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A hydrogenotrophic Sulfurimonas is globally abundant in deep-sea oxygen-saturated hydrothermal plumes

  • Inagaki, F., Takai, K., Kobayashi, H., Nealson, K. H. & Horikoshi, K. Sulfurimonas autotrophica gen. nov., sp. nov., a novel sulfur-oxidizing e-proteobacterium isolated from hydrothermal sediments in the Mid-Okinawa Trough. Int. J. Syst. Evol. Microbiol. 53, 1801–1805 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Timmer-Ten Hoor, A. A new type of thiosulphate oxidizing, nitrate reducing microorganism: Thiomicrospira denitrificans sp. nov. Neth. J. Sea Res. 9, 344–350 (1975).

    Article 
    CAS 

    Google Scholar 

  • Cai, L., Shao, M. & Zhang, T. Non-contiguous finished genome sequence and description of Sulfurimonas hongkongensis sp. nov., a strictly anaerobic denitrifying, hydrogen- and sulfur-oxidizing chemolithoautotroph isolated from marine sediment. Stand. Genom. Sci. 9, 1302–1310 (2014).

    Article 

    Google Scholar 

  • Wang, S., Jiang, L., Liu, X., Yang, S. & Shao, Z. Sulfurimonas xiamenensis sp. nov. and Sulfurimonas lithotrophica sp. nov., hydrogen- and sulfur-oxidizing chemolithoautotrophs within the Epsilonproteobacteria isolated from coastal sediments, and an emended description of the genus Sulfurimonas. Int. J. Syst. Evol. Microbiol. 70, 2657–2663 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Takai, K. et al. Sulfurimonas paralvinellae sp. nov., a novel mesophilic, hydrogen- and sulfur-oxidizing chemolithoautotroph within the Epsilonproteobacteria isolated from a deep-sea hydrothermal vent polychaete nest, reclassification of Thiomicrospira denitrificans as Sulfurimonas denitrificans comb. nov. and emended description of the genus Sulfurimonas. Int. J. Syst. Evol. Microbiol. 56, 1725–1733 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Hu, Q., Wang, S., Lai, Q., Shao, Z. & Jiang, L. Sulfurimonas indica sp. nov., a hydrogen- and sulfur-oxidizing chemolithoautotroph isolated from a hydrothermal sulfide chimney in the Northwest Indian Ocean. Int. J. Syst. Evol. Microbiol. 71, 1466–5034 (2021).

    Article 

    Google Scholar 

  • Wang, S. et al. Sulfurimonas sediminis sp. nov., a novel hydrogen- and sulfur-oxidizing chemolithoautotroph isolated from a hydrothermal vent at the Longqi system, southwestern Indian ocean. Antonie Van Leeuwenhoek 114, 813–822 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wang, S. et al. Characterization of Sulfurimonas hydrogeniphila sp. nov., a novel bacterium predominant in deep-sea hydrothermal vents and comparative genomic analyses of the genus Sulfurimonas. Front. Microbiol. 12, 626705 (2021).

  • Labrenz, M. et al. Sulfurimonas gotlandica sp. nov., a chemoautotrophic and psychrotolerant epsilonproteobacterium isolated from a pelagic redoxcline, and an emended description of the genus Sulfurimonas. Int. J. Syst. Evol. Microbiol. 63, 4141–4148 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Henkel, J. V. et al. Candidatus Sulfurimonas marisnigri sp. nov. and Candidatus Sulfurimonas baltica sp. nov., thiotrophic manganese oxide reducing chemolithoautotrophs of the class Campylobacteria isolated from the pelagic redoxclines of the Black Sea and the Baltic Sea. Syst. Appl. Microbiol. 44, 126155 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ratnikova, N. M. et al. Sulfurimonas crateris sp. nov., a facultative anaerobic sulfur-oxidizing chemolithoautotrophic bacterium isolated from a terrestrial mud volcano. Int. J. Syst. Evol. Microbiol. 70, 487–492 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Han, Y. & Perner, M. The globally widespread genus Sulfurimonas: versatile energy metabolisms and adaptations to redox clines. Front. Microbiol. 6, 989 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • López-garcía, P. et al. Bacterial diversity in hydrothermal sediment and epsilonproteobacterial dominance in experimental microcolonizers at the Mid-Atlantic Ridge. Environ. Microbiol. 5, 961–976 (2003).

    Article 
    PubMed 

    Google Scholar 

  • Nakagawa, S. et al. Distribution, phylogenetic diversity and physiological characteristics of epsilon-Proteobacteria in a deep-sea hydrothermal field. Environ. Microbiol. 7, 1619–1632 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Huber, J. A. et al. Isolated communities of Epsilonproteobacteria in hydrothermal vent fluids of the Mariana Arc seamounts. FEMS Microbiol. Ecol. 73, 538–549 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • Meier, D. V. et al. Niche partitioning of diverse sulfur-oxidizing bacteria at hydrothermal vents. ISME J. 11, 1545–1558 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mino, S. et al. Endemicity of the cosmopolitan mesophilic chemolithoautotroph Sulfurimonas at deep-sea hydrothermal vents. ISME J. 11, 909–919 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Akerman, N. H., Butterfield, D. A., Huber, J. A., Huber, J. A. & Paul, J. B. Phylogenetic diversity and functional gene patterns of sulfur-oxidizing subseafloor Epsilonproteobacteria in diffuse hydrothermal vent fluids. Front. Microbiol. 4, 185 (2013).

  • Rogge, A., Vogts, A., Voss, M. & Labrenz, M. Success of chemolithoautotrophic SUP05 and Sulfurimonas GD17 cells in pelagic Baltic Sea redox zones is facilitated by their lifestyles as K- and r -strategists. Environ. Microbiol. 19, 2495–2506 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • German, C. R. et al. Diverse styles of submarine venting on the ultraslow spreading Mid-Cayman Rise. Proc. Natl Acad. Sci. USA 107, 14020–14025 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sylvan, J. B., Pyenson, B. C., Rouxel, O., German, C. R. & Edwards, K. J. Time-series analysis of two hydrothermal plumes at 9°50’ N East Pacific Rise reveals distinct, heterogeneous bacterial populations. Geobiology 10, 178–192 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Perner, M. et al. In situ chemistry and microbial community compositions in five deep-sea hydrothermal fluid samples from Irina II in the Logatchev field. Environ. Microbiol. 15, 1551–1560 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Haalboom, S. et al. Patterns of (trace) metals and microorganisms in the Rainbow hydrothermal vent plume at the Mid-Atlantic Ridge. Biogeosciences 17, 2499–2519 (2020).

    Article 
    CAS 

    Google Scholar 

  • Li, J. et al. Distribution and succession of microbial communities along the dispersal pathway of hydrothermal plumes on the Southwest Indian Ridge. Front. Mar. Sci. 7, 581381 (2020).

    Article 

    Google Scholar 

  • Dick, G. J. et al. The microbiology of deep-sea hydrothermal vent plumes: ecological and biogeographic linkages to seafloor and water column habitats. Front. Microbiol. 4, 124 (2013).

  • Dick, G. J. The microbiomes of deep-sea hydrothermal vents: distributed globally, shaped locally. Nat. Rev. Microbiol. 17, 271–283 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • German, C. R. & Seyfried, W. E. in Treatise on Geochemistry 2nd edn (eds Holland, H. D. & Turekian, K. K.), 8, 191–233 (Elsevier, 2014).

  • Kadko, D., Baross, J. & Alt, J. The magnitude and global implications of hydrothermal flux. Geophys. Monogr. Ser. 91, 446–466 (1995).

    Google Scholar 

  • German, C. R. et al. Volcanically hosted venting with indications of ultramafic influence at Aurora hydrothermal field on Gakkel Ridge. Nat. Commun. 13, 6517 (2022).

  • Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Konstantinidis, K. T., Rosselló-móra, R. & Amann, R. Uncultivated microbes in need of their own taxonomy. ISME J. 11, 2399–2406 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Murray, A. E. et al. Roadmap for naming uncultivated Archaea and Bacteria. Nat. Microbiol. 5, 987–994 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Eren, A. M. et al. Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J. 9, 968–979 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Dick, G. J. & Tebo, B. M. Microbial diversity and biogeochemistry of the Guaymas Basin deep-sea hydrothermal plume. Environ. Microbiol. 12, 1334–1347 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lesniewski, R. A., Jain, S., Anantharaman, K., Schloss, P. D. & Dick, G. J. The metatranscriptome of a deep-sea hydrothermal plume is dominated by water column methanotrophs and lithotrophs. ISME J. 6, 2257–2268 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sheik, C. S. et al. Spatially resolved sampling reveals dynamic microbial communities in rising hydrothermal plumes across a back-arc basin. ISME J. 9, 1434–1445 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Reed, D. C. et al. Predicting the response of the deep-ocean microbiome to geochemical perturbations by hydrothermal vents. ISME J. 9, 1857–1869 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Han, Y. & Perner, M. The role of hydrogen for Sulfurimonas denitrificans’ metabolism. PLoS ONE 9, 8–14 (2014).

    Google Scholar 

  • Ilbert, M. & Bonnefoy, V. Insight into the evolution of the iron oxidation pathways. Biochim. Biophys. Acta 1827, 161–175 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yu, H. & Leadbetter, J. R. Bacterial chemolithoautotrophy via manganese oxidation. Nature 583, 453–458 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Andrews, S. C. Iron storage in bacteria. Adv. Microb. Physiol. 40, 281–351 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Pitcher, R. S. & Watmough, N. J. The bacterial cytochrome cbb 3 oxidases. Biochim. Biophys. Acta 1655, 388–399 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Sousa, F. L. et al. The superfamily of heme–copper oxygen reductases: types and evolutionary considerations. Biochim. Biophys. Acta 1817, 629–637 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Park, B. et al. Cultivation of autotrophic ammonia-oxidizing archaea from marine sediments in coculture with sulfur-oxidizing bacteria. Appl. Environ. Microbiol. 76, 7575–7587 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fuchs, G. Alternative pathways of carbon dioxide fixation: insights into the early evolution of life? Annu. Rev. Microbiol. 65, 631–658 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Bayer, B. et al. Metabolic versatility of the nitrite-oxidizing bacterium Nitrospira marina and its proteomic response to oxygen-limited conditions. ISME J. 15, 1025–1039 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yamamoto, M., Arai, H., Ishii, M. & Igarashi, Y. Role of two 2-oxoglutarate: ferredoxin oxidoreductases in Hydrogenobacter thermophilus under aerobic and anaerobic conditions. FEMS Microbiol. Lett. 263, 189–193 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yamamoto, M., Ikeda, T., Arai, H., Ishii, M. & Igarashi, Y. Carboxylation reaction catalyzed by 2-oxoglutarate:ferredoxin oxidoreductases from Hydrogenobacter thermophilus. Extremophiles 14, 79–85 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Berg, I. A. Ecological aspects of the distribution of different autotrophic CO2 fixation pathways. Appl. Environ. Microbiol. 77, 1925–1936 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • French, C. E., Bell, J. M. L. & Ward, F. B. Diversity and distribution of hemerythrin-like proteins in prokaryotes. FEMS Microbiol. Lett. 279, 131–145 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Isaza, C. E., Silaghi-dumitrescu, R., Iyer, R. B., Kurtz, D. M. & Chan, M. K. Structural basis for O2 sensing by the hemerythrin-like domain of a bacterial chemotaxis protein: substrate tunnel and fluxional n terminus. Biogeochemistry 45, 9023–9031 (2006).

    Article 
    CAS 

    Google Scholar 

  • Kendall, J. J., Barrero-tobon, A. M., Hendrixson, D. R. & Kelly, D. J. Hemerythrins in the microaerophilic bacterium Campylobacter jejuni help protect key iron–sulphur cluster enzymes from oxidative damage. Environ. Microbiol. 16, 1105–1121 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Nariya, S. & Kalyuzhnaya, M. G. Hemerythrins enhance aerobic respiration in Methylomicrobium alcaliphilum 20Z R, a methane-consuming bacterium. FEMS Microbiol. Lett. 367, fnaa003 (2020).

  • Sheng, Y. et al. Superoxide dismutases and superoxide reductases. Chem. Rev. 114, 3854–3918 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Anantharaman, K., Breier, J. A., Sheik, C. S. & Dick, G. J. Evidence for hydrogen oxidation and metabolic plasticity in widespread deep-sea sulfur-oxidizing bacteria. Proc. Natl Acad. Sci. USA 110, 330–335 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Dede, B. et al. Niche differentiation of sulfur-oxidizing bacteria (SUP05) in submarine hydrothermal plumes. ISME J. 16, 1479–1490 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Schlindwein, V. (ed.) The Expedition of the Research Vessel ‘Polarstern’ to the Antarctic in 2013 (ANT-XXIX/8). Reports on polar and marine research, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research, 672, 111 (2014); https://doi.org/10.2312/BzPM_0672_2014

  • Boetius, A. The Expedition PS86 of the Research Vessel POLARSTERN to the Arctic Ocean in 2014. Reports on polar and marine research, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research, 685, 133 (2015); https://doi.org/10.2312/BzPM_0685_2015

  • Boetius, A. & Purser, A. The Expedition PS101 of the Research Vessel POLARSTERN to the Arctic Ocean in 2016. Reports on polar and marine research, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research, 706, 230 (2017); https://doi.org/10.2312/BzPM_0706_2017

  • Varliero, G., Bienhold, C., Schmid, F., Boetius, A. & Molari, M. Microbial diversity and connectivity in deep-sea sediments of the South Atlantic Polar Front. Front. Microbiol. 10, 665 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pernthaler, A., Pernthaler, J. & Amann, R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl. Environ. Microbiol. 68, 3094–3101 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Alm, E. W., Oerther, D. B., Larsen, N., Stahl, D. A. & Raskin, L. The oligonucleotide probe database. Appl. Environ. Microbiol. 62, 3557–3559 (1996).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).

    Article 

    Google Scholar 

  • Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).

  • Hassenrück, C., Quast, C., Rapp, J. & Buttigieg, P. Amplicon (GitHub, accessed 15 April 2019); https://github.com/chassenr/NGS/tree/master/AMPLICON

  • Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm: robust and fast clustering method for amplicon-based studies. PeerJ 2, e593 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner. United States (2014). https://www.osti.gov/servlets/purl/1241166

  • Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kopylova, E., Noe, L. & Touzet, H. Sequence analysis SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Gruber-vodicka, H. R., Seah, B. K. & Pruesse, E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems 5, e00920 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. https://doi.org/10.14806/ej.17.1.200 (2011).

  • Zhang, J., Kobert, K., Fluori, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Varghese, N. J. et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 43, 6761–6771 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wheeler, T. J. & Eddy, S. R. nhmmer: DNA homology search with profile HMMs. Bioinformatics 29, 2487–2489 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Preprint at bioRxiv https://doi.org/10.1101/2022.07.11.499243 (2022).

  • Manni, M., Berkeley, M. R., Seppey, M. & Zdobnov, E. M. BUSCO: assessing genomic data quality and beyond. Curr. Protoc. 1, e323 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Laslett, D. & Canback, B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 32, 11–16 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Haft, D. H. et al. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 248, 726–731 (2015).

    Google Scholar 

  • Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kristensen, D. M. et al. A low-polynomial algorithm for assembling clusters of orthologous groups from intergenomic symmetric best matches. Bioinformatics 26, 1481–1487 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Søndergaard, D., Pedersen, C. N. S. & Greening, C. HydDB: a web tool for hydrogenase classification and analysis. Sci. Rep. 6, 34212 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Garber, A. I. et al. FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genome and metagenome assemblies. Front. Microbiol. 11, 37 (2020).

  • Passardi, F. et al. PeroxiBase: the peroxidase database. Phytochemistry 68, 1605–1611 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lucchetti-miganeh, C., Goudenège, D., Thybert, D., Salbert, G. & Barloy-hubler, F. SORGOdb: superoxide reductase gene ontology curated database. BMC Microbiol. 11, 105 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 4–8 (2016).

    Google Scholar 

  • Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Mistry, J. et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412–D419 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Tu, Q., Lin, L., Cheng, L., Deng, Y. & He, Z. NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes. Bioinformatics 35, 1040–1048 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Li, H. et al. A cross-species alignment tool (CAT). BMC Bioinformatics 8, 349 (2007).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Vasimuddin, M., Misra, S., Li, H. & Aluru, S. Efficient architecture-aware acceleration of BWA-MEM for multicore systems. In 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, 314–324, doi: 10.1109/IPDPS.2019.00041 (2019); https://ieeexplore.ieee.org/document/8820962

  • Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E. & Zanini, F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics 38, 2943–2945 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Criscuolo, A. & Gribaldo, S. BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol. Biol. 10, 210 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jalili, V. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Res. 48, 395–402 (2020).

    Article 

    Google Scholar 

  • Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kalyaanamoorthy, S. et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Berger, S. A., Krompass, D. & Stamatakis, A. Performance, accuracy, and web server for evolutionary placement of short sequence reads under maximum likelihood. Syst. Biol. 60, 291–302 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2, W256–W259 (2019).

    Article 

    Google Scholar 

  • Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • van Dongen, S. & Abreu-goodger, C. in Bacterial Molecular Networks: Methods and Protocols, Methods in Molecular Biology (eds van Helden, J. et al.) 281–295 (Springer, 2012).

  • Altschup, S. F., Gish, W., Pennsylvania, T. & Park, U. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article 

    Google Scholar 

  • Nguyen, L., Schmidt, H. A., Haeseler, A., Von & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chernomor, O., von Haeseler, A. & Minh, B. Q. Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol. 65, 997–1008 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Delmont, T. O. & Eren, A. M. Linking pangenomes and metagenomes: the Prochlorococcus metapangenome. PeerJ 6, e4320 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jensen, L. J. et al. eggNOG: automated construction and annotation of orthologous groups of genes. Nucleic Acids Res. 36, 250–254 (2008).

    Article 

    Google Scholar 

  • Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–360 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  • Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.6-4. https://CRAN.R-project.org/package=vegan (2022).

  • Robinson, M. D., Mccarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Reiner-Benaim, A. FDR control by the BH procedure for two-sided correlated tests with implications to gene expression data analysis. Biom. J. 49, 107–126 (2007).

    Article 
    PubMed 

    Google Scholar 

  • Villanueva, R. A. M. & Chen, Z. J. ggplot2: elegant graphics for data analysis (2nd ed.). Meas. Interdiscip. Res. Perspect. 17, 160–167 (2019).

  • Diepenbroek, M. et al. Towards an integrated biodiversity and ecological research data management and archiving platform: the German Federation for the Curation of Biological Data (GFBio). In Informatik 2014 – Big Data Komplexität meistern Proc. 232 (eds Plödereder, E. et al.) 1711–1725 (Gesellschaft für Informatik, 2014).

  • Schmidt, K., Koschinsky, A., Garbe-Schönberg, D., de Carvalho, L. M. & Seifert, R. Geochemistry of hydrothermal fluids from the ultramafic-hosted Logatchev hydrothermal field, 15°N on the Mid-Atlantic Ridge: temporal and spatial investigation. Chem. Geol. 242, 1–21 (2007).

    Article 
    CAS 

    Google Scholar 

  • Perner, M. et al. The influence of ultramafic rocks on microbial communities at the Logatchev hydrothermal field, located 15 degrees N on the Mid-Atlantic Ridge. FEMS Microbiol. Ecol. 61, 97–109 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Douville, E. et al. The rainbow vent fluids (36°14’N, MAR): the influence of ultramafic rocks and phase separation on trace metal content in Mid-Atlantic Ridge hydrothermal fluids. Chem. Geol. 184, 37–48 (2002).

    Article 
    CAS 

    Google Scholar 

  • Ji, F. et al. Geochemistry of hydrothermal vent fluids and its implications for subsurface processes at the active Longqi hydrothermal field, Southwest Indian Ridge. Deep Sea Res. I 122, 41–47 (2017).

    Article 
    CAS 

    Google Scholar 


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