Metagenomic insights into production of zero valent sulfur from dissimilatory sulfate reduction in a methanogenic bioreactor

Journal Pre-proof Metagenomic insights into production of zero valent sulfur from dissimilatory sulfate reduction in a methanogenic bioreactor

Wenwen Fang, Zhiwei Liang, Yulong Liu, Jiayuan Liao, Shanquan Wang PII:

S2589-014X(19)30195-1

DOI:

https://doi.org/10.1016/j.biteb.2019.100305

Reference:

BITEB 100305

To appear in:

Bioresource Technology Reports

Received date:

11 June 2019

Revised date:

8 August 2019

Accepted date:

9 August 2019

Please cite this article as: W. Fang, Z. Liang, Y. Liu, et al., Metagenomic insights into production of zero valent sulfur from dissimilatory sulfate reduction in a methanogenic bioreactor, Bioresource Technology Reports(2019), https://doi.org/10.1016/ j.biteb.2019.100305

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

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Metagenomic insights into production of zero valent sulfur from dissimilatory sulfate reduction in a methanogenic bioreactor

Wenwen Fanga,b, Zhiwei Lianga,b , Yulong Liua, Jiayuan Liaoa and Shanquan Wanga,b,c* a

School of Environmental Science and Engineering, Sun Yat-Sen University,

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Environmental Microbiomics Research Center, Sun Yat-Sen University, Guangzhou,

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b

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Guangzhou, China 510006;

Guangdong Provincial Key Laboratory of Environmental Pollution Control and

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c

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China 510006;

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Remediation Technology, Guangzhou, China 510006

*Corresponding author: Shanquan Wang ([email protected])

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Abstract

Dissimilatory sulfate reduction mediated by sulfate-reducing microorganisms (SRMs) has a pivotal role in the sulfur cycle, from which the generation of zero valent sulfur (ZVS) represents a novel pathway. Molecular details in the sulfite reduction to sulfide are still in debate. Also, the community composition and metabolic potential in

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sulfate-to-ZVS microbial communities remain to be elucidated. In this study, we

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employed genome-centric metagenomics approach to investigate the major players in a

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sulfate-to-ZVS bioreactor (ZVS-SR). Totally 51 metagenome assembled genomes

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(MAGs) were retrieved from the ZVS-SR microbiome, most belonging to phyla

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Proteobacteria, Actinobacteria, Bacteroides and Chloroflexi. Major players possibly responsible for ZVS generation included Desulfobacter, Desulfococcus, Desulfobacula

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and Desulfobacterales. A Desulfobacterales bacterium (SRB-bin23) was selected for

implications.

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subsequent detailed characterization of genome-encoded metabolic pathways and key

Keywords:

dissimilatory

functional genes involved in ZVS generation. This study expands our knowledge on the dissimilatory sulfate reduction in SRMs and may have important environmental

sulfate

reduction,

microorganisms (SRMs), zero valent sulfur

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metagenomics,

sulfate

reducing

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1. Introduction The sulfur cycle is a major biogeochemical process on earth (Anantharaman et al. 2018), and more than 50% of the organic carbon in marine sediments was mineralized via sulfate reduction (Thiel et al., 2018). Of the sulfur cycle, dissimilatory sulfate reduction mediated by sulfate-reducing microorganisms (SRMs) might represent the

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earliest energy metabolism of anaerobic life (Oliveira et al., 2008; Anantharaman et al., 2018; Eickmann et al., 2018). The canonical pathway of the dissimilatory sulfate

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reduction is sulfate reduction to sulfide via sulfite without the production of zero valent

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sulfur (ZVS) (Zhou et al., 2011; Xu et al., 2013; Havig et al., 2017). Interestingly, ZVS

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generation from the dissimilatory sulfate reduction was recently observed in a coculture

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and represented a novel pathway, in which anaerobic methanotrophic archaea (ANME)

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was proposed to couple the anaerobic methane oxidation with the sulfate reduction to ZVS (Milucka et al., 2012). This report provided experimental evidence, for the first

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time, to confirm the ZVS generation from the dissimilatory sulfate reduction. Very recently, we also observed the novel ZVS generation pathway in a light-blocking methanogenic bioreactor (ZVS-SR) (Fang et al., 2019), of which the key functional microorganisms and their metabolic potential involving in ZVS production from dissimilatory sulfate reduction await identification and characterization. Metagenomics has come a long way since the term was first introduced by Handelsman et al (1998). This analytical strategy was used to examine environmental microbiome using a suit of genomic tools to directly access their genetic content (Zhi et al., 2014; Bouhajja et al., 2016). The metagenomics could circumvent the 3

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unculturability of a large portion of microorganisms and access to their genomic contents, the biggest roadblocks to advance environmental microbiology (Stewart et al., 2012). Therefore, metagenomic analysis is an effective strategy to discover and describe inaccessible environmental microorganisms in their whole complexity, and to provide a comprehensive overview of their metabolic potential (Gillan et al., 2015). Recently, the

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enormous potential of metagenomics to promote both our understanding and resource exploration of ecosystems has become clear (Riesenfeld et al., 2004; Imhoff et al., 2011).

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Consequently, the metagenomic analysis could be employed to identify the key

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functional microorganisms and characterize their metabolic potential for the

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dissimilatory sulfate reduction.

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In this study, we employed the genome-centric metagenomic analysis to retrieve

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metagenomic-assembled genomes (MAGs) of major lineages, particularly the SRMs, in the ZVS-SR. Specific objectives include: (1) characterization of composition and

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function of the ZVS-SR microbiome, and (2) identification of SRMs and their metabolic potential. Results generated from this study may expand our understanding of the sulfur cycle and shed lights on future application in sulfate-containing wastewater treatment.

2. Materials and Methods

2.1. Sample collection and DNA extraction

To investigate the community composition and function of the ZVS-producing microbiome, a light-blocking methanogenic SBR bioreactor with a working volume of 10 L was setup and operated at 30℃ for 400 days (Fang et al., 2019). Samples for 4

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genomic DNA (gDNA) extraction were collected on day 210, when the ZVS-SR bioreactor was operated under stable condition. Cells were harvested by centrifugation at 16000× g for 10min at 4℃. After removing the supernatant and resuspending microbial cell, community gDNA extraction was performed according to the manufacturer's instructions using the FastDNA Spin Kit for Soil (MP Biomedicals,

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Carlsbad, CA, United States) (Hamilton et al., 2013; Maza-Márquez et al., 2017 ). The quality and quantity of the gDNA were evaluated with Quantus Fluorometer (Promega,

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Madison, WI, USA). The results showed that the gDNA was purified in good integrity.

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2.2. Metagenomic sequencing, assembly and annotation

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Shotgun metagenomic sequencing was provided by BGI (Shenzhen, China) using a

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2×150bp pair end run on Illumina Hiseq 4000 platform. In total, 36 Gb of raw

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sequencing data was obtained. Adaptor trimming and quality control were done by BGI with Q20 >90% and Q30 >85%, generating roughly 36 Gb of clean data. The quality of

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metagenomic sequencing data was checked with FastQC (Andrews, 2010). Metagenomic reads were assembled with SPAdes v3.11.1 using default settings (Bankevich et al., 2012). The quality of assembled scaffold was checked by QUAST (Gurevich et al., 2013). Sequence coverage for each assembled scaffold was calculated by mapping raw reads from the sample to assembled contigs using Bowtie2 with default parameters (Langmead and Salzberg, 2012). These scaffolds were binned using MetaBAT (version 0.32.4) with the parameters “-m 2000 --unbinned” (Kang et al., 2015), which considering both tetranucleotide frequencies and the coverage of these scaffolds. The retrieved bins from MetaBAT were evaluated for taxonomic assignment, 5

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genome completeness, potential contamination and strain heterogeneity using CheckM (Parks et al., 2015). A total of 114,591,602 reads were further optimized to obtain high-quality genomes as described previously (Chen et al., 2018). The draft genome was automatically annotated using Prokka (Seemann, 2014). Potentially missed assignments were manually corrected. Reads were only classified at the genus level to

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ensure accuracy.

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2.3.Phylogenetic tree reconstruction

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Taxonomies of the 51 MAGs populations were determined by using a concatenated

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ribosomal protein (RP) tree as described (Hug et al., 2016). Briefly, 16 RPs (L2, L3, L4,

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L5, L6, L14, L15, L16, L18, L22, L24, S3, S8, S10, S17, S19) were each aligned separately using MUSCLE v3.8.31 with a modified method published recently (Hug et

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al., 2016). The 16 RP subunits in the selected bacterial species were independently

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aligned with MAFF v7.245, trimmed to remove unaligned N and C termini residues using default parameters with Gblocks bersion 0.91b (Talavera et al., 2007), and concatenated to reconstruct a maximum likelihood tree with 100 bootstrap values using PHYML v3.2.0 (Guindon et al., 2010). Ortholog groups were predicted via OrthoMCL (Fischer et al., 2011) and OrthoDB (Zdobnov et al., 2016). Enzyme orthologues were mapped to the species tree with Evolview v2 (He et al., 2016; Wang et al., 2019). This analysis sampled a total of 204 bootstrap replicates before being stopped by the autoMRE algorithm.

2.4.Identification of sulfate reduction genes 6

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Genome-specific metabolic potential for sulfate/sulfite reduction was determined in an iterative manner by: (I) using Hidden Markov Model to identify dissimilatory anaerobic sulfite reductase encoding genes (dsrA, dsrB and dsrC) by hit to Pfam database, KEGG database and COG database (Gutiérrez-Preciado et al., 2018); (II) identifying genes for the reduction of sulfate to sulfite by searching all predicted

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proteins against sat and aprAB hmm profiles from the KEGG database (Tan et al.,

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2019).

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2.5. Sequence data accession

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Metagenomic sequencing reads were deposited into the EMBL sequence read

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3. Results and discussion

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archive with the accession number of PRJEB32704.

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3.1. Community composition of the ZVS-producing microbiome

To investigate the community composition and function of the ZVS-producing microbiome, the genome-centric metagenomic approach was employed to retrieve metagenomic-assembled genomes (MAGs) of major lineages in the ZVS-SR bioreactor. Shotgun sequencing and assembly generated a metagenome of 805,532 contigs totaling to 155.2 Mb sequences with an N50 of 32,414 bp. Population genome binning of the assembled metagenomes enabled the recovery of 51 MAGs with >70% completeness and <5% contamination. The contig clusters corresponding to these retrieved genomes were shown in the plot of coverage vs. GC% (Fig. 1), which were well clustered at the 7

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species level. Once the initial scaffold membership of each population bin had been refined, all reads mapping to those scaffolds and their associated reads were de novo assembled to produce individual population draft genomes (Chen et al., 2018). Completeness of these genomic bins were identified to be 71.0-99.1% by using Phylosift (Darling et al., 2014) based on their housekeeping genes. These populations

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spanned 44 genera over 13 phyla (Fig. 2), providing coverage for 65% of the microbiome calculated by mapping reads to binned scaffolds (Fig. 3). No contamination

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was observed in bins 10, 13, 14, 27, 3, 30, 32, 35, 40 and 51, and other bins had slight

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contamination (0.15-4.5%) following the observed split coverage (Table 1).

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Phylogenetic analysis based on the concatenated 16 ribosomal protein (RP) sequences, as well as functional genes involving in sulfate reduction, showed the

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community composition of the ZVS-SR microbiome. At the phylum level, most

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metagenomic sequencing reads were mapped to the phyla Proteobacteria (58%), Actinobacteria (15%), Bacteroides (11%), Chloroflexi (9%) and Planctomycetes (1%). The major players for sulfate reduction included SRMs of Desulfobacter, Desulfococcus, Desulfobacula and Desulfobacterales (Fig. 3). These populations are well-characterized sulfate-reducing bacteria (SRB), and commonly found in sulfate reduction bioreactors (Cao et al., 2014; Baker et al., 2015).

After the discovery of the syntrophic anaerobic oxidation of methane (Hinrichs et al., 1999), a number of hypotheses have been proposed for the ZVS generation in microcosms consisting of anaerobic methanotrophic archaea (ANME) and SRB (Knittel

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et al., 2009; Yu et al., 2018). Nonetheless, only a single example of ZVS generation from the dissimilatory sulfate reduction was observed recently in a batch experiment, in which ANME coupled the anaerobic methane oxidation with the sulfate reduction to ZVS (Milucka et al., 2012). And recent attempts to enrich and isolate the SRB from the ANME-SRB co-culture using zero-valent sulfur were unsuccessful (Wegener et al.,

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2016). Moreover, both metagenomic and metatranscriptomic analyses of ANME showed that the sulfate reduction pathways are likely used for assimilation but not

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dissimilation of sulfate (Meyerdierks et al., 2010). On the other hand, marker genes or

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proteins for canonical dissimilatory sulfate reduction have been only detected in SRB

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(Yu et al., 2018). Thus, ZVS generation from the dissimilatory sulfate reduction could

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be mediated by sulfate-reducing bacteria (Meyerdierks et al., 2010; Yu et al., 2018).

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Notably, in our study, sulfate reduction genes were not observed in other lineages other than the above-mentioned SRB (Desulfobacter, Desulfococcus, Desulfobacula and

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Desulfobacterales) (Fig. 3).Consequently, our study provide the first experimental evidence to confirm the ZVS generation from dissimilatory sulfate reduction mediated by the SRB, rather than previously reported archaeal populations (Milucka et al., 2012; Yu et al., 2018).

3.2. Dissimilatory sulfate reduction genes in the ZVS-SR bioreactor

SRB employ a complete gene set (i.e., satA, aprA, aprB, dsrA, dsrB, dsrC and dsrD) for the dissimilatory sulfate reduction in the ZVS-SR bioreactor. The normal dissimilatory sulfate reduction process mediated by SRB is sulfate reduction to sulfide

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via sulfite without the production of zero valent sulfur (ZVS) (Zhou et al., 2016; Havig et al., 2017; Zhang et al., 2019). In the dissimilatory sulfate reduction process, dissimilarity sulfite reductase (Dsr) genes (i.e., dsrA, dsrB, dsrC and dsrD) are “signature” genes in SRB (Zhou et al., 2016), of which the dsrA and dsrB are paralogous genes and may originate from the very early gene duplication. As shown in

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Figure 4, the dsrAB genes from SRB are clustered together, sharing high sequence similarities (86.1-100%) with homologous genes in their close lineages. In line with the

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dsrAB phylogeny, the SRB dsrC genes retrieved from ZVS-SR microbiome share high

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sequence similarities (76.5-100%) with previously reported dsrC genes involved in

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dissimilatory sulfate reduction (Fig. 4). Previous studies suggested a novel mechanism

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for the process of sulfite reduction involving both DsrAB and DsrC (Oliveira et al.,

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2008; Wenk et al., 2018), and in vivo tests suggested the essential role of DsrC and its CysA in the dissimilatory sulfate reduction (Santos et al., 2015): DsrAB and DsrC could

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assemble as α2β2γ2 arrangement, and sulfite bind to the active site of DsrAB and is reduced via four-electron transfers to form an S0 intermediate. Then, the S0 is transferred to Cys-104C of DsrC to form a persulfide. Once DsrC undocks from DsrAB, it can reduce the persulfide, releasing H2S and forming an oxidized DsrC, which could be reduced by the DsrMKJOP membrane complex and further bind to DsrAB to enter another sulfate reduction cycle. Notably, the presence of dsrD genes in the MAGs of SRB further confirms their involvement in sulfate reduction, because the dsrD genes were exclusively identified in SRB and could be a biomarker gene to differentiate the SRB from sulfide-oxidizing bacteria (SOB) (Table 2) (Rabus et al., 2015). Although the 10

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exact function of the DsrD is unclear, the presence of winged-helix domains in its structure and its association with other core proteins of the dsr complex (DsrABC) suggested a regulatory role of DsrD in microbial sulfite reduction (Mizuno et al., 2003; Anantharaman et al., 2018). The concatenated Dsr tree showed that Desulfobacter (SRB-bin 10), Desulfococcus (SRB-bin 32), Desulfobacula (SRB-bin 24) and

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Desulfobacterales (SRB-bin 23) could play a key role in sulfite reduction and ZVS generation in the ZVS-SR microbiome (Fig. 4; Fig 5; Table 2). Fang et al. (2019)

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reported that a higher amount of ZVS generation from dissimilatory sulfate reduction

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was observed in serum bottles with a higher concentration of sulfate. A possible reason

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might be due to that SRMs could utilize sulfate-to-ZVS as an alternative pathway to

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sulfate-to-sulfide to increase the thermodynamic favorability of the dissimilatory sulfate

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reduction and alleviate the inhibitive effect of sulfide. Consequently, ZVS generation from the dissimilatory sulfate reduction might be as a result of normal SRB metabolism

al., 2019).

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under an unfavorable condition, e.g., inhibitive high-concentrations of sulfide (Fang et

3.3.Genome-encoded metabolic potential in a Desulfobacterales bacterium

Based on the genome completeness and presence of dissimilatory sulfate reduction genes, SRB-bin23 taxonomically assigned to Desulfobacterales was chosen for subsequent detailed characterization of genome-encoded metabolic pathways (Fig.5), particularly the carbon metabolism and respiratory electron transport chains. Inside SRB-bin23 cells, the assimilated acetate was converted into acetyl-CoA and further subjected to the TCA cycle, providing a carbon source for anabolism and reducing 11

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equivalents for the dissimilatory sulfate reduction. Before sulfate reduction, sulfate molecules were transported across the cell membrane via a facilitator superfamily-type transporter encoded by the sulP gene. In the multistep dissimilatory sulfate reduction process (Fig. 4), sulfate was first activated by ATP sulfurylase (sat) to adenosine 5’-phosphosulfate (APS, sulfur oxidation state +6) due to the thermodynamically

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unfavorable reduction of sulfate to sulfite. Then the APS was reduced to sulfite (sulfur oxidation state +4) through the mediation by APS reductase (AprAB) and the essential

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membrane complex qmoABC, similar to the process in other reported sulfate-reducing

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bacteria and archaea (Pereira et al., 2011). Details in the sulfite reduction to sulfide as

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the final step in the dissimilatory sulfate reduction have been still in debate, which

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might involve a series of intermediates, in particular thiosulfate, trithionate and bisulfite

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(Brunner and Bernasconi, 2005; Milucka et al., 2012). In our ZVS-SR bioreactor, ZVS was generated and accumulated, as a parallel pathway with sulfate reduction to sulfide,

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during the dissimilatory sulfate reduction (Fang et al. 2019). The ZVS might be released from the DsrC-bound ZVS and further generate polysulfide to increase the overall thermodynamic favorability when a high concentration of sulfide present in the ZVS-SR bioreactor. After that, the ZVS will be exported across the cytoplasmic membrane by ThiFS. Also, during sulfite reduction to ZVS and sulfide, DsrM and DsrK were involved in the electron transport.

Metagenomics studies revealed that bacteria of Desulfobacter, Desulfococcus, Desulfobacula and Desulfobacterales may involve in ZVS generation. In future studies, pure cultures could be employed to further verify the generation of ZVS from the 12

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dissimilatory sulfate reduction. Furthermore, meta-omic analyses, together with heterogeneous expression of key functional genes involving in the ZVS generation, may reveal molecular mechanisms underlying the ZVS generation from the dissimilatory sulfate reduction.

4. Conclusion

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Based on the genome-centric metagenomic analyses, this study identified the

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functional microorganisms that may involve in ZVS generation including Desulfobacter,

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Desulfococcus, Desulfobacula and Desulfobacterales. These SRMs employ a complete

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gene for the dissimilatory sulfate reduction. Inside SRMs, ZVS generation from the

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dissimilatory sulfate reduction might be mediated by DsrC and DsrD under unfavorable conditions, e.g., inhibitive high-concentrations of sulfide. This study provides

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metagenomic insights into ZVS generation from dissimilatory sulfate reduction in

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sulfate-reducing microcosms, shedding light on future elucidation of the molecular mechanism of the sulfate-to-ZVS generation.

Acknowledgements

This study was supported by the Key Program of National Natural Science Foundation of China (51638005)

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Figure Legends Figure 1. Extraction of bin-genomes from the metagenome assembly of ZVS-SR 21

Journal Pre-proof

microbiome. Bin-genome clusters were established using the estimated taxonomic classification of contigs (color of dots), contig lengths (size of dots), contig GC content and contig relative frequency.

Figure 2. Phylogenetic tree of the 51 MAGs retrieved from ZVS-SR microbiome. The tree was constructed based on an alignment of their concatenated ribosomal protein

of

sequences.

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Figure 3. Genome completeness and relative abundance (shown as bar graph) of

-p

retrieved MAGs from ZVS-SR microbiome. The right-hand columns showed the

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presence (closed circle) and absence (blank) of dissimilatory sulfate reduction genes in

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the MAGs.

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Figure 4. Paralogous rooting analysis of dsrABCD genes. Consensus phylogeny of the dsrA, dsrB, dsrC and dsrD gene sequences were reconstructed by neighbor-joining

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distance method (42 sequences). Scale bar indicates 0.5 sequence divergence.

Figure 5. Sulfate-to-ZVS relevant metabolism inferred from draft genome of a Desulfobacterales bacterium. Pathways associated with sulfur and carbon metabolisms were shown in green and blue colors, respectively. Genes with unknown functions were in grey color.

22

Journal Pre-proof Table1 Genome statistics of MAGs recovered from the ZVS-SR community Bin

Phylogenetic

No.of

Total

Contamina GC (%)

Number

affiliation

contigs

N50

length (bp)

tion (%)

Thiobacillus

82.96

2221365

0.661

7483

1.105

bin.2 bin.3

Anaerolinea Desulfosarcin

6.93 17.5

2752421 2979572

0.559 0.568

10130 27328

4.545 0

bin.4 bin.5

Smithella a Thauera

19.86 618.6

3125474 2692107

0.547 0.677

36563 7447

4.086 0.755

bin.6

Draconibacte

23.32

3551643

0.474

29441

1.612

bin.7 bin.8

15.71 62.25

2050973 2843441

0.621 0.672

12789 5301

0.645 2.678

bin.9 bin.10

Desulfuromon rium Desulfobulbus as Anaerolineae Desulfococcus

37.44 12.83

4028047 4122266

0.628 0.556

35887 63864

1.09 0

Melioribacter

6.38

3284387

0.339

10631

2.067

Anaerolineae Desulfatirhab

107.35 67.53

3734560 3520318

0.665 0.541

17073 19720

1.01 0

bin.14 bin.15

Mollicutes dium Alistipes

17.94 14.23

1333740 2698785

0.325 0.497

11546 19256

0 1.093

bin.16 bin.17

Chloroflexi Thermovirga

9.56 6.2

2473323 1752594

0.49 0.566

31571 8235

2 0.847

bin.18

Collinsella

39.48

1684449

0.666

9508

0.833

bin.19 bin.20

Bacteroides Phycisphaerae

5.43 8.2

3176893 3951251

0.451 0.625

4124 8829

4.38 1.136

bin.21 bin.22

0.64 26.57

4105257 3026129

0.636 0.5

20458 57777

1.111 2.419

bin.23

Sphaerobacter Bacteroidetes idae Desulfobacter

129.13

3650327

0.586

177637

0.744

bin.24 bin.25

Desulfobacula ales Nitrospira

23.89 18.43

3807127 3776510

0.537 0.624

78366 212504

1.29 3.818

bin.26 bin.27

Thalassolituus Proteobacteria

6.65 9.83

2922694 2395341

0.559 0.634

12848 14399

1.648 0

bin.28 bin.29

26.65 21.77

3056591 2703467

0.702 0.693

7520 14007

0.215 0.18

bin.30

Thermoleophi Tetrasphaera lum Thermotogale

6.68

2009123

0.45

7591

0

bin.31 bin.32

Anaerolineae s Desulfobacter

8 41.22

3073879 3481972

0.649 0.479

4721 50095

1.828 0

bin.33 bin.34

Desulfobulbac Sulfurimonas eae Kineococcus

109.85 14.65

3163806 2458330

0.593 0.44

118848 109597

1.935 1.024

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re

lP na

Jo ur

bin.35

of

bin.11 bin.12 bin.13

-p

bin.1

70.76

2867566

0.658

18207

0

Burkholderial Turneriella es Marmoricola Rhodococcus

93.19 6.81

3726746 3247554

0.651 0.596

80554 6553

0.233 1.685

7.04 28.82

3046648 4373858

0.695 0.703

4458 7625

1.381 1.563

29.21 9.58

2812649 3527625

0.532 0.666

59585 11802

0 2.272

bin.42

Desulfuromus Phycisphaerae a Bacteroides

194.9

3422848

0.5

37900

3.216

bin.43 bin.44

Acidobacteria Deltaproteoba

116.58 6.15

4535123 3467931

0.714 0.423

7784 4800

4.444 3.602

bin.45 bin.46

Pirellula cteria Hyphomicrobi

9.56 9.11

7090772 2840032

0.642 0.626

19744 25747

1.724 1.419

bin.47

Unknown um Anaerolineae Propionimicro

39.52

2847225

0.409

26463

1.818

bin.48 bin.49

52.23 13.79

3856555 2571341

0.646 0.657

28395 20474

2.373 0.662

bin.50 bin.51

Synergistetes bium Phycisphaera

10.75 5.66

2743053 2618177

0.6 0.661

2743053 2618177

0.154 0

bin.36 bin.37 bin.38 bin.39 bin.40 bin.41

23

Journal Pre-proof Table2 Differentiating characteristics between Bin-23 and other sulfate-reducing microorganisms Proteobacteria

Proteobacteria

Proteobacteria

Bin-23

Desulfobacter

Desulfovibrio

Characteristics

Firmicutes Desulfotomac

Thermodesulfo vibrio

Size (Mb)

3.65

3.33

3.10

2.33

1.92

GC (%)

58.6

47.3

60.25

45.8

36.7

Gene

3163

2779

3030

2345

1912

H2/CO2

+

-

+

+

-

Acetate

-

+

-

+

-

Lactate

+

+

+

+

+

Sulfate

+

+

+

Nitrate

-

-

DsrA

+

+

DsrB

+

+

DsrC

+

DsrD

+

Reference

This study

lP

ulum

Nitrospirae

Electron

Electron acceptors

of

donors:

+

-

+

+

-

+

+

-

+

+

+

+

-

+

+

-

-

Parks et al.,

Mancini et al.,

ro

+

-

-p

Dissimilatory s ulfite reductase

na

re

gene

2017

Jo ur

Note: ‘-’Negative and ‘+’positive.

24

Anantharaman Hu et al., 2016

2011

et al., 2016

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Highlights: 1. Totally 51 MAGs were retrived;

Jo ur

na

lP

re

-p

ro

of

2. SRB are responsible for the sulfate-to-ZVS.

25

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5