Developing a microfluidic-based epicPCR reveals diverse potential hosts of the mcrA gene in marine cold seep

Wenli Shen , Danrui Wang , Jiangtao Li , Yue Liu , Yinzhao Wang , Xingsheng Yang , Xi Peng , Bingliang Xie , Lei Su , Ziyan Wei , Qing He , Zhiyi Wang , Kai Feng , Wenbin Du , Ye Deng

mLife ›› 2025, Vol. 4 ›› Issue (1) : 70 -82.

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mLife ›› 2025, Vol. 4 ›› Issue (1) : 70 -82. DOI: 10.1002/mlf2.12159
ORIGINAL RESEARCH

Developing a microfluidic-based epicPCR reveals diverse potential hosts of the mcrA gene in marine cold seep

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Abstract

Anaerobic methanotrophic (ANME) microbes play a crucial role in the bioprocess of anaerobic oxidation of methane (AOM). However, due to their unculturable status, their diversity is poorly understood. In this study, we established a microfluidics-based epicPCR (Emulsion, Paired Isolation, and Concatenation PCR) to fuse the 16S rRNA gene and mcrA gene to reveal the diversity of ANME microbes (mcrA gene hosts) in three sampling push-cores from the marine cold seep. A total of 3725 16S amplicon sequence variants (ASVs) of the mcrA gene hosts were detected, and classified into 78 genera across 23 phyla. Across all samples, the dominant phyla with high relative abundance (>10%) were the well-known Euryarchaeota, and some bacterial phyla such as Campylobacterota, Proteobacteria, and Chloroflexi; however, the specificity of these associations was not verified. In addition, the compositions of the mcrA gene hosts were significantly different in different layers, where the archaeal hosts increased with the depths of sediments, indicating the carriers of AOM were divergent in depth. Furthermore, the consensus phylogenetic trees of the mcrA gene and the 16S rRNA gene showed congruence in archaea not in bacteria, suggesting the horizontal transfer of the mcrA gene may occur among host members. Finally, some bacterial metagenomes were found to contain the mcrA gene as well as other genes that encode enzymes in the AOM pathway, which prospectively propose the existence of ANME bacteria. This study describes improvements for a potential method for studying the diversity of uncultured functional microbes and broadens our understanding of the diversity of ANMEs.

Keywords

anaerobic oxidation of methane / cold seep / epicPCR / mcrA gene

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Wenli Shen, Danrui Wang, Jiangtao Li, Yue Liu, Yinzhao Wang, Xingsheng Yang, Xi Peng, Bingliang Xie, Lei Su, Ziyan Wei, Qing He, Zhiyi Wang, Kai Feng, Wenbin Du, Ye Deng. Developing a microfluidic-based epicPCR reveals diverse potential hosts of the mcrA gene in marine cold seep. mLife, 2025, 4(1): 70-82 DOI:10.1002/mlf2.12159

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References

[1]

Hamdan LJ, Wickland KP. Methane emissions from oceans, coasts, and freshwater habitats: new perspectives and feedbacks on climate. Limnol Oceanogr. 2016; 61: S3–S12.

[2]

Cai C, Zhang X, Wu M, Liu T, Lai C-Y, Frank J, et al. Roles and opportunities for microbial anaerobic oxidation of methane in natural and engineered systems. Energy Environ Sci. 2021; 14:4803–4830.

[3]

Niu M, Deng L, Su L, Ruff SE, Yang N, Luo M, et al. Methane supply drives prokaryotic community assembly and networks at cold seeps of the South China Sea. Mol Ecol. 2022; 32:660–679.

[4]

Thauer RK. Anaerobic oxidation of methane with sulfate: on the reversibility of the reactions that are catalyzed by enzymes also involved in methanogenesis from CO2. Curr Opin Microbiol. 2011; 14:292–299.

[5]

Wang Y, Wegener G, Hou J, Wang F, Xiao X. Expanding anaerobic alkane metabolism in the domain of Archaea. Nat Microbiol. 2019; 4:595–602.

[6]

Wang Y, Feng X, Natarajan VP, Xiao X, Wang F. Diverse anaerobic methane- and multi-carbon alkane-metabolizing archaea coexist and show activity in Guaymas Basin hydrothermal sediment. Environ Microbiol. 2019; 21:1344–1355.

[7]

Niu M, Fan X, Zhuang G, Liang Q, Wang F. Methane-metabolizing microbial communities in sediments of the Haima cold seep area, northwest slope of the South China Sea. FEMS Microbiol Ecol. 2017; 93:1–13.

[8]

Biddle JF, Cardman Z, Mendlovitz H, Albert DB, Lloyd KG, Boetius A, et al. Anaerobic oxidation of methane at different temperature regimes in Guaymas Basin hydrothermal sediments. ISME J. 2012; 6:1018–1031.

[9]

Evans PN, Boyd JA, Leu AO, Woodcroft BJ, Parks DH, Hugenholtz P, et al. An evolving view of methane metabolism in the Archaea. Nat Rev Microbiol. 2019; 17:219–232.

[10]

Bhattarai S, Cassarini C, Lens PNL. Physiology and distribution of archaeal methanotrophs that couple anaerobic oxidation of methane with sulfate reduction. Microbiol Mol Biol Rev. 2019; 83:e0007418.

[11]

Spencer SJ, Tamminen MV, Preheim SP, Guo MT, Briggs AW, Brito IL, et al. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. ISME J. 2016; 10:427–436.

[12]

Sakowski EG, Arora-Williams K, Tian F, Zayed AA, Zablocki O, Sullivan MB, et al. Interaction dynamics and virus-host range for estuarine actinophages captured by epicPCR. Nat Microbiol. 2021; 6:630–642.

[13]

Qin H, Wang S, Feng K, He Z, Virta MPJ, Hou W, et al. Unraveling the diversity of sedimentary sulfate-reducing prokaryotes (SRP) across Tibetan saline lakes using epicPCR. Microbiome. 2019; 7:71.

[14]

Preheim SP, Olesen SW, Spencer SJ, Materna A, Varadharajan C, Blackburn M, et al. Surveys, simulation and single-cell assays relate function and phylogeny in a lake ecosystem. Nat Microbiol. 2016; 1:16130.

[15]

Hultman J, Tamminen M, Pärnänen K, Cairns J, Karkman A, Virta M. Host range of antibiotic resistance genes in wastewater treatment plant influent and effluent. FEMS Microbiol Ecol. 2018; 94:fiy038.

[16]

Wei Z, Feng K, Wang Z, Zhang Y, Yang M, Zhu YG, et al. High-throughput single-cell technology reveals the contribution of horizontal gene transfer to typical antibiotic resistance gene dissemination in wastewater treatment plants. Environ Sci Technol. 2021; 55:11824–11834.

[17]

Xie B, Wang J, Nie Y, Tian J, Wang Z, Chen D, et al. Type IV pili trigger episymbiotic association of Saccharibacteria with its bacterial host. Proc Natl Acad Sci USA. 2022; 119:e2215990119.

[18]

Zheng W, Zhao S, Yin Y, Zhang H, Needham DM, Evans ED, et al. High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome. Science. 2022; 376:eabm1483.

[19]

Sánchez Barea J, Lee J, Kang D-K. Recent advances in droplet-based microfluidic technologies for biochemistry and molecular biology. Micromachines. 2019; 10:412.

[20]

Wang P, Wang F, Xu M, Xiao X. Molecular phylogeny of methylotrophs in a deep-sea sediment from a tropical west Pacific Warm Pool. FEMS Microbiol Ecol. 2004; 47:77–84.

[21]

Knittel K, Boetius A. Anaerobic oxidation of methane: progress with an unknown process. Annu Rev Microbiol. 2009; 63:311–334.

[22]

Siegert M, Krüger M, Teichert B, Wiedicke M, Schippers A. Anaerobic oxidation of methane at a marine methane seep in a Forearc Sediment Basin off Sumatra, Indian Ocean. Front Microbiol. 2011; 2:249.

[23]

Ettwig KF, Butler MK, Le Paslier D, Pelletier E, Mangenot S, Kuypers MMM, et al. Nitrite-driven anaerobic methane oxidation by oxygenic bacteria. Nature. 2010; 464:543–548.

[24]

Yao X, Wang J, He M, Liu Z, Zhao Y, Li Y, et al. Methane-dependent complete denitrification by a single Methylomirabilis bacterium. Nat Microbiol. 2024; 9:464–476.

[25]

Graw MF, D’Angelo G, Borchers M, Thurber AR, Johnson JE, Zhang C, et al. Energy gradients structure microbial communities across sediment horizons in deep marine sediments of the South China Sea. Front Microbiol. 2018; 9:729.

[26]

Walsh EA, Kirkpatrick JB, Pockalny R, Sauvage J, Spivack AJ, Murray RW, et al. Relationship of bacterial richness to organic degradation rate and sediment age in subseafloor sediment. Appl Environ Microbiol. 2016; 82:4994–4999.

[27]

Böer SI, Hedtkamp SIC, van Beusekom JEE, Fuhrman JA, Boetius A, Ramette A. Time- and sediment depth-related variations in bacterial diversity and community structure in subtidal sands. ISME J. 2009; 3:780–791.

[28]

Kallmeyer J, Pockalny R, Adhikari RR, Smith DC, D’Hondt S. Global distribution of microbial abundance and biomass in subseafloor sediment. Proc Natl Acad Sci USA. 2012; 109:16213–16216.

[29]

Hoehler TM, Jørgensen BB. Microbial life under extreme energy limitation. Nat Rev Microbiol. 2013; 11:83–94.

[30]

Hoshino T, Doi H, Uramoto GI, Wörmer L, Adhikari RR, Xiao N, et al. Global diversity of microbial communities in marine sediment. Proc Natl Acad Sci USA. 2020; 117:27587–27597.

[31]

Brito IL. Examining horizontal gene transfer in microbial communities. Nat Rev Microbiol. 2021; 19:442–453.

[32]

Dong X, Zhang C, Peng Y, Zhang HX, Shi LD, Wei G, et al. Phylogenetically and catabolically diverse diazotrophs reside in deep-sea cold seep sediments. Nat Commun. 2022; 13:4885.

[33]

Milucka J, Ferdelman TG, Polerecky L, Franzke D, Wegener G, Schmid M, et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature. 2012; 491:541–546.

[34]

Hoehler TM, Alperin MJ, Albert DB, Martens CS. Field and laboratory studies of methane oxidation in an anoxic marine sediment—evidence for a methanogen-sulfate reducer consortium. Global Biogeochem Cycles. 1994; 8:451–463.

[35]

Meyerdierks A, Kube M, Kostadinov I, Teeling H, Glöckner FO, Reinhardt R, et al. Metagenome and mRNA expression analyses of anaerobic methanotrophic archaea of the ANME-1 group. Environ Microbiol. 2010; 12:422–439.

[36]

Chang T, Gavelis GS, Brown JM, Stepanauskas R. Genomic representativeness and chimerism in large collections of SAGs and MAGs of marine prokaryoplankton. Microbiome. 2024; 12:126.

[37]

Zhang Z, Qu Y, Li S, Feng K, Wang S, Cai W, et al. Soil bacterial quantification approaches coupling with relative abundances reflecting the changes of taxa. Sci Rep. 2017; 7:4837.

[38]

Duffy DC, McDonald JC, Schueller OJA, Whitesides GM. Rapid prototyping of microfluidic systems in poly(dimethylsiloxane). Anal Chem. 1998; 70:4974–4984.

[39]

Steinberg LM, Regan JM. Phylogenetic comparison of the methanogenic communities from an acidic, oligotrophic fen and an anaerobic digester treating municipal wastewater sludge. Appl Environ Microbiol. 2008; 74:6663–6671.

[40]

Hugerth LW, Wefer HA, Lundin S, Jakobsson HE, Lindberg M, Rodin S, et al. DegePrime, a program for degenerate primer design for broad-taxonomic-range PCR in microbial ecology studies. Appl Environ Microbiol. 2014; 80:5116–5123.

[41]

Kibbe WA. OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Res. 2007; 35: W43–W46.

[42]

Wu Y, Feng K, Wei Z, Wang Z, Deng Y. ARDEP, a rapid degenerate primer design pipeline based on k-mers for amplicon microbiome studies. Int J Environ Res Public Health. 2020; 17:5958.

[43]

Roman VL, Merlin C, Virta MPJ, Bellanger X. EpicPCR 2.0: technical and methodological improvement of a cutting-edge single-cell genomic approach. Microorganisms. 2021; 9:1649.

[44]

Feng K, Zhang Z, Cai W, Liu W, Xu M, Yin H, et al. Biodiversity and species competition regulate the resilience of microbial biofilm community. Mol Ecol. 2017; 26:6170–6182.

[45]

Feng K, Wang S, He Q, Bonkowski M, Bahram M, Yergeau E, et al. CoBacFM: core bacteria forecast model for global grassland pH dynamics under future climate warming scenarios. One Earth. 2024; 7:1275–1287.

[46]

Yang X, Feng K, Wang S, Yuan MM, Peng X, He Q, et al. Unveiling the deterministic dynamics of microbial meta-metabolism: a multi-omics investigation of anaerobic biodegradation. Microbiome. 2024; 12:166.

[47]

Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011; 27:2957–2963.

[48]

Kong Y. Btrim: a fast, lightweight adapter and quality trimming program for next-generation sequencing technologies. Genomics. 2011; 98:152–153.

[49]

Edgar RC. UNOISE2: improved error-correction for illumina 16S and ITS amplicon sequencing. bioRxiv. 2016.

[50]

Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007; 73:5261–5267.

[51]

Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “all-species living tree project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014; 42: D643–D648.

[52]

Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004; 32: D23–D26.

[53]

Bateman A, Coin L, Durbin R, Finn RD, Hollich V, Griffiths-Jones S, et al. The Pfam protein families database. Nucleic Acids Res. 2004; 32: D138–D141.

[54]

Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front Microbiol. 2013; 4:291.

[55]

Ma K. Methanosaeta harundinacea sp. nov., a novel acetate-scavenging methanogen isolated from a UASB reactor. Int J Syst Evol Microbiol. 2006; 56:127–131.

[56]

L’Haridon S, Haroun H, Corre E, Roussel E, Chalopin M, Pignet P, et al. Methanohalophilus profundi sp. nov., a methylotrophic halophilic piezophilic methanogen isolated from a deep hypersaline anoxic basin. Syst Appl Microbiol. 2020; 43:126107.

[57]

Liang L, Sun Y, Dong Y, Ahmad T, Chen Y, Wang J, et al. Methanococcoides orientis sp. nov., a methylotrophic methanogen isolated from sediment of the East China Sea. Int J Syst Evol Microbiol. 2022; 72:005384.

[58]

Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017; 27:824–834.

[59]

Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015; 31:1674–1676.

[60]

Bertrand D, Shaw J, Kalathiyappan M, Ng AHQ, Kumar MS, Li C, et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat Biotechnol. 2019; 37:937–944.

[61]

Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018; 6:158.

[62]

Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017; 11:2864–2868.

[63]

Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020; 36:1925–1927.

[64]

Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010; 11:119.

[65]

Yu T, Cui H, Li JC, Luo Y, Jiang G, Zhao H. Enzyme function prediction using contrastive learning. Science. 2023; 379:1358–1363.

[66]

Kanehisa M, Sato Y, Kawashima M. KEGG mapping tools for uncovering hidden features in biological data. Prot Sci. 2021; 31:47–53.

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2025 The Author(s). mLife published by John Wiley & Sons Australia, Ltd on behalf of Institute of Microbiology, Chinese Academy of Sciences.

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