Dissecting microbial community structure and methane-producing pathways of a full-scale anaerobic reactor digesting activated sludge from wastewater treatment by metagenomic sequencing
© Guo et al.; licensee BioMed Central. 2015
Received: 12 December 2014
Accepted: 24 February 2015
Published: 14 March 2015
Anaerobic digestion has been widely applied to treat the waste activated sludge from biological wastewater treatment and produce methane for biofuel, which has been one of the most efficient solutions to both energy crisis and environmental pollution challenges. Anaerobic digestion sludge contains highly complex microbial communities, which play crucial roles in sludge treatment. However, traditional approaches based on 16S rRNA amplification or fluorescent in situ hybridization cannot completely reveal the whole microbial community structure due to the extremely high complexity of the involved communities. In this sense, the next-generation high-throughput sequencing provides a powerful tool for dissecting microbial community structure and methane-producing pathways in anaerobic digestion.
In this work, the metagenomic sequencing was used to characterize microbial community structure of the anaerobic digestion sludge from a full-scale municipal wastewater treatment plant. Over 3.0 gigabases of metagenomic sequence data were generated with the Illumina HiSeq 2000 platform. Taxonomic analysis by MG-RAST server indicated that overall bacteria were dominant (~93%) whereas a considerable abundance of archaea (~6%) were also detected in the anaerobic digestion sludge. The most abundant bacterial populations were found to be Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria. Key microorganisms and related pathways involved in methanogenesis were further revealed. The dominant proliferation of Methanosaeta and Methanosarcina, together with the functional affiliation of enzymes-encoding genes (acetate kinase (AckA), phosphate acetyltransferase (PTA), and acetyl-CoA synthetase (ACSS)), suggested that the acetoclastic methanogenesis is the dominant methanogenesis pathway in the full-scale anaerobic digester.
In short, the metagenomic sequencing study of this work successfully dissected the detail microbial community structure and the dominated methane-producing pathways of a full-scale anaerobic digester. The knowledge garnered would facilitate to develop more efficient full-scale anaerobic digestion systems to achieve high-rate waste sludge treatment and methane production.
KeywordsWaste activated sludge Anaerobic digestion Metagenomic sequencing Microbial community Methanogenesis pathway Biological wastewater treatment
Activated sludge process is the most widely used biological wastewater treatment technology. During its over 100-years development, many novel and modified processes have been proposed in order to efficiently meet the more and more stringent discharge and emission limits [1,2]. However, substantial amounts of excess sludge are generated during wastewater treatment, which require further treatment. This accounts for around 60% of the total operational costs of the overall wastewater treatment plant (WWTP) . As one of the most efficient solutions to both energy crisis and environmental pollution challenges, anaerobic digestion is widely applied to reduce the amount of excess sludge, eliminate pathogens and produce methane . In general, the anaerobic digestion process can convert about 40 ~ 60% of the organic solids (excess sludge) to methane (CH4), which is a highly valuable hydrocarbon biofuel, generating 36.5 MJ/m3 in combustion .
Anaerobic digestion sludge contains highly complex microbial communities, which play critical roles in excess sludge treatment, in particular determining the sludge reduction performance and the methane production efficiency. Many molecular methods, such as denaturing gradient gel electrophoresis (DGGE), fluorescent in situ hybridization (FISH), 16S rRNA gene and other marker gene low-throughput sequencing, have been previously applied to investigate the microbial community structure in anaerobic systems . However, these low-throughput approaches are not able to completely reveal the detailed microbial community structure due to the extremely complex communities and overwhelming genetic diversities in anaerobic digestion sludge, especially for those low abundant populations though playing important role in the system. Moreover, the approaches based on clone library sequencing of the 16S rRNA gene for ecological investigations of functional microorganisms may result in an overestimation or underestimation of both their numbers and the diversity due to their inherent bias of amplification [7,8].
High-throughput sequencing methods, such as Illumina sequencing and 454 pyrosequencing technologies, have been recently applied as novel and promising methods to characterize the phylogenetic composition and functional potential of complex community [9,10]. So far, several metagenomic studies have been conducted on microbial community analysis in anaerobic digesters using 454 pyrosequencing [11-13]. Compared to 454 pyrosequencing, Illumina sequencing offers significantly greater throughput and is a more cost-effective approach to study the complex environmental microorganisms [14,15]. To date, Illumina sequencing has been applied in several studies with complex microbial communities, such as soil , ocean , human gut microbes  and activated sludge [19,20]. However, so far, little effort has been dedicated to using Illumina sequencing to analyze in detail the microbial community structure including the rare members of the community as well as their functions in anaerobic digesters . In addition, the dominated methane-producing pathway in full-scale anaerobic digesters for treating excess sludge is still unclear.
The aim of this study was to characterize the metagenomic community composition and reveal functional traits in a typical full-scale anaerobic digester. Toward this end, we extracted DNA from a full-scale anaerobic digestion sludge, and conducted high-throughout (around 3.0 gigabases) metagenomic sequencing on the Illumina HiSeq 2000 platform. The microbial community structures, functional profiles, and metabolic pathways of the anaerobic digestion sludge were revealed. In particular, key microorganisms involved in hydrolysis, acidogenesis, acetogenesis and methane production were comprehensively analyzed based on the obtained metagenomics data. Furthermore, the possible genes associated with methanogenesis pathways were highlighted. This study provides insights into the dominant functions of microbes in full-scale anaerobic digesters, thereby facilitating the development of more efficient full-scale systems to achieve a high-rate sludge reduction and methane production.
Results and discussion
Operational performance of the full-scale anaerobic digester
This full-scale anaerobic digester was fed with excess activated sludge (around 900 m3 per day) with water content of approximately 96%. The temperature was kept around 35°C, i.e. a typical mesophilic digestion process. The detailed operational conditions and the performance of the full-scale anaerobic digester are summarized in Additional file 1: Table S1 (SI). During the sampling period, the anaerobic digester demonstrated a good performance in terms of volatile solids destruction (51% on the average), nutrient balance, and pathogen destruction (above 90%). The volatile fatty acids (VFAs) in the effluent of the digester were significantly low (lower than 800 mg/L), indicating that the anaerobic digestion system was functioning efficiently in converting VFAs to biogas (methane). The daily methane (CH4) production rate was around 1500 m3/d. The average methane composition accounted for about 70.8% in the biogas.
Microbial compositions in anaerobic digester
Most of members belonging to the Firmicutes phylum are syntrophic bacteria that can degrade various VFAs, which were often detected in both activated sludge systems and anaerobic digesters . Within the phylum of Firmicutes, Clostridia (72.5% of all the Firmicutes sequences) and Bacilli (22.6%) form the majority of the classes (Additional file 1: Table S3). The class of Clostridia is well-known in fermenters. The predominance of Clostridia in the anaerobic digestion sludge was associated with a high-rate of hydrolysis and VFA fermentation occurred in the anaerobic digester studied, which was confirmed by the reactor performance data (Additional file 1: Table S1). The genera Streptococcus and Halothermothrix belonging to the phylum of Firmicutes also showed a high abundance based on the metagenomics data (Figure 2).
The major classes within the phylum of Bacteroidetes were found to be Bacteroidia, Cytophagia, Flavobacteriia and Shingobacteriia (Additional file 1: Table S2). The percentage of Bacteroidia was distinctly higher than those of other classes. Similar with the Clostridia class, the Bacteroidaceae family belonging to Bacteroidetes (class) is also well-known comprise fermentative bacteria, which generally play the important role in hydrolyzing and fermenting organic materials and producing organic acids, CO2 and H2 during the anaerobic digestion process .
Methanomicrobia were the major class in the phylum of Euryarchaeota, taking 85.4% of all the Euryarchaeota sequences in the anaerobic digestion sludge (Additional file 1: Table S2). The predominance of Methanomicrobia is associated with the abundant methanogens in the sample, in which abundant Methanosaeta and Methanosarcina are detected (further discussed below).
At the genus level, there are over 2900 different taxa that can be classified (Figure 2). These data demonstrate the extraordinary microbial diversity of anaerobic digestion sludge. The top 50 representing abundant genera in the sample were selected, as shown in Additional file 1: Table S3 (SI). Ten genera have the percentages higher than 1% in the anaerobic digestion sludge. At the genus level, Candidatus Cloacamonas is the most dominant taxon in the anaerobic digestion sludge. As reported in previous work , Candidatus Cloacamonas acidaminovorans is probably a hydrogen-producing syntrophic bacterium that is widely present in many anaerobic digesters.
Recently, the microbial diversity in full-scale biogas production reactors has been reported using metagenomics sequencing [13,21,26]. The current study showed that Proteobacteria was the most dominant phylum, followed by Firmicutes, Bacteroidetes, and Actinobacteria, which are consistent with a previous study , in which microbial community structure of two full-scale anaerobic digesters operated in municipal WWTPs were revealed through llumina high-throughput sequencing. However through using 454 pyrosequencing of 16S rRNA gene sequences, Sundberg et al.  found that the dominant populations included the phyla Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Chloroflexi and Spirochaetes in biogas production reactors digesting sewage sludge, while Firmicutes were the most prevalent in codigesting various combinations of wastes from restaurants, households and slaughterhouses. Similarly, a meta-analysis of all publicly available 16S rRNA gene sequences from microbial communities of anaerobic digesters fed with a variety of feedstocks demonstrated that many dominant populations belong to the phyla Chloroflexi and Proteobacteria . Li et al.  also conducted metagenomic analysis of a solid-state biogas reactor based on 647 Mb of data from 454 pyrosequencing. Their results revealed that the most prevalent fermentative microbes are derived from Clostridiales (Firmicutes). These various dominant populations might be associated with different influent characteristics and operational conditions, which have been reported to strongly influence microbial community structure [13,27-29]. At the WWTP studied in this work, a fraction of industrial wastewater (taking account about 10-20% of the total inflow) was fed into the activated sludge process, subsequently changing the characteristics of the sludge that was fed into the anaerobic digester.
Global gene functional profiles
The genes involved in amino acid metabolism were detected in reads assigned to “valine, leucine and isoleucine biosynthesis (11695 reads)”, “glycine, serine and threonine metabolism (11027 reads)”, and “cysteine and methionine metabolism (6460 reads)”, which are the three most dominant groups. These amino acids including valine, leucine, isoleucine, glycine, serine, threonine, cysteine and methionine are known to be commonly involved in Stickland reactions. There are principally two pathways in which amino acids can be fermented: (1) pairs of amino acids can be fermented through the Stickland reaction; and (2) single amino acids can be degraded in a process that requires the cooperation with hydrogen-utilizing bacteria . Moreover, the above taxonomic assignment indicated that Clostridiales are the first predominant at the order level. Considering that the Stickland reaction has only been reported previously with Clostridial species , thus, it is most likely that the first pathway, i.e. Stickland reaction, is the predominant amino acid fermentation pathway in this full-scale anaerobic digester. Many of the enzymes involved in the amino acid degradation, such as S-adenosylmethionine synthetase [EC:184.108.40.206], cystine reductase [EC:220.127.116.11], cysteine synthase A [EC:18.104.22.168], alpha-aminoadipic semialdehyde synthase [EC:22.214.171.124; 126.96.36.199] and lysine 2,3-aminomutase [EC:188.8.131.52] are annotated with high reads numbers. These observations are indeed associated with good acidogenesis performance in the anaerobic digester (Additional file 1: Table S1).
There are also abundant reads matching the genes for “carbohydrate metabolism”, mainly including “glycolysis/gluconeogenesis (6,198 reads)”, “pentose phosphate pathway (4918 reads)”, “amino sugar and nucleotide sugar metabolisms (4,525 reads)”, “citrate cycle (TCA cycle, 4149 reads)”, “fructose and mannose metabolism (3223 reads)” and “starch and sucrose metabolism (3141 reads)”, as shown in Figure 3. These annotation observations further confirmed the findings that abundant species in this full-scale anaerobic digester are involved in carbohydrate digestion and energy conversion .
Key microorganisms involved in anaerobic digestion process
Dissecting the pathways involved in methanogenesis
Based on the obtained results, the abundances of genes encoding enzymes in acetoclastic pathway are much higher than that involved in hydrogenotrophic and methylotrophic pathways. For instance, the abundances of AckA and PTA are 395 and 157 hits, respectively, while the abundances of FmdA and FTR are 55 and 39 hits, respectively. Compared to the genes of the hydrogenotrophic pathway, the abundances of genes in methylotrophic pathway were the lowest among the three methanogenesis pathways. The obtained results suggested that acetoclastic pathway is likely the major pathway of methane production in anaerobic digestion processes [21,37]. However, it should be noted that the abundance of genes in methanogenesis pathway was based on metagenomics (DNA level), rather than metatranscriptomics or metaproteomics (RNA or protein level), which are required to further explore the active functions involved in the methanogenesis pathway in future study.
Sampling of full-scale anaerobic digestion sludge
The anaerobic digestion sludge was collected from the anaerobic digester from a full-scale WWTP, Beijing, China. This WWTP treats a mean influent flow of 1,000,000 m3/day and services a population of approximately 2,400,000 people in Beijing. The excess sludge from the biological treatment process is removed via the secondary clarifiers and enters the sludge treatment units together with the primary sludge. The sludge treatment processes consists of thickening tanks, anaerobic mesophilic digestion and dewatering. The process diagram and the detailed operational condition are shown in Additional file 1: Figure S3 and Table S1 (Supporting information, SI), respectively. Samples were mixed with 100% ethanol at a ratio of 1:1 (volume/volume) immediately after being collected from the full-scale anaerobic digester, then transferred to the lab using an ice-box and stored at −20°C before the DNA extraction.
Genomic DNA extraction was conducted within 24 hours after sampling. Around 2 mL sample was centrifuged at 3750 g for 5 min to collect the sludge pellet by removing the supernatant. DNA extractions were performed using the FastDNA SPIN Kit for Soil (QBIOgene Inc., Carlsbad, CA, USA), according to the manufacturer’s instructions. DNA quality was assessed using gel electrophoresis (1% agarose) and DNA concentrations were determined using a Qubit Fluorometer (Thermo, USA). The DNA concentration of anaerobic digestion sludge was 580 ng/μL.
DNA library construction and sequencing
The metagenomic sequencing was conducted using Illumina HiSeq 2000 platform by the Beijing Genomic Institute at Shenzhen, China. The extracted DNA sample was afterwards processed according to the genomic DNA sample preparation kit protocol (Illumina). The DNA fragmentation was firstly performed using Covaris S2 Ultrasonicator. The DNA fragments were then processed by end reparation, A-tailing, adapter ligation, DNA size-selection, PCR reaction and products purification based on Illumina HiSeq 2000 instructions. For sequencing, a library consisting of approximate 170 bp fragments was constructed. The base-calling pipeline (version Illumina Pipeline-0.3) was used to process the raw fluorescence images and call sequences. The sequencing depth of 3.0 Gb reads was applied for the sample metagenomic datasets. The metagenomic reads were trimmed using a minimum quality score of 30, a minimum read length of 35 bp and allowing no ambiguous nucleotides. The parameters adopted for overlapping were as follows: at least 20 nt length of the overlap region was required, and at most two mismatches were allowed.
Unassembled DNA sequences were annotated using the Metagenomics Rapid Annotation (MG-RAST) server (v3.1). MG-RAST not only enables phylogenetic and metabolic reconstructions, but also provides protein similarities analysis, including both function annotation and function classification . In the present study, 3.0 Gbp DNA dataset (MG-RAST ID: 4536159.3) was used for most of the analysis. Taxonomic profiles were calculated by Best Hit classification at the E-value cutoff of 10−5 with minimum alignment length of 50 bp based on all the annotation source databases used by MG-RAST. The distribution of taxonomic domains, phyla, orders, families and genus for the annotations was analysed in detail. Concerning taxonomic profiles, percentages shown in the study referred to those classified at a certain taxonomic level.
Functional profiling was conducted by the gene annotation with SEED Subsystems using Hierarchical classification at E-value cutoff of 10−5 and minimum alignment length of 17 amino acids [21,39], respectively, in MG-RAST, and visualized using KEGG mapper. Most of the genes were successfully classified into the hierarchical metabolic categories.
To investigate gene profile characteristic for the anaerobic microbial community, the total sequencing reads were annotated against the databases of Clusters of Orthologous Groups of proteins (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG, v59) [40,41] databases using BLASTP (v2.2.21) with the E-value cutoff of 10−5. Detailed analysis of the anaerobic digestion sludge was conducted to count and compare the hit numbers of the sequences of corresponding enzymes subunits in the methanogenesis pathways. The module ‘KEGGviewer’ in MEGAN was used to analyze pathways [42,43]. Proteins glutathione-independent formaldehyde dehydrogenase (FdhA), hydrogenase subunit A(EchA), formylmethanofuran dehydrogenase subunit A (FmdA), formylmethanofuran-tetrahydromethanopterin N-formyltransferase (FTR), methenyltetrahydromethanopterin cyclohydrolase (MCH), methylenetetrahydromethanopterin dehydrogenase (MTD), coenzyme F420-dependent N5, N10-methenyltetrahydromethanopterin reductase (MER), tetrahydromethanopterin S-methyltransferase (MtrA), [methyl-Co(III) methanol-specific corrinoid protein]:coenzyme M methyltransferase (MtaA), methyl-coenzyme M reductase alpha subunit (McrA), acetate kinase (AckA), acetyl-CoA synthetase (ACSS), phosphate acetyltransferase (PTA), heterodisulfide reductase subunit A (HdrA), acetyl-CoA decarbonylase/synthase complex subunit beta (CdhC) play important roles in recognized methanogenesis pathways, but lack good representative sequences in the eggNOG and KEGG databases at the time of this study. To accurately discover them, BLASTX results were manually analysed through keyword searches based on NCBI-nr annotations, in which genes representing top BLASTX matches were recovered from GenBank. Confirmation of methanogenesis genes was conducted by manually aligning the matched sequences against NCBI-nr database (9 June 2014) using BLAST with E-value cutoff of 10−10.
This study successfully dissected the detailed microbial community structure and the key methane-producing pathways of a full-scale anaerobic digester through applying metagenomics approach. Taxonomic analysis indicated Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria are the four most abundant bacterial populations in anaerobic digestion sludge. For full-scale anaerobic digester treating sewage sludge, the production of methane is achieved through consortia of microorganisms (hydrolysers, fermenters, acetogens and methanogens) working in a step-wise reaction. The members of the order of Halanaerobiales (mainly the genus Halothermothrix) are the major hydrolysers, while the Clostridia class and the Bacteroidaceae family are the dominant fermenters in the system. Clostridium, Treponema, Eubacterium, Thermoanaerobacter and Moorella are found to play important roles on acetate production at the acetogenesis step. The dominant proliferation of the acetoclastic methanogens (Methanosaeta and Methanosarcina), together with the functional affiliation of enzymes-encoding genes (Ack, PTA, ACSS, etc.), strongly suggested that the acetoclastic methanogenesis might be the dominant methanogenesis pathway in the anaerobic digester. Further studies directly based on metatranscriptomics or metaproteomics are necessary to further explore the active functions in the full-scale biogas production digester.
This work is supported by Natural Science Foundation of China (51208009) and Natural Science Foundation of Beijing (8132008). We also acknowledge the support from Specialized Research Fund for the Doctoral Program of Higher Education (20121103120010). Jianhua Guo acknowledges the support from the Australian Research Council Discovery Early Career Researcher Award (DE 130101401) and the University of Queensland ECR Project. Bing-Jie Ni acknowledges the support of Australian Research Council Discovery Early Career Researcher Award (DE130100451).
- van Loosdrecht MCM, Brdjanovic D. Anticipating the next century of wastewater treatment. Science. 2014;344:1452–3.View ArticleGoogle Scholar
- Guo J, Peng Y, Wang S, Ma B, Ge S, Wang Z, et al. Pathways and organisms involved in ammonia oxidation and nitrous oxide emission. Crit Rev Env Sci Tec. 2013;43:2213–96.View ArticleGoogle Scholar
- Canales A, Pareilleux A, Rols JL, Goma G, Huyard A. Decreased sludge production strategy for domestic wastewater treatment. Water Sci Technol. 1994;30:97–106.Google Scholar
- Appels L, Baeyens J, Degreve J, Dewil R. Principles and potential of the anaerobic digestion of waste-activated sludge. Prog Energ Combust. 2008;34:755–81.View ArticleGoogle Scholar
- Amani T, Nosrati M, Sreekrishnan TR. Anaerobic digestion from the viewpoint of microbiological, chemical, and operational aspects - a review. Environ Rev. 2010;18:255–78.View ArticleGoogle Scholar
- Vanwonterghem I, Jensen PD, Ho DP, Batstone DJ, Tyson GW. Linking microbial community structure, interactions and function in anaerobic digesters using new molecular techniques. Curr Opin Biotech. 2014;27:55–64.View ArticleGoogle Scholar
- Ye L, Zhang T, Wang TT, Fang ZW. Microbial structures, functions, and metabolic pathways in wastewater treatment bioreactors revealed using high-throughput sequencing. Environ Sci Technol. 2012;46:13244–52.View ArticleGoogle Scholar
- Aird D, Ross MG, Chen W-S, Danielsson M, Fennell T, Russ C, et al. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 2011;12:R18.View ArticleGoogle Scholar
- Bragg L, Tyson GW. Metagenomics Using Next-Generation Sequencing. In: Paulsen IT, Holmes AJ, editors. Environmental Microbiology: Methods and Protocols, vol. 1096. 2nd ed. New York City: Humana Press; 2014. p. 183–201. Methods in Molecular Biology.View ArticleGoogle Scholar
- Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol. 2013;31:533–8.View ArticleGoogle Scholar
- Wong MT, Zhang D, Li J, Hui RKH, Tun HM, Brar MS, et al. Towards a metagenomic understanding on enhanced biomethane production from waste activated sludge after pH 10 pretreatment. Biotechnol Biofuels. 2013;6:38.View ArticleGoogle Scholar
- Li A, Chu Y, Wang X, Ren L, Yu J, Liu X, et al. A pyrosequencing-based metagenomic study of methane-producing microbial community in solid-state biogas reactor. Biotechnol Biofuels. 2013;6:3.View ArticleGoogle Scholar
- Sundberg C, Al-Soud WA, Larsson M, Alm E, Yekta SS, Svensson BH, et al. 454 pyrosequencing analyses of bacterial and archaeal richness in 21 full-scale biogas digesters. FEMS Microbiol Ecol. 2013;85:612–26.View ArticleGoogle Scholar
- Mardis ER. The impact of next-generation sequencing technology on genetics. Trends Genet. 2008;24:133–41.View ArticleGoogle Scholar
- Glenn TC. Field guide to next-generation DNA sequencers. Mol Ecol Resour. 2011;11:759–69.View ArticleGoogle Scholar
- Mackelprang R, Waldrop MP, DeAngelis KM, David MM, Chavarria KL, Blazewicz SJ, et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature. 2011;480:368–U120.View ArticleGoogle Scholar
- Mason OU, Scott NM, Gonzalez A, Robbins-Pianka A, Baelum J, Kimbrel J, et al. Metagenomics reveals sediment microbial community response to Deepwater Horizon oil spill. ISME J. 2014;8:1464–75.View ArticleGoogle Scholar
- Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–U70.View ArticleGoogle Scholar
- Albertsen M, Hansen LBS, Saunders AM, Nielsen PH, Nielsen KL. A metagenome of a full-scale microbial community carrying out enhanced biological phosphorus removal. ISME J. 2012;6:1094–106.View ArticleGoogle Scholar
- Ju F, Guo F, Ye L, Xia Y, Zhang T. Metagenomic analysis on seasonal microbial variations of activated sludge from a full-scale wastewater treatment plant over 4 years. Environ Microbiol Rep. 2014;6:80–9.View ArticleGoogle Scholar
- Yang Y, Yu K, Xia Y, Lau FTK, Tang DTW, Fung WC, et al. Metagenomic analysis of sludge from full-scale anaerobic digesters operated in municipal wastewater treatment plants. Appl Microbiol Biot. 2014;98:5709–18.View ArticleGoogle Scholar
- Ariesyady HD, Ito T, Okabe S. Functional bacterial and archaeal community structures of major trophic groups in a full-scale anaerobic sludge digester. Water Res. 2007;41:1554–68.View ArticleGoogle Scholar
- Garcia-Peña EI, Parameswaran P, Kang DW, Canul-Chan M, Krajmalnik-Brown R. Anaerobic digestion and co-digestion processes of vegetable and fruit residues: Process and microbial ecology. Bioresour Technol. 2011;102:9447–55.View ArticleGoogle Scholar
- Traversi D, Villa S, Lorenzi E, Degan R, Gilli G. Application of a real-time qPCR method to measure the methanogen concentration during anaerobic digestion as an indicator of biogas production capacity. J Environ Manage. 2012;111:173–7.View ArticleGoogle Scholar
- Pelletier E, Kreimeyer A, Bocs S, Rouy Z, Gyapay G, Chouari R, et al. “Candidatus Cloacamonas acidaminovorans”: Genome sequence reconstruction provides a first glimpse of a new bacterial division. J Bacteriol. 2008;190:2572–9.View ArticleGoogle Scholar
- Nelson MC, Morrison M, Yu Z. A meta-analysis of the microbial diversity observed in anaerobic digesters. Bioresour Technol. 2011;102:3730–9.View ArticleGoogle Scholar
- Ziganshin AM, Liebetrau J, Proeter J, Kleinsteuber S. Microbial community structure and dynamics during anaerobic digestion of various agricultural waste materials. Appl Microbiol Biot. 2013;97:5161–74.View ArticleGoogle Scholar
- Regueiro L, Veiga P, Figueroa M, Alonso-Gutierrez J, Stams AJM, Lema JM, et al. Relationship between microbial activity and microbial community structure in six full-scale anaerobic digesters. Microbiol Res. 2012;167:581–9.View ArticleGoogle Scholar
- Vanwonterghem I, Jensen PD, Dennis PG, Hugenholtz P, Rabaey K, Tyson GW. Deterministic processes guide long-term synchronised population dynamics in replicate anaerobic digesters. ISME J. 2014;8:2015–28.View ArticleGoogle Scholar
- Ramsay IR, Pullammanappallil PC. Protein degradation during anaerobic wastewater treatment: derivation of stoichiometry. Biodegradation. 2001;12:247–57.View ArticleGoogle Scholar
- Papagianni M. Recent advances in engineering the central carbon metabolism of industrially important bacteria. Microb Cell Fact. 2012;11:50.View ArticleGoogle Scholar
- Noor E, Eden E, Milo R, Alon U. Central carbon metabolism as a minimal biochemical walk between precursors for biomass and energy. Mol Cell. 2010;39:809–20.View ArticleGoogle Scholar
- Ferry JG. Enzymology of one-carbon metabolism in methanogenic pathways. FEMS Microbiol Rev. 1999;23:13–38.View ArticleGoogle Scholar
- Sandoval Lozano CJ, Vergara Mendoza M, Carreno De Arango M, Castillo Monroy EF. Microbiological characterization and specific methanogenic activity of anaerobe sludges used in urban solid waste treatment. Waste Manage. 2009;29:704–11.View ArticleGoogle Scholar
- Ragsdale SW, Pierce E. Acetogenesis and the Wood-Ljungdahl pathway of CO2 fixation. Biochimica Et Biophysica Acta-Proteins and Proteomics. 2008;1784:1873–98.View ArticleGoogle Scholar
- Liu Y, Whitman WB. In: Wiegel J, Maier RJ, Adams MWW, editors. Incredible Anaerobes: From Physiology to Genomics to Fuels, vol. 1125. New York: HighWire Press, Blackwell Publishing; 2008. p. 171–89. Annals of the New York Academy of Sciences.Google Scholar
- Yu Y, Lee C, Hwang S. Analysis of community structures in anaerobic processes using a quantitative real-time PCR method. Water Sci Technol. 2005;52:85–91.Google Scholar
- Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, et al. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics. 2008;9:386.View ArticleGoogle Scholar
- Jung JY, Lee SH, Kim JM, Park MS, Bae J-W, Hahn Y, et al. Metagenomic analysis of kimchi, a traditional Korean fermented food. Appl Environ Microb. 2011;77:2264–74.View ArticleGoogle Scholar
- Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006;34:D354–7.View ArticleGoogle Scholar
- Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28:33–6.View ArticleGoogle Scholar
- Mitra S, Rupek P, Richter DC, Urich T, Gilbert JA, Meyer F, et al. Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG. BMC Bioinformatics. 2011;12:S21.View ArticleGoogle Scholar
- Huson D, Mitra S, Ruscheweyh H, Weber N, Schuster S. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011;21:1552–60.View ArticleGoogle Scholar
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