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Biodegradation of winery biomass wastes by
developing a symbiotic multi-fungal consortium
Submitted in total fulfilment requirements for the degree of
Doctor of Philosophy By
Avinash Vasant Karpe
Department of Chemistry and Biotechnology Faculty of Science, Engineering and Technology
Swinburne University of Technology
August 2015
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Abstract
Australia is sixth largest global wine grape producer with an annual production
of 1.75 million metric tonnes in 2012-13. Owing to its poor digestibility and minor
phenolic toxicity, winery waste (about a half of total grape biomass) has limited use as
either animal feed or compost fertilizer. During fermentation, Saccharomyces cerevisiae
generates about 12-15% ethanol from the raw material, with the residual biomass
(approx. 85%) consisting of spent wash, seeds, marcs and pomace generating landfill
waste.
Fungi from the divisions Ascomycota and Basidiomycota are considered as
efficient biomass degraders. These fungi not only hydrolyse the complex molecules like
cellulose to simple sugars, but also generate metabolites of industrial and medicinal
importance. However, the inherent nature of the lignocellulosic complex makes it
recalcitrant towards numerous physical, chemical or biological treatments. Besides their
lower inherent activities, cellulases suffer from product inhibition from cellulose and
lignin degradation products, thus making the biomass degradation more difficult. Also,
no naturally evolved organism possesses the entire spectrum of the necessary biomass
degrading biochemical machinery.
The experiments described herein explore the possibilities of developing a
mixed fungal consortium to achieve enhanced grape biomass degradation. The
processes utilized cultures of Ascomycota fungi (Trichoderma harzianum, Aspergillus
niger, Penicillium chrysogenum and Penicillium citrinum) alone or together with a
Basidiomycota fungus (Phanerochaete chrysosporium). The conventional and popular
submerged fermentation (SmF) and the emerging Solid State Fermentation (SSF)
methods were tested. Statistical analysis was performed to generate an optimised mixed
fungal “cocktail” and achieve a bioreactor-based degradation process with increased
biomass degradation. Additionally, co-culturing the optimized ascomycete fungal
mixture with Ph. chrysosporium resulted in enhanced degradation. In addition to
classifying and characterizing the important metabolites generated and consumed during
the biodegradation process, metabolomics was used to identify the critical points of
fungal metabolism which can be altered to improve the overall bioconversion process.
iv
Initial experiments involved single cultures of the ascomycetes added to thermal
pre-treated (autoclaved) winery waste. Pre-treatment resulted in 18% mass loss of
biomass with enzyme activities, especially that of β-glucosidase, improving in most
cultures (compared to non-autoclaved waste), indicating considerable hydrolyzation of
cellulose and hemicelluloses. P. chrysogenum was observed to degrade about 9% lignin
in the presence of free sugars. For SSF, the ratio between substrate and fungal growth
medium was kept at 1:1. Cellulase and xylanase activities were moderately or
considerably higher in SSF conditions with respect to SmF. However, β-glucosidase
activities were higher under SmF than SSF. Lignin degradation in SSF conditions was
considerably lower at 2.2 % as compared to 9% in SmF.
To balance the overall enzyme activities and achieve higher lignin degradation
while minimizing competitive and product inhibitions, full factorial statistical modelling
was used to determine an optimal fungal mixture. Following autoclaving pre-treatment,
a mixture of A. niger: P. chrysogenum: T. harzianum: P. citrinum in a percent ratio of
60:14:4:2 was applied for biomass degradation. The substrate to medium ratio was held
at 0.39. It was observed that the enzyme activities of cellulases, glucosidase and
xylanases increased considerably, especially xylanase activities which increased from
1430 U/mL in monoculture to 3550 U/mL in optimized conditions. Total lignin
degradation of 17.9 % in 5 days was noted. Metabolic profiling indicated considerable
production of sugar alcohols, fatty acids and secondary metabolites, such as gallic acid.
Further improvement in degradation of thermal pre-treated waste was achieved
by a cocktail of Ph. chrysosporium and the statistically optimized mix of ascomycetes.
Over 16 days, the nature of fungal mix prevented product inhibition of cellulases, β-
glucosidase and xylanase activities, thus resulting in enhanced activities. Xylanase
activity increased about 2-fold (> 6000 U/mL) with respect to ascomycete mixture.
Considerable laccase and lignin peroxidase activities were observed, which was
surprising since Ph. chrysosporium is generally unable to express laccase activities
when grown on various substrates. Metabolic profiling displayed lignin degradation
products such as suberic acid, 1, 3 benzenedicarboxylate and cadaverine, which were
further degraded during the course of fermentation. Similarly, considerable amounts of
commercially important metabolites such as sugar alcohols, sugar acids and fatty acids
were generated.
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Metabolic flux profiling of A. niger and P. chrysogenum indicated considerable
accumulation of free sugars. GC-MS based 2H2O-flux profiling of A. niger and P.
chrysogenum showed 17 and 36 active metabolic pathways, respectively, merging in
and out of glycolysis and TCA cycle pathways. Fungal enzymes encountered product
inhibition after 5 days of fermentation, indicating the need for continuous batch
conditions of lower intervals of 4-5 days to generate better degradation. Based on the
assessment of A. niger metabolic pathways, it is proposed that adding keto acid
carboxylases will reroute amino acid biosynthesis pathways to butanol isomers, which
are widely used as fuel molecules. Moderate generation of xylitol, an intermediate of
ethanol production, was observed during P. chrysogenum metabolism. This pentose
sugar oxidation pathway was absent in the other ascomycetes under investigation.
Moderate lignin and tannin degradation was also observed during the early phase of
biomass degradation. It is proposed that P. chrysogenum forms an ideal candidate for
inclusion in a fungal consortium to minimize pentose based enzyme inhibition and
convert this carbohydrate into sugar alcohols, eventually leading to ethanol production.
Overall, the results of this study show that a mixed fungal approach in
combination with simple pre-treatment process such as autoclaving is able to generate
considerable amount of biomass degradation. This degradation generates numerous
metabolites such as sugars, alcohols and fatty acids which can be applied to generate
biofuel molecules and metabolites of medicinal interests.
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Dedicated in the loving memory of
Devendra R Jagtap
17th Jun. 1987 – 2nd Oct. 2013
A brother and the best of friends
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Acknowledgements
This thesis has been a result of an arduous, yet a momentous journey of some
considerable years of my life. So, as it happens with every journey, there are a number
of people who have been instrumental in shaping the story of this journey. My gurus,
Professors Enzo Palombo and Ian Harding and Dr. David Beale of all the people have
been the biggest constructors of my PhD’s journey. Enzo, you not only guided me as a
master, but also acted like my godfather during all these times. Ian, the PhD would not
have been as cheery without your numerous stories and corrections. Lastly, David; you
have been like an elder and wiser friend, guiding me on the basis of your own research
experiences. You are the people who have still kept my hunger for research and
knowledge alive. I cannot thank you enough gentlemen!
I would also like to thank my colleagues at Faculty and Science, Engineering
and Technology, Swinburne University and CSIRO. You have made this journey more
of a fun and entertaining rather than a boring one. I am grateful to Chris Key, Dr.
Huimei Wu, Savithri Galappathie, Angela McKellar, Ngan Nguyen, Dr. Rebecca
Alfred, Dr. Adrian Dinsdale, Dr. François Malherbe, Dr. Daniel Eldridge and Professor
Mrinal Bhave. This acknowledgement will remain incomplete without mentioning Dr.
Jacqui McRae from The Australian Wine Research Institute, who provided the research
with grape substrate to work on.
The story of this journey will especially remain incomplete with mentioning vast
number of friends who always made the life ‘Greater than 9000’. The gang of
‘Breakers’ in India, Rohan Shah, Ravi Nirmal, Snehal Jadhav, Amol Ghodke, Shaan
Agrawal, Balasaheb Sonawane, Richi Shah, Maitri Patel, Dr Atul Kamboj, Matt Quinn,
Simon Grossemy, Dr. Vi Khanh Truong, Dr Mohammad Al Kobaisi, Dr. Shakuntla
Gondalia and Nainesh Godhani, my PhD journey would have been not possible without
you guys. Thanks!
Finally, big thanks to my parents, brother and other family members for
unconditional love, unshakable belief and very long patience. Although, no thanks is big
enough, that’s the only thing I can give you. I know that I have made you proud and
hope that all your sacrifices were worth it!
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Declaration
I, Avinash V Karpe, hereby declare, to best of my knowledge, the thesis entitled
“Biodegradation of winery biomass wastes by developing a symbiotic multi-fungal
consortium” is no more than 100,000 words in length, exclusive of tables, figures,
appendices, references and footnotes. I also declare that this thesis, in complete or in
part has been submitted or previously published for any other degree, diploma or
independent publishing by me or any other person. Where the work is a result of
collaborations and joint research, the thesis and its resultant research articles indicate
contributions of all contributors and respective workers. I also declare that this thesis
has been appropriately edited; however, the extent of the editing addressed only the
style and grammar of the thesis, and not its substantive content.
-------------------------------
Avinash V Karpe
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Publications arising from this thesis work
Peer-reviewed journal articles
Karpe AV, Harding IH & Palombo EA (2014) Comparative degradation of
hydrothermal pretreated winery grape wastes by various fungi. Industrial Crops &
Products 59: 228-233
Karpe AV, Beale DJ, Harding IH & Palombo EA (In press) Optimization of degradation
of winery-derived biomass wastes by Ascomycetes. Journal of Chemical Technology &
Biotechnology. DOI:10.1002/jctb.4486
Karpe AV, Beale DJ, Morrison PD, Harding IH & Palombo EA (2015) Untargeted
Metabolic Profiling of Vitis vinifera during Fungal Degradation. FEMS Microbiology
Letters. 362 (10), fnv060
Karpe AV, Beale DJ, Godhani, N, Morrison PD, Harding IH & Palombo EA (2015)
Metabolic profiling of winery-derived biomass waste degradation by Aspergillus niger.
Journal of Chemical Technology & Biotechnology, DOI: 10.1002/jctb.4749
Conference presentations
Karpe AV, Beale DJ, Harding IH & Palombo EA. Application of metabolomics in
fungal mediated winery biomass degradation. XIVth International Congress of
Mycology and Eukaryotic Microbiology, Montréal, Canada; 07/2014 (Oral
presentation)
Karpe AV, Beale DJ, Harding IH & Palombo EA. Optimization of degradation of
winery-derived biomass waste by Ascomycetes. Australian Society for Microbiology
Annual General Meeting & Exposition, Melbourne, Australia; 07/2014 (Oral
presentation)
Karpe AV, Harding IH & Palombo EA. Comparative degradation of grape pomace
from winery wastes by various fungal cultures. 4th International Conference for Young
Chemists, Penang, Malaysia; 01/2013 (Oral presentation)
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Related miscellaneous peer-reviewed journal articles
Beale DJ, Karpe AV, Jadhav, S, Muster, T & Palombo EA (2015) 'Omic' approaches
and their use in the assessment of microbial influenced corrosion of metals. Corrosion
Reviews
Jadhav, S, Gulati, V, Fox, EM, Karpe, A, Beale, DJ, Sevior, D, Bhave, M & Palombo,
EA. (2015). Rapid identification and source-tracking of Listeria monocytogenes using
MALDI-TOF mass spectrometry. International Journal of Food Microbiology 202, 1-9
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Table of contents
Abstract ..................................................................................................................... iii
Dedication ................................................................................................................. vi
Acknowledgements ................................................................................................... vii
Declaration ................................................................................................................ viii
Publications arising from the thesis .......................................................................... ix
Table of contents ....................................................................................................... xi
List of tables .............................................................................................................. xvii
List of figures ............................................................................................................ xviii
Annotation ................................................................................................................. xxv
1. Chapter 1 ............................................................................................................. 1
1.1. Overview .................................................................................................. 2
1.2. Problems associated with biomass degradation ....................................... 3
1.3. Various biomass degradation approaches ................................................ 4
1.4. Aims and strategies of the project ............................................................. 5
2. Chapter 2 ............................................................................................................. 7
2.1. Biomass composition ....................................................................................... 8
2.1.1. Cellulose ..................................................................................................... 8
2.1.2. Hemicelluloses ............................................................................................ 10
2.1.2.1. Xylans ..................................................................................................... 10
2.1.2.2. Xyloglucans ............................................................................................ 11
2.1.2.3. Mannans ................................................................................................. 11
2.1.2.4. β-glucans ................................................................................................ 12
2.1.2.5. Pectins .................................................................................................... 14
2.1.3. . Lignins ........................................................................................................ 15
2.2. Biomass biodegradation ................................................................................... 18
2.2.1. Biodegradation: Issues and problems ......................................................... 19
2.2.2. Structure of lignocellulose complex ........................................................... 19
2.2.3. Microbial and chemical limitations ............................................................ 21
2.2.4. Enzymatic limitations ................................................................................. 21
xiii
2.2.5. Product inhibition ........................................................................................ 21
2.3. Biomass pre-treatment ..................................................................................... 22
2.3.1. Physical pre-treatment methods .................................................................. 22
2.3.1.1. Milling .................................................................................................... 23
2.3.1.2. Irradiation and sonication ....................................................................... 23
2.3.2. Physico-chemical treatment ........................................................................ 24
2.3.3. Chemical treatments .................................................................................... 26
2.3.3.1. Acid pre-treatment .................................................................................. 26
2.3.3.2. Alkali pre-treatment ............................................................................... 27
2.3.3.3. Organosolv treatments ............................................................................ 27
2.3.4. Biological pre-treatment ............................................................................. 28
2.4. Fungal degradation of biomass ........................................................................ 28
2.4.1. Cellulases .................................................................................................... 29
2.4.2. Hemicellulases ............................................................................................ 32
2.4.3. Lignases ...................................................................................................... 33
2.4.3.1. Laccases ................................................................................................. 35
2.4.3.2. Heme peroxidases .................................................................................. 38
2.4.3.2.1. Lignin peroxidases .......................................................................... 38
2.4.3.2.2. Manganese peroxidases ................................................................... 39
2.4.3.2.3. Versatile peroxidases ...................................................................... 42
2.5. Biodegradation methods .................................................................................. 45
2.5.1. First generation: Submerged fermentation .................................................. 45
2.5.2. Second Generation: Solid State Fermentation ............................................ 47
2.5.3. Third generation: Consolidated Bioprocessing ........................................... 50
2.5.4. Symbiotic fermentation: SSF and thermophilic degradation ...................... 53
2.6. Application of metabolomics in fungal biomass degradation ......................... 56
2.7. Overview .......................................................................................................... 59
3. Chapter 3 ............................................................................................................. 60
3.1. Chemicals ........................................................................................................ 61
3.1.1. Chemicals and media ................................................................................... 61
3.1.2. Growth media .............................................................................................. 61
3.1.3. Enzymes ...................................................................................................... 63
xiv
3.1.4. Buffers and Reagents .................................................................................. 63
3.1.5. Maintenance of storage and other conditions .............................................. 65
3.2. Organisms ........................................................................................................ 66
3.3. Genomic identification of fungi and bacteria .................................................. 67
3.4. Grape samples .................................................................................................. 67
3.5. Fungal and bacterial growth conditions ........................................................... 67
3.6. Calculating bacterial and fungal concentrations .............................................. 68
3.7. Biochemical tests ............................................................................................. 69
3.7.1. Determination of total soluble sugars ......................................................... 69
3.7.2. Determination of reducing sugars ................................................................ 69
3.7.3. Determination of pentoses .......................................................................... 70
3.7.4. Total protein content ................................................................................... 70
3.7.5. Lignin content measurement ....................................................................... 70
3.7.6. Total nitrogen and carbon content ............................................................... 71
3.8. Enzyme assays ................................................................................................. 72
3.8.1. Cellulase activity assay ............................................................................... 72
3.8.2. β-glucosidase activity assay ......................................................................... 73
3.8.3. Xylanase activity assay ................................................................................ 73
3.8.4. Laccase activity assay ................................................................................. 74
3.8.5. Lignin peroxidase activity assay ................................................................. 75
3.9. Silyl derivatization and gas chromatography-mass spectrometry(GC-MS) ..... 76
3.9.1. Silyl derivatization ...................................................................................... 76
3.9.2. GC-MS analysis .......................................................................................... 77
3.10. Chemometric and statistical analysis ............................................................ 78
3.10.1. Biochemical analysis and enzyme assays ................................................. 78
3.10.2. Statistical modeling for degradation optimization ................................... 78
3.10.3. Metabolic profiling and flux analysis ....................................................... 79
4. Chapter 4 ............................................................................................................. 81
4.1. Introduction ...................................................................................................... 82
4.2. Overview .......................................................................................................... 83
4.3. Results and discussions ................................................................................... 84
4.3.1. Effects of hydrothermal pre-treatment ........................................................ 84
xv
4.3.2. Utilization of Total Soluble Sugars ............................................................ 85
4.3.3. Utilization of reducing sugars and pentoses ............................................... 87
4.3.4. Change in the lignin content ....................................................................... 90
4.3.5. Cellulase activity ......................................................................................... 92
4.3.6. β-glucosidase activity ................................................................................. 94
4.3.7. Xylanase activity ......................................................................................... 96
4.4. Conclusions ..................................................................................................... 97
4.5. Experimental summary .................................................................................... 98
5. Chapter 5 ............................................................................................................. 100
5.1. Introduction ...................................................................................................... 101
5.2. Overview .......................................................................................................... 102
5.3. Results and discussions ................................................................................... 102
5.3.1. Total Protein Content .................................................................................. 102
5.3.2. Reducing sugars .......................................................................................... 104
5.3.3. Lignin mineralization .................................................................................. 107
5.3.4. Cellulase activities during SSF ................................................................... 110
5.3.5. β-glucosidase activities .............................................................................. 112
5.3.6. Xylanase activities ...................................................................................... 115
5.4. Conclusions ..................................................................................................... 118
5.5. Summary .......................................................................................................... 119
6. Chapter 6 ............................................................................................................. 121
6.1. Introduction ...................................................................................................... 122
6.2. Overview .......................................................................................................... 123
6.3. Results and discussions ................................................................................... 124
6.3.1. Compositional analysis of grape waste ....................................................... 124
6.3.2. Statistics: Design of experiment and analysis ............................................. 125
6.3.3. Grape biomass degradation ......................................................................... 126
6.3.4. Enzyme activities ........................................................................................ 128
6.3.5. Grape biomass degradation in bioreactor ................................................... 137
6.3.6. Cellulolytic enzyme production in bioreactor culture ................................ 137
6.3.7. Gas Chromatography-Mass Spectrometry (GC-MS) .................................. 138
xvi
6.3.8. Metabolic output of mixed fungal degradation ........................................... 139
6.4. Conclusions ..................................................................................................... 145
6.5. Summary .......................................................................................................... 146
7. Chapter 7 ............................................................................................................. 148
7.1. Introduction ...................................................................................................... 149
7.2. Overview .......................................................................................................... 149
7.3. Results and discussions ................................................................................... 150
7.3.1. Lignin degradation ...................................................................................... 150
7.3.2. Cellulase activity ......................................................................................... 152
7.3.3. β-glucosidase activity ................................................................................. 154
7.3.4. Xylanase activity ......................................................................................... 156
7.3.5. Ligninase activities ..................................................................................... 158
7.3.5.1. Laccase activity ...................................................................................... 158
7.3.5.2. Lignin peroxidase activity ...................................................................... 160
7.3.6. Metabolic output of sequential fermentation .............................................. 162
7.3.6.1. Mass spectral analysis and PLS-DA ...................................................... 163
7.4. Conclusions ..................................................................................................... 174
7.5. Summary .......................................................................................................... 175
8. Chapter 8 ............................................................................................................. 176
8.1. Introduction ...................................................................................................... 177
8.2. Overview .......................................................................................................... 178
8.3. Results and discussions .................................................................................... 179
8.3.1. A. niger metabolic profile ........................................................................... 179
8.3.2. Mass spectral analysis and PLS-DA ........................................................... 180
8.3.3. Metabolic flux of A. niger during biomass degradation .............................. 186
8.3.3.1. Glucose junction ..................................................................................... 186
8.3.3.2. Glyceraldehyde-3-phosphate junction .................................................... 187
8.3.3.3. Acetyl-CoA junction .............................................................................. 192
8.3.3.4. Succinate junction .................................................................................. 197
8.3.4. Glycolysis and TCA cycle .......................................................................... 197
8.3.5. P. chrysogenum metabolic profile .............................................................. 198
xvii
8.3.6. Mass spectral analysis and PLS-DA ........................................................... 200
8.3.7. Metabolic flux of P. chrysogenum during biomass degradation ................ 206
8.3.7.1. Glucose/ glucose-1-phosphate junction ................................................. 206
8.3.7.2. Glycerate-3-phosphate junction ............................................................. 211
8.3.7.3. Acetyl CoA junction ............................................................................... 215
8.4. Conclusions ..................................................................................................... 221
8.5. Summary .......................................................................................................... 222
9. Chapter 9 ............................................................................................................. 224
9.1. General discussions ......................................................................................... 225
9.1.1. Hydrothermal pre-treatment and submerged fermentation .................. 226
9.1.2. Solid state fermentation ....................................................................... 227
9.1.3. Statistical optimization of fungal biomass degradation ....................... 227
9.1.4. Biomass degradation by co-culture of basidiomycetes and ascomycetes
.............................................................................................................. 228
9.1.5. Fungal metabolic flux analysis to improve biomass degradation ........ 229
9.2. Close and future aspects .................................................................................. 230
Bibliography ........................................................................................................ 232
Appendices .......................................................................................................... 264
xviii
List of tables
Table 2.1 Major classification of different mannans ................................................ 12
Table 3.1 General composition of various minerals/ organic sources in the growth media
for isolating and characterizing cellulolytic organisms ............................ 62
Table 3.2 Composition of buffers and reagents used during the experiments .......... 62
Table 3.3 Micro-organisms isolated from various sources and characterized by Sanger
sequencing ................................................................................................. 67
Table 3.4 Working protocol for laccase assay .......................................................... 74
Table 3.5 Working protocol for lignin peroxidase assay .......................................... 75
Table 6.1 General composition of Shiraz grape waste .............................................. 125
Table 6.2 ANOVA for Cellulases, Xylanases and β-glucosidase using Adjusted SS for
tests ........................................................................................................... 133
Table 6.3 Total cellulase, xylanase and β-glucosidase activities (U/mL) of different
fungi under experimental conditions of Design of Experiment ................ 135
Table 6.4 Most significant features generated during the optimized biomass degradation
as identified by the volcano plot with their fold change (FC) and P values
.................................................................................................................... 143
Table 7.1 The table represents most significantly generated metabolites by D8 (end of
Ph chrysosporium incubation) and D16 (end of overall degradation) of winery
biomass waste degradation process ........................................................... 168
Table A1 List of most significant metabolites differentiated on the basis of D2O
mediated metabolic flux of A niger during winery biomass degradation . 271
Table A2 List of most significant metabolites differentiated on the basis of D2O
mediated metabolic flux in P chrysogenum .............................................. 276
xix
List of figures
Figure 2.1 Fragment of cellulose with the reducing, non-reducing and cellobiose component of
cellulose ........................................................................................................... 9
Figure 2.2 Hydrogen bonds in cellulose molecule, represented by dashes (---) ................... 10
Figure 2.3 (A) Structure of xylopyranose, which is the primary component of Xylans. (B)
Structure of β-galactopyranose molecule. (C) Structure of 4-O-methyl glucopyranoxyl
uronic acid (MeGLcA), which is a primary component of glucurunoxylans. (D)
Structure of arabinose (arabinofuranose) ............................................................. 11
Figure 2.4 General structure of a portion of β-glucan polymer. The arrows mark the presence of
β (1, 3) glycosidic bonds ...................................................................................... 13
Figure 2.5 General structure of pectin chain, which is supposed to have in it the components like
RG I, XGA, HG and RG II, attached either in a linear manner or as the side chains to
pectic chain .......................................................................................................... 15
Figure 2.6 Structures of (A) Hydroxycinnamyl alcohols-p-coumaryl alcohol, coniferyl alcohol
and sinapyl alcohol and (B) derived structural units H, G and S ......................... 16
Figure 2.7 General structure of lignocellulose complex ....................................................... 20
Figure 2.8 Structure of copper centres Type 1, type 2 and type 3 of laccase enzyme catalytic
cluster .................................................................................................................. 36
Figure 2.9 A general representation of laccase catalysis mechanism of 2, 6-dimethoxy phenol
using Cu2+ site .................................................................................................... 37
Figure 2.10 A conceptual design of MBM reactor to achieve a CBP to generate ethanol from pre-
treated wheat straw .............................................................................................. 52
Figure 4.1 Total soluble sugar content (Kg/m3) in fungal cultures of non-autoclaved, autoclaved
and untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples
are significantly different to untreated samples (p < 0.05) .................................. 86
Figure 4.2 Reducing sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and
untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples are
significantly different to untreated samples (p < 0.05) ........................................ 88
xx
Figure 4.3 Pentose sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and
untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples are
significantly different to untreated samples (p < 0.05) ........................................ 89
Figure 4.4 Lignin content (%) in fungal cultures of non-autoclaved, autoclaved and untreated
Shiraz grape waste. Values for non-autoclaved and autoclaved samples are
significantly different to untreated samples (p < 0.05) ........................................ 91
Figure 4.5 Cellulase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved
Shiraz grape waste. Values for non-autoclaved and autoclaved samples are
significantly different between the samples (p ≤ 0.05), except for the asterisk (*)
marked values (p > 0.05) ..................................................................................... 93
Figure 4.6 β-glucosidase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved
Shiraz grape waste. Values for non-autoclaved and autoclaved samples are
significantly different between the samples (p ≤ 0.05) ........................................ 95
Figure 4.7 Xylanase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved
Shiraz grape waste. Values for non-autoclaved and autoclaved samples are
significantly different between the samples (p ≤ 0.05) ........................................ 97
Figure 5.1 Total protein content of fungal degraded Shiraz grape waste over 2 weeks of SSF.
Data from autoclaved fermented samples are used for comparison purposes. All the
values are the mean of triplicate data (P ≤ 0.05) .................................................. 103
Figure 5.2 Total reducing sugar content of fungal degraded Shiraz grape waste over 2 weeks of
SSF. Data from autoclaved fermented samples are used for comparison purposes. The
values are the mean of triplicate data and P ≤ 0.05, except for the bar marked with the
asterisk ................................................................................................................. 104
Figure 5.3 Lignin content of fungal degraded Shiraz grape waste over 2 weeks of SSF. The
untreated sample refers to the lignin content of grape samples before any treatment or
addition of growth medium. The values are means of triplicate data (P < 0.05) . 107
Figure 5.4 Comparison of cellulase activity (U/mL) in the fungal degraded Shiraz grape waste
over 2 weeks of SSF compared to autoclaved pre-treated samples. The values are
means of triplicate data and P < 0.05, except for the bars marked with an asterisk (*)
.............................................................................................................................. 111
Figure 5.5 Comparison of β-glucosidase activity (U/mL) in the fungal degraded Shiraz grape
waste over 2 weeks of SSF compared to autoclaved pre-treated samples. The values
are means of triplicate data and P < 0.05 ............................................................. 114
xxi
Figure 5.6 Comparison of xylanase activity (U/mL) in the fungal degraded Shiraz grape waste
over 2 weeks of SSF compared to autoclaved pre-treated samples. The values are
means of triplicate data and P < 0.05 ................................................................... 116
Figure 6.1 Total protein content of 1 week SSF degraded grape biomass and hydrothermal pre-
treated submerged grape biomass. The values from untreated sample are for
comparison purposes. All values are average of triplicate data with p < 0.05 between
the treatment methods .......................................................................................... 127
Figure 6.2 Main effects plot showing the relationship between various fungal cultures across
different growth media in terms of cellulase activity. The values represented in
parentheses denote standard errors ...................................................................... 129
Figure 6.3 Main effects plot showing the relationship between various fungal cultures across
different growth media, in terms of β-glucosidase activity. The values represented in
parentheses denote standard errors ...................................................................... 130
Figure 6.4 Main effects plot showing the relationship between various fungal cultures across
different growth media, in terms of xylanase activity. The values represented in
parentheses denote standard errors ...................................................................... 131
Figure 6.5 Matrix plot shows the fungal enzyme activities under different conditions. The top X-
axis labels of 1, 2, 3 and 4 refer to T. harzianum, A. niger, P. chrysogenum and P.
citrinum, respectively .......................................................................................... 132
Figure 6.6 Enzyme activities observed in individual cultures under different conditions compared
to that of the optimized mixed culture (n=3) ....................................................... 137
Figure 6.7 (A) PCA score scatter plot displaying the metabolite output pattern of mixed fungal
degradation of grape biomass. (B) DModX line plot of PCA metabolites. .......... 140
Figure 6.8. (A) PLS-DA score scatter plot and (B) Loading scatter plot of degraded Shiraz
substrate according to fungal species analysed using GC-MS ............................ 142
Figure 6.9 Important features selected by volcano plot with fold change threshold (x-axis) 2 and
t-tests threshold (y-axis) 0.05 .............................................................................. 145
Figure 7.1 Total lignin content and lignin degradation over 16 days by the co-culture of wood-rot
fungus, Ph. chrysosporium and statistically optimized Ascomycota mix ........... 151
Figure 7.2 Cellulase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.
chrysosporium and statistically optimized Ascomycota mix ............................... 153
xxii
Figure 7.3 β-glucosidase activity observed over 16 days by the co-culture of wood-rot fungus,
Ph. chrysosporium and statistically optimized Ascomycota mix ........................ 155
Figure 7.4 Xylanase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.
chrysosporium and statistically optimized Ascomycota mix ............................... 157
Figure 7.5. Laccase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.
chrysosporium and statistically optimized Ascomycota mix ............................... 159
Figure 7.6 Lignin peroxidase activity observed over 16 days by the co-culture of wood-rot
fungus, Ph. chrysosporium and statistically optimized Ascomycota mix ........... 161
Figure 7.7 PCA model of winery biomass degradation by the co-culture of Ph. chrysosporium
and statistically optimized ascomycota mix ........................................................ 162
Figure 7.8 ‘DModX’ or ‘Distance of observation’ plot of winery biomass degradation by the co-
culture of Ph. chrysosporium and statistically optimized ascomycota mix. The
samples refer to Control (1-5), Day 4 (6-10), Day 8 (11-15), Day 12 (16-20) and Day
16 (21-25) ............................................................................................................ 163
Figure 7.9. PLS-DA model of winery biomass degradation by the co-culture of Ph.
chrysosporium and statistically optimized ascomycota mix ................................ 164
Figure 7.10A. OPLS-DA model of winery biomass degradation by the co-culture of Ph.
chrysosporium and statistically optimized ascomycota mix. ............................... 165
Figure 7.10B. Loading scatter plot of winery biomass degradation by the co-culture of Ph.
chrysosporium and statistically optimized ascomycota mix ................................ 166
Figure 7.11 Volcano plots displaying significantly generated metabolites (D8 and D16) during
winery biomass degradation by the co-culture of Ph. chrysosporium and statistically
optimized ascomycota mix .................................................................................. 167
Figure 8.1 Principal Component Analysis of Aspergillus niger flux over 8 days during winery
biomass degradation ............................................................................................ 179
Figure 8.2 ‘DModX’ or ‘Distance of observation’ plot of Aspergillus niger flux over 8 days of
winery biomass degradation ................................................................................ 180
Figure 8.3 Partial Least Square- Discriminant Analysis derived score scatter plot of Aspergillus
niger flux over 8 days during winery biomass degradation ................................. 181
Figure 8.4 Partial Least Square- Discriminant Analysis derived loading scatter plot of
Aspergillus niger flux over 8 days during winery biomass degradation .............. 182
xxiii
Figure 8.5 Volcano plot displaying the differential expressing metabolites in deuterated and non-
deuterated media .................................................................................................. 183
Figure 8.6 Metabolic pathways for A. niger mediated SSF degradation of winery biomass waste
over 8 days ........................................................................................................... 184
Figure 8.7 Changes in the composition of metabolites leading to or from the ‘glucose/glucose-6-
phosphate’ junction in the glycolysis pathway during A. niger mediated grape waste
biomass degradation ............................................................................................ 187
Figure 8.8. Changes in the features of the D-ribulose metabolism path of the pentose phosphate
pathway ................................................................................................................ 188
Figure 8.9. Changes in the features of the D-ribulose metabolism path of pentose phosphate
pathway ................................................................................................................ 189
Figure 8.10. Changes in the features of the D-ribose metabolism path of pentose phosphate
pathway ................................................................................................................ 189
Figure 8.11. Changes in the features of gluconate metabolism path of pentose phosphate pathway
.............................................................................................................................. 190
Figure 8.12. Changes in the features of significant shikimate pathway metabolites ............... 191
Figure 8.13. Changes in the significant metabolite features of significant fucose degradation
pathway ................................................................................................................ 191
Figure 8.14. Changes in the features of acetyl-CoA and most significantly observed pathway
intermediates of valine, leucine and isoleucine biosynthesis in A. niger ............. 194
Figure 8.15. Changes in the features of acetyl-CoA and R-3-hydroxy butanoate, metabolites
leading to BHP biosynthesis in A. niger .............................................................. 194
Figure 8.16. Medium chain fatty acid conversion by A. niger during degradation of Shiraz grape
waste over 8 days ................................................................................................. 196
Figure 8.17. Intermediate metabolites of urea cycle and Yang cycle/methionine salvage pathway
.............................................................................................................................. 196
Figure 8.18. Principal Component Analysis of P. chrysogenum flux over 8 days during winery
biomass degradation ............................................................................................ 199
Figure 8.19. DModX’ or ‘Distance of observation’ plot of Aspergillus niger flux over 8 days of
winery biomass degradation ................................................................................ 200
xxiv
Figure 8.20. PLS-DA derived score scatter plot of P. chrysogenum flux over 8 days during winery
biomass degradation ............................................................................................ 201
Figure 8.21. PLS-DA derived loading scatter plot of P. chrysogenum flux over 8 days during
winery biomass degradation ................................................................................ 202
Figure 8.22. Volcano plot displays the differentially expressing metabolites in deuterated and non-
deuterated media of P. chrysogenum ................................................................... 203
Figure 8.23. Metabolic pathway of P. chrysogenum during SSF based degradation of winery
biomass waste over 8 days ................................................................................... 204
Figure 8.24. Metabolic variation of glucose and L-sorbose metabolism by P. chrysogenum during
winery biomass waste degradation of Shiraz grapes ........................................... 207
Figure 8.25. Metabolism of melibiose with respect to glucose by P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days .................................... 208
Figure 8.26. Metabolism of chitin biosynthesis intermediates with respect to glucose-1-phosphate
by P. chrysogenum during winery biomass waste degradation of Shiraz grapes over 8
days ...................................................................................................................... 209
Figure 8.27. Biosynthesis and metabolism of melanin pigments by P. chrysogenum during SSF
based winery biomass waste degradation ............................................................ 210
Figure 8.28. Metabolism of glycerate-3-phosphate and sugars from pentose phosphate pathway by
P. chrysogenum during winery biomass waste degradation of Shiraz grapes over 8
days ...................................................................................................................... 211
Figure 8.29. Metabolism of pentose acids by P. chrysogenum during winery biomass waste
degradation of Shiraz grapes over 8 days ............................................................ 212
Figure 8.30. Xylitol production by P. chrysogenum during xylose metabolism by winery biomass
waste degradation of Shiraz grapes over 8 days .................................................. 213
Figure 8.31. Propanol production by P. chrysogenum during metabolism by winery biomass waste
degradation of Shiraz grapes over 8 days ............................................................ 214
Figure 8.32. Pectin degradation by P. chrysogenum during metabolism by winery biomass waste
degradation of Shiraz grapes over 8 days ............................................................ 215
Figure 8.33. Isoprenoid biosynthesis by P. chrysogenum during metabolism by winery biomass
waste degradation of Shiraz grapes over 8 days. α-tocopherol has been scaled on the
secondary axis ...................................................................................................... 217
xxv
Figure 8.34. Lignin and tannin degradation via benzoate degradation pathway by P. chrysogenum
during metabolism by winery biomass waste degradation of Shiraz grapes over 8 days
.............................................................................................................................. 219
Figure 8.35. Penicillin production via lysine degradation pathway by P. chrysogenum during
metabolism by winery biomass waste degradation of Shiraz grapes over 8 days 220
Figure 8.36. Fatty acid biosynthesis by P. chrysogenum during metabolism by winery biomass waste
degradation of Shiraz grapes over 8 days ............................................................ 221
xxvi
Annotations
All the acronyms including chemical names, general terminologies, SI units used in the
thesis.
AATCC American Association of Textile Chemists and Colourists
ABS Australian Bureau of Statistics
ABTS 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)
Acetyl-CoA Acetyl-coenzyme A
AFEX Ammonia fibre explosion
AIL Acid Insoluble Lignin
AIR Acid Insoluble Residue
ANOVA Analysis of variance
AP Apiogalacturonan
AR Analytical grade
ASL Acid Soluble Lignin
AWRI Australian Wine Research Institute
AX Arabinoxylans
BOD Biochemical Oxygen Demand
BSA Bovine Serum Albumin
BSTFA N,O-Bis(trimethylsilyl)trifluoroacetamide
CAZy Carbohydrate active enzyme classification
CBH Cellobiohydrolase
CBP Consolidated Bio-processing
xxvii
CDH Cellobiose dehydrogenase
CHD Coronary Heart Disease
CMCase Carboxymethyl cellulase
COD Chemical Oxygen Demand
COVAIN Covariance-Inverse
CSIRO Commonwealth Scientific and Industrial Research Organization
DAHP 7-phospho-2-deoxy-3-D-arabino heptanoate
Dcrit Critical value of DModX
DMAPP Dimethylallyl pyrophosphate
DNSA 3, 5-dinitrosalicylic acid
EC Enzyme Commission number
EG Endoglucanases
EI Electron ionization
EPA Environment Protection Agency
eV Electron Volt
FAO Food and Agriculture Organization
FC Fold change
FEBF Palm empty fruit bunch fibre
FF Furfurals
FPA Filter Paper Activity
GC-MS Gas Chromatography-Mass Spectrometry
GDHB γ-glutaminyl-3,4-dihydroxybenzene
xxviii
GH Glycoside hydrolase
HCW Hot-compressed water
HETCOR Heteronuclear correlation spectra
HMF- Hydroxymethyl furfurals
HPLC High Pressure Liquid Chromatography
HSD Honestly significant difference
HT Hydrothermal treatment
IBM International business Machines
IPP Isopentyl pyrophosphate
IU International unit
IUPAC International Union of Pure and Applied Chemistry
kDa Kilo-Daltons
KDC 2-keto-acid decarboxylases
KEGG Kyoto Encyclopaedia of Genes and Genomes
kGy Kilo Gray
kHz Kilo-Hertz
kJ Kilo-Joules
kPa kilo Pascals
Kw Molal ionisation product of water
kWh Kilowatt- hours
LC-MS Liquid chromatography-mass spectrometry
LDL Low density lipoproteins
xxix
LiP Lignin peroxidase
LSD Least significant difference
MBM Multi-species biofilm membrane reactor
MCFAs Medium chain fatty acids
MEP 4-phospho-2-C-methyl erythritol
MnP Manganese peroxidase
MSI Metabolomics Standard Initiative
NA Nutrient Agar
NAG N-acetyl glucosamine
NB Nutrient Broth
NIH National Institutes of Health
NIST National Institute of Standards and Technology
NMR Nuclear magnetic resonance
OIV L'Organisation Internationale de la Vigne et du Vin
OPLS-DA Orthogonal PLS-DA
PCA Principal Component Analysis
PDA Potato Dextrose Agar
PKs Polyketide synthases
PLS-DA Partial Least Square-Discriminant Analysis
pNPG para-Nitrophenyl β-D-glucopyranoside
PRESS Model's predictive ability
RG I Rhamnogalacturonan I
xxx
RG II Rhamnogalacturonan II
RSD Residual standard deviation
RuBisCO Ribulose biphosphate carboxylase
SE Steam explosion
SmF Submerged fermentation
SPSS Statistical Package for the Social Sciences
SS Adjusted sums of squares
SSF Solid state fermentation
SW Supercritical water
TCA Tricarboxylic acid
TG-FTIR Thermo-gravimetric analyser coupled with Fourier Transform Infrared
Spectrometry
TIC Total Ion Chromatogram
TMCS Trimethylchlorosilane
VP Versatile peroxidases
1
CHAPTER 1
Introduction
Chapter 1 Introduction
2
1. Introduction
1.1. Overview
Fermentation is chiefly used for liquor production from various sources which
include barley, grapes, and molasses from sugarcane. Brewery wastes consist of dry
matter (25%), proteins (20-30%), ethers (3%), crude fibres (20%) and amino acids
(Agblevor et al., 2003, Alonso et al., 2004, Bowling et al., 2011, Rjiba et al., 2007). In
any fermentation process, the organisms like Saccharomyces cerevisiae are able to
ferment to give ethanol of up to 12-15% from the raw material, which is either used as
beer, ale or wine after clarification and as whisky, brandy or rum etc. after distillation
(depending on the source of raw material). Rest of 80-85% of raw material with unused
live or dead fermenting organisms is discarded as spent wash, which in addition to (used
as solvent) adds up to 6-15 times the actual volume of alcohol produced (Ozdural, 2004,
Strong and Burgess, 2008). The Biochemical Oxygen Demand and Chemical Oxygen
Demand (COD) values in these spent washes range about 20000-60000 ppm and 50000-
200000 ppm, respectively (Mohana et al., 2009, Wakelyn, 2006).
In wine industry, besides the spent wash and other post-fermentation wastes, raw
wastes like grape seeds, skin, pomace, marcs, stalks and skin pulp, which were
traditionally dumped after the fermentation process. However, it has been reported that
these parts are composed of valuable components like ethanol, organic acids, oil,
phenolics, colloids and dietary fibres (Arvanitoyannis et al., 2006). Presence of various
secondary metabolites has been reported antioxidants in winery wastes derived from
grapes. Grape seeds have been reported to contain polymers of flavanols like cathechins
and proanthocyanidins have been reported to be antiulcer, anti-carcinogenic and
antiviral in nature (González-Paramás et al., 2003). The polyphenols obtained from the
grape peels, marcs and stalks have been reported to lower Coronary Heart Disease
(CHD) and low density lipoproteins (LDLs) (Alonso et al., 2002, Guendez et al., 2005).
Various workers have reported fungi as the primary decomposers of cellulose,
hemicelluloses and pectins, followed by bacteria, acting as the secondary degraders in
most instances (Kumar et al., 2008, Szostak-Kotowa, 2004, Wood and Garcia-
Campayo, 1994). A large number of microbes are employed for degradation of
wastewaters from breweries. These not only reduce BOD and COD levels by about 80-
90%, but also are responsible for conversion of complex biological molecules to simple
ones, many of which serve as the sources of biofuels. These include fungi like
Chapter 1 Introduction
3
Aspergillus niger, Aspergillus fumigatus, Aspergillus niveus and Penicillium lignorum
(Rao et al., 2007, Sánchez, 2009). Thermophiles such as Clostridium thermocellum are
efficient bioethanol producers with an increased tolerance towards ethanol (Georgieva
et al., 2007, Rani and Seenayya, 1999). These organisms can be utilised in a symbiotic
manner to degrade the biomass and produce biofuel molecules at commercially viable
volumes.
However, fungi belonging to the division Ascomycota, such as Trichoderma
spp., Aspergillus spp. and Penicillium spp., are known for their biomass degrading
ability. These fungi have been well studied for their high cellulase and hemicellulase
enzyme production (Brijwani et al., 2010, Klyosov, 1987b). Such enzymes have the
potential to be used in generating important molecules such as alcohols, flavonoids,
organic acids and phenolics (Arvanitoyannis et al., 2006, Sánchez, 2009, Strong and
Burgess, 2008). A number of fungi from division Basidiomycota, or more commonly
known as white and brown-rot fungi are also known for considerable lignin degradation,
a major impediment in biomass degradation. These fungi include species such as
Phanerochaete spp. and Trametes spp. which have been known for their ability to
completely mineralize the lignin component of biomass to CO2 and H2O (Sánchez,
2009, Singh Arora and Kumar Sharma, 2010).
1.2. Problems associated with biomass degradation
Although, biomass degradation seems like a straightforward process, due to the
involvement of large amounts of factors, it is a very complex process. One of the main
factors related to the biomass degradation is the structure of the biomass itself. The
lignocellulose complex, which forms the basic structure of biomass, is made up of
complex links between cellulose, hemicelluloses and lignins (Eriksson, 1990,
Grisebach, 1985, Leistner, 1985, Brune, 2014). This structure limits the access of
enzymes in the lignocellulose complex to degrade the constituent molecules such as
cellulose, hemicellulose and lignins. In addition to this, there is no single organism that
can degrade all the components. Lignocellulosic sources like woods contain various
molecules like terpenes, phenolics and aliphatics and do not contain any transport water,
which ceases or slows down any microbial reaction significantly (Eriksson, 1990,
Sjöström, 1993). In addition to these, the enzymes which catalyse the lignocellulose
complex themselves have numerous limitations. Lignins not only act as the physical
Chapter 1 Introduction
4
barriers to the microbial degradation, but also as the non-productive binders to the
cellulases (Alvira et al., 2010, Andrić et al., 2010a, Esteghlalian Ali et al., 2000). In
addition to the above conditions, cellulases themselves have very low catalytic activity
(Sainz, 2009, Zhang and Lynd, 2002). Product inhibition is a long known phenomenon,
which adds up the difficulties for lignocellulose degradation. It has been reported by
numerous workers, especially in cellulose degradation, that cellobiose, the chief product
of cellulose degradation and glucose, the minor product, act as the inhibitors for the
cellulases (Baldrian and Valášková, 2008, Carere et al., 2008, Klyosov, 1987b, Sainz,
2009).
1.3. Various biomass degradation approaches
There are various approaches which can be applied on individual level or in
combination with different methods to improve the biomass degradation and its
successive conversion to products of interest. One of the preliminary methods of
biomass degradation consists of pre-treatment procedures such as acid or alkali
treatment or steam blowing techniques. The biomass applied to such treatments then can
be degraded by normal microbial processes. For example, a successive hydrothermal
pretreatment and fungal treatment produces greater lignocellulose degradation as
compared to regular fermentation (Papadimitriou, 2010).
Solid state fermentation (SSF) is one of the second generation bioconversion
processes for the production of industrially important molecules like bio-hydrogen and
bio-ethanol. The method employs a mixture of variable organisms and/or derived
enzymes from them in a single step to generate a considerable higher bioconversion of
biomass as compared to submerged fermentation (Brijwani et al., 2010, Kausar et al.,
2010, Lee, 1997, Sarkar et al., 2012). As the water content in SSF is equal to or slightly
higher than the substrate, the method allows greater aeration and surface attachment to
the filamentous fungi. This approach allows for a production of vast array of
lignocellulolytic enzymes. It also increases the amount of substrate to be degraded in a
single step from 1-2% in submerged fermentation to more than 10% in SSF (Lee, 1997,
Ng et al., 2010).
Chapter 1 Introduction
5
Consolidated Bio-processing (CBP) is one of the upcoming techniques which
employ a number of different strategies to improve the biomass degradation process.
Briefly, it involves a single step process to degrade the substrate and convert it in to the
product of interest. This not only saves considerable amounts of time, but also saves a
number of other resources such as raw materials and more importantly, finance, thereby
making it an economic technique. CBP involves numerous strategies such as genomic
strain improvements in the microbial populations involved in biomass degradation to
yield the products of interest, metabolic engineering and utilization of multiple
organisms, including thermophilic microbes to enhance the one-step degradation and
successive bioconversion process (Demain et al., 2005, Lynd et al., 2005).
One of the simpler methods of biomass degradations however is the
establishment of microbial symbiotic system in order to improve the degradation. To
overcome numerous limitations associated with first and second generation biomass
degradation processes, mixed fungal culture degradation has been suggested (Brijwani
et al., 2010, Chu et al., 2011). It is known that, apart from satisfactory cellulase
production, Aspergillus sp. generate highly efficient xylanases and β-glucosidases in
exceptional quantities (Betini et al., 2009, Singhania, 2012). Additionally, Penicillium
spp. can be used for lignin mineralization to enhance the overall degradation process
(Rodríguez et al., 1994, Rodriguez et al., 1996).
1.4. Aims and strategies of the project
The aims associated with this project, regarding improvement in degradation of
winery derived biomass waste are listed below. The list also briefly overviews the
strategies applied for each aim.
• Applying hydrothermal pre-treatment method followed by fungal biomass
degradation to improve the biomass degradation under the submerged fermentation
conditions.
• Application SSF by the fungi belonging to division Ascomycota and
Basidiomycota. One of the primary reasons for using this strategy was to conserve
the amount of water used in the process and increasing the amount of substrate
being degraded during the process.
Chapter 1 Introduction
6
• Application of statistical based approach to obtain a balance between submerged
fermentation and SSF based approaches. Statistical models were created from the
results based on submerged fermentation and SSF. The models were then used to
predict the optimized mixture of fungi to be used and the ratio of medium and
substrate to increase the biomass degradation.
• Metabolome analysis of fungal mediated biomass degradation in order to further
improve the biomass degradation. Unique metabolites and metabolic pathways for
individual fungi were tracked by metabolic flux analysis. The critical points in
these pathways were observed for altering the entire pathways to generate the
products of interest during biomass degradation.
• Generation of sequential biomass degradation process involving basidiomycetes,
followed by ascomycetes, in combination with application of metabolic pathway
analysis to achieve further improvement in biomass degradation.
7
CHAPTER 2
Literature review
Chapter 2 Literature review
8
2.1. Biomass composition
Grapes are one of the major global horticultural crops with an estimated production
of 69.1 million metric tonnes during 2012. Of this, approximately 80% are wine grapes
(OIV, 2013). Australia is one of the major grape producing regions and during 2012-13,
1.75 million metric tonnes of grapes were crushed for wine production (ABS, 2013).
Wineries produce large amounts of biomass waste, amounting to about 50-60 % of total
grape crushed during the process (Devesa-Rey et al., 2011, Gerling, 2011). In addition
to this, wineries have high wastewater generation of as much as 8 litres per bottle of
wine (Christ and Burritt, 2013). Winery wastes have been classified as pollutants by the
European Union and subsequent treatment and post-product processing are required to
make these wastes less hazardous (Devesa-Rey et al., 2011).
Grape wastes consist of grape berries and plant-derived fibres, grape seeds, skin,
marcs, stalk and skin pulp. Unlike other agricultural by-products, grape biomass waste
has limited use as an animal feed stock due to its poor nutrient value and low
digestibility (high concentration of tannins and polyphenols). The polyphenols also slow
down microbial utilization of this biomass. In addition, the winery grapes have high
amounts of potassium (1.2-7.3 %) and acidity and thus have limited application as either
animal feed or agricultural compost fertilizer. As a result, a majority of these winery
biomass wastes end up as toxic landfill waste (Devesa-Rey et al., 2011).
Fermentation is mainly used for liquor production from various sources which
include barley, grapes, and molasses from sugarcane. Brewery wastes consist of dry
matter (25%), proteins (20-30%), ethers (3%), crude fibres (20%) and amino acids. The
major components of dry grape biomass waste are cellulose, pectins and lignins
(including tannins).
2.1.1. Cellulose
Cellulose is the most abundant and most studied polysaccharide and constitutes
the basic structure of cell walls of all plants and algae. It comprises about 20-50% of
plant biomass and is the largest contributor to the lignocellulose complex, which
comprises cellulose, lignin, hemicellulose and trace amounts of protein and fatty acids
(Eriksson, 1990, Hori, 1985, Sánchez, 2009). Cellulose chiefly occurs in the primary
Chapter 2 Literature review
9
layer of the plant cell wall and its presence decreases in the secondary and tertiary cell
wall layers. The molecule occurs as an aggregated, lateral microfibril network, which
form mesh-like structures of a highly complex nature, which has only been partially
understood until now even by employing various complicated methods like NMR, X-
ray crystallography and electron microscopy (O'Sullivan, 1997, Fernandes et al., 2011).
Figure 2.1. Fragment of cellulose with the reducing, non-reducing and cellobiose component of
cellulose
Cellulose is an almost linear molecule made up of β-D-glucopyranose, linked by
β-1, 4-polyanhydroglucose with cellobiose as the smallest repetitive unit (Figure 2.1),
thus, is a β-glucan (Kumar et al., 2008, O'Neill et al., 2004, Stone, 1958). Until the
1970s, glucose and cellobiose were considered the monomer units of cellulose.
However, by applying model-building strategies, it has been more recently confirmed
that glucose is not the main basic structural component of cellulose, but rather
cellobiose is. Interestingly, cellobiose is a disaccharide, made up of two glucose units
linked by β-1, 4 bonds (O'Sullivan, 1997).
The microfibrils forming the cellulose molecule are arranged in a parallel
manner, thus giving the whole molecule a defined crystalline nature. The individual
glucopyranose structures have been reported not only linked to each other by covalent
bonds, but also with the neighbouring pyranose molecules, lying in a parallel fashion by
Hydrogen bonds and Van der Waals forces of attraction (Figure 2.2). This makes the
whole molecule highly rigid and tolerant to natural degradation compared to other
biopolymers, which are more heterogeneous; cellulose is made of only single β-4-D-
pyranose structure (Dashtban et al., 2009, Eriksson, 1990, McNeil et al., 1984, Wood
and Garcia-Campayo, 1994).
Chapter 2 Literature review
10
Figure 2.2. Hydrogen bonds in cellulose molecule, represented by dashes (---).
Cellulose molecule has been long known as the molecule with a very high
degree of polymerization, which varies from 6000 in certain algae to 14000 in cotton
(Nakamura et al., 2002, Zieher, 2010), with a fold occurring every 30 nm (Fengel, 1971,
O'Sullivan, 1997). Crystallographic methods showed that the native cellulose (cellulose
isolated from the plants) is about 70% of crystalline and 30% amorphous. The minor
amorphous nature of cellulose makes it slightly susceptible to degradation by various
enzymes and other chemicals (McNeil et al., 1984, O'Sullivan, 1997, Wood and Garcia-
Campayo, 1994).
2.1.2 Hemicelluloses
Various hemicelluloses have been reported to be directly or indirectly associated
with cellulose. They include sugars such as xylans, xyloglucans, mannans, pectins,
homogalacturonans, rhamnogalacturonans, arabinans and galactans (Kumar et al., 2008,
Pérez et al., 2002).
2.1.2.1. Xylans
Xylans are the most numerous of all hemicelluloses, composing about 25% of
all saccharides, second only to cellulose to which they are tightly bound by covalent and
other bonds in a complex manner (Fengel, 1971, Goksu et al., 2007, Hansen and
Plackett, 2008) (Figure 2.3).
They are made of β (1, 4) D-xylopyranoses with non-crystalline hexose uronic
acids such as galacturonic acid (Figure 2.3) attached to them.
Chapter 2 Literature review
11
Figure 2.3. (A) Structure of xylopyranose, which is the primary component of Xylans. (B) Structure
of β-galactopyranose molecule. (C) Structure of 4-O-methyl glucopyranoxyl uronic acid (MeGLcA),
which is a primary component of glucurunoxylans. (D) Structure of arabinose (arabinofuranose).
The xylans are further classified as homoxylans, glucurunoxylans, arabino-
glucurunoxylans, arabinoxylans (AX), glucuruno arabinoxylans and heteroxylans,
depending on the types of substitutions made in xylose chain.
2.1.2.2. Xyloglucans
Chiefly related with the seed cotton and primary cell walls of numerous
dicotyledons, xyloglucans are multidimensional hemicelluloses composed of various
monosaccharides such as D-glucose, D-xylose and D-galactose as common components
in varied ratios joined by β-1, 4 linkages. The core generally consists of a β-(1, 4)
glucan skeleton to which xylose is attached by α-(1, 6) linkages along with fucose,
galactose or arabinose (in minor concentrations) (Hori, 1985, Hayashi, 1989).
2.1.2.3. Mannans
Mannans have been classified into three classes consisting of galactomannan;
linear mannans, glucomannans and galactoglucomannans (Table 2.1). These polymers
have been reported in numerous plants like date, coffee (Meier, 1958, Wolfrom et al.,
1961), ivory nut (Aspinall et al., 1953, Nieduszynski and Marchessault, 1972),
Arabidopsis (Chanzy et al., 1984, Handford et al., 2003) and numerous algae. They also
occur in wood in conjugation with β (1, 4) D-glup (Alonso-Sande et al., 2009, Franz,
1973).
Chapter 2 Literature review
12
Table 2.1 Major classification of different mannans
Mannans Source Properties References Glucomannans Angiosperms,
Gymnosperms
Linear β (1, 4) D-manp and D-glup chains. DP of 15-80. Amorphous, heavy acetylation for cellular defence
(Brink and Vries, 2011, Chanzy et al., 1984, Handford et al., 2003, Meier, 1958, Moreira and Filho, 2008, Nieduszynski and Marchessault, 1972, Northcote, 1972, Painter, 1983, Petkowicz et al., 2007)
Galactomannan Angiosperms [β (1, 4) D-manp and D-glup chains] attached to α (1, 6) D-galp. High molecular weight and high DP. Variations: β (1,3)D-manp attached to β (1,4)D-galp
(Dea and Morrison, 1975, Ishurd et al., 2004)
Galactoglucomannans Angiosperms Most numerous of all mannans. Randomized β (1, 4)-D-glup and D-manp linkages.
(Moreira and Filho, 2008, Northcote, 1972)
2.1.2.4. β-glucans
β-glucans are the generic names for the non-cellulosic polysaccharides grouped
as β (1, 3), β (1, 4) and β (1, 3; 1, 4) linked D-glucans (Figure 2.4). They have been
reported to be restricted to members of the Poaceae (formerly Graminae) family
(McNeil et al., 1984); however, they also have been found in lichen and fungi (Fontaine
et al., 2000, Lazaridou et al., 2003, Lazaridou et al., 2004). Additionally, β (1, 3; 1, 4)
glucans have been reported in cotton (Li and Brown, 1993). The polymers have been
reported in seed endosperms of wheat (Philippe et al., 2006), barley (Coles, 1979,
Seefeldt et al., 2009), oat and rice (Brown et al., 1997). They are reportedly formed in
golgi membranes by β-glucan synthetase (Autio, 2006, Tsuchiya et al., 2005).
Chapter 2 Literature review
13
Figure 2.4. General structure of a portion of β-glucan polymer. The arrows mark the presence of β
(1, 3) glycosidic bonds.
The polymers are linear in nature and are made up of D-glup molecules linked
by both β (1, 3) and β (1, 4) glycosidic linkage either separately [as in (1, 3) and β (1, 4)
glucans] and simultaneously [β (1, 3; 1, 4) glucan]. Additionally, trace amounts of
arabinose and xylose also have been found in association with these polymers
(Lazaridou et al., 2003, Lazaridou et al., 2004, McNeil et al., 1984).
Amongst the two types of glycosidic bonds, β (1, 4) bonds are the majority
(about 70%) with β (1, 3) constituting the remaining portion. β (1, 4) bonds are always
located on the reducing ends of the polymer and form continuous adjacent linkages. β
(1, 3) bonds, on the other hand, do not form continuous linkages (Buliga et al., 1986,
Woodward et al., 1983).
Three types of molecules, namely cellobiosyl sugars, cellotriosyl sugars and
cellodextrins, were reportedly released upon the actions of bacterial endoglucanase
enzymes; β-D-glucanohyrolase from Bacillus subtilis (Carpita, 1996) and endo-(1, 3),
(1, 4) -β-glucanase from B. amyloliquifaciens (Tsuchiya et al., 2005). These molecules
comprised 2-3 β (1, 4) linked molecules, which constitutes about 90% of the polymer.
The rest of the polymer is composed of about 4-15% β (1, 4) linked D-glup residues.
Most researchers have reported the presence of cellotriosyl and cellotetraosyl residues
as the smallest components obtained by glucanase action. These molecules are linked to
each other by β (1, 3) glycosidic linkages (Burton and Fincher, 2009, Burton et al.,
2010, Tsuchiya et al., 2005) at the O-3 or O-4 positions (Burton and Fincher, 2009).
However, the ratio of β (1, 4) and β (1, 3) linkages vary from plant to plant and is highly
Chapter 2 Literature review
14
random (thus, determining functional properties of the polymers in different species)
with variations from 1.15:1 in sorghum to 2.6:1 in other cereals like barley (Burton and
Fincher, 2009, Burton et al., 2004).
Among the constituent oligosaccharides, cellotriosyl makes up the major part (~
90%) and cellotetraosyl comprises the remainder (Burton and Fincher, 2009). Due to
these variations, the structure and the molecular weight of β-glucans differ significantly
across different species. The molecular weight of oat β-glucans has been reported as 20
kDa by size exclusion chromatography (Liu and White, 2011, Vaikousi et al., 2004).
2.1.2.5. Pectins
Pectins are complex polysaccharides composed of 1, 4-D-galacturonic acid
residues. These polysaccharides are localized chiefly on the primary cell wall region of
plant tissue with components covalently and ionically bound to each other to give the
pectic compounds an overall complex structure (Burton et al., 2010, Caffall and
Mohnen, 2009, O'Neill et al., 2004, Pérez et al., 2003). Due to this heterogeneous
nature, pectins have a range of molecular weights (from 50-100 kDa) and differential
chemical structures (Pérez et al., 2003). Apart from α-1, 4-galacturonic acid (as the
main chain), pectins also contain substituted residues of rhamnogalacturonan I. The Gal
A is in 4C1 chair conformation with a carboxyl group in the centre, surrounded by
various linkages and ionic covalent bonds (Burton et al., 2010, O'Neill et al., 2004).
Figure 2.5. General structure of pectin chain, which is supposed to have in it the components like
RG I, XGA, HG and RG II, attached either in a linear manner or as the side chains to pectic chain
(Adapted from Mohnen, 2008).
Chapter 2 Literature review
15
Pectins are currently distinguished in five classes which include polysaccharides
such as rhamnogalacturonan I (RG I), rhamnogalacturonan II (RG II),
homogalacturonan (Martens et al.), xylogalacturonan (von Gal Milanezi et al.) and
apiogalacturonan (AP) (Figure 2.5). They form one of the larger parts of primary cell
wall along with hemicelluloses and make up about 35% of the total primary cell wall
composition. They function in various important aspects such as growth, cell and
organogenesis, cell-cell adhesion, defence, abscission, ionic binding and cell maturity
(Naran et al., 2008, O'Neill et al., 2004, Ridley et al., 2001, Vincken et al., 2003).
Although they are found in both Type I and Type II primary cell walls, their chief
occurrence is in the former where they play an important role in cell-cell adhesion by
forming a gelatinous region, which in turn affects the porosity of the cell wall. This
disrupts H-bond formation in cellulose and xyloglucans, thus allowing the cell wall to
expand and insert new molecules in its structure without breakage (Bouton et al., 2002).
2.1.3. Lignins
Lignins are the second most abundant biopolymers after cellulose. In most
plants, especially, in higher plants, they function as the prominent components of
xylem. Due to their hydrophobic nature, they aid in water transport throughout the plant
system. This nature also makes them tolerant towards the biodegradation (Boerjan et al.,
2003).
Lignin biosynthesis and deposition occurs in the secondary cell wall after the
cell has completed its growth. During primary growth, cellulose and hemicellulose are
deposited on the cell wall, followed by sequential deposition of lignin in the secondary
walls. However, most lignin deposition takes place in the next stage, which is followed
by cell death (Baucher et al., 1998).
Lignins are made up of hydroxycinnamyl alcohols i.e. p-coumaryl alcohol,
coniferyl alcohol and sinapyl alcohol (Figure 2.6) and their methoxy derivatives
(Boerjan et al., 2003, Higuchi, 2006, Vanholme et al., 2010). The chief methoxy
products of these three hydroxy cinnamyl alcohols, which form the structural units of
lignin polymers, are known as general p-hydroxyphenyl units (H), general guaiacyl
units (G) and general syringyl unit (S) (Baucher et al., 1998, Boerjan et al., 2003,
Higuchi, 2006, Ralph et al., 2004, Vanholme et al., 2010). However, due to the very
complex nature of the lignin polymer, they have not been successfully isolated and
Chapter 2 Literature review
16
characterized (Vanholme et al., 2010) from natural sources. Higher plants have greater
amounts of G and S units, while grasses and other monocots are rich in H units
(Baucher et al., 1998, Boerjan et al., 2003, Yang et al., 2010a).
Figure 2.6. Structures of (A) Hydroxycinnamyl alcohols-p-coumaryl alcohol, coniferyl alcohol and
sinapyl alcohol and (B) derived structural units H, G and S.
During lignin formation, the p-hydroxycinnamyl alcohols interact with either of
the units (H, G or S) to form monolignols, which are secondary precursors. Besides
monolignols, there are at least three major groups in lignins which form a major part of
the lignin polymer. These include acetylated lignin units, ferulates and dihydroxy
coniferyl alcohol and guaiacylpropane-1, 3-diol derived units. Acetylated lignin units
are derived from two or more lignin monomers like p-coumaryl alcohol, sinapyl
alcohol, 5-hydroxyconiferyl alcohol, hydroxycinnamyl aldehydes, hydroxybenzyl
aldehydes, hydroxycinnamate esters, dihydrocinnamyl alcohols, arylpropane -1, 3-diol,
aryl glycerols, hydroxycinnamyl acetates, hydroxycinnamyl p-hydroxybenzoates,
hydroxycinnamyl p-coumarates and tyramine hydroxycinnamates. These molecules are
primarily attached at γ-positions to form acetylated units. They may be regarded as the
foremost of all lignin components, constituting more than half of the lignin polymer (Lu
and Ralph, 2002).
Ferulates are formed chiefly of hydroxycinnamate esters and guaiacyl units of
monolignols. They are one of the important groups of lignin polymers, although, they
are not as abundant as the acetylated groups. However, these molecules and their
hydroxy derivatives provide important polysaccharide-polysaccharide crosslinking,
which is vital for lignocellulose binding, thus creating strong bonds in the
Chapter 2 Literature review
17
lignocellulosic complex. The ferulates and their derivatives are chiefly found in the
Poaceae family where they form the centre for aggregation to form lignin polymer
(Ishii, 1997, Ralph et al., 1995).
The third and minor group of lignin forming units are derivatives of dihydroxy
coniferyl alcohol and guaiacyl propane-1, 3-diol, which are respectively formed by their
association with guaiacyl unit. As their name suggests, they are chiefly found in
conifers (Gymnosperms) and are minor elements in angiosperms (both monocots and
dicots). The chief linkages by which monolignols and their derivatives join each other
to form lignin polymers are β-O-4 and β-5, 5-5 dibenzodioxocin and 4-O-5 biphenyl
ether linkages. Additionally, β-β-resinol and β-1-oligomer couplings have also been
reported (Ralph et al., 2004).
Among these, β-O-4 linkages are most common and most readily favoured
between the monolignols. Additionally, 5-5-dibenzodioxocin related linkages show
some unexpected 8-membered ring formation by the process of radical coupling of β-O-
4 bonds in rings, followed by an internal trapping of an external ring structure. These
structures form branching points of lignins. In addition, due to their higher complexity
compared to β-O-4 linkages, they are difficult to break open during degradation
processes (Ralph et al., 2004).
During lignification, β-O-4 coupling leads to the formation of two isomers of β-
ether units called erythro- and threo- units, formed in approximately equal amounts.
These two isomers differ significantly in their physical and chemical properties, with
erythro- isomers having higher degradability under alkaline conditions (Kirk, 1984).
The β-β coupling occurring in monolignols, which also occurs in lignins, forms
resinols which are the initiation points for lignin polymer formation. However, the type
of β-β coupling differs in softwoods and in grasses, wherein β-β coupling appears as 4-
O-5 links in the former (Önnerud and Gellerstedt, 2003). In grasses, these bonds are
probably formed due to the prevention of –OH group addition to quinone methide
groups under acidic conditions, which contrasts to softwood β-β coupling, wherein,
addition of –OH groups assist the bond formation (Ralph et al., 2004, Zhang et al.,
2006).
β-1 couplings are the least common of all the linkages found in lignins due to
their high complexity with respect to the other linkages (Lundquist et al., 1967, Weng
and Chapple, 2010). The linkage is found as the secondary type of bond in already
present β-O-4 linkage. These types of linkages occur between G-G and G-S units and
Chapter 2 Literature review
18
are prevalent in conifers and softwoods such as birch (Li et al., 1997), poplar
(Ämmälahti et al., 1998) and spruce (Zhang et al., 2006). However, syringyl based (S)
β-1 linkages have been reported in grass fibres (Ralph et al., 2004, Zhang et al., 2006).
Apart from the previously discussed major molecules, a minor amount of
phenolics are also associated with lignins. Those form a minor portion in softwoods, but
are found in larger amount in grasses in their acetylated form (Boerjan et al., 2003,
Ralph et al., 2004). Some of the Poaceae family members contain as high as 17%
phenolic content in lignin present as acetylated γ-p-coumarate derivatives. The
phenolics are highly complex in their occurrence in the lignin structure and the
mechanism of their structure and involvement in lignification is still unknown (Liu et
al., 2011, Ralph et al., 2004).
Other molecules that have been reported to be associated with lignins have been
determined by the method of pyrolysis of lignins by Thermo-gravimetric analyser
coupled with Fourier Transform Infrared Spectrometer (TG-FTIR). These include
phenol, methoxy phenol, guaiacol and cresol in addition to simpler molecules like
methanol, formaldehyde, acetaldehyde, acetic acid and some simple hydrocarbons with
CO, CO2 and H2O (Liu et al., 2011).
2.2. Biomass degradation
Numerous agricultural activities, especially harvesting and post-harvest
processes generate significant amounts of biomass wastes globally. It has been
estimated that the major crops; corn, barley, oat, rice, sorghum and sugarcane generate
about 73.9 × 1010 metric tonnes of biomass waste per year (Kim and Dale, 2004). The
global agriculture sector plays a major role in generating waste biomass. As of 2012,
reported global agricultural production was about 2.16 × 1010 metric tonnes of which
only 3.43 × 109 metric tonnes of processed products were produced. In addition, the
Food and Agriculture Organization (FAO) of the United Nations indicated the
generation of about 1.06 × 1010 metric tonnes of agricultural wastes during 2011 of
which 5.62 × 108 metric tonnes was of lignocellulosic nature (FAO, 2015). Most of this
biomass waste remains underutilized. The following sections discuss some of the issues
which limit biomass degradation, particularly in terms of obtaining by-products of
commercial interest. Possible solutions to achieve biomass degradation by micro-
organisms, especially fungi, will also be discussed.
Chapter 2 Literature review
19
2.2.1. Biodegradation: issues and problems
Due to their abundance, lignocelluloses have been long considered as a good
substrate for microbial degradation processes. From the late 1970s, several workers
have reported the processing of lignocellulose complexes to derive single cell proteins
(Poulsen and Petersen, 1988); biofuels like ethanol, H2 and CH4 (Demain, 2014, Pauly
and Keegstra, 2008) and metabolites such as polyphenols and flavonoids (Arnous and
Meyer, 2009, Pinelo et al., 2006, Vicens et al., 2009) have been obtained, albeit in very
low quantities. Despite this research, biodegradation of the lignocellulose structure faces
numerous problems which have not yet been fully resolved. Some of the chief issues
related to lignocellulose degradation are given below.
2.2.2. Structure of lignocellulose complex
As mentioned earlier, the lignocellulose complex is made up of three major
components, cellulose, hemicelluloses and lignins, and other components
(glycoproteins, lipids, free amino acids and other metabolites) which interact with the
major components (Eriksson, 1990, Grisebach, 1985, Leistner, 1985). Among the major
components, lignin forms the outermost structure of lignocelluloses and is the most
complex molecule. Unlike cellulose, lignin is made up of wide range of aromatic and
aliphatic molecules (Eriksson, 1990, Grisebach, 1985, Leistner, 1985). Over the years
workers have proposed a generally accepted model for lignocelluloses (Fengel, 1971,
O'Sullivan, 1997, Chundawat et al., 2011) as given in Figure. 2.7.
Chapter 2 Literature review
20
Figure. 2.7. General structure of lignocellulose complex.
The lignocellulose system is chiefly associated with the secondary cell wall of
the plants as the primary cell wall has been reportedly composed of about 90-95%
cellulose with rest of the components being either of the hemicellulosic components,
xylan or glycoprotein (Carpita, 1996, Singh et al., 2009).
Most microbial or chemical degradation methods have not been successful
against lignin due to its variable and intractable nature (Baucher et al., 1998, Boerjan et
al., 2003, Ralph et al., 2004). Hemicelluloses include various types of molecules packed
together by H-bonds. Other molecules are equally complex, such as pectins which are
composed of as many as five different polysaccharides (10-50 saccharide molecules
long) (Mohnen, 2008, Naran et al., 2008, Ridley et al., 2001). Pectins are highly varied
and consist of galactose (or galacturonic acid) rich residues called homogalacturonans,
rhamnogalacturonans, arabinogalacturonans and xylogalacturonans (Burton and
Fincher, 2009, Caffall and Mohnen, 2009, Naran et al., 2008).As yet, no single
microorganism has been reported that can degrade such a wide range of biomolecules.
The amount of hemicelluloses also determines the ease of biodegradation of
lignocellulosic complexes. As mentioned earlier, cellulose microfibrils are surrounded
by a layer of hemicelluloses and lignins (Fengel, 1971, O'Sullivan, 1997).
Due to their very high complexity, hemicelluloses increase the recalcitrant
nature of the overall lignocellulose complex. The recalcitrant nature of hemicelluloses
increases due to their high levels of cross-interactions with cellulose microfibrils
(Brijwani et al., 2010, Carpita and Gibeaut, 1993, Zieher, 2010).
Chapter 2 Literature review
21
2.2.3. Microbial and chemical limitations
As discussed above, due to the varied nature of lignins and hemicelluloses, there
is no single organism that can degrade all the components of ligniocellulose.
Lignocellulosic sources like woods contain various molecules such as terpenes,
phenolics and aliphatics and do not contain transport water, which ceases or
significantly slows down any microbial reaction (Eriksson, 1990, Sjöström, 1993).
Quinones and some flavonoids, which inhibit microbes and microbial enzymes
function, have been reported from many plants, especially woody ones from the
Leguminosae family (Dalbergia plants) and Acacia, (Leistner, 1985).
2.2.4. Enzymatic limitations
One of the other major factors impeding biomass degradation is the physical
interaction of major enzymes, i.e. cellulases and hemicellulases, with their respective
substrates. Lignins not only act as physical barriers to microbial degradation, but also as
non-productive binders of cellulases (Alvira et al., 2010, Duarte et al., 2012b). In
addition to the above conditions, the cellulase enzyme complex overall has very low
catalytic activity. In Trichoderma spp., one of the highest producers of cellulases, it has
been observed that the specific activity of celluloses on their substrates is very low as
compared to the other enzymes (Sainz, 2009, Zhang and Lynd, 2002). Cellulose activity
on filter paper has been shown only at 0.6-0.7 IU/mg of enzyme, which corresponds to
a catalytic constant of 0.5-0.6/sec for the enzymes, which is well below glucoamylase
activity of 69 IU/mg corresponding to a catalytic constant of 58/sec (Klyosov, 1987b).
Additionally, within the cellulases, activity varies leading to decreased reaction rates or
complete inhibition. Within the same experimental set up, it was reported
that carboxymethyl cellulase and xylanases have activities of about 350 IU/ml and 500
IU/ ml, respectively, as against cellobiohydrolases (8 IU/ ml) and β-glucosidase (8
IU/ml) (Andrić et al., 2010a, Hideno et al., 2011, Juhász et al., 2005).
2.2.5. Product inhibition
Product inhibition is a well-known phenomenon which adds to the difficulties of
lignocellulose degradation. It has been reported by numerous workers, especially with
respect to cellulose degradation, that cellobiose (the chief product of cellulose
degradation) and glucose (the minor product) act as cellulase inhibitors (Baldrian and
Chapter 2 Literature review
22
Valášková, 2008, Duarte et al., 2012b). The main reason for this is the exceptionally
low catalytic activity of β-glucosidase as compared to the other enzymes present in the
cellulase enzyme complex (Baldrian and Valášková, 2008, Hideno et al., 2011,
Klyosov, 1987b). Additionally, the glucose produced in the process is unable to be
oxidised or reduced and so cannot be easily remediated from the system (Kim et al.,
2012a, Liu et al., 2012, Liu and White, 2011, Sainz, 2009), i.e. there is an inability of
many cellulolytic microbes to utilize it during simultaneous cellulolytic processes
(Baldrian and Valášková, 2008). It has been reported that concentrations of glucose and
cellobiose as low as 6% inhibit the enzymatic catalysis of cellulose (Sainz, 2009). In
the case of lignin and hemicellulose degrading enzymes, much has been reported about
the enzyme kinetics, especially inhibition and competition kinetic mechanisms.
However, xylanases are probably inhibited due to cellobiose as they are conjugated with
cellulose (Qing et al., 2010).
2.3. Biomass pre-treatment
Pre-treatment forms one of the important steps in biomass conversion towards
commercially/industrially useful products. The process breaks several linkages and
bonds between cellulose, hemicelluloses and lignins, exposing considerable amounts of
hemicelluloses and celluloses to subsequent enzyme/microbial based degradation
(Sarkar et al., 2012). There are several physical, chemical and biological methods of
biomass pre-treatment, which are used according to the biomass type, source and
required type of product recovery. No single pre-treatment is adequate for complete
biomass conversion. However, a mix of two or more types (physical-chemical,
physical-biological and chemical-biological) has been observed to generate higher
biodegradation (Khuong et al., 2014, Kovacs et al., 2009, Lu et al., 2013, Maeda et al.,
2011, Pribowo et al., 2012).
2.3.1. Physical pre-treatment methods
These methods are generally applied to increase the overall surface area of
biomass, thus exposing higher amounts of cellulose and hemicellulosic residues. This
leads to a decrease in recalcitrance, enabling subsequent chemical, enzymatic or
microbial treatment to be more effective (due to the availability of a higher proportion
of substrate). There are various types of physical pre-treatments; however they can be
Chapter 2 Literature review
23
broadly categorised as milling, irradiation, extrusion, hydrothermal treatment and
expansion.
2.3.1.1. Milling
Milling is a very common method applied in biomass pre-treatments. There are
several types of milling methods used including ball milling, hammer milling, grinding,
and chipping. Milling and grinding break several linkages between biomass
components, thus decreases the particle size and increasing the overall surface area.
These techniques are not necessarily very efficient in themselves, but they enhance the
effectiveness of any following pre-treatments such as steam explosion or hydrothermal
treatment (Karimi et al., 2013).
The economic viability of the entire milling process varies according to the
actual and required particle size of biomass substrates, total moisture content and nature
(high lignin or low lignin) of the waste. Particle size is one of the major factors we hope
to control by physical pre-treatment processes. A very high particle size decreases the
efficiency of any following chemical or biological conversion, whereas particles of very
fine size can lead to aggregation, which results in substrate channelling in liquid-based
treatments. This not only decreases efficiency of the following treatment, but also the
economic viability of the entire process. The amount of energy required for wheat straw
milling to 0.8 mm particle size was observed as 51.6 kWh per metric tonne of biomass
(Sarkar et al., 2012). Similarly, the process of milling followed by 2 mm sieving and
screw pressing was observed to utilize 27.7 kWh of energy and 0.6 m3 of water per
metric tonne, while multi-sieving followed by pulping and dispersion utilized about
83.5 kWh of energy and 1.1 m3 of water per metric tonne (Bernstad et al., 2013). The
examples show that the economic viability of the entire process decreases considerably
as the number of physical treatment processes increase.
2.3.1.2. Irradiation and sonication
Irradiation methods use the property of polar bond vibrations to expand the
biomass, subsequently causing an internal explosion, resulting in the breakage of
several bonds and other linkages. Milling can be utilized to decrease the particle size,
although it is not an essential pre-process for microwave irradiation. The process
Chapter 2 Literature review
24
requires an aqueous medium due to its effects on polar bonds in the biomass. It has been
observed that microwave-assisted heating in the presence of water causes an
endothermic deviation peaking at 134°C, followed by a decrease in heat flow at 170°C
due to depolymerisation. The crystallanity of cellulose decreases considerably at 180°C
due to an increase in the rotating ability of polar bonds, which also leads to a further
decomposition of amorphous cellulose. Microwave products have a calorific value of
33.1 kJ/g against conventional pyrolysis (30 kJ/g). Similarly, microwave treatment has
been shown to be economically viable as the heat of combustion is lower (about 18
kJ/g) in microwave-treated products compared to conventional pyrolysis (25 kJ/g) at
280°C (Budarin et al., 2010). Another method of irradiation is the electron beam, which
uses variation in magnetic fields to rotate the polar bonds in biomass. This process
results in room temperature disruption in cellulose and hemicellulose structures and
some mineralization of lignins. Recently, it was observed that the degree of
polymerization of cellulose in hardwood Kraft pulp and bamboo chips decreased almost
2.5-3-fold following a radiation dose of 15 kGy. However, this dose of electron beam
radiation was not very effective on hemicelluloses (Ma et al., 2014).
A more common pre-treatment method is sonication which has been
increasingly used in recent research. The process utilizes ultrasonic frequencies ranging
from 16-100 kHz to disrupt linkages and bonds in the biomass. In an alternating
sequence, cavitation bubbles are formed and imploded in the biomass containing
aqueous phase, thus increasing local temperature and pressure at minute scales, causing
a physical breakdown on lignocellulose components. It has been reported that
sonication of 20-25 minutes at 20-25 kHz in alkaline conditions removed about 80%
lignin and hemicellulose and more than 95% cellulose from sugarcane bagasse. Variable
but promising results from numerous biomass substrates have indicated this method to
be a potentially simpler technique to improve biomass conversion in a direct/indirect
process (Karimi et al., 2013, Rehman et al., 2013).
2.3.2. Physico-chemical treatment
These methods utilize both physical and chemical (mild and strong) techniques
to decompose/degrade the lignocellulose complex. As mentioned earlier, standalone
physical methods such as milling and pyrolysis require higher amounts of input energy
to break down lignocellulose complex. They are not highly economical treatments, with
Chapter 2 Literature review
25
an efficiency of about 45-55% bioconversion. Microwave, although more energy and
time viable, is also only able to convert about half of the biomass (Sarkar et al., 2012).
It has been widely observed that the application of more than one type of
treatment increases biomass conversion efficiency. Physico-chemical techniques utilize
the physical attributes of temperature and pressure along with the chemical attributes of
water, acids and alkaline chemicals to either separate biomass components or alter the
recalcitrant structure to make it more degradable. Hydrothermal treatment (HT), steam
explosion (SE), liquid hot water treatment (LWH) and ammonia fibre explosion
(AFEX) are some of the most commonly used physico-chemical methods used at
laboratory and industrial scales.
Hydrothermal treatment utilizes the property of the molal ionisation product
(Kw) of water for biomass hydrolysis. The Kw of water is dependent on the temperature
of the system. Kw increases from 0.64 × 10-14 at 18°C to 54.6 × 10-14 at 100°C and 268
× 10-14 at about 150°C. Kw reaches its highest value at about 250°C, when it is 634 ×
10-14. This increase in ionic product is important as it is directly proportional to the
water solubility. The solubility of substrates (such as cellulose and hemicelluloses)
increases with the rise in Kw value. This relationship is always taken into account when
applied to several hydrothermal processes such as hot-compressed water (HCW), Steam
explosion (SE) and supercritical water (SW) treatments. Therefore, temperatures around
250°C or above are applied in these processes (Ando et al., 2000, Goto et al., 2004). It
was observed that autoclaving 130°C for 1 hour resulted in about 11.7 % increase in
dissolved organic carbon, indicating a mass loss of co-mingled household waste.
However, due to the increasing Kw, this conversion increased to about 16% at 200°C.
Although, considerable amounts of cellulose (13%) and some hemicellulose (6%) were
recovered by autoclaving at 160°C, lignin loss was absent or negligible in samples
treated at 200°C (Papadimitriou, 2010).
SE utilizes chemical catalysts such as H2SO4 or SO2 to decompose the biomass,
albeit at lower temperature with respect to HT. Experiments on Douglas fir softwood
chips showed that with the mild H2SO4 treated samples, an SO2 based SE at 120°C
resulted in 80-90% hemicellulose release within 1 hour of treatment (Shevchenko et al.,
2000).These methods are simple in nature, however have major drawbacks in the
requirements of high temperature systems (>160°C) with or without corrosive
Chapter 2 Literature review
26
chemicals and the generation of considerable hydroxymethyl furfurals (HMF) and
furfurals (FF) which, if present in a microbial degradation system, cause severe growth
inhibition (Sanchez and Bautista, 1988, Duarte et al., 2012b).
Ammonia fibre explosion (AFEX) is a type of SE which utilizes NH3 instead of
acids or SO2-based catalysis. The conditions are similar to those in SE; however the
alkaline conditions produced due to NH3 mean that inhibitors such as HMF and
furfurals are not generated in this process. Similar to SE, this method is efficient for
biomasses with considerable hemicellulose and moderate cellulose content but cannot
reduce or mineralize the lignin component of the lignocellulose complex. Also, the
higher costs associated with NH3 limits the use of AFEX in industrial applications
(Sarkar et al., 2012).
2.3.3. Chemical treatments
Chemical treatments use strong alkaline or acidic reagents in diluted forms
under room temperature or elevated temperatures. Although oxidising agents such as
H2SO4 and NaOH are frequently used, SO2, CO2, formic acid and acetic acid have also
been used.
2.3.3.1. Acid pre-treatment
Acid pre-treatment is one of the most common methods used in research and in
limited industrial processes. Dilute acids are used at elevated temperatures of around
100-120°C. They cause substantial breakage of hydrogen bonds in cellulose and
especially in hemicelluloses. This process decreases the crystallanity of cellulose to a
considerable extent, thus increasing its degradability in follow-up microbial degradation
processes. Acidic treatment causes monomerization of hemicelluloses to produce free
pentose sugars in the aqueous phase. In an SE experiment where 3% H2SO4 was
applied at 120°C, the release of about 14-36% pentoses and 18-27 % hexoses was
observed (Shevchenko et al., 2000). However, up to 90% hemicellulose removal has
been reported (Karimi et al., 2013). Although this method in itself is cost effective as
compared to milling, pyrolysis and AFEX, it has its own disadvantages. The
requirement of high temperature conditions in acid pre-treatment enhances reactor
vessel corrosion. Thus, acid-tolerant vessels are required, which increases overall
Chapter 2 Literature review
27
expenses. Besides, this process, like HT, generates moderate quantities of inhibitory
molecules such as HMF and furfural in addition to the loss of sugars due to their linkage
with acid-soluble lignins. However, the major costs involved are due to downstream
neutralization of acid-treated biomass which involves expensive alkaline treatments and
generates considerable amounts of wastewaters (Karimi et al., 2013, Sarkar et al.,
2012).
2.3.3.2. Alkali pre-treatment
Alkaline treatment is more focussed towards lignin removal as opposed to
cellulose or hemicellulose degradation, and is widely used in paper and pulp industries.
The process specifically degrades lignin in substantial proportions allowing the
exposure of cellulose and hemicellulose to downstream catalysis. As opposed to acid
pre-treatment, alkali treatments do not require elevated temperatures. An application of
2% NaOH in combination with ultrasonic treatment for 20 minutes at 50°C has been
reported to remove about 81% hemicellulose and 91% lignin from sugarcane bagasse.
Follow-up bacterial treatment with Cellulomonas flavigena (MTCC 7450) resulted in
about 91% glucose yield of the theoretical maximum (Velmurugan and Muthukumar,
2012). In recent experiments, an application of 5% NaOH at 121°C for 1 hour resulted
in a decrease of lignin in sugarcane bagasse from 23.5% to 5.2%. It also resulted in the
loss of about 15.5% xylan. However, glucan content increased by about 28%.
Subsequent biodegradation by the fungus Phlebia spp. MG-60 resulted in about an
ethanol yield of 66% of the theoretical maximum (Khuong et al., 2014). One of the
issues related with alkaline treatment of biomass is the transformation of the cellulose
component. Utilization of alkaline compounds such as NaOH in long-term treatments
results in mercerization, a process that converts natural cellulose (Cellulose I) to
mercerized cellulose (Cellulose II), which is more recalcitrant towards numerous
biodegradation techniques (Karimi et al., 2013, O'Sullivan, 1997).
2.3.3.3. Organosolv treatments
Organosolv treatments utilize organic solvents in either standalone (e.g. 100%
methanol) mode or in combination (e.g. methanol: acetonitrile: acetone in a percentile
ratio of 40:20:40) to degrade biomass. The method uses polar or non-polar solvents
such as methanol, ethanol, and acetone in the presence of catalysts such as formic acid,
Chapter 2 Literature review
28
acetic acid, sulphuric acid or hydrochloric acid. Due to the use of highly volatile
molecules, this process is used for lignin removal due to their high hydrophobicity and
their high solubility in non-polar solvents.
The process has been reported to generate as much as 97% glucose from rice
straw cellulose (Sarkar et al., 2012). Similarly, a Pinus radiata substrate treated with an
acetone-water mixture at 195°C for 5 minutes resulted in an ethanol yield of
approximately 99.5%. The disadvantages of this treatment method are its high costs and
volatility of non-polar molecules which are flammable and explosive at elevated
treatment temperatures (Haghighi Mood et al., 2013).
2.3.4. Biological pre-treatment
Biological pre-treatments rely on microbial cells, especially wood rot fungi, to
degrade biomass. The chief aim of this method is delignification, although numerous
fungi also hydrolyse cellulose and hemicellulose components. Fungi such as white-rot
and brown-rot fungi are utilized for this pre-treatment. White-rot fungi such as Ph.
Chrysosporium, T. versicolor and Phlebia spp. are able to produce universal biomass
degradation with lignin reduction followed by cellulose and hemicellulose hydrolysis.
On the other hand, brown-rot fungi such as Postia spp. and Laetiporus spp. reduce only
the lignin component of biomass, leaving cellulose and hemicellulose needing further
treatment. The application of these fungi in biomass treatment is highly cost-effective
compared to chemical and physical pre-treatment, however the overall effectiveness of
standalone biological pre-treatment is still low without pre-treatment. In combination,
the pre-treatment process of milling and Acremonium spp. enzymes treatment has been
found to be highly effective and has reported to yield as much as 90% hexose and 77%
pentose degradation (Sarkar et al., 2012).
2.4. Fungal degradation of biomass
Cellulose is the chief source of carbon in lignocellulose complex, as it is the
richest source of glucose, the chief source of carbon for the microorganisms. Cellulose,
however, exists in the core of the complex, surrounded by layers of hemicellulose and
lignins (Figure 2.7) and is often inaccessible. To improve access to the cellulose, fungi
have to penetrate through these outer layers. Numerous workers have shown that fungi
Chapter 2 Literature review
29
are better degraders than the bacteria in this aspect (Alvira et al., 2010, Arantes and
Saddler, 2010, Zuroff and Curtis, 2012). Fungi ranging from Trichoderma reesei to
some of the less studied brown/white-rot fungi apply a battery of enzymes to achieve
this. The enzymes include cellulases (complexes of endo- and exo-glucanases and
glucosidases), hemicellulases (such as xylanases, mannanases and arabinases) and
lignases (Dashtban et al., 2009, Pérez et al., 2003, Sánchez, 2009).
Lignocellulosic fungi secrete large amounts of different enzymes which degrade
a wide variety of substrates to make the utilizable carbon source available. The systems
differ in composition to hemicellulases, whose secretions, even under the similar
conditions (Baldrian and Valášková, 2008, Dashtban et al., 2009, Sánchez, 2009, Sipos
et al., 2010) depends on substrate variability. Fungi reportedly produce cellulases with
varied individual enzyme composition. For example, it was reported that T. koningii
produced 31.3 U/ml of endoglucanse which is significantly greater than T. reesei (5
U/ml) and A. niger (7.5 U/ml) (Liu et al., 2012).
2.4.1. Cellulases
Cellulases are complex enzyme systems which generally consist of the three
enzyme classes; endoglucanases, exoglucanases and β-glucosidases.
Endoglucanase (EC 3.2.1.4) form the first enzymes to be secreted from the
lignocellulolytic enzyme complex when degrading a biomass substrate. These are also
called endo-1, 4-glucanases or carboxymethyl cellulases (CMCases). These are the
enzymes that convert crystalline cellulose to the amorphous form making it more
accessible to cellobiohydrolases (Dashtban et al., 2009, Sweeney and Xu, 2012, Zhang
and Lynd, 2002). At present, 10 glycoside hydrolases (GH) of endo-1, 4-glucanases are
known. Among those, 5-6 are known to occur in most of the cellulolytic fungi.
Endoglucanases generally bind through H-bonds in amino acid. Residues within
the enzyme cleft to the cellulose molecule, then either stretch the glycosidic bonds, thus
breaking them, or distort the normal chair 4C1 orientation to the skew/boat orientation,
thus changing the glucosyl structure from -1 to +1 (Davies et al., 2003, Davies et al.,
1995). Almost all EGs have highly specific substrate activity. Some families of EGs,
such as Cel45 EGs, are especially cellulolytic, thus are unable to act on any other
substrates (like hemicelluloses), while others, like Cel5 EGs (arabinoxylan,
mannan/galactomannan and xyloglucan), are variable in their activity (Cantarel et al.,
Chapter 2 Literature review
30
2009, Davies et al., 1995, Gloster et al., 2007, Lawoko et al., 2000, Sweeney and Xu,
2012, Vincken et al., 1997, Vlasenko et al., 2010).
Among the EGs, Cel5 shows higher activities on amorphous cellulose than
crystalline cellulose. However, it is also reported to show less activity on amorphous
cellulose, which occurs in conjugation with lignins. Other EGs such as Cel7 and Cel12
have wider activity, as stated earlier. Cel45 activity is dependent chiefly on H-bonding
to cellulose molecules and not on π stacking as in most of the known EGs. Due to this,
the enzyme does not does not distort the β-glycoside linkage, but elongates it until it
breaks (Gloster et al., 2007, Hilge et al., 1998, Hirvonen and Papageorgiou, 2003,
Karlsson et al., 2002, Schagerlöf et al., 2007, Sweeney and Xu, 2012, Vlasenko et al.,
2010).
EGs are temperature sensitive enzymes with their optimal activity at 60°C and
pH range of 4-5. The stability for different EGs varies with many being highly stable at
40-50°C. Cel5 EGs, however, have been reported to be fairly stable and have been
shown to have good activity at 80°C, probably due to their thermophilic origin
(Baldrian and Valášková, 2008, Cantarel et al., 2009, Dashtban et al., 2009,
Maheshwari et al., 2000, Vlasenko et al., 2010). Unlike CBHs, EGs are monomeric in
nature and vary from 22-45 kDa. However, in some fungi such as Scletrotium rolfsii,
they have been reported to be about 80 kDa and to have catalytic groves (clefts) of up to
4 kDa (Uzcategui et al., 1991).
Cellobiohydrolases (CBH) or exoglucanases (EC 3.3.1.91) are known to be the
main cellulase components. They act by degrading the extended or distorted cellulose
molecules to convert it into cellobiose. In addition to any amorphous cellulose, CBH
also degrades crystalline cellulose. Like endoglucanases, CBHs are classified into
families (glycoside hydrolases; GH). Three families, GH6, GH7 and GH48, are known
to be the precursors of CBHs. Among these, CBH I is related to GH7 and is commonly
found in cellulolytic fungi. CBH II is known to occur in numerous fungi and reportedly
belongs to the GH6 family (Sweeney and Xu, 2012). The enzymes target β-1, 4-
glycosidic linkages as their cleavage sites on cellulose, while CBH II enzymes are
known to catalyse the non-reducing ends of the polymer. In some fungi, such as
Phanerochaete chrysosporium, CBH I has been reported to belong to the group of
enzymes called CBH58 and CBH62 (Baldrian and Valášková, 2008). Recently, CBH I
has been classified in the GH7 family and CBH II in GH6 (Cantarel et al., 2009).
Chapter 2 Literature review
31
CBH I is the chief enzyme in cellulose degradation, comprising 70% of the total
cellulase system (Colussi et al., 2012, Divne et al., 1994, Sánchez, 2009, Sweeney and
Xu, 2012). All the CBH I enzymes are non-glycosylated monomers of about 45-66 kDa.
The single enzyme is composed of a core and carbohydrate binding domains (CBDs).
The enzyme is primarily made up of β-sheets with the remaining part made of α-helix
content (about 25-32%) and β-strand loops joined by nine disulphide bridges
comprising the remainder. The CBD tunnels in CBH I are twice as long as those of
CBH II and consist of loops extending from the –COOH terminus. The tunnels are
complex network of glycosidic interacting amino acid sites (Divne et al., 1994,
Francieli Colussi et al., 2011, Sweeney and Xu, 2012).
The operating conditions of CBH are similar to those of the endoglucanases.
They show an optimum activity from 40°C-60°C between pH 4-5. However, it has been
reported that 50°C and pH 5 are the optimal conditions, while temperatures below 25°C
and above 55°C slowed enzyme activity (Baldrian and Valášková, 2008, Colussi et al.,
2012, Dashtban et al., 2009, Francieli Colussi et al., 2011, Sweeney and Xu, 2012).
CHBs are inhibited by their major (cellobiose) and minor (glucose) products
with concentrations of 3-6 % of either or both sugars inhibiting the cellulolytic action of
the enzyme by competitive inhibition (Baldrian and Valášková, 2008, Igarashi et al.,
1998, Klyosov, 1987b).
β-glucosidases (EC 3.2.1.21) are exo-glycoside hydrolases which catalyse the β-
1, 4 glycosidic bonds in cellobiose or other β-oligodextroses to produce glucose. Like
endoglucanases and exoglucanases, β-glucosidases are also categorised among GH
families 1 and 3 (Dashtban et al., 2009, Henrissat and Davies, 1997).
These enzymes have been reported from a large group of organisms from
bacteria to mammals (Dan et al., 2000, Dashtban et al., 2009, Eyzaguira et al., 2005,
Sweeney and Xu, 2012). β-glucosidases are generally produced by numerous
cellulolytic organisms (especially fungi) due to the competitive inhibition of endo- and
exo- glucanases by their end products (cellobiose). However, in the case of Ph.
chrysosporium, β-glucosidases do not effectively degrade cellobiose and their primary
substrates have been reported as β-glucans. Nonetheless, they also degrade cellobiose at
significant levels (Igarashi et al., 1998, Nijikken et al., 2007).
The molecular weights of β-glucosidases, as estimated by gel filtration
chromatography, have been reported as approximately 46 kDa (Nijikken et al., 2007).
However, the molecular weights determined by amino acid sequencing and SDS-PAGE
Chapter 2 Literature review
32
were 52.6 and 53 kDa, respectively (Tsukada et al., 2006a), illustrating a degree of
polydispersity – different techniques will emphasise larger sizes leading to a higher
average molecular weight, and vice versa. In plants, the enzyme has been reported to
occur in multimeric forms, namely dimers (Burmeister et al., 2000, Czjzek et al., 2001,
Nijikken et al., 2007, Verdoucq et al., 2004), tetramers (Aguilar et al., 1997, Chi et al.,
1999) and hexamers (Sue et al., 2006).
As with the other cellulose enzyme constituents, β-glucosidase also suffers from
product inhibition, primarily from glucose and furfurals. In addition, the catalytic
activity of the enzyme has been found to be very low as compared to cellobiohydrolases
and endoglucanases (Baldrian and Valášková, 2008, Juhász et al., 2005, Kim et al.,
2012a).
2.4.2. Hemicellulases
Like cellulases, hemicellulases are a complex mixture of numerous enzyme
catalysing specific substrates. Due to the large number of sugars that constitute
hemicelluloses, the number of individual enzymes in hemicellulases is far greater than
the number of enzymes in cellulases. Hemicellulases are composed mainly of xylanases,
xyloglucanases, β-xylosidases, β-mannases, arabinofuranosidases and α-L-arabinases
(Dashtban et al., 2009, Shallom and Shoham, 2003). In addition, accessory
hemicellulases include esterases like xylan esterase, ferruloyl esterase and glucuronoyl
esterase (Dashtban et al., 2009, Sweeney and Xu, 2012).
Xylanases are a group of various hemicellulases responsible for the degradation
of various xylans by hydrolysing their β-(1, 4) linked D-xylopyranoside units. They
comprise up to 1% of total fungal lignocellulolytic enzymes and may work
symbiotically with other hemicellulases and cellulases, thereby facilitating a consortial
mix for degradation of biomass substrates. Like cellulases and numerous
hemicellulases, xylanases are also classified as glycoside hydrolases and most belong to
the GH 8, 10, 11, 30 and 43 families (Dashtban et al., 2009, Sweeney and Xu, 2012,
Cantarel et al., 2009).
The majority of fungal xylanases are reported to have optimal activities and
stability at a pH of 4 to 6. While a number of fungal species are known to have
xylanases which are active in extreme conditions, this property is seen chiefly in
extremophilic bacteria such as Thermonospora fusca (Bachmann and McCarthy, 1991,
Chapter 2 Literature review
33
Dhiman, 2008). Also, in some species, the optimal enzyme activity has been seen at
more acidic levels as in Aspergillus kawachii (2-6) and A. niger (3.0) (Ito et al.,
1992).The xylanases obtained from Aspergillus spp. have been repeatedly shown to be
active at pH 5 to 6. Unlike cellulases, xylanases have been reported to have high
activities of 650-930 U/ mg protein. It is also reported that in some species, certain
additives such as nitrogen increase this activity up to 30-36 U/mL as against 1.2 U/mL
under nitrogen depleted conditions. The optimal temperature for A. niger and A.
ochraceus xylanases in solid fermentation is about 65°C, while that of A. niveus is in the
range of 55- 65°C (Betini et al., 2009). Xylanases are more stable towards temperature
change, compared to cellulases, as exampled by Aspergillus cultures, where more than
70% of activity was retained at 75°C. However, even under optimal conditions, those
enzymes have a very low half-life of about 1 hour, which reduces significantly as the
temperature rises (Betini et al., 2009).
Although xylanase activity in a fermentation filtrate is not directly inhibited by
free sugars, they probably limit the range of activity over the degradation time. SDS-
PAGE and zymogram analyses show the enzyme size to be about 30 kDa. However, the
enzymes have ability to change conformation and pass through very small spaces (10
kDa cut-off membranes). They can thus catalyse a high amount of substrate in plant
cells (von Gal Milanezi et al., 2012). The enzyme follows Michaelis-Menten kinetics
with Km and Vmax of 47.08 mg/mL and 3.02 IU/mL, respectively. (Edivaldo Filho et al.,
1993, von Gal Milanezi et al., 2012).
Most fungal xylanases are active and stable at 40-55°C, with the optimum
condition dependent on the type of substrate. Where most Aspergillus spp. displayed
activity in 45-60°C, A. niger displayed optimal activity at 40°C on xylan (Garcia
Medeiros and Hanada, 2003), 48°C on wheat bran (Zhao et al., 2006)) and 45-50°C
(von Gal Milanezi et al., 2012) on sugarcane bagasse.
2.4.3. Lignases
Lignases are the group of enzymes that hydrolyse the lignin component in the
lignocellulose complex. Fungi, especially, those belonging to division Basidiomycota,
are however known for their lignin degrading abilities (Baldrian and Valášková, 2008).
Basidiomycetes are categorized as either ‘white-rot’ fungi or ‘brown-rot’ fungi,
depending on the nature of their lignin degradation. White-rot fungi comprise species
Chapter 2 Literature review
34
such as Phanerochaete spp., Trametes spp. and Ganoderma spp., while brown-rot fungi
include species of Fomitopsis spp. and Postia spp.
Basidiomycetes form the major portion of wood rotting fungi and can degrade
cellulose and hemicellulose in addition to the lignin component of biomass owing to the
large array of enzymes they produce. Numerous white-rot basidiomycota are known to
degrade a wide spectrum of biomass components. For example, Trametes spp. M23 has
been reported to effectively degrade humic acid and other humic substances in addition
to utilizing the free sugars and cellulosic components in the growing medium,
particularly in low nitrogen media (Grinhut et al., 2011). Humic substances are the
organic materials chiefly consisting of lignin, cutin, cutan, tannin and humic substances.
Some other basidiomycetes, such as Phlebia spp., Physisporinus rivulous and
Dichomitus squalens, degrade lignin selectively without affecting the other components.
The brown-rot basidiomycetes, such as Postia spp. and Laetiporus spp., can
degrade cellulosic and hemicellulosic substrates, but are unable to degrade lignins; a
property similar to some biomass degrading ascomycetes, as previously discussed
(Dashtban et al., 2010).
Most of these fungi use Fenton-like reactions in order to degrade the lignins in
biomass. This process involves a variety of enzymes which utilize the –OH- radicals
generated from the reaction of hydrogen peroxide and ferrous ions (Fe2+) or manganese
ions (Mn2+). The –OH- thus generated oxidises numerous lignin components to produce
small molecular weight polyphenols and quinones (Grinhut et al., 2011). Some of the
basidiomycetes, such as Phanerochaete velutina, have been observed to use this
property periodically or sequentially to degrade hemicellulose followed by cellulose.
The same property was noticed during the degradation of lignin, where selective lignin
degradation was observed for approximately the first 45 days of degradation
experiment. During this time, vanillin and dihydroxybenzoic acid (phenolic acids),
pimaric acid and palustric acid (resin acids) and sterols such as stigmasterols and
pinosylvin were observed to be degraded or oxidised (Valentín et al., 2010).
Most of the fungal lignases are extracellular in nature, i.e. they are secreted by
the lignin degrading fungi and act externally to the organism, in this case oxidising the
lignin components. This mechanism is activated to access the more useful and energy
Chapter 2 Literature review
35
beneficial components, namely cellulose and hemicelluloses. Fungal lignases can be
classified in two broad categories of phenol oxidases and heme peroxidases. The first
category comprises enzymes such as laccases, while the second category is comprised
of enzymes such as lignin peroxidases, manganese peroxidases and versatile
peroxidases.
2.4.3.1. Laccases
Laccases (EC 1.10.3.2) belong to the phenol oxidase group of lignases and the
family of multimeric copper oxidases that catalyse single electron oxidation processes
in many phenolic compounds. These enzymes have been reported from a number of
fungi associated with ascomycetes and deuteromycetes. However, most of the
commercial enzymes have been sourced from basidiomycetes. Whereas, the white-rot
fungi are commercially the major producers of laccases, brown-rot fungi such as Postia
placenta and Fomitopsis pinicola have been shown to produce comparatively lesser
amounts of laccases. Laccase has also been reported to be produced by Trichoderma
spp., however, only in the spore stage. These enzymes are glycoproteins with a
carbohydrate content of up to 20%. They are large molecules with a molecular weight
of about 60-80 KDa. Molecular oxygen is utilized by these enzymes as the electron
acceptor and is reduced to water in the following equation:
O2 + 4e- + 4H+= 2H2O
Laccases generally contain four copper atoms at their active site centres,
although fewer have been occasionally reported. These copper centres are bound by one
cysteine, ten histidine and, in some cases, one phenylalanine residues. The laccase
catalytic clusters made up of four copper atoms are classified into type 1, type 2 and
type 3 coppers. The catalytic region with the copper centre in the type 1 structure is
generally co-ordinated between two histidine nitrogens and one cysteine sulphur region
(Figure 2.8). This particular arrangement gives a typical blue coloration to the enzymes.
This co-ordination geometry is generally referred to as a distorted trigonal bipyramidal
and is unusual due to its intermediary between Cu+ and Cu2+ states. Type 2 and 3
structures have their active region copper ions arranged in a trinuclear centre (Figure
2.8). These copper centres are co-ordinated to eight histidine residues, arranged in four
His-X-His motifs (Manole et al., 2008, Singh Arora and Kumar Sharma, 2010).
Chapter 2 Literature review
36
Figure 2.8. structure of copper centres (a) Type 1, (b) Type 2 and (c) Type 3 of laccase enzyme
catalytic cluster.
Due to the presence of numerous types of catalytic centres, laccases are able to
oxidise a large number of substrates such as catechol, hydroquinone and guaiacol. Other
substrates, however, are too large to accommodate in the active site of catalytic region.
Chemical mediators are then required, acting as intermediate substrates which become
oxidised and are then able to interact with the actual target substrates with high redox
potential (Singh Arora and Kumar Sharma, 2010).
Laccases have been reported to possess high catalytic activity in acidic
conditions, although each laccase has its own pH optimum. The pH optimum also
changes depending on the nature of substrate being targeted. For example, it was found
that the highest laccase activity for tannic acid and syringaldazine oxidation was
observed at pH 5, whereas, the optimum pH for vanillic acid and hydroquinone
oxidation was around pH 4 (Manole et al., 2008). The nature of the substrate also
determines the other catalytic properties and, eventually, the catalytic preferences of the
laccases. It has been shown that ortho- compounds such as guaiacol, caffeic acid,
catechol, gallic acid and pyrogallol are better substrates than para-compounds such as
orcinol, resorcinol and phloroglucinol (Blaich and Esser, 1975). The temperature range
of laccase activities lies in mesophilic conditions i.e. from 20°C to 37°C, although more
stable thermophilic fungal laccases have been reported (Manole et al., 2008).
It has been shown that laccases are one of the most necessary components in
lignin degradation. In the case of Pycnoporus cinnabarinus, it has been demonstrated
that laccase mutants were unable to degrade Kraft lignin. Laccases cause radical
coupling by substituting electrons from phenolic hydroxyl groups to form phenoxy
radicals by the mechanism of one-electron subtraction. These radicals undergo partial
(a) (b) (c)
Chapter 2 Literature review
37
polymerization via an alkyl-aryl cleavage in their propyl side chains. There are three
types of reactions catalysed by laccases – C1-C2 cleavage (Cα-Cβ), C1-aryl cleavage
(alkyl-aryl) and C1 oxidation. In the presence of a free radical, an electron is donated to
the central cupric (Cu2+) site to convert it to the cuprous state (Cu+). Molecular oxygen
then converts this cuprous state back to the cupric state (Manole et al., 2008, Singh
Arora and Kumar Sharma, 2010) (Figure 2.9).
Figure 2.9. A general representation of the laccase catalysis mechanism of 2, 6-dimethoxy phenol
using the Cu2+ site.
As mentioned above, laccase activity is influenced by a number of factors.
Similarly, a number of conditions influence the production of laccases. Substrates rich
in nitrogen, such as malt or yeast extracts, have been reported to increase laccase
production and activity. An interesting observations made was (Srinivasan et al., 1995)
the production of laccases by Phanerochaete chrysosporium when cultured on cellulose
medium supplemented with a nitrogenous source such as ammonium tartarate.
Numerous other compounds, especially phenolics such as gallic acid, humic acid
ferulates and benzoates, also induce laccase production. Similar to other enzymes such
as cellulases and glucosidases, laccases are generally inhibited or repressed by sugars.
However, this is not as widespread as in cellulases and laccases in some fungi behave
variably to the concentrations of different sugars (Singh Arora and Kumar Sharma,
2010). Other reagents such as fatty acids, thiourea, glutathione, L-cysteine and ion
groups such as halides, cyanides, hydroxide Mg2+, Ca2+, Mn2+ and Zn2+ are known to
inhibit laccase activity (Dashtban et al., 2010, Singh Arora and Kumar Sharma, 2010)
Chapter 2 Literature review
38
2.4.3.2.Heme peroxidases
Heme peroxidases are ligninases which use heme groups as a cofactor during
their catalysis process, and may have one of several different catalytic cores. For
example, the catalytic core of lignin peroxidases is made up of ferrous ions, while that
of manganese peroxidases is made chiefly of manganese ions (Mn2+) or less commonly
Ca2+ or Cd2+ ions.
2.4.3.2.1. Lignin peroxidases
Lignin peroxidases (LiPs) are a group of isozymes which depolymerize non-
phenolic lignins and β-O-4 non-phenolic lignin compounds by H2O2-based oxidation
(EC 1.11.1.14). In addition to these polymers, the LiPs degrade numerous phenolic
molecules including guaiacol, vanillin, catechol and syringic acid. These enzymes were
first isolated from Ph. chrysosporium in 1983 and have been reported since to occur
widely in a number of white-rot fungi such as T. versicolor and Panus spp.
Interestingly, the enzymes also have been reported in lignin active bacteria such as
Acinetobacter spp. and Streptomyces spp. (Doyle et al., 1998, Baldrian and Valášková,
2008, Dashtban et al., 2010).
LiPs are isozymes and have molecular weights of about 36-50 KDa. The number
of isozymes varies in each fungal species, e.g., T. versicolor LiP is made up of 16
different isozymes (Janusz et al., 2013), while 15 isozymes form LiP for Ph.
chrysosporium (Ollikka et al., 1993). LiPs convert the target substrates to intermediate
radicals by single- or multi-step electron transfer mechanisms. These intermediate
radicals, due to their reactivity, become depolymerized. Unlike laccases, these isozymes
do not require mediator molecules or ions to catalyse substrates with high redox
potential. Also, unlike laccases, the heme sites with the enzyme active site are located
deep within the peroxidase enzyme unit. The substrate has to either pass through a
protein channel or a molecule such as veratryl alcohol attaches to the active site through
these channels and mediates the substrate oxidation. The enzymes catalyse the
substrates in a two-step reaction where, in the presence of an oxidizer such as H2O2, the
catalytic Fe3+ centre is oxidised by a one-step electron transfer to form an oxoferryl
compound. This radicalized enzyme then reacts with the substrate in a second reaction.
The equation of this process is shown below.
Chapter 2 Literature review
39
(Fe3+)POx + H2O2 (Fe(IV)=O)2+POx + H2O
(Fe(IV)=O)2+POx + Substrate (Fe(IV)=O)2+POx +Product
(Fe(IV)=O)2+POx + Substrate (Fe3+)Pox + Product
Protoporphyrin acts as the heme-possessing prosthetic group in these enzymes.
The Fe3+ reaction centre is sandwiched between the four pyrrole nitrogens of the heme
group and a nitrogen atom in the imidazole group of histidine. There is thus a strong
pentacoordinated linkage, generally referred to as a Fe-imidazolic nitrogen (Fe-Nε2)
linkage. The ability to weaken this linkage is directly proportional to the ability to
catalyse the high redox potential substrate. In LiPs, Ser177 and Asp201 residues are
responsible for the weakening of this linkage (Conesa et al., 2002).
LiPs oxidise their substrates under acidic conditions, similar to cellulases,
hemicellulases and laccases. However, their pH optima are generally in the pH range of
3-3.5 and this range can further widen due to the variability of substrates. For example,
the pH optimum of LiP during the catalysis of veratryl alcohol and 4-[(3,5-difluoro-4-
hydroxyphenyl) azo] benzenesulfonate (DFAD) has been reported within the range of
2.5-3, whereas for 2, 2’-azinobis- (3-ethylbenzthiazoline)-6-sulfonic acid (ABTS), the
range is shifted to 3-3.5 (Doyle et al., 1998). The temperature zones for LiPs range from
25°C to 50°C. However, considerable activities of some isozymes of Ph. chrysosporium
LiPs have been reported at 60°C. One of the peculiar observations made for LiP is their
variable temperature optimum with respect to the pH. For example, Tuisel et al (Tuisel
et al., 1990) reported that the temperature optimum changes from 25°C at pH 2.5 to
45°C at pH 3.5 and 45-60°C at pH 4.5. Similar to other biomass-degrading enzymes,
LiPs have a low shelf-life and are generally stable at rest for about 48 hours when
incubated up to 50°C. However, about 20% enzyme loss has been recorded for a
working enzyme at 40°C in about 21 hours. Similarly, outside the range of pH 4-7.5, the
enzymes lose all activity within 5 hours. LiPs are generally not inhibited by some
halides, an uncommon feature among the biomass-degrading enzymes. They are
inhibited by EDTA, Mn2+, cyanides, azides and excess H2O2 in the absence of any
reducing agent (Tuisel et al., 1990).
2.4.3.2.2. Manganese Peroxidases
Manganese peroxidases (MnPs) (EC 1.11.1.13) are also glycoproteins, similar to
LiPs, which play a crucial role in lignin degradation (EC 1.11.1.13). These enzymes
Chapter 2 Literature review
40
were first reported in 1985 in Ph. chrysosporium (Dashtban et al., 2010). Since then,
enzyme isoforms have been regularly reported from numerous basidiomycetes including
Trametes versicolor, Agaricus bisporus and Panus tigrinus. The number of MnP
isoforms varies in different fungal species. Ceriporiopsis subvermispora has been
reported to possess 13 different putative isoforms (Fernández-Fueyo et al., 2012),
whereas, Physisporinus rivulosus grown on spruce wood was reported to be composed
of 4 different isomers (Hakala et al., 2005). The most widely known MnP producers,
Ph. chrysosporium and T. versicolor, possess 5 (Fernandez-Fueyo et al., 2012) and 3
MnP isozymes (Carabajal et al., 2013), respectively.
Similar to LiPs, these enzymes have iron protoporphyrin from heme sources as
the catalytic core. As the name suggests, the catalysis process employed by these
enzymes is based on peroxidase-dependent oxidation mediated by manganese. The
Mn2+ cation, in the presence of a peroxide group such as H2O2, is oxidised to Mn3+ on
the enzyme surface. This more chemically active oxidised ion then forms a complex
with chelating agents such as oxalate or malonate and forms a redox mediator for the
oxidative degradation of phenolic substrates. The chelating agents act as the
physiological regulators of MnP, where they aid in dissociating Mn3+ ion from the
enzyme’s surface, thereby enhancing enzyme activity. The attachment of Mn3+ also
causes oxidation of the chelating agent, such as oxalic acid, to produce a formate radical
(HCO-2) which forms a superoxide (O-
2) in a chemical reaction with O2. The chelated
form of Mn3+ then acts as a strong redox mediator and oxidises phenolic lignin and
related structures (Saroj et al., 2013), as illustrated in the reaction.
(Fe3+)MnPH2O2→H2O�⎯⎯⎯⎯⎯⎯� [(Fe4+) = O]MnP
Mn2+→Mn3+�⎯⎯⎯⎯⎯⎯⎯⎯� [(Fe4+) = O]MnP
[(Fe4+) = O]MnPMn2+→Mn3+�⎯⎯⎯⎯⎯⎯⎯⎯� (Fe3+)MnP
The enzymes are also able to degrade non-phenolic polymers if, for example,
organic acids are present which form a complex with Mn3+ (Dashtban et al., 2010). In
the absence of H2O2, which is the primary exogenous inducer, MnP oxidizes the Cα-Cβ
linkage of lignin to generate reduced aryl products and H2O2. This process not only
causes a primary degradation of lignin, but also supplements the reaction machinery
Chapter 2 Literature review
41
with the generated H2O2 which is used by the peroxidase to further oxidise lignin
molecules (Saroj et al., 2013).
The iron in the centre of MnPs is in the Fe3+ state and is linked via a
pentacoordinated linkage to a proximal histidine residue. A hexa-coordinated linkage
has also been observed with the Mn2+ ion in Ph. chrysosporium, which is the chief
catalytic moiety of MnP. This ion is linked to Glu35, Glu39 and Asp179 amino acid
residues in their carboxylic positions. The remaining three linkages are with two water-
based oxygen atoms and a heme propionate oxygen (Conesa et al., 2002).
Similar to cellulases and laccases, MnPs are generally produced as isozymes
encoded by closely-related gene clusters. Thus, differences in expression levels of those
genes determine the variable MnP activities in different fungi. Five genes related to
MnP expression (mnp 1, mnp 2, mnp 3, mnp 4 and mnp 5) have been reported in Ph.
chrysosporium (Kersten and Cullen, 2007), while 13 are reported in another wood-
degrading basidiomycete, Ceriporiopsis subvermispora (Fernandez-Fueyo et al., 2012).
The common occurrence of the MnP gene cluster has been recently shown in Ph.
crassa, where a single gene cluster of WD1694 has been observed to possess at least 4
MnP genes (mnpA2, mnpA3, mnpB2 and mnpB3) (Takano et al., 2013). The MnP genes
from C. subvermispora were found to be highly up-regulated in the presence of ball-
milled aspen as the sole carbon source (as compared to glucose) (Fernandez-Fueyo et
al., 2012).
Another element determining MnP activity is nitrogen. In the presence of
supplemented N2, Plebia radiata has been reported to generate high MnP activities over
an 18-day period incubation under submerged conditions in yeast extract glucose
medium supplemented with succinate. In semi-solid cultures, this activity was
considerably enhanced with the addition of crushed charcoal (Mäkelä et al., 2013). It
has been also reported that numerous white-rot fungi, especially Ph. chrysosporium,
accumulate extracellular oxalic acid in higher quantities during lignin degradation as it
stimulates MnP activities by increasing the stability of Mn3+ ions present in the enzyme
active sites (Zeng et al., 2010). Addition of Mn2+ also increases MnP activity. The MnP
encoding genes mnp1 and mnp 2 of Ph. chrysosporium are positively affected by
increased Mn2+ in a low N2 medium, whereas mnp 3 is not (Kersten and Cullen, 2007).
Similarly, absence of Mn2+ is known to cause a large decrease in MnP activity (Grinhut
et al., 2011). On the other hand, high concentrations of FeSO4 in the fermentation
medium, especially above 0.1 g/L, were observed to not only suppress overall fungal
Chapter 2 Literature review
42
growth, but also MnP activity (Bak et al., 2009). This was a surprising observation
since this mineral is suggested to be one of the important cofactors in Fenton reactions
causing lignin degradation. It has been shown that exogenous addition of a heme group
up to a critical concentration considerably increases MnP production and activity.
Addition of 1 g/L heme increased the activity of MnP in an expression host, Pichia
pastoris SMD1168H, and resulted in an increase of MnP production from 6 U/L to 185
U/L. However, the increase was more prominent at 2490 U/L after addition of 0.5 g/L
heme under similar conditions. The authors proposed that the higher heme
concentration in the fermentation medium increased the bio-bleaching difficulties of
recombinant strains (Jiang et al., 2008). Supplementation of Tween-80 to nitrogen-rich
medium has also been shown to improve MnP activity. (Fujian et al., 2001) showed that
an addition of 0.1 % Tween-80 to medium containing 0.1 % (NH4)2SO4 increased MnP
activity to 1375 U/L within 4 days of solid state fermentation.
MnPs are generally monomeric and, in addition to Mn2+, have been observed to
be induced by several other ions such as Ca2+, Cd2+and Sm3+. A number of mediators in
addition to veratryl alcohol are known to induce and enhance the activities of these
enzymes. These mediators include organic acids, unsaturated fatty acids and thiols.
MnPs are mostly mesophilic in nature and are produced at room temperature or at
around 37°C. However, on a wider scale, a temperature range of 30-60°C within a pH
range of 2.5 and 6.5 results in good MnP activity in various organisms (Dashtban et al.,
2010).
2.4.3.2.3. Versatile peroxidases
Versatile peroxidases (EC 1.11.1.16) (VPs) are recently discovered lignases.
They are oxidoreductases belonging to Class II fungal peroxidases (with LiPs and
MnPs) and have been further classified as reactive-black-5: hydrogen-peroxide
oxidoreductases (EC 1.11.1.16). They are glycoproteins and possess the catalysing
abilities of both LiPs and MnPs, i.e., they have the ability to oxidize general LiP and
MnP substrates such as phenolics and Mn2+ ions. In contrast to other peroxidases, they
have not been found in Ph. chrysosporium, but are found in various other wood rotting
fungi such as Pleurotus spp. and Bjerkandera spp. during the late 1990s (Ruiz-Duenas
et al., 2001). It is known that LiPs are unable to catalyse their substrates in the absence
of mediators such as veratryl alcohol and MnPs are not very efficient in the absence of
Chapter 2 Literature review
43
Mn2+ ions. VPs, on the other hand, are not restricted by limited availability or absence
of these mediators (Dashtban et al., 2010, Ruiz-Duenas et al., 2001).
Although VPs and LiPs share the ability to use veratryl alcohol, the major VP
mediators/substrates are Mn2+, hydroquinones, ferulic acid, dyes and α-napthol. Indeed,
VPs possess greater affinity towards these mediators than veratryl alcohol. Additionally,
like MnPs, VPs show their maximal activities in the presence of Mn2+ (Ruiz-Duenas et
al., 2001). Pleurotus ostreatus, a common edible mushroom, has been reported to
possess nine mnp genes, of which mnp 2, 4 and 5 are classified as VP-encoding genes
for VP2, VP4 and VP5 enzymes, respectively. The genes mnp 3, 6, 7, 8 and 9 are highly
expressed in Mn2+-supplemented medium, while mnp 4 shows predominant expression
in Mn2+-deficient medium. The expression of this gene is dependent on the fungal age
and it shows exceptionally high expression during tropophase, early idiophase and
idiophase. Depending on the age of fungal culture, the substrates were also found to
differ. Whereas this enzyme could degrade other substrates to considerable levels during
every fungal growth stage, some azo dyes such as Orange II were only degraded during
the idiophase in Mn2+-deficient medium, where the activities of MnPs and LiPs were
negligible (Knop et al., 2014).
Similar to other peroxidases, VPs have a heme centre which is sandwiched
between four pyrrole nitrogens of the heme group and a nitrogen atom in the imidazole
group of histidine by pentacoordinated linkage. This linkage is generally referred to as
an Fe-imidazolic nitrogen (Fe-Nε2) linkage. However, in contrast to other peroxidases
where there are generally two histidine residues flanking the heme group (proximal and
distal), one of the histidines in VPs is substituted by either one aspartate/glutamate or
one cysteine and one glutamate. There are multiple catalytic sites present on the VP
surface and these can be broadly classified into two parts. The Mn2+ oxidation site
possesses multiple acidic Mn2+ cation binding residues while the lignin oxidation site
oxidises lignin-related substrates by a tryptophan-mediated electron transfer
mechanism. Additionally, VPs, especially VP4, has been observed to possess about 20
lysine residues in their structure compared to other peroxidases such as MnPs which
contain about half this number (Fernández-Fueyo et al., 2014). The following chemical
equation shows a schematic representation of phenolic substrate degradation by VPs by
utilizing both H2O2 and Mn2+ as mediators, thus showing combined activities of both
LiPs and MnPs.
Chapter 2 Literature review
44
VPH2O2→H2O�⎯⎯⎯⎯⎯⎯� VPRed I
Mn2+→Mn3+�⎯⎯⎯⎯⎯⎯⎯⎯� VPRed I
Mn3+→Mn2+�⎯⎯⎯⎯⎯⎯⎯⎯� VPRed II
VPRed IIMn2+→Mn3+�⎯⎯⎯⎯⎯⎯⎯⎯� VPRed II
Mn3+→Mn2+�⎯⎯⎯⎯⎯⎯⎯⎯� VP
Detailed studies conducted on the evolutionary distribution of fungal biomass
degrading enzymes, it was shown that VPs are closely related to LiPs than MnPs
(Floudas et al., 2012). However, VPs were shown to be evolved from short MnPs (those
possessing a peptide of 21 amino acids starting with the N-terminus and ending at a
KEX2 cleavage site with dibasic Arg-Arg or Lys-Arg residues before the main protein
sequence), which in turn evolved from long MnPs. Due to their intermediary nature,
VPs did not lose the parental active sites for both Mn2+ binding and Trp-homolog sites
responsible for lignin oxidation (Floudas et al., 2012), the former of which (Mn2+)
appeared to be lost from LiPs during evolution. Follow-up analysis also indicated the
presence of the copies of genes encoding VPs in white-rot fungi such as T. versicolor
and 1 copy of putative VP genes in Ph. Chrysosporium (Choi et al., 2014). However,
the presence active forms of these genes remains to be characterized.
A recent secretome analysis of T. versicolor during the fermentation of tomato
juice medium supplemented with Cu2+ and Mn2+ revealed the presence of a VP enzyme.
The purified VP, denoted ‘VP ID 26239’, had a molecular weight of 44.6 kDa and 45.5
kDa, as determined by HPLC-SEC and SDS-PAGE, respectively. The enzyme
displayed greater glycosylation of about 16% as compared to other peroxidases. This
property (glycosylation) resulted in a change of molecular weight from previous
observations which were based on its sequence length of 38.1 kDa. The level of post-
translation glycosylation also had an effect on altered pI of less than 4, which differs
from the theoretical pI value range of 4-7.5. Of the five secreted proteins related to
Class II heme-peroxidases, four belonged to MnPs while the last protein chain was
shown to be related to VP. This protein displayed a VP peculiar Trp-residue required for
both lignin oxidation and long range electron transfer (LRET) pathway types of LRET
I, II and III. Among all the five secretome Class II heme-peroxidases, only VP
displayed the presence of active sites responsible for the LRET II pathway. The overall
activity of purified VP (540 U) was considerably less than that of MnP I, II and III,
which displayed the activities of more than 3800 U (Carabajal et al., 2013).
Chapter 2 Literature review
45
These enzymes are also inactivated in an indirect manner due to product
inhibition of the LiP active site by the addition of excess H2O2. This is due to the co-
operative nature of VP with other peroxidases such as LiPs and MnPs, as confirmed by
the presence of biphasic kinetics-based oxidation of humic acid and fulvic acid.
Isothermal titration calorimetry combined with LC-ESI-MS indicated that VPs have the
ability to degrade humic substances such as humic acid, as observed from its m/z shift
from a range of 500-1000 Da to lower values of up to 393 Da (Siddiqui et al., 2014).
VPs have become attractive enzymes for lignin degradation purposes, especially
in the paper and pulp industries. However, owing to their low copy number in most of
basidiomycetes, they are not as highly secreted as MnPs or LiPs. However, successful
expression of mnp2, 4 and 5 genes, encoding VP 2, 4 and 5, respectively¸ in other fungi
such as Aspergillus nidulans has been reported. Additionally, LiPs and MnPs can also
be re-engineered to convert them in to VPs by adding Mn2+ and Trp-homolog based
LRET pathways, as has been recently shown in Ph. chrysosporium (Dashtban et al.,
2010).
2.5. Biodegradation methods
2.5.1. First generation: Submerged Fermentation
Submerged fermentation is the classical fermentation method used for ethanol
production and the manufacture of fermented food. In the case of biomass degradation,
this method was, thus, the earliest experimented procedure. While it has been very
successful for the production of various alcohols and other fermented products from
sugar sources, it is not necessarily very efficient in degradation of complex and
recalcitrant biomass. . Nonetheless, this method is reported to generate good activities
for selective enzymes such as β-glucosidases.
Submerged fermentation/shake flask fermentation or ‘SmF’ is an aqueous phase
fermentation process where the medium-to-substrate ratio is generally high (in excess of
10:1). Although the technique is well-established for wine and related alcohol
production, its industrial applications flourished in Western countries during late 1940s
for enhanced penicillin production from P. chrysogenum. The basic methods have been
greatly optimized due to the requirement for scaling up of SmF (> 3000-5000 litres).
Such optimizations include supplementation of oxygen, heat exchange, agitation,
prevention of foaming and culture loads. SmF remains the most important standardized
Chapter 2 Literature review
46
fermentation process largely because it has been thoroughly investigated and optimised
(Humphrey, 1998).
During the past 3-4 decades, the importance of glycoside hydrolase (GH)
enzymes has increased significantly in a large number of bioprocess industries. These
enzymes, especially cellulases, are utilized primarily in cotton industries, juice
extraction and, most importantly, in paper manufacturing and recycling. They are also
increasingly used for the production of biofuels such as ethanol. Cellulases are one of
the highest produced enzymes worldwide and a majority of these enzymes, either from
bacterial or fungal sources, are commercially produced by SmF-based technologies
which are easy to manage with simple handling and monitoring systems (Singhania et
al., 2010).
However, even with optimization, SmF methods are inadequate for either
enhanced production of lignocellulolytic enzyme or effective degradation of the
lignocellulose complex. Numerous fungal-based experiments have shown that shake
flask methods tend to produce cellulases and hemicellulases upon addition of pure,
crystalline sources of cellulose, such as Solka-Floc or Avicel (Sipos et al., 2010). It was
also shown that, under shake flask conditions, the specific activities of cellulases, β-
glucosidases and xylanases were 0.6, 1.9 and 16.9 U/mg, respectively, when Solka-Floc
was used as the sole carbon source (Hideno et al., 2011). However, these activities
dropped to 0.4, 0.5 and 10.2 U/mg, respectively, when milled rice straw was used as the
carbon source. These results agree with previous observations (Juhász et al., 2005).
(Viniegra-González et al., 2003) argue that the observed productivity (Γobs) curve
corresponds directly to the ratio of broth volume (P) and time (t) of fermentation. This
relationship is represented in the equation:
𝛤𝑜𝑏𝑠 = �𝑃𝑡�𝑚𝑎𝑥
In the case of SmF of A. niger during invertase production, this curve showed
hyperbolic saturation with a maximal equilibrium level of biomass (Xm) density of 14.6
g/L, whereas, the curve displayed a linear relationship in a solid state fermentation
(SSF) system with an Xm value of over 35 g/L. This was irrespective of the higher level
of inhibition in SSF conditions as compared to SmF conditions.
It has been long debated that SmF conditions create a stress environment for
fungal growth after their spore stage. It has been observed in recent studies that SmF
Chapter 2 Literature review
47
conditions, especially due to agitation under excess water, may result in mycelial
rupture. Secretome studies of Neurospora sitophila grown under SmF conditions
displayed the presence of five intracellular proteins (formate dehydrogenase, ubiquitin-
60S ribosomal protein L40, superoxide dismutase, enolase and fructose biphosphatase)
in the extracellular filtrate (Li et al., 2013). These proteins are known to be involved in
the metabolism of glyoxylate and dicarboxylate, glycolysis, gluconeogenesis and
protection against oxidative stress. The presence of these enzymes in the secretome
indicated the possibilities of fungal cell lysis.
Another issue related to SmF is the necessity of sterilization. Due to the aqueous
conditions, SmF is more favourable for bacterial growth as compared to fungi, thus,
SmF is highly prone to bacterial contamination. To prevent this, the medium is
generally sterilized, primarily by hydrothermal methods such as autoclaving or
steaming. However, the application of these treatments generally results in the
generation of furfurals and low molecular weight organic and fatty acids which inhibit
enzyme activity and overall fungal growth (Merino and Cherry, 2007, Sanchez and
Bautista, 1988).
2.5.2. Second Generation: Solid State Fermentation
Solid State Fermentation (SSF) is a microbial bioprocessing method, conducted
at very low levels of free water. This technique has been for a long time in bread
making and for at least 3000 years for food processing in Asian countries. For example,
the Koji process for food fermentation utilizes Aspergillus oryzae. Production of sake is
another example of SSF and utilizes Trichoderma spp. Both of these applications are
well-known in Japanese and Chinese culture. In Europe, SSF is also used to make
traditional French blue cheese (Couto and Sanromán, 2006, Hölker and Lenz, 2005).
One of the main reasons for its lesser use in Western cultures was the optimization of
penicillin production by SmF and its increased economic viability with quicker
productions during the late 1940s. This was followed by further optimization of SmF
linked to the production of various antibiotics, thus improving yields and decreasing
microbial inhibitory factors (Hölker and Lenz, 2005, Humphrey, 1998, Singhania et al.,
2010).
SSF provides the natural growth conditions to fungi, especially the filamentous
fungi. Fungal spores and, ultimately, the enzymes generated in SSF exhibit higher
Chapter 2 Literature review
48
tolerance to the environmental conditions. Thus, higher quantities of enzymes are
produced under SSF conditions than SmF conditions. This pattern becomes more
evident in mixed fungal cultures where a number of different enzymes are generated
and utilized for various purposes. A number of experiments have shown that the fungal
and enzymatic behaviours in SSF conditions are generally synergistic or symbiotic and
competitive inhibition is not a widespread phenomenon (Gruntjes, 2013, Singhania et
al., 2010).
In the last 2-3 decades, SSF has been widely used for a number of industrial
bioprocesses such as biomass conversion. Degradation and bioconversion of the
lignocellulose complex from various biomass sources has increased the production of
industrial metabolites, biofuels and secondary metabolites which can be used for
medicinal purposes. The use of biomass addresses the issues related to managing the
considerable amounts of waste material generated during numerous agricultural and
allied processes. SSF employs a mixture of different organisms and/or enzymes derived
from them in a single step to generate considerably higher bioconversion of biomass as
compared to SmF (Brijwani et al., 2010, Kausar et al., 2010, Lee, 1997, Sarkar et al.,
2012). As the water content in SSF is equal to or slightly higher than the substrate, the
method allows greater aeration and surface attachment for the filamentous fungi,
allowing production of a vast array of lignocellulolytic enzymes. SSF increases the
amount of substrate to be degraded in a single step from 1-2% in SmF to more than 10%
(Lee, 1997, Ng et al., 2010) and reduces the need for separate enzyme production,
isolation and application in biomass degradation. Thus, SSF fulfils all the requirements
for bioconversion in a single step which makes it more cost-effective than SmF (Betini
et al., 2009, Brijwani et al., 2010, Dashtban et al., 2010, Sarkar et al., 2012).
During SSF degradation of steam-exploded wheat straw, increased protein
production of more than 100-fold was displayed by Neurospora sitophila compared to
SmF. This reflected an increase in enzyme activities. For example, CMCase-specific
activities increased from 0.2 U/mg in SmF to about 0.6 U/mg under SSF conditions.
Similarly, β-glucosidase activities increased from 0.12 U/mg to 0.42 U/mg. Secretome
analysis showed that more cellulolytic enzymes were released during wheat straw
degradation in SSF conditions as compared to SmF (Li et al., 2013). In basidiomycete-
mediated degradation of eucalyptus, it was shown that some strains of Lentinus edodes
and Pleurotus spp. had considerably better activities of cellulases, laccases and MnPs.
Chapter 2 Literature review
49
In particular, MnP activity increased many-fold in SSF as compared to SmF conditions
(Elisashvili et al., 2008).
Numerous biodegradation procedures adopt a pre-treatment process during
which the biomass substrate is first degraded using a fungal culture, followed by either
enzyme-based degradation or mixed fungal fermentation. The uniformity of the biomass
not only results in a production of high amounts of lignocellulolytic enzymes, but also
produces a considerably higher degradation of biomass. Recently, (Cheng and Liu,
2012) showed the effects of pre-treatment in hydrogen production from milled
cornstalk. SSF meditated by Trichoderma reesei Rut-30 was applied to this substrate,
followed by sludge seeding at 35°C and 55°C. It was observed that within 4 days of
seeding, the 6-day pre-treated substrate generated about 200 mL H2, when incubated at
55°C. Additionally, considerable amounts of low molecular weight fatty acids and
ethanol were produced. In a similar pattern, rice straw pre-treated with Ph.
chrysosporium displayed considerable LiP and MnP activities. It was observed that after
30 days of pre-treatment, these enzymes had degraded about 33% of the lignin. Follow-
up enzymatic degradation resulted in the degradation of about 17% of the glucan and
3% of the xylan (Bak et al., 2009).
Other types of pre-treatments include physical processes such as autoclaving,
acid/alkali hydrolysis, ammonia and steam explosion (Sarkar et al., 2012). Pre-
treatment of palm empty fruit bunch fibre (EFBF) with a combination of steam and 5%
acetic acid at 110°C/30 minutes resulted in the release of about 100 mg xylose and 280
mg glucose per gram of substrate after enzymatic degradation (Hassan et al., 2013).
Experiments involving steam pre-treatment followed by Trichoderma atroviride TUB
F-1663-mediated degradation were also conducted on wheat straw, sugarcane bagasse
and milled spruce biomass (Kovacs et al., 2009). These experiments yielded increased
cellulose and xylan degradation and the cellulase, glucosidase and xylanase activities
increased to 56, 3 and 750 IU/g, respectively, indicating that the steam pre-treatment
had increased the degradation levels considerably.
Another type of SSF application is the production of biofuel molecules
especially ethanol. SSF of pre-steamed and SO2 -treated corn stalk by Ph.
chrysosporium was shown to cause loss of 34% biomass with 41% Klason lignin
reduction. Additionally, a follow-up anaerobic submerged fermentation resulted in the
generation of 1.7% ethanol. However, this production further increased to 3% with
yeast co-culture (Shrestha et al., 2008).
Chapter 2 Literature review
50
Similar to SmF-based biomass degradation, fungal degradation under SSF is
also dependent on the nutrient content of the biomass. It is well known that the
composition of the substrate strongly influences its degradation efficiency. Various
chemical components such as free proteins, sugars and structural components such as
cellulose and lignin affect the overall efficiency of degradation, usually providing a
barrier to such degradation (Duarte et al., 2012b). The carbon-nitrogen (C/N) ratio of
the substrate also plays an important role in SSF biodegradation with higher nitrogen
content resulting in greater fungal degradation ability (Brijwani et al., 2010). The higher
the nitrogen content, especially from organic sources (proteins, for example), the greater
the overall enzyme activities (Kim et al., 2012a). During degradation by A. niger, A.
niveus and A. ochraceus, for example, it was noticed that high nitrogen content
additives such as yeast extract and peptone improved xylanase production. The authors
of this research also noted that a mixture of corncob: wheat bran and rice straw: wheat
bran in the ratio of 1:1 increased the xylanase activity by 10-fold, indicating the
importance of nutritional supplementation (Betini et al., 2009). One of the other factors
affecting SSF is the availability of oxygen and its level of permeability through the
biomass structure. This particular aspect is one of the most critical factors for biomass
degradation, but has often been neglected during SSF process optimization.
Additionally, the substrate particle size also played an important role in O2 availability.
Although O2 distribution was observed to be more homogeneous on larger sized
particles, it did not result in higher fungal growth due to its decreased adsorption ability.
However, the fungi showed higher O2 consumption on smaller sized (0.4 cm) particles,
causing greater O2 utilization and, thus, showing greater decrease of air permeability
(Wang et al., 2014).
SSF is reported as an intermediate step in the development of Consolidated Bio-
processing (see below), which has been estimated to reduce the production costs of
biofuels by at least 4-5 fold (Lynd et al., 2005, Yamada et al., 2013).
2.5.3. Third generation: Consolidated Bioprocessing
Consolidated bioprocessing (CBP) is a relatively new methodology for
improving biomass conversion to products of commercial interest (Brethauer and
Studer, 2014, Lynd et al., 2005). CBP combines the individual processes of biomass
hydrolysis and subsequent fermentation to generate products such as ethanol in a single
Chapter 2 Literature review
51
step. It eliminates the time-consuming separate biological pre-treatment process. It also
eliminates the need for separate fermentations of different sugars, such as tetroses or
pentoses, which cannot be fermented by general industrial fermenters such as
Saccharomyces cerevisiae (Lynd et al., 2005).
The combination of multiple processes in a single step makes CBP a very
efficient and economical method. However, there is no single microorganism which can
ferment the entire spectrum of sugars generated in biomass degradation. In addition, the
majority of microorganisms have lower tolerance towards metabolic inhibitory products
such as saccharides, alcohols, fatty acids and organic acid salts (Duarte et al., 2012b,
Lynd et al., 2005).
CBP relies on the development of cellulolytic organisms via strategies such as
metabolic engineering or genetic engineering of native species. The first strategy has
been reported to generate about 0.47 g ethanol/g hexose using the thermophilic
bacterium, Geobacillus thermoglucosidasius. Clostridium thermocellum and C.
cellulolyticum, two thermophilic biomass-degrading bacteria, have been reported to
generate about 50 g/L ethanol on pre-treated cellulose. The second strategy involves the
(recombinant DNA technology) heterologous expression of cellulolytic enzymes in
naturally-occurring biomass-degrading microorganisms. These strategies have been
successful at the laboratory scale, however, success under industrial conditions has not
yet been reported (Olson et al., 2012).
Compared to bacterial CBP, developments in fungal CBP have been limited and
not many fungi have been tested for CBP until very recently (Olson et al., 2012). In
terms of biofuel, and especially ethanol production, utilization of fungi such as
Aspergillus, Rhizopus, Fusarium and Trichoderma has been reported (Hasunuma et al.,
2013a). Among these, Trichoderma spp., especially Trichoderma reesei, has been
observed to generate moderate amounts of ethanol while simultaneously degrading
cellulosic biomass. One of the major limitations of this fungus, however, is its strict
aerobic growth, which prevents the production of commercially-viable levels of
alcohols such as ethanol. The utilization of facultative ethnologenic organisms, such as
Pichia stipites, Candida spp. and S. cerevisiae, can improve this situation. Bacteria such
as Zymomonas mobilis also have considerable potential to improve this process. This
bacterium removes certain deficiencies of yeast-based fermentation, such as
heterologous enzyme secretion and mass accumulation, resulting in recent
developments in CBP (Linger and Darzins, 2013).
Chapter 2 Literature review
52
A recent report on CBP for ethanol production involved construction of a
laboratory-scale multi-species biofilm membrane reactor (MBM reactor), as shown in
Figure 2.10.
Figure 2.10. The figure shows a conceptual design of the MBM reactor for CBP to generate ethanol
from pre-treated wheat straw (Diagram taken from Brethauer and Studer, 2014).
Experimental analyses of Avicel-based CBP using the MBM reactor showed that
a co-culture of S. cerevisiae and T. reesei RUT C30 generated 7.2 g/L ethanol in the gas
phase upon exogenous addition of β-glucosidase. When acid-treated wheat straw
replaced Avicel as the carbon source, addition of Scheffersomyces stipites (formerly
Pichia stipites) generated about 9 g/L ethanol with complete utilization of xylose. The
authors proposed that either the addition of a high β-glucosidase-producing organism or
an ethnologenic strain, such as Dekkera bruxxelensis, would further improve ethanol
production (Brethauer and Studer, 2014).
In a fungal (mushroom)-only based CBP experiment, (Kamei et al., 2014)
described the use of spent sawdust waste from Lentinula edodes cultivation as a
substrate for white-rot fungus, Phlebia spp. MG-60. They observed about 45% ethanol
production from the mushroom sawdust waste in about 400 hours due to increased
saccharification. A slightly different approach utilizing a two-step process of alkali pre-
treatment followed by Phlebia spp. MG-60 biodegradation was utilized for ethanol
production from sugarcane bagasse. After a biodegradation period of 10 days, 4.5 g/L
ethanol was generated, yielding about 66% of the theoretical maximum (Khuong et al.,
2014).
Besides the above-mentioned symbiotic or synergistic approaches, recombinant
DNA technologies have been widely used in developing CBP for biofuel production.
However, their application to filamentous fungi remains limited due to the complex
Chapter 2 Literature review
53
functional and expression systems. Hence, CBP has been more successful with simpler
microorganisms, namely bacteria and yeasts; Indeed, S. cerevisiae has been the subject
of numerous recombinant applications for CBP. Endoglucanases from several
organisms including Trichoderma spp., Aspergillus spp., Clostridium thermocellum and
Bacillus subtilis have been successfully expressed in this yeast. However, due to the
exogenous nature of these genes and the high level of metabolic pressure created on
host cells, extracellular endoglucanase production is lower than in the organisms of
origin (Linger and Darzins, 2013). The other successful gene expression system is that
of E. coli. A number of genes related to exoglucanase and cellulose-binding domains
from Cellulomonas fimi have been expressed in E. coli in addition to the β-glucosidase
gene from Thermobifida fusca. Similarly, pyruvate decarboxylase and alcohol
dehydrogenase genes were expressed in E. coli KO11. This strain utilized sugar beet
pulp as a carbon source and was able to produce ethanol. Other bacteria (B. subtilis, B.
coagulans, Corynebacteria glutamicum and lactic acid bacteria) have been successfully
developed as gene expression systems for CBP (Hasunuma et al., 2013b).
Overall, there is no single microorganism which has the ability to fulfil every
requirement of biomass conversion. Although various technologies have evolved to the
point of developing a commercial-scale CBP process, most of the fundamental research
is still in progress. Considerable developments have been made into bacterial-based
CBP processes by introducing recombinant expression systems. However, significant
research remains to be done in relation to integrating major biomass degraders, i.e.,
filamentous fungi, into CBP.
2.5.4. Symbiotic fermentation: SSF and thermophilic degradation
Symbiotic fermentation may be carried out as part of CBP. The strategy is to
develop a symbiotic consortium of different micro-organisms or their lignocellulolytic
enzymes in either a single batch or continuous batch fermentation. It has been shown
previously that fungi such as Trichoderma spp. and Aspergillus spp. have the ability to
co-culture during cellulose degradation (Brijwani et al., 2010, Gupte and Madamwar,
1997). A similar approach has been investigated in lignocellulose degradation by
termites (Brune, 2014) and lignin degradation by co-culture of wood-rot fungi (Qi-he et
al., 2011). In addition, it has been also shown that lignocellulose degradation is
enhanced in a co-culture of different fungi (Singhania et al., 2010).
Chapter 2 Literature review
54
While testing the effects of a number of filamentous fungi on a rice straw
composting process, it was observed that cultures of T. viride and A. niger were able to
grow in a synergistic manner to degrade cellulose and hemicellulose. In the same
experiment, the commensal behaviour of a number of other strains was assessed, but
these were found to be present at very low levels or absent (Kausar et al., 2010). A
slightly different approach was adopted by Chu et al. (2011). Instead of a pure, known
fungal culture, mixed microbiota was taken from biogas digested sludge and inoculated
onto wheat stalk. The system produced 37 mL/ g of H2 after 200 hours of incubation. In
addition, the concentration of volatile fatty acids, such as acetate, propionate and
butyrate, increased considerably. This was in addition to about 75% cellulose being
solubilized during this process (Chu et al., 2011). A fungal mix was also observed to
degrade soybean hulls supplemented with wheat bran (a 4:1 mix) during an SSF
process. This mix was then degraded by a composite culture of T. reesei and A. oryzae
added in an equivalent ratio. It was observed that after the SSF process of 4 days, the
cellulase and β-glucosidase activities in mixed culture reached 10.8 U/g as compared to
6.5-6.7 U/g in the individual cultures (Brijwani et al., 2010). A similar increase in
enzymatic activities was observed in a mixed fungal culture of T. reesei RUT C30 and
A. niger on corn stover pre-treated by nano-shearing (Lu et al., 2013).It was observed
that the overall activities of cellulases and glucosidases increase considerably under the
symbiotic consortium conditions.
Another form of symbiotic fermentation consists of a sequential fermentation
where the first biomass degrader is used for pre-treatment followed by the second
biomass degrader during the main degradation step. In this process, the enzymes
released by the first biodegrading microorganism act in symbiosis with the second
biodegrading organism to not only enhance the degradation, but also to achieve
bioconversion to the product of interest (Cheng and Liu, 2012). The authors reported
that corn stalk substrate pre-treated with T. reesei RUT C30 by SSF and then seeded
with winery sludge was able to generate H2 gas up to 200 mL within 6 days.
In addition to ascomycetes, wood-rot fungi are known to generate important
molecules such as ethanol in addition to degrading difficult biomass complexes. Corn
stalks pretreated with hot water and steeping SO2 was applied to Ph. Chrysosporium-
mediated degradation. This was followed by depleting total oxygen from this medium to
generate anaerobic conditions. The medium was further fermented at 35°C for up to 11
Chapter 2 Literature review
55
days. It was observed that the total mass and lignin mass loss were about 34% and 41%,
respectively, and the production of ethanol was about 1.7 % (Shrestha et al., 2008).
A recent study demonstrated a symbiotic relationship between bacterial
cellulosomes and fungal extracellular cellulases. It was shown that, individually, C.
thermocellum cellulosomes were able to convert 100% Avicel to monosaccharides and
oligosaccharides, as opposed to about 40% achieved by fungal cellulases. Similarly,
about 40% Whatman no. 1 filter paper conversion was achieved by cellulosome
application, whereas, about 10% conversion was achieved with fungal cellulases. These
studies show the enhanced degrading ability of celulosomes on crystalline cellulose.
Contrastingly, fungal cellulases work better on pre-treated biomass substances such as
switchgrass and poplar, where they showed more than 80% conversion as opposed to
less than 50% conversion by celulosomes. Interestingly, when both of these were mixed
in equivalent amounts in an Avicel-containing medium, their synergistic behaviour
resulted in 100% bioconversion within 24 hours. This study suggested the possibilities
of using enhanced biomass degradation using thermophilic bacteria and general
biomass-degrading fungi in symbiosis for various purposes, including biofuel
production (Resch et al., 2013). A recent experiment displaying the synergistic
properties of cellulolytic enzymes was recently reported. Crude, filtered and dialysed
fungal enzyme samples obtained from SSF were mixed to an already operating SmF
biodegradation sample. The resulting symbiosis of Trichoderma and Aspergillus
enzymes caused the degradation of cellulose and hemicelluloses, as evidenced from a
conversion of up to 64% biomass to reducing sugars. Additionally, under the anaerobic
conditions, these enzyme mix in combination with S. cerevisiae was able to generate up
to 43 g/L ethanol (about 84% of the theoretical maximum) within 7 hours (Pirota et al.,
2014).
Thermophilic fungi can also be included in symbiotic consortia, similar to the
approach of using thermophilic bacteria described above. It is known that most
cellulolytic enzymes have their maximal activities in the range of 50-60°C
(temperatures that are typically achieved during biodegradation processes, such as
composting). However, most biomass-degrading fungi cannot survive at these
temperatures. The application of thermophilic fungi can overcome this problem and has
the potential to increase the biomass degradation potential of fungal systems. A recent
secretome analysis of the thermophilic fungus Thermomyces lanuginosus SSBP
growing on corn cobs showed the presence of 74 different proteins, a number of which
Chapter 2 Literature review
56
were found to be glycoside hydrolase enzymes. Although, other enzyme activities were
comparable to mesophilic fungi, this fungus was observed to possess an exceptionally
high xylanase activity of 3500 U/mL at 70°C with a wide pH range of activity (3-12)
(Winger et al., 2014). Another thermophilic cellulolytic fungus, Myceliophthora
thermophila, was recently shown to display cellulase activities (when grown on pre-
treated wheat straw at 70 °C) of 200 nmol min-1mg protein-1, equivalent to Trichoderma
reesei but higher xylanase activity of about 4 nmol min-1mg protein-1 at its optimal
temperature. One of the peculiar features of M. thermophila was the rate of reaction or
biomass degradation, which was much faster than either Trichoderma spp. or
Aspergillus spp. (van den Brink et al., 2013).
The natural symbiotic mechanism of biomass degradation and conversion to
important products requires careful selection. The process also avoids the much debated
technique of genetic engineering under in-vivo conditions due to better yields without
resorting to genetic engineering techniques. The output of symbiotic consortia can be
further optimized in their natural growth conditions by the application of new
technologies such as metabolomics and metabolic engineering.
2.6. Application of metabolomics in fungal biomass degradation
The study of metabolomics has the potential to provide biochemical information
in order to understand and characterise the various mechanisms related to fungal
biomass degradation. Metabolomics have been applied to investigate bacterial processes
related to preventative health (Bi et al., 2013, Marcinowska et al., 2011), environmental
pollution (Beale et al., 2013a), food (Beale et al., 2014) and fungal metabolism on
various substrates including benzoic acid and Chardonnay grape berries (Hong et al.,
2012, Matsuzaki et al., 2008). However, within the context of fungal-mediated biomass
degradation, its application has been limited. Analysis of the metabolic flux, however,
has been shown to have the capacity to enable a good understanding of the nature, time-
dependence and substrate-based limitations in regard to the metabolism of fungal cells.
Metabolomics also assist in extricating the correlation between cell phenotypes and
their metabolic patterns and stoichiometry (Meijer et al., 2009). Metabolic flux studies
have previously been applied in toxicology and medicine (Maier et al., 2009, Niklas et
al., 2010), plant proteomes (Nelson et al., 2014) and microbial respiratory systems
(Driouch et al., 2012, Pedersen et al., 1999).
Chapter 2 Literature review
57
Numerous methods are available for metabolic flux analysis. The majority of
these involve the use of heavy isotopes due to their considerably superior tracking
abilities. In commonly utilized analyses, such as nuclear magnetic resonance (NMR)
and gas chromatography-mass spectrometry (GC-MS), metabolite tracking is performed
using isotopes such as 1H, 2H, 13C, 14N, 15N and 18O. While every isotope has its
advantages and shortcomings, 13C-molecules have been used by most researchers
(Nelson et al., 2014), probably because of its wide-spread use in general chemistry.2H is
a much more abundant element in nature, comprising about half of the peptide atomic
population (Yang et al., 2010b).It is much more economical as compared to other
isotopes, has a comparatively higher invasive rate and a slower decay rate (Kim et al.,
2012b). Complete 2H labelling is not feasible, however, due to the limited tolerance of
multicellular organisms, thereby resulting in partial labelling, generally from 8% (Kim
et al., 2012b) to 30% (Yang et al., 2010b).
Limited metabolomics-based research has been performed on biomass
conversion compared to clinical applications. However, a number of studies in this area
have recently been described. For example, the supramolecular effects of pre-treatment
and successive microbial degradation of rice straw have recently been described (Ogura
et al., 2013). This biomass was pre-treated by ZrO2 ball milling for 6 hours followed by
metabolic analysis by 2D 13C-1H heteronuclear correlation spectra (HETCOR) in solid
state NMR and thermo-gravimetric analyses. The pre-treated biomass was
supplemented with paddy soil and the products were analysed by 1H-NMR
spectroscopy. The presence of syringyl, guaiacyl, hydroxyphenyl and coumarate
derivatives suggest considerable lignin linkage breakdown. Similarly, sugars and sugar
acids resulted from degradation of the cellulose and hemicellulose components.
Deformation of cellulose was noticed from the appearance of the C4 peak (NMR
spectroscopy) of crystalline cellulose and increasing intensity of amorphous cellulose,
indicating its conversion to the amorphous form. With respect to other pre-treatments,
the activation energy of ball-milling reduced considerably, from 166 KJ/mol to 96
KJ/mol, suggesting an increase in amorphous cellulose. Successive microbial
degradation of this biomass resulted in further generation of pentoses, hexoses and
disaccharides (from 3-4 ppm) and small chain fatty acids, especially butyrate (up to 2.2
ppm).Principal Component Analysis suggested the termination of degradation at 21
days (Ogura et al., 2013). In an extension to this experiment, bacterial cellulose
Chapter 2 Literature review
58
generated by Gluconacetobacter xylinus was degraded by anaerobic sludge and the
products were analysed by solid state-, solution state- and gas state- NMR. While solid
state-NMR showed decreasing crystallanity of cellulose over 84 hours, solution state-
NMR indicated that the major metabolite output consisted of small-chained fatty acids
and ethanol. These were generated during the early phase of fermentation and most
were re-utilized after 60 hours of fermentation. Similarly, CO2 and CH4 were generated
by oxidation and reduction of acetate, as detected by gas state-NMR (Yamazawa et al.,
2013).
The metabolomic approach can also be applied to prevent or minimise process
inhibition, either competitive or product, as shown in recent research using GC-MS-
based analysis (Zha et al., 2014) . It was observed that biomass pre-treatment products
such as hydroxymethyl furfural (HMF) and furfural prolonged the log-phase of yeast
growth, thereby allowing considerable growth inhibition. Similarly, other metabolites
such as aldehydes, phenolics, vanillin and sorbic acid, negatively affected the growth
rate. It was observed that S. cerevisiae counters the effects of HMF and furfural by
converting these molecules to their respective acids. It was also noted that amino acids,
when initially present in considerable amounts, compensated for the inhibitory and
toxic effects of various molecules during fermentation (Zha et al., 2014). Metabolic
profiling can be applied to obtain a metabolic flux, following which metabolic
engineering tools such as induction of expression systems can be utilized. These
techniques not only decrease the overall inhibition, but also aid in developing systems
which generate desired products. This has been reported recently where a Zymomonas
mobilis ethanolic pathway system was expressed in Sphingomonas spp. A1, to convert
alginate to ethanol (Takeda et al., 2011).
Due to the complex nature of biomass composition and the allosteric nature of
most lignocellulolytic enzymes, standard biochemical tests have proven less effective
compared to metabolomic approaches for deciphering biomass conversion. Moreover,
metabolic profiling methods indicate signature metabolites and critical pathway points
which can be exploited to not only improve the biomass degradation process, but also to
convert this biomass to the products of industrial and medicinal interests.
Chapter 2 Literature review
59
2.7. Overview
This chapter presented an overview of biomass composition and fungal
degradation of biomass components, and included a discussion of the various methods
and techniques to improve biodegradation. In the earlier sections, the chapter dealt with
biomass composition and specific characteristics of various biomass components. These
components include cellulose, hemicellulose molecules such as xylans, xylosides,
glucans, mannans and pectins and lignin, tannin and phenolic components.
Later sections described various issues and problems related to biomass
degradation, highlighting the parameters which are still prevalent in industrial
bioprocessing and prevent the utilization of biomass as the major source of energy
production and medicinal metabolite generation. To counter these limitations, various
fungal-mediated biomass processing have been developed. Major enzymes secreted by
biomass-degrading fungi and their effects with possible commercial outputs have been
reviewed. These enzymes include cellulases, hemicellulases and ligninases from a wide
variety of fungi such as ascomycetes and basidiomycetes. Various methods of applying
these fungi and their improvement in symbiotic and SSF conditions have been indicated
by numerous researchers to be the effective measures of not only enhancing biomass
degradation, but also to convert these primary products of biodegradation to secondary
products with maximal economic viability.
Lastly, this chapter reviewed the novel approach of metabolomic analysis and its
application to further develop symbiotic fungal consortia to obtain superior
bioconversion with minimal inhibition effects. Overall, this review presents numerous
approaches for biomass waste utilization to generate potentially useful products which
are important in numerous industrial and medicinal applications. Various approaches
such as SSF, fungal consortia and some consolidated bioprocessing will be later utilized
during the course of this thesis to improve the biomass degradation. The degradation
approaches will be analysed by numerous biochemical and enzyme activity analyses.
Metabolomic approaches using GC-MS techniques will be used to find the metabolic
profile of grape biomass degradation. The technique will also aid in finding the critical
points in fungal biomass degradation pathways to further improve the overall
bioconversion.
60
CHAPTER 3
Materials and methods
61
3.1. Chemicals
3.1.1. Chemicals and media
The chemicals used for all the experiments were purchased from Sigma-Aldrich
Co. LLC (Sydney, Australia) and were of analytical grade (AR) levels, unless specified.
These included sugars such as glucose, fructose, galactose, arabinose, cellobiose,
mannose, xylose, cellulose and para-Nitrophenyl β-D-glucopyranoside (pNPG); and
other chemicals such as cysteine hydrochloride, syringaldazine, ABTS, tyrosine, 3, 5-
dinitrosalicylic acid (DNSA), Bovine Serum Albumin (BSA), pyridine and deuterium
oxide (2H2O).
3.1.2. Growth media
Bacterial and fungal growth media of Nutrient Agar (NA), Potato Dextrose Agar
(PDA), Nutrient Broth (NB) and Sabouraud broth were purchased from Oxoid
(Basingstoke, UK).
AATCC (American Association of Textile Chemists and Colourists) mineral
salts iron medium, Clostridium thermocellum medium and Clostridium stercorarium
medium were made in-house as per the pre-requisites (Atlas, 2010). The composition of
each of these media is given in Table 3.1. The cellulose/ biomass content changed from
30 g/L in the submerged fermentation (SmF) to 500 g/L in solid state fermentation
(SSF). The pH level of AATCC medium was kept at 5 ± 0.2 during all the fungal
growth conditions in submerged and solid state conditions, unless specified otherwise.
For the bacterial growth purposes, the pH was set at 7.4 ± 0.2. Similarly, the pH level
was maintained at 7.4 ± 0.2 in Clostridium thermocellum and Clostridium stercorarium
media during every experiment, unless specified otherwise.
62
Table 3.1. General composition of various minerals and organic sources in the growth media for
isolating and characterizing cellulolytic organisms.
Component AATC
C (g/L)
Cl. thermocellum
(g/L)
Cl. stercorarium
(g/L)
NH4NO3 3.0 - -
KH2PO4 2.5 1.0 1.65
K2HPO4 2.0 1.0 -
MgSO4 0.2 - -
FeSO4.7H2O 0.1 0.00125 -
NH4SO4 - - 1.6
Yeast extract - 6.0 1.0
NaCl - - 0.96
Cysteine
HCl.H2O
- 0.3 0.5
CaCl2 - 0.05 0.096
Resazurin - 0.001 0.001
Urea - 2.0 -
MgCl2 - 0.5 -
Cellobiose - 5.0 -
MOPS - 10.0 -
Trisodium citrate - 30 -
Cellulose/biomass 30-500 - 30.0
63
3.1.3. Enzymes
The enzymes used in the study included cellulase (from Trichoderma viride),
xylanase (from Aspergillus niger), β-glucosidase (from almonds), laccase (from
Trametes versicolor), peroxidase (from Horseradish) and tyrosinase (from mushroom).
All the enzymes were purchased from Sigma-Aldrich Co LLC (Sydney, Australia).
3.1.4. Buffers and reagents
All the buffers used for the study were made in the laboratory using AR grade
constituents, (unless specified) are given in Table 3.2
Table 3.2. Composition of buffers and reagents used during the experiments
Reagent/Buffer Composition
DNSA reagent 1 g 3, 5-DNSA, 300 g sodium potassium tartrate were
dissolved in deionised water. 200 mL of 2M NaOH was
added and volume made to 1 L with deionised water.
Mollisch’s reagent 5% α-napthol solution was made up to 1 L with 95% H2SO4
Biuret reagent 1.5 g CuSO4.5H2O and 6 g NaK-tartrate were dissolved in
500 mL deionised water. 300 mL 10% NaOH was then added
and total volume was made to 1 L with deionised water.
Bial’s reagent 160 mg ethanolic orcinol was dissolved in 80 mL
concentrated HCl and 0.2 mL 10% FeCl3. Total volume was
made to 1 L with deionised water.
0.05 M citrate
buffer
10.5 g citric acid monohydrate was dissolved in 800 mL
deionized water and pH was adjusted to 4.5 with NaOH
solution. Total volume was made to 1 L with deionised water
so that the final pH was 4.8.
0.1 M acetate buffer 13.608 g sodium acetate was dissolved in 900 mL deionized
water and pH was adjusted to 5.0 by 1 M HCl. Total volume
was made to 1 L with deionised water.
0.2 M Na2CO3
solution
21.191 g Na2CO3 was dissolved in deionized water and total
volume was made to 1 L with deionized water.
64
Table 3.2. Composition of buffers and reagents used during the experiments (…continued).
Reagent/Buffer Composition
0.01 M phosphate
buffer, pH 7
0.12 g NaH2PO4 (monobasic) was dissolved in 900 mL
deionized water followed by adding 2 g BSA. The pH was
adjusted to 7 and total volume was made to 1 L with
deionised water.
0.1 M phosphate
buffer, pH 6.5
13.609 g KH2PO4 (monobasic) was dissolved in deionized
water and pH was adjusted to 6.5 with 1 M KOH. Total
volume was made to 1 L with deionised water.
0.1 M phosphate
buffer, pH 5
13.609 g KH2PO4 (monobasic) was dissolved in deionized
water. The pH was adjusted to 5 with 1 M KOH. Total
volume was made to 1 L with deionised water.
0.04 M phosphate
buffer, pH 6.8
5.443 g KH2PO4 (monobasic) was dissolved in deionized
water followed by 2.5 g BSA and 5 g triton X-100. The pH
was adjusted to 6.8 with 1 M KOH. Total volume was made
to 1 L with deionised water.
Tris-HCl buffer 0.1 M Tris (12.114 g/L) was dissolved in deionized water
followed by addition of 0.1 N HCl (9.8 mL/L). The pH was
adjusted to 7.8 and total volume was made to 1 L with
deionised water.
50X Tris acetate
EDTA (TAE) buffer
242 g Tris was dissolved in deionized water with 57.1 mL
glacial acetic acid and 100 mL 0.5 M EDTA. Final pH was
adjusted to 8 and total volume was made to 1 L with
deionised water.
Syringaldazine
solution
0.0778 g syringaldazine was dissolved in absolute methanol
to produce 0.216 mM solution.
ABTS solution 4.993 g ABTS was dissolved in 0.1 M KH2PO4 (monobasic).
The pH was adjusted to 5 with 1 M KOH and total volume
was made to 1 L with deionised water.
0.3 % H2O2 solution 10 mL of 30 % H2O2 was added to deionised water and total
volume was made to 1 L with deionised water.
65
Table 3.2. Composition of buffers and reagents used during the experiments (…continued).
Reagent/Buffer Composition
0.125 M Sodium
tartrate buffer, pH 3
28.76 g sodium tartrate was dissolved in deionized water and
pH was adjusted to 3 with 1M HCl. Total volume was made
to 1 L with deionised water.
Veratryl alcohol
solution
1.682 g (w/v) was dissolved in deionized water and total
volume was made to 1 L with deionised water.
0.002 M H2O2
solution
0.1706 mL of 35% H2O2 was dissolved in deionised water to
make the final volume of 1 L.
Cellulase solution Cellulase (from Trichoderma viride, 9.8 U/mg) was
dissolved in citrate buffer, pH 4.8 at a concentration of 1
mg/mL and later used at appropriate dilutions. It was stored
at -20°C until used.
β-glucosidase
solution
β-glucosidase (from almond, 6.9 U/mg) was dissolved in ice
cold tris HCl buffer, pH 7.8 at a concentration of 1 mg/mL
and later used at appropriate dilutions. It was stored at -20°C
until used.
Xylanase solution Xylanase (from Trichoderma viride, 105 U/mg) was
dissolved in citrate buffer, pH 4.8 at a concentration of 1
mg/mL and later used at appropriate dilutions. It was stored
at -20°C until used.
Laccase solution Laccase (from Agaricus bisporus, 0.8 U/mg) was dissolved
in 0.1 M KH2PO4 (monobasic) buffer at a concentration of 1
mg/mL and was later used at appropriate dilutions. It was
stored at -20°C until used.
Lignin peroxidase
solution
Lignin peroxidase (0.1 U/mg) was dissolved in 0.1 M
KH2PO4 (monobasic) buffer at a concentration of 1 mg/mL
and was later used at appropriate dilutions. It was stored at -
20°C until used.
3.1.5. Maintenance of storage and other conditions
Fungal degraded samples were snap frozen by using liquid nitrogen and later
stored at the temperature of -80°C until further analysis. Fungal degraded samples
66
utilized for metabolomic purposes were freeze dried at -52°C in a Cryodos-50 freeze
drier (Telstar Industrial, S.L., Terrassa, Spain) and again stored at -80°C until further
analysis.
All glassware and bioreactors utilized in the process were thoroughly washed
and applied to autoclaving at 121°C, 101 kPa for 20 minutes before utilization.
Similarly, the non-utilized biomass waste from fungal degradation, unused fungal and
bacterial cultures and any organic waste products were autoclaved at 121°C, 101 kPa
for 20 minutes prior to discarding.
3.2. Organisms
Ascomycota fungi Aspergillus niger (ATCC 10577) and Saccharomyces
cerevisiae (ATCC 287) were collected from the culture collection at Swinburne
University of Technology (Hawthorn, Victoria, Australia). Trichoderma harzianum
(AG 46) and Penicillium chrysogenum (AG 47) were obtained from Agpath Pty. Ltd.
(Vervale, Victoria, Australia). Basidiomycota fungi Phanerochaete chrysosporium
(16543), Trametes versicolor (4483) and Ganoderma applanatum (2403) were kindly
provided by Dr. Geoff Dumsday, CSIRO Manufacturing Flagship (Clayton, Victoria,
Australia). All fungi were first grown in Sabouraud agar for 5 days before any
subculturing. Ascomycota fungi were incubated at 30°C, whereas basidiomycota fungi
were incubated at 35°C. Fungal cultures were stored in petri plates at 4°C on a short-
term basis and under medicinal paraffin oil (specific gravity 0.865-0.89) immersion
slants at 4°C for long-term basis.
3.3. Genomic identification of fungi and bacteria
Thermophilic anaerobic bacterium was isolated from the domestic compost
samples held in Department of Chemistry and Biotechnology, Swinburne University of
Technology. The compost samples were cultured on Clostridium thermocellum
medium. This bacterium was used in the accessory experiments and establishing
primary bacterial cellulose degradation at thermophilic conditions (60°C). However, it
was not applied during advanced biomass degradation studies. Besides this, fungi were
isolated from Tamborine National Park, Gold Coast, Queensland, Australia. These fungi
were identified by ITS region sequencing as given below. However, due to their
67
similarities to standard fungi already available in the laboratory, they were not used for
further experiments. The details of these fungi are provided in Table 3.3 below.
Table 3.3. Micro-organisms isolated from various sources and characterized by Sanger sequencing.
Type Primers Identification GenBank ID
Bacteria 27F forward
1513R reverse
Brevundimonas spp. clone viniA711 KC161226
Fungus ITS1 forward
ITS4 reverse
Trichoderma harzianum isolate
Viniti KF316951
Fungus ITS1 forward
ITS4 reverse
Candida homilentoma isolate swin KF316952
Fungus ITS1 forward
ITS4 reverse
Fusarium spp. VinV4 ITS 1, partial
sequence KF316953
Fungus ITS1 forward
ITS4 reverse
Nectria haematococca isolate PVK KF316954
3.4. Grape samples
Post-fermentation wastes of Vitis vinifera vars. Shiraz, Grenache and Cabernet
were obtained from the Australian Wine Research Institute (AWRI), Glen Osmond,
South Australia; Australia. These samples were stored at 4°C in airtight containers
unless used. The grape biomass waste was oven-dried at 70°C for 96 hours and ground
using HR2094 domestic blender (Philips Electronics Australia, North Ryde, NSW;
Australia). The dried, ground substrate was further oven dried for 96 hours at 70°C
before its application to experimental conditions.
3.5. Fungal and bacterial growth conditions
All the fungi mediated biodegradation processes, whether submerged
fermentation or solid state fermentation, were carried out in 250 mL conical flasks. The
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flasks were filled up to 150 mL levels during submerged fermentation with AATCC
mineral medium. This was followed by addition of dried grape biomass at the
concentration levels of 30 g/L. During solid state fermentation, 10 g of dried grape
biomass was put in to the flasks with 10 mL AATCC mineral medium. The flasks were
tightly sealed with non-absorbent cotton before fungal spore transfer. The flasks
categorised for submerged fermentation were autoclaved at carbohydrate cycle of
115°C for 10 minutes. The flasks categorised for solid state fermentation were not
autoclaved. After the addition of fungal spores, these flasks were incubated at 30°C/ 200
rpm for 1 week duration.
The conditions similar to fungal submerged fermentation were applied to
bacterial shake flask conditions. The only exception was replacement of grape biomass
by pure cellulose at a concentration of 30 g/L.
3.6. Calculating bacterial and fungal concentrations
Bacterial and fungal concentrations were monitored and regulated for all
experimental purposes. For bacteria, this concentration was kept at 1×109 cells/mL,
while the fungal spore concentration was kept at 1×107 cells/mL, which was in line with
numerous previous experiments performed with different fungi (Betini et al., 2009, Gal
Milanezi et al., 2012, Gao et al., 2008, Gupte and Madamwar, 1997, Teixeira et al.,
2010). Initially, the fungi grown on Sabouraud agar plates and bacteria grown on
nutrient agar plates were harvested and added to aseptic 0.1% tween-20 solution. This
method proved to be simple, yet efficient to remove debris material from solid bacterial
cultures.
Fungi obtained from standard collections were inoculated on Sabouraud agar
petri plate. All fungi inoculated plates were incubated at 30°C for 5 days. The grown
fungal biomass consisting of mycelia and spores was then sampled from these plates
and added to 10 mL sterile tween 20 solutions (0.1% in distilled sterile water). The
mixture was then vortexed for about 30 seconds and filtered through sterile Whatman
no. 1 filter paper (pore size: 11 μm). Spores present in the filtrate were counted by
microscopic observation on a haemocytometer and calculated to the required spore
count by using the equation given below.
69
𝑆𝑝𝑜𝑟𝑒 𝑛𝑜. = 𝑁 × 1𝑉
× 𝑆 Eq. 3.1
Where,
N = number of spores observed in an area
V = volume of haemocytometer under consideration (0.004 mm3)
S = Total volume of sample considered for final calculation (1 mL=103 mm3)
These calculated amount of spores/bacterial cells were then inoculated in the respective
growth media, under submerged or solid state fermentation conditions.
3.7. Biochemical tests
3.7.1. Determination of total soluble sugars
Total soluble sugars in non-degraded and degraded grape pomace were
quantitatively determined by Mollisch’s assay. Filtrate was used as the sample, 0.02 ml
of which was mixed with 0.2 ml ethanolic α-napthol. This was followed by a slow and
careful addition of 1 ml 95% H2SO4. The mixture was gently vortexed after 10 minutes
of incubation at room temperature followed by a further incubation at the same
temperature for 30 minutes. The sugar content was then determined by taking the
absorbance at 490 nm and interpolating the values with the glucose standard (Ahmed,
2005).
3.7.2. Determination of reducing sugars
The quantitative determination of reducing sugars in the filtrate of degraded
grape waste was performed by the dinitrosalicylic acid (DNSA) assay. Grape waste
filtrate (100 μL sample) was mixed with 1ml DNSA and incubated in a boiling water
bath for 5 minutes followed by cooling on ice to stop further reaction and bring the
sample to room temperature. The absorbance was taken at 540 nm to determine the
concentration of reducing sugars. A glucose gradient was used to derive the standard
reducing sugar (Plummer, 1987).
70
3.7.3. Determination of pentoses
The pentose sugar concentration was quantified by Bial’s assay (Pramod and
Venkatesh, 2006). Grape waste filtrate (200 μL) was mixed with 1 mL Bial’s reagent
(80 mg ethanolic orcinol in 40 mL concentrated HCl and 0.1 mL 10% FeCl3) and kept
in a boiling water bath for 5 minutes. The samples were then cooled to room
temperature on ice to terminate the reaction and the absorbance was taken at 660 nm.
The concentration was determined on the basis of the standard curve of arabinose.
3.7.4. Total protein content
Protein determination was performed using Biuret assay. Appropriately diluted
filtrate in the volume of 0.2 ml from the degraded samples was mixed with 0.8 ml
Biuret reagent. The mixture was vortexed and incubated at room temperature for 30
minutes. Total protein content was determined by absorbance at 546 nm. Bovine serum
albumin was used as the standard for the test.
3.7.5. Lignin content measurement
Lignins were determined as Acid Soluble Lignin (ASL) and Acid Insoluble
Lignin (AIL) by the NREL procedure (Sluiter et al., 2011). 0.1 g dried grape was
incubated in 1 mL 72% H2SO4 at 30°C for 1 hour. This was followed by dilution of the
acid hydrolysed sample to 4% H2SO4 by addition of deionized H2O. The mixture was
then autoclaved at 121°C for 1 hour followed by cooling to room temperature. The
supernatant was collected as the ASL fraction after a brief centrifugation. The pellet was
rinsed with distilled water and was dried at 105°C for 4 hours to ensure complete
drying. The dried sample was weighed as Acid Insoluble Residue and was vaporized in
a muffle furnace at 575°C for 1 hour followed by cooling to room temperature. The
weight of this sample was considered as ash. ASL in each sample was determined by
the absorbance of the centrifuged filtrate at 320 nm using the equation 3.2:
%𝐴𝑆𝐿 = 𝐴𝐵𝑆 × 𝑣𝑜𝑙𝑢𝑚𝑒 × 𝐷𝑓𝜀×𝑊𝑆1 × 𝑝𝑎𝑡ℎ𝑙𝑒𝑛𝑔𝑡ℎ
× 100 Eq. 3.2
Where,
71
ABS = Absorbance at 320 nm
Volume = Volume of total filtrate (30.35 mL)
ε = Absorptivity of biomass at 320 nm (30 L/g.cm)
WS1 = Oven dried weight of sample (milligrams)
Pathlength = Pathlength of the cell (1 cm)
AIL was determined by the ratio of difference between dry Acid Insoluble
Residue (AIR) and ash to the original dry weight of grape waste as given in the equation
below (Equation 3.3).
% 𝐴𝐼𝐿 = 𝑊𝑆2− 𝑊𝑆3𝑊𝑆1
× 100 Eq. 3.3
Where,
WS1 = Oven dried weight of sample (milligrams)
WS2 = Weight of AIR (milligrams)
WS3 = Weight of ash (milligrams)
The total lignin content was calculated as the cumulative ASL and AIL.
3.7.6. Total nitrogen and carbon content
Total nitrogen and carbon content were measured using a Carbon and Nitrogen
analyser (CN 2000, Leco Corporation, St. Joseph, Michigan, USA). Following
calibration and drift correction, 100-130 mg oven dried control and fermented samples
were fed into the analyser. Total nitrogen and carbon content was calculated by
automated sampling.
All the samples were dried at 105°C for 4 hours before analysis to remove any
traces of moisture. The analyser utilizes a convection furnace to oxidise the test samples
at elevated temperatures. Under the current analysis, 1050°C temperature was applied,
which was specified by the supplier. Ultra high purity grade oxygen (Grade 4.5) was
used to oxidize the samples by both direct supplementations over the sample and
background purging in a controlled pulsed mechanism. The analyser utilizes the
72
properties of thermal conductivity to determine total nitrogen percentage, while it uses
an infra-red analyser to detect the total carbon released from a given sample.
All blanks were determined by placing empty ceramic crucibles and adjusting
the ‘zero’ or ‘Blank’ readings. Supplied standards of EDTA (41.05% C, 5.55% H and
9.58% N), rye flour (44.04% C, 6.51% H, 1.83% N and 0.143 % S), complete coal
(71.93 % C and 1.24 % N) and soil (2.7 % C, 0.028 % N and 0.028 % S) were used to
calibrate the nitrogen and carbon contents. All the samples and calibration standards
were used in at least triplicates for establishing validity and increasing the accuracy of
analysis.
3.8. Enzyme assays
The activities of various lignocellulose degrading enzymes was studied which
involved cellulase, β-glucosidase, xylanase. Ligninases activity assays were performed
with laccase and Lignin peroxidase. The assay protocols are mentioned below. For
assay purposes, the crude filtrates from fungal degraded winery biomass were used and
were mixed with citrate buffer in appropriate ratio (for cellulase and xylanase assays) or
relevant phosphate buffers (for β-glucosidase, laccase and peroxidase assays). These
filtrates were centrifuged at 2500 g for 15 minutes at 0°C to separate any fungal and
grape biomass. The supernatant from this centrifugation process was transferred to fresh
tubes. The tubes were applied to snap freezing by liquid nitrogen and stored at -80°C
until further use.
3.8.1. Cellulase activity assay
The crude filtrate obtained (as mentioned above) was thawed on ice bath before
any use. This filtrate was mixed in equal proportion (v/v) of citrate buffer (0.05 M, pH
4.8). The mixture solution was briefly vortexed and stored at -20°C until used further,
while during the use, it was always stored in ice. Cellulase activity was measured in
terms of Filter Paper Activity (FPA) as per IUPAC protocols (Ghose, 1987). Whatman
no. 1 filter paper (50 mg) was used as the substrate and was added to 0.5 ml of
appropriately diluted filtrate in 0.05 M sodium citrate buffer (pH 4.8). The mixture was
vortexed and incubated at 50°C for 1 hour. DNSA reagent (3 mL) was added to this
73
reaction mixture before boiling for 5 minutes. The reaction was quenched on ice prior to
adding 20 ml of de-ionized water. The sample mixture was then vortexed vigorously
and allowed to settle for 20 minutes at room temperature. Absorbance was taken at 540
nm to determine the cellulose activity in terms of FPA. One International Unit (IU) of
cellulase is defined as the amount of enzyme required to liberate 1 µmol glucose per
minute under assay conditions.
3.8.2. β-glucosidase activity assay
The filtrate sample was mixed in equal proportion (v/v) with ice cold tris HCl
buffer (pH 7.8) and then further diluted in phosphate buffer supplemented with 0.2%
BSA (0.04 M, pH 6.8). This mixture was kept at -20°C until used further, while during
the use, it was always stored in ice. β-glucosidase activity was determined by p-
nitrophenyl-β-D-glucoside (pNPG) assay according to the method of Kovacs et al.
(Kovacs et al., 2009) with slight modifications. Briefly, 1 ml of sodium acetate buffer
(0.1 M, pH 5) and 0.5 ml of 0.02 M p-nitrophenyl-β-D-glucosidase (pNPG) were added
to appropriately diluted enzyme sample in sodium acetate buffer. The mixture was
incubated at 50°C for 5 minutes. The reaction was terminated by addition of 2 ml
Na2CO3 solution (0.2 M). β-glucosidase activity was determined by measuring the
optical density against water at 400 nm. One IU of β-glucosidase is defined as the
amount of enzyme required to liberate 1 µmol p-nitrophenol per minute under assay
conditions.
3.8.3. Xylanase activity assay
The crude filtrate obtained (as mentioned above) was thawed on ice bath before
any use. This filtrate was mixed in equal proportion (v/v) of citrate buffer (0.05 M, pH
4.8). The mixture solution was briefly vortexed and stored at -20°C until used further,
while during the use, it was always stored in ice. Xylanase activity was measured by
Highely’s method (Highley, 1997). 1.8 ml of Birchwood xylan (1%) in 0.05 M Na-
citrate buffer was mixed with 200 µl of appropriately diluted enzyme sample and
incubated at 50°C for exactly 5 minutes. 3 ml DNSA reagent was added to this mixture
before boiling for 5 minutes. The reaction was terminated on ice. Absorbance was taken
74
at 540 nm to determine enzyme activity. One IU of xylanase is defined as the amount of
enzyme required to liberate 1 µmol xylose per minute under assay conditions.
3.8.4. Laccase activity assay
The crude filtrate obtained (as mentioned above) was thawed on ice bath before
any use. This filtrate was mixed in equal proportion (v/v) of potassium phosphate buffer
(0.1 M, pH 6.5). The mixture solution was briefly vortexed and stored at -20°C until
used further, while during the use, it was always stored in ice. Laccase activity was
measured by using slight variations mentioned previously (Manole et al., 2008). Crude
filtrate was appropriately diluted in potassium phosphate buffer. Fresh potassium
phosphate buffer was added with 0.5 mL of filtrate sample. The mixture was
equilibrated at 37°C for 5 minutes before the addition of methanolic syringaldazine
(0.216 mM). Table 3.4 elaborates the test method of laccase activity.
Table 3.4. Working protocol for laccase assay
Components Sample Blank
KH2PO4 (0.1 M) 2.2 2.2
Filtrate sample 0.5 0
H2O 0 0.5
Equilibration at 37°C for 5 minutes
Syringaldazine 0.3 0.3
Total volume 3.0 3.0
Upon addition of syringaldazine, the testing cuvette with sample was quickly
inverted for a quick vortex mixing of all components. Laccase activity was measured by
spectrophotometer at 525 nm wavelength. The reaction time in cuvette was 10 minutes
and difference in final and initial absorbance was taken in to account for calculation.
The activity was measured by equation 3.4.
𝐿𝑎𝑐𝑐𝑎𝑠𝑒 � 𝑈𝑚𝐿� = �(∆𝐴𝑠𝑎𝑚𝑝𝑙𝑒−∆𝐴𝑏𝑙𝑎𝑛𝑘)×𝑉×𝐷𝑓�
𝜀×𝑉1 Eq. 3.4
Where,
ΔA = difference of absorbance (final absorbance – initial absorbance)
75
V = total volume of mixture (3 mL)
Df = dilution factor
ε = extinction coefficient of syringaldazine (65000 M-1 cm-1)
V1 = volume of enzyme used (0.5 mL)
The enzyme activity was expressed in U/mL, where one unit (U) of Laccase
activity was defined as the amount of enzyme catalysing the oxidation of 1 μmole
syringaldazine to form quinone per minute at 30°C, pH 6.5 in a 3 mL reaction mixture.
3.8.5. Lignin peroxidase (LiP) activity assay
The crude filtrate obtained (as mentioned above) was thawed on ice bath before
any use. This filtrate was mixed in equal proportion (v/v) of potassium phosphate buffer
(0.1 M, pH 6.5). The mixture solution was briefly vortexed and stored at -20°C until
used further, while during the use, it was always stored in ice. LiP activity was
measured by using the method mentioned previously (Solarska, 2009). Crude filtrate
was appropriately diluted in Na2HPO4-citric acid buffer (0.2 M). Fresh Na2HPO4-citric
acid buffer was added with 0.5 mL of filtrate sample followed by veratryl alcohol (0.02
M). The mixture was equilibrated at 37°C for 5 minutes before the addition of H2O2
(0.004 M) to initiate the reaction. Table 3.5 elaborates the test method of lignin
peroxidase activity.
Table 3.5. Working protocol for lignin peroxidase assay
Components Sample Blank
Na2HPO4-citric acid 0.84 0.84
Filtrate sample 1.56 0
Veratryl alcohol 0.3 0.3
Equilibration at 37°C for 5 minutes
H2O2 0.3 0.3
Total volume 3.0 3.0
Upon addition of H2O2, the testing cuvette with sample was quickly inverted for
a quick vortex mixing of all components. LiP activity was measured by
spectrophotometer at 310 nm wavelength. The reaction time in cuvette was 5 minutes
76
and difference in final and initial absorbance was taken in to account for calculation.
The activity was measured by the equation 3.5 given below.
𝐿𝑖𝑔𝑛𝑖𝑛 𝑝𝑒𝑟𝑜𝑥𝑖𝑑𝑎𝑠𝑒 � 𝑈𝑚𝐿� = �(∆𝐴𝑠𝑎𝑚𝑝𝑙𝑒−∆𝐴𝑏𝑙𝑎𝑛𝑘)×𝑉×𝐷𝑓�
𝜀×𝑉1 Eq. 3.5
Where,
ΔA = difference of absorbance (final absorbance – initial absorbance)
V = total volume of mixture (3 mL)
Df = dilution factor
ε = extinction coefficient of veratryl alcohol (9300 M-1 cm-1)
V1 = volume of enzyme used (1.56 mL)
The enzyme activity was expressed in U/mL, where one unit (U) of lignin
peroxidase activity was defined as the amount of enzyme catalysing the oxidation of 1
μmole veratryl alcohol to form veratraldehyde per minute at 30°C, pH 4.5 in a 3 mL
reaction mixture.
3.9. Silyl derivatization and gas chromatography-mass spectrometry(GC-MS)
3.9.1. Silyl derivatization
Silyl derivatization was utilized for GC-MS based analysis of fungal metabolism
during the biomass degradation process. The necessity of derivatization was felt as the
aqueous samples from the fermentation process not only overload the chromatography
column and mass spectra detector, but also damages the column by bleeding it. Besides,
the polar molecules such as sugars, some amino acids and alcohols cannot be detected
readily by GCMS unlike many volatile compounds. Therefore, a derivatization agent
such as N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA) is utilized. BSTFA is
tagged to the metabolite of interest and converts it in to a non-polar volatile molecule,
which can then be easily detected by GC-MS based approaches.
The silyl derivatization was started as a conventional thermal heating process.
Samples derived from the optimized bioreactor degradation process were further
analysed by gas chromatography-mass spectrometry (GC-MS). A 1 ml aliquot of
methanol (LC grade, ScharLab, Sentemanat, Spain) was added to 40 (±2) mg post
degraded freeze dried sample, then vortexed briefly before centrifugation at 572.5 g/4°C
77
for 15 minutes. A 50 µL aliquot of the supernatant was then transferred to a fresh tube
and dried in an RVC 2-18 centrifugal evaporator at 40°C/210 g (MARTIN CHRIST
Gefriertrocknungsanlagen GmbH; Osterode, Germany). All samples were stored at -
80°C until further use (Ng et al., 2012). In order to derivatize the samples for GC-MS
analysis, 40 µL methoxamine HCl (2% in pyridine) was added to each sample and
incubated for 45 minutes at 37°C. To complete the derivatization, silylation was
performed by adding 70µL BSTFA in 1% TMCS. Samples were then incubated for an
additional hour at 70°C. Samples were diluted with 190 µL pyridine, vortexed and
centrifuged at 15682 g for 5 minutes before transferring to GC-MS vials.
However, due to the requirement of more time for this derivatization process, a
new microwave assisted derivatization process was developed after optimization. This
process not only reduced the time of derivation to about 3 minutes as compared to about
2.5 hours by thermal heating process, but also decreased the formation of number of
unnecessary metabolites which were classified as artefacts during the GC-MS process.
In order to derivatize the samples for GC-MS analysis, 40.0 µL methoxyamine HCl (20
mg/mL in pyridine) followed by 70.0 µL N,O-Bis(trimethylsilyl)trifluoroacetamide
(BSTFA) in 1% trimethylchlorosilane (TMCS) were added to the dried samples.
Samples were then briefly vortexed before being transferred to clean GC-MS vials.
These were then derivatized in a Multiwave 3000 microwave (PerkinElmer Inc.,
Melbourne, Australia) for 3 minutes at 120°C/ 600 W before transferring to the GC-MS
for analysis. Pre-derivatized 13C-Sorbitol (Kovats Retention Index = 1918.76, m/z =
620.00 [10µg/mL, HPLC grade, Sigma-Aldrich, Castle Hill, NSW, Australia]) was
added as the second internal standard at this point in order to verify instrument stability
over the run time. This increased the reliability and prevented any miscalculations
arising from sample loss during GC-MS.
3.9.2. GC-MS analysis
The derivatized samples were analysed using an Agilent 6890B Gas
Chromatograph (GC) oven coupled with a 5973A Mass spectrometer (MS) detector
(Agilent Technologies, Mulgrave, Victoria, Australia), as described previously (Beale et
al., 2013b, Beale et al., 2014). The GC-MS system was fitted with a 30 m DB-5MS
column, 0.25mm ID and 0.25 µm film thickness. All injections were performed in
78
splitless mode with 1.0 µL volume; the oven was held at an initial temperature of 70°C
for 2.0 min before increasing to 325°C at 7.5°C min-1; the final temperature was held for
4.5 min. The transfer line was held at 280°C and the detector voltages at 1054 V. Total
Ion Chromatogram (TIC) mass spectra were acquired from 45 to 550 m/z, at an
acquisition frequency of 1.08 spectra s-1. The ionisation source used was electron
ionization (EI) and the energy was 70 eV. The solvent delay time of 7.5 minutes
ensured that the source filament was not saturated and damaged with derivatization
reagent. Data acquisition and spectral analysis were performed using MassHunter.
Quantitative identification of the compounds was performed according to the
Metabolomics Standard Initiative (MSI) Chemical Analysis Workgroup (Sumner et al.,
2007) using standard GC-MS reference metabolite libraries of Wiley, NIST 11 and
NIST EPA/NIH using Kovats retention Indices based on the referenced n-alkane
retention times (C8-C40 Alkanes Calibration Standard, Sigma-Aldrich, Castle Hill,
NSW, Australia). For peak integration, a 5 point detection filtering (default settings)
was set with a start threshold of 0.2 and stop threshold of 0.0 for 10 scans per sample.
3.10. Chemometric and statistical analysis
3.10.1. Biochemical analysis and enzyme assays
Statistical analysis for the analysis of biochemical tests and enzyme assay was
performed by one way analysis of variance (one-way ANOVA). All data were presented
as the mean values of triplicate data samples with their standard error. The consistency
and deviations between the data were analyzed by one way ANOVA using IBM®
SPSS® 20.0 statistics software.
3.10.2. Statistical modeling for degradation optimization
For developing the statistically optimized method for improved fungal
degradation, statistical modelling possessing predictive abilities was used. Experimental
design and statistical analysis was performed using a full factorial design method
developed using Minitab® 16 (Minitab Pty Ltd., Sydney, Australia). One of the simplest
methods for complex experimental design, it can analyse multiple factors with each
factor containing multiple levels of experimental conditions. In our experiments, we
used two factors of fungi (4 types) and methods (2 types) optimising cellulases,
79
xylanases and β-glucosidases (3 factors), thus yielding a 24 experimental condition
model (Montgomery, 2008). For the experimental design, maximum enzyme activities
were the primary responses sought after. However, the responses for reducing sugar and
lignin concentrations were also taken into consideration for the experimental design.
3.10.3. Metabolic profiling and flux analysis
Data generated by GC-MS process for metabolite profiling and metabolic flux
analysis were applied to various statistical approaches. GC-MS quantitative analysis
was performed by Quantitative analysis system of MassHunter workstation software,
version B.06.00/ Build 6.0.388.0 (Agilent Technologies, Mulgrave, Victoria, Australia).
The data was generated in a tabular form and was readily exported to Excel® 2010
spread sheet (Microsoft Corporation, Redmond, WA, USA). The earliest data filtration
was done manually where the peak features generated from non-derivatized samples or
‘artefact peak features’ were removed. Chemometric and statistical and analysis was
undertaken for this filtered data using SIMCA 13, a chemometric software package
(Umetrics AG, Umeå, Sweden), and MetaboAnalyst 2.0, an online statistical package
(TMIC, Edmonton, Canada). Peak areas were taken in to consideration for statistical
analyses. Chromatography peaks were considered significant where the signal to noise
ratio > 50, the Fold Change (FC) was > 2.0, and p-values were ≤ 0.05. Similarly, the
metabolites with FC values < 0.5 were considered as the fungal degraded/ utilized
metabolites. The data generated by mass spectral analyses were thus normalized with
respect to internal standards (RSD = 7.39%), where a magnitude of 1 fold change (FC)
referred to the concentration of 10 mg/L. Also, the FC values of less than 0.5 were
indicated as fungal utilized metabolites. This was followed by Principal Component
Analysis (PCA) based on score scatter
To accommodate the outliers and differentiating between the groups based on
metabolic pattern, which PCA could not fully accomplish; Partial Least Square-
Discriminant Analysis (PLS-DA) was employed. PLS-DA is used to analyse large
datasets and has the ability to assess linear/polynomial correlation between variable
matrices by lowering the dimensions of the predictive model, enabling easy
discrimination between samples and the metabolite features that cause the
discrimination (Wold, et al., 2001).
80
Where the PLS-DA was unable to provide satisfactory discrimination,
Orthogonal PLS-DA (OPLS-DA) was used. Orthogonal PLS-DA (OPLS-DA) was, thus
applied to yield better metabolite discrimination, leading to a better grouping and better
model predictability. Whereas, PLS-DA builds the model from a two-component
system of systematic and residual variables, OPLS-DA utilizes a three component
system. In addition to residual variables, the systematic variability component in OPLS-
DA is derived from adding the correlated variability (predictive) and non-correlated
variability (orthogonal) between X-Y axis components. Generally, due to the presence
of single Y-axis component, OPLS provides only one predictive component. However,
multiple T-axes components are represented as the orthogonal variations to generate a
multiple predictive OPLS-DA model (Trygg and Wold, 2002). For single response, X-
axis variation is represented by equations 3.6 and 3.7 below.
𝑋 = 𝑙𝑥′� + 𝑡𝑝′ + 𝑇0𝑃′0 + 𝑒 Eq. 3.6
and, Y-axis based prediction is represented by:
𝑌 = 𝑦′� + 𝑡𝑞′ + 𝑓 Eq. 3.7
Where, TP’ and TQ’ = matrix products
T = score vector
P’ and Q’ = loading vectors
e and f = residual variables
The FC values obtained in MetaboAnalyst 2.0 were replotted on Excel® 2010
spread sheet to distinguish significant metabolites. These metabolites were then
validated by calculating their Kovats retention indices.
To analyse metabolic flux, in addition to abovementioned statistical approaches,
MATLAB statistical software was also used. The identified metabolites were crossed-
checked against the KEGG database of metabolic pathways
(http://www.kegg.jp/kegg/pathway.html). The data obtained was then applied to
MATLAB 2014a statistical software using Covariance-Inverse (COVAIN) script
(Doerfler et al., 2014). The resultant data were applied as a correlation between the
different metabolites in a time series analysis.
81
CHAPTER 4
Fungal degradation of hydrothermal pretreated
winery grape wastes
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
82
4.1. Introduction
Fermentation is the preferred method for alcohol production from various
sources such as grape, corn and barley. In the fermentation process, microorganisms
such as Saccharomyces cerevisiae are able to generate up to 12-15% alcohol from the
raw material, which is used as either beverage alcohol or as an industrial reagent. The
rest, and the bulk, of the material are discarded as spent wash, and may include unused
live or dead fermenting organisms. Wine waste will be the focus of study here. Its waste
consists of dry matter including crude fibres, grape seeds, skin, waste, marcs, stalk and
skin pulp, proteins, ethers and amino acids. Apart from the moisture content, the major
components of grape waste are dietary fibre (30-40%), lipids (0.5-1%), soluble sugars
(2-3%), proteins (2.5-3.5%) and ash (2-3%). Dietary fibre consists mainly of cellulose
(27-37%), pectins (37-40%) and lignins (33-35%) (Bravo and Saura-Calixto, 1998, Ping
et al., 2011, Vicens et al., 2009).
Various fungi such as Trichoderma spp., Aspergillus spp. and Penicillium spp.
have been reported as extensive biomass degraders owing to their ability to generate an
array of enzymes including endo- and exo-glucanases, β-glucosidase, xylanases,
arabinofuranosidases and pectinases (Brink and Vries, 2011, González-Centeno et al.,
2010). This degradation generates useful industrial and medicinal biomolecules such as
ethanol, flavonoids, phenolic compounds, anthocyanins and hydroxybenzoic acid
(Arvanitoyannis et al., 2006, Sánchez, 2009, Strong and Burgess, 2008). Additionally,
fungi such as Penicillium spp. can be used for lignin mineralization during the
degradation process (Rodríguez et al., 1994, Singh Arora and Kumar Sharma, 2010).
These fungi, and ultimately the enzymes derived from them, convert the
lignocellulose complex to various soluble sugars, which are then converted into other
secondary products. However, due to the relatively recalcitrant nature of the
lignocellulose complex, pretreatment is usually required to facilitate access of fungal
enzymes to the cellulose and hemicelluloses which are the primary sources of carbon
for these organisms. Hydrothermal treatment has been shown to be a comparatively
efficient pretreatment technique for the breakdown of these biomolecules
(Papadimitriou, 2010).
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
83
4.2. Overview
Hydrothermal treatment has been shown to be a comparatively efficient
pretreatment technique for the breakdown of recalcitrant biomolecules such as lignin.
The experiments described here were performed to study the effects of fungal
degradation on the composition of winery grape waste to achieve biodegradation of its
lignocellulose components, namely cellulose, hemicelluloses and lignins. The
degradation patterns of various ascomycete fungi such as P. chrysogenum, P. citrinum,
A. niger and T. harzianum were compared, and optimsed, so as to formulate the ideal
fungal/enzymatic combination to yield maximum bioconversion.
4.3. Results and Discussion
During the pilot protocol, fungi were grown in Yeast Mannitol broth to develop
a starter culture. This was followed by their subculturing on Sabouraud agar for spore
propagation at 30°C for 48 hours. The spores were then filtered from fungal mycelia
and were transferred to growth media consisting of pure cellulose as the only carbon
source. These media included American Association of Textile Chemists and Colourists
(AATCC) iron minimal nutrient medium, Clostridium stercorarium and Clostridium
thermocellum media (Atlas, 1993); LC-3 and LC-4 media (Van Gool et al., 2011) . The
biochemical tests and enzyme assays were performed for the pure cellulose degradation
during various pilot protocols with and without the provision of hydrothermal pre-
treatment through autoclaving. It was observed that due to the minimal nature of
AATCC, this medium induced higher cellulose degradation as compared to other media.
Also, FeSO4.7H2O, which was only present in this medium, might have played a role in
enhancing the fungal cellulose degradation. It is known that Fe2+ ions increase the
biomass degradation abilities, especially during the stage of pyrolysis (autoclaving
being one of the examples). It has been reported that addition of a Fe2+ ion source, such
as FeSO4, causes cellulose degradation during pyrolysis with minimal release of either
CO2 or CO. This is in contrast with the other moieties such as ZnCl2 or NiCl2, which
increase the release of these gases during pyrolysis. Additionally, Fe2+ also releases
significant amounts of H2, which is known as an important ingredient of a large number
of enzyme reactions (Williams and Horne, 1994). Also, due to its minimal nutrient
nature, AATCC was found to favour fungal growth during cellulose degradation with
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
84
respect to that of bacterial growth, which ensured minimal treatment associated with
this medium as compared to others.
After the medium composition optimization, cellulose was replaced by post-
fermented crushed grape biomass waste. The compositional analysis of Shiraz grapes
indicated the presence of soluble proteins (2.2%), total soluble sugars (1.6%), reducing
sugars (1.8%), acid insoluble lignins (35.2%), acid soluble lignins (0.8%), ash +
insoluble proteins (2.8%) and cellulose + hemicellulose (57.4%).
4.3.1. Effects of hydrothermal pre-treatment
Grape pomace samples were added to AATCC medium at a dose of 30 g/L. The
pH of this medium was adjusted to 5.6 before autoclaving it at 121°C for 15 minutes.
The pre-treated grapes were collected. The filtrate was separated from the solids by
centrifuging at 15300 g for 15 minutes at 4°C. The resultant filtrate was further filtered
using Whatman no. 1 filter paper to remove any particulates and was then used for the
determination of free sugars and soluble total protein content. The solid part was taken
and oven-dried at 105°C for 48 hours before any compositional analysis. This part was
then analyzed for lignin content and total carbon and nitrogen content.
Autoclaving altered the composition of winery grape waste, with an overall
decrease in dry mass. The overall dry mass of the grape pomace was observed to be
reduced by about 18%, from 3.00 g to 2.56 g, in a non-autoclaved grape pomace. The
autoclaving process also increased the Total Soluble Sugar (Bengtsson and Åman)
content by about 4 times from 16.9 kg/m3 to 66 kg/m3 (Figure 4.1). Pentose content in
the filtrate increased from 1 kg/m3 to 10 kg/m3. The reducing sugars also increased
significantly from 1.76 kg/m3 to 2.21 kg/m3.
It has been reported by numerous workers that biomass tends to undergo
hydrolysis during hydrothermal processing. Although most of the reports have used
temperatures around 180°C, hydrolysis of biomass has also been documented to occur
at autoclaving temperatures (Papadimitriou, 2010). The molal ionic product of water
(Kw) is dependent on the temperature of the system. Kw increases from 0.64 × 10-14 at
18°C to 54.6 × 10-14 at 100°C and 268 × 10-14 at about 150°C. Kw reaches its highest
value at about 250°C, when it is 634 × 10-14. This increase in ionic product is important
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
85
as it is directly proportional to the solubility of water. The solubility of substrates (such
as cellulose and hemicelluloses) increases with the rise in Kw value. This relationship is
always taken into account when applied to several hydrothermal processes such as hot-
compressed water (HCW), steam explosion (SE) and supercritical water (SW)
treatments. Therefore, temperatures around 250°C or above are applied in these
processes (Ando et al., 2000, Goto et al., 2004). As compared to HCW or SE processes,
the Kw value during autoclaving is minimal, but is nevertheless sufficient to ensure a
substantially high substrate solubilisation without yielding strong microbial inhibitors
such as 2-furfural and 5-hydroxymethyl furfural (5-HMF). These are generated during
hydrothermal treatments, especially during SE treatment (Klinke et al., 2004, Sanchez
and Bautista, 1988). This allows efficient subsequent microbial degradation without
requiring multiple rinsing of substrate with deionised water to remove these inhibitors,
thereby saving important time and resources.
4.3.2. Utilization of Total Soluble Sugars
During the incubation period, TSS was observed to be significantly utilized by
all the cultures. However, the most significant utilizers were T. harzianum and A. niger,
which utilized about 27.3 kg/m3 and 26 kg/m3 of TSS present, respectively. On the other
hand, P. notatum and P. citrinum were not able to utilize TSS as effectively, displaying
only 17.4 kg/m3 and 7.9 kg/m3 TSS utilization, respectively (Figure. 4.1).
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
86
Figure 4.1. Total soluble sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and
untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples are significantly
different to untreated samples (p < 0.05).
However, none of the fungi were able to utilize the complete TSS in the
respective media. In T. harzianum, it was probably due to a very low β-glucosidase
activity as compared with the endoglucanases and exoglucanases in the similar
cellulolytic system. While endoglucanases have a high catalytic activity of about 35-70
IU/ mg, the overall cellulolytic activity becomes very low to 0.6-0.7 IU/ mg due to low
β-glucosidase activity. Product inhibition by glucose and cellobiose, the final products
of cellulose degradation effectively reduces the β-glucosidase activity (Klyosov, 1987a,
Liu et al., 2012, Lynd et al., 2005, Sweeney and Xu, 2012, Andrić et al., 2010b). A.
niger has been reported to have lower endoglucanse and exoglucanase activities [1.11
IU/ mg and 8 IU/mg, respectively] as compared to T. harzianum. However, it has a very
high β-glucosidase activity at 12.5 IU.mg, which might explain its comparative TSS
utilization against T. harzianum (Liu et al., 2012). P. citrinum secretes significant
amounts of endoglucanases [111.5 IU/ mg] and exoglucanases (Dutta et al., 2008, Ng et
al., 2010, Dutta et al., 2007). However, its production ability of β-glucosidase has not
been reported for the broth cultures, although, it has been reported during the solid state
fermentation under the optimal source of carbon and nitrogen sources (Camassola and
0
10
20
30
40
50
60
70
80
T. harzianum A. niger P. chrysogenum P. citrinum
Tota
l sol
uble
suga
rs (K
g/m
3)
Treated Untreated Non-autoclaved
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
87
Dillon, 2007, Gao et al., 2008, Ng et al., 2010). This may explain the comparative
inability of the Penicillium cultures to utilize a substantial amount of TSS in the media.
4.3.3. Utilization of reducing sugars and pentoses
Reducing sugars such as glucose are the primary carbon sources for microbial
growth. Cellulose is the most abundant polysaccharide and makes up the basic structure
of plant cell walls. It is an almost linear molecule made up of the reducing sugar β-D-
glucopyranose, linked by β-1, 4-polyanhydroglucose with cellobiose (also a reducing
sugar) as the smallest repetitive unit, thus is a β-glucan (Fengel, 1971).
It was observed that the process of autoclaving released considerable amounts
free sugars in the filtrate. The final sugar concentration suggests complete utilization of
these sugars by growing fungi. The subsequent degrading process of cellulose and
hemicelluloses generated further free sugars (2.2 kg/m3). Noticeable amounts of these
sugars were then consumed by fungi during their growth. It was observed that
substantial amounts of reducing sugars accumulated in the autoclaved samples. The
highest amount of sugar utilization was observed for T. harzianum and P. citrinum
cultures, where the concentration of reducing sugars decreased from 2.2 kg/m3 to 1
kg/m3 and 1.5 kg/m3, respectively (a decrease of 1.2 kg/m3 and 0.7 kg/m3, respectively).
A. niger also showed noticeable degradation amounting to about 0.4 kg/m3 utilization as
the reducing sugar concentration decreased from 2.2 kg/m3 to 1.8 kg/m3 (Figure 4.2),
whereas P. chrysogenum did not show significant utilization . The fungal degraded non-
autoclaved grapes accumulated lower amounts of reducing sugars, ranging from 0.9
kg/m3 (A. niger) to 1.4 kg/m3 (P. citrinum).
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
88
Figure 4.2. Reducing sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and
untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples are significantly
different to untreated samples (p < 0.05).
Noticeable amount of sugars were utilized by T. harzianum as the sugar
concentration decreased by 0.8 kg/m3 (from 1.8 kg/m3). A similar result was seen in A.
niger culture, where the reducing sugars decreased to 0.9 kg/m3 from 1.9 kg/m3
(untreated control). By contrast, P. chrysogenum and P. citrinum cultures both
accumulated 1.4 kg/m3 sugars, decreasing from 1.8 kg/m3 in the untreated control. Thus,
they were able to utilize only 0.4 kg/m3 of sugars. Overall, proportionally higher
amounts of reducing sugars were observed to be utilized in autoclaved substrates as
compared to non-autoclaved substrates.
It was observed (Figure 4.3) that high amounts of pentose sugars accumulate in
autoclaved samples. The pentose sugar content of non-autoclaved substrate cultured
with T. harzianum, A. niger, P. chrysogenum and P. citrinum was 5.6 kg/m3, 7.6 kg/m3,
5.1 kg/m3 and 5.6 kg/m3, respectively. These values increased significantly in the
autoclaved samples where the pentose content increased to 9.1 kg/m3, 11.1 kg/m3, 8.5
kg/m3 and 9.1 kg/m3 in the same order (Figure 4.3). This may indicate higher
hemicellulosic degradation in autoclaved samples, as also reflected in the higher
0.0
0.5
1.0
1.5
2.0
2.5
T. harzianum A. niger P. chrysogenum P. citrinum
Red
ucin
g su
gars
(kg/
m3 )
Non-autoclaved AutoclavedAutoclaved control Untreated control
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
89
xylanase activities in those substrates as compared to the non-autoclaved substrates (see
below).
Figure 4.3. Pentose sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and
untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples are significantly
different to untreated samples (p < 0.05).
Hydrothermal heating weakens the lignin crosslinks within cellulose and
hemicellulose, thus exposing them to physical, chemical and/or biological degradation
(Hassan et al., 2013, Papadimitriou, 2010). Further, the hydrothermal process, as it
occurs in an autoclave, enhances the breakdown of hemicellulosic polymers to form its
constituent pentose and hexose sugars, as well as other derived products such as
galacturonic and/or glucuronic acids. The highly heterogeneous composition of
hemicelluloses makes them more susceptible to physical and chemical reactions
compared to cellulose. The noticeable content of hexoses and minor amount of heptoses
might explain the extraordinary utilization of reducing sugars in T. harzianum and P.
citrinum cultures (Hassan et al., 2013, Papadimitriou, 2010).
The initial low pH (5.6) probably intensified the degradation process of
hemicelluloses and cellulose in both cultures (Hassan et al., 2013, Takashima and
Tanaka, 2008). The further release of sugars and their ultimate utilization explains the
degradation ability of the fungal cells. Additionally, T. harzianum, A. niger and P.
0
2
4
6
8
10
12
T. harzianum A. niger P. chrysogenum P. citrinum
Pent
ose
cont
ent (
kg/m
3 )
Non-autoclaved Autoclaved
Autoclaved control Untreated Control
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
90
citrinum are known to produce a significant amount of hemicellulose-hydrolyzing
enzymes such as xylanases, arabinases and pectinases among many others (Brink and
Vries, 2011, Kumar et al., 2008).
4.3.4. Change in the lignin content
Lignins are abundant biopolymers, second only to cellulose. In most plants,
especially in higher plants, they function as the prominent components of xylem. They
are composed of hydroxycinnamyl alcohols specifically p-coumaryl alcohol, coniferyl
alcohol and sinapyl alcohol, and their methoxy derivatives. Due to their hydrophobic
nature, they aid in efficient water transport throughout the plant system. However, this
very nature also makes them tolerant towards biodegradation (Boerjan et al., 2003,
Vanholme et al., 2010). Lignins not only act as the physical barriers to microbial
degradation, but also as non-productive binders of cellulases, thus acting as cellulase
inhibitors (Duarte et al., 2012b)
The total lignin (Acid Soluble Lignin and Acid Insoluble Lignin) was observed
to increase in every sample when compared to the untreated control (Figure 4.4). This
was expected since T. harzianum and A. niger do not have the ability to degrade lignin
and Penicillium spp. only have limited ability to mineralize lignin (Rodríguez et al.,
1994). As other material is degraded, the remaining material, lignin, will increase as a
percent content.
Autoclaving was previously observed to degrade considerable amounts of
sugars, thereby substantially increasing the lignin content of the substrate from an
average of about 36% in untreated controls to about 63% in autoclaved substrates.
Subsequent fungal growth consumed the sugars, but their inability to mineralize lignin
resulted in further lignin accumulation in the growth media. This increase was
especially noticeable in T. harzianum and P. citrinum cultures, where the lignin content
spiked from 65.7 % to 68 % and 63.8 % to 74 %, respectively, thus, showing inability
of these fungi to degrade lignin. Interestingly, however, P. chrysogenum was able to
mineralize some lignin in autoclaved substrate as total lignin in the culture showed a
decrease of about 9 %, from 64% to 53.1 % (Figure 4.4). On the other hand, the non-
autoclaved substrate from the P. chrysogenum culture displayed an increase of about
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
91
14% lignin from 36.1% to 50.3%, thus, suggesting the inability of this fungus to
degrade lignin in non-pretreated substrates.
Figure 4.4. Lignin content (%) in fungal cultures of non-autoclaved, autoclaved and untreated Shiraz
grape waste. Values for non-autoclaved and autoclaved samples are significantly different to
untreated samples (p < 0.05).
Similar changes were observed with autoclaved substrate cultured with A. niger.
Similar to P. chrysogenum, a decrease in lignin content by 4.2 % (from 63.7 % to 59.6
%) was observed in this culture. This was unexpected since A. niger has not been
reported to have any lignin degrading properties. The observation encourages further
study on the degradation pattern of A. niger on winery wastes. One of the other
interesting observations was the limited increase in lignin content in non-autoclaved
substrate cultured with A. niger. In this culture, the lignin content rose slightly from
35.8 % in the untreated control to 39.5% in the non-autoclaved sample. One of the
reasons for this might be a lower cellulase activity of A. niger, which resulted in
minimal substrate degradation, thereby, almost conserving the original lignin
composition.
Although all fungi used in this study generated substantial amounts of cellulases
and hemicellulases, all but P. chrysogenum do not have the ability to synthesize
lignases. P. chrysogenum, however, generates lignases which degrade the low molecular
0102030405060708090
T. harzianum A. niger P. chrysogenum P. citrinum
Lig
nin
cont
ent (
%)
Non-autoclaved Autoclaved
Autoclaved control Untreated control
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
92
weight lignin components such as cinnamic acid, ferulic acid and vanilic acid
(Rodríguez et al., 1994). In the case of A. niger, the fungus might possess minor lignin
mineralization ability for its major substrates, cellulose and hemicellulose. Indeed, there
are few data available regarding the quantification of lignin degradation/transformation
by these fungi on wooden substrates (Hassan et al., 2013, Singh Arora and Kumar
Sharma, 2010). To authors’ knowledge, the current study is the first to investigate the
degradation of the lignin component of winery biomass waste.
4.3.5. Cellulase activity
Cellulose is the main source of carbon in lignocellulose complexes as it is the
richest source of glucose, the primary source of carbon for most microorganisms.
However, cellulose exists in the core of lignocellulosic complexes, surrounded by
hemicellulose and lignins. To achieve access to cellulose, fungi have to penetrate these
layers (Fengel, 1971). Cellulases are enzyme complexes with slight variations in their
constituent enzyme ratios. However, three enzymes types; endoglucanase, exoglucanase
and β-glucosidase, are most common and are found in almost every lignocellulose
degrading fungus. Aerobic ascomycetes such as T. harzianum and Aspergillus spp. have
cellulases composed of endoglucanase and exoglucanase, reportedly of varied
individual enzyme composition depending on substrate availability and specificity
(Kovacs et al., 2009, Liu et al., 2012, Sipos et al., 2010). Endoglucanases (EC 3.2.1.4)
are the enzymes that convert the crystalline cellulose to the amorphous form making it
more accessible to enzymes like cellobiohydrolases. Cellobiohydrolases or exo
glucanases (EC 3.3.1.91) have been shown to be one of the main cellulase components
(Sweeney and Xu, 2012). They act by degrading the extended or distorted cellulose
molecules to convert them into cellobiose as the product. In addition to any amorphous
cellulose, cellobiohydrolases also degrade crystalline cellulose.
Cellulase activity is thus effectively measured by combined enzyme activities of
both endo and exoglucanases (Ghose, 1987).The highest cellulase activity of about 45
U/mL was observed in the A. niger culture, significantly higher than the enzyme
activities of 19.6 U/mL, 24.1 U/mL and 15.1 U/mL observed in T. harzianum, P.
chrysogenum and P. citrinum cultures, respectively (Figure 4.5). This is in contrast to
reportedly higher activities in Trichoderma with respect to Aspergillus (Kovacs et al.,
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
93
2009, Liu et al., 2012). One of the reasons for this might be the limited requirement for
enzyme production due to the availability of large amounts of free sugars which result
from the process of autoclaving the substrate. Another reason might be significant
product inhibition of the fungal cellulases by free sugars produced, most of which are
known to be product inhibitors of cellulolytic enzymes (Brink and Vries, 2011, Duarte
et al., 2012a, Duarte et al., 2012b).
Figure 4.5. Cellulase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved
Shiraz grape waste. Values for non-autoclaved and autoclaved samples are significantly different
between the samples (p ≤ 0.05), except for the asterisk (*) marked values (p > 0.05).
The cellulase activities of the non-autoclaved samples were found to be 48.1
U/mL, 16.6 U/mL, 9.2 U/mL and 64.6 U/mL in T. harzianum, A. niger, P. chrysogenum
and P. citrinum cultures, respectively. The most contrasting difference of activities was
seen in T. harzianum and P. citrinum cultures where the cellulase activities were about
3-4 times higher in non-autoclaved samples as compared to autoclaved samples (Figure
4.5). This was expected in T. harzianum cultures as the species is known (and was
observed) to have a higher cellulase activity, but very low β-glucosidase activity. This
results in product inhibition of Trichoderma cellulase by glucose and cellobiose thus
lowering the activity, as seen in the autoclaved samples, which had considerable
amounts of those free sugars. However, the considerable difference of cellulase activity
*
0
10
20
30
40
50
60
70
80
T. harzianum A. niger P. chrysogenum P. citrinum
Cel
lula
se a
ctiv
ity (U
/mL
)
Non-autoclaved Autoclaved
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
94
in the P. citrinum culture is more difficult to interpret, since it displayed higher β-
glucosidase and xylanase activity in autoclaved and non-autoclaved substrates (Sections
4.3.6 and 4.3.7). Higher cellulase activity was observed in autoclaved substrate further
degraded by A. niger. This was expected since the species, although having
comparatively lower cellulase activity possesses high β-glucosidase activity which
prevented product inhibition of its cellulase enzyme.
Product inhibition is a well-known phenomenon which contributes to the
difficulties observed in lignocellulose degradation. It has been reported by numerous
authors, especially in relation to cellulose degradation, that cellobiose (the chief product
of cellulose degradation) and glucose (the minor product) act as inhibitors of cellulases.
The main reason for this is the exceptionally low catalytic activity of β-glucosidase as
compared to the other enzymes present in the cellulase enzyme complex (Duarte et al.,
2012b, Kovacs et al., 2009, Liu et al., 2012, Sipos et al., 2010).
4.3.6. β-glucosidase activity
β-glucosidases are exo-glycoside hydrolases which catalyse the β-1, 4 glycosidic
bonds in cellobiose or other β-oligodextroses to give glucose as the by-product. Like
endoglucanases and exoglucanases, β-glucosidases are also categorised as Glycoside
Hydrolase families 1 and 3 (Dashtban et al., 2009, Henrissat and Davies, 1997). β-
glucosidases are generally produced by numerous cellulolytic organisms (especially
fungi) due to the competitive inhibition of endo- and exo- glucanases by their
degradation end products of cellobiose.
The activity and secreted amount of β-glucosidase is one of the important
contributing factors towards cellulase activities. β-glucosidase hydrolyses the β-(1-6)
glycosidic bonds in cellobiose (the smallest functional unit of the cellulose molecule) to
glucose units which can be readily utilized by chemical or microbial actions (Brink and
Vries, 2011, Kim et al., 2012a). However, numerous previously reported works and the
current findings suggested very low activity (26 U/mL) of these enzymes in
Trichoderma spp. (Baldrian and Valášková, 2008, Juhász et al., 2005, Kovacs et al.,
2009) as compared to other fungi (Figure 4.6), especially, A. niger (Singhania, 2012),
which displayed enzyme activity of 155 U/mL in our work. As found with the other
cellulose enzyme constituents, β-glucosidase also suffers from product inhibition,
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
95
especially from cellobiose and glucose. This might explain the overall lower cellulase
activity in the T. harzianum culture. As seen earlier in Figures 4.1 and 4.2, high
amounts of sugars observed after hydrothermal pre-treatment may have increased the
product inhibition of β-glucosidase in the T. harzianum culture, thereby decreasing the
activity over the course of fermentation. In addition to this, the catalytic activity of the
enzyme has been found to be very low as compared to other enzymes like
cellobiohydrolases and endoglucanases (Baldrian and Valášková, 2008, Juhász et al.,
2005, Kim et al., 2012a). Penicillium cultures also displayed significant β-glucosidase
activities, which probably resulted in their overall high cellulolytic activities (Figure
4.6).
Figure 4.6. β-glucosidase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved
Shiraz grape waste. Values for non-autoclaved and autoclaved samples are significantly different
between the samples (p ≤ 0.05).
The β-glucosidase activity of T. harzianum from the non-autoclaved substrate
(16.8 U/mL) did not vary much from the autoclaved substrate, although, it showed a
decrease of about 9 U/mL over the fermentation period. Similar results were seen in the
P. citrinum culture where β-glucosidase activity in non-autoclaved substrate was 160.9
U/mL as compared to 180.1 U/mL in autoclaved substrate. However, considerably
lower activities were found in the non-autoclaved substrates cultured with A. niger and
020406080
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Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
96
P. chrysogenum. The β-glucosidase activity of the autoclaved substrate cultured with A.
niger was 154.9 U/mL, which was more than 3 times the value of the non-autoclaved
substrate (49.6 U/mL). Similarly, the activity in P. chrysogenum culture reduced from
84.9 U/mL in autoclaved substrate to 40.6 U/mL in non-autoclaved substrate, a greater
than two-fold decrease in activity. Overall, the higher β-glucosidase activity in
autoclaved substrate as compared to non-autoclaved substrate indicates the hydrolysis
of a major part of cellulose and hemicelluloses to disaccharides, such as cellobiose,
which is the substrate for enzymes such as β-glucosidase (Figure 4.6).
4.3.7. Xylanase activity
Xylans are perhaps most numerous of all hemicelluloses, comprising about 25%
of all saccharides, second only to cellulose to which they are tightly bound by covalent
and other bonds in a complex manner (Fengel, 1971, Goksu et al., 2007, Hansen and
Plackett, 2008). They are comprised of β (1, 4) D-xylopyranoses with non-crystalline
hexoses such as galactose attached to them.
Xylanases are a group of differential hemicellulases responsible for the
degradation of various xylans by hydrolysing their β-(1, 4) linked D-xylopyranoside
units. Comprising up to 1% of total fungal lignocellulolytic enzymes, xylanases may
work symbiotically with other hemicellulases and cellulases, thereby facilitating a
consortial mix for degradation of biomass substrate. Like cellulases and numerous
hemicellulases, xylanases are also classified as glycoside hydrolases (GH) and most of
them belong to families of GH 8, 10, 11, 30 and 43 (Dashtban et al., 2009, Sweeney and
Xu, 2012, Cantarel et al., 2009).
Xylanase activities across all the fungi were lower than expected. The highest
activity of 335 U was found in A. niger followed by P. citrinum and P. chrysogenum at
234 U and 191.1 U, respectively (Figure 4.7). T. harzianum displayed the lowest
enzyme activity of 38.7 U. The low activity of xylanase reflects the presence of very
low amounts of hemicellulose substrates in the medium following thermal hydrolysis.
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
97
Figure 4.7. Xylanase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved
Shiraz grape waste. Values for non-autoclaved and autoclaved samples are significantly different
between the samples (p ≤ 0.05).
The xylanase activities in non-autoclaved substrates were lower than that of the
autoclaved substrate. Enzyme activities of 13.8 U, 277.3 U and 201.1 U were observed
in T. harzianum, A. niger and P. citrinum non-autoclaved cultures, respectively. The
most significant decrease was seen in P. chrysogenum, where the xylanase activity
decreased dramatically from 191.1 U in autoclaved substrate to 7.2 U in non-autoclaved
substrate. The activities reported here are significantly lower than those reported
previously (Betini et al., 2009, Hideno et al., 2011). The higher activity of xylanase in
autoclaved substrate indicates possible hydrolysis of hemicelluloses to oligomers rather
than mono-saccharides, which acted as the substrates to xylanases. It was seen that
although xylanase activity in fermentation filtrates was not inhibited by free sugars, it
probably limited the range of activity over the degradation time.
4.4. Conclusions
In this study, degradation of winery discarded grape waste was assessed after
hydrothermal treatment, followed by degradation with T. harzianum, A. niger, P.
chrysogenum or P. citrinum. Variation in the percent composition of cellulose,
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Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
98
hemicelluloses and lignin in the grape waste was determined by various assays after an
incubation period of 5 days.
Significant amounts of reducing sugars and pentoses were secreted in the
medium after the hydrothermal treatment, presumably resulting from the partial and/or
total degradation of cellulose and hemicelluloses, respectively. Subsequent fungal
treatments not only utilized the free sugars in the medium, but also enhanced further
degradation of the grape waste. The process of autoclaving increased the composition of
total soluble sugars to 66 kg/m3, reducing sugars to about 22.6 kg/ m3 and pentose
concentration from about 0.94 kg/m3 in non-autoclaved sample to 9.9 kg/m3 to 10% in
the autoclaved sample. Acid insoluble lignin, which forms the majority of lignin
content, increased to about 63.7%, showing an increase of 28.5% (from 35.2%) in the
non-treated sample.
Total lignin was noticeably mineralized by P. chrysogenum and to a minor
extent by A. niger over the incubation period. The other fungi did not show any lignin
degrading abilities. A. niger displayed substantial xylanase, β-glucosidase and xylanase
activities, marginally higher than P. citrinum except for β-glucosidase.
Although T. harzianum showed comparable cellulase activities, it displayed
lower xylanase and β-glucosidase activities with respect to the other fungi. Autoclaving
hydrolyzed hemicelluloses and crystalline cellulose, converting the latter to a more
degradable amorphous form. Overall, a significant increase in the efficiency of fungal
enzyme activities and subsequent degradation of winery waste was observed after
hydrothermal treatment compared to that seen in a normal fermentation process.
4.5. Experimental summary
The experiments described in this chapter form one of the intermediate protocols
to improve the fungal biodegradation of winery derived biomass waste. The objective of
this protocol was to optimize fungal mediated winery biomass degradation. For the
experimental purposes, fungi belonging to division Ascomycota, such as T. harzianum,
A. niger, P. chrysogenum and P. citrinum, were used.
The fungi inoculated after autoclaving utilized considerable amounts of sugars
as discussed in section 4.3. Penicillium spp. are known to degrade lignin to some extent,
Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes
99
especially the smaller lignin structures. Not surprisingly, P. chrysogenum was observed
to degrade minor amounts of lignin during the fermentation process which could have
resulted from loosening or breaking of the linkages between lignins, cellulose and
hemicelluloses during autoclaving process. This process might have provided more
access for biomass degrading enzymes to degrade lignins and cellulose.
Overall, it was observed that a simple autoclaving process could improve the
biomass degradation abilities of fungi during a successive fermentation process. Also,
the pre-treatment autoclaving process decreased the time required to degrade grape
biomass considerably. Current methodologies are focussed on individual fungal cultures
in combination with economically viable physical processes in order to obtain
maximum lignocellulose degradation for gradual production of ethanol, H2 and other
commercially important molecules. The initial protocol was directed at producing a
standard positive control with respect to the cellulose degradation by a different
combination of organisms.
100
CHAPTER 5
Solid state fermentation of winery
biomass waste by Ascomycota fungi
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
101
5.1. Introduction
Solid state fermentation (SSF) is one of the second generation bioconversion
processes for the production of industrially important molecules such as bio-hydrogen
and bio-ethanol (Lee, 1997, Brijwani et al., 2010, Sarkar et al., 2012, Kausar et al.,
2010). The method employs either single microbial cultures or a mixture of various
organisms and/or their derived enzymes, in a single step and usually generates a
considerably higher bioconversion of biomass as compared to submerged fermentation.
This approach eliminates the need for a separate pre-treatment method such as
hydrolysis. Strong hydrolysing techniques such as steam explosion, water-compression
and ammonia freeze explosion methods do strongly hydrolyse lignocellulose
components; however they may decrease successive microbial bioconversion (and
degradation) efficiency. This results from the generation of several low molecular
weight molecules such as furfural, 5-hydroxymethylfurfural, small chain organic acids,
phenolics and inorganics, any of which can cause enzyme inhibition of fermentation
micro-organisms (Dashtban et al., 2009, Klinke et al., 2004). In many cases, this
microbial inhibition can be overcome by washing. However, this results in the
generation of considerable wastewater, which is of environmental concern and counter-
productive to the ultimate aim of a cleaner society. These pre-treatment processes also
increase the biomass treatment and successive bioconversion costs. SSF-based
approaches, eliminate the production of microbial inhibitory molecules, ameliorating
the need for washing, and are worth testing for their competiveness against pre-
treatment followed by fungal degradation.
As the water content in SSF is equal to or slightly higher than the substrate, the
method allows greater aeration and surface attachment of filamentous fungi. This
approach allows for the production of a vast array of lignocellulolytic enzymes. It also
increases the amount of substrate degraded in a single step from 1-2% in submerged
fermentation to more than 10% in SSF (Lee, 1997, Ng et al., 2010). The process also
reduces the need for separate enzyme production, isolation and application in biomass
degradation as these all occur in a single step, thus, reducing critical error points and
reducing economic inputs (Betini et al., 2009, Brijwani et al., 2010, Dashtban et al.,
2009, Sarkar et al., 2012). The SSF process is also reported as an intermediate step in
the development of Consolidated Bio-processing (CBP), which has been estimated to
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
102
reduce the production costs of biofuels by a factor of 4-5 or more (Lynd et al., 2005,
Yamada et al., 2013).
5.2. Overview
The experiments described in this chapter were performed to study the
comparative degradation of winery grape wastes by various fungi, and across two
biodegradation processes (SSF and submerged media). Further, they were aimed to
specifically to achieve biodegradation of the winery grape waste lignocellulose
components. One of the primary reasons for using the SSF strategy was to conserve the
amount of water used in the process and increase the amount of substrate being
degraded during the process. The ratio between fungal substrate and growth media was
kept at 1:1 across the various fungi and for both SSF and submerged media, which
meant that the substrate concentration increased from 3% in submerged fermentation to
50% in SSF. The degradation patterns of various white-rot fungi (P. chrysogenum and
P. citrinum), brown-rot fungi (A. niger) and T. harzianum were compared over 2 weeks,
so as to formulate the ideal fungal/enzymatic combination and the optimum time-frame
to yield maximum degradation. These experiments form a process in the development
of a CBP method for the production of biofuel molecules, such as ethanol or hydrogen.
5.3. Results and discussion
5.3.1. Total Protein Content
It was observed that the total protein content in the filtrate dropped significantly
from 11.5 Kg/m3 after 1 week to 1.9 kg/m3 after 2 weeks in T. harzianum cultures. The
content in A. niger also dropped significantly, from 4 kg/m3 to 2.3 mg/ml. The total
content drop in Penicillium spp. was however, not as striking. P. chrysogenum
displayed a drop of 2.2 kg/m3 from 10.6 kg/m3. Moreover, no drop was observed for P.
citrinum cultures, with total protein content staying at 6.2 kg/m3 (Figure 5.1).
Total protein content in the filtrate reflects not only the amount of enzymes
(especially, lignocellulolytic enzymes) secreted, but also reflects the growth stage of
individual fungal cells. The fungi release high amounts of proteins in the filtrate during
their log phase of growth. However, as the stationary phase approaches, the amount of
proteins in the filtrate decreases (Kausar et al., 2010). Thus, the protein content in the
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
103
filtrate varies across the organisms, type of substrate (Sipos et al., 2010) and stage of
incubation.
Figure 5.1. Total protein content of fungal degraded Shiraz grape waste over 2 weeks of SSF. Data
from autoclaved fermented samples are used for comparison purposes. All the values are the mean of
triplicate data.
It has been reported that the presence of specific hemicellulose sugars in the
growth medium increases proteinase activity. However, this activity is inversely
proportional to the cellulase or avicelase activities (Poulsen and Petersen, 1988). This
relation probably reflects the increased activities of cellulase and β-glucosidase and
decreased activities of xylanase in most of the fungal cultures during the current studies.
The protein content in the autoclaved grape substrates (fungal degradation time
of 5 days) was much higher than the SSF cultures. The observed protein concentrations
in T. harzianum and A. niger were 31.9 and 21.9 kg/m3, respectively, by far the highest
values obtained. The total protein in Penicillium cultures differed to a lesser degree.
Against concentrations of 10.6 kg/m3 and 6.22 kg/m3 in SSF cultures after 1 week, the
protein concentrations were observed to be at 11.9 kg/m3 and 13 kg/m3 in P.
chrysogenum and P. citrinum, respectively. The increase in total protein content for
autoclaved samples was not reflected in an increase of any enzyme activity observed in
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Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
104
those samples, suggesting that the proteins probably originated from the grape substrate
due to the autoclaving process and accumulated in the filtrate. This observation is in
agreement with previous work (Izydorczyk et al., 2000).
5.3.2. Reducing sugars
The reducing sugars in two of the cultures increased significantly from 1 to 2
weeks, whilst the other two did not significantly alter. Specifically, the reducing sugar
levels in T. harzianum cultures decreased from 8.6 kg/m3 to 6.7 kg/m3, whilst of P.
citrinum cultures showed a minimal drop from 7.8 kg/m3 to 7.3 kg/m3. In both cases, the
change was within experimental error, however, the sugar levels in the A. niger and P.
chrysogenum cultures increased significantly by almost 12 kg/m3 (5.7 mg/ml to 17
kg/m3) and 22 kg/m3 (16 kg/m3 to 38.3 kg/m3), respectively (Figure 5.2).
Figure 5.2. Total reducing sugar content of fungal degraded Shiraz grape waste over 2 weeks of
SSF. Data from autoclaved fermented samples are used for comparison purposes. The values are the
mean of triplicate data.
The reducing sugar content in the untreated grape biomass was found to be 1.7
Kg/m3, which increased in each fungal culture under the experimental conditions. It was
also observed that the sugar content increased considerably during the second week, T.
harzianum and P. citrinum being the exceptions. One of the peculiar observations about
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Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
105
the T. harzianum cultures was the high accumulation of reducing sugars during week 1
of SSF (8.7 kg/m3) which was followed by a drop to 6.7 kg/m3 after the second week.
One of the reasons behind this could be product inhibition of Trichoderma cellulases. It
is known that Trichoderma spp. produce considerable amounts of cellulases (Baldrian
and Valášková, 2008, Klyosov, 1987b). However, the cellulases produced by these
fungi suffer from a lower catalytic activity as compared to other enzymes such as
amylases and cellulases derived from other biomass degraders such as Aspergillus spp.
and Phanerochaete spp. The fungus also generates lower amounts of glucosidases
which are responsible for catalyzing products of cellulose degradation such as
cellobiose. This deficiency contributes to the problem of enhanced product inhibition
(Andrić et al., 2010a, Duarte et al., 2012b, Klyosov, 1987b, Kumar et al., 2008).
By contrast, P. chrysogenum was observed to generate high levels of reducing
sugars at week 1, which increased to even higher values after 2 weeks. This was
observed to be directly proportional to the considerable increase in cellulase activity of
this fungus over 2 weeks (as compared to week 1) and a considerably higher β-
glucosidase activity during the same time. As a result, this fungus probably suffered
from very low product inhibition, which was observed prominently in both the T.
harzianum and P. citrinum cultures. It has been reported that enzyme activities change
according to the nature of substrate (Juhász et al., 2005). Lignin composition also plays
a role in the efficiency of biomass degradation, with the lignin concentration directly
proportional to non-specific inhibition as is discussed in section 5.3.3. Under the current
experimental conditions, it was observed that the grape biomass wastes, due to their
structural complexity, induced higher activities in Penicillium spp. and Aspergillus
niger rather than T. harzianum.
In all cases, the content of reducing sugars in the autoclaved substrate cultures
was considerably lower than that of the SSF cultures. Previous analysis has shown that
the amount of reducing sugars increased considerably in the autoclaved media (Chapter
4, section 4.3.3), which forms a major source of carbon for the fungi. This initial growth
was observed to utilize the substantial amounts of sugars in the medium. Reducing
sugar contents were found to be 1.5 kg/m3, 1.8 kg/m3, 2 kg/m3 and 1.5 kg/m3 in
autoclaved substrates cultured with T. harzianum, A. niger, P. chrysogenum and P.
citrinum, respectively. The most significant difference in reducing sugar content was
observed in P. chrysogenum cultures, where the level was lowered by about a factor of
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
106
8 than that of 1 week SSF. However, as with the protein content, this difference of sugar
utilization was not reflected in increased enzyme activities, except for A. niger which
displayed a marginally increased cellulase activity (Figure 5.4) and significantly
enhanced β-glucosidase activity (Figure 5.5). The process also seemed to increase β-
glucosidase activity in P. citrinum cultures as compared to the SSF process.
Fungi, especially those belonging to divisions Ascomycota and Basidiomycota,
produce better degradation under solid state conditions than submerged fermentations
due to the provision of higher surface area. Additionally, during the current SSF
experimental set up, autoclaving was not used as the pre-treatment method. As
discussed earlier, free sugars are probably released in the growth medium during
autoclaving as a direct result of hydrolyzation of cellulose and hemicellulosic
components of the biomass. This would not, therefore, happen under the current SSF
experimental protocol. Fungi may, therefore, grow slowly during the initial phase but
induce quicker cellulase and hemicellulase production which in turn causes rapid
cellulose and hemicellulose degradation generating high amounts of reducing sugars
(Figure 5.2). The non-hydrolyzation of cellulose and hemicelluloses also makes the
overall process less prone to the product inhibition of cellulases and hemicellulases
caused by free monosaccharides (e.g. glucose) or disaccharides (e.g. cellobiose) (Kumar
et al., 2008, Duarte et al., 2012b).
Reducing sugars, such as cellobiose, glucose, xylose and arabinose, are the most
common units in cellulose and hemicelluloses and are produced during chemical or
microbial hydrolysis of those molecules. However, it is known that the cellulolytic
enzymes from numerous fungi such as Trichoderma spp. are highly susceptible to
product inhibition, which can decrease further enzyme activity of those fungi. This is
compounded by a very low natural β-glucosidase activity (Baldrian and Valášková,
2008, Klyosov, 1987b). The experimental observations are consistent with these
previous reports as they show a decreasing reducing sugar trend in T. harzianum
cultures, which was directly proportional to significantly decreased cellulase and
xylanase activities in the second week. This resulted in a lower amount of biomass
degradation in T. harzianum cultures. A. niger has also been reported to possess very
high β-glucosidase activity in addition to its moderate cellulase production (Maeda et
al., 2011, Singhania, 2012).
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
107
5.3.3. Lignin mineralization
Lignins are a group of polymers seemingly arranged in a random and complex
manner. Lignins form the most abundant biomass after cellulose and can comprise up to
40% of total biomass. Lignins are bounded to both cellulose and hemicelluloses in a
highly complex structural pattern (Dashtban et al., 2009, Fengel, 1971). In most plants,
especially, in higher plants, they function as the prominent components of xylem. Due
to their hydrophobic nature, they aid in efficient water transport throughout the plant.
However, this very nature also makes them tolerant towards biodegradation (Boerjan et
al., 2003).
Figure 5.3. Lignin content of fungal degraded Shiraz grape waste over 2 weeks of SSF. The
untreated sample refers to the lignin content of grape samples before any treatment or addition of
growth medium.
Acetylated lignin units are derived from two or more lignin monomers such as
p-coumaryl alcohol, sinapyl alcohol, 5-hydroxyconiferyl alcohol, hydroxycinnamyl
aldehyde, hydroxybenzyl aldehyde, hydroxycinnamate ester, dihydrocinnamyl alcohol,
arylpropane -1, 3-diol, aryl glycerol, hydroxycinnamyl acetate, hydroxycinnamyl p-
hydroxybenzoate, hydroxycinnamyl p-coumarate and tyramine hydroxycinnamate. They
may be regarded as the foremost of all lignin components, constituting more than half of
the lignin polymer (Lu and Ralph, 2002). Lignin degradation or removal is one of the
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Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
108
chief aspects of biomass degradation as they act as non-productive inhibitors of
cellulolytic enzymes, especially cellulases, which have a strong binding tendency to
lignins (Alvira et al., 2010, Duarte et al., 2012b). The importance of this property
further increases in grape waste due to its high content of lignin-derived products such
as phenolics and tannins (Bravo and Saura-Calixto, 1998, González-Centeno et al.,
2010).
According to literature, most of the fungi used in the current study are
considered as non-lignolytic. Indeed, there was an absence of lignin-degrading enzymes
such as laccases and peroxidases in those fungi (data not shown). However, Penicillium
spp. has been previously reported to possess minor lignin mineralization abilities. These
fungi, especially, P. chrysogenum, are known to produce up to 8% degradation of low
molecular weight lignin molecules under appropriate conditions (Rodríguez et al., 1994,
Rodriguez et al., 1996).
Results obtained during the given experiments indicated a similar trend, but to a
lesser degree. As determined from previous experiments (see Section 4.3.4), the lignin
composition of Shiraz grape waste, and prior to treatment, was found to be about 36%,
which is in agreement with previous reports (Bravo and Saura-Calixto, 1998, González-
Centeno et al., 2010). The lignin content following various treatments is given in Fig
5.3. The terms “Untreated’ and ‘Control’ refer to the substrates before and after the
addition of AATCC mineral medium, respectively The total lignin content increased in
all the cultures, in variable amounts, following SSF treatment (Figure 5.3). This is
presumed due to cellulose and hemicellulose degradation (reducing the total amount of
material), but little lignin degradation (increasing the content of the lignin in the
remaining sample). The untreated grape samples consisted of about 36% lignin.
Probably due to the addition of AATCC-based minerals, this level increased to about
46.9% in control samples (treated with S. cerevisiae). As the substrate was not subjected
to autoclaving before SSF, it did not display a sharp increase in lignin content caused by
hydrolyzation of cellulose and hemicellulose. Therefore, the lignin contents observed
during autoclave pre-treated substrate followed by submerged fermentation (Chapter 4)
were not taken in to consideration during the current experiments.
Used as a control due to its inability to degrade biomass, the lignin content from
S. cerevisiae was expected (data not presented in graph). The lignin content of this yeast
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
109
increased to about 47%, which was 0.1% above that of the control sample (46.9%). In
P. chrysogenum and P. citrinum cultures, the lignin contents were found at 44.8 % and
49.1%, respectively. These cultures displayed a drop of 2.2% and 1.6% lignin content,
which, although not amounting to considerable lignin degradation, showed a similar
trend to that observed in the literature mentioned above. During the current experiments,
negligible laccase activity was observed in Penicillium cultures, but lignin peroxidases
and tyrosinase activities were absent. The laccase enzyme group present in wood rot
fungi such as Trametes spp. is responsible for a majority of lignin oxidation. The azo
group (R-N=N-R’), which is present in both azo dyes such as Acid Red B, is also found
in a majority of lignin molecule structures. Probably due to this, the laccases from
Penicillium spp. have been effective in exhibiting minor lignin degradation. However, it
has been suggested that, unlike Trametes spp., laccases in Penicillium spp. show bio-
adsorption properties rather than the actual biodegradation of lignin residues (Gou et al.,
2009).
A. niger is known as a lignin non-degrader. This inability was clearly observed
during the experiments here. The lignin content in this culture increased to about 65%,
which was an increase of about 18%. However, this sharp increase could not be
accounted for any other content due to the process involved in lignin content
determination (Sluiter et al., 2011). However, the probability of this occurring due to a
lower cellulose and hemicellulose degradation of grape waste cannot be ruled out. The
lignin content observed in A. niger treated SSF grape contrasts to previous observations
(Kausar et al., 2010) where lignin mineralization was mediated by a consortium of A.
niger and T. viride, even though both fungi are individually known as non-lignolytic in
nature.
One of the surprising results obtained was from T. harzianum cultures. The
lignin content observed in this fungus amounted to 43.6%, which was a drop of about
3.4%. This decrease in lignin was unexpected since no laccase, lignin peroxidase or
tyrosinase activities were found in this fungus. Kirk and Farrell (1987) reviewed 18
strains of Trichoderma spp., including T. harzianum and reported that none of these
fungi possess lignolytic activities. Therefore, the apparent degradation may have been
the result of cellular adsorption to the substrate rather than an actual degradation (Kirk
and Farrell, 1987). In an experiment performed with 14 isolates of Trichoderma spp.,
only one type of phenol oxidase (of only one subtype - classified as a laccase system
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
110
enzyme), was isolated in minor quantities from some selective fungi. However, no
correlation was observed between this isolated enzyme and lignin degradation as it had
been observed to be in an inactive state (Assavanig et al., 1992). These observations, in
addition to the current experimental observations on enzyme activities (see sections
below), confirmed that lignin degradation of about 3.4% observed was probably due to
the adsorption mechanism or deposition of some polymers on the substrate by this
fungus which were not hydrolysed by sulphuric acid during the process of lignin
determination.
5.3.4. Cellulase activities during SSF
Cellulase activity is measured as the combined activities of endoglucanases and
exoglucanases. The cellulose activity of autoclaved (control) cultures is compared with
SSF at week 1 and at week 2 for the four chosen fungi in Fig 5.4. T. harzianum cultures
displayed a large decrease in cellulase activity from 39 U/mL in week 1 to 12.9 U/mL in
week 2, probably caused by gradually increasing product inhibition during this period.
It has been reported that Trichoderma spp. produce considerable amounts of cellulases
which are prone to lower catalytic activities on their substrates as compared to other
enzymes (Sainz, 2009, Zhang and Lynd, 2002). Cellulose activity on filter paper is only
0.6-0.7 IU/mg of enzyme. This contrasts to glucoamylase activity of 69 IU/mg
(Klyosov, 1987b). The fungus also generates low amounts of glucosidases which are
responsible for catalyzing the products of cellulose degradation, such as cellobiose. This
deficiency contributes to enhance product inhibition (Andrić et al., 2010a, Duarte et al.,
2012b, Klyosov, 1987b, Kumar et al., 2008).
Cellulase activity of P. citrinum did not show drastic change, although it
dropped from 27.7 U/mL in week 1 to 21.9 U/mL in week 2 (Figure 5.4).Contrastingly,
A. niger and P. chrysogenum displayed an increment in cellulase activity. The activity
of A. niger cellulase increased from 28.9 U/mL in week 1 to 43.5 U/mL in week 2,
while that of P. chrysogenum went from 30.7 U/mL to 97.6 U/mL during the same
period.
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
111
Figure 5.4. Comparison of cellulase activity (U/mL) in the fungal degraded Shiraz grape waste over
2 weeks of SSF compared to autoclaved pre-treated samples. The values are means of triplicate data.
The overall cellulase activities in all SSF cultures employed in the current
investigation were either comparable or substantially higher than the submerged
fermentation from previous experiments (Chapter 4) and other literature reports
(Brijwani et al., 2010, Kovacs et al., 2009, Liu et al., 2012, Sipos et al., 2010).
Due to low β-glucosidase production ability, the resultant cellulase activity in T.
harzianum was severely depleted over the second week. Although, the cellulase activity
was expected to gradually gradual increase over 2 weeks, it was observed to decrease
over the second week. Additionally, the second week cellulase activity of T. harzianum
was unexpectedly lower than the cellulase activities of P. chrysogenum over the same
period. This suggests that grape waste is a better substrate for P. chrysogenum T.
harzianum. However, this claim needs further investigation.
It has been reported that xylan in the biomass acts as one of the competitive
inhibitors of cellulose enzymes (Duarte et al., 2012a, Zhang et al., 2012). Thus, a
significantly lower activity is likely to occur in organisms with lower xylanase activity,
consistent with the results to be shown in Section 5.3.6.
0
20
40
60
80
100
120
T. harzianum A. niger P. chrysogenum P. citrinum
Cel
lula
se a
ctiv
ity (U
/mL
)
1 week 2 weeks Autoclaved
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
112
The cellulase activity of the autoclaved substrate varied according to the
organism and was found to be generally lower than the SSF grapes (week 1 or week 2).
The cellulase activity of T. harzianum cultured on the autoclaved substrate was about
19.7 U/mL (see fig. 4.5), slightly higher than week 2 SSF substrate (12.9 U/mL), but
considerably lower than week 1 SSF (39 U/mL). A similar trend was seen in P.
chrysogenum cultures where this activity was 24.1 U/mL as compared to week 1 (30.7
U/mL) and week 2 (97.6 U/mL) SSF. P. citrinum displayed a slightly lower cellulase
activity for the autoclaved substrate (15.2 U/mL) than either week 1 or week 2 SSF. The
results for week 2 SSF for A. niger cultures were in stark contrast, with cellulase
activity (see fig. 4.5) much higher than for the autoclaved grape waste (45.1 U/mL) and
the week 2 SSF (see fig. 4.5). This could be partly due to a higher β-glucosidase activity
as compared to the grapes applied to SSF. A. niger is generally known to possess high
β-glucosidase activity (Shin et al., 2011). The process of autoclaving probably increased
the amount of oligosaccharides and cellobiose in the filtrate which induced the higher
production of β-glucosidase enzyme. This probably hydrolysed cellobiose, one of the
product inhibitor of cellulases, during the initial incubation period.
Cellulase activities are also affected by the lignin composition of the biomass.
Generally, the lignin concentration is inversely proportional to cellulase activity.
Lignins not only act as physical barriers to microbial degradation, but also as non-
productive binders of cellulases (Alvira et al., 2010, Andrić et al., 2010a, Esteghlalian
Ali et al., 2000, Duarte et al., 2012b). Lignins form the outermost structure of
lignocelluloses and are the most complex molecules of all three biomass components,
i.e. cellulose, hemicellulose and lignins. Unlike cellulose, lignins are made up of a wide
range of aromatic and aliphatic molecules. They are linked to hemicelluloses and
cellulose by highly complex linkages and bonds, which increase the overall recalcitrant
nature of lignocellulose complex towards cellulase degradation (Eriksson, 1990,
Grisebach, 1985, Leistner, 1985). In the case of T. harzianum and P. citrinum, this was
the likely reason for low cellulase activities.
5.3.5. β-glucosidase activities
Contrary to other enzyme groups, β-glucosidase activity in all cultures
marginally or significantly increased over two weeks (Figure 5.5). T. harzianum
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
113
displayed a minor increase in enzyme activity from 27.9 U/mL to 30.2 U/mL and
displayed an activity which was lower than the other fungal cultures. This observation
was not surprising since it is well known that Trichoderma spp. have limited β-
glucosidase activity compared to other biomass-degrading fungi.
A similar marginal increase was observed with P. chrysogenum cultures where
enzyme activities increased marginally from 106 U/mL after 1 week to 118.9 U/mL
after 2 weeks. Although, this increase was minimal, the overall activity was high, and
considered enough to prevent or minimise the product inhibition of cellulase. As seen
previously (section 5.3.4), the highest cellulase activity was observed in P.
chrysogenum cultures. It can be argued from these results that once activities of β-
glucosidase reach a threshold level, the overall cellulase activity is improved or, at least,
enzyme inhibition is prevented. One of the surprising observations was seen in A. niger
cultures, where the enzyme activities were observed to be lower than expected. The
fungus displayed an increase in enzyme activity of only about 20 U/mL, from 28.7
U/mL after the first week to 50.2 U/mL after two weeks of SSF. These values are
considerably lower than previously reported. For example, an activity of 128 U/g was
observed with corn stover after 4 days on incubation (Gao et al., 2008, Singhania,
2012). Similarly, β-glucosidase activities of 68.3 U/g have been reported with Ca(OH)2
pre-treated sugarcane bagasse degradation after 8 days of incubation (Gupte and
Madamwar, 1997). However, the activities of β-glucosidase have been reported to
increase either after a pre-treatment as observed by Gupte and Dutta (1997) or at higher
temperatures of 35-45°C as shown by Gao et al (2008). A similar increase was observed
after hydrothermal pre-treatment (autoclaving) of grape biomass as discussed below and
elsewhere (Chapter 4, section 4.3.6).
The most significant increase, however, was observed in P. citrinum cultures,
which displayed a major increase of about 181 U/mL; from 30.3 U/mL after the first
week to 211.5 U/mL after the second week (Figure 5.5). P. citrinum is known to
generate β-glucosidase with high enzyme activities under various conditions. However,
the activities observed during the current experiments were found to be even higher than
those reported previously, e.g. P. citrinum degradation of rice bran (Ng et al., 2010).
The high activity in the current study after 2 weeks SSF contrasts with a moderate
activity after just 1 week SSF. Previously studies with Penicillium spp., for example,
which showed activities of 59 U/g after 4 days of SSF (Camassola and Dillon, 2007, Ng
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
114
et al., 2010). but were marginally greater than the reported 26.6 U/mL after 3 days of
fermentation (Maeda et al., 2011) and much greater than the 1.835 U/mL observed on
sugarcane bagasse after 15 days (Castro et al., 2010). It is likely that the nature and
composition of substrate, i.e. grape biomass, has a positive effect on β-glucosidase
activity with respect to the time of fermentation.
Figure 5.5. Comparison of β-glucosidase activity (U/mL) in the fungal degraded Shiraz grape waste
over 2 weeks of SSF compared to autoclaved pre-treated samples. The values are means of triplicate
data.
Although, the overall composition of lignocellulolytic enzymes has been
observed to be well balanced in Penicillium spp. and the production level of β-
glucosidase is significant (Castro et al., 2010), the current β-glucosidase activities and
production by both the Penicillium spp. were much higher than earlier reported by
others (Camassola and Dillon, 2007, Castro et al., 2010, Maeda et al., 2011), but
marginally higher than 156.5 U/g (Ng et al., 2010) and the previously worked out
submerged fermentation experiment (Chapter 4).
This increase in β-glucosidase activities of all the fungi might be indicative of
higher enzyme production and activity during SSF as compared to the submerged
0
50
100
150
200
250
T. harzianum A. niger P. chrysogenum P. citrinum
β-gl
ucos
idas
e ac
tivity
(U/m
L)
1 week 2 weeks Autoclaved
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
115
fermentation. Additionally, the substrate used in the current study (non-autoclaved
grape waste) may have contributed to the increased enzyme activity compared to other
substrates previously studied (Kovacs et al., 2009, Liu et al., 2012, Ng et al., 2010,
Sipos et al., 2010).
The β-glucosidase activities in T. harzianum cultures grown on autoclaved
grapes were lower than those of the SSF grapes. The enzyme activity in T. harzianum
was observed at 25.8 U/mL. In P. chrysogenum cultures, this activity was observed to
be 84.9 U/mL. β-glucosidase activity in P. citrinum cultures grown on autoclaved
grapes was observed at 180.1 U/mL, lower than that observed after 2 weeks of SSF, but
significantly higher than week 1 SSF culture. However, one of the contrasting outcomes
seen in these cultures was the comparatively lower cellulase activity even with
considerable β-glucosidase activity, which was unexpected. One of the reasons for this
might be an inherently lower cellulase activity of this fungal species. The lower
cellulase activity is in line with previously reported experiments which showed the
activities at 1.7 U/mL and 1.1 U/mL on wheat bran and rice straw, respectively, after 5
days (Dutta et al., 2008) and 4.8 U/g and 0.6 U/g on rice straw and rice bran,
respectively, after 4 days (Ng et al., 2010).
β-glucosidase activity in A. niger cultures grown on autoclaved grapes (154.9
U/mL) was much higher than the substrates undergoing SSF. This activity was also
higher than the activities reported by other Aspergillus spp. grown on SSF corn stover
(Gao et al., 2008) or on untreated or pre-treated sugarcane bagasse, wheat bran, wheat
straw, wheat bran and groundnut shells (Gupte and Madamwar, 1997). This β-
glucosidase activity probably translated into the comparatively higher cellulase activity
of A. niger observed on autoclaved substrate (see figures 4.5 and 5.4). Similar positive
correlations between β-glucosidase activities and cellulase activities have been observed
in the literature findings mentioned above.
5.3.6. Xylanase activities
It is known that β-glucosidases are similar to β-xylanases. Some belong to the
same family of GH5 Glycoside hydrolases (Pollet et al., 2010)) and their activities are
usually proportional to each other. In all the cultures, a high xylanase activity was
observed by the end of week 1. However, this activity declined rapidly during the
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
116
second week, although, in almost all the cases, the activity was higher than that on the
autoclaved substrate. A. niger showed highest xylanase activity after the first week at
1430 U/mL, which rapidly dropped to 214.3 U/mL (Figure 5.6).
Figure 5.6. Comparison of xylanase activity (U/mL) in the fungal degraded Shiraz grape waste over
2 weeks of SSF compared to autoclaved pre-treated samples. The values are means of triplicate data.
A. niger is a well-known xylanase producing fungus and has been used for
commercial production of this enzyme. As mentioned before with respect to other
enzymes, xylanase activities are also dependent on the nature of substrate on which the
fungus is growing. Betini et al (2009) reported utilization of several substrates for
xylanase production such as corn cob, eucalyptus sawdust, oatmeal, industrial sludge,
rice straw and sugarcane bagasse and found lower xylanase activities over 4 days at
30°C. However, on a mixture of corncob, wheat bran and rice straw, this activity
increased 3-10 times as compared to individual substrates. When grown on wheat bran,
the fungus also displayed enhanced xylanase activity of about 928 U under the same
conditions (Betini et al., 2009). Much higher xylanase activities of 3182 U/g were
obtained in a mixture of wheat bran and sugarcane bagasse within a period of 3 days
(Lu et al., 2008). Also noted from these experiments and previous observations was that
xylanase activities increase earlier than either cellulases or glucosidases. The enzyme
0
200
400
600
800
1000
1200
1400
1600
T. harzianum A. niger P. chrysogenum P. citrinum
Xyl
anas
e ac
tivity
(U/m
L)
1 week 2 weeks Autoclaved
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
117
activity is very high during the initial phases of biomass degradation and gradually
depletes, either due to a complete xylan sugar utilization or product inhibition.
T. harzianum displayed considerable depletion of about 550 U/mL between
week 1 and week 2 of SSF (Figure 5.6). The activity decreased from 772.7 U/mL at the
end of first week to 114.9 U/mL after 2 weeks. Although, Trichoderma spp. generally
produces large amounts of cellulases, they appear to produce poor quantities of
xylanases. Also, it was observed that, similar to cellulase and β-glucosidase activities,
xylanase activity of T. harzianum was lower on grape biomass with respect to other
fungi used in these experiments. (Chen et al., 1997) observed low xylanase activity (20
U/mL, 35°C, 5 days) and a gradual depletion of this activity after 4 days of
Trichoderma spp. fermentation. However, this may be due to the submerged nature of
this fermentation rather than SSF, where this activity was much higher at 320 U/mL
within 3 days (Maeda et al., 2011).
P. chrysogenum also displayed substantial depletion (620 U/mL) from 936.8
U/mL after the first week to 320 U/mL after week 2 of SSF. Grape biomass appeared to
a better substrate for Penicillium spp. as these fungi have been reported to generate
lower xylanase activities. For example, xylanases generated by P. chrysogenum
displayed the activities of 6.5 U/mL on wheat bran after 4 days, while an even lower
activity of 1.4 U/mL was observed on sugarcane pulp after 6 days (Okafor et al., 2007).
Similarly, the xylanase activities of Penicillium spp. were up to only 168 U/mL in
various supplemented basal media at 30°C after the 4th day of fermentation (Adsul et
al., 2007). One of the peculiar properties of xylanase activity was observed in P.
citrinum cultures with a decrease of about 270 U/mL (from 1100.8 U/mL to 822.5
U/mL) over 2 weeks of SSF. This decrease of xylanase activity in P. citrinum was not
as significant as in the other cultures (Figure 5.6). These results indicated that the
xylanase enzyme produced by P. citrinum culture was observed to possess a higher
stability as compared to other fungi used in the current experiments.
The xylanase activities of the majority of the fungal cultures grown on
autoclaved grape were found to be substantially lower than those in SSF cultures. T.
harzianum, P. chrysogenum and P. citrinum displayed xylanase activities at 38.7 U/mL,
191.1 U/mL and 234.2 U/mL, respectively. However, the xylanase activity of A. niger
was found to be 335.3 U/mL. The activity, although, much lower than SSF after week 1,
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
118
was substantially higher than SSF after week 2. The trend of xylanase activity was
found to be somewhat similar to β-glucosidase activity. However, this seemingly was
due to β-glucosidase itself as its higher activity in A. niger culture probably prevented
accumulation of cellobiose. The only exception to this relationship between β-
glucosidase and xylanase activities was noticed in P. citrinum, which displayed a lower
xylanase activity with respect to its β-glucosidase activity. These results showed that the
fungal xylanase activities were considerably higher in SSF rather than submerged
fermentations either with or without pre-treatment processes, and were in agreement
with results obtained previously (Betini et al., 2009, Brijwani et al., 2010, Lu et al.,
2008, Maeda et al., 2011).
5.4. Conclusions
The experiments presented in this chapter were performed to assess the
comparative degradation of grape biomass waste by Trichoderma harzianum,
Aspergillus niger, P. chrysogenum and P. citrinum in an SSF system over 2 weeks.
Total proteins were observed to decrease in all cultures but the extent was considerably
greater in T. harzianum and A. niger than P. chrysogenum and P. citrinum cultures. In
particular, the levels remained more or less constant at about 6.2 kg/m3 in P. citrinum
culture over two weeks. This decrease possibly reflected the lowered activities of
xylanases of all the fungi. However, other enzyme activities were mostly unaffected.
Reducing sugars in T. harzianum and P. citrinum cultures dropped marginally
while they displayed a sharp rise in A. niger and P. chrysogenum cultures. It was
observed that the concentration of reducing sugars correlated directly to the cellulase
activities in all cultures. As expected, the cellulase activity of T. harzianum and P.
citrinum decreased over two weeks of SSF, while that of A. niger and P. chrysogenum
increased during the same period. The cellulase activities over 2 weeks in all fungal
cultures correlated positively with reducing sugar accumulation, indicating possible
product inhibition of the respective enzymes due to increasing concentrations of
cellobiose/glucose in the media.
T. harzianum cultures displayed very minor increases in β-glucosidase activities
between the first and second week in which was not surprising as this fungus cannot
produce high amounts of β-glucosidases. A. niger, P. chrysogenum and P. citrinum
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
119
cultures, on the other hand, displayed considerable rises in β-glucosidase activities over
the same period. Xylanase activities dropped in every culture as the fermentation
progressed. The highest activity of about 1430 U/mL was observed in A. niger after one
week. This rapid rise followed by a gradual decrease in xylanase activities was
consistent with observations from the literature.
None of the fungi were able achieve considerable lignin degradation in the SSF
process over 2 weeks. Lower amounts of lignins were degraded in the substrate applied
to SSF with respect to the same substrate subjected to hydrothermal pre-treatment
process before fungal degradation (Chapter 4). Nonetheless, higher lignocellulolytic
enzyme activities were observed in SSF as compared to submerged fermentation.,
Further improvement in lignin degradation and the need to minimise product inhibition
are required to increase the overall bioconversion process for the production of
important molecules like industrial alcohols, acids and numerous metabolites.
5.5. Summary
Fungal enzyme activities during SSF of grape biomass were found to be significantly
higher, in most of the cases observed, than submerged fermentation (Chapter 4) or in
previously reported works (Juhász et al., 2005, Kovacs et al., 2009, Sipos et al., 2010).
The cellulase and β-glucosidase activities were either comparable or substantially
higher in the SSF process than in the autoclaved substrates, except for β-glucosidase
activity in A. niger cultures. Similarly, xylanase activities were significantly higher than
the steam pre-treated grape waste degraded by the fungi over 1 week. However, the
cellulase and xylanase activities over SSF grape wastes decreased during the second
week of incubation. Lignin mineralization was surprisingly lower in SSF as compared
to submerged fermentation. It is known that SSF provides better growth conditions for
fungi by increasing the surface area for fungal attachment. The low substrate particle
size (typically 1-4 mm) (Gupte and Madamwar, 1997, Shi et al., 2008) increase the
aeration and thus increase the degradation of more recalcitrant molecules such as lignin
(Lee, 1997). However, longer incubation was observed to decrease the activity of
numerous enzymes. Overall, a better cellulose and hemicellulose degradation was
observed in SSF as compared to autoclaved substrate, as evident from the increased
Chapter 5 SSF of winery biomass wastes by Ascomycota fungi
120
enzyme activities. However, the experiment highlighted a need to improve lignin
degradation further.
121
CHAPTER 6
Optimizing degradation of winery-derived biomass waste by
Ascomycetes
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
122
6.1. Introduction
Grapes are one of the major global horticultural crops with an estimated
production of 69.1 million tonnes during 2012 of which approximately 80% were wine
grapes (OIV, 2013). Australia is an important grape producing region and 1.75 million
tonnes of grapes were crushed for wine production during 2012-13 (ABS, 2013). Like
the rest of the world, Australian wineries produce large amounts of biomass waste,
amounting to about 50-60% of the total grape crushed during the process (Devesa-Rey
et al., 2011, Gerling, 2011). In addition, wineries are a high wastewater generator,
producing as much as 8 litres per bottle of wine (Christ and Burritt, 2013). The problem
is ubiquitous and, in recent years, winery wastes have been classified as pollutants by
the European Union (Wang et al.). Subsequent treatment, specifically post-product
processing, is thus required to make winery wastes less hazardous, both in nature and
volume (Devesa-Rey et al., 2011). Grape wastes consist of grape berries, plant-derived
fibres, grape seeds, skin, marcs, stalk and skin pulp. Unlike other agricultural by-
products, grape biomass waste has limited use as an animal feed stock due to its poor
nutrient value and low digestibility (high concentration of tannins and polyphenols).
The polyphenols also slow down microbial utilization of this biomass. A majority of
these winery biomass wastes thus end up as toxic landfill (Devesa-Rey et al., 2011).
The major components of grape biomass waste are cellulose, pectins and lignins
(including tannins). Fungi belonging to the division Ascomycota, such as Trichoderma
spp., Aspergillus spp. and Penicillium spp., are known for their biomass degrading
ability and should prove useful in grape biomass degradation. The fungi have been well
studied for their ability to produce high levels of degradative enzymes, such as
cellulases and hemicellulases (Brink and Vries, 2011, Klyosov, 1987b). Such enzymes
also have potential in generating useful metabolic by-products such as alcohols,
flavonoids, organic acids and phenolics (Sánchez, 2009).
Thus, there is great potential for biomass degradation as well as utilization of the
degradation products. However, the fungal enzymes, depending on their parent fungi,
have several limitations. A limitation of cellulases, for example, is their low
comparative activity, on a weight-by-weight basis, compared to many other enzymes
such as amylases (Klyosov, 1987b). Enzymes capable of degrading grape biomass
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
123
suffer greatly from product inhibition, especially by cellobiose (direct inhibition) and
glucose (indirect inhibition), even at low concentrations (Andrić et al., 2010a).
Several strategies have been employed to overcome these limitations. For
example, the use of mixed fungal culture degradation has been suggested and reported
(Brijwani et al., 2010, Chu et al., 2011). This enables the different strengths of different
fungi to be utilised at the same time. For example, it is known that, apart from
satisfactory cellulase production, Aspergillus spp. generates highly efficient xylanases
and β-glucosidases in exceptional quantities (Betini et al., 2009). Penicillium spp. can
be used for lignin mineralization to enhance the overall degradation process (Rodríguez
et al., 1994). The use of multiple fungi simultaneously would, hopefully, result in
enhanced degradation as a direct result of their complementary degradation pathways.
Other strategies could involve pre-treatment designed to increase access of enzymes to
celluloses and hemicelluloses and the overall surface area of the substrate for more
efficient breakdown (Papadimitriou, 2010).
6.2. Overview
The experiments described herein explore a statistical-based optimization of
mixed fungal cultures to achieve enhanced grape biomass degradation. The optimization
experiments utilized cultures of Trichoderma harzianum, Aspergillus niger, Penicillium
chrysogenum and Penicillium citrinum for degradation purposes.
The conventional and popular submerged fermentation and the emerging Solid
State Fermentation (SSF) methods were tested. As seen in the previous chapter when a
single fungal organism was used, the submerged fermentation method for grape
biomass degradation had some advantages over the SSF method. For example, β-
glucosidase activities were often observed to be considerably higher during pre-treated
biomass degradation by submerged fermentation than SSF. Also, due to pre-treatment
involved in the submerged fermentation method, lignin degradation was higher than in
SSF. However, the enzyme activities responsible for a major part of degradation were
considerably higher during SSF. Part of the purpose of the work in this chapter is to
determine whether or not these trends remain when using a mixed fungal culture.
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
124
When using a single culture, each of the processes (submerged fermentation and
SSF) suffered some limitations, which would be amplified on a commercial scale. For
example, strong enzyme inhibition was observed in submerged fermentation of pre-
treated grapes. The low substrate concentration in this process (30 g/L) was observed to
generate large amounts of waste water. Conversely, a major limitation observed in SSF
was lower lignin degradation over 2 weeks. In both cases, many of these limitations
might be expected to be reduced if a mixed culture were to be used.
Statistical analysis was performed to generate an optimised mixed fungal
“cocktail” and achieve a bioreactor-based degradation process with increased biomass
degradation. The activities of all enzymes and other parameters were used as the input
values to generate an optimal working design using statistical modelling.
Finally, metabolomic techniques were used to analyse the degradation products
and to identify several metabolites of industrial and medicinal interest. The
biodegradation process was scaled up from flasks to a small industrial scale bioreactor
to increase the reproducibility for future commercial scale biomass degradation.
6.3. Results and Discussions
6.3.1. Compositional analysis of grape waste
It is well known that the composition of the substrate strongly influences its
degradation efficiency. Various non-structural components such as free proteins,
sugars and structural components such as cellulose and lignin affect the overall
efficiency of degradation, usually providing a barrier to such degradation (Duarte et
al., 2012b).The carbon-nitrogen (C/N) ratio plays an important role in biodegradation.
It has been found that a higher the nitrogen content in the substrate results in greater
degradation (Brijwani et al., 2010). This may also reflect the observation that the
higher the nitrogen content, especially from organic sources (e.g. proteins), the greater
the overall enzyme activities (Kim et al., 2012a).
The total compositional analysis of untreated grape wastes used in this study is
given in Table 6.1.
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
125
Table 6.1. General composition of Shiraz grape waste (n=3).
Component Content (g/100 g
mass)
Composition (previous works)
Proteins 2.22 ± 0.01 3.3 ± 0.1 (González-Centeno et al., 2010)
Total sugars 1.92 ± 0.18 2.1 ± 0.01 1 (González-Centeno et al.,
2010)
Lignins 35.96 ± 0.13 36.2 ± 0.1 (Bravo and Saura-Calixto,
1998)
Cellulose +
Hemicellulose
57.84 ± 0.13 47- 65 (Deng et al., 2011, Spigno et al.,
2008)
Ash 2.81 ± 0.01 3 ± 0.1 (González-Centeno et al., 2010)
6.3.2. Statistics: Design of experiment and analysis
Experimental design and statistical analysis was performed using a full factorial
design method developed using Minitab® 16 (Minitab Pty Ltd., Sydney, Australia). One
of the simplest methods for complex experimental design, it can analyse multiple factors
with each factor containing multiple levels of experimental conditions. In the current
experiments, we used fungi type (4 types) and degradation methods (2 types) to provide
8 factors, each of which were optimised for the component cellulases, xylanases and b-
glucosidases (3 factors), thus yielding a 24 experimental condition model (Montgomery,
2008).
The model used a regression analysis given by the equation:
𝑦 = 𝑏0 + 𝑏1𝑥1 + 𝑏2𝑥2 + 𝑏3𝑥3 + 𝑏4𝑥1𝑥2 + 𝑏5𝑥2𝑥3 + 𝑏6𝑥1𝑥3 + 𝑏7𝑥1𝑥2𝑥3 + 𝑒𝑖 Eq. 6.1
where,
b0 = co-efficient of intercept
b (1,…, 7) = factor mean difference
x (1, 2, 3) = Factor variables
ei = residual for ith unit
Predictability of the model response can be assessed by the equation:
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
126
𝑅𝑝𝑟𝑒𝑑2 = 1− 𝑃𝑅𝐸𝑆𝑆𝑆𝑆
= 1 −∑ � 𝑒𝑖
1−ℎ𝑖�2
𝑛1
∑(𝑦𝑖−ӯ)2 Eq. 6.2
where,
n= number of observations
ei= ith residual
hi= ith diagonal element of predictor matrix [𝑥(𝑥′𝑥)−1𝑥−1]
yi= ith observed response
ӯ= mean response
PRESS = model's predictive ability
SS = adjusted sums of squares
An increasing response value indicates a higher predictive ability of the given
model (Montgomery, 2008).
For the current experimental design, maximum enzyme activities were the
primary responses. However, the responses for reducing sugar levels and lignin levels
were also used for experiment development.
6.3.3. Grape biomass degradation
In this study, it was observed that the process of hydrothermal treatment
increased the protein content in the medium as compared to that in the SSF process
(Figure 6.1). To maintain similar conditions, the values of hydrothermal pre-treated
substrate were compared with SSF samples which had been treated for one week only.
The observed protein concentration in T. harzianum cultured under the submerged
condition was 31.9 kg/m3, however, this value decreased under SSF conditions, where it
was 11.5 kg/m3. A significant decrease in total protein content was also observed for A.
niger cultures where the protein content decreased from 21.9 kg/m3 to 4.5 kg/m3 which
was also reflected in a decrease in β-glucosidase activity (see Figure 6.3).
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
127
Figure 6.1. Total protein content of 1 week SSF degraded grape biomass and hydrothermal pre-
treated submerged grape biomass (labelled “autoclaved”). The values from untreated sample are for
comparison purposes. All values are averages of triplicate data with p < 0.05 in all cases.
In addition to protein content, lignin content is an important factor to measure, as
it can determine ultimate biomass degradation. Lignins are aromatic polymers and are
very complex in their chemical structure (compared to celluloses and hemicelluloses).
They form a considerable challenge for degradation, both chemically and biologically
(Higuchi, 2006). They are bound to both cellulose and hemicellulose polymers by
various cross linkages (Dashtban et al., 2009, Fengel, 1971), thus inhibiting the
degradation of those polymers as well. Lignin degradation, or at least its removal and
separation from cellulose and hemicellulose, is therefore one of the chief aims of
biomass degradation. Lignin can act as a non-productive inhibitor of cellulolytic
enzymes, especially cellulases which tend to have strong binding to lignins (Duarte et
al., 2012b). The importance of this intractability is particularly relevant to grape waste
because of the high content of lignin-derived products such as phenolics and tannins
(Bravo and Saura-Calixto, 1998, González-Centeno et al., 2010). In this study, it was
observed that P. chrysogenum was the only fungus able to degrade lignin to an
appreciable extent. Degradation of 4.7 % and 2.2% lignin content were seen under the
submerged and SSF conditions, respectively. None of the other fungi used were found
0
5
10
15
20
25
30
35
T. harzianum A. niger P. chrysogenum P. citrinum
Prot
ein
cont
ent (
kg/m
3 )
1 week SSF Autoclaved Untreated
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
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to be lignolytic organisms. However, Penicillium spp. has been previously reported to
possess minor lignin mineralization abilities, including P. chrysogenum, and are known
to produce lignin degradation (about 8%) under appropriate conditions (Rodríguez et
al., 1994). It is quite possible that the lignin degrading ability of P. chrysogenum results
in an overall increased biomass degradation efficiency, thus requiring considerably less
protein as compared to other fungi under similar conditions.
Finally, autoclaving caused a considerable reduction in winery grape biomass
waste. Overall, dry mass decreased by about 18% in autoclaved grape waste. It has been
reported by numerous authors that biomass undergoes hydrolysis during hydrothermal
processing. Although most of the reports have utilized temperatures around 180°C, the
hydrolysis of biomass has been documented to occur even at autoclaving temperature of
121°C (Papadimitriou, 2010). This occurs due to the increase in ion products of water
with a simultaneous decrease in the dielectric constant as the temperature rises. These
factors have been reported to significantly increase the solvent capacity of water (Goto
et al., 2004). This, in turn, increases biomass degrading ability of the system, as
observed during course of the mentioned experiment.
6.3.4. Enzyme activities
Cellulase activity was measured as the combined activities of endoglucanases
and exoglucanases as measured by the filter paper assay. The production of cellulases,
β-glucosidases and xylanase is important from a biodegradation perspective. High
activities of β-glucosidases play a crucial role as they prevent the product inhibition of
cellulases (Duarte et al., 2012b, Klyosov, 1987b). To assess the overall activity of these
enzymes in all fungal cultures and to generate a better grape biomass degradation
method, process optimization was carried out. Generally, 1:1 cellulase:β-glucosidase
activities have been reported to yield highest outputs under SSF conditions (Brijwani et
al., 2010, Chahal, 1985), although the exact ratio varies across the species and type of
substrates.
The highest activity of cellulase under the full factorial design was noted as 43.7 U/mL,
as described by a main effects plot (Figure 6.2).
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
129
Figure 6.2. Main effects plot showing the relationship between various fungal cultures across
different growth media in terms of cellulase activity. The values represented in parentheses denote
standard errors.
The overall equation measured in terms of cellulase activity was (Eq. 6.3), where
𝑦 = 29.03 + 0.32𝑥1 + 6.54𝑥2 − 1.55𝑥3 − 7.06𝑥1𝑥2 + 9.20𝑥2𝑥3 − 0.69𝑥1𝑥2 −
2.62𝑥1𝑥2𝑥3 Eq. 6.3
The highest activities of β-glucosidase were observed at 181.4 U/mL as described by
the main effects plot (Figure 6.3). The equation for β-glucosidase was (Eq. 6.4), where
𝑦 = 79.81− 53.45𝑥1 + 12.17𝑥2 + 15.83𝑥3 − 32.21𝑥1𝑥2 + 31.31𝑥2𝑥3 −
42.34𝑥1𝑥2 + 31.64𝑥1𝑥2𝑥3 Eq. 6.4
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
130
Figure 6.3. Main effects plot showing the relationship between various fungal cultures across
different growth media, in terms of β-glucosidase activity. The values represented in parentheses
denote standard errors.
The highest activities of xylanase were observed at 1414 U/mL, as observed in main
effects plot (Figure 6.4). The equation for xylanase was (Eq. 6.5), where
𝑦 = 611.17 − 240.25𝑥1 + 263.43𝑥2 − 52.18𝑥3 + 79.13𝑥1𝑥2 − 128.04𝑥2𝑥3 +
43.51𝑥1𝑥2 − 411.37𝑥1𝑥2𝑥3 Eq. 6.5
P. citrinum
P.chrysogen
um
A. niger
T. harzi
anum
110
100
90
80
70
60
50
40
30
20
SSF
Submerged
Fungi
β-gl
ucos
idas
e ac
tivity
(U/m
L)
Method
(1.1)
(0.69)(0.54)
(0.48)
(0.94)
(0.47)
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
131
Figure 6.4. Main effects plot showing the relationship between various fungal cultures across
different growth media, in terms of xylanase activity. The values represented in parentheses denote
standard errors.
The full factorial modelling method also generated a complete interaction matrix plot
which displayed the overall activities of different enzymes during both SSF and
submerged fermentation (Figure 6.5). Relationships between different conditions can
be inferred from the interaction plot, based on which the statistical model predicted the
optimal conditions, i.e. appropriate ratio between different fungi and ratio between
substrate and medium to achieve maximum amount of biomass degradation within the
least possible time.
P. citr
inum
P. chryso
genum
A. niger
T. harzi
anum
1100
1000
900
800
700
600
500
400
300
200
SSF
Submerged
Fungi
Xyla
nase
act
ivity
(U/m
L)
Method
(19.58)
(11.62)
(15.81)
(17.74)
(11.34)
(21.03)
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
132
Figure 6.5. Matrix plot shows the fungal enzyme activities under different conditions. The top X-
axis labels of 1, 2, 3 and 4 refer to T. harzianum, A. niger, P. chrysogenum and P. citrinum,
respectively.
The fitted ANOVA model was generated using the data provided in Figures 6.2,
6.3 and 6.4, and is given below in Table 6.2. High predictive responses (cumulative Q2
= 0.929) were observed across the species and experimental conditions, suggesting good
predictive capability of the model used for the enzyme activities of different fungi under
varied conditions of growth (Equations 6.1, 6.2, 6.3, 6.4, 6.5).
4321
40
30
201600
800
0200
100
0
Cel
lula
ses
Xyl
anas
es
Fungi
β-gl
ucos
idas
eSubmerged
SSF
Method
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
133
Table 6.2. ANOVA for Cellulases, Xylanases and β-glucosidase using Adjusted SS for Tests
Enzyme Source DF Seq SS Adj MS F P
Cellulase Fungi 3 440.6 146.87 42.05 0.001
Method 1 164.75 164.75 47.17 0.003
Fungi*Method 2 822.93 274.31 78.53 0
Error 16 55.89 3.49
Total 23 1484.17
S = 1.86896 R2 = 96.23%
Xylanase Fungi 3 784082 261361 984.86 0.001
Method 1 4061345 4061345 15304.04 0
Fungi*Method 2 147477 49159 185.24 0.002
Error 16 4246 265
Total 23 4997150
S = 1.54204 R2 = 99.88%
β-glucosidase Fungi 3 784082 261361 984.86 0.001
Method 1 4061345 4061345 15304.04 0
Fungi*Method 2 147477 49159 185.24 0.002
Error 16 4246 265
Total 23 4997150
S = 16.2904 R2 = 99.92%
The highest activity of cellulase under the full factorial design was observed at
43.7 U/mL, while those of xylanase and β-glucosidase were observed at 1414 U/mL and
181.4 U/mL, respectively. The cellulase activity with the autoclaved substrate varied as
per organism, but was found to be generally lower than for SSF. For example, the
cellulase activity of T. harzianum for the autoclaved substrate was 19.7 U/mL, lower
than SSF (39 U/mL). A similar trend was seen in the P. chrysogenum culture where the
activity was 24.1 U/mL, lower than for SSF (30.7 U/mL) and for the P. citrinum culture
where the activity was 15.2 U/mL, lower than for SSF conditions (27.7U/mL). The
exception was the A. niger culture, where cellulase activity in the autoclaved grape
(45.1 U/mL) was higher than for SSF conditions (28.9 U/mL) (Table 6.3). This could
be partly due to a higher β-glucosidase activity as compared to the grapes applied to
SSF. A. niger is generally known to possess high β-glucosidase activity (Shin et al.,
2011). The process of autoclaving is likely to have increased the amount of
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
134
oligosaccharides and cellobiose in the filtrate which in turn increased the production of
β-glucosidase. This probably led to hydrolysis of cellobiose, one of the product
inhibitors of cellulases, during the initial incubation period.
The β-glucosidase activity of T. harzianum was observed at 25.8 U/mL under
submerged conditions, comparable to 27.9 U/mL from SSF. A similar trend was seen
with P. chrysogenum, where the activity was observed at about 106.3 U/mL under SSF
conditions as opposite to 84.9 U/mL under submerged conditions. β-glucosidase activity
in the P. citrinum culture with autoclaved grapes was 180.1 U/mL, which was
significantly higher than the 1 week SSF culture (30.4 U/mL). Thus, one of the
contrasting outcomes seen in this culture is the comparatively low cellulase activity
even with considerably high β-glucosidase activity, a result which was unexpected. One
plausible explanation for this might be an inherently lower cellulase activity of P.
citrinum. Conversely, the β-glucosidase activity in the A. niger culture with autoclaved
grapes was much higher than the SSF substrates. The activity in the autoclaved
substrate was found to be 154.9 U/mL compared to 29.1 U/mL in SSF. The high β-
glucosidase activity was probably related to the comparatively high cellulase activity of
A. niger on autoclaved substrate.
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
135
Table 6.3. Total cellulase, xylanase and β-glucosidase activities (U/mL) of different fungi under
experimental conditions of Design of Experiment.
It is known that β-glucosidases are similar to xylan degrading enzymes,
especially xylosidases, with some of them belonging to the same family of GH5
glycoside hydrolases. Their activities are generally interdependent and are directly
proportional to each other (Pollet et al., 2010). Confounding this is that cellobiose, the
Method Fungi Cellulases
(U/mL)
Xylanases
(U/mL)
β-glucosidase
(U/mL)
SSF P. chrysogenum 31.2 926.9 105.7
SSF P. citrinum 28.1 1046.2 30.1
Submerged T. harzianum 16.7 32.1 26.4
Submerged P. citrinum 21.2 221.0 178.6
SSF A. niger 29.4 1414.0 28.8
Submerged A. niger 39.2 315.1 156.6
SSF T. harzianum 39.8 703.2 29.6
Submerged P. chrysogenum 25.7 156.3 86.0
SSF A. niger 28.5 1414.0 29.2
Submerged T. harzianum 21.2 42.0 23.2
Submerged A. niger 43.7 360.2 153.4
SSF P. chrysogenum 30.8 926.9 106.2
SSF P. citrinum 27.6 1046.2 30.5
Submerged P. chrysogenum 21.2 230.9 83.7
Submerged P. citrinum 16.7 235.9 180.5
SSF T. harzianum 38.4 703.2 24.3
SSF P. citrinum 27.6 1046.2 30.5
Submerged T. harzianum 21.2 42.0 27.8
SSF T. harzianum 38.9 703.2 26.9
SSF A. niger 29.0 1414.0 29.2
Submerged P. chrysogenum 25.7 186.2 85.1
Submerged A. niger 43.7 330.3 154.8
SSF P. chrysogenum 30.3 926.9 107.1
Submerged P. citrinum 21.2 245.8 181.4
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
136
chief substrate for β-glucosidases, is inhibitory towards xylanase activity (Brijwani et
al., 2010). In this case, T. harzianum, A. niger, P. chrysogenum and P. citrinum
displayed xylanase activities for autoclaved grape waste at 38.7 U/mL, 335.3 U/mL,
191.1 U/mL and 234.2 U/mL, respectively. These activities were much lower than for
the SSF substrate, which showed substantially higher xylanase activities of 772.7 U/mL,
1430.6 U/mL, 936.8 U/mL and 1100.8 U/mL, respectively. This trend of xylanase
activity was found to be similar to that of β-glucosidase activity (Table 6.3), however
the correlation is probably not a causal relationship and simply reflects the
interdependence noted above.
The higher activity of β-glucosidase in the SSF cultures, particularly in the A.
niger culture, presumably prevents accumulation of cellobiose, in turn resulting in
higher xylanase activity. The only exception to the relationship between β-glucosidase
and xylanase was noticed in P. citrinum, which displayed much lower comparative
xylanase activity with respect to its β-glucosidase activity for the SSF culture. It has
been reported that xylans in biomass act as one of the competitive inhibitors of cellulase
enzymes (Duarte et al., 2012b). Thus, a significantly lower activity is likely to occur in
the organisms with lower xylanase activity, which was mostly consistent with the
current experimental outcomes.
The main effects plots and matrix plot generated were able to provide optimum
relationships necessary to increase the degradation abilities (Figures 6.2, 6.3 6.4 and
6.5). The Full Factorial Design was able to provide the optimum conditions and mix of
various fungi for maximizing the degradation of grape biomass. As predicted by the
model, the substrate: media ratio was adjusted to 0.3:1 for optimal results while the
percent fungal ratio for A. niger: P. chrysogenum: T. harzianum: P. citrinum of
60:14:4:2 was selected so as to yield maximum possible output without considerable
enzyme inhibition.
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
137
6.3.5. Grape biomass degradation in bioreactor
The pre-dried and ground grape waste was weighed, adjusted to a 0.3:1 ratio
with AATCC medium and sterilized by autoclaving at 115°C for 10 minutes in a 3.6 L
bioreactor with controlled pH, oxygen, temperature, nutrient feed and agitation speed
(Infors AG, Bottmingen, Switzerland). The fungal cultures were added at the design
levels determined from the Minitab modelling (Tables 2 and 3). The treated grape
waste was incubated with this fungal culture for 1 week at 30°C with constant agitation
at 200 rpm. This incubation procedure was identical to that of the individual species and
the results were compared with individual species results (Figure 6.6).
Figure 6.6. Enzyme activities observed in individual cultures under different conditions compared to
that of the optimized mixed culture (n=3).
6.3.6. Cellulolytic enzyme production in bioreactor culture
The activities of all the enzymes were observed to increase under the statistically
optimized conditions (Figure 6.6). After 5 days of culturing, cellulase activity in the
bioreactor was observed at 78.4 U/mL, more than twice that of A. niger, (the fungus
which displayed the highest individual cellulase activity) by itself (under the submerged
fermentation conditions). A less dramatic, but still considerable, increase was seen in
0
500
1000
1500
2000
2500
3000
3500
4000
0
50
100
150
200
250
300
Subm
erge
d
SSF
Subm
erge
d
SSF
Subm
erge
d
SSF
Subm
erge
d
SSF
T. harzianum A. niger P. chrysogenum P. citrinum MixedX
ylan
ase
activ
ity (U
/mL
)
Cel
lula
se/β
-glu
cosi
dase
act
ivity
(U/m
L)
Cellulase β-glucosidase Xylanase
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
138
the activity of β-glucosidase. Under the statistically optimized bioreactor conditions, the
β-glucosidase activity was observed at 250.9 U/mL, considerably higher than that of P.
citrinum, the fungus which displayed the highest β-glucosidase activity when used
individually. The most significant increase was seen in xylanase activity, which was
observed at 3544.7 U/mL, almost twice that of the activity seen during the process
optimization stage for individual species. Cellulase activities observed during the
bioreactor-mediated mixed fermentation were similar to those of the SSF process at day
15, while the activities of β-glucosidase and xylanase were considerable higher.
A significant spike in xylanase activity is probably one of the main reasons for
the increase in activities of other enzymes. It is known that the hemicellulases form
complex cross linking with cellulose and lignins in a biomass (Sánchez, 2009).
Xylanases, especially those derived from P. citrinum, have been reported to be active
across a wide pH range and temperature conditions spanning 30-50°C (Dutta et al.,
2007). Also, due to the close association between cellulases and β-glucosidase, the
activity of xylanase is dependent on the activities of these enzymes and vice-versa.
A considerable decrease in lignin content was observed in the degraded
substrate, where 17.9% lignin was found to be degraded or mineralized during the
process. This rate was considerably higher than the 4.7 % and 2.2% found for the
control in submerged and SSF treatments, respectively.
6.3.7. Gas Chromatography-Mass Spectrometry (GC-MS)
Samples derived from the optimized bioreactor degradation process were further
analysed by gas chromatography-mass spectrometry (GC-MS). A 1 ml aliquot of
methanol (LC grade, ScharLab, Sentemanat, Spain) was added to 40 mg post degraded
freeze dried sample, then vortexed briefly before centrifugation at 572 g /4°C for 15
minutes. A 50 µL aliquot of the supernatant was then transferred to a fresh tube and
dried in an RVC 2-18 centrifugal evaporator at 40°C/210 g (Martin Christ
Gefriertrocknungsanlagen GmbH; Osterode, Germany). All samples were stored at -
80°C until further use (Ng et al., 2012). In order to derivatize the samples for GC-MS
analysis, 40 µL methoxamine HCl (2% in pyridine) was added to each sample and
incubated for 45 minutes at 37°C. To complete the derivatization, silylation was
performed by adding 70µL BSTFA in 1% TMCS. Samples were then incubated for an
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
139
additional hour at 70°C. Samples were diluted with 190 µL pyridine, vortexed and
centrifuged at 15682 g for 5 minutes before transferring to GC-MS vials.
GC-MS was performed as previously explained in ‘Materials and methods
section’ (3.10.3).
6.3.8. Metabolic output of mixed fungal degradation
The mixed fungal degradation products were analysed by GC-MS to yield a
metabolic profile of ca. 220 peak features, which is generally expected for a microbial
metabolism processes. The data generated by mass spectral analyses were displayed as
the magnitude of 1 fold change (FC) with respect to the peak areas between mixed
fungal culture samples and control samples. This was followed by Principal Component
Analysis (PCA) based on score scatter (Figure 6.7 A) and DModX plots (Figure 6.7
B).
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
140
Figure 6.7. (A) PCA score scatter plot displaying the metabolite output pattern of mixed fungal
degradation of grape biomass. (B) DModX line plot of PCA metabolites. Note: Each point on the
scatter plot refers to single sample, with R2X (cumulative) = 91.6 % and Q2 (cumulative) = 80.3
%.All the values are within the Dcrit value range of 0.05, as marked by dotted line.
(A)
(B)
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
141
PCA is a method for correlating variable transformation to non-correlated
variables. During PCA, these variables, or components, are arranged in a decreasing
order of importance to minimise the dimensionality of data under consideration.
Generally, 2-3 principal components are used to obtain a PCA dataset, however, the
number of components can increase based on the complexity of data. PCA modelling is
performed based on the equation (Equation 6.6) given below.
𝑋 = 1 × 𝑋� + 𝑇𝑃′ + 𝐸 Eq. 6.6
Where,
X = Correlation of data matrix
𝑋� = Average of x variables in the dataset
P = matrix showing influence of variables
E = residual matrix or deviation between original values and projected values
The DModX (Distance of Observation) plot of PCA is the normalized
observational distance between variable set and X modal plane and is proportional to a
variable’s residual standard deviation (RSD). Dcrit (critical value of DModX) is derived
from the F-distribution and calculates the size of observational area under analysis.
Values which are twice that of Dcrit’ values are generally considered as moderate
outliers (Jackson, 2005).
In order to interrogate the data further, the samples were processed using Partial
Least Square-Discriminant Analysis (PLS-DA). PLS-DA is used to analyse large
datasets and has the ability to assess linear/polynomial correlation between variable
matrices by lowering the dimensions of the predictive model, enabling easy
dissemination between the samples and the metabolite features that cause the
dissemination (Wold et al., 2001).
Of the 220 metabolites, 78 were identified as statistically significant by PLS-DA
(Figure 6.8). Moreover, the change in metabolite concentration during degradation was
analysed by one way ANOVA using Fisher’s least significant difference method
(Fisher’s LSD) and Tukey’s Honestly Significant Difference (Tukey’s HSD).
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
142
Figure 6.8. (A) PLS-DA score scatter plot and (B) loading scatter plot of degraded Shiraz grape
waste according to fungal species analysed using GC-MS. Note: the PLS-DA plot eclipse represents
the 95% confidence interval. The black star labels on the loading scatter plot refer to each fungal
group; with each yellow circles referring to single metabolite feature.
(A)
(B)
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
143
Numerous metabolites, including organic acids, alcohols, sugars, sugar acids and
amino acids, were observed to be either consumed or generated during the biomass
degradation process. Table 6.4 represents the most significant metabolites analysed by
one way ANOVA.
Table 6.4. Most significant features generated during the optimized biomass degradation as
identified by the volcano plot with their fold change (FC) and P values
Peaks FC value P-value
Galactonic acid-1,4-lactone 12.031 2.06 e-7
Hexacosanoic acid 711.99 9.89 e-7
FructoseBP 177.47 3.34 e-6
DL-Fucose 3.373 4.07 e-6
Ethyl phosphoric acid 16174 6.89 e-6
Arabinofuranose 27.731 6.95 e-6
alpha-DL-Lyxofuranoside 54.94 6.98 e-6
2-Monopalmitin 9.5698 8.89 e-6
myo- Inositol 49.643 1.06 e-5
Phosphoric acid 143890 1.06 e-5
Oleanolic acid 4170.7 1.15 e-5
Gulose 5.3276 1.17 e-5
D-Fructose O-methyloxime Results 229.8 1.20 e-5
α-D-Mannopyranoside 269.66 1.20 e-5
D-Fructose 207.12 1.34 e-5
Glyceric acid 4215.4 1.35 e-5
Heptadecanoic acid 310.16 1.37 e-5
Arabitol 4.0971 1.63 e-5
Tetradecanoic acid 1760 1.80 e-5
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
144
Table 6.4. Most significant features generated during the optimized biomass degradation as
identified by the volcano plot with their fold change (FC) and P values (…continued)
Peaks FC value P-value
Saccharic acid 4.1996 2.09 e-5
Gallic acid 38.383 2.14 e-5
D- Mannitol 119.86 2.17 e-5
Hexadecanoic acid 17611 2.30 e-5
Docosanoic acid 322 2.32 e-5
β-Sitosterol 18.036 2.57 e-5
Maleic acid, 2-methyl- (2TMS) Results 0.068538 0.000125
Xylitol, 1,2,3,4,5-pentakis-O-(trimethylsilyl) 0.18821 0.000701
Phosphoric acid, bis(trimethylsilyl) 2,3-
bis[(trimethylsilyl)oxy] propyl ester
0.19696 0.002067
Glycerol-3-phosphate (4TMS) Results 0.19714 0.00207
Stigmasterol trimethylsilyl ether 0.20688 4.94 e-5
1,2,3-Propanetricarboxylic acid, 2-
[(trimethylsilyl)oxy]-,tris(trimethylsilyl) ester
0.29432 0.009638
Citric acid (4TMS) Results 0.29432 0.009638
D-Xylonic acid, 2,3,5-tris-O-(trimethylsilyl)-,γ-lactone 0.30923 0.003607
A volcano plot was further applied to assess the metabolite input and output. It
was observed that a majority of metabolites present in the substrate were consumed by
the fungi during the degradation process whilst other metabolites were generated
(Figure 6.9).
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
145
0
1
2
3
4
5
6
7
8
-5 0 5 10 15 20
- Log
10 (P
-val
ue)
Log2 (Fold Change)
Control Optimized mix
Figure 6.9. Important features selected by volcano plot with fold change threshold (x-axis) 2 and t-
tests threshold (y-axis) 0.05. Note: both fold changes and p-values are log transformed. The further
its position away from the (0, 0), the more significant the feature is. The yellow circles represent the
significant metabolites, while the black triangles represent non-significant metabolites.
The primary significantly accumulated metabolites were stigmasterol, maleic
acid, xylitol and glycerol. Although statistically less significant, other metabolites of
interest generated were γ-lactone-d-xylonic acid, d-glucopyranoside, citric acid,
ethylene and arabinitol.
6.4. Conclusions
Recently, winery wastes have been classified as pollutants by the European
Union and post-product processing is required to lower their hazardous nature.
Individual fungal enzymes have limited capacity so mixed fungal degradation combined
with pre-treatment can decrease biomass recalcitrance for more efficient breakdown.
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
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The aim of this part of the thesis was the application of a statistical based
approach to obtain optimization for both submerged fermentation and SSF. Statistical
models were created from the results based on submerged fermentation and SSF
experiments. The models were then used to predict the optimized mixture of fungi to be
used as well as the ratio of medium to substrate. The resultant optimised conditions
were then tested and compared across submerged fermentation and SSF against the
most successful single organism result for each case.
Winery biomass degradation by a mixture of Trichoderma harzianum,
Aspergillus niger, Penicillium chrysogenum and P. citrinum in submerged fermentation
and SSF was evaluated. Higher cellulase and β-glucosidase activities were observed in
SSF and submerged fermentation, respectively. Statistical modelling predicted the
fungal percentage ratio of 60:14:4:2 for A. niger: P. chrysogenum: T. harzianum: P
citrinum with a substrate: medium ratio of 0.39:1.
Under the optimized conditions, cellulase, xylanase and β-glucosidase activities
increased to 78.5, 3544.7 and 250.9 U/mL, respectively. Cellulase and xylanase activity
both increased more than two-fold. Lignin degradation increased to 17.9% with respect
to submerged fermentation (P. chrysogenum) under optimized conditions within 5 days.
GC-MS analysis identified 78 significant metabolites, of which stigmasterol, glycerol,
maleic acid, xylitol and citric acid were the most significant generated by fungal
degradation
Enhanced degradation of winery-derived biomass was achieved using mixed
fungal cultures and GC-MS analysis indicated the production of commercially
important metabolites during the process.
6.5. Summary
A series of experiments were performed to obtain an optimized protocol for
degrading winery biomass waste. Both submerged fermentation and SSF processes were
assessed using the fungi T. harzianum, A. niger, P. chrysogenum and P. citrinum.
Following degradation, the parameters of cellulase, xylanase and β-glucosidase
activities, carbon/nitrogen content and lignin content were determined. A full factorial
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
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design to predict the optimal ratio of fungi and substrate to medium ratios was
employed to demonstrate the effectiveness of a mixed fungi culture.
Enzyme activities and lignin degradation were observed to be considerably
enhanced under these optimized conditions, illustrating the significance of a multi-
factorial design to mixed culture degradation. In addition to noticeable increases in
cellulase and β-glucosidase activities, a greater than two fold increase was seen in
xylanase activity during the mixed fungal fermentation within 5 days as against 7 days
in submerged fermentation and SSF. In addition, several metabolites of industrial and
medicinal interest such as alcohols, acids and monosaccharides were observed to be
produced during the mixed fermentation process.
Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes
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CHAPTER 7
Degradation of hydrothermally pre-
treated winery biomass by a co-culture of
basidiomycetes and ascomycetes
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
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7.1. Introduction
Grapes are one of Australia’s major agricultural products although the waste
material (lignocellulosic biomass and water) generated by wineries is an environmental
concern. It has been reported that a symbiotic consortium of lignocellulose degrading
micro-organisms effectively degrades such biomass (Chu et al., 2011), and is
considerably more efficient than monoculture fungal degradation and lignin related
metabolite degradation (Gou et al., 2009). However, there have been few reports of
mixed or sequential fermentation of biomass using different phyla of fungi. Numerous
fungi, especially those belonging to division Basidiomycota, are known for their lignin
degrading abilities (Baldrian and Valášková, 2008). Basidiomycetes are categorized as
either ‘white-rot’ fungi or ‘brown-rot’ fungi, depending on the nature of their lignin
degradation (white-rot fungi being more effective than brown-rot fungi). White-rot
fungi include Phanerochaete spp., Trametes spp. and Ganoderma spp., while brown-rot
fungi include Fomitopsis spp. and Postia spp.
Fungi belonging to the division Ascomycota, such as Trichoderma spp.,
Aspergillus spp. and Penicillium spp., are known for their biomass degrading ability and
should prove useful in grape biomass degradation. The fungi have been well studied for
their ability to produce high levels of cellulase and hemicellulase degrading enzymes
(Brink and Vries, 2011, Klyosov, 1987b). Basidiomycetes form the major wood rotting
fungi and can degrade the cellulose, hemicellulose and lignin component of the biomass
due to the large array of enzymes they produce (Grinhut et al., 2011).
7.2. Overview
As mentioned above, utilizing a mixed fungal culture to degrade biomass
produces a higher degradation compared to fungal monocultures. Indeed, the work
described in this thesis has shown that a mixed ascomycete fungal culture was more
effective than the respective monocultures. This chapter involves the study of biomass
degradation achieved by a mixture of a white-rot fungus, Phanerochaete chrysosporium
and the optimised mix of ascomycetes described in the previous chapter. It is
hypothesized that the combination of a wood-rot basidiomycete and the mixture of
ascomycetes would produce better lignocellulose degradation than the ascomycetes
alone.
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
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7.3. Results and Discussions
Grape samples were added to AATCC minimal iron medium in an Infors HT 3.6
L bioreactor (Infors AG, Bottmingen, Switzerland) and autoclaved using a ‘gentle’
cycle (115°C). The substrate and medium ratio was maintained at 0.39:1, as determined
by the statistical modelling in the preceding chapter. After cooling to room temperature,
this medium was oxygenated overnight. A spore suspension from a 120-hour culture of
Ph. chrysosporium (about 1× 107 spores/mL) was added to the bioreactor for grape
biomass degradation. This degradation was allowed to proceed for 8 days after which a
mixture of A. niger: P. chrysogenum: T. harzianum: P. citrinum (percentage ratio of
60:14:4:2) was added and the degradation allowed to proceed for a further 8 days.
Degradation was performed at 30°C and 200 rpm for the entire experiment. Samples
were taken every 4 days and analysed for lignin content and enzyme activity, namely
cellulase, β-glucosidase, xylanase, laccase, lignin peroxidase and tyrosinase. Similarly,
the samples were analysed by GC-MS in order to quantify the degradation products
generated at the end of each stage of degradation.
7.3.1. Lignin degradation
As discussed earlier, lignins form a considerable part of grape biomass. The
winery waste grapes obtained for experimentation here consisted of a just over 36%
lignin (35.4 % AIL + 0.7% ASL). It was also observed that a major part of this lignin
was not degraded during autoclaving. However, considerable amounts of sugars were
released into the medium due to the degradation of cellulosic and hemicellulosic
components, forming monosaccharides and oligosaccharides. Autoclaving was observed
to dramatically increase the total lignin content to about 63.7%, indicative that other
ingredients are degraded leaving a high percentage content of lignin behind. This point,
prior to addition of degradation agents, is referred to as the control.
Lignin degradation was enhanced during the co-culture of Ph. chrysosporium
and ascomycetes (Figure 7.1) and, interestingly, that lignin degradation tapered off
slowly and gradually by the end of experimental period. At the end of day four (D4), the
total lignin content in the sample dropped to 59.4%. This content then dropped further
to 54.7%, 48.2% and 41.2% after D8, D12 and D16, respectively (Figure 7.1).
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
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Figure 7.1. Total lignin content and lignin degradation over 16 days by the co-culture of wood-rot
fungus, Ph. chrysosporium, and the optimized Ascomycota mix.
Although the duration of this lignin degradation experiment was twice that of
the mixed ascomycete degradation described in Chapter 6, overall degradation was
considerably higher than the former process. One of the issues related to the mixed
ascomycota degradation, as will be discussed in the next chapter, was the autolytic
property of P. chrysogenum (and possibly of P. citrinum) towards the nutritional
deficiency period. Autophagy is quite common in filamentous fungi, especially during
the carbon depletion period of their growth. It is utilized by these fungi to recycle the
nutrient resources for their survival. Chitinases are a group of enzymes responsible for
the onset of autophagy in P. chrysogenum. Although, the enzymes are produced by P.
chrysogenum along with chitin synthase enzymes, their activity increases considerably
towards the carbon starvation period, i.e. from the 4th day of incubation (Sámi et al.,
2001). These enzyme activities have been shown to decrease the hypal concentration in
the tested samples (Sámi et al., 2001), indicating P. chrysogenum autolysis, primarily
after D5 of incubation. The application of Ph. chrysosporium may have enabled the
entire system to counter this deficiency by initiating lignin degradation, thus causing a
release of considerable amounts of free sugar into the medium to be later utilized by
ascomycetes during their early growth phase.
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7.3.2. Cellulase activity
Cellulases are enzyme complexes with moderately variable enzyme constituents.
Three enzyme classes, endoglucanases, exoglucanases and β-glucosidases, are most
common and are found in almost every lignocellulose degrading fungus. Aerobic
ascomycetes, such as T. harzianum and Aspergillus sp., have cellulases composed of
endoglucanases and exoglucanases, reportedly of varied individual enzyme composition
depending on substrate availability and specificity (Kovacs et al., 2009, Liu et al., 2012,
Sipos et al., 2010). Numerous experiments, including those reported in the previous
chapters of this thesis, have investigated cellulase activities of these fungi under
different conditions with variable carbon sources. There have been fewer reports,
however, for Ph. chrysosporium cellulase activity although, it is known to have
lignocellulolytic activity (Dashtban et al., 2010). Previous reports have indicated lower
cellulase activities of up to 1 U/mL on cotton stalks (Shi et al., 2008), CMC and avicel
(Uzcategui et al., 1991). Contrasting results were observed under the current
experimental conditions. Although, the overall cellulase activities were not as high as
the mixed ascomycete experiment, they were consistently higher than most of the
monoculture fungal cultures either in submerged or SSF conditions. As expected,
cellulase activities dropped during the latter phase of the Ph. chrysosporium degradation
period (i.e. D8) and early phase of ascomycete mix inoculation, i.e. by D12. The
cellulase activities displayed a considerable increase towards the end of degradation
period, possibly due to an increased production of these enzymes by ascomycetes.
Additionally, these fungi also possibly decreased the overall cellulase inhibition by
utilizing the sugars. Cellulase activity was about 82.5 U/mL on D4, while it dropped to
64.9 U/mL by D12. However, by D16, it again increased to 75.7 U/mL (Figure 7.2).
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
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Figure 7.2. Cellulase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.
chrysosporium, and the optimized Ascomycota mix.
It has been noted that the carbon and nitrogen ratio is an important factor which
decides the efficiency of biomass degradation by microbes, especially fungi (Mäkelä et
al., 2013). It has been found that the higher the nitrogen content in the substrate, the
greater the degradation ability of fungi (Brijwani et al., 2010). In addition, the higher
the nitrogen content, especially from organic sources (proteins, for example), the greater
the overall enzyme activities (Kim et al., 2012a). As observed during the previous
chapter (Chapter 6, Section 6.3.1), soluble proteins comprised 2.2% of the Shiraz grape
biomass, which may have induced an exceptionally high cellulase activity of Ph.
chrysosporium during the early phase of biodegradation. The results obtained in these
experiments contrasted to the observations reported in a previous study by (Shi et al.,
2008) where a prolonged pre-treatment for more than 15 days resulted in lower
enzymatic activities during subsequent biomass degradation. One of the other reasons
for the absence (or at least reduction) of enzyme inhibition could be the presence of the
enzyme cellobiose dehydrogenase (CDH). This enzyme is an oxidoreductase and is
generally categorised as a hemofavoenzyme (due to presence of both heme and flavin
domains). CDH specifically binds to cellulose and oxidises cellobiose as its substrate to
convert it to either glucose or other monosaccharide lactones (Bao et al., 1993). The
presence of CDH may explain the increased cellulase activity at the end of the
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fermentation at D16, where the total cellulase activity increased from 64.8 U/ml to 75.7
U/mL.
7.3.3. β-glucosidase activity
β-glucosidase activity plays an important role in cellulase efficiency and overall
biomass degradation. β-glucosidases are generally produced by numerous cellulolytic
organisms (especially fungi) due to the competitive inhibition of endo- and exo-
glucanases by their end product of cellobiose. β-glucosidase hydrolyses the β-(1-4)
glycosidic bonds in cellobiose (the smallest functional unit of the cellulose molecule) to
glucose units which can be readily utilized in microbial biochemical reactions (Brink
and Vries, 2011, Kim et al., 2012a).
Autoclaving altered the composition of winery grape waste, with an overall
decrease in dry mass. This pre-treatment process has been reported previously to affect
the biomass composition due to changes in the molal activities of water (Papadimitriou
and Barton, 2007). Hydrothermal heating weakens the lignin crosslinks within cellulose
and hemicellulose, thus exposing them to physical, chemical and/or biological
degradation (Papadimitriou, 2010). Further, the hydrothermal process, as it occurs in an
autoclave, enhances the breakdown of hemicellulosic polymers to form their constituent
pentose and hexose sugars, in addition to other derived products such as galacturonic
and/or glucuronic acids. The highly heterogeneous composition of hemicelluloses
makes them more susceptible to physical and chemical reactions compared to cellulose.
The presence of considerable amounts of oligosaccharide residues resulting from
autoclaving possibly led to exceptionally high β-glucosidase activity during the early
phase of grape biomass degradation.
Secretion of CDH by Ph. chrysosporium results in a two way degradation of
cellulose (and ultimately the biomass) by separate pathways. Due to the complex nature
of CDH, it has an independent pathway for cellulose degradation. The other pathway
consists of cellulase activities followed by β-glucosidase based degradation, similar to
numerous other biomass degrading fungi (Tsukada et al., 2006b). Although, Ph.
chrysosporium is not one of the major β-glucosidase generating fungi, about 11 different
types of β-glucosidase enzyme types have been reported in this organism; belonging to
either glycoside hydrolase (GH) classes GH3 or GH5 (Martinez et al., 2004).
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
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During the current experiment, Ph. chrysosporium displayed higher β-
glucosidase activity than the individual fungi or the combined ascomycetes. However,
the activity of 316.5 U/mL at the first measured point, D4, dropped to 253 U/mL by D8
and 193.8 U/mL by D12 before increasing again at D16 to 445.5 U/mL. This decrease
during degradation is probably due to the overall inhibition of fungal activity caused by
increasing nutrient starvation (Figure 7.3).
Figure 7.3. β-glucosidase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.
chrysosporium, and the optimized Ascomycota mix.
Ph. chrysosporium generates both intra- and extra-cellular β-glucosidases (Smith
and Gold, 1979) and the overall activity varies according to nature of the substrate.
However, the β-glucosidase activity observed during first four days of the current
experiment was found to be similar to previous observations (Lymar et al., 1995). β-
glucosidase activity increased considerably to 445.5 U/mL at the end of biodegradation
period (D16), indicating the combined activities of β-glucosidases from Ph.
chrysosporium and ascomycetes. Ascomycetes such as A. niger and P. citrinum are
known to generate β-glucosidase enzyme with high enzyme activities under various
conditions. However, the activities observed during the current experiments were found
to be even higher than ones reported previously (e.g. on rice bran (Ng et al., 2010)). It is
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possible that the nature and composition of the substrate, i.e. grape biomass, might have
a positive effect on β-glucosidase activity with respect to the time of fermentation.
7.3.4. Xylanase activity
Xylanase activity is an important parameter to measure hemicellulose
degradation as a major part of hemicellulose is comprised of xylans. Xylanases are a
group of differential hemicellulases responsible for the degradation of various xylans by
hydrolysing their β-(1, 4) linked D-xylopyranoside units. Comprising up to 1% of total
fungal lignocellulolytic enzymes, xylanases may work symbiotically with other
hemicellulases and cellulases, thereby facilitating a consortium for degradation of
biomass substrates (Dashtban et al., 2009, Cantarel et al., 2009).
During the current experiments, noteworthy xylanase activity was observed
during Ph. chrysosporium based grape biomass degradation. During the early phase of
degradation, i.e., D4, a xylanase activity of 1626.4 U/mL was observed, considerably
higher than A. niger (a commercial producer of xylanase) (Sections 4.3.7 and 5.3.6).
Although, the activity dropped by D8 to 1493.9 U/mL, it was still higher than the
ascomycete xylanase activities mentioned above and in ‘cellulose + xylan’ based
substrate degradation (Hori et al., 2011). This was surprising considering the low levels
of xylanase secreted by Ph. chrysosporium in cellulose and wood supplemented growth
media (Sato et al., 2007). This activity increased significantly after the addition of the
ascomycetes mix at the end of D8. By D12, xylanase activity was observed at 6131.5
U/mL. Although, this activity dropped considerably by D16 to 4661 U/mL, it was
significantly higher than any xylanase activity measured in previous experiments
(Figure 7.4).
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
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Figure 7.4. Xylanase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.
chrysosporium, and the optimized Ascomycota mix.
Due to its wider spectrum of lignocellulose degradation, Ph. chrysosporium has
the ability to degrade the hemicellulosic component of its substrate. With respect to
other enzymes such as cellulases and β-glucosidases, xylanase and xylosidase activities
are reported to be considerably higher in Ph. chrysosporium. However, as with the other
enzymes, these activities also depend on the nature of substrate. For example, corn stalk
has been shown to enhance xylanase activities with respect to avicel or oat spelts
(Dobozi et al., 1992).
The observations noted in this experiment indicated enhancement of xylanase
activities, probably due to nature of substrate and symbiotic behaviour between fungal
hemicellulases (of Ph. chrysosporium and ascomycetes) and cellulases. Xylanases not
only increased the overall activities of hemicellulases and cellulases, but also prevented
an overall product inhibition of these enzymes. Carbohydrate Active Enzyme (CAZy)
classification lists xylanases in subclass GH5 (http://www.cazy.org/GH5.html), which is
one of the largest groups of glycoside hydrolase (GH) enzymes. This subclass consists
of glucanases, mannanases, glucosidases, xylanase, xyloglucanase and galactanase. Due
to close relatedness of these enzymes, both in genomic and proteomic nature, they have
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generally been reported to work in very close associations (Huy et al., Das et al., 2013,
Cantarel et al., 2009).
7.3.5. Ligninase activities
Lignases are the group of enzymes that hydrolyse the lignin component in
lignocellulose complex to smaller molecules. As discussed earlier, lignin is the second
most abundant carbon sink after cellulose. However, unlike cellulose, lignin is formed
of more than one type of molecule and, due to its complex structure, the normally
employed ascomycetes such as Aspergillus spp. and Trichoderma spp. do not have the
capacity to degrade lignin (Dashtban et al., 2010). However, numerous other fungi do -
especially those belonging to division Basidiomycota (Baldrian and Valášková, 2008).
Fungal lignases can be classified into the two broad categories of phenol
oxidases and heme peroxidases. The first category comprises enzymes such as laccases,
while the second category is comprised of enzymes such as lignin peroxidases.
7.3.5.1. Laccase activity
Laccases belong to the phenol oxidase group of lignases. They further belong to
the family of multimeric copper oxidases and catalyse single electron oxidation
processes in a large number of phenolic compounds. Laccases are complex, having
multiple catalytic centres, and this enables them to oxidise a large number of substrates
including catechol, hydroquinone and guaiacol (Dashtban et al., 2010)
During the current experiment, considerable laccase activity was observed
during the early phase of degradation, i.e. during the period of Ph. chrysosporium based
degradation. The initial laccase activities (D4) were low at about 24 U/mL. However, by
D8, the activities increased considerably to about 74 U/mL. After this time, the activities
dropped again to 34 U/mL by D12 and 18 U/mL by D16 (Figure 7.5).This result was
expected since the ascomycetes inoculated after D8 did not possess any laccase
activities, as has been observed during the previous studies (data not shown).
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
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Figure 7.5. Laccase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.
chrysosporium, and the optimized Ascomycota mix.
It has been shown that the process of adsorption not only decreases the
competitive and product inhibition but also increases the half-life of an enzyme complex
as compared to the free enzyme. A recent experiment (Wu et al., 2014) showed that
Trametes versicolor laccases retained their activities for longer when adsorbed on
humus and humic acid related minerals as compared to free laccase, which displayed a
sharp decrease in activity after just 4 days. However, during the first 8 days, laccase
activities were observed to increase in the current experiments which might indicate a
positive effect of grape biomass on this enzyme’s activity. One of the reasons for this
phenomenon might be the autoclaving pre-treatment process of the grape biomass.
Although the previous experiments indicated that autoclaving was not very efficient in
hydrolysing the lignin component, this process breaks or weakens numerous types of
lignin-lignin and lignin-cellulose bonds, thereby increasing lignin exposure
(Papadimitriou, 2010) and, consequently, fungal lignin degradation efficiency.
Generally, Ph. chrysosporium does not generate or exhibit insignificant amounts
of laccases during biomass degradation, although it possesses laccase encoding genes.
Substrates rich in nitrogen, such as malt or yeast extracts, have been reported to increase
the laccase production and activities (Srinivasan et al., 1995). Additionally, brewery and
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apple pomace waste has also been reported to induce laccase activities to exceptional
levels (>700 U/g) in the presence of inducers such as copper sulphate during Ph.
chrysosporium mediated degradation (Gassara et al., 2010). Similarly, under the current
experimental conditions, the high content of nitrogen in the grape biomass and the
considerable nitrogenous content of the basal medium (3g/L NH4NO3) induced and
later enhanced the laccase activity during early phase of degradation (up to D8).
7.3.5.2.Lignin peroxidase activity
Lignin peroxidases (LiP) are a group of isozymes which depolymerize the non-
phenolic lignins and β-O-4 non phenolic lignin compounds by H2O2 based oxidation. In
addition to these polymers, the LiPs are known to degrade numerous phenolic molecules
such as guaiacol, vanillin, catechol and syringic acid. These enzymes were first isolated
from Ph. chrysosporium in 1983 and have since been reported to occur widely in a
number of white-rot fungi.
During the current experiment, highest LiP activities were observed during first
8 days of biomass degradation. By D4, LiP activity was observed at 126.5 U/mL, which
increased to 147 U/mL after D8 (Figure 7.6). Higher enzyme activity was expected
during the early phase of grape biomass degradation. However, lignin peroxidase
generally has a pH optimum at the low pH of around 3.0 to 3.5 and this value can be
even lower depending on the substrates (Doyle et al., 1998). Similar to laccase, LiP
activities are also influenced by inducing entities. Oxygen and nitrogen act as positive
inducers of LiP activities causing marginal to considerable increase in LiP activities
(Belinky et al., 2003, Martı́nez, 2002). Similarly, veratryl alcohol is known to enhance
LiP activities in brewery and pomace to considerable levels after a prolonged incubation
of about 9 days; although, this activity was either absent or insignificant during the
earlier phase (Gassara et al., 2010). Due to the presence of oxygen and nitrogenous
compounds, the activities during the early phase in the current experiments were
comparable to previous observations around D8 (Gassara et al., 2010), but considerably
better in earlier and later periods.
As expected, due to the inability of ascomycetes to produce LiP, overall activity
decreased by D12 to 102.5 U/mL and further by D16 to 61.5 U/mL (Figure 7.6).
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
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Figure 7.6. Lignin peroxidase activity observed over 16 days by the co-culture of wood-rot fungus,
Ph. chrysosporium, and the optimized Ascomycota mix.
The drop of LiP activity from D12 to D16, although considerable, was surprising
since none of the previous studies had detected enzyme activity during long
fermentation periods (Gassara et al., 2010, Fujian et al., 2001).Possible explanations for
this may be the consistently high cellulase activities combined with considerably higher
levels of β-glucosidase activities during the later phase of grape biomass, which either
increased or maintained the levels of glucose and other sugars. Dosoretz et al. (1990)
has shown that an increasing glucose in Ph. chrysosporium medium minimises the loss
of LiP activities. Increasing concentrations of glucose in the medium resulted in
inhibition of proteases, which act as LiP repressors (Dosoretz et al., 1990). One of the
other possibilities may be the presence of soybean peroxidase in the degradation
medium. This enzyme, secreted by Ph. chrysosporium, is a catalytically similar enzyme
to LiP. However, the enzyme has high thermostability and longer half-life- as compared
to LiP, as it has been observed to maintain its stability long-term below 70°C and for up
to 2.5 hours at 85°C. (McEldoon and Dordick, 1996). Whether or not this enzyme is
produced, and what activity that would lead to during winery biomass waste
degradation by Ph. chrysosporium remains to be tested.
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7.3.6. Metabolic output of sequential fermentation
A complete metabolomic profile was obtained by GC-MS at regular intervals
(D4, D8, D12 and D16) to analyse the metabolic behaviour of the fungi utilized during
the biomass degradation process. The aim was to increase the understanding of temporal
fungal metabolic behaviour by assessing the levels of accumulation or utilization of
metabolites during the biomass degradation process. The mixed fungal degradation
product was analysed by GC-MS (5 replicates per sample) to yield a metabolic profile
of ca. 339 peak features, which is generally expected for a microbial metabolism
processes. The data generated by mass-spectral analysis were then exported to
Microsoft® Excel to manually normalize peak areas with respect to the internal standard.
This data was then applied to SIMCA 13 for preliminary multivariate Principal
Component Analysis (PCA). The score scatter and DModX plots are shown in Figure
7.7 and Figure 7.8, respectively.
Figure 7.7. PCA model of winery biomass degradation by the co-culture of Ph. chrysosporium and
the optimized ascomycota mix. Note: Each point on the scatter plot refers to single sample, with R2X
(cumulative) = 87.9% and Q2 (cumulative) = 67.6%. Each point on the plot refers to single treated
sample. Note: the OPLS-DA plot eclipse represents the 95% confidence interval
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
163
Figure 7.8. ‘DModX’ or ‘Distance of observation’ plot of winery biomass degradation by the co-
culture of Ph. chrysosporium and the optimized ascomycota mix. The samples refer to Control (1-5),
Day 4 (6-10), Day 8 (11-15), Day 12 (16-20) and Day 16 (21-25).
7.3.6.1. Mass spectral analysis and PLS-DA
In order to interrogate the data further, the samples were processed using Partial
Least Square-Discriminant Analysis (PLS-DA). Although, PLS-DA improved the data
predictability to about 78.9% with respect to PCA, the metabolite segregation by PLS-
DA was unable to group the metabolites based solely on their discriminant levels
(Figure 7.9).
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
164
Figure 7.9. PLS-DA model of winery biomass degradation by the co-culture of Ph. chrysosporium
and the optimized ascomycota mix. Note: Each point on the scatter plot refers to single sample, with
R2X (cumulative) = 95.1%, R2Y (cumulative) = 94.1% and Q2 (cumulative) = 78.9%. Each point on
the plot refers to single treated sample. Note: the OPLS-DA plot eclipse represents the 95%
confidence interval
Orthogonal PLS-DA (OPLS-DA) was, thus, applied to yield better metabolite
discrimination, leading to better grouping and model predictability. Whereas PLS-DA
builds the model from a two-component system of systematic and residual variables,
OPLS-DA utilizes a three component system. In addition to residual variables, the
systematic variability component in OPLS-DA is derived from adding the correlated
variability (predictive) and non-correlated variability (orthogonal) between X-Y axis
components. Generally, due to the presence of single Y-axis component, OPLS
provides only one predictive component. However, multiple T-axes components are
represented as the orthogonal variations to generate a multiple predictive OPLS-DA
model (Trygg and Wold, 2002). For single response, X-axis variation is represented by
equations 7.1 and 7.2 below.
𝑋 = 𝑙𝑥′� + 𝑡𝑝′ + 𝑇0𝑃′0 + 𝑒 Eq. 7.1
and, Y-axis based prediction is represented by:
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
165
𝑌 = 𝑦′� + 𝑡𝑞′ + 𝑓 Eq. 7.2
Where, TP’ and TQ’ = matrix products
T = score vector
P’ and Q’ = loading vectors
e and f = residual variables
Of the 339 metabolites, 153 were identified as statistically significant by OPLS-
DA (Figure 7.10). Moreover, the change in metabolite concentration during
degradation was analysed by one way ANOVA using Fisher’s least significant
difference method (Fisher’s LSD) and Tukey’s Honestly Significant Difference
(Tukey’s HSD).
Figure 7.10A. OPLS-DA model of winery biomass degradation by the co-culture of Ph.
chrysosporium and the optimized ascomycota mix. Note: Each point on the scatter plot refers to
single sample, with R2X (cumulative) = 94.1%, R2Y (cumulative) = 91.8% and Q2 (cumulative) =
79.7%. Each point on the plot refers to single treated sample. Note: the OPLS-DA plot eclipse
represents the 95% confidence interval.
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
166
Figure 7.10B. Loading scatter plot of winery biomass degradation by the co-culture of Ph.
chrysosporium and the optimized ascomycota mix. Note: The black star labels on the loading scatter
plot refer to each fungal group; with each yellow circles referring to single metabolite feature.
The filtered sample data was applied to univariate differential analyses methods
of T-test, analysis of variance (Vranova et al., 2013). Although, the metabolic outputs of
all samples, i.e. D4, D8, D12 and D16, were taken into account, major metabolite
turnovers were observed after day 8 and day 16 of degradation. Based on the volcano
plot generated from these tests, the total number of generated metabolites by fungal
biomass degradation was discriminated from the degraded or utilized metabolites
(Figure 7.11). The filtered data are displayed as the magnitude of fold change (FC) with
respect to the peak areas between mixed fungal culture samples and control samples.
The FC cut-off values are set at 2-fold change in a metabolite noting that a single-fold
change of metabolite refers to 10 mg/L of 13C-sorbitol, which was used as an internal
standard. Due to considerable degradation of cellulose and hemicellulose, hexose and
pentose production were expected.
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
167
Figure 7.11. Volcano plots displaying significantly generated metabolites during winery biomass
degradation (D8 and D16) by the co-culture of Ph. chrysosporium and the optimized ascomycota
mix.
168
Table 7.1. The table represents most significantly generated metabolites by D8 (end of Ph. chrysosporium incubation) and D16 (end of overall degradation) of winery
biomass waste degradation process.
DAY 8 Metabolites KEGG
ID m/z RT FC p-value InChI code
Ribitol, D- (5TMS) C01068 513.052 22.09/19.16 7.0702 5.83E-07 SUZLPERYXSOGNY-ACDBMABISA-N
Tyrosine, DL- (3TMS) C00082 397.732 22.098 7.0702 5.83E-07 WMWBCQXPKSQMOK-UHFFFAOYSA-N
Compound_58 73.2 21.85 6.9319 1.03E-06 Mannitol, D- (6TMS) C00392 615.259 21.935 6.9319 1.03E-06 USBJDBWAPKNPCK-
MOUTVQLLSA-N Compound_60 21.657 6.8961 1.01E-06 alpha-DL-Lyxofuranoside, methyl 2,3,5-tris-O-(trimethylsilyl)-
C00476 380.699 18.2 6.6012 4.40E-07 WZJRJUAWWFXXSN-UHFFFAOYSA-N
Xylose, D- (1MEOX) (4TMS) C00181 467.895 18.09 6.6005 4.40E-07 ZBEJHGUYYNELJI-RCCFBDPRSA-N
D-Leucrose R000405 342.297 18.19 6.6001 4.40E-07 alpha-D-Ribofuranoside, methyl 2,3,5-tris-O-(trimethylsilyl)-
C00121 380.699 18.202 6.5941 4.45E-07 WZJRJUAWWFXXSN-UHFFFAOYSA-N
Galactonic acid (6TMS) C00880 629.242 22.772 6.2367 1.71E-06 IVIMVRAMMWQMHJ-WZYRSQIMSA-N
Octanedioic acid, bis(trimethylsilyl) ester C08278 318.556 18.791 4.9772 6.22E-06 LWDVKORSFQXPHL-UHFFFAOYSA-N
Glucose, D- (1MEOX) (5TMS) C00031 570.102 21.35 4.5298 0.011337 LLVFXXKDPAPSCJ-DXBBTUNJSA-N
9,12-Octadecadienoic acid (Z,Z)-, trimethylsilyl ester
C01595 352.626 25.526 4.298 1.28E-05 MXGBYOVWUYSJSN-UTJQPWESSA-N
Propanedioic acid, bis(trimethylsilyl) ester C00383 248.424 9.802 3.6293 0.00683 ATCKJLDGNXGLAO-UHFFFAOYSA-N
169
Table 7.1. The table represents most significantly generated metabolites by D8 (end of Ph. chrysosporium incubation) and D16 (end of overall degradation) of winery
biomass waste degradation process (…continued)
DAY 8 Metabolites KEGG ID m/z RT FC p-value InChI code (-)-Riboflavin C00255 376.365 25.679 3.4399 3.04E-06 AUNGANRZJHBGPY-
SCRDCRAPSA-N 1,3-Benzenedicarboxylic acid, bis(trimethylsilyl) ester
C14097 310.493 20.121 3.2662 0.006857 XBYWPSUAELKTET-UHFFFAOYSA-N
Inulotriose (impurity: 1-Kestose) R000842 504.438 25.746 3.1825 2.68E-05 Glucopyranose, D- (5TMS) C00031 541.062 18.89/22.55 2.9742 0.000683 PPFHNIVPOLWPCF-
AWGDKMGJSA-N alpha-D-Xylopyranose, 1,2,3,4-tetrakis-O-(trimethylsilyl)-
C00181 438.854 18.959 2.9733 0.000687 KEOUSSOURMHEKN-UHFFFAOYSA-N
Lysine, L- (4TMS) C00047 434.912 21.871 2.6924 0.015148 NAMWUGJQNPIVND-KRWDZBQOSA-N
Cadaverine tetratms C01672 390.902 21.78 2.5958 0.006888 VLLJOGZFMDUFGI-UHFFFAOYSA-N
Per(trimethylsilyl)-D-ribose C00121 380.699 17.487 2.5228 0.061273 WZJRJUAWWFXXSN-UHFFFAOYSA-N
L-Asparagine C00152 132.118 10.374 2.4911 0.037694 DCXYFEDJOCDNAF-REOHCLBHSA-N
Propanoic acid, 3-[(trimethylsilyl)oxy]-, trimethylsilyl ester
C00163 234.440 9.525 2.0314 0.091801 IHWRJZSKTAMEMR-UHFFFAOYSA-N
DAY 16 Galactonic acid (6TMS) C00880 629.242 22.772 23.647 1.15E-08 IVIMVRAMMWQMHJ-
WZYRSQIMSA-N Per-O-(trimethylsilyl)-alpha-D-galactofuranuronic acid
C00333 555.045 19.832 22.936 2.57E-10 SNQPUYIVUCUQEU-UHFFFAOYSA-N
2,3,4-Trihydroxybutyric acid tetraTMS C01620 424.828 19.838 22.936 2.57E-10 IEXXQBIANMZJJP-LSDHHAIUSA-N
170
Table 7.1. The table represents most significantly generated metabolites by D8 (end of Ph. chrysosporium incubation) and D16 (end of overall degradation) of winery
biomass waste degradation process (…continued)
DAY 16 Metabolites KEGG ID m/z RT FC p-value InChI code (-)-Perillyl alcohol C02452 152.234 19.896 22.936 2.57E-10 NDTYTMIUWGWIMO-
SNVBAGLBSA-N 1,3-Benzenedicarboxylic acid, bis(trimethylsilyl) ester
C14097 310.493 20.121 21.039 3.13E-10 XBYWPSUAELKTET-UHFFFAOYSA-N
Propanedioic acid, bis(trimethylsilyl) ester C00383 248.424 9.802 16.289 0.001619 ATCKJLDGNXGLAO-UHFFFAOYSA-N
Butanoic acid, 3-[(trimethylsilyl)oxy]-, trimethylsilyl ester
C01089 248.466 9.804 16.289 0.001619 YWRIHOYCAHATJN-UHFFFAOYSA-N
alpha-D-Ribofuranoside, methyl 2,3,5-tris-O-(trimethylsilyl)-
C00121 380.699 18.202 16.121 1.65E-09 WZJRJUAWWFXXSN-UHFFFAOYSA-N
alpha-DL-Lyxofuranoside, methyl 2,3,5-tris-O-(trimethylsilyl)-
C00476 380.699 18.2 15.988 1.75E-09 WZJRJUAWWFXXSN-UHFFFAOYSA-N
Xylose, D- (1MEOX) (4TMS) C00181 467.895 18.09 15.987 1.75E-09 ZBEJHGUYYNELJI-RCCFBDPRSA-N
D-Leucrose R000405 342.297 18.19 15.986 1.75E-09 Ribitol, D- (5TMS) C01068 513.052 22.09/19.16 13.296 5.42E-08 SUZLPERYXSOGNY-
ACDBMABISA-N Tyrosine, DL- (3TMS) C00082 397.732 22.098 13.296 5.42E-08 WMWBCQXPKSQMOK-
UHFFFAOYSA-N Octanedioic acid, bis(trimethylsilyl) ester C08278 318.556 18.791 12.962 5.60E-09 LWDVKORSFQXPHL-
UHFFFAOYSA-N Pentanedioic acid, 2-[(trimethylsilyl)oxy]-, bis(trimethylsilyl) ester
C02630 364.658 16.895 12.034 0.1097 GFDIGKRHNMDEJF-GFCCVEGCSA-N
1,4-Benzenedicarboxylic acid, bis(trimethylsilyl) ester
C06337 310.493 20.11 12.007 0.03366 XNDCJULFVFHXBR-UHFFFAOYSA-N
Compound_60 21.657 10.759 5.17E-08
171
Table 7.1. The table represents most significantly generated metabolites by D8 (end of Ph. chrysosporium incubation) and D16 (end of overall degradation) of winery
biomass waste degradation process (…continued)
DAY 16 Metabolites KEGG ID m/z RT FC p-value InChI code alpha-D-Glucose 1,6-bisphosphate potassium salt hydrate
C01231 379.214 10.61/20.96 9.8338 4.48E-07 OVRXVCAVDXBPQT-QMKHLHGBSA-J
Glucose, D- (1MEOX) (5TMS) C00031 570.102 21.35 8.8565 0.005113 LLVFXXKDPAPSCJ-DXBBTUNJSA-N
Propanoic acid, 3-[(trimethylsilyl)oxy]-, trimethylsilyl ester
C00163 234.440 9.525 7.8948 0.002846 IHWRJZSKTAMEMR-UHFFFAOYSA-N
3-Methyl-1,3-bis(trimethylsilyloxy)butane 248.509 9.509 7.8089 0.29133 FHZUQOQCMZAXQH-UHFFFAOYSA-N
D-Arabinose 4TMS C00216 467.896 18.522 7.8025 2.97E-07 ZBEJHGUYYNELJI-VWQWLJPISA-N
Glucose, 1,6-anhydro, beta-D- (3TMS) C00221 378.684 18.826 7.7984 2.96E-07 OEPKLYQQZLBNKR-HHHGZCDHSA-N
Per(trimethylsilyl)-D-ribose C00121 380.699 17.487 7.7868 0.006846 WZJRJUAWWFXXSN-UHFFFAOYSA-N
Glucopyranose, D- (5TMS) C00031 541.062 18.89/22.55 7.4222 6.72E-06 PPFHNIVPOLWPCF-AWGDKMGJSA-N
alpha-D-Galactose 1-phosphate dipotassium salt pentahydrate
C00446 317.250 18.415 7.3892 2.34E-07 ZYBRMLNCQMITLC-JFYWUTKTSA-L
Compound_57 443.6 21.695 7.1796 1.14E-06 Phenylalanine, DL- (2TMS) C00079 309.552 17.713 7.1789 1.61E-06 DWNFNBPPSFIIBG-
AWEZNQCLSA-N Vitamin E acetate C13202 472.744 17.270 6.1295 0.14582 ZAKOWWREFLAJOT-
UHFFFAOYSA-N alpha-D-Xylopyranose, 1,2,3,4-tetrakis-O-(trimethylsilyl)-
C00181 438.854 18.959 4.9366 2.09E-05 KEOUSSOURMHEKN-UHFFFAOYSA-N
Compound_84 216.6 24.468 4.7245 2.48E-05
172
Table 7.1. The table represents most significantly generated metabolites by D8 (end of Ph. chrysosporium incubation) and D16 (end of overall degradation) of winery
biomass waste degradation process (…continued)
DAY 16 Metabolites KEGG ID m/z RT FC p-value InChI code 1,3-Diphenyl-1-trimethylsilyloxy-1-pentene 310.505 23.197 4.5244 3.84E-05 SDLWASLJDGYSTN-
SILNSSARSA-N alpha-D-Glucose-1-phosphate, dipotassium salt dihydrate
C00103 276.179 18.197 3.3807 0.032003 JXLMWHHJPUWTEV-QMKHLHGBSA-L
Compound_74 147.2 24.462 3.218 0.000682 1H-Indole, 7-methyl-1-(trimethylsilyl)- C08313 203.355 10.384 2.9527 0.030054 FSBIHSWERZRMQS-
UHFFFAOYSA-N Creatinine (3TMS) C00791 329.662 10.371 2.9061 0.030415 XLSJWTRDAZBATG-
UHFFFAOYSA-N L-Asparagine C00152 132.118 10.374 2.9061 0.030415 DCXYFEDJOCDNAF-
REOHCLBHSA-N d-(+)-Xylose, tetrakis(trimethylsilyl) ether C00181 467.895 19.68 2.6816 0.0039 ZBEJHGUYYNELJI-
UHFFFAOYSA-N Compound_81 340.8 25.898 2.1797 0.086601
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
173
Figure 7.10 and Table 7.1 represent the metabolites generated after Day 8 and
Day 16 of winery biomass degradation. During the first 8 days of biodegradation, Ph.
chrysosporium degraded the overall lignocellulose complex to generate a wide variety
of products, as expected. These mainly consisted of sugars such as glucose, xylose,
lyxose and galactose, sugar alcohols, short chain fatty acids and sugar acids.
During this early phase, considerable numbers of lignin degradation products
were also observed, including octanedioic acid, 1, 3-benzenedicarboxylic acid and
cadaverine. These were released into the medium by Ph. chrysosporium within the first
4 days of initiating the biodegradation process. By Day 4, 160 mg/L of octanedioic acid
(generally referred to as suberic acid) was accumulated in the medium. However, by
Day 8 this accumulation dropped considerably to 32 mg/L indicating its further
degradation by the fungus. This fatty acid is known to be produced in plants for defence
against primary or secondary infections (Best et al., 2012) and has been linked with
humic and fulvic acids, which are the products of lignin components of plant biomass
(Xiaoli et al., 2008). The concentration, however, dropped considerably (12.3 mg/L) by
Day 16, due to fungal degradation first by Ph. chrysosporium and later by ascomycetes.
Similar to suberic acid, 1, 3-benzenedicarboxylic acid has been found in
association with humic and fulvic substances and has been observed in significant
quantities in humic substances and plant biomass pre-treatment processes (Yip et al.,
2009, Olivella et al., 2002). In the current experiments, this metabolite was observed to
be present in the grape biomass in very low quantities (23.2 mg/L), indicating that a
majority of it may be present in the bound form in the lignin complex and therefore not
detected. The subsequent autoclaving and Ph. chrysosporium based oxidation resulted
in very high concentrations of this metabolite during the early phase of biomass
degradation. Such high levels of 1, 3-benzenedicarboxylic acid have also been observed
during the pyrolysis of bamboo biomass (Yip et al., 2009). During the early phase of
Phanerochaete spp. based degradation, the concentration increased to about 2290 mg/L,
indicating considerable lignin degradation. Also, similar to suberic acid, the
concentration of 1, 3-benzenedicarboxylic acid dropped considerably to 108.8 mg/L by
Day 16, indicating its metabolism by Ph. chrysosporium and ascomycetes.
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
174
7.4. Conclusions
The winery biomass degradation process described in this chapter utilized a
basidiomycete fungus, Ph. chrysosporium, and a statistically optimized mix (percent
ratio of 60:14:4:2) of A. niger, P. chrysogenum, P. citrinum and T. harzianum as
determined during a previous experiment (Chapter 6). Pre-treatment of the biomass by
autoclaving was followed by Ph. chrysosporium degradation for 8 days and subsequent
co-culturing for a further 8 days with the ascomycete mix.
During the total biodegradation period of 16 days, a considerable amount of
lignin was degraded by enzyme mediated oxidation. At the end of the biodegradation,
about 22.5% lignin degradation was observed not only due to the breaking of numerous
lignocellulose complex linkages, but also from the ligninase activities of Ph.
chrysosporium followed by the lignin mineralization ability of the ascomycete fungi
mix.
Cellulase activities remained stable throughout the degradation period, while β-
glucosidase and xylanase activities increased significantly towards the later period of
degradation. The increment of these activities was due to the enhanced CDH enzyme,
which probably prevented product inhibition of cellulases and β-glucosidases. Very
high xylanase activities were recorded during the Ph. chrysosporium: Ascomycota mix.
These values were recorded as the highest for any experiment conducted in this thesis,
possibly due to the symbiotic nature of these enzymes with not only other
hemicellulases, but also with cellulases and glucosidases. Considerably higher laccase
activities were noticed during the earlier phase (up to Day 8) of winery biomass
degradation. This was peculiar since Ph. chrysosporium generally does not have
capability of laccase secretion. However, the high nitrogen content of the grape waste
and supplementation of nitrogenous sources in the media are likely to have induced
laccase activity in this fungus. Similarly, considerable lignin peroxidase (LiP) activities
were observed during first 8 days of biomass degradation. Surprisingly, LiP activity did
not decrease significantly after the commencement of co-culturing with ascomycetes.
This was unexpected since LiP has a very short half-life. One of the possibilities for this
stable LiP activity could be the presence of another similarly catalytic enzyme (such as
soybean peroxidase) with a longer half-life and possessing greater temperature
tolerance. Another reason for this might be repression of proteases caused by the
Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes
175
presence of considerable levels of glucose. This protease repression also might have
caused LiP stabilization for an extended period of biomass degradation.
Many metabolites arising from the initial degradation of lignin accumulated
during the early phase of biomass degradation due to a combination of hydrothermal
pre-treatment, and Ph. chrysosporium, degradation. However, this accumulation
decreased considerably during the later phase of degradation due to metabolism of
numerous fungal mediated biodegradation.
7.5. Summary
In summary, the rapid degradation of winery biomass waste was improved
considerably by application of hydrothermal pre-treatment and subsequent application
of a multi-fungal consortium. Due to the nature of this system, product and competitive
inhibition of lignocellulose degrading enzymes, especially cellulases and β-glucosidase,
was decreased considerably, leading to more efficient biomass degradation.
Composition of the biomass also played an important role as higher levels of nitrogen
and oxygen not only induced laccase activity, but also had roles in suppressing
inhibition of ligninase enzyme over an extended duration. Numerous metabolites were
generated during the biomass degradation process including sugars, sugar acids, sugar
alcohols and lignin degradation products. However, the biomass degradation could be
further improved by generating a more complex microbial system, particularly the
inclusion of a thermophilic bacterium (or fungus) to assist the current mixed fungal
system. Additionally, a wider spectrum of metabolites remains to be identified which
can be achieved by using a combination of GC-MS and LC-MS based analytical
approaches.
176
CHAPTER 8
Metabolic profiling of ascomycetes during SSF based winery biomass
degradation
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
177
8.1. Introduction
Fungi belonging to the division Ascomycota, such as Aspergillus spp. and
Penicillium spp., are known for their biomass degrading abilities. These fungi have been
well studied for their high cellulase and hemicellulase enzyme production (Brink and
Vries, 2011, Klyosov, 1987b). These enzymes have the potential to be used in
generating important molecules such as alcohols, flavonoids, organic acids and
phenolics (Arvanitoyannis et al., 2006, Sánchez, 2009, Strong and Burgess, 2008). It is
known that, apart from cellulase production, Aspergillus spp. generate xylanases and β-
glucosidases in exceptional quantities (Betini et al., 2009, Singhania, 2012).
Additionally, as observed in the previous chapters, P. chrysogenum possess a
considerable cellulolytic ability in addition to a limited lignolytic activity, which is
absent in all other ascomycetes used under this project.
The field of metabolomics has the potential to provide biochemical information
in order to understand and characterise the various mechanisms related to fungal
biomass degradation. Among numerous areas, metabolomics has been applied to
investigate bacterial processes related to preventative health (Bi et al., 2013,
Marcinowska et al., 2011), environmental pollution (Beale et al., 2013a), food (Beale et
al., 2014) and fungal metabolism on various substrates such as benzoic acid and
Chardonnay grape berries, respectively (Hong et al., 2012, Matsuzaki et al., 2008).
However, within the context of fungal-mediated biomass degradation, its application
has been rather limited. Additionally, the analysis of the metabolic flux has the capacity
to enable a greater understanding of the nature, time dependence and substrate-based
limitations in regard to the metabolism of fungal cells. The process also assists in
understanding of correlation between cell phenotypes and their metabolic patterns and
stoichiometry (Meijer et al., 2009). Metabolic flux studies have previously been applied
in toxicology and medicine (Maier et al., 2009, Niklas et al., 2010), plant proteome
(Nelson et al., 2014), bacterial and fungal respiratory systems (Driouch et al., 2012,
Pedersen et al., 1999).
Numerous methods are available for metabolic flux analysis. The majority of
these involve the use of heavy isotopes due to their considerably superior tracking
abilities. In commonly utilized analyses, such as nuclear magnetic resonance (NMR)
and gas chromatography-mass spectrometry (GC-MS), metabolite tracking is performed
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
178
using isotopes such as 1H, 2H, 13C, 14N, 15N and 18O. While every isotope has its
advantages and shortcomings, 13C-molecules have been used by most researchers
(Nelson et al., 2014). However, 2H is a much more abundant element in nature,
comprising about half of the peptide atomic population (Yang et al., 2010b). Besides, it
is much more economical as compared to other isotopes, has a comparatively higher
invasive rate and a slower decay rate (Kim et al., 2012b). However, complete 2H
labelling is not feasible due to the limited tolerance of multicellular organisms, thereby
resulting in partial labelling, generally from 8% (Kim et al., 2012b) to 30% (Yang et al.,
2010b).
8.2. Overview
The current study described herein studied the metabolic flux of the periodic
metabolic change during the degradation of winery-derived biomass waste by A. niger
and P. chrysogenum. The study utilized 2H-flux due to its rapid incorporation in fungal
metabolism as compared to 13C. The flux experiment was designed to differentiate the
metabolites derived from the substrate and those generated by the fungi. This process
also aided in differentiating and quantifying significant metabolites generated by fungi
at different intervals, thereby, optimizing a particular process in order to maximize the
generation of specific products or generate numerous products in significant quantities
in a single bioconversion process setup.
8.3. Results and discussions
8.3.1. A. niger metabolic profile
A number of studies related to the degradation of various substrates by
Aspergillus spp. have been reported. However, the study of the degradation mechanism
of these fungi on winery biomass wastes has not been reported. The current section,
thus, contributes towards the understanding of metabolic behaviour over time and the
levels of accumulation or utilization of metabolites during the biomass degradation
process. The data generated by mass-spectral analysis was then exported to Microsoft®
Excel to manually normalize obtained peak areas with respect to the internal standard.
This data was then applied to ‘SIMCA 13’ based preliminary multivariate analysis of
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
179
Principal Component Analysis (PCA), represented in the score scatter (Figure 8.1) and
DModX (Figure 8.2) plots.
Figure 8.1. Principal Component Analysis of Aspergillus niger flux over 8 days during winery
biomass degradation. Note: Each point on the scatter plot refers to single sample, with R2X
(cumulative) = 90.3% and Q2 (cumulative) = 73.5%. Samples 1-27 (green circles) refer to D0 to D8
of deuterated samples, while samples 31-57 (yellow triangles) to D0 to D8 of control samples (all
triplicates). Note: the PCA plot eclipse represents the 95% confidence interval
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
180
Figure 8.2. ‘DModX’ or ‘Distance of observation’ plot of Aspergillus niger flux over 8 days of
winery biomass degradation. Note: Samples 1-27 refer to D0 to D8 of deuterated samples, while
samples 31-57 refer to D0 to D8 of control samples (all triplicates)
8.3.2. Mass spectral analysis and PLS-DA
The mass spectrometry analysis yielded ca. 640 peaks in growth media
supplemented with H2O and 30% D2O. The analysis was performed by PLS-DA
(Figures 8.3 and 8.4), which enabled easy discrimination between samples and the
metabolite features, as discussed previously (Section 6.3.8) and elsewhere(Wold et al.,
2001).
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
181
Figure 8.3. Partial Least Square- Discriminant Analysis derived score scatter plot of Aspergillus
niger flux over 8 days during winery biomass degradation. Note: Each point on the scatter plot
refers to single sample, with R2X (cumulative) = 69.3%, R2Y (cumulative) = 93.1% and Q2
(cumulative) = 86.8%. Samples 1-27 (green circles) refer to D0 to D8 of deuterated samples, while
samples 31-57 (yellow triangles) refer to D0 to D8 of control samples. Note: the PLS-DA plot eclipse
represents the 95% confidence interval.
The data obtained from SIMCA 13 required analyses for further discrimination
of the unique metabolites from a large group to more accurate levels. MetaboAnalyst
2.0 was, thus utilized to further differentiate and classify the most significant
metabolites in D2O and non-D2O based media. Metaboanalyst 2.0 employs a pre-
processing data filtering and normalization before its application to differential
expression analyses and two-group or multi-group analysis (Xia et al., 2012).
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
182
Figure 8.4. Partial Least Square- Discriminant Analysis derived loading scatter plot of Aspergillus
niger flux over 8 days during winery biomass degradation. Note: Five pointed star labels denote the
days of analysis (D2O day 0 to D2O day 8 and AATCC day 0 to AATCC day 8). Green circles
represent all the metabolites under consideration and their orientation. The metabolites in the vicinity
of five pointed star labels represent most significant metabolites.
The filtered sample data was applied to univariate differential analyses methods
of T-test, analysis of variance (Vranova et al.). Based on volcano plot model generated
from these tests, the total number of differentially expressed metabolites in D2O was
reduced to 37 (Figure 8.5; Appendix 3, Table 1).
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.5. Volcano plot displaying the differential expressing metabolites in deuterated and non-deuterated media. Note: The significant metabolites expressed in deuterated medium (black triangles) were taken into consideration for further metabolic flux analyses.
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.6. Metabolic pathways for A. niger mediated SSF degradation of winery biomass waste over 8 days. Note: The following table illustrates all the metabolite numbers in this pathway.
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
186
8.3.3. Metabolic flux analysis of A. niger during winery biomass degradation
The identified metabolites were then crossed-checked against the KEGG
database of metabolic pathways (http://www.kegg.jp/kegg/pathway.html). The data
obtained was then applied to MATLAB 2014a statistical software using Covariance-
Inverse (COVAIN) script (Doerfler et al., 2014). The resultant data were applied as a
correlation between the different metabolites in a time series analysis. Glycolysis
leading to the tricarboxylic acid (TCA) cycle was considered as the basic skeletal
pathway with respect to any other upstream or downstream metabolic pathways. The
major junctions were marked at glucose, glyceraldehyde-3-phosphate, acetyl-coenzyme
A (acetyl-CoA) and succinate, where an increase or decrease in other metabolites was
considered with respect to these metabolites. Overall, including glycolysis-TCA, 18
different pathways were projected on the basis of biosynthesis or degradation of
metabolites during the 8 day fermentation (Figure 8.6)
8.3.3.1. Glucose junction
Three pathways led in or out of glucose or glucose-6-phosphate. These included
galactose metabolism, N-acetyl glucosamine synthesis towards the biosynthesis of
chitins and sucrose / psicose degradation leading towards the glycolysis pathway. Major
degradation of L-sorbose leading to glucose was observed over 8 days of biomass
degradation. A considerable amount of this degradation was observed in samples D2 to
D5, when the sorbose concentration dropped from 564 mg/L to 5.9 mg/mL. During the
same period, the glucose concentration expectedly dropped from 66 mg/L to 1.8 mg/L.
However, the glucose concentration increased to 56 mg/L in D7, probably reflecting
sorboseglucose conversion (Figure 8.7).
The occurrence of N-acetyl glucosamine (NAG) is insignificant in grape
biomass. However, it forms a considerable proportion of fungal cell wall (Bernard and
Latgé, 2001). As expected, a constant increase of NAG from 5.4 mg/L in D0 to 83.5
mg/L in D7 was observed, indicating a constant increase in fungal biomass during the
biodegradation process (Figure 8.7). All the soluble sugars, such as glucose, galactose
and melibiose, showed uniform increments in accumulation from D5 onwards, probably
marking a late cellulase/β-glucosidase inhibition.
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.7. Changes in the composition of metabolites leading to or from the ‘glucose/glucose-6-
phosphate’ junction in the glycolysis pathway during A. niger mediated grape waste biomass
degradation.
8.3.3.2. Glyceraldehyde-3-phosphate junction
It was observed that a total of five pathways of biosynthesis or degradation
merged into the glycolysis pathway at the glyceraldehyde-3-phosphate junction. A
majority of these consisted of pentose phosphate pathways, related to the metabolism of
D-ribulose, L-arabinose, ribose and pectins. One pathway belonged to L-tyrosine
metabolism.
In the D-ribulose metabolism pathway, it was observed that the concentration of
glyceraldehyde-3-phosphate was inversely proportional to the sugar concentration over
the entire degradation period. Glyceraldehyde-3-phosphate accumulation reduced from
112 mg/L to 4.3 mg/L, barring D5, when it unexpectedly spiked to 95.5 mg/L. D-
ribulose displayed a constant increase from 5.4 mg/L to 83.5 mg/L during this time.
Sedoheptulose-7-phosphate remained more or less constant except in D5, when it
dropped to 6.3 mg/L (Figure 8.8).
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.8. Changes in the features of the D-ribulose metabolism path of the pentose phosphate
pathway
In the second part of pentose phosphate pathway, L-arabinose metabolism, all
the metabolites except glyceraldehyde-3-phosphate displayed accumulation. Especially
interesting were the sudden spikes in L-arabinose content from 12.2 mg/L in D4 to 84
mg/L in D5 and decrease in xylitol concentration from 37.4 mg/L in D4 to 15.2 mg/L in
D5 (Figure 8.9).
Ribose metabolism, in addition to gulonate and gluconate metabolism, also
displayed similarities to arabinose metabolism. However, from D4 onwards, the
concentration of both ribose and 5-phospho-alpha-D-ribose-1-diphosphate (PRPP)
displayed a direct proportionality to glyceraldehyde-3-phosphate (Figures 8.10 and
8.11). Molecules such as arabinose, arabitol, gulonates, gluconates and other pentose
acids and alcohols are primary degradation products of hemicellulose components of
biomass, such as xylans and pectins (Gírio et al., 2010). From the observed metabolic
behaviour of these degradation products over the fermentation time, it could be easily
interpreted that treatment with A. niger for 4-5 days would not only make a majority of
these components available to a further independent bioconversion, but also will
prevent the product inhibition of xylan degrading enzymes, thereby, further improving
the biomass degradation rate (Duarte et al., 2012b).
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Ribulose pathway (days)
Glyceraldehyde-3-Phosphate D- RibuloseD-Erythrose-4-Phosphate Sedoheptulose-7-Phosphate
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.9. Changes in the features of the D-ribulose metabolism path of pentose phosphate pathway.
Figure 8.10. Changes in the features of the D-ribose metabolism path of pentose phosphate pathway
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Pentose phosphate pathway (days) Glyceraldehyde-3-Phosphate L-Arabinose Xylitol Xylonate Gulconate
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.11. Changes in the features of gluconate metabolism path of pentose phosphate pathway
One of the other metabolic paths leading from glyceraldehyde-3-phosphate via
phosphoenol pyruvate was the shikimate pathway. Shikimate (or shikimic acid) and 3,
5-Diiodo-L-tyrosine were the only metabolites of this pathway which were observed
during our experiments. The shikimate pathway is one of the most important pathways
in plants as shikimate is the only source of benzene ring structures to synthesize further
complex biomolecules. Therefore, shikimate is the source of aromatic amino acids,
alkaloids, terpenoids, coumarins, stilbenes, tannins and lignins. Many of these
molecules serve in structural, defence or growth induction mechanisms in plants
(Bochkov et al., 2012). In addition, shikimate is used for the commercial production of
Oseltamivir phosphate (Tamiflu®), which is used to treat influenza H1N1 and H5N1
infections (Cortés-Tolalpa et al., 2014). In the context of this experiment, the shikimate
pathway was considered necessary to evaluate the lignin degradation potential of A.
niger. It was observed that the shikimate concentration decreased rapidly from 548
mg/L to 8.8 mg/L over the first four days of biomass degradation. After this, it
continued to decrease further over the next four days and reached 4.6 mg/L in D7. This
seemed more or less proportional to glyceraldehyde-3-phosphate during the first four
days. A minor part of the shikimate decrease may be explained in the increment of 3, 5-
diiodo-L-tyrosine from 0.5 mg/L to 3 mg/L over 8 days (Figure 8.12).
0
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cent
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Glyceraldehyde-3-Phosphate D-Glucuronate 2-Keto-D-gluconate
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.12. Changes in the features of significant shikimate pathway metabolites.
Figure 8.13. Changes in the significant metabolite features of significant fucose degradation
pathway.
This molecule has been reported as an intermediate metabolite in the formation
of fungal melanins, such as DOPA melanin, γ-glutaminyl-3,4-dihydroxybenzene
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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(GDHB) melanin and catechol melanin (Bell and Wheeler, 1986). Due to the increase in
fungal mass, as evident from an increase in NAG (Figure 8.7), the accumulation of 3,
5-diiodo-L-tyrosine was expected. However, due to the status of shikimate as a
precursor to virtually all molecules with a benzene ring, it could be possible that during
the degradation process, A. niger converted shikimate into a large number of secondary
metabolites, which appeared as statistically insignificant during the metabolic pathway
analysis.
Fucose was observed to decrease during the overall fermentation period. The
initial concentration of 548.6 mg/L decreased rapidly to 7.9 mg/L within four days of
fermentation, remaining more or less linear thereafter (Figure 8.13). The outcome was
expected as this sugar is not only one of the chief precursors of nucleotide sugars, but
also is an active metabolite in fructose and mannose metabolism leading to glycolysis.
Due to this role in metabolic pathways, it forms a considerable part of fruit cell walls,
enabling them to be resistant towards numerous microbial degrading enzymes (Vincken
et al., 1996).
8.3.3.3. Acetyl-CoA junction
Acetyl-CoA forms one of the most important junction points towards the
beginning of the TCA cycle. It acts as an intermediate between pyruvate, the end
product of glycolysis and citrate, the starting point of the TCA cycle. It is estimated that
38 pathways directly or indirectly merge in or lead from TCA/glycolysis at the acetyl-
CoA junction (KEGG, 2014). In the current experimental analyses, seven metabolic
pathways either merged at the acetyl-CoA junction via pyruvate or led from the TCA
cycle with acetyl-CoA as the precursor metabolite. These included isoleucine
biosynthesis, butanoate metabolism, xylene degradation, fatty acid biosynthesis, valine
biosynthesis and secondary metabolite biosynthesis.
In recent years, it has been observed in bacteria such as Geobacter
sulfurreducens and Methanocaldococcus jannaschii that a major proportion of
isoleucine biosynthesis flux is attributed to the citramalate pathway. The enzyme,
citramalate synthase, condenses pyruvate and acetyl-CoA to form R-citramalate (or 2-
methyl malate), which under the subsequent enzyme is converted to L-isoleucine (Risso
et al., 2008, Drevland et al., 2007). During our flux analysis based on 2H2O labelled
metabolites, it was observed that the acetyl-CoA concentration dropped rapidly from
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
193
143.3 mg/L in D0 to 31.6 mg/L in D3. Barring an increment in D4, this concentration
further decreased to 9.8 mg/L in D7. Under the same conditions, citramalate
concentrations steadily increased from 8 mg/L to 11.9 mg/L in 7 days (Figure 8.14). To
our knowledge, this study shows the first reported occurrence of isoleucine biosynthesis
from pyruvate and acetyl-CoA condensation. However, further experiments are
important to confirm the level of importance of R-citramalate during isoleucine
biosynthesis in fungi, such as A. niger. The other metabolites observed which belonged
to the valine, leucine and isoleucine biosynthesis pathway were 2-ketovaline/ 2-oxo
isovalerate and 4-methyl-2-oxopentanoate/ 2-oxoisocaproate. They serve as the
precursors for L-valine and L-leucine synthesis, respectively. It was observed that,
barring D5, the concentration of this intermediate increased from 78.6 mg/L to 99.3
mg/L. However, the concentration of 4-methyl-2-oxopentanoate remained more or less
constant at 0.3 mg/L during the entire fermentation process (Figure 8.14). It has been
suggested that the elimination of acetolactate group enzymes such as acetohydroxy acid
synthase and application of 2-keto-acid decarboxylases (KDCs) followed by alcohol
dehydrogenases (Xia et al.) during the generation of 2-ketovaline in E. coli eliminates
downstream amino acid biosynthesis and alters the pathway towards 1-butanol, 2-
butanol and 3-methyl-butanol production, some of the major commercial fuel molecules
(Atsumi et al., 2008, Si et al., 2014).
One of the metabolites related to butanoate metabolism, R-3-hydroxy butanoate,
is among the most abundant R-3-hydroxy alkanoate polymers (PHBs). It is an important
ingredient for microbial spore formation. However, the industrial importance of these
metabolites lies in their use as biodegradable plastics. The metabolite is generated from
a double condensation of acetyl-CoA to first form acetoacetyl-CoA, followed by
acetoacetate and R-3-hydroxy butanoate, which is catalysed by the enzyme PHB
synthase (Thakor et al., 2006). In the current studies, it was observed that the
concentration of acetyl-CoA was inversely proportional to R-3-hydroxy butanoate
during the initial phase of biomass degradation. The concentration of acetyl-CoA
dropped by about 42 mg/L, which was also reflected in an increase of 40 mg/L of R-3-
hydroxy butanoate over the initial four days of fermentation. However, after this period,
probably due to inhibition of cellulolytic enzymes leading to decreasing levels of sugars
in the culture, A. niger may have utilized the polymer for nutritional or spore forming
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
194
purposes. This reflected the reduction of R-3-hydroxy butanoate levels by about 39
mg/L from D5 to D8 (Figure 8.15).
Figure 8. 14. Changes in the features of acetyl-CoA and most significantly observed pathway
intermediates of valine, leucine and isoleucine biosynthesis in A. niger.
Figure 8.15. Changes in the features of acetyl-CoA and R-3-hydroxy butanoate, metabolites leading
to BHP biosynthesis in A. niger.
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Amino acid biosynthesis (days) Acetyl-CoA (R)-Citramalate 2-Ketovaline 4-methyl-2-oxopentanoate
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Acetyl-CoA R-3-Hydroxy butanoate
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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In the fatty acid metabolism pathways, hexadecanoate was observed to degrade
marginally over the eight days of fermentation from 9.6 mg/L to 6.4 mg/L (Figure 4C).
It is reported that grapes, especially the grape seeds, store considerable amounts of
unsaturated fatty acids. Hexadecanoate, or palmitic acid, forms about 6-13% of total oil
content of grape biomass (Sabir et al., 2012). Medium chain fatty acids (MCFAs) are
produced by fungal cells during the fermentation process. However, after a period of 3-
4 days post-fermentation, the MCFAs drop the internal cell pH of fungi, thereby
inhibiting further growth and resultant fermentation (Edwards et al., 1990, Legras et al.,
2010). During our experiment, dodecanoate was observed to increase from 78 mg/L in
D0 to 99.3 mg/L in D7. However, the major increase was observed during the initial
four days, when the concentration increased from 78.6 mg/L to 96.8 mg/L, indicating
considerable fungal growth. The increase plateaued after four days, indicating possible
fungal inhibition (Figure 8.16). The fatty acid increment curve confirmed the outputs
from pentose sugar metabolism indicating that a continuous batch fermentation of four
days will enhance the overall biomass degradation as compared to single batch
fermentation.
Minor quantities of urea and S-methyl-5-thio-D-ribose-1-phosphate were also
observed during the flux analysis. The former is generated in the urea cycle with L-
aspartate as the precursor. L-aspartate also serves as the precursor to ethylene
biosynthesis via the Yang cycle/methionine salvage pathway to which S-methyl-5-thio-
D-ribose-1-phosphate serves as an intermediate metabolite and a catalysed product of S-
adenosine methionine (Larsson et al., 2011). However, according to our experiment, the
concentration of S-methyl-5-thio-D-ribose-1-phosphate remained more or less stable
(2.9 mg/L in D0 and 3.9 mg/L in D7), implying that this metabolite originates from the
grape biomass rather than being a fungal product. The urea content initially increased
from 1.6 mg/L on Day 1 to 9.6 mg/L, perhaps owing to active fungal metabolism
(Figure 8.17). However, as observed with sugar and fatty acid metabolism, possible
product inhibition after day 4 caused a decrease in total urea content to 1 mg/L,
probably due to its catalysis in an aqueous environment to ammonia and CO2.
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
196
Figure 8.16. Medium chain fatty acid conversion by A. niger during degradation of Shiraz grape
waste over 8 days
Figure 8.17. Intermediate metabolites of urea cycle and Yang cycle/methionine salvage pathway.
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Fatty acid metabolism (days)
Acetyl-CoA Hexadecanoate Dodecanoate
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Acetyl-CoA L-Alanine Urea S-Methyl-5-thio-D-Ribose-1-Phosphate
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
197
8.3.3.4. Succinate junction
Succinate is one of the very important components of metabolic pathways as it forms
one of the active parts of the TCA cycle. However, due to its role as an intermediate
metabolite of citrate or malate in the TCA cycle, it generally does not remain in high
concentrations in the fungal cell (Brown et al., 2013). In the current study, the only
pathway merging into the TCA cycle at the succinate junction was pentanoate
degradation. The concentration of pentanoate increased from 1.6 mg/L to 20.8 mg/L
during the initial two days, during which succinate decreased from 5.5 mg/L to 2.1
mg/L. However, the fatty acid gradually degraded over the fermentation period to 1.9
mg/L during the same time, while succinate levels increased to 63.4 mg/L, thus showing
an inverse relation with respect to pentanoate. Pentanoate and iso-pentanoate are the
major C5 fatty acids occurring in plant essential oils and are catalysed by hydroxyacyl-
CoA dehydrogenase followed by methylmalonyl epimerase to succinate (Layden et al.,
2013).
8.3.4. Glycolysis and TCA cycle
It was seen across the different pathways that the concentration of a number of
metabolites was inversely proportional to that of their pathways’ junction point
metabolites in glycolysis and TCA cycle pathways. It was also observed that A. niger
degraded the biomass better during the first four days of fermentation, after which the
accumulation of hexoses such as glucose and medium chain fatty acids started to inhibit
the fungal biodegradation efficiency. In the case of glycolysis and the TCA cycle, it was
observed that almost all the metabolites displayed a decreased concentration over the
initial four days of fermentation, suggesting active biomass degradation by fungi. This
was reflected in the increase of metabolites leading to biosynthesis of medium chained
fatty acids, branched-chain hydrophobic amino acids and chitin. A considerable increase
of 2-ketovaline indicated its possible use to produce biofuel molecules such as iso-
butanol by the post-fermentation application of 2-keto-acid decarboxylases after
eliminating acetolactate group enzymes (Atsumi et al., 2008, Si et al., 2014).
Additionally, it has been observed that a pre-treatment prior to SSF and a mixed fungal
degradation considerably increases the biomass degradation. A continuous batch
fermentation of the degraded biomass in a simultaneous and/or sequential manner by
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
198
Saccharomyces cerevisiae, Pichia stipitisi or Escherichia coli has been shown to utilize
the mixed pentoses and hexoses (Gupta et al., 2009, Kim et al., 2010) generated by A.
niger enzymes. The in-process utilization of these sugars not only converts those sugars
to products such as ethanol, but also prevents the product inhibition of A. niger
cellulolytic enzymes.
8.3.5. P. chrysogenum metabolic profile
Penicillium chrysogenum or Penicillium notatum, as it was previously known is
one of the most recognised fungi. This fungus, belonging to division ascomycota, has
been used for a very long time for penicillin manufacturing, one of the most used
antibiotics in the world. However, this fungus has been known to be a highly versatile
eukaryote as, apart from penicillin production, it has been known to degrade several
types of lignocellulosic wastes. Similar to other ascomycetes such as Trichoderma spp.
and Aspergillus spp., it has been known to produce enzymes such as cellulases and
hemicellulases among many. Also, unlike some of the other species such as those
mentioned above, Penicillium spp., and especially, P. chrysogenum although minor, has
an inherent property of lignin degradation. It has been observed to degrade the lignin
component of varied sources such as wheat straw, pine lignocellulose (Rodríguez et al.,
1994, Rodriguez et al., 1996) and grape biomass wastes from wineries. However, due to
lower enzyme productions as compared to Trichoderma and Aspergillus fungi,
Penicillium spp. are not widely utilized in bioprocess industry. However, as discussed in
previous chapters, Penicillium spp., especially, P. chrysogenum has been observed to
produce comparable winery biomass degradation with respect to A. niger, while its
activities were considerably higher than Trichoderma harzianum. Besides this, it also
degraded a minor amount of lignins, a property found to be absent in the other species
used during various experiments.
Similar to A. niger, P. chrysogenum has been heavily reported in several
industrial processes, especially, penicillin production. The metabolic profiling of this
fungus has been reported from numerous groups for the purpose of developing
metabolic engineering to enhance penicillin production from this fungus. However, the
metabolic behaviour of this fungus on biomass degradation has been neglected. While
few researches mention biomass degradation properties of this fungus, none of the
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
199
currently reported researches have presented any metabolic behaviour of P.
chrysogenum on any type of biomass conversion. The current thesis therefore forms one
of the foundation works for metabolic profiling of this P. chrysogenum during winery
biomass waste degradation over periodic intervals. The approach used for metabolic
flux analysis was similar to that discussed earlier during A. niger mediated biomass
degradation. This data was then applied to ‘SIMCA 13’ based preliminary multivariate
analysis of Principal Component Analysis (PCA). The score scatter (Figure 8.18) and
DModX (Figure 8.19) plots.
Figure 8.18. Principal Component Analysis of P. chrysogenum flux over 8 days during winery
biomass degradation. Note: Each point on the scatter plot refers to single sample, with R2X
(cumulative) = 84.4% and Q2 (cumulative) = 68.9%. Samples D2O-28 to D2O-54 (brown triangles)
refer to D0 to D8 of deuterated samples, while samples AA-82 to AA-108 (yellow squares) refer to
D0 to D8 of control samples (all triplicates). Note: the PCA plot eclipse represents the 95%
confidence interval
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
200
Figure 8.19. ‘DModX’ or ‘Distance of observation’ plot of P. chrysogenum flux over 8 days of
winery biomass degradation. Note: Samples D2O-28 to D2O-54 refer to D0 to D8 of deuterated
samples, while samples AA-82 to AA-108 refer to D0 to D8 of control samples (all triplicates)
8.3.6. Mass spectral analysis and PLS-DA
The mass spectrometry analysis yielded ca. 640 peaks in growth media
supplemented with H2O and 30% D2O, whose analysis was performed by PLS-DA
(Figures 8.20 and 8.21).
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
201
Figure 8.20. PLS-DA derived score scatter plot of P. chrysogenum flux over 8 days during winery
biomass degradation. Note: Each point on the scatter plot refers to single sample, with R2X
(cumulative) = 79.7%, R2Y (cumulative) = 99.6% and Q2 (cumulative) = 93.1%. Samples D2O-28 to
D2O-54 (brown triangles) refer to D0 to D8 of deuterated samples, while samples AA-82 to AA-108
(yellow squares) refer to D0 to D8 of control samples (all triplicates). Note: the OPLS-DA plot
eclipse represents the 95% confidence interval.
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
202
Figure 8.21. PLS-DA derived loading scatter plot of P. chrysogenum flux over 8 days during winery biomass degradation. Note: Five pointed star labels denote the days of analysis (2H2O day 0 to D2O day 8 and AATCC day 0 to AATCC day 8). Green circles represent all the metabolites under consideration and their orientation.
The data obtained from SIMCA 13 required analyses for further discrimination
of the unique metabolites from a large group to more accurate levels. MetaboAnalyst
2.0 was, thus utilized to further differentiate and classify the most significant
metabolites in D2O and non-D2O based media. Metaboanalyst 2.0 employs a pre-
processing data filtering and normalization before its application to differential
expression analyses and two-group or multi-group analysis (Xia et al., 2012). The
filtered sample data was applied to univariate differential analyses methods of T-test,
analysis of variance (Vranova et al.). Based on volcano plot model generated from these
tests, the total number of differentially expressed metabolites in D2O was reduced to 94
(Figure 8.22; Appendix 3, Table 2).
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
203
Figure 8.22. Volcano plot displays the differentially expressing metabolites in deuterated and non-
deuterated media of P. chrysogenum. Note: The significant metabolites expressed in deuterated
medium (yellow circles) were taken into consideration for further metabolic flux analyses.
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Figure 8.23. Metabolic pathway of P. chrysogenum during SSF based degradation of winery biomass waste over 8 days. Note: Numbers in the table represent
identities of GCMS detected pathway metabolites.
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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8.3.7. Metabolic flux analysis of P. chrysogenum during winery biomass
degradation
The identified metabolites were then crossed-checked against the KEGG
database of metabolic pathways (http://www.kegg.jp/kegg/pathway.html). The data
obtained was then applied to MATLAB 2014a statistical software using Covariance-
Inverse (COVAIN) script (Doerfler et al., 2014). The resultant data were applied as a
correlation between the different metabolites in a time series analysis. Glycolysis
leading to the tricarboxylic acid (TCA) cycle was considered as the basic skeletal
pathway with respect to any other upstream or downstream metabolic pathways. The
major junctions were marked at glucose, glycerate-3-phosphate, acetyl-coenzyme A
(acetyl-CoA) and fumarate, where an increase or decrease in other metabolites was
considered with respect to these metabolites. The pathways merging at glycolysis
junction point of glyceraldehyde-3-phosphate was mapped with respect to glycerate-3-
phosphate, the immediate next metabolite. Similarly, the pathways merging at pyruvate
junction were mapped with acetyl-CoA. Overall, including glycolysis-TCA, 23 distinct
(and 36 individual) pathways with respect to glycolysis and TCA were projected on the
basis of biosynthesis or degradation of metabolites during the 8 day fermentation
(Figure 8.23).
8.3.7.1. Glucose/ glucose-1-phosphate junction
Glucose is one of the most important molecules in any metabolic pathway. It
marks the initial metabolite of glycolysis and TCA cycle pathways; the pathways which
are required for energy production from storage molecules in all organisms. During the
experiments, initial glucose concentration on D0 was about 4.7 mg/L in the substrate.
This was expected since the winery biomass waste consists of very low amounts of this
sugar. Due to the fungal degradation, however, this concentration increased to about
85.1 mg/L on D5. This concentration gradually decreased to 1.3 mg/L on D7, indicating
the utilization of this sugar by the fungus. However, an increase to about 28.7 mg/L on
D8 suggested accumulation of glucose due to a probable onset of cellulase enzyme
inhibition from D7 onwards (Figure 8.24).
It was observed that metabolic pathways of 7 different sugars and sugar alcohols
linked directly to glucose. These consisted primarily of galactose, melibiose, L-sorbose,
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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maltose and psicose. Contrary to the previous studies (Barbe et al., 2001), L-sorbose
was observed to be in higher concentrations in Shiraz grape biomass. Considerable
amount of this sugar was utilized by P. chrysogenum. The initial concentration of 632.6
mg/L detected on D0 rapidly dropped to 16.1 mg/L by D4. This was inversely
proportional to glucose concentration, indicating that a considerable amount of sorbose
was converted to glucose via a rapid sorbitol reduction process, as observed from a
constantly low sorbitol levels throughout the experimental period (Figure 8.24). The
probability of a rapid sorbitol conversion can be inferred from the previous observations
which indicated the presence of very low concentrations or complete absence of sorbitol
in grapes (Pilando and Wrolstad, 1992) in contrast to other fruit sources such as pear or
pineapple.
Figure 8.24. Metabolic variation of glucose and L-sorbose metabolism by P. chrysogenum during
winery biomass waste degradation of Shiraz grapes.
Melibiose, which was earlier reported as a trace sugar in grape berries (Kliewer,
1965), is a disaccharide made up of α (1-6) linkage between glucose and galactose. As
contrary to previous reports about the low concentration of melibiose(Bhat et al., 2010,
Kliewer, 1965), the current experimental observations showed the presence of about 80
mg/L of melibiose, which increased to about 101.3 mg/L from D0 to D5 and later
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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dropped to 6.7 mg/L by D7, thus indicating a considerable metabolism, directly
proportional to glucose metabolism (Figure 8.25).
Figure 8.25. Metabolism of melibiose with respect to glucose by P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
N-acetyl glucosamine (NAG) is one of the other major metabolite synthesized
during fungal growth on a substrate; it being an essential component of fungal cell wall.
It was observed that NAG content of P. chrysogenum degraded biomass initially
increased from 4.51 mg/L on D0 to 111.4 mg/L on D5. However, it then depreciated
rapidly to 39.9 mg/L by D8 (Figure 8.26). Although unexpected, this depletion in NAG
levels was not surprising due to a possible autophagy by P. chrysogenum. Autophagy is
not very uncommon in filamentous fungi, especially during the carbon depletion period
of their growth. It is utilized by these fungi to recycle the nutrient resources for their
survival. In case of P. chrysogenum, autolysis has been linked with proteolysis activity
of fungus after 3 days of substrate degradation.
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.26. Metabolism of chitin biosynthesis intermediates with respect to glucose-1-phosphate by
P. chrysogenum during winery biomass waste degradation of Shiraz grapes over 8 days.
Proteases showed enhanced activities during earlier phase of Penicillium
metabolism. However, these activities dropped sharply after 5th day of incubation,
coinciding with ammonia release, thus marking the onset of autophagy (McIntyre et al.,
2000). Chitinases are the other group of enzymes responsible for the onset of autophagy
in P. chrysogenum. Although, the enzymes are produced by P. chrysogenum along
chitin synthase enzymes, their activity increases considerably towards the carbon
starvation period, i.e. from 4th day of incubation (Sámi et al., 2001). These enzyme
activities have shown to decrease the hypal concentration in the tested samples,
indicating P. chrysogenum autolysis, primarily after D5 of incubation. Our results were
found to be in agreement with these previously performed works and showed a
considerable depletion of NAG after D5. Similarly, N-acetyl mannosamine, one of the
intermediates of chitin biosynthesis, also displayed an overall depletion from 3.4 mg/L
to 0.25 mg/L during P. chrysogenum mediated biomass degradation.
Similar to A. niger, P. chrysogenum was also observed to generate melanin
products via tryptophan metabolism. One end product, melatonin and an intermediate,
3, 5-diiodo-L-tyrosine were detected in culture samples tested. Melanins are some of the
numerous polyketides produced by filamentous fungi. Their production is induced by a
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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group of enzymes called as Polyketide synthases (PKS enzymes), which in turn, are
regulated by PKS genes. Although, the number of these genes vary according to
species, they occur in high concentrations (23 copies) in Penicillium spp. (Woo et al.,
2010). Although, melanins have been observed from disease causing fungi such as
Penicillium marneffei, as reported by Woo et al (2010), the pigments have also been
observed during penicillin production by P. chrysogenum due to de-repression of
melanin synthesis genes (Sigl et al., 2011). Current experiments showed that the
concentration of melatonin and 3, 5-diiodo-L-tyrosine increased during the lag phase of
fungal growth. Melatonin deposition increased from 6.1 mg/L on D0 to 9.8 mg/L on D3,
whereas, 3, 5-diiodo-L-tyrosine concentration increased from 2.1 mg/L to 5.7 mg/L
during the same duration. However, during the later phases, this concentration dropped
rapidly to 3.7 mg/L and 0.6 mg/L on D8 for melatonin and 3, 5-diiodo-L-tyrosine,
respectively (Figure 8.27). This drop in the melanin levels can be attributed to the late
autolysis observed in P. chrysogenum culture, as indicated by the decrease in chitin
biosynthesis intermediates (Figure 8.26).
Figure 8.27. Biosynthesis and metabolism of melanin pigments by P. chrysogenum during SSF
based winery biomass waste degradation.
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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8.3.7.2.Glycerate-3-phosphate junction
Glycerate-3-phosphate or 3-phosphogleceric acid is the downstream metabolite
of glyceraldehyde-3-phosphate in glycolysis pathway. During the experiments, it was
observed that at least 6 pathways converged at Glycerate-3-phosphate junction either
directly or via glyceraldehyde-3-phosphate (not detected as a statistically significant
metabolite during P. chrysogenum flux). These consisted of pentose phosphate
pathways, lecithin metabolism via serine biosynthesis, terpenoids metabolism, deoxy
sugar metabolism, glycerol biosynthesis and pectin metabolism. The molecule is
generated during glycolysis pathway by type I and type III D-Glycerate kinases in the
cell cytoplasm, as has been reported from Saccharomyces spp. (yeast) and Nostoc spp.
(bacteria) (Bartsch et al., 2008). In our experiments, owing to hemicellulose degradation
by P. chrysogenum, numerous monosaccharides such as D-ribulose, erythrose, xylose
accumulated during the earlier phase of degradation, especially, during first 3 days of
SSF. These accumulated underwent fungal mediated metabolism to generate glycerate-
3-P. Due to this, glycerate-3-P increased from an initial concentration of 160.9 mg/L to
252.5 mg/L during first 3 days (Figure 8.28). However, owing to biosynthesis of
downstream molecules such as serine and glycerol, this concentration depleted
considerably to 12.9 mg/L by D8.
Figure 8.28. Metabolism of glycerate-3-phosphate and sugars from pentose phosphate pathway by
P. chrysogenum during winery biomass waste degradation of Shiraz grapes over 8 days.
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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The only sugar showing a late accumulation during the fermentation was D-
ribulose. Initial concentration of this pentose sugar was 4.5 mg/L on D0, which
increased to 6 mg/L. However, this concentration increased to 40 mg/L by D8, possibly
showing the onset of either xylanase inhibition. However, it has been long known that
ribulose and ribulose-5-phosphate are two of the most important metabolites of pentose
phosphate pathway. It is known that CO2 is assimilated in the metabolic pathways,
especially in TCA or Calvin cycle via ribulose and ribulose-5-phosphate. This CO2
fixation or acquisition is catalysed by ribulose biphosphate carboxylase (RuBisCO).
This pathway is very common in plants, algae and photosynthetic bacteria.
Additionally, RuBisCO-like enzymes catalyse a reverse TCA pathway, causing the
generation of ribulose and ribulose-1-phosphate in sulphur bacteria such as Chlorobium
limicola and Thiocapsa roseopersicina (van der Meer et al., 1998). However, in non-
photosynthetic organisms such as fungi, ribulose is one of the intermediates of reduction
of pentose sugar alcohols such as arabitol (Guo et al., 2006) or from pentose acid
reduction of molecules such as gluconates or idonates, as observed in our studies
(Figure 8.29). In an inversely proportional relation to ribulose, concentration of these
acids depleted from about 6.2 mg/L on D6 to about 3.4 mg/L on D8.
Figure 8.29. Metabolism of pentose acids by P. chrysogenum during winery biomass waste
degradation of Shiraz grapes over 8 days.
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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One of the interesting features observed during the metabolic profiling of P.
chrysogenum during the SSF of winery biomass waste was the accumulation of pentose
alcohols such as arabitol and xylitol. As mentioned above, arabitol was more or less
reduced to ribulose, thereafter merging in to glycolysis pathway at glycerate-3-
phosphate junction. Due to this, its accumulation in culture sample remained in a range
of 18-19 mg/L during the degradation period. Xylose is one of the major hemicellulosic
residues. However, a large number of wild type fungi, including yeasts are unable to
utilize this sugar. During this experiment, it was observed that xylose accumulated in
the early phase of P. chrysogenum mediated degradation due to xylanase activities for
hemicellulose degradation. During this period, xylose concentration increased more
than two-folds from 15.1 mg/L on D0 to 37.7 mg/L on D3. However, this concentration
depleted to 1.4 mg/L by D8, displaying xylose metabolism either towards glycolysis
cycle via xylulose-5-phosphate or xylitol biosynthesis. During the experiments, it was
also noticed that during xylose depletion period, considerable amounts of xylitol
accumulated in the media. Xylitol was observed to accumulate during later phase of
fermentation and its concentration (2.6 mg/L, D0) increased to 24 mg/L by D8 (Figure
8.30).
Figure 8.30. Xylitol production by P. chrysogenum during xylose metabolism by winery biomass
waste degradation of Shiraz grapes over 8 days.
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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P. chrysogenum therefore, was observed to possess the property of xylose
conversion. This property can be applied to co-generate biofuel molecules such as
ethanol while biodegradation of lignocellulose components. Studies utilizing S.
cerevisiae (Lee et al., 2003) and Candida maltosa (Guo et al., 2006) based D-
xylulokinase catalysing activities to generate ethanol from xylose have been reported.
One of the other peculiar results observed was the propanol biosynthesis via glycerol
metabolism pathway. Although, not generated in high concentration as xylitol, the SSF
process utilizing P. chrysogenum showed the potential to generate greater amounts of
propanol, one of the other important molecules which can be used as a biofuel molecule.
By D6, about 6.2 mg/L propanol was generated. However, probably due to the
stationary growth phase of this fungus, this metabolite was reutilized as an energy
source by Penicillium, thereby; ultimately depleting the propanol levels to 0.6 mg/L.
similarly, Sn-glycerol-3-phosphate was produced during the earlier growth phase and
reached its peak value of 18 mg/L at D6, followed by a depletion t 1.1 mg/L by D8
(Figure 8.31).
Figure 8.31. Propanol production by P. chrysogenum during metabolism by winery biomass waste
degradation of Shiraz grapes over 8 days.
From the obtained results, it can be deduced that P. chrysogenum has the ability
to generate propanol during the lag-phase of its growth. However, the experiments also
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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highlighted the necessity for extraction of propanol from the medium before the onset of
stationary phase. The study also indicates a necessity to further improve the Penicillium
mediated biomass degradation to generate higher quantities of industrially useful
alcohols such as propanol and glycerol.
P. chrysogenum was observed to possess pectin degradation as well, a feature
observed less prominently during A. niger metabolic flux. The concentration of
glucuronate (Figure 8.11) was more or less stationary during A. niger mediated biomass
degradation. Additionally, threonate and galacturonate degradation was almost absent.
However, P. chrysogenum was found to degrade pectin substrate of biomass via inositol
metabolism pathway. Galacturonate concentration dropped from 79.6 mg/L on D3 to 3.1
mg/L on D8. Additionally, glucuronate concentration depleted from 4.3 mg/L on D0 to
0.2 mg/L on D8 (Figure 8.32).
Figure 8.32. Pectin degradation by P. chrysogenum during metabolism by winery biomass waste
degradation of Shiraz grapes over 8 days.
8.3.7.3.Acetyl CoA junction
Acetyl-CoA is arguably the most distinct molecule in metabolic pathway as it
forms a connecting linkage between glycolysis and TCA cycle. Due to its presence at
this critical position, large number of pathways either mere in or merge out at Acetyl-
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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CoA junction. In the current experiments, about 7 pathways merged at this junction
directly and 3 pathways merged (indirectly) via pyruvate. These pathways consisted of
metabolisms of isoprenoids, fatty acids such as Hexadecanoate, dodecanoate,
octadecanoate, linoleate and docosanoate; secondary metabolites and amino acids such
as leucine, isoleucine, lysine, cysteine, methionine and lysine.
Unlike glycerate-3-phosphate, overall concentration of acetyl-CoA remained
unexpectedly low during the entire SSF period. This was in contrast to the levels
observed during A. niger metabolism, where, these levels were higher than 100 mg/L
during early phase of metabolism (Section 8.3.3.3). However, one of the reasons for
this might be the biosynthesis of amino acids, fatty acids and isoprenoids, as explained
in this section.
One of the major biosynthesis seen from acetyl-CoA junction was isoprenoid
biosynthesis, resulting in the formation of α-tocopherol via mevalonate pathway.
Tocopherols are the large groups of isoprenoids generally produced by the plants as
their defence molecules in response to fungal infection. González-Candelas et al (2010)
during their experiments reported the presence of multiple ESTs responsible for
secondary metabolite production in citrus plants via 7-phospho-2-deoxy-3-D-arabino
heptanoate (DAHP). This molecule was also observed during the current experiments as
an intermediate metabolite during melanin pigment synthesis (Figure 8.27). Another
molecule acting as α-tocopherol biosynthesis intermediate observed during the
metabolic profiling was 4-(Cystediene-5diphospho)-2-C-methyl-D-erythritol. It has
been reported in numerous researches and had been reviewed that this molecule belongs
to 4-phospho-2-C-methyl erythritol (MEP) pathway and is one of the most widely used
precursor for α-tocopherol biosynthesis in plant plastids and prokaryotes. However, in
fungi and other eukaryotes, this isoprenoid is biosynthesized from acetyl-CoA as its
starter metabolite. Isopentyl pyrophosphate (IPP) acts its active isoprene precursor in
mevalonate pathway, which combines with dimethylallyl pyrophosphate (DMAPP) to
form geranyl pyrophosphate (GPP). GPP serves as the immediate precursor to
carotenoids, tocopherols and numerous other fat soluble vitamins (Demain, 2014).
During the experiments, it was observed that although, α-tocopherol was found
at levels less than 1 mg/L, its precursors i.e., mevalonate and MEP were present at
higher concentrations.
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.33. Isoprenoid biosynthesis by P. chrysogenum during metabolism by winery biomass
waste degradation of Shiraz grapes over 8 days. α-tocopherol has been scaled on the secondary axis.
At D0, the culture sample consisted of 73 mg/L and 2.2 mg/L of these
metabolites, respectively. This indicated a significant presence of some isoprenoids in
the grape biomass before the onset of its degradation. However, the fungus probably
synthesized α-tocopherol chiefly via mevalonate pathway, and surprisingly, to a minor
extent by MEP pathway, which is not a common way of isoprenoid synthesis in fungi.
Mevalonate levels increased to 109.8 mg/L by D3, which was directly proportional to a
considerable increase in α-tocopherol levels from 0.003 mg/L to 0.03 mg/L during the
same period. After D3, the drop in mevalonate levels to 67.4 mg/L by D7 resulted in
depletion of α-tocopherol to 0.01 mg/L. However, this Tocopherol concentration
increased to 0.03 mg/ L on D8, probably as a result of increasing MEP levels from 2.2
mg/L (D0) to 5.7 mg/L (D7) (Figure 8.33). Other isoprenoids were not detected in
significant levels by GCMS analysis. An application of LCMS based approach is
expected to detect these metabolites to resolve these deficiencies.
Tannins and lignins are some of the major constituents of grape biomass and can
comprise up to 35-40% of dry weight of this biomass (Bravo and Saura-Calixto, 1998).
It has been demonstrated that P. chrysogenum has the ability to degrade minor amounts
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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of low molecular weight lignin components such as vanilates and ferrulates (Rodríguez
et al., 1994, Rodriguez et al., 1996). Additionally, Penicillium spp. have been also
known to possess tannin degrading enzymes such as tannin acyl hydrolase, which
catalyses the hydrolyzation of tannic acid (Sharma and Saxena, 2012). However, this
property has not been widely reported in P. chrysogenum.
During SSF based degradation of Shiraz grape biomass, P. chrysogenum was
observed to free up and then degrade syrinagate or syringic acid component of grape
tannins via benzoate degradation pathway. Initial syringate content of grape biomass
was recorded at 1.03 ± 0.01 mg/L (D0). Considering that the majority of syringate ends
up in grape juice used for wine production, the obtained results we in line with previous
observations that had reported Shiraz grape to contain up to 3 mg/L of syringate
(Fanzone et al., 2012). This metabolite was observed to accumulate to about 3.2 mg/L
during early phase (D3) of P. chrysogenum mediated degradation. However, this
concentration depleted to 0.6 mg/L by D8 (Figure 8.34).
Lignin degradation has been previously observed in P. chrysogenum cultures, as
mentioned above. During this experiment, small molecular weight lignins, especially
polypropanoids such as vanillates, caffetaes and ferrulates were possibly degraded, as
indicated by the presence of 4-hydroxy benzoate. At D0, this metabolite was
accumulated at the level of 6.1 mg/L. An increase in its concentration (15.4 mg/L) by
D3 indicated that the lignin degradation started during the early phases of P.
chrysogenum growth. This was further confirmed by a heavy accumulation of 4-amino
benzoate, immediate degradation product of 4-hydroxy benzoate. The level of 4-amino
benzoate increased rapidly from 4.4 mg/L on D0 to 34 mg/L on D3. During the later
phases, concentration of benzoates dropped (14 mg/L and 2.5 mg/L of 4-amino
benzoate and 4-hydroxy benzoate, respectively, by D8) due to their degradation via
benzoate pathway (Figure 8.34).
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
219
Figure 8.34. Lignin and tannin degradation via benzoate degradation pathway by P. chrysogenum
during metabolism by winery biomass waste degradation of Shiraz grapes over 8 days
During the SSF based degradation, P. chrysogenum also displayed the
production of penicillin, especially during the log phase of its growth. It was observed
that lysine degradation pathway was utilized for penicillin production as evidenced from
the presence of considerable amounts of glutarate and L-pipecolate. L-pipecolate or 2-
piperidine carboxylic acid is an intermediate of lysine synthesis. However, in P.
chrysogenum, it also acts as the precursor to 2-aminoadipic acid, an important starter
metabolite for penicillin biosynthesis. L-pipecolate dehydrogenase catalyses L-
pipecolate first, to L-2aminoadipate-6-semialdehyde, followed by L-2-aminoadipate. L-
2-aminoadipate then enters penicillin biosynthesis pathway by N-(5-amino-5-
carboxypentanoyl)-L-cysteinyl-D-valine synthase to form isopenicillin N, which is the
precursor to all forms of penicillins (Vranova et al., 2013).
The lag phase of P. chrysogenum growth displayed slow lysine degradation, as
evident from lowered accumulation of glutarate. During first 3 days, glutarate levels
slightly dropped from 4.8 mg/L to 3.8 mg/L. This possibly was due to diversion of
lysine towards penicillin production, as L-pipecolate, the intermediate metabolite in
penicillin production started accumulating during this period. The original concentration
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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(D0) of L-pipecolate increased from 4.1 mg/L to 7.7 mg/L by D3. During the later
period, the concentrations of L-pipecolate dropped, especially after D7, most likely due
to autolysis of P. chrysogenum caused by nutrient starvation (Figure 8.35).
Figure 8.35. Penicillin production via lysine degradation pathway by P. chrysogenum during metabolism by winery biomass waste degradation of Shiraz grapes over 8 days.
One of the most distinct pathways leaving from acetyl-CoA was fatty acid
biosynthesis. Unlike A. niger, fatty acids were not produced in higher quantities by P.
chrysogenum over the entire biomass degradation period. However, 5 fatty acids
(hexadecanoate, octadecanoate, 9Z-octadecanoate, docosanoate and linoleate) were
significantly produced against just 2 (hexadecanoate and dodecanoate) observed during
A. niger metabolism. Similar to A. niger metabolic pattern, fatty acids accumulated over
the entire period of SSF. Especially, hexadecanoate concentration increased from 1.8
mg/L to 14.5 mg/L over 8 days (Figure 8.36). It is normally observed during a
microbial degradation that after a period of 3-4 days post-fermentation, the MCFAs
drop the internal cell pH of fungi, thereby inhibiting further growth and resultant
fermentation (Edwards et al., 1990, Legras et al., 2010). However, due to the low
accumulation of all fatty acids, the enzyme and growth inhibition was delayed to about
6 days in P. chrysogenum as compared to 4 days in A. niger.
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Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
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Figure 8.36. Fatty acid biosynthesis by P. chrysogenum during metabolism by winery biomass waste degradation of Shiraz grapes over 8 days.
8.4. Conclusions
A targeted metabolomic approach was used to understand the metabolic flux
during winery waste biomass degradation by A. niger and P. chrysogenum. Deuterium
oxide was used as metabolite tagging molecule in an 8 day experiment. Successive
applications of multivariate statistics and covariance inverse statistics to GC-MS data
were used to characterise the metabolic output of winery waste biomass degradation. It
was observed that 37 unique metabolites related to 18 different pathways were chiefly
involved during the A. niger metabolic process, while 94 unique metabolites related to
23 distinct pathways were involved in P. chrysogenum metabolism.
It was found that product inhibition started at the fifth day of fermentation under
solid state fermentation conditions in A. niger. On the other hand, inhibition started after
D6 in P. chrysogenum mediated biomass degradation. However, one of the peculiar
observations noticed with P. chrysogenum was the onset of autophagy or autolysis
during the later period of incubation (D7-D8).
0
2
4
6
8
10
12
14
16
18
0 3 7 8
Con
cent
ratio
n (m
g/L
)
Fatty acid metabolism duration (days) Acetyl-CoA Octadecanoate Hexadecanoate
(9Z)-Octadecanoate Docosanoate Linoleate
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
222
The addition of external enzyme complexes after the fourth day may result in the
generation of bio-fuel molecules such as 1-butanol and 2-butanol by altering the amino
acid biosynthesis pathways. Additionally, an initial mixed fungal treatment and
continuous fermentation with selective yeasts after day 5 will not only prevent the
product inhibition, but will also considerably increase the rate of biomass degradation.
P. chrysogenum, on other hand displayed the production of numerous commercially
important metabolites such as propanol and xylitol. Additionally, it also displayed the
degradation of lignin and tannin degradation, which makes it one of the important
contributors for developing the process for an efficient conversion of biomass to
biofuels and other commercially important metabolites. However, due to feedback
inhibition and individual enzymatic limitations, both of these fungi have not proven to
be highly effective. However, it has been observed that in a symbiotic consortium
followed by simple pre-treatments, they prove to be highly effective in degrading
biomass, causing up to about 18% lignin degradation (see Section 6.3.6), which
otherwise is less than 6% in case of single culture of P. chrysogenum .
8.5. Summary
Abovementioned chapter describes the assessment of metabolic pathways in
biomass degrading fungi, A. niger and P. chrysogenum. These fungi were selected due
to their overall better cellulolytic enzyme activities. Additionally, P. chrysogenum was
selected based on its lignin degrading ability observed during the previous experiments
under submerged and SSF conditions.
The pathways were built upon the 2H2O based metabolic flux of those fungi
over a period of 8 days during SSF based degradation of winery biomass waste. GC-MS
approach was used for data generation. These data were then applied to quality control
and were filtered by multivariate statistical approaches such as PCA, PLS-DA, t-tests
and one way ANOVA. Unique metabolites, based on 2H2O labelling were then obtained
for further analysis. 37 metabolites were obtained in A. niger degraded samples,
whereas, 94 unique metabolites were obtained in P. chrysogenum degraded samples.
These metabolites were then applied to Covariance-Inverse (COVAIN) statistical
toolbox was applied in MATLAB© toolbox to generate metabolic behavioural pattern
over the entire period of biomass degradation.
Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation
223
It was observed that both the fungi degraded more efficiently during their early
growth phase. After this, both product and competitive inhibitions began, causing
decrease in the efficiency of biomass degradation. For A. niger, growth stationary phase
and inhibitions started from 5th day of fermentation onset. P. chrysogenum culture, on
the other hand displayed the decrease in efficiency after 6th day of SSF. After this time,
degradation ability of P. chrysogenum not only decreased, but it also displayed
autolysis, due to nutrient starvation.
During the SSF, A. niger displayed a greater degradation of cellulosic and
hemicellulosic residues, as evident from high accumulation of free hexoses and
pentoses. However, this fungus was also prone to an early inhibition, most likely due to
high amounts of fatty acid deposition. Although, P. chrysogenum was not effective as A.
niger, especially during hemicellulose degradation, it was able to degrade tannins and
lignins to some extent, a feature absent in Aspergillus spp. Also, its culture accumulated
considerably low amounts of fatty acids, thereby showing a late inhibition as compared
to A. niger.
From the metabolic flux analysis, it could be suggested that these fungi, if kept
under a continuous batch fermentation of about 5 days, will produce greater biomass
degradation than single batch fermentation. Additionally, eliminating the leucine,
isoleucine and valine biosynthesis pathways by adding external enzyme complexes such
as 2-keta acid decarboxylases is likely to reroute the metabolic pathway towards butanol
isomers biosynthesis, which have the utility as biofuel molecules. Similarly, minor
amounts of propanol and xylitol were observed in P. chrysogenum. Improving the P.
chrysogenum SSF conditions by preventing autolysis is expected to generate ethanol,
which is also considered as an important biofuel molecule.
224
CHAPTER 9
General Discussions and Conclusions
Chapter 9 General Discussions and Conclusions
225
9.1. General Discussions
Grapes are one of the major global horticultural crops, with a production of
about 70 million tonnes in 2012-13. Majority of these are classified as wine grapes,
Australia is 6th largest global wine grape producer. However, such a massive production
also generates considerable pre- and post-process wastes. It is estimated that about half
of grape biomass ends up as waste. Also, wineries generate massive amounts of
wastewater during wine production. Owing to its poor digestibility and minor phenolic
toxicity, the winery waste has a very limited use as either animal feed or compost
fertilizer. End result, it ends up as a landfill waste. Therefore, novel techniques for its
bioconversion are necessary.
Lignocellulose complex in biomass is made up of three major components of
cellulose, hemicelluloses and lignins. Apart from there, there are glycoproteins, lipids,
free amino acids and other metabolites, which occur in the interaction with the major
components. Among those, lignins form the outermost structure of lignocelluloses and
are the most complex molecules of all the three. Due to the varied nature of lignins and
hemicelluloses, there is no single organism that can degrade all the components. Other
major reason impeding the biomass degradation is interaction of majority of enzymes
like cellulases and hemicellulases with their respective substrates. A number of products
generated by biomass degradation themselves cause product inhibitions of
lignocellulolytic enzymes.
The experiments described herein explore the possibilities of developing mixed
fungal consortium to achieve enhanced grape biomass degradation. The degradation
processes utilized cultures of ascomycota fungi such as Trichoderma harzianum,
Aspergillus niger, Penicillium chrysogenum and Penicillium citrinum, and
basidiomycota fungus Ph. chrysosporium for degradation purposes. The conventional
and popular submerged fermentation and the emerging Solid State Fermentation (SSF)
methods were tested. Statistical analysis was performed to generate an optimised mixed
fungal “cocktail” and achieve a bioreactor-based degradation process with increased
biomass degradation. Additionally, co-culturing was experimented with Ph.
chrysosporium and statistically optimized ascomycete fungal mixture. Metabolomics
was applied in classifying and characterizing important metabolites during the
biodegradation process. It was used to pin point the critical points of fungal
Chapter 9 General Discussions and Conclusions
226
biodegradation process, which can be altered to improve the required bioconversion
process.
9.1.1. Hydrothermal pre-treatment and Submerged fermentation
Routinely used autoclaving method was used as a pre-treatment technique on
grape biomass waste. According to numerous literature sources, hydrothermal pre-
treatment processes such as hot-compressed water (HCW), steam explosion (SE),
ammonia fibre explosion (AFEX) and supercritical water (SW) treatments degrade the
biomass contents, especially celluloses and hemicelluloses to greater degrees. These
methods also have been observed to degrade lignin to considerable levels (Ando et al.,
2000, Goto et al., 2004, Papadimitriou, 2010). However, these techniques have been
known to solubilize considerable levels of lignin composition to free phenolics. These
resultant metabolites not only inhibit the overall lignocellulose degrading enzyme
activities, but also have adverse effects on fungal growth. Additionally, other
metabolites such as furfurals are generated in considerable amounts due to hydrothermal
treatments (Duarte et al., 2012b). However, the GC-MS analysis of grape biomass
before and after the process of autoclaving indicated an absence of considerable levels
of phenolics. Additionally, it was observed that although autoclaving was able to
hydrolyse hemicelluloses and cellulose to considerable extents, this method did not
generate any furfural molecules, possibly due a lower molal ionic product of water as
compared to HCW or SE processes.
Autoclaving resulted in 18% mass loss of biomass, thus indicating its
considerable effects. The biochemical analysis showed a considerable release of
pentoses and other free sugars due to this process. During the initial phase of growth,
presence of free sugars possibly enhanced an overall fungal growth, thus enabling fungi
to increase overall biomass degradation rate in less amount of time with respect to a
non-treated biomass substrate. Enzyme activities especially that of β-glucosidase
improved in many cultures, indicating a considerable hydrolyzation of cellobiose and
other disaccharides forming functional units of cellulose and hemicelluloses. The
hydrothermal treatment was also observed to solubilize some linkages in lignins.
Resultantly, in the presence of free sugars, fungi such as P. chrysogenum were able to
degrade about 9% lignin, a majority of which were possibly smaller chained lignins.
Chapter 9 General Discussions and Conclusions
227
9.1.2. Solid state fermentation
The SSF was performed to study the comparative fungal degradation of winery
grape wastes to achieve biodegradation of its lignocellulose components. One of the
primary reasons for using this strategy was to conserve the amount of water used in the
process and increase the amount of substrate being degraded during the process. The
ratio between substrate fungal growth media was kept at 1:1, which meant that the
substrate concentration increased from 3% in submerged fermentation to 50% in
SSF.The degradation patterns of various fungi such as T. harzianum, A. niger, P.
chrysogenum and P. citrinum were compared over 2 weeks.
Cellulase activities were moderately or considerably higher in SSF conditions
with respect to pre-treated submerged fermented biomass. Similarly, xylanase activity
was significantly higher in SSF conditions than the SmF. However, β-glucosidase
activities were either comparable or were higher under SmF conditions than SSF. The
cellulase activities increased considerably in some fungal cultures, while it remained
stationary or decreased in others. This indicated that long term fermentation did not
necessarily improve the biodegradation. Xylanase activities decreased significantly after
week 2 with respect to week 1. The only improvement was in β-glucosidase activity,
where it was considerable in some and marginal in other cultures after 2 weeks of SSF.
The lignin degradation in SSF conditions was considerably lower at 2.2 % as compared
to 9% in pre-treated SmF conditions. For primary enzymes which degrade macro
molecules such as cellulose and xylans, SSF proved to be a better candidate; it still
resulted in the inhibition of those enzymes due to lower activities of secondary enzymes
such as β-glucosidases, which further hydrolyze the products of cellulose hydrolysis
which created product inhibition of those primary enzymes. The longer duration of
week 2 improvements was considered as a less economically viable trait. Due to
abovementioned shortcomings of SmF and SSF processes, a necessity of a new working
model was felt to improve overall degradation efficiency.
9.1.3. Statistical optimization of fungal biomass degradation
The conventional and popular submerged fermentation and the emerging Solid
State Fermentation (SSF) methods were observed to have their advantages at some
Chapter 9 General Discussions and Conclusions
228
aspects. However, each of the processes suffered from some limitations, which on
commercial scale would accumulate to larger scales.
To balance the overall enzyme activities combined with higher lignin
degradation, with minimizing competitive and product inhibitions, a statistical approach
was applied. The enzyme activities from SmF and SSF processes were applied to a
combination coefficient correlation and ANOVA to generate a predicted method with
higher biomass degradation efficiency.
The statistical approach yielded a combination of all ascomycota fungi in
variable percent ratio. The fungal mixture of A. niger: P. chrysogenum: T. harzianum:
P. citrinum in a percent ratio of 60:14:4:2 was made and applied for biomass
degradation. Additionally, the pre-treatment method of autoclaving was provided to
biomass before fungal inoculation. Substrate to medium ratio was suggested to be held
at 0.39 to improve biodegradation. It was observed that the enzyme activities of
cellulases, glucosidase and xylanases increased considerably. Especially, the xylanase
activities increased more than 2-folds. Also the total lignin degradation during the
biodegradation process was 17.9 % in a shorter duration of 5 days, as compared to 7
days in SSF. This method indicated the positive synergistic approach of numerous fungi
under the same conditions. This fungal behaviour was observed to not only compensate
each other’s limitations, but also to produce a better biomass conversion than the
individual processes. The metabolic profile indicated a generation of several hexoses,
pentoses and fatty acids along important medicinal metabolites such as gallic acid.
9.1.4. Biomass degradation by co-culture of basidiomycete and ascomycete
The next experiments involved the study of biomass degradation achieved by the
simultaneous mixture of a white-rot fungus, Phanerochaete chrysosporium and
statistically optimized mix of ascomycetes. It was hypothesized that addition of wood-
rotting basidiomycetes would produce better lignin degradation as compared to a mixed
ascomycete culture. This treatment, followed after the pre-treatment method application
of autoclaving or ‘hydrothermal pre-treatment’ was predicted to considerably improve
an overall biomass degradation and conversion.
Chapter 9 General Discussions and Conclusions
229
The grape samples were pre-treated by autoclaving in a bioreactor before
applying to fungal degradation. The ratio between substrate and medium was
maintained at 0.39:1, as determined by the statistical modelling. Ph. chrysosporium
degradation was allowed for 8 days and was followed by a mixture of A. niger: P.
chrysogenum: T. harzianum: P. citrinum in a percentage ratio of 60:14:4:2 for further 8
days. It was observed that over 16 days, due to the nature of fungal mix, cellulase
activities did not suffer product inhibition and displayed considerable activities. Similar
observations were noted regarding β-glucosidase and xylanase activity, especially the
latter’s activity increased about 2-folds with respect to that observed in ascomycota
fungal mixture. In a peculiar observation, considerable laccase activities were observed
during early phase of degradation, which was surprising since Ph. chrysosporium is
generally unable to express laccase activities on numerous substrates. However, it is
predicted that due to moderately high nitrogen content of grape biomass and added
nitrogen supplementation resulted in elevated laccase expression during degradation
process. Good activities of lignin peroxidase were also observed. Metabolic profiling
displayed considerable production of lignin degradation products such as suberic acid,
1, 3 benzenedicarboxylate and cadaverine, which were further degraded by fungal cells
in the course of fermentation. Similarly, considerable amounts of commercially
important metabolites such as sugar alcohols, sugar acids and fatty acids were
generated.
9.1.5. Fungal metabolic flux analysis to improve biomass degradation
Among numerous areas, metabolomics has been applied to investigate bacterial
processes related to preventative health, environmental pollution and food research.
However, within the context of fungal-mediated biomass degradation, its application
has been rather limited. The process assists in understanding of correlation between cell
phenotypes, metabolic patterns and stoichiometry. It also enables better understanding
of the nature, time dependence and substrate-based limitations of fungal metabolism.
Metabolic flux during the degradation of winery-derived biomass waste by A.
niger and P. chrysogenum was studied. The study utilized 2H-flux due to its rapid
incorporation in fungal metabolism as compared to 13C. The flux experiment was
designed to differentiate the metabolites derived from the substrate and those generated
Chapter 9 General Discussions and Conclusions
230
by the fungi. This process also aided in differentiating and quantifying significant
metabolites generated by fungi at different intervals, thereby, optimizing a particular
process in order to maximize the generation of specific products or generate numerous
products in significant quantities in a single bioconversion process setup.
Over an 8 day period, metabolic flux profile of both fungi indicated considerable
biomass degrading ability, as evidenced from considerable accumulation of free sugars.
GC-MS based 2H2O-flux profile of A. niger showed about 17 active pathways merging
in and out of glycolysis and TCA cycle pathways. It was observed that the fungal
enzymes encountered product inhibition from 5 days of initiation, thus, indicating a
necessity of continuous batch degradations of lower intervals to generate better
degradation. Considerable amounts of amino acid biosynthesis were observed. It is
proposed that a pathway alteration by the addition of keto acid carboxylases will reroute
these pathways from amino acid synthesis to butanol isomers, which are widely used as
fuel molecules.
P. chrysogenum metabolic flux displayed about 36 active pathways merging in
and out of glycolysis and TCA cycle pathways. Similar to A. niger, this fungus also
encountered enzyme inhibition from day 5 onwards during biomass degradation.
Additionally, this fungus was observed to be prone to autolysis in a nutrient drought
conditions. This indicated a necessity of lower duration continuous fermentation,
similar to A. niger. Also observed was moderate generation of xylitol, an intermediate
of ethanol production with a property of pentose sugar oxidation, a property absent in
other ascomycetes under study. Moderate lignin and tannin degradation was also
observed during early phase of biomass degradation, as evidenced from the presence of
their degradation intermediates. It is proposed that P. chrysogenum forms a good
candidate for development of fungal consortium; especially to minimize pentose based
enzyme inhibitions and its ability to convert them in to sugar alcohols leading to ethanol
production.
9.2. Close and Future Aspects
The process elaborated in this document was focussed towards developing a
system of multiple fungi to improve the winery biomass waste conversion. The project
dealt with applying the simple yet effective and economically viable methods in the
Chapter 9 General Discussions and Conclusions
231
process of developing a waste management system for winery grape biomass, which
remains a considerable environmental hazard due to its massive production in Australia.
The project successfully demonstrated that cost effective techniques such as
autoclaving, followed by a multiple fungal consortium was not only able to improve the
degradation of winery biomass waste, but also minimised the inhibition of degrading
enzymes. Additionally, the process was able to degrade considerable lignin composition
with respect to monoculture micro-organisms and numerous standalone physio-
chemical techniques. Considerable sugar alcohols, which form the intermediates of
biofuels such as ethanol and butanol, were generated during multi-fungal fermentation.
However, a considerable development is still required to develop more complex
consortia of biomass degrading fungi and thermophilic bacteria in order to improve
bioconversion efficiency.
The project also showed the need of increasing the application of metabolomics
in biomass degradation process. Metabolic flux profiling in A. niger and P.
chrysogenum was able to provide the critical points in their biomass degradation
pathways. A further work on those critical points or critical pathways in collaboration
with other ‘omic’ approaches such as proteomics, transcriptomics and genomics is
expected not only to improve the biodegradation efficiency, but also the production of
metabolites of commercial/ industrial interests. The preliminary work performed on
these ascomycetes can be easily applied on other fungi of commercial interests to
enhance their output efficiencies.
232
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264
Appendices
265
Appendix 1
Calibration curves of biochemical tests and enzyme assays
Figure A1. Standard curve for reducing sugars by DNSA assay @ 540 nm.
Figure A2. Standard curve for total sugars by Mollisch’s assay @ 400 nm.
y = 0.0586x + 0.0026
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5
Abs
orba
nce
(Au)
@ 5
40 n
m
Glucose Concentration (mg/mL)
y = 0.3098x + 0.0017
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 1 2 3 4 5 6
Abs
orba
nce
(Au)
@ 4
90 n
m
Glucose concentration (mg/mL)
266
Figure A3. Standard curve for pentose sugars by Bial’s assay @ 490 nm.
Figure A4. Standard curve for total proteins by Biuret assay @ 546 nm.
y = 1.7671x - 0.0022
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 0.02 0.04 0.06 0.08 0.1 0.12
Abs
orba
nce
(490
nm
)
Arabinose concentration (mg/mL)
y = 0.0523x - 0.0022
0
0.05
0.1
0.15
0.2
0.25
0.3
0 1 2 3 4 5 6
Abs
orba
nce
@ 5
46 n
m
BSA concentration (mg/mL)
267
Figure A4. Standard curve to determine β-glucosidase activity by release of p-
Nitrophenol from pNPG @ 400 nm.
y = 0.0301x + 0.0064
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0 1 2 3 4 5 6
Abs
orba
nce
@ 4
00 n
m
p-Nitrophenol concentration (mg/mL)
268
Appendix 2
Sequence information of isolated and characterized bacteria and fungi during the
project
Locus: KC161226 Length: 690 bp DNA: linear
Definition: Uncultured Brevundimonas spp. clone viniA711 16S ribosomal RNA gene,
partial sequence.
Origin:
gttcggaata gctcctggaa actggtggta atgccgaatg tgcccttcgg
gggaaagatt tatcgccttt agagcggacc gcgtctgatt agctatttgg
tgaggtaatg gctcaccaag gcgacgatca gtagctggtc tgacaggatg
accatccaca ttgggactga gacacggacc aaactcctac gggaggcaac
actggggaat cttgcgcaat gggcgaaagc ctgacgcttc catgccgggt
gaatgatgaa ggtcttagga ttgtaaaatt ctttcaccgg ggacgataat
gacggtaccc ggagaagaag ccccggctaa cttcgtgcca ccagccgcgg
taatacgaag ggggctagcg ttgctcggaa ttactgggcg taaagggcgc
gtaggcggac atttaagtca ggggtgaaat cccagagctc aactctggaa
ctgcctttga tactgggtgt cttgagtgtg atacaggtat gtggaactcc
gactgtatag gtgaaattcg tacatattca gaagaaaacc agtggcaaat
gcgacatact ggctcattac tgacgctgag gcgcgaaatc ctgaggagca
aacaggatta cataccctgg tagtccactc cgtaaacaat gattgctagt
tgtcgggctg catgcacctc ggtgacgcac ctaacacatt
Locus: KF316951 Length: 254 bp DNA: linear
Definition: Trichoderma harzianum isolate Viniti 18S ribosomal RNA gene, partial
sequence; internal transcribed spacer 1, 5.8S ribosomal RNA gene, and internal
transcribed spacer 2, complete sequence; and 28S ribosomal RNA gene, partial
sequence.
Origin:
269
atgcctgtcc gaacgtcctt tcaaccctcg aaaccctccg gggggtcggc
gttggggaac ggccctgcct ctggcggtgg ccgtctccca aatacagtgg
cggtctcgcc ccaacctctc ctgcgcaata atttgcacac tcccatcggg
agcgcggcgc gtccacagcc gttaaacacc caacttctga aatgttgacc
tcggatcagg taggaatacc cgctgaactt aagcatatca ataagcggaa gaaa
Locus: KF316952 Length: 373 bp DNA: linear
Definition: Candida homilentoma isolate swin 18S ribosomal RNA gene, partial
sequence; internal transcribed spacer 1, 5.8S ribosomal RNA gene, and internal
transcribed spacer 2, complete sequence; and 28S ribosomal RNA gene, partial
sequence.
Origin:
tgagtattct ataacttaac acacaattaa ttagtcaaca caaacataac
ctcaaaactt tcaacaacgg atctcttggt tctcgcatcg atgaagaacg
cagcgaaatg cgataagtaa tatgaattgc agattgtgaa tcatcgaatc
tttgaacgca cattgcaccg tgtggcattc cacacggtat gcctgtttga
gcgtggtttc tctctcagcc cgcgttcctt tttttaagga gcttgggctg
gcggtgagcg gcacacgagt gtttgcttga aagctagtac gactgatagt
acactttaca caaactcccc ctcaaatcag gtaggactac ccgctgaact
taagcatatc aataagcgga gga
Locus: KF316953 Length: 239 bp DNA: linear
Definition: Fusarium spp. VinV4 internal transcribed spacer 1, partial sequence; 5.8S
ribosomal RNA gene and internal transcribed spacer 2, complete sequence; and 28S
ribosomal RNA gene, partial sequence.
Origin:
ctttcaacaa cggatctctt ggctctggca tctatgaaaa acgcaccaaa
atgtgataaa taatgtgaat tgcttaattc agtgaatcat caaatctttg
aacgcacatt gcgcccgcca ttattctggc gggcatgcct gttcaagcgt
270
cattacaacc ctcacgcccc cgggcctggc gttggggacc ggtcgaaccg
ccttttgacc cccccaaccc ccccgggggg tcggcgttg
Locus: KF316954 Length: 398 bp DNA: linear
Definition: Nectria haematococca isolate PVK 18S ribosomal RNA gene, partial
sequence; internal transcribed spacer 1, 5.8S ribosomal RNA gene, and internal
transcribed spacer 2, complete sequence; and 28S ribosomal RNA gene, partial
sequence.
Origin
tgggtaaggg taataactca tcaccctgtg acatacctaa acgttgcttc
ggcgggaata gacggccccg taaaacgggc cgccgccgcc agaggaccct
taactctgtt tctataatgt ttcttctgag taaaacaagc aaataaatta
aaactttcta caacggatct cttggcactg gcatcaatga ataacgctgc
gaaatgcgaa aagtaatgtg aattgcataa ttcattgaat ctgcaactct
tagaacgcac attgaccccc ctcctattca ggttggcagc cagtaagcca
ttgactacac tccgtaggac catgagcatg ccggagacgg aatatttagc
actataatac tgtcaggggc atggtaccaa cggttcactt tcagtgga
271
Appendix 3 Table A1. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux of A. niger during winery biomass degradation
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
Galactose oxime hexakis(trimethylsilyl) 2129.565 628.2 C00124 7.4475 2.40e-09 VHAWQMKIXQYXH
C-UHFFFAOYSA-N
Glucose oxime hexakis(trimethylsilyl) 2129.482 628.3 C00031 7.3366 2.56e-09 VHAWQMKIXQYXH
C-UHFFFAOYSA-N
Xylitol 5TMS 1699.223 513.1 C00379 2.7273 0.017916 SUZLPERYXSOGNY-
UHFFFAOYSA-N
1-O-Methyl-alpha-D-Galactoside 1798.013 194.2 C01019 2.4776 0.12344 UIDVFSCIFIMDHO-
SPOLIRPYSA-N
Shikimic acid (4TMS) 1807.463 462.9 C00493 2.4542 0.13224 LMFAUEGOCMAFIW
-KZNAEPCWSA-N
2,3-Dimethylsuccinic acid 1701.943 146.1 C00042 2.3732 0.023395 KLZYRCVPDWTZLH-
UHFFFAOYSA-N
D-Ribulose 1472.389 150.1 C00309 2.3732 0.023395 ZAQJHHRNXZUBTE-
NQXXGFSBSA-N
N-acetyl-D-glucosamine 1702.012 221.2 C00140 2.3732 0.023395 MBLBDJOUHNCFQT-
UHFFFAOYSA-N
272
Table A1. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux of A. niger during winery biomass degradation (…continued)
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
3,5-Diiodo-L-Tyrosine 2424.713 432.9 C02645 1.8409 0.028522 NYPYHUZRZVSYKL-
UHFFFAOYSA-N
2-keto-d-gluconic acid 5TMS 2142.731 555.1 C06473 1.6884 0.00055 VBUYCZFBVCCYFD-
JJYYJPOSSA-N
D-Psicose, pentakis(trimethylsilyl) ether (mixed anomers) 2142.814 488.6 C06468 1.6884 0.00055 BJHIKXHVCXFQLS-
UHFFFAOYSA-N
2-Keto-L-gulonic acid hydrate 2142.897 194.14 C14899 1.6884 0.00055 WTAHRPBPWHCMH
W-LWKDLAHASA-N
D-(+)-Melibiose monohydrate 2748.823 360.3 C05402 1.6608 0.035676 RGFAEJSLSYUKCO-
RWCKWOMLSA-N
Sedoheptulose-1-phosphate 2118.387 290.2 C05382 1.6517 0.020511 JPTRNFAYXMBCLJ-
VVXDDPDLNA-N
D-Altro-2-Heptulose, anhydrotetrakis-O-(trimethylsilyl)- 2118.387 824.5 C02076 1.6512 0.020569 JDDUOQRGYRTILL-
QCWLDUFUSA-N
Hexanoic acid, 5-oxo-, trimethylsilyl ester 1307.325 218.3 C06762 1.6157 0.22696 PXSCQEMSXNKQJK-
UHFFFAOYSA-N
(+/-)-1,2,4-Butanetriol 1358.391 106.1 C01089 1.5923 0.055938 ARXKVVRQIIOZGF-
UHFFFAOYSA-N
273
Table A1. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux of A. niger during winery biomass degradation (…continued)
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
D-alpha-Hydroxyglutaric acid disodium 1349.982 192.1 C00026 1.5179 0.079283 GFDIGKRHNMDEJF-
GFCCVEGCSA-N
L-(+)-Citramalic acid 1461.449 148.1 C02612 1.447 0.35398 XFTRTWQBIOMVPK-
UHFFFAOYSA-N
D-(+)-Malic acid 1426.514 134.1 C00149 1.4346 0.11695 BJEPYKJPYRNKOW-
REOHCLBHSA-N
3-Trimethylsiloxycapric acid, trimethylsilyl ester 1441.805 276.5 C00249 1.425 0.40954 HWIXAIPBPDYHHJ-
UHFFFAOYSA-N
Arabinose, 2,3,4,5-tetrakis-O-(trimethylsilyl)- 1731.758 438.9 C00259 1.4202 0.31946 DWTGGLMCGFIELV-
UHFFFAOYSA-N
d-Ribose, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, O-
methyloxime BP
1756.810 422.5 C00119 1.4201 0.31952 ZBEJHGUYYNELJI-
RGEXLXHISA-N
D-Glucuronic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-,
trimethylsilyl ester
2412.068 555.0 C00191 1.324 0.10818 HDGFBGPNRURMOV-
UHFFFAOYSA-N
Butanal, 2,3,4-tris[(trimethylsilyl)oxy]-3-
[[(trimethylsilyl)oxy] methyl]-, O-methyloxime, (S)-
1649.556 467.9 C00279 1.2591 0.62864 OKWPSPPSKQXZNS-
XMHGGMMESA-N
274
Table A1. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux of A. niger during winery biomass degradation (…continued)
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
Ribose, D- (1MEOX) (4TMS) 1656.452 467.9 C00121 1.2591 0.62864 ZBEJHGUYYNELJI-
XMHGGMMESA-N
Urea (2TMS) 1461.332 205.0 C00086 1.2557 0.55849
MASDFXZJIDNRTR-
UHFFFAOYSA-N
Pentanoic acid, 3-methyl-3,5-bis[(trimethylsilyl)oxy]-,
methyl ester
1461.449 306.5 C00803 1.2556 0.5585 KKMAJHUYGPAZQG-
UHFFFAOYSA-N
Acetic acid, [(trimethylsilyl)oxy]-, trimethylsilyl ester 1448.569 220.4 C00024 1.2477 0.53841 MAEQOWMWOCEXK
P-UHFFFAOYSA-N
Glyceraldehyde (1MEOX) (2TMS) 1216.385 263.5 C00118 1.2136 0.46383 MNQZXJOMYWMBO
U-VKHMYHEASA-N
Xylonic acid, 2,3,5-tris-O-(trimethylsilyl)-, gamma-lactone,
D-
1644.570 364.7 C00502 1.1732 0.75409 RJMKTJPGOIYTQZ-
UHFFFAOYSA-N
Dodecanoic acid, n- ( 1TMS) 1649.688 272.5 C02679 1.154 0.75502 RHDZBWSXEZTMPK-
UHFFFAOYSA-N
Sorbopyranose, 1,2,3,4,5-pentakis-O-(trimethylsilyl)-, L- 2274.988 488.6 C00247 1.0591 0.80946 PJXWXHJDJODISP-
UHFFFAOYSA-N
275
Table A1. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux of A. niger during winery biomass degradation (…continued)
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
Butanoic acid, 2-(methoxyimino)-3-methyl-, trimethylsilyl
ester
1052.143 217.3 C00141 1.0513 0.028223 CALQXVJPGUTRRJ-
CSKARUKUSA-N
D-5-Deoxyribofuranose, 5-S-methyl-5-thio-1,2,3-tris-O-
(trimethylsilyl)-
1667.271 396.8 C04188 1.0148 0.95195 VSJSAJSCVZDVTO-
UHFFFAOYSA-N
2-Ketoisocaproic acid oxime, bis(trimethylsilyl)- derivative 1165.945 289.5 C00233 1.0033 0.79154 RBTBRFPPEAKNKU-
UHFFFAOYSA-N
276
Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum.
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
2-Ketoisocaproic acid oxime 1165.973 289.5187 C00233 1.1426 8.88e-14 RBTBRFPPEAKNKU-UHFFFAOYSA-N
Butanoic acid, 3-methyl-3-[(trimethylsilyl)oxy]-, trimethylsilyl ester 1205.645 262.4933 C04405 1.8101
0.000922
CEMBCVWMQGTNRL-UHFFFAOYSA-N
Benzoic acid 1244.733 194.3025 C00180 2.164
9.67e-05
VFFKJOXNCSJSAQ-UHFFFAOYSA-N
Serine, L- (2TMS) 1253.029 249.455 C00065 1.6295
0.01423
MWERNBWITQOYEZ-QMMMGPOBSA-N
Ethanolamine (3TMS) 1262.909 277.627 C00189 1.6522
0.009685
MZTOEZRSUGVLOG-UHFFFAOYSA-N
Phosphoric acid, bis(trimethylsilyl) 2,3-bis[(trimethylsilyl)oxy]propyl ester 1755.238 460.7982 C00009 0.5694
0.035744
OSNKBCBCOYHYNF-UHFFFAOYSA-N
2-Deoxy ribose O,O',O''-tris(trimethylsilyl)- 1298.497 350.6739 C01801 1.0695
0.60459
WJEJOIVWMARKGR-UHFFFAOYSA-N
2-Ketoisocaproic acid, trimethylsilyl ester 1349.757 202.3229 C02504 1.0696
0.60412
MCPFMKJHOFUFFW-UHFFFAOYSA-N
Succinic acid (2TMS) 1312.052 262.451 C00042 1.0626
0.6436
QUUDZINXVRFXLB-UHFFFAOYSA-N
Propanoic acid, 2,3-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester 1325.581 322.6207 C00163 1.0696
0.60413
VTLZEKUPFUIGJQ-UHFFFAOYSA-N
Glyceric acid (3TMS) 1326.021 322.621 C00258 1.0696
0.60412
VTLZEKUPFUIGJQ-NSHDSACASA-N
Fumaric acid (2TMS) 1347.824 260.435 C00122 1.0798
0.64332
OITVFMRNHJZOHF-BQYQJAHWSA-N
277
Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum (…continued).
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
Mevalonic acid-1,5-lactone (1TMS) 1362.839 202.323 C00418 1.0838
0.67966
CNPIMSSHNMVFPM-UHFFFAOYSA-N
Threonic acid-1,4-lactone (2TMS) 1367.765 262.451 C01620 1.4582
0.000956
ASNCBNVBJKSVIT-BDAKNGLRSA-N
2(3H)-Furanone, dihydro-3,4-bis[(trimethylsilyl)oxy]-, cis- 1367.867 262.451 C03393 1.4581
0.000957
ASNCBNVBJKSVIT-BDAKNGLRSA-N
Glutaric acid (2TMS) 1401.385 276.477 C00489 1.5381
0.009207
UWZGPXOJVOOLSG-UHFFFAOYSA-N
Uridine-5'-diphosphogalactose disodium salt 1409.930 610.266 C00052 2.8227
1.52e-05
PKJQEQVCYGYYMM-OUJOOSCPSA-L
β-Alanine (3TMS) 1423.217 305.637 C00099 1.9927
0.000205
MKLCEJSOJVLNMP-UHFFFAOYSA-N
Acetic acid, [(trimethylsilyl)oxy]-, trimethylsilyl ester 1448.576 220.4136 C00033 0.1546
6.50e-05
MAEQOWMWOCEXKP-UHFFFAOYSA-N
Butanoic acid, 2-methyl-3-[(trimethylsilyl)oxy]-, trimethylsilyl ester 1451.390 262.4933 C00141 1.0629
0.78476
REMFBAMFOOTXSI-UHFFFAOYSA-N
Butyric acid, 4-amino- (3TMS) 1525.218 319.663 C00334 1.4476
0.041965
HMZQEGNJWXKANK-UHFFFAOYSA-N
3,3,3-Tris(trimethylsilyl)propan-1-ol 1527.767 276.638 C05979 1.9332
0.000881
BDERNNFJNOPAEC-UHFFFAOYSA-N
L-(+)-Tartaric acid 1543.186 150.0868 C00898 1.0372
0.84074
FEWJPZIEWOKRBE-LWMBPPNESA-N
L-Arabinonic acid, γ-lactone 1543.259 C01114 148.11
4 1.037
0.84149
CUOKHACJLGPRHD-YVZJFKFKSA-N
278
Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum (…continued).
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
α-D-Galactofuranosiduronic acid, methyl 2,3,5-tris-O-(trimethylsilyl)-, methyl ester 1543.395 438.7359 C00426 1.0289
0.87843
PUYBWLWFNZAWFM-UHFFFAOYSA-N
Glutaric acid, 2-hydroxy- (3TMS) 1553.943 364.658 C00026 2.3018
0.05978
GFDIGKRHNMDEJF-GFCCVEGCSA-N
D-Ribonic acid, 2,3,5-tris-O-(trimethylsilyl)-, γ-lactone 1586.376 364.6574 C01685 1.3237
0.057253
RJMKTJPGOIYTQZ-UHFFFAOYSA-N
D-Arabino-Hexitol, 2-deoxy-1,3,4,5,6-pentakis-O-(trimethylsilyl)- 1591.007 527.078 C04691 1.3529
0.17245
ULCPMKYKEVLMSQ-UHFFFAOYSA-N
D-Erythro-Pentitol, 2-deoxy-1,3,4,5-tetrakis-O-(trimethylsilyl)- 1591.040 424.8709 C11435 1.3529
0.17245
HXWBUBLOJVCOEO-UHFFFAOYSA-N
2-Piperidine carboxylic acid, 1-(trimethylsilyl)-5-[(trimethylsilyl)oxy]-, trimethylsilyl ester 1593.623 273.5193 C00408 1.5593
0.005006
CPDPZJUOATWIOD-UHFFFAOYSA-N
Pentanedioic acid, 3-methyl-3-[(trimethylsilyl)oxy]-, bis(trimethylsilyl) ester 1596.584 378.684 C06007 1.6915
0.000694
SWDDFRVEZJUJJD-UHFFFAOYSA-N
Fucose, DL- (1MEOX) (4TMS) 1602.643 481.923 C01019 1.0973
0.56492
SXUVIFIZLGMRBJ-ORJDBQSNSA-N
D-Ribofuranose, 1,2,3,5-tetrakis-O-(trimethylsilyl)- 1609.245 438.8544 C00121 1.1589
0.4671
LDFPXMNJVPETIY-UHFFFAOYSA-N
α-DL-Arabinopyranose, 1,2,3,4-tetrakis-O-(trimethylsilyl)- 1609.103 438.8544 438.8544
1.1586
0.46794
KEOUSSOURMHEKN-YYIAUSFCSA-N
D-Xylopyranose, 1,2,3,4-tetrakis-O-(trimethylsilyl)- 1621.056 438.85438
438.85438
2.5262
1.11e-06
KEOUSSOURMHEKN-UHFFFAOYSA-N
Xylonic acid, 2,3,4-tris-O-(trimethylsilyl)-, δ-lactone, D- 1621.404 364.6574 364.6574
1.804
8.01e-05
RWGTVHCZLUFFAU-UHFFFAOYSA-N
279
Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum (…continued).
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
Lyxose, D- (1MEOX) (4TMS) 1656.440 467.8956 C00476 1.505
0.04653
ZBEJHGUYYNELJI-KZNAEPCWSA-N
Xylose, D- (1MEOX) (4TMS) 1656.455 467.8956 C00181 1.505
0.04653
ZBEJHGUYYNELJI-RCCFBDPRSA-N
D-5-Deoxyribofuranose, 5-S-methyl-5-thio-1,2,3-tris-O-(trimethylsilyl)- 1667.270 396.7654
4 C04188 1.4259
0.25347
VSJSAJSCVZDVTO-UHFFFAOYSA-N
Erythritol (4TMS) 1680.215 410.845 C00503 2.0797
0.00275
ZBFVOPVQUXWTHM-IYBDPMFKSA-N
2,3,5-Tri-o-trimethylsilyl-xylono-1,5-lactone 1680.290 148.114 C02266 2.0797
0.00275
XXBSUZSONOQQGK-FLRLBIABSA-N
Mannose, 6-deoxy-2,3,4,5-tetrakis-O-(trimethylsilyl)-, L- 1686.424 481.923 C00507 2.8706
6.32e-07
SXUVIFIZLGMRBJ-HMMYKYKNSA-N
Xylitol 5TMS 1699.245 513.052 C00379 1.1663
0.72239
SUZLPERYXSOGNY-UHFFFAOYSA-N
2,3-Dimethylsuccinic acid 1701.972 146.1412 C00091 6.255
0.000768
KLZYRCVPDWTZLH-UHFFFAOYSA-N
D-Ribulose 1472.212/ 1184.853
150.13 C00309 6.255
0.000768
ZAQJHHRNXZUBTE-NQXXGFSBSA-N
N-acetyl-D-glucosamine 1702.041 221.2078 C00140 6.255
0.000768
MBLBDJOUHNCFQT-UHFFFAOYSA-N
Ribitol, D- (5TMS) 1713.261 513.052 C00474 2.5064
0.00227
SUZLPERYXSOGNY-ACDBMABISA-N
Arabitol, D- (5TMS) 1718.419 513.052 C01904 1.4384
0.006646
SUZLPERYXSOGNY-RTBURBONSA-N
280
Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum (…continued).
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
Putrescine (4TMS) 1731.797 376.876 C00134 1.189
0.40852
HALMUIBMTWWREW-UHFFFAOYSA-N
Glycerol-3-phosphate, DL- (4TMS) 1754.952 460.798 C00093 2.9436
0.000658
OSNKBCBCOYHYNF-HNNXBMFYSA-N
Sorbopyranose, 1,2,3,4,5-pentakis-O-(trimethylsilyl)-, L- 1808.785/ 2274.986
541.0615 C00247 2.2887
0.008467
PJXWXHJDJODISP-UHFFFAOYSA-N
Citric acid (4TMS) 1812.142 480.848 C00158 1.5279
0.098174
VFGAVMGYDWDESE-UHFFFAOYSA-N
Fructose, D- (1MEOX) (5TMS) 1864.373/ 2729.258
541.062 C00095 1.5059
0.046356
PLNWQGWZBNJIQM-UHFFFAOYSA-N
Trimethylsilyl 3,5-dimethoxy-4-(trimethylsilyloxy)benzoate 1890.594 342.5349 C10833 1.8027
0.000322
KWOFGVXBWXVAJU-UHFFFAOYSA-N
Galacturonic acid, D- (1MEOX) (5TMS) 1934.981 584.087 C00333 1.2679
0.20619
FKLGHFYFJKVJFQ-WOCUKSDZSA-N
Benzoic acid, 3,4,5-tris(trimethylsiloxy)-, trimethylsilyl ester 1950.357 458.844 C00156 1.23
0.15086
KCIFUQKNKZTHGI-UHFFFAOYSA-N
Inositol, 1,2,3,4,5,6-hexakis-O-(trimethylsilyl)-, scyllo- 2019.775 613.2426 C06153 1.1663
0.31948
FRTKXRNTVMCAKI-QJJHVYBSSA-N
1H-Indole-3-acetic acid, 1-(trimethylsilyl)-, ethyl ester 2034.280 275.41824
C00954 1.15
0.37418
ZYFCMPGFRITKAM-UHFFFAOYSA-N
Hexadecanoic acid, n- (1TMS) 2042.652 328.606 C00249 1.8481
0.001537
HKNNSWCACQFVIK-UHFFFAOYSA-N
Inositol, myo- (6TMS) 2082.670 613.243 C00137 1.8481
0.001537
FRTKXRNTVMCAKI-HPEVMFQJSA-N
281
Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum (…continued).
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
D-Mannose, 2-(acetylamino)-2-deoxy-, tetrakis(trimethylsilyl) deriv. 2084.912 509.932 C00645 1.2383
0.27276
DPZWOYBZRJCTQP-UHFFFAOYSA-N
Sedoheptulose TMS 2118.444 643.2686 C02076 1.6839
0.000789
UZWIKIWXLGTVJV-UHFFFAOYSA-N
Glucose oxime hexakis(trimethylsilyl) 2129.521 628.2572 C00031 1.7574
0.086144
VHAWQMKIXQYXHC-UHFFFAOYSA-N
Galactose oxime hexakis(trimethylsilyl) 2129.641 628.2572 C00124 1.7594
0.085678
VHAWQMKIXQYXHC-UHFFFAOYSA-N
2-keto-d-gluconic acid 5TMS 2142.758 584.087 C06473 1.3093
0.27124
VBUYCZFBVCCYFD-JJYYJPOSSA-N
D-Psicose (mixed anomers) 2142.837 180.156 C06468 1.3093
0.27124
BJHIKXHVCXFQLS-UHFFFAOYSA-N
2-Keto-L-gulonic acid hydrate 2142.936 194.1394
C15673 1.3093
0.27124
WTAHRPBPWHCMHW-LWKDLAHASA-N
Linoleic acid ethyl ester 2158.334 308.4986 C01595 2.0934
0.063675
FMMOOAYVCKXGMF-MURFETPASA-N
Ethyl Oleate 2164.847 310.5145 D04090 2.7904
0.011851
LVGKNOAMLMIIKO-QXMHVHEDSA-N
Oleic acid, trimethylsilyl ester 2225.456 354.6425 C00712 3.9425
0.006763
GAODWSYPSJBKGB-SEYXRHQNSA-N
Galactitol, D- (6TMS) 2243.612/ 2515.697
615.259 C01697 1.0966
0.726
USBJDBWAPKNPCK-NVPYSNMXSA-N
Octadecanoic acid, trimethylsilyl ester 2279.143 356.6584 C01530 1.1007
0.40744
DDLPZVTUKLKVQB-UHFFFAOYSA-N
282
Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum (…continued).
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
Sucrose, D- (8TMS) 2289.902 919.746 C00089 1.2191
0.18686
JDAONBLATRVCRV-YYEYMFTQSA-N
Inulobiose (impurity: Sucrose) 2595.917/ 2951.808
342.297 C01711 1.7779
0.007807
WOHYVFWWTVNXTP-SQOWBOMSSA-N
Galactopyranoside, 1-O-methyl-, α-D- (4TMS) 2290.969 482.907 C02985 1.3395
0.052446
UIDVFSCIFIMDHO-SPOLIRPYSA-N
D-(-)-Citramalic acid 2291.697 148.11402
C02612 1.2201
0.18532
XFTRTWQBIOMVPK-RXMQYKEDSA-N
D-Altrose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)- 2333.160 527.0349 C06464 1.1783
0.39585
DMYXNOJXHNOHDN-WAPOTWQKSA-N
Melatonin 2343.197 232.2783 C01598 1.0512
0.72168
DRLFMBDRBRZALE-UHFFFAOYSA-N
D-Glucuronic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, trimethylsilyl ester 2412.129 555.045 C00191 2.4483
0.000416
HDGFBGPNRURMOV-UHFFFAOYSA-N
3,5-Diiodo-L-Tyrosine 2424.825 432.9816 C01060 2.9471
7.63e-05
NYPYHUZRZVSYKL-UHFFFAOYSA-N
Mannonic acid, 2,3,4,6-tetrakis-O-(trimethylsilyl)-, lactone 2520.426 466.8645 C03107 1.1105
0.60704
VNGTZLYNGGLPIZ-VQHPVUNQSA-N
α-D-Glucose-1-phosphate, dipotassium salt dihydrate 2523.959 276.179 C00103 0.7856
0.24065
JXLMWHHJPUWTEV-QMKHLHGBSA-L
Gulose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)- 2562.538/ 2875.852
541.0615 C15923 1.0192
0.92422
PPTMWEDTYQRQBC-UHFFFAOYSA-N
D-(+)-Melibiose monohydrate 2748.831 360.3118 C05402 2.5419
8.65e-05
RGFAEJSLSYUKCO-RWCKWOMLSA-N
283
Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum (…continued).
Metabolite Kovats
Index
m/z KEGG
ID
FC P-value InChI Key
Trehalose, α, α'-, D - (8TMS) 2759.013 919.746 C01083 1.1582
0.31803
YQFZNYCPHIXROS-UHFFFAOYSA-N
Maltose, octakis(trimethylsilyl)- 2893.459 919.7454 C00208 1.3086
0.21046
QJZFVYJIGWEKIR-UHFFFAOYSA-N
Docosanoic acid, trimethylsilyl ester 3020.779 412.7647 C08281 1.0835
0.67229
BJBBFOVJCKMBAL-UHFFFAOYSA-N
Tocopherol, α- (1TMS) 3101.754 502.888 C02477 1.2082
0.28607
MJWXSUWKOCZGBM-NCLPYWGKSA-N
Appendices
284
Appendix 4
P. chrysogenum metabolic flux: Secondary data
Figure A5. Periodic concentration of glycolysis pathway metabolites detected in P. chrysogenum
during winery biomass waste degradation of Shiraz grapes over 8 days.
Figure A6. Periodic concentration of TCA cycle metabolites detected in P. chrysogenum during
winery biomass waste degradation of Shiraz grapes over 8 days.
020406080
100120140160180
0 4 5 7 8
Con
cent
ratio
n (m
g/L
)
Gycolysis duration (days) Glucose-1-P D-Glucose UDP-Galactose Galactitol
D-Galactose Glycerate-3-P Acetyl-CoA
0
100
200
300
400
500
600
0 4 5 7 8
Con
cent
ratio
n (m
g/L
)
TCA duration (days)
Citrate 2-oxo-glutarate Succinyl-CoA Succinate Fumarate
Appendices
285
Figure A7. Gluconolactone metabolism pathway metabolites detected in P. chrysogenum during
winery biomass waste degradation of Shiraz grapes over 8 days.
Figure A8. Maltose metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
0102030405060708090
100
0 4 5 7 8
Con
cent
ratio
n (m
g/L
)
Glucono-1,5-lactone metabolism duration (days)
D-Glucose D-Glucono-1,5-lactone
0
50
100
150
200
250
300
350
0 4 5 7 8
Con
cent
ratio
n (m
g/L
)
Maltose metabolism duration (days)
D-Glucose α, α'-Trehalose Maltose
Appendices
286
Figure A9. Sucrose metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
Figure A10. Fructose metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
0102030405060708090
100
0 4 5 7 8
Con
cent
ratio
n (m
g/L
)
Sucrose metabolism duration (days)
D-Glucose Sucrose Psicose
0
50
100
150
200
250
300
350
400
0 4 5 7 8
Con
cent
ratio
n (m
g/L
)
Fructose metabolism duration (days)
Glucose-1-P Inulobiose D-Fructose
Appendices
287
Figure A11. Lecithin metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
Figure A12. Terpenoid metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
0
50
100
150
200
250
300
0 3 6 8
Con
cent
ratio
n (m
g/L
)
Lecithin metabolism duration (days) Glycerate-3-P L-Serine Propanol L-Tartarate Ethanolamine
0
0.01
0.02
0.03
0.04
0.05
0.06
0
50
100
150
200
250
300
0 3 6 8
Con
cent
ratio
n (m
g/L
)
Con
cent
ratio
n (m
g/L
)
Terpenoid metabolism duration (days) Glycerate-3-P
4-(Cystediene-5-diphospho)-2-C-methyl-D-erythritol
α-Tocopherol
Appendices
288
Figure A13. Deoxy sugar metabolism pathway metabolites detected in P. chrysogenum during
winery biomass waste degradation of Shiraz grapes over 8 days.
Figure A14. Fucose metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
0
50
100
150
200
250
300
0 3 6 8
Con
cent
ratio
n (m
g/L
)
Deoxy sugar metabolism duration (days) Glycerate-3-P D-Ribonate D-Ribose 5-P-α-D-Ribose-1-P Deoxyribose
0
50
100
150
200
250
300
0 3 6 8
Con
cent
ratio
n (m
g/L
)
Fucose metabolism duration (days)
Glycerate-3-P L-Rhamnose L-Fucose L-Fucose-1-P
Appendices
289
Figure A15. Pectin metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
Figure A16. Isoleucine metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
0
50
100
150
200
250
300
0 3 6 8
Con
cent
ratio
n (m
g/L
)
Pectin metabolism duration (days) Glycerate-3-P 2-Keto-D-gluconate 2-Dihydro-L-idonate
02468
1012141618
0 3 7 8
Con
cent
ratio
n (m
g/L
)
Isoleucine biosynthesis duration (days)
Acetyl-CoA (R)-2,3-Dihydroxy-3-methyl valerate
Appendices
290
Figure A17. Yan cycle metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
Figure A18. Arginine metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz grapes over 8 days.
0
10
20
30
40
50
60
0 3 7 8
Con
cent
ratio
n (m
g/L
)
Yang cycle duration (days)
Acetate L-Serine S-Methyl-5-thio-D-ribose-1-P
02468
1012141618
0 3 7 8
Con
cent
ratio
n (m
g/L
)
Arginine metabolism duration (days)
Acetyl-CoA Fumarate Putrescine
Appendices
291
Figure A19. Ascorbate metabolism pathway metabolites detected in P. chrysogenum during winery
biomass waste degradation of Shiraz
0
20
40
60
80
100
120
0 3 7 8
Con
cent
ratio
n (m
g/L
)
Ascorbate metabolism duration (days) Acetyl-CoA L-Gulose D-Glucuronate L-Threonate
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