Effects of glucose overloading on microbial community structure and biogas production in a laboratory-scale anaerobic digester
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Eects 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
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 48% 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 aected 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
In biogas production from organic materials, the in-
tegrated action of several types of microorganisms,
which perform dierent 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
classied into hydrolytic, fermentative organic acid-producing, acetate-producing and, in the terminal step,
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 dierent kinds of disturbance
is still meagre, particularly regarding the communitiesstructural characteristics.
In the present study, we investigated the eects 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.
2.1. 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@example.com (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) 237243
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 l1 d1.Two experiments with glucose additions were carried
out, separated by ve months, where extra glucose wasadded representing approximately 15 (Experiment B)
and 25 (Experiment C) times (in gVS l1 d1) the normaldaily 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). Briey, 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)triuoroacetamide (BSTFA), quanti-
cations of the lipid components with gas chromato-
graphy (GC) with a ame ionization detector, and
identications 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 quantications were madeby using the methyl ester of the fatty acid 19:0 as in-
ternal standard and identications were facilitated by
comparisons with standard mixtures (standards were
obtained from Larodan Fine Chemicals AB, Lim-
hamnsgaardens allee 9, SE-216 16 Malmoo, 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 coecient 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 ow 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
(Schnuurer 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 ow of carbon dioxide for
losses of bicarbonate from the reactor uid 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 dier-
ences among samples could be detected.
3.1. Eects on microbial communities
During the two sampling periods, a total of 22 die-
rent PLFAs and one PLEL were quantied in the re-
actor contents (Table 1). There were traces of otherdiethers that could not be quantied. Nineteen of the
normal fatty acids were common to both experiments.
Branched-chain and saturated PLFAs with uneven
238 I. Sundh et al. / Bioresource Technology 89 (2003) 237243
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 diered by a
factor of two between the two overloading experiments.
Total PLFA concentration peaked after 34 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 eect 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 dierent 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 dierent 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 (0.61.6%) and did not changesignicantly 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 l1).
Average concentrations of normal phospholipid fatty acids (PLFAs; in mol % for the individual fatty acids) and archaeal diether lipids (PLEL; in
nmolml1) 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 (nmolml1) 160 (2.7) 212 (39.9) 121 (2.7) 182 (40.3)PLEL (nmolml1) 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.
I. Sundh et al. / Bioresource Technology 89 (2003) 237243 239
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 l1, after 15 and 25times overloading, respectively. The alkalinity gradually
returned to initial values after the glucose was consumed
The ow of biogas increased substantially as an im-
mediate result of the glucose addition. The ow of car-
bon dioxide returned to pre-glucose rates sooner than
the ow of methane. As long as free glucose was avail-able, carbon dioxide ow 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 rstsamples were taken approximately 5 min after addition).
The calculation assumes that the degradation of the
regular substrate was not aected 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).
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; Delbees 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-
240 I. Sundh et al. / Bioresource Technology 89 (2003) 237243
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 12% of the
The diether lipids from methanogens corresponded to
48% 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 48% 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 78% (Ince et al., 1997). Analyses of
relative amounts of small subunit (SSU) rRNA have
yielded ratios of methanogens/eubacteria of 510% 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 (Grin 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 dierentmethods 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 dier-
ences in the microbial community structure between thetwo experiments. This dierence 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.
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
Glucose in reactor immediately (5 min) after addition 5.68CO2 leaving reactor
CO2 production from regular feed )1.55CH4 leaving reactor
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 ow 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 g1 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) 237243 241
community structure are illustrated by a general decline
in the concentration of i16:0 during Experiment B, and
the general dierence 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 23
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
34 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 eective
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 coecient 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
ow were smaller during the rst 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 reectance (NIR) spectra of the process slurry. A
NIR spectrum can be used as a ngerprint 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.
We are grateful for the excellent technical assistance
of Anette Levin, Gunnel Fransson and Sylvia Roonn. Wealso thank Anna Schnuurer for constructive comments onthe manuscript. Financial support was given by The
Swedish National Energy Administration (Contract
APHA, 1985. Standard Methods for the Examination of Water and
Waste Water, 16th ed. APHA, AWWA, WPCF. American Public
Health Association, Washington, DC.
Delbees, C., Moletta, R., Godon, J.-J., 2001. Bacterial and archaeal 16S
rDNA and 16S rRNA dynamics during an acetate crisis in an
anaerobic digestor ecosystem. FEMS Microbiol Ecol. 35, 1926.
Dollhopf, S.L., Hashsham, S.A., Dazzo, F.B., Hickey, R.F., Criddle,
C.S., Tiedje, J.M., 2001. The impact of fermentative organisms on
carbon ow in methanogenic systems under constant low-substrate
conditions. Appl. Microbiol. Biotechnol. 56, 531538.
Fernandez, A., Huang, S., Seston, S., Xing, J., Hickey, R., Criddle, C.,
Tiedje, J., 1999. How stable is stable? Function versus community
composition. Appl. Environ. Microbiol. 65, 36973704.
Fernandez, A., Hashsham, S.A., Dollhopf, S.L., Raskin, L., Glagol-
eva, O., Dazzo, F.B., Hickey, R.F., Criddle, C.S., Tiedje, J.M.,
242 I. Sundh et al. / Bioresource Technology 89 (2003) 237243
2000. Flexible community structure correlates with stable commu-
nity function in methanogenic bioreactor communities perturbed
by glucose. Appl. Environ. Microbiol. 66, 40584067.
Godon, J.-J., Zumstein, E., Dabert, P., Habouzit, F., Moletta, R.,
1997. Molecular microbial diversity of an anaerobic digestor as
determined by small-subunit rDNA sequence analysis. Appl.
Environ. Microbiol. 63, 28022813.
Grin, M.E., McMahon, K.D., Mackie, R.I., Raskin, L., 1998.
Methanogenic population dynamics during start-up of anaerobic
digesters treating municipal solid waste and biosolids. Biotechnol.
Bioeng. 57, 342355.
Guckert, J.B., Hood, M.A., White, D.C., 1986. Phospholipid ester-
linked fatty acid prole changes during nutrient deprivation of
Vibrio cholerae: Increases in the trans/cis ratio and proportions of
cyclopropyl fatty acids. Appl. Environ. Microbiol. 52, 794801.
Gujer, W., Zehnder, A.J.B., 1983. Conversion processes in anaerobic
digestion. Water Sci. Technol. 15, 127167.
Hashsham, S.A., Fernandez, A.S., Dollhopf, S.L., Dazzo, F.B.,
Hickey, R.F., Tiedje, J.M., Criddle, C.S., 2000. Parallel processing
of substrate correlates with greater functional stability in metha-
nogenic bioreactor communities perturbed by glucose. Appl.
Environ. Microbiol. 66, 40504057.
Hedrick, D.B., Richards, B., Jewell, W., Guckert, J.B., White, D.C.,
1991. Disturbance, starvation, and overfeeding stresses detected by
microbial lipid biomarkers in high-solids high-yield methanogenic
reactors. J. Ind. Microbiol. 8, 9198.
Hedrick, D.B., White, T., Guckert, J.B., Jewell, W.J., White, D.C.,
1992. Microbial biomass and community structure of a phase-
separated methanogenic reactor determined by lipid analysis. J.
Ind. Microbiol. 9, 193199.
Ince, O., Anderson, G.K., Kasapgil, B., 1997. Composition of the
microbial population in a membrane anaerobic reactor system
during start-up. Water Res. 31, 110.
Jarvis, AA., Nordberg, AA., Mathisen, B., Svensson, B.H., 1995.Stimulation of conversion rates and bacterial, activity in a silage-
fed two-phase biogas process by initiating liquid recirculation.
Antonie van Leeuwenhoek 68, 317327.
Koga, Y., Morii, H., Akagawa-Matsushita, M., Ohga, M., 1998.
Correlation of polar lipid composition with 16S rRNA phylogeny
in methanogens. Further analysis of lipid component parts. Biosci.
Biotechnol. Biochem. 62, 230236.
Lechevalier, H., Lechevalier, M.P., 1988. Chemotaxonomic use of
lipidsan overview. In: Ratledge, C., Wilkinson, S.G. (Eds.),
Microbial Lipids, Vol. 1. Academic Press, London (Chapter 12).
Nichols, P.D., Guckert, J.B., White, D.C., 1986. Determination of
monounsaturated fatty acid double-bond position and geometry
for microbial monocultures and complex consortia by capillary
GC-MS of their dimethyl disulphide adducts. J. Microbiol.
Methods 5, 4955.
Nordberg, AA., Hansson, M., Sundh, I., Nordkvist, E., Carlsson, H.,
Mathisen, B., 2000. Monitoring of a biogas process using electronic
gas sensors and near-infrared spectroscopy (NIR). Water Sci.
Technol. 41, 18.
Raskin, L., Zheng, D., Grin, M.E., Stroot, P.G., Misra, P., 1995.
Characterization of microbial communities in anaerobic bioreac-
tors using molecular probes. Antonie van Leeuwenhoek 68, 297
Schink, B., 1988. Principles and limits of anaerobic degradation:
Environmental and technological aspects. In: Zehnder, A.J.B.
(Ed.), Biology of Anaerobic Microorganisms. John Wiley & Sons,
New York (Chapter 14).
Schnuurer, A., Schink, B., Svensson, B.H., 1996. Clostridium ultunense
sp. nov, a mesophilic bacterium oxidizing acetate in syntrophic
association with a hydrogenotrophic methanogenic bacterium. Int.
J. Syst. Bacteriol. 46, 11451152.
Schropp, S.J., Phelps, T.J., Mikell, A.T., White, D.C., 1988. The
relationship of eubacterial and methanogenic community structure
in anaerobic digesters. In: Klass, D.L. (Ed.), Energy From Biomass
and Wastes X. Elsevier Applied Science Publishers, London, pp.
Sekiguchi, Y., Kamagata, Y., Syutsubo, K., Ohashi, A., Harada, H.,
Nakamura, K., 1998. Phylogenetic diversity of mesophilic and
thermophilic granular sludges determined by 16S rRNA gene
analysis. Microbiology 144, 26552665.
Sundh, I., Nilsson, M., Borgaa, P., 1997. Variation in microbialcommunity structure in two boreal peatlands as determined by
analysis of phospholipid fatty acid proles. Appl. Environ.
Microbiol. 63, 14761482.
Virtue, P., Nichols, P.D., Boon, P.I., 1996. Simultaneous estimation of
microbial phospholipid fatty acids and diether lipids by capillary
gas chromatography. J. Microbiol. Methods 25, 177185.
Vogels, G.D., Keltjens, J.T., Drift, C., 1988. Biochemistry of methane
production. In: Zehnder, A.J.B. (Ed.), Biology of Anaerobic
Microorganisms. John Wiley & Sons, New York (Chapter 13).
White, D.C., Bobbie, R.J., Herron, J.S., King, J.D., Morrison, S.J.,
1979. Biochemical measurements of microbial mass and activity
from environmental samples. In: Costerton, J.W., Colwell, R.R.
(Eds.), Native Aquatic Bacteria: Enumeration, Activity, and
Ecology, ASTM STP 695. American Society for Testing and
Materials, pp. 6981.
Xing, J., Criddle, C., Hickey, R., 1997. Long-term adaptive shifts in
anaerobic community structure in response to a sustained cyclic
substrate perturbation. Microb. Ecol. 33, 5058.
Zumstein, E., Moletta, R., Godon, J.-J., 2000. Examination of
two years of community dynamics in an anaerobic bioreactor
using uorescence polymerase chain reaction (PCR) single-strand
conformation polymorphism analysis. Environ. Microbiol. 2, 69
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Effects of glucose overloading on microbial community structure and biogas production in a laboratory-scale anaerobic digesterIntroductionMethodsReactor systemMicrobial biomass and community structureAnalytical methods for gas and glucoseFate of added glucoseData analysis
ResultsEffects on microbial communitiesProcess parameters