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Page 1: Effects of glucose overloading on microbial community structure and biogas production in a laboratory-scale anaerobic digester

Effects of glucose overloading on microbial community structureand biogas production in a laboratory-scale anaerobic digester

Ingvar Sundh a,*, Helena Carlsson a, �AAke Nordberg b, Mikael Hansson b, Berit Mathisen b

a Department of Microbiology, Swedish University of Agricultural Sciences, P.O. Box 7025, SE-750 07 Uppsala, Swedenb Swedish Institute of Agricultural and Environmental Engineering, P.O. Box 7033, SE-750 07 Uppsala, Sweden

Received 8 October 2002; received in revised form 10 February 2003; accepted 25 February 2003

Abstract

This study characterizes the response of the microbial communities of a laboratory-scale mesophilic biogas process, fed with a

synthetic substrate based on cellulose and egg albumin, to single pulses of glucose overloading (15 or 25 times the daily feed based on

VS). The microbial biomass and community structure were determined from analyses of membrane phospholipids. The ratio be-

tween phospholipid fatty acids (PLFAs; eubacteria and eucaryotes) and di-ethers (PLEL; archaea) suggested that methanogens

constituted 4–8% of the microbial biomass. The glucose addition resulted in transient increases in the total biomass of eubacteria

while there were only small changes in community structure. The total gas production rate increased, while the relative methane

content of the biogas and the alkalinity decreased. However, the biomass of methanogens was not affected by the glucose addition.

The results show that the microbial communities of biogas processes can respond quickly to changes in the feeding rate. The glucose

overload resulted in a transient general stimulation of degradation rates and almost a doubling of eubacterial biomass, although the

biomass increase corresponded to only 7% of the glucose C added.

� 2003 Elsevier Science Ltd. All rights reserved.

Keywords: Biogas; Microbial community structure; PLFA; Substrate overload; Methanogenesis; Di-ether lipid

1. Introduction

In biogas production from organic materials, the in-

tegrated action of several types of microorganisms,

which perform different degradation steps, results in the

sequential degradation of polymeric carbohydrates,

proteins and fats (Gujer and Zehnder, 1983; Schink,

1988). Functionally, the organisms can be broadly

classified into hydrolytic, fermentative organic acid-producing, acetate-producing and, in the terminal step,

methane-producing, organisms.

Due to low energy yields, many anaerobic microor-

ganisms grow slowly, the methanogens in particular.

Therefore, in completely mixed systems with relatively

short retention times, disturbance due to, for example,

poor composition or overloading of the substrate, ac-

cumulation of inhibitory substances, or presence of an-thropogenic contaminants, may inhibit the active

organisms and result in serious malfunctioning of the

process. However, knowledge of the response of themicrobial communities to different kinds of disturbance

is still meagre, particularly regarding the communities�structural characteristics.

In the present study, we investigated the effects of

substrate overload, with a single dose of glucose, on the

size and structure of the microbial community and on

the biogas production in a laboratory scale biogas pro-

cess fed with a synthetic substrate. The microbial bio-mass and community structure were determined from

analysis of membrane phospholipids. In addition, the

fate of the added glucose was determined by construc-

tion of a C budget.

2. Methods

2.1. Reactor system

The reactor was a continuously mixed glass tank of8 l active volume, operated under mesophilic condi-

tions (37 �C). It was started with digester contents froma larger reactor which had operated with the same

*Corresponding author. Tel.: +46-18-673210; fax: +46-18-673392.

E-mail address: [email protected] (I. Sundh).

0960-8524/03/$ - see front matter � 2003 Elsevier Science Ltd. All rights reserved.

doi:10.1016/S0960-8524(03)00075-0

Bioresource Technology 89 (2003) 237–243

Page 2: Effects of glucose overloading on microbial community structure and biogas production in a laboratory-scale anaerobic digester

substrate for three years. The substrate consisted of a

mixture of microcrystalline cellulose, egg albumin and

vitamins in a mineral medium (Nordberg et al., 2000).

The experimental reactor was automatically fed every

12th h and the hydraulic retention time was 20 d. The

regular feeding corresponded to 1.0 gVS l�1 d�1.

Two experiments with glucose additions were carried

out, separated by five months, where extra glucose wasadded representing approximately 15 (Experiment B)

and 25 (Experiment C) times (in gVS l�1 d�1) the normal

daily feeding rate, with cellulose and albumin. The

glucose was added as a single dose, which replaced one

regular feeding, after which normal feeding rates were

resumed. A second reactor operated in an identical

manner served as a control and did not receive glucose

at any stage.

2.2. Microbial biomass and community structure

The microbial communities were characterized fromanalyses of normal phospholipid fatty acids (PLFAs)

from eucaryotes and bacteria, and of phospholipid die-

ther lipids (PLELs) from methanogenic archae. Samples

for lipid analysis were collected immediately before and

after the glucose addition, after 4 h, and then at intervals

during a 20 day period. 2-ml aliquots of the reactor

contents were immediately frozen in extraction tubes

and stored at )20 �C until analysis. Detailed descri-ptions of the lipid extractions have been given previously

(Sundh et al., 1997). Briefly, the total lipid fraction of

the reactor contents was extracted in a one-phase chlo-

roform/methanol/water extraction. The lipids were then

fractionated with silicic acid chromatography. The polar

fraction (containing the phospholipids) was subjected to

an alkaline methanolysis, after which the normal fatty

acids were recovered as methyl esters. The subsequentderivatizations of the intact diether lipids with

bis(trimethylsilyl)trifluoroacetamide (BSTFA), quantifi-

cations of the lipid components with gas chromato-

graphy (GC) with a flame ionization detector, and

identifications with a GC with a mass selective detector

were made according to Virtue et al. (1996). However, in

contrast to them, we used helium as the carrier gas and a

30 m HP1-MS column. The quantifications were madeby using the methyl ester of the fatty acid 19:0 as in-

ternal standard and identifications were facilitated by

comparisons with standard mixtures (standards were

obtained from Larodan Fine Chemicals AB, Lim-

hamnsg�aardens all�ee 9, SE-216 16 Malm€oo, Sweden). Therelative amounts of cis and trans isomers of monoun-

saturated PLFAs were determined after dimethyldisul-

phide (DMDS) derivatizations followed by GC-MSanalysis (Nichols et al., 1986).

The coefficient of variation (CV) for PLFA determi-

nations in replicate samples from the reactor was 19%,

both for the total concentration and the mean for indi-

vidual PLFAs. For the diether PLEL the CV was 42%.

2.3. Analytical methods for gas and glucose

The total gas flow from the reactor was continously

registered using a gas meter with a volume of 50 ml percycle, calibrated against a wet gas meter (Schlumberger

Industries, Meterfabriek B.V.). Methane and carbon

dioxide concentrations in the reactor gas were measured

with gas chromatography, and as the gas volume ab-

sorbed in 7 M NaOH, respectively (Jarvis et al., 1995).

The glucose concentration was measured with HPLC

(Schn€uurer et al., 1996) and alkalinity with acid titration(APHA, 1985).

2.4. Fate of added glucose

A carbon budget for the added glucose was con-

structed for the total duration of Experiment B, i.e. one

hydraulic retention time. The following sinks for the

added glucose were considered: 1. Total amounts ofmethane and carbon dioxide leaving the reactor (also

corrected for gas produced from the regular substrate).

2. Glucose passing the reactor without being metabo-

lized. 3. Increase in microbial biomass in the reactor

contents. 4. Microbial biomass washed out of reactor.

5. Propionate and acetate washed out of reactor.

We did not correct the flow of carbon dioxide for

losses of bicarbonate from the reactor fluid due to dropsin pH and alkalinity, since both parameters returned to

initial values well before the end of the experiment.

2.5. Data analysis

In order to detect general, systematic variation in thefatty acid composition, the PLFA data were treated with

principal component analysis (PCA), performed with

the JMP software. ‘‘Standardized principal compo-

nents’’ calculations were used, where the principal

component scores were scaled to have unit instead of

canonical variance. By investigating two-dimensional

plots of the principal components (PCs), general differ-

ences among samples could be detected.

3. Results

3.1. Effects on microbial communities

During the two sampling periods, a total of 22 diffe-

rent PLFAs and one PLEL were quantified in the re-

actor contents (Table 1). There were traces of otherdiethers that could not be quantified. Nineteen of the

normal fatty acids were common to both experiments.

Branched-chain and saturated PLFAs with uneven

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numbers of carbons were most prominent, showing that

gram-positive and anaerobic gram-negative eubacteria

dominated the community. With the exception of small

amounts of 18:2, polyunsaturated fatty acids were

missing, demonstrating a lack of eucaryotic microor-

ganisms. The ratio of PLEL to PLFAs is an estimate ofthe relative contribution of methanogens to the micro-

bial biomass. With respect to complete phospholipid

molecules, the average amount of PLEL was 3.5% and

8.3% of the PLFAs in the two experiments.

In both experiments, the total PLFA concentration

roughly doubled in response to glucose addition,

showing that the eubacterial biomass increased sub-

stantially (Fig. 1). On the other hand, there was noobvious change in PLELs (Fig. 1), and hence not in

methanogenic biomass. However, the latter differed by a

factor of two between the two overloading experiments.

Total PLFA concentration peaked after 3–4 days and

then gradually declined, returning to its initial level

approximately three weeks after glucose addition.

The glucose overloadings resulted in time-dependent

changes in the PLFA composition in the reactor. Whenthe PLFA data from the two experiments were separately

treated by PCA, regular changes with time were evident

(Fig. 2).Most of the quantitatively dominating fatty acids

were high in PC1, i.e. they had a peak in concentration 2–

4 days after the glucose addition. Most of the fatty acids

responsible for the separation of early and late samples in

the PCA (e.g. 16:1x7, 18:1x9, 18:0) had overall lowconcentrations (Table 1). Thus, the time-dependent

changes demonstrated by the PCA are caused both by an

increase in total PLFA concentration and by changes in

the relative contributions of individual fatty acids.

In a simultaneous PCA with all samples, the generalpattern of movement with time in the score plot, was

similar in both experiments, showing that the effect of

the glucose on the microbial community structure was

similar. However, the two overloading experiments were

clearly separated in the score plot, showing that they

had slightly different PLFA composition overall. The

main causes for this separation were the three fatty acids

occurring in only one of the experiments (i17:1, 20:0,cy21:0), and the overall different concentrations of 18:2,

i16:0 and PLEL.

The abundances of the trans isomers of the mono-

unsaturated PLFAs were determined in Experiment C.

The average trans/cis ratio of 16:1x7, 18:1x7 and18:1x9 was generally low (0.6–1.6%) and did not changesignificantly during the course of the experiment. These

low ratios show that the nutrient supply to the microbialcommunity was generally high.

3.2. Process parameters

During normal feeding rates with cellulose and al-

bumin, glucose was below the detection limit (0.2 g l�1).

Table 1

Average concentrations of normal phospholipid fatty acids (PLFAs; in mol % for the individual fatty acids) and archaeal diether lipids (PLEL; in

nmolml�1) in a mesophilic biogas reactor fed a synthetic substrate mixture and receiving instant overloadings with glucose

PLFA 15 Times overloading (B) 25 Times overloading (C)

Normal After overload Normal After overload

i13:0 0.81 (0.010) 1.05 (0.35) 1.47 (0.11) 1.33 (0.19)

a13:0 0.51 (0.027) 0.74 (0.13) 0.70 (0.093) 0.90 (0.12)

i14:0 4.9 (0.069) 5.5 (0.61) 4.4 (0.16) 5.5 (0.77)

14:0 2.9 (0.11) 2.3 (0.61) 2.2 (0.097) 3.2 (1.1)

i15:0 20.6 (0.17) 22.9 (4.1) 25.4 (0.29) 26.4 (3.9)

a15:0 28.3 (0.059) 30.5 (2.0) 25.8 (0.30) 24.6 (1.5)

15:0 2.9 (0.072) 7.2 (2.2) 6.6 (0.46) 11.1 (2.6)

i16:0 11.1 (0.064) 7.4 (2.4) 4.5 (0.075) 3.6 (0.60)

16:1x7 0.81 (0.005) 0.67 (0.17) 1.1 (0.24) 0.99 (0.32)

16:0 5.9 (0.051) 4.4 (1.2) 5.9 (0.13) 4.9 (0.91)

i17:1 0.81 (0.26) 0.51 (0.18)

i17:0 5.4 (0.011) 4.0 (0.64) 5.8 (0.11) 4.8 (1.5)

a17:0 5.0 (0.004) 3.9 (0.62) 5.1 (0.12) 3.7 (0.68)

17:0 1.6 (0.011) 2.6 (1.09) 1.5 (0.050) 2.0 (0.58)

18:2 2.4 (0.055) 1.6 (0.43) 1.4 (0.31) 0.84 (0.23)

18:1x9 2.8 (0.031) 2.0 (0.30) 4.4 (0.16) 2.7 (0.54)

18:1x7 0.54 (0.001) 0.42 (0.11) 0.47 (0.026) 0.39 (0.056)

18:0 2.1 (0.072) 1.5 (0.31) 2.6 (0.12) 1.6 (0.26)

10Me18:0 0.38 (0.085) 0.23 (0.029) 0.24 (0.12)

20:1 0.62 (0.010) 0.46 (0.082) 0.50 (0.051) 0.47 (0.079)

20:0 0.37 (0.042) 0.33 (0.17)

cy21:0 0.21 (0.11)

Total PLFAs (nmolml�1) 160 (2.7) 212 (39.9) 121 (2.7) 182 (40.3)

PLEL (nmolml�1) 3.86 (0.98) 3.42 (0.98) 7.33 (0.26) 7.62 (0.73)

Data are given separately for normal, pre-overload, conditions and for a three-week period following overload. SD is shown within parenthesis.

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The added glucose was rapidly consumed and the con-

centration had returned to non-detectable levels after 2–

3 days (Fig. 3). Simultaneously, the alkalinity droppedfrom roughly 7, to 5 and 4 g CaCO3 l�1, after 15 and 25

times overloading, respectively. The alkalinity gradually

returned to initial values after the glucose was consumed

(Fig. 3).

The flow of biogas increased substantially as an im-

mediate result of the glucose addition. The flow of car-

bon dioxide returned to pre-glucose rates sooner than

the flow of methane. As long as free glucose was avail-able, carbon dioxide flow exceeded methane production.

Thereafter, the biogas composition changed back to

dominance by methane. The regular feeding occasions

were evident as regular cycles of increased production of

methane and carbon dioxide.

The sum of the glucose sinks considered amounted to

3.17 mol C (Table 2). This is 56% of the glucose carbon

in the reactor immediately after the addition (the firstsamples were taken approximately 5 min after addition).

The calculation assumes that the degradation of the

regular substrate was not affected by the addition. This

is not likely, and on the converse assumption, that there

was no degradation of regular feed after the addition,

the total recovery was 6.90 mol C, equivalent to 121% of

the glucose carbon (Table 2).

4. Discussion

Comparatively short and saturated, branched- or

straight-chain fatty acids with up to 17 carbons domi-

nated among the normal ester-linked fatty acids in the

reactor. These fatty acids are dominant in many gram-

positive and anaerobic gram-negative eubacteria. Otherstudies have suggested that members of Clostridium,

other low GC gram-positive eubacteria, Spirochaeta,

Bacteroides, and Cytophaga can be prominent in the

eubacterial community in biogas processes (Godon

et al., 1997; Sekiguchi et al., 1998; Fernandez et al.,

1999, 2000; Delb�ees et al., 2001; Dollhopf et al., 2001).The fatty acid compositions of Spirochaeta and Bacte-

roides, which are common sugar fermenters in anaerobic

Fig. 1. (a) Total PLFA and (b) total PLEL concentrations in a biogas

reactor after instant glucose overloading. Experiment B, 15 times the

normal daily feed (diamonds); Experiment C, 25 times the normal

daily feed (triangles).

Fig. 2. PCA of PLFA and PLEL data from a biogas reactor after

receiving an instant glucose overloading of 25 times the daily feed. (a)

scores; (b) loadings. Numbers in the score plot denote days after glu-

cose addition.

240 I. Sundh et al. / Bioresource Technology 89 (2003) 237–243

Page 5: Effects of glucose overloading on microbial community structure and biogas production in a laboratory-scale anaerobic digester

systems, show a high resemblance to that in the reactor

(Lechevalier and Lechevalier, 1988), indicating that

these bacterial groups were dominants in the commu-

nity. Members of Clostridium, other low GC gram-positives and Cytophaga, on the other hand, all have

mainly 14:0, 16:0, 16:1, and 18:1 (Lechevalier and

Lechevalier, 1988). These PLFAs occurred in much

lower concentrations, showing that the latter eubacterial

groups possibly formed sub-dominant populations.

Eucaryotes were principally absent, as 18:2 was the only

polyunsaturated PLFA detected, making up 1–2% of the

PLFAs.

The diether lipids from methanogens corresponded to

4–8% of the PLFAs, based on moles of complete

phospholipid molecules. This means that assuming that

the ratio of phospholipid to biomass is the same in the

eubacteria and the methanogens, the methanogenic

biomass was roughly 4–8% of the eubacterial. This iswithin the range of biomass ratios reported in other

studies employing lipid analysis (Schropp et al., 1988;

Hedrick et al., 1991, 1992). Similarly, in a pilot-scale

reactor system treating brewery wastewater, direct mi-

croscopic cell counts gave a methanogenic to eubacterial

cell number ratio of 7–8% (Ince et al., 1997). Analyses of

relative amounts of small subunit (SSU) rRNA have

yielded ratios of methanogens/eubacteria of 5–10% inanaerobic reactors treating sludge from wastewater

treatment plants (Raskin et al., 1995) and during treat-

ment of a simulated organic fraction of municipal solid

waste (Griffin et al., 1998). Although conversion factors

that are necessary to convert amounts of membrane

lipids or SSU rRNA to actual biomass may vary con-

siderably, estimates of the relative methanogenic bio-

mass in biogas reactors based on completely differentmethods seem to converge around a value of 10%.

Apart from the few weeks immediately following the

glucose addition, the reactor maintained a quite stable

performance with respect to chemical parameters, both

before and after the glucose additions (data not shown).

Despite a stable performance, the PCA analysis of the

fatty acid data revealed that there were general differ-

ences in the microbial community structure between thetwo experiments. This difference was smaller, but per-

sisted, if the PLFAs that were detected in only one of the

experiments were excluded in the PCA (data not

shown). Additionally, slow and gradual changes in

Fig. 3. Glucose concentrations (diamonds) and alkalinity (triangles) in

a biogas reactor after glucose overloading. (a) Experiment B,15 times

the normal daily feed; (b) Experiment C, 25 times the daily feed.

Table 2

Recovery of glucose carbon (mol C) after a single pulse of glucose overloading (15 times daily feed) of a mesophilic biogas reactor fed with a synthetic

liquid substrate

Glucose in reactor immediately (�5 min) after addition 5.68

CO2 leaving reactora 2.75

CO2 production from regular feed )1.55CH4 leaving reactor

a 3.46

CH4 production from regular feed )2.18Glucose passing the reactorb 0.19

Incorporated into microbial biomassc 0.20

Flushed out as microbial biomassd 0.20

Flushed out as propionate+ acetateb ;e 0.10

Total C recovery, assuming unchanged utilization of regular feed 3.17

Total C recovery, assuming no utilization of regular feed 6.90

The calculations take one hydraulic retention time into account.a Estimated as the sum of hourly flow rates for gas leaving the reactor.b The time-weighted average concentration multiplied by total volume washed out.c Estimated from the increase in PLFA content, assuming 100 lmol PLFA g�1 dw of cells and that C makes up 50% of the dw (White et al., 1979).d Calculated as for glucose passing reactor, but only for increase compared to the initial ‘‘background’’ biomass.e Calculated from data presented in Nordberg et al. (2000).

I. Sundh et al. / Bioresource Technology 89 (2003) 237–243 241

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community structure are illustrated by a general decline

in the concentration of i16:0 during Experiment B, and

the general difference in diethers between the experi-

ments. Along the same line, recent studies based on

molecular inventories of biogas reactors indicate that

there may be substantial changes in the microbial

communities despite a rather stable process performance

(Fernandez et al., 1999, 2000; Zumstein et al., 2000).In this study, the responses to glucose overloading

were similar to those found in other studies in similar

systems (Xing et al., 1997; Hashsham et al., 2000).

However, in our experiments, the general microbial re-

sponse was a stimulation of degradation. For example,

all glucose was taken up by microorganisms in only 2–3

days, and with regard to the entire Experiment B, most

of the glucose carbon was recovered as raised produc-tion rates of methane and carbon dioxide (Table 2).

Additionally, the biomass of eubacteria increased, as

evidenced by a peak in total PLFA concentration after

3–4 days. However, since only a very small part of the

energy was available for growth, the total eubacterial

biomass increase corresponded to not more than 7.0%

of the glucose C (Table 2). Along the same lines, the

trans/cis ratios of the monounsaturated PLFAs werequite low (1.6% at most), suggesting that the microbial

community experienced good nutritional conditions

(Guckert et al., 1986; Hedrick et al., 1992).

It is a bit surprising that while there was an obvious

increase in eubacterial biomass, there was no clear

change in methanogenic biomass (Fig. 1). In an effective

biogas process like this reactor, most of the carbon

mineralization is channelled through methanogenesis.Indeed, the total increase in gas production over the

length of the experiment was represented by almost

equimolar amounts of methane and carbon dioxide,

which is expected for carbohydrate degradation in a

biogas reactor (Gujer and Zehnder, 1983). In part, the

lack of methanogenic biomass increase can be explained

by the comparably low energy yield for methanogenesis,

and the low growth rates of methanogens in general(Vogels et al., 1988). In any case, the diether data must

be interpreted with care, since the diether content varies

among methanogens (Koga et al., 1998), implying that a

species shift may potentially mask a biomass increase. In

addition, the coefficient of variation for the diether data

was high (42%).

Our budget calculations did not account for all the

added glucose. This was not unexpected, for at least fourreasons: 1. Flush-out of intermediates other than acetate

and propionate was not considered. 2. Some of the

glucose carbon may have ended up in biomass growing

on the walls of the reactor. This was hardly a major sink,

however, since there was no visible growth on the walls.

3. The conversion factor for calculation of dry weight of

biomass from PLFA concentration is uncertain. 4. The

background gas production from the regular feed may

have been overestimated, since the daily peaks in gas

flow were smaller during the first week after the glucose

addition, indicating that the regular feed may have been

utilized at lower rates for a period after the glucose

shock. A slower degradation of the regular feed in this

period also complies well with the fact that when the

assumption was made that the regular feed was not used

at all, this resulted in an obvious overestimate in re-covery (126%).

The lipid analyses used in this study monitored major

changes in the microbial community, especially in the

eubacterial biomass, but also in the community structure.

By comparisonwith the chemical parameters, the changes

in the community could be explained in the perspective of

process dynamics. However, the PLFA analysis would

not be a suitable parameter for monitoring the state ofbiogas processes. Therefore, the glucose overloading ex-

periments also included on-line measurements of the near

infrared reflectance (NIR) spectra of the process slurry. A

NIR spectrum can be used as a fingerprint of the chemical

composition of the process slurry, and NIR spectroscopy

has a big potential for on-line monitoring and regulation

of biogas production processes. Correlations ofNIR data

with total PLFA concentrations using partial least square(PLS) modelling gave strong relationships (Nordberg

et al., 2000). This shows that the changes in the biological

components of the biogas process studied here can be

detected by NIR. Such sensitivity to biological compo-

nents is a good characteristic of parameters used to con-

trol the performance and ‘‘health’’ of biogas processes.

Acknowledgements

We are grateful for the excellent technical assistance

of Anette Levin, Gunnel Fransson and Sylvia R€oonn. Wealso thank Anna Schn€uurer for constructive comments onthe manuscript. Financial support was given by The

Swedish National Energy Administration (Contract

P10545-1).

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