Appl Microbiol Biotechnol (2014) 98:9095–9106 DOI 10.1007/s00253-014-5906-1

ENVIRONMENTAL BIOTECHNOLOGY

Microbial community composition and dynamics in high-temperature biogas reactors using industrial bioethanol waste as substrate Immo Röske & Wael Sabra & Heiko Nacke & Rolf Daniel & An-Ping Zeng & Garabed Antranikian & Kerstin Sahm

Received: 28 February 2014 / Revised: 19 June 2014 / Accepted: 20 June 2014 / Published online: 12 July 2014 # Springer-Verlag Berlin Heidelberg 2014

Abstract Stillage, which is generated during bioethanol production, constitutes a promising substrate for biogas production within the scope of an integrated biorefinery concept. In this study, a microbial community was grown on thin stillage as mono-substrate in a continuous stirred tank reactor (CSTR) at a constant temperature of 55 °C, at an organic loading rate of 1.5 goTS/L*d and a retention time of 25 days. Using an amplicon-based dataset of 17,400 high-quality sequences of 16S rRNA gene fragments (V2–V3 regions), predominance of Bacteria assigned to the families Thermotogaceae and Elusimicrobiaceae was detected. Dominant members of methane-producing Euryarchaeota within the CSTR belonged to obligate acetoclastic Methanosaetaceae and hydrogenotrophic Methanobacteriaceae. In order to investigate population dynamics during reactor acidification, the organic loading rate was increased abruptly, which resulted in an elevated concentration of volatile fatty acids. Acidification led to a decrease in relative abundance of Bacteria accompanied with stable numbers of Archaea. Nevertheless, the abundance of Methanosaetaceae increased while that of Methanobacteriales decreased successively. These I. Röske : G. Antranikian : K. Sahm (*) Institute of Technical Microbiology, Hamburg University of Technology, Kasernenstraße 12, Hamburg 21073, Germany e-mail: [email protected] W. Sabra : A.8 bp) or more than two primer mismatches were removed from the dataset by using the QIIME 1.4 software pipeline (Caporaso et al. 2010). Afterwards, denoising was performed by applying the QIIME scripts ‘denoise_wrapper.py’ and ‘inflate_denoiser.py’. The remaining primer sequences were truncated using the program cutadapt (Martin 2011). In addition, chimeras were removed by using the UCHIME program and the Greengenes gold dataset ‘gold_strains_gg16_aligned.fasta’ (19 March 2011) as reference (DeSantis et al. 2006; Edgar et al. 2011). On the basis of the preprocessed datasets, determination of operational taxonomic units (OTUs) was performed using UCLUST (Edgar 2010). Taxonomic classification of OTUs and calculation of rarefaction curves were performed using QIIME. With respect to taxonomic classification, sequences were compared to the most recent copy of the SILVA ribosomal RNA database (SSU Ref NR 108) using BLASTN (Pruesse et al. 2007). Additionally, a customized script was used to remove all OTUs from the OTU table that have been classified as chloroplasts. DGGE PCR amplification of bacterial 16S rRNA gene fragments was performed using the primers GM5for_GC (5′CCTACGGGAGGCAGCAG-3′) and GM907rm (5′-CCGT CAATTCMTTTGAGTTT-3′) (Muyzer et al. 1993, 1995). On the 5′ end of the forward primer, a GC clamp (5′-CGCC CGCCGCGCCCCGCGCCCGTCCCGCCGCCCCCGCC CG-3′) was attached (Sheffield et al. 1989). Archaeal 16S rRNA gene fragments were amplified by using the primers 0376for_GC (5′-CCCTACGGGGCGCAGCAG-3′) (developed in this study) and 0691rev (5′-GGATTACARGATTT CAC-3′) (Watanabe et al. 2004). As described above, a GC clamp was included on the 5′ end of the forward primer. The PCR reactions for both primer sets were conducted in 50 μL volumes containing 0.2 μL Taq-DNA polymerase (5 U/μL) (Thermo Fisher Scientific, St. Leon-Rot, Germany), 5 μL of 10-fold PCR reaction buffer, 5 μL dNTPs (2.5 mM each), 3 μL of MgCl2 (2.5 mM), 0.25 μL of each of the primers (10 mM) and 1 to 4 μL of extracted total DNA (10–100 ng). The PCR conditions were as follows: initial denaturation at 94 °C for 5 min, followed by denaturation at 94 °C for 30 s, annealing at 64 °C for 1 min using a temperature gradient ranging from 64 to 55 °C (1 °C touchdown every two cycles), and 12

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additional cycles at a constant annealing temperature of 55 °C were conducted. Annealing of the methanogenic primer set was performed permanently at 52 °C. Chain elongation occurred at 72 °C for 1 min, and a final elongation step was performed at 72 °C for 8 min. PCR products were analyzed on 1 % agarose gels in order to check expected amplicon lengths. DGGE was performed with a DCode Universal Mutation Detection System (Bio Rad Laboratories, Munich, Germany) as described by Muyzer and Smalla (1998). The bacterial PCR products were analyzed on a 6 % (w/v) polyacrylamide gel in 1X TAE buffer (pH 7.5) with a denaturant gradient from 20 to 80 % (100 % denaturant was a mixture of 7 M urea and 40 % [v/v] formamide). Electrophoresis was performed at constant conditions of 55 °C and 75 V for 16 h. PCR products, obtained with the methanogen-specific primer set, were analyzed using an 8 % (w/v) polyacrylamide gel in 1X TAE buffer (pH 7.5) with a denaturant gradient from 25 to 60 % (100 % denaturant was a mixture of 7 M urea and 40 % [v/v] formamide). Electrophoresis was conducted under conditions of 60 °C and 180 V for 6.5 h. Due to the application of the newly designed primer 0376for_GC, the conditions used for DGGE analysis with archaeal 16S rRNA gene sequences had to be developed using a perpendicular DGGE and subsequent time travel experiments. After electrophoresis, gels were stained in Roti®-GelStain solution (Carl Roth, Karlsruhe, Germany) and visualized by UV transilluminator. Sequencing of 16S rRNA genes and phylogenetic analyses DGGE bands were removed from the gel using a scalpel and washed in 100 μL ddH2O for 1 min. After this, 20 μL ddH2O was added, and the samples were stored for 5 h at 4 °C. Using 1 μL of recovered DNA, a reamplification was performed by PCR conditions and primers as described above. PCR products were purified using GeneJET PCR Purification Kit (Thermo Fisher Scientific Inc., St. Leon-Roth, Germany) and sequenced using the reverse primers, respectively. The obtained sequences were compared to 16S rRNA gene sequences provided by the National Center for Biotechnology Information database using BLASTN (www.ncbi.nlm.nih. gov/BLAST). All sequences generated in this study were deposited in the EMBL Nucleotide Sequence Database (accession numbers HG967645-HG967653). Primer in silico evaluation For DGGE application, we constructed a new forward primer, which is specific for methanogenic Euryarchaeota designated as 0376for_GC. In combination with primer 0691rev, from Watanabe et al. (2004), 80 or 45 % of all methanogenic type strains were covered with or without one mismatch, respectively. 16S rRNA gene sequences not covered by the combination of the two primers mainly relate to hyperthermophilic Methanococci and mesophilic Methanobacteriaceae.

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Fluorescence in situ hybridization Fixation and hybridization for FISH analysis were conducted with samples taken from the CSTRs as described previously (Snaidr et al. 1997). Due to the presence of typical aggregates within the CSTRs, a pretreatment was necessary by squeezing the fixed samples through a 0.9 mm cannula until aggregates disappeared. An aliquot of 2.5 μL was dispersed on a poly-L-lysine-coated microscopic slide, dehydrated and hybridized with the corresponding probe for 90 min at 46 °C. In this study, the following oligonucleotide probes were utilized with stringency conditions (underlined) as published correspondingly: EUB338 (most Bacteria, 35 %), ARC915 (most Archaea, 35 %), MX825 (specific for Methanosaetaceae, 50 %) and MB1174 (specific for Methanobacteriales, 45 %) (Amann et al. 1990; Raskin et al. 1994). Oligonucleotide probes were commercially synthesized and 5′-labelled with the indocyanine dye Cy3. After a washing step at 48 °C for 20 min, DNA containing cells was stained with 2 μg/mL of 4′,6′-diamidino2-phenylindol (DAPI) on ice for 5 min. For evaluation, 10 to 12 randomly chosen fields were used for each probe section, which corresponded to a minimum of 1,000 DAPI-stained cells. To calculate the standard error of the mean, standard deviation was divided by the square root of the number of chosen fields. The following pure cultures were used as positive and negative controls for FISH analysis: Methanosaeta thermophila (DSMZ 6194), Methanothermobacter thermautotrophicus (own isolate) and Petrotoga mobilis (DSMZ 10674). We used the nonsense probe NONEUB338 (reverse complement of EUB338) in order to control nonspecific binding of labelled probes (Wallner et al. 1993).

Results Reactor conditions and operation A continuous stirred tank reactor was used to investigate the prokaryotic microbial community involved in the anaerobic digestion of thin stillage at 55 °C. The reactor was inoculated with a sample taken from a full-scale 5,000 m3 biogas plant fed with corn, wheat and chicken manure operated at 50 °C. Feeding occurred daily at an initial loading rate of 1.5 goTS/L*d and a retention time of 25 days. Thin stillage was the only substrate, and no additional supplements were added. The pH was monitored but not adjusted. After an adaptation phase of 5 months, a steady state was obtained, at which no VFAs were detected in the reactor. Simultaneously, ammonia concentration decreased from 2.0 to 0.4 g/L, while the pH value decreased from 8.0 to 7.4. After adaptation, the mean specific biogas production reached values between 650 and 700 LN/kgoTS, containing a methane ratio ranging from 55 to 60 %. With this monosubstrate fermentation, the maximum loading rate for a stable performance without adding trace elements was 2.1 goTS/L*d.

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Microbial community composition based on 16S rRNA gene tags Pyrosequencing of partial 16S rRNA genes from the CSTR yielded approximately 14,300 bacterial and 3,100 archaeal high-quality sequences with an average read length of 385 bp. As can be seen in Fig. 1, the bacterial population was dominated by the phylum Thermotogae (>50 % of all bacterial sequences), and all OTUs belonged to a single family, the Thermotogaceae, and were affiliated to Defluviitoga tunisiensis (Ben Hania et al. 2012). The second most abundant group was the phylum Elusimicrobia, formerly known as Termite Group 1, which amounted to 27 % of the Bacteria. The family Elusimicrobiaceae represents the whole phylum. OTUs belonging to the phyla Firmicutes (11.2 %), Chloroflexi (6.3 %) and Synergistetes (3.4 %) constituted up to 20.9 % of the bacterial community. OTUs of the Firmicutes were classified as Ruminococcaceae (4 %), OPB54 (1.5 %), Lachnospiraceae (1 %), Clostridiales Incertae Sedis III (1 %), Thermoanaerobacteraceae (1 %), Peptococcaceae (0.7 %) and Thermodesulfobiaceae (0.5 %). All members of Chloroflexi belonged to the family Anaerolineaceae, and all Synergistetes were related to the family Synergistaceae. The methanogenic community comprised Methanomicrobia (34 %) (Fig. 1c). Within the Methanomicrobia, the family of acetoclastic Methanosaetaceae constituted 98.5 %, demonstrating that acetate plays an important role as an intermediate for methane production during biogasification from thin stillage. Only 1.5 % of the Methanomicrobia were associated with the metabolically versatile Methanosarcinaceae, known to utilize various substrates such as acetate, H2/CO2, methanol and methylamines for methanogenesis (Boone et al. 1993). Fig. 1 Relative abundances of phylogenetic groups of Bacteria and Euryarchaeota, which are involved in the production of methane from thin stillage. Based on 14,300 partial 16S rRNA gene sequences derived from Bacteria, the dataset offers a considerable survey of the decomposing community. For this purpose, phylum levels (a) were subdivided to family levels (b). Analysis of Euryarchaeota (c) based on a dataset of 3,100 partial sequences of 16S rRNA genes, resulting in three methanogenic families. Abundances lower than 0.5 % are classified to Other

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Hydrogenotrophic Methanobacteria consisted of the genera Methanothermobacter (>26.5 %) and Methanobacterium (>7 %), utilizing H2/CO2 for methanogenesis, but unable to convert acetate. This dataset was used to determine rarefaction curves. Diversity of the microbial community In order to calculate rarefaction curves, OTUs were identified at genetic dissimilarity of 3 and 20 % (Fig. 2). At 20 % genetic distance, both the bacterial and the archaeal rarefaction curves reached saturation, indicating that almost the full extent of taxonomic diversity at this genetic distance was covered by the surveying effort. At 3 % genetic dissimilarity, the rarefaction curve of Bacteria was not saturated, and the richness estimator (Chao1) indicated that 58.4 % of the bacterial community was recovered by the surveying effort (Chao and Bunge 2002). This demonstrates that the taxonomic diversity at this genetic distance was not fully surveyed, although a major fraction thereof was covered. The rarefaction curve of the Archaea at the species level reached saturation, indicating that the full extent of taxonomic diversity was analyzed. The maximum number of OTUs for the bacterial CSTR community predicted at 3 % genetic distance was 288 belonging to 22 predicted phyla, while the maximum predicted number of methanogenic OTUs was 9, belonging to 4 phyla. Population dynamics during acidification The failure of biogas reactors can be attributed, among other reasons, to inhibition of methanogens by the presence of toxic compounds in the digestate and pH values below 5.5 (Kim et al. 2003; Leitão et al. 2006; Zhu and Beland 2006). Methanogens are affected

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Fig. 2 Rarefaction analysis of Bacteria (a) and Euryarchaeota (b) at a genetic distance of 3 and 20 % present in the thin stillagebased CSTR. The curves were calculated based on an amplicon sequencing-derived dataset containing partial 16S rRNA gene sequences spanning the V2 and V3 regions

by a number of substances, including ammonia, sodium, VFAs, heavy metals and sulphide (Lin and Chen 1999; Schnürer et al. 1999). In order to study the effect of reactor failure on the microbial community structure, reactor disturbance was mimicked by abruptly increasing the organic loading rate (OLR) thereby inducing acidification (Fig. 3). This experiment was started after the 5 months of stabilization. When substrate overloading occurred by raising the OLR from 1.5 to 3.5 goTS/L*d, the biogas production rate increased from 3,500 up to 8,900 mL/d, and the pH dropped from 7.4 to 7.0. Acidification was accompanied by increasing concentrations of acetic (2.7 g/L) and propionic acid (3.2 g/L). Consequently, the pH continued to fall to 6.5 while the biogas production rate collapsed (670 mL/d). In order to avoid an irreversible reactor failure, feeding was stopped after 11 days of overload. FISH analysis revealed a stable contribution of 14 % of all Euryarchaeota between the start and the stop of overloading (sampling points S1 and S2, Fig. 4). At the

same time, the relative abundance of acetoclastic Methanosaetaceae decreased slightly from 39 to 35 % of all Archaea (accounting for approximately 5 % of DAPI-stained cells). Methanobacteriales on the other hand increased from 54 to 64 %. Relative abundance of Bacteria dropped significantly from 77 to 61 %. Comparing the DGGE band patterns between the sampling points S1 and S2, no striking shifts were observed for Bacteria and Archaea (Fig. 5). By DNA extraction, reamplification and sequencing, the most intensive band (Fig. 5a, band no. 2) was identified to be affiliated with D. tunisiensis (Table 1). DNA affiliated with M. thermautotrophicus and M. thermophila was identified in two intensive bands (Fig. 5b, band nos. 6 and 7) of methanogenic Archaea. After the pH had been adjusted to 7.5 by adding NaOH, the feeding was resumed with an OLR of 1.5 goTS/L*d, and biogas production started to increase (S3). The concentrations of VFAs were still high, but a reduction was apparent. FISH-based quantification disclosed a slight

Fig. 3 Volumetric biogas production rate, variation of pH and major VFAs in the biogas reactor fed with thin stillage. In order to analyze the microbial community by FISH and DGGE, samples were collected at six time points (green asterisks). After fairly constant conditions had been obtained, the reactor was overloaded by more than doubling the loading

rate of thin stillage (S1), leading to a pH decrease and reactor failure (S2). Simultaneously, acetate and propionate accumulated at high concentrations. After resetting the pH to 7.5, the biogas production rate normalized (S3), and organic acids were metabolized successively (S4) until primarily process conditions were achieved (S5 and S6)

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During this phase, a trend of the relative abundance of Bacteria is noticeable, reaching its initial level of around 78 % of all DAPI-stained cells. A slight decrease of the relative abundance of methanogenic Euryarchaeota (11 %) occures, while the number of Methanosaetaceae is still high (53 % of Euryarchaeota). Hydrogenotrophic Methanobacteriales returned to the relative abundance of 44 % of all Euryarchaeota but did not rebound to its initial level. According to the DGGE band patterns, both bacterial and methanogenic diversities remained stable.

Discussion

Fig. 4 Community dynamics of Bacteria and methanogenic Euryarchaeota determined by FISH analysis. The relative abundances of Bacteria and Euryarchaeota are given as percentages of DAPI-stained cells (a). The relative abundances of methanogenic groups are calculated with respect to total abundance of Euryarchaeota (b). Standard errors of 0.9 to 6.6 % are determined among separate fields (n ≥10 fields). Points of sampling (S1–S6) are given in Fig. 3

increase of Archaea up to 17 % and a significant decrease of Bacteria to 49 % (Fig. 4). At this point, the highest abundance of Archaea and the lowest abundance of Bacteria were measured. Within Archaea, the relative abundance of Methanosaetaceae recovered to 39 %, while hydrogenotrophic Methanobacteriales decreased from 64 to 43 %. Simultaneously, DGGE analysis revealed the appearance of a band that was affiliated to mixotrophic Methanosarcina thermophila (Fig. 5b, band no. 8). The entire biogas process started to stabilize, when the pH was adjusted to 7.6 and acetic and propionic acid reached values below 0.5 and 2.0 g/L, respectively (S4). Biogas production rate increased to 3,900 mL/d. At this sampling point, we counted 61 % Bacteria and 16 % Archaea. The remaining 23 % could not be detected by FISH but were only stained with DAPI. While Methanobacteriales stabilized at 42 %, the acetate-utilizing Methanosaetaceae increased to 55 % of all Euryarchaeota. On the other hand, the number of all cells detectable by FISH decreased by approximately 14 % during the entire acidification process. This indicates a decreased cellular rRNA content, possibly corresponding to decreasing overall viability. In DGGE, the band affiliated with M. thermophila disappeared while those derived from M. thermautotrophicus and M. thermophila were still present. Between the sampling time points S5 and S6, process conditions were comparable to the starting phase, with very low concentrations of acetate (50 %) in a biogas reactor fed with stillage. This supports the study of Sasaki et al. (2013) who, based on TRFLP and clone analyses, suggested that D. tunisiensis may act as a key player in thermophilic biogas systems. The second most abundant group, Elusimicrobiaceae, is widespread in environments such as soil or animal gastrointestinal tracts (Geissinger et al. 2009; Herlemann et al. 2007; Hongoh et al. 2003). Both Elusimicrobiaceae and Thermotogales have been reported to utilize sugars to produce acetate, lactate, ethanol, H2 and CO2 (Geissinger et al. 2009; Ravot et al. 1996).

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Fig. 5 DGGE analysis from the biogas reactor communities. Time points of sampling (S1–S6) are shown in Fig. 3. Lane S0 represents the initial inoculum diversity. 16S rRNA gene fragments, including V2 and V3 regions, were amplified using GM5for_GC and GM907rm or 0376for_GC and 0691rev for Bacteria (a) or methanogenic Euryarchaeota (b), respectively. Bands marked with an arrow were excised and reamplified for sequence analyses. The denaturant gradient ranges were 20 to 80 % and 25 to 60 % for sections a and b, respectively. Sequencing results for specific bands are listed in Table 1

In contrast to our results, many studies on the microbiology of anaerobic digesters have shown considerably different communities, frequently with high numbers of Betaproteobacteria, Bacteroidetes, Chloroflexi and Firmicutes (España-Gamboa et al. 2012; Kröber et al. 2009; Rademacher et al. 2012a; Rivière et al. 2009; Sundberg et al. 2013; Zhang et al. 2003; Ziganshin et al. 2011). In the process of anaerobic digestion of sewage sludge, these so-called core groups account for nearly 30 % of the total bacterial population as Rivière et al. (2009) demonstrated. However, in our reactor, only two of these core

groups were present (Chloroflexi and Firmicutes) not exceeding 17.5 %. The elevated temperature, low feeding rate and withholding trace elements can be the reason for the difference in community structure with high amounts of Thermotogae and Elusimicrobia but deficient of Betaproteobacteria and Bacteroidetes. Although not dominant, small numbers of Firmicutes represented by Lachnospiraceae, OPB54, Peptococcaceae, Ruminococcaceae, Thermoanaerobacteraceae and Clostridiales Incertae Sedis III were detected in our system by pyrosequencing

Table 1 Sequence matches of bands excised from the DGGE-gel in Fig. 5 obtained from the initial inoculum and the thin stillage-based CSTRs Band

Acc. no.

Closest relative (similarity)a

Order

1 2 3 4 5 6 7 8

HG967645 HG967646 HG967648 HG967649 HG967650 HG967651 HG967652 HG967653

Sedimentibacter saalensis (96 %) Defluviitoga tunisiensis (100 %) Methanobrevibacter smithii (81 %) Methanosaeta concilii (100 %) Methanolinea mesophila (94 %) Methanothermobacter thermautotrophicus (99 %) Methanosaeta thermophila (97 %) Methanosarcina thermophila (100 %)

Clostridiales Thermotogales Methanobacteriales Methanosarcinales Methanomicrobiales Methanobacteriales Methanosarcinales Methanosarcinales

a

Results of the closest relative analysis were derived from the NCBI database including cultured microorganisms

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analysis. In addition, isolation experiments yielded Firmicutes like Clostridium thermosuccinogenes, Bacillus smithii, Bacillus thermoamylovorans, Anoxybacillus rupiensis, and Thermoanaerobacterium thermosaccharolyticum (data not shown). Various experimental biases, such as primer coverage, insufficient hydrolysis of Gram-positives or GC content, may influence taxon representation in sequence-based studies (Danhorn et al. 2012). However, a recent study that applied the same DNA isolation strategy and selected primer set revealed no specific bias concerning the phylum Firmicutes (Sahm et al. 2013). As expected, organisms of the genus Enterococcus, including typical pathogens, were not detected by 16S rRNA gene analysis in our pyrosequencing data, despite the presence of the nonpathogenic chemoorganotrophic Lachnospiraceae as key indicator for faecal pollution (Newton et al. 2011). This is most likely due to the reactor’s thermophilic conditions and provides evidence that biogas production at elevated temperature significantly reduces the risk of pathogenicity within the biogas reactor effluent (Burtscher et al. 1998; Nosrati et al. 2006). This is particularly valuable because fermentation residues are commonly used as fertilizer in agriculture. FISH analysis revealed a relative contribution of 11 to 17 % Archaea to the total microbial community in the reactors, corresponding to previous studies (Jaenicke et al. 2011; Krakat et al. 2010; Wirth et al. 2012). Pyrosequencingderived results demonstrated high numbers of acetateutilizing Methanosaetaceae. The high abundance of Methanosaetaceae was also confirmed by microscopic analysis, showing long filaments with weak autofluorescence. In spite of their low specific growth rate compared to various hydrogenotrophic Methanobacteriales, Methanosaetaceae may represent an important producer of methane in biogas reactors with low ammonia and acetate concentrations (Hori et al. 2006). Also, reduced concentrations of VFAs can promote the occurrence of acetoclastic methanogens in biogas reactors, whereas the accumulation of VFAs may induce a drastic increase of hydrogenotrophic methanogens (Ahring 1995; Hori et al. 2006). This was supported by studies performed on sludge digesters with low organic acid concentration, demonstrating that the presence of different groups of acetoclastic methanogens strongly depends on the organic load (Karakashev et al. 2006). Thus, under low organic loading, mixotrophic Methanosarcinaceae, which were scarcely present in our CSTR, are not able to compete with Methanosaetaceae, because Methanosaetaceae have a significantly higher substrate affinity (Jetten et al. 1992). In addition, hydrogenotrophic Methanobacteriaceae represented by Methanobacterium and Methanothermobacter using H2/CO2 as substrate for methanogenesis were detected. Studies that analyzed Methanobacteriaceae within biogas reactors at high temperatures are numerous, clearly

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demonstrating their frequent appearance (Demirel and Scherer 2008; Hori et al. 2006; Krakat et al. 2010; Rademacher et al. 2012a). Additionally, isolation experiments often yielded pure cultures of M. thermautotrophicus, supporting our results obtained from DGGE and amplicon sequencing. Furthermore, Methanobacteriaceae might be promoted by high temperature, which leads to an increased H 2 pressure, kno wn to stimula te the growth of hydrogenotrophic methanogens (Chen 1983; Zinder 1995). In summary, it should be noted that the results obtained from amplicon sequencing were supported by FISH and DGGE band analyses. All dominating groups identified in the pyrosequencing approach affiliated to Thermotogaceae, Methanosaetaceae, Methanobacteriaceae and Methanosarcinaceae were also prominent in the DGGE profiles, and high abundance of Methanosaetaceae and Methanobacteriaceae was detected by FISH. In general, biogas plants are not operated continuously due to maintenance, lack of substrates, inhibition by contaminants and acidification. Interruption of biogas production can rapidly reduce profitability of a single biogas plant. To simulate process failure and to observe recovery, one reactor was overloaded with thin stillage. Parallel to an increase in biogas formation, higher concentrations of acetate and propionate could be detected. However, the relative abundance of Bacteria decreased significantly during reactor acidification. This might be explained by the negative effect of low pH on the most abundant D. tunisiensis, reported to have a growth optimum at pH 6.9 (Ben Hania et al. 2012). During this phase, the abundance of Methanobacteriales increased, probably due to the additional availability of H2/CO2. Working with maize silage, Munk et al. (2010) observed a similar effect during acidification. On the other hand, the relative abundance of acetate-consuming Methanosaetaceae only marginally decreased during acidification. Consistent to our findings, Lee et al. (2009) obtained comparable results by conducting qPCR of the methanogenic population in batch experiments supplied with whey permeate wastewater, also rich in protein. We could demonstrate that the population of Methanosaetaceae, present in our CSTR, was not affected significantly by elevated concentrations of acetate (approximately 2.5 g/L). At the sampling point S3, both Methanobacteriales and Methanosaetaceae added up to only 82 %, whereas the number of all Euryarchaeota increased. This effect might be explained by the demonstrated appearance of Methanosarcinaceae in the DGGE analysis. The increase of Methanosarcinaceae was previously reported to parallel the increase in the acetate level in the reactor and hence can be considered as an indicator of the reactor imbalance (Griffin et al. 1998; Lee et al. 2009; McMahon et al. 2001). The degradation curves of acetate and propionate show successive degradation of the two acids. As propionate cannot be used for methanogenesis directly, conversion to acetate, CO2 and H2 by secondary fermenters is required (Stams et al. 1998).

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Furthermore, low concentrations of acetate are favourable for oxidation of propionate (Dong et al. 1994). This biphasic degradation, which is syntrophically conducted by acetogenic bacteria and methanogens, may explain the successive increase of the methanogenic population within the CSTR (Scholten and Conrad 2000; Xu et al. 2009). At least the abundance of Bacteria seemed to have recovered to the initial level when the reactor was no longer overloaded. Stable succession from Methanobacteriales to Methanosaetaceae was observed. While hydrogenotrophic Archaea dominated the community before the induction of acidification, Methanosaetaceae became the dominant family after the disturbance when process conditions normalized. This study investigated the microbial community in anaerobic digesters fed with thin stillage as mono-substrate. A stable community was obtained, clearly dominated by the bacterial families Thermotogaceae and Elusimicrobiaceae, found for the first time in anaerobic digestion of stillage. The archaeal communities appeared to be highly stable and resilient in our system, dominated by acetoclastic Methanosaetaceae and hydrogenotrophic Methanobacteriales. Analyses of microbial communities and their dynamics, especially during changes of process conditions, are of great interest for the future of biogas technology. Understanding the dynamics of microbial communities may help in process management decisions, such as whether to increase the OLR or to reduce the retention time. Acknowledgments This project was funded by the German Federal Ministry for Education and Research (BMBF) through the Project Management Jülich (PtJ) as part of the Cluster Biorefinery2021 (grant no. 0315559A). I. Röske and W. Sabra gratefully acknowledge the receipt of a scholarship and the SynBio grant from the State Excellence Initiative Hamburg.

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