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. Microbial Cell Factories
Dissecting microbial community structure and methane-producing pathways of a full-scale anaerobic reactor digesting activated sludge from wastewater treatment by metagenomic sequencing
Jianhua Guo 0 1 3
Yongzhen Peng 1 3
Bing-Jie Ni 0
Xiaoyu Han 1 2 3
Lu Fan 0
Zhiguo Yuan 0
0 Advanced Water Management Centre (AWMC), The University of Queensland , St Lucia, Brisbane, QLD 4072 , Australia
1 Key Laboratory of Beijing for Water Quality Science and Water Environmental Recovery Engineering, Engineering Research Center of Beijing, Beijing University of Technology , Beijing 100124 , Peoples' Republic of China
2 Beijing Drainage Group Co., Ltd , Beijing 100022 , Peoples' Republic of China
3 Key Laboratory of Beijing for Water Quality Science and Water Environmental Recovery Engineering, Engineering Research Center of Beijing, Beijing University of Technology , Beijing 100124 , Peoples' Republic of China
Background: 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. Results: 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. Conclusions: 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.
Waste 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
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
Results and discussion
Operational performance of the full-scale anaerobic
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 35C, 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
Overall, Illumina sequencing yielded above 3.0 Gb reads.
After quality filtering, the anaerobic digestion sludge yielded
more than 2.6 Gb high quality reads. To reveal microbial
composition, taxonomic annotation was conducted by Best
Hit classification at the E-value cutoff of 105 with
minimum alignment length of 50 bp  based on the entire
available source databases in MG-RAST. Figure 1 shows
that Bacteria were the dominant domain in the sample,
Figure 1 Taxonomic profiling at the Domain level of the studied anaerobic digestion sludge. Total DNA sequences were assigned to
Bacteria, Eukaryota, Archaea, Viruses, and other sequences.
accounting for 93.0% of anaerobic digestion sludge DNA
sequences. Moreover, the abundance of Archaea in the
anaerobic digestion sludge (5.6%) was slightly higher than those
of previous studies [12,21], i.e., below 4.7% of their reads
were assigned to Archaea. Sequences from Eukaryota and
Viruses only accounted for 1.1% and 0.2% in the anaerobic
digestion sludge, respectively. For details, the interactive
Krona chart of the full taxonomy can be found in Additional
file 1: Figure S1.
For a better understanding of the microbial community
structure in anaerobic digestion sludge, taxonomic
affiliation at different levels was analyzed (Figure 2). At the
phylum level, the most abundant bacterial populations were
found to be Proteobacteria, Firmicutes, Bacteroidetes, and
Actinobacteria, accounting for 41.2%, 12.5%, 9.6%, and 5.2%
of all the Bacteria reads, respectively. Proteobacteria are
important microbes in anaerobic digestion process because
most of Alpha-, Beta-, Gamma-, and Deltaproteobacteria
are well-known the glucose, propionate, butyrate, and
acetate-utilizing microbial communities . At the most
abundant phylum Proteobacteria, Alphaproteobacteria was
identified as the most dominant class, having 36.4% of all
the classified Proteobacteria reads.
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
Figure 2 Pie and bar charts showing taxonomic assignments at the various levels for anaerobic digestion sludge based on
metagenomic sequencing data (A: Phylum; B: Class; C: Order; D: Family; E: Genus).
454 pyrosequencing of 16S rRNA gene sequences,
Sundwere the most prevalent in codigesting various
combinaberg et al.  found that the dominant populations
intions of wastes from restaurants, households and
slaughtercluded the phyla Firmicutes, Bacteroidetes, Actinobacteria,
houses. Similarly, a meta-analysis of all publicly available
Proteobacteria, Chloroflexi and Spirochaetes in biogas
pro16S rRNA gene sequences from
microbial communities of
duction reactors digesting sewage sludge, while Firmicutes
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
Global gene functional profiles
To reveal the functional profiling of the full-scale anaerobic
digestion sludge, the total reads were annotated according
to categories of the COG and KEGG databases (Figure 3
and Additional file 1: Figure S2). COG annotation analysis
showed that 43.2% of the total reads were related to
metabolism and 19.6% of them were assigned to cellular processes
and signaling, whereas about 21.6% corresponded to
housekeeping genes involved in information-related processes in
anaerobic digestion sludge (Figure 3 and Additional file 1:
Table S3). The obtained results are comparable with a
previous study , in which approximately 28% of the total
reads were assigned to one or more COG functional
categories and a large number of reads were associated with
the metabolism. In the category of metabolism, the most
abundant metabolic type was energy production and
conversion (9.7%), followed by amino acid transport and
metabolism (9.6%) as well as carbohydrate transport and
metabolism (6.1%). These metabolic activities are well
linked with the conversion of excess activated sludge into
methane during anaerobic digestion .
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:188.8.131.52], cystine
reductase [EC:184.108.40.206], cysteine synthase A [EC:220.127.116.11],
alpha-aminoadipic semialdehyde synthase [EC:18.104.22.168;
22.214.171.124] and lysine 2,3-aminomutase [EC:126.96.36.199] are
0 2 4 6 8 10 12
Figure 3 Functional categories of anaerobic digestion sludge in COG categories. Name of subcategories in COG database are listed on the
left, and the corresponding major categories are list on the right.
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 .
Based on the annotation of functional genes using SEED
subsystems in MG-RAST, it was found that the subsystem
of carbohydrates was the most abundant one, followed by
protein metabolism, amino acids and derivatives (Additional
file 1: Table S4). The Level 2 subsystem of carbohydrate was
further analyzed and compared. As shown in Figure 4,
central carbohydrate metabolism and one-carbon metabolism
are the major function in Level 2 subsystems. Central
carbohydrate metabolism is used to describe the integration of
pathways of transporting and oxidation of main carbon
sources inside the cell . It involves a complex series of
enzymatic steps to convert external substrate (e.g., sugars)
into metabolic precursors, such as acetyl-CoA, pyruvate and
d-fructose-6-phosphate . These precursors are then
utilized to generate the cell biomass . One-carbon
metabolism was also annotated with a high abundance, accounting
for 2.33% of the identified carbohydrate subsystem in
anaerobic digestion sludge. One-carbon metabolisms convert
complex organic matter to simple one-carbon compounds,
which play important roles in the process of methanogenesis
and are generally present in methanogenic Archaea .
Key microorganisms involved in anaerobic digestion
The anaerobic digestion process generally consists the
four stages, i.e. hydrolysis, fermentation, acetogenesis and
methanogenesis . Various microorganisms are
involved in each step and cooperated with each other to
achieve high-rate sludge reduction and methane
formation in anaerobic digestion. According to the sequencing
data of this work, the members of the order of
Halanaerobiales (mainly the genus Halothermothrix) dominated in
the anaerobic digester, which are predicted to hydrolyze
polymers to monomers with their enzymes, converting
particulate materials into dissolved materials at the first
stage. Subsequently, the fermentative bacteria, mainly the
Clostridia class and the Bacteroidaceae family in this
study, performed the acidogenic process at the second
stage and produced VFA, CO2 and H2. At the third stage,
acetogenic bacteria further converted these products to
acetate by utilizing obligate hydrogen-producing
acetogens or homoacetogens via the pathway of CO2 reduction
with the acetyl-CoA synthase as the key enzyme. In fact,
there are more than 20 bacterial genera that contain over
100 reported acetogenic species in literature . Our
results suggests that Clostridium, Treponema, Eubacterium,
Thermoanaerobacter and Moorella are the dominant
acetogenic bacteria in the studied anaerobic digester as
shown in Figure 5, consistent with a previous reports .
Biological methane production is the last step of
anaerobic digestion, in which methanogens are the key
microorganisms producing methane as the end product.
Methanogens include a phylogenetically diverse group
belonging to the Archaea. These methanogens are
classified into five well-established orders: Methanobacteriales,
Methanococcales, Methanomicrobiales, Methanosarcinales,
and Methanopyrales. In this study, key genera involved in the
Figure 4 Abundances of major Level 2 subsystems in anaerobic digestion sludge derived from Level 1 subsystem of carbohydrate based
on SEED subsystems (The E-value cutoff of 105 and minimum alignment length of 17 aa was used as the annotation parameters).
0 50000 100000 150000
Number of Reads
Figure 5 Key genera of acetogenic bacteria detected in the full-scale anaerobic digester.
methanogenesis pathway were further analysed based on the
obtained sequencing data (Figure 6). The results indicated that
the five most dominant methanogenic genera are
Methanosaeta (26.2% of all the methanogens), Methanospirillum
(13.1%), Methanosarcina (12.8%), Methanoculleus (11.1%)
and Methanoregula (7.6%) in the full-scale anaerobic
digestion sludge. Among them, only two genera are
known to use acetate for methanogenesis, i.e.
Methanosaeta and Methanosarcina. Methanosaeta is a specialist
that uses acetate exclusively, whereas Methanosarcina is a
relative generalist that can utilize methanol, methylamine
and acetate, as well as hydrogen and carbon dioxide
for methane production . Hydrogenotrophic
methanogens can reduce CO2 to CH4 with H2 as the primary
electron donor, as well as formate. A diverse group of
hydrogenotrophic methanogens (e.g. Methanospirillum,
Methanoculleus and Methanoregula) was detected in the
full-scale anaerobic digester; however, their abundances
were lower than acetoclastic methanogens. Moreover, the
methylotrophic methanogens such as Methanococcoides,
Methanohalophilus and Methanolobus are also found with
a relatively lower reads number in the anaerobic digestion
sludge. In addition, a number of Methanosphaera
belonging to the order Methanobacteriales were also found
in the anaerobic digestion sludge, which are able to use
Dissecting the pathways involved in methanogenesis
In order to reveal the dominant methanogenesis pathway,
functional enzyme-encoding genes for the relevant
Figure 6 Key genera involved in methanogenesis process.
methanogenesis pathways in the anaerobic digester were
identified and annotated with reference to a
methanogenesis genes database extracted from KEGG (Figure 7).
According to current knowledge, there are mainly three
recognized methanogenic pathways, including acetoclastic,
hydrogenotrophic and methylotrophic pathways . For
hydrogenotrophic methanogenesis, CO2 is reduced
successively to CH4 through a series of intermediates, including
formyl, methylene, and methyl levels. The methyl group
is then transferred to Coenzyme M, forming methyl-CoM.
The methyl-CoM is reduced to CH4 through methyl
coenzyme M reductase (Mcr) at the final step (blue line in
Figure 7). For the acetoclastic pathway, acetate is firstly
converted to acetyl-CoA, in which Methanosarcina utilizes
the low-affinity acetate kinase (AK)-phosphotransacetylase
(PTA) system to activate acetate to acetyl-CoA, while
Methanosaeta uses the high-affinity adenosine
monophosphate (AMP)-forming acetyl-CoA synthetase. Then
acetylCoA is converted to a methyl group and subsequently to
methane through the key enzymes of Cdh, Mtr and Mcr
(red line in Figure 7). For methylotrophic pathway, the
methyl-groups from methylated compounds or methane
are transferred to a methanol-specific corrinoid protein
(green dashed line in Figure 7). Methyl-CoM subsequently
enters the methanogenesis pathway and is then reduced to
methane via Mcr reductase.
Figure 7 Hits numbers of genes involved in the relevant methanogenesis pathways in anaerobic digestion. The three known pathways
involved in methanogenesis are colored differentially. The acetoclastic pathway is shown in red, the hydrogenotrophic pathway is marked in
green, and the methylotrophic pathway is presented in green. FdhA, glutathione-independent formaldehyde dehydrogenase; EchA, hydrogenase
subunit A; FmdA, formylmethanofuran dehydrogenase subunit A; FTR, formylmethanofuran-tetrahydromethanopterin N-formyltransferase; MCH,
methenyltetrahydromethanopterin cyclohydrolase; MTD, methylenetetrahydromethanopterin dehydrogenase; MER, coenzyme F420-dependent N5,
N10-methenyltetrahydromethanopterin reductase; MtrA, tetrahydromethanopterin S-methyltransferase; MtaA, [methyl-Co(III) methanol-specific
corrinoid protein]:coenzyme M methyltransferase; McrA, methyl-coenzyme M reductase alpha subunit; AckA, acetate kinase; ACSS, acetyl-CoA
synthetase; PTA, phosphate acetyltransferase; HdrA, heterodisulfide reductase subunit A; CdhC, acetyl-CoA decarbonylase/synthase complex
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 m /
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 20C before the DNA
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 manufacturers 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/L.
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
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
(MGRAST ID: 4536159.3) was used for most of the analysis.
Taxonomic profiles were calculated by Best Hit classification
at the E-value cutoff of 105 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 105 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
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 105. 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
Nformyltransferase (FTR), methenyltetrahydromethanopterin
cyclohydrolase (MCH), methylenetetrahydromethanopterin
dehydrogenase (MTD), coenzyme F420-dependent
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
heterodisulfide reductase subunit A (HdrA), acetyl-CoA
decarbonylase/synthase complex subunit beta (CdhC)
play important roles in recognized
pathways, but lack good representative sequences in
the eggNOG and KEGG databases at the time of this
study. To accurately discover them, BLASTX results
manually analysed through
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
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
Additional file 1: Table S1. Operation condition and performance of
the second-stage anaerobic digester in a full-scale WWTP. Table S2.
Percentage of dominant class in major phylum from Bacteria and
Archaea. Table S3. Abundances of Top 50 genera in the ADS sample.
The abundance is presented in terms of percentages of the total
sequences in the sample. Table S4. Level 1 subsystems in the ADS
sample, annotated by SEED sub-systems databases with E-value cutoff of
1e-5 and minimum alignment length of 17 aa. Figure S5. The interactive
Krona chart of the full taxonomy. Figure S6. KEGG mapper for the
anaerobic digestion sample. Figure S7. Schematic diagram of the
full-scale wastewater treatment plant and the sampling point (sampling
point as shown by a red star).
The authors declare that they have no competing interests.
All authors contributed intellectually via scientific discussions during the
work and have read and approved the final manuscript. JG performed the
sample preparation, sequence analyses, data interpretation and compiled the
manuscript. BJN participated in experimental design and data interpretation,
and reviewed the manuscript. XH carried out the anaerobic digestion and
physical/chemical characterization. LF conducted parts of sequence analysis.
YP and ZY contributed to data interpretation and manuscripts final revision.
JG, YP and ZY conceived and coordinated the study.
JG. is an Assistant Professor of School of Environmental and Energy Engineering,
Beijing University of Technology (China), and DECRA Fellow at Advanced Water
Management Centre, The University of Queensland. YP. is a Full Professor at
School of Environmental and Energy Engineering, Beijing University of
Technology (China). BN. and LF. are research fellow and post-doc, respectively, at
Advanced Water Management Centre, The University of Queensland. XH. is a PhD
student at Beijing University of Technology (China). ZY. is a Full Professor at
Advanced Water Management Centre, The University of Queensland.
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).
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