Canfield, D. E. & Farquhar, J. in Fundamentals of Geobiology 49–64 (John Wiley & Sons, 2012).
Fike, D. A., Bradley, A. S. & Rose, C. V. Rethinking the ancient sulfur cycle. Annu. Rev. Earth Planet. Sci. 43, 593–622 (2015).
Wasmund, K., Mußmann, M. & Loy, A. The life sulfuric: microbial ecology of sulfur cycling in marine sediments. Environ. Microbiol. Rep. 9, 323–344 (2017).
Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).
Mateos, K. et al. The evolution and spread of sulfur cycling enzymes reflect the redox state of the early Earth. Sci. Adv. 9, eade4847 (2023).
Canfield, D. E. & Raiswell, R. The evolution of the sulfur cycle. Am. J. Sci. 299, 697–723 (1999).
Poulton, S. W. Sulfide oxidation and iron dissolution kinetics during the reaction of dissolved sulfide with ferrihydrite. Chem. Geol. 202, 79–94 (2003).
Canfield, D. E. Reactive iron in marine sediments. Geochim. Cosmochim. Acta 53, 619–632 (1989).
Anantharaman, K. et al. Expanded diversity of microbial groups that shape the dissimilatory sulfur cycle. ISME J. 12, 1715–1728 (2018).
Müller, A. L., Kjeldsen, K. U., Rattei, T., Pester, M. & Loy, A. Phylogenetic and environmental diversity of DsrAB-type dissimilatory (bi)sulfite reductases. ISME J. 9, 1152–1165 (2015).
Diao, M. et al. Global diversity and inferred ecophysiology of microorganisms with the potential for dissimilatory sulfate/sulfite reduction. FEMS Microbiol. Rev. 47, fuad058 (2023).
Jørgensen, B. B., Findlay, A. J. & Pellerin, A. The biogeochemical sulfur cycle of marine sediments. Front. Microbiol. 10, 849 (2019).
Canfield, D. E. et al. A cryptic sulfur cycle in oxygen-minimum-zone waters off the Chilean coast. Science 330, 1375–1378 (2010).
Pester, M., Knorr, K.-H., Friedrich, M. W., Wagner, M. & Loy, A. Sulfate-reducing microorganisms in wetlands – fameless actors in carbon cycling and climate change. Front. Microbiol. 3, 72 (2012).
Henkel, J. V. et al. A bacterial isolate from the Black Sea oxidizes sulfide with manganese(IV) oxide. Proc. Natl Acad. Sci. USA 116, 12153–12155 (2019).
Hayes, J. M. & Waldbauer, J. R. The carbon cycle and associated redox processes through time. Philos. Trans. R. Soc. B 361, 931–950 (2006).
Jørgensen, B. B. & Nelson, D. C. in Sulfur Biogeochemistry—Past and Present 63–81 (Geological Society of America, 2004).
Hansel, C. M. et al. Dominance of sulfur-fueled iron oxide reduction in low-sulfate freshwater sediments. ISME J. 9, 2400–2412 (2015).
Flynn, T. M., O’Loughlin, E. J., Mishra, B., DiChristina, T. J. & Kemner, K. M. Sulfur-mediated electron shuttling during bacterial iron reduction. Science 344, 1039–1042 (2014).
Findlay, A. J., Pellerin, A., Laufer, K. & Jørgensen, B. B. Quantification of sulphide oxidation rates in marine sediment. Geochim. Cosmochim. Acta 280, 441–452 (2020).
Michaud, A. B. et al. Glacial influence on the iron and sulfur cycles in Arctic fjord sediments (Svalbard). Geochim. Cosmochim. Acta 280, 423–440 (2020).
Elsgaard, L. & Jørgensen, B. B. Anoxie transformations of radiolabeled hydrogen sulfide in marine and freshwater sediments. Geochim. Cosmochim. Acta 56, 2425–2435 (1992).
Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).
Raiswell, R. & Canfield, D. E. The iron biogeochemical cycle past and present. Geochem. Perspect. 1, 1–220 (2012).
Osorio, H. et al. Anaerobic sulfur metabolism coupled to dissimilatory iron reduction in the extremophile Acidithiobacillus ferrooxidans. Appl. Environ. Microbiol. 79, 2172–2181 (2013).
Malik, L. & Hedrich, S. Ferric iron reduction in extreme acidophiles. Front. Microbiol. 12, 818414 (2021).
Thorup, C., Schramm, A., Findlay, A. J., Finster, K. W. & Schreiber, L. Disguised as a sulfate reducer: Growth of the Deltaproteobacterium Desulfurivibrio alkaliphilus by sulfide oxidation with nitrate. mBio 8, e00671-17 (2017).
Wang, F. et al. Structure of microbial nanowires reveals stacked hemes that transport electrons over micrometers. Cell 177, 361–369.e10 (2019).
Shi, L., Fredrickson, J. K. & Zachara, J. M. Genomic analyses of bacterial porin-cytochrome gene clusters. Front. Microbiol. 5, 657 (2014).
Portela, P. C. et al. Widespread extracellular electron transfer pathways for charging microbial cytochrome OmcS nanowires via periplasmic cytochromes PpcABCDE. Nat. Commun. 15, 2434 (2024).
Nübel, T., Klughammer, C., Huber, R., Hauska, G. & Schütz, M. Sulfide:quinone oxidoreductase in membranes of the hyperthermophilic bacterium Aquifex aeolicus (VF5). Arch. Microbiol. 173, 233–244 (2000).
Schütz, M., Maldener, I., Griesbeck, C. & Hauska, G. Sulfide-quinone reductase from Rhodobacter capsulatus: requirement for growth, periplasmic localization, and extension of gene sequence analysis. J. Bacteriol. 181, 6516–6523 (1999).
Finneran, K. T., Johnsen, C. V. & Lovley, D. R. Rhodoferax ferrireducens sp. nov., a psychrotolerant, facultatively anaerobic bacterium that oxidizes acetate with the reduction of Fe(iii). Int. J. Syst. Evol. Microbiol. 53, 669–673 (2003).
Baker, I. R., Conley, B. E., Gralnick, J. A. & Girguis, P. R. Evidence for horizontal and vertical transmission of Mtr-mediated extracellular electron transfer among the bacteria. mBio 13, e02904–e02921 (2021).
Nixon, S. L., Bonsall, E. & Cockell, C. S. Limitations of microbial iron reduction under extreme conditions. FEMS Microbiol. Rev. 46, fuac033 (2022).
Jakobsen, R. & Postma, D. Redox zoning, rates of sulfate reduction and interactions with Fe-reduction and methanogenesis in a shallow sandy aquifer, Rømø, Denmark. Geochim. Cosmochim. Acta 63, 137–151 (1999).
Sorokin, D. Y., Tourova, T. P., Mussmann, M. & Muyzer, G. Dethiobacter alkaliphilus gen. nov. sp. nov., and Desulfurivibrio alkaliphilus gen. nov. sp. nov.: two novel representatives of reductive sulfur cycle from soda lakes. Extremophiles 12, 431–439 (2008).
Schippers, A. & Jørgensen, B. B. Oxidation of pyrite and iron sulfide by manganese dioxide in marine sediments. Geochim. Cosmochim. Acta 65, 915–922 (2001).
Finster, K. Microbiological disproportionation of inorganic sulfur compounds. J. Sulfur Chem. 29, 281–292 (2008).
Sun, J., Wei, L., Yin, R., Jiang, F. & Shang, C. Microbial iron reduction enhances in-situ control of biogenic hydrogen sulfide by FeOOH granules in sediments of polluted urban waters. Water Res. 171, 115453 (2020).
Kjeldsen, K. U. et al. On the evolution and physiology of cable bacteria. Proc. Natl Acad. Sci. USA 116, 19116–19125 (2019).
Santos, T. C., Silva, M. A., Morgado, L., Dantas, J. M. & Salgueiro, C. A. Diving into the redox properties of Geobacter sulfurreducens cytochromes: a model for extracellular electron transfer. Dalton Trans. 44, 9335–9344 (2015).
Kappler, A. et al. An evolving view on biogeochemical cycling of iron. Nat. Rev. Microbiol. 19, 360–374 (2021).
Lovley, D. R. & Holmes, D. E. Electromicrobiology: the ecophysiology of phylogenetically diverse electroactive microorganisms. Nat. Rev. Microbiol. 20, 5–19 (2022).
Filman, D. J. et al. Cryo-EM reveals the structural basis of long-range electron transport in a cytochrome-based bacterial nanowire. Commun. Biol. 2, 219 (2019).
Johs, A., Shi, L., Droubay, T., Ankner, J. F. & Liang, L. Characterization of the decaheme c-type cytochrome OmcA in solution and on hematite surfaces by small angle x-ray scattering and neutron reflectometry. Biophys. J. 98, 3035–3043 (2010).
Antunes, J. M. A., Silva, M. A., Salgueiro, C. A. & Morgado, L. Electron flow from the inner membrane towards the cell exterior in Geobacter sulfurreducens: Biochemical characterization of cytochrome CbcL. Front. Microbiol. 13, 898015 (2022).
Walker, D. J. et al. Electrically conductive pili from pilin genes of phylogenetically diverse microorganisms. ISME J. 12, 48–58 (2018).
Wang, F., Craig, L., Liu, X., Rensing, C. & Egelman, E. H. Microbial nanowires: type IV pili or cytochrome filaments? Trends Microbiol. 31, 384–392 (2023).
Wang, Y. et al. Structural mechanisms of Tad pilus assembly and its interaction with an RNA virus. Sci. Adv. 10, eadl4450 (2024).
Gu, Y. et al. Structure of Geobacter pili reveals secretory rather than nanowire behaviour. Nature 597, 430–434 (2021).
Sonani, R. R. et al. Tad and toxin-coregulated pilus structures reveal unexpected diversity in bacterial type IV pili. Proc. Natl Acad. Sci. USA 120, e2316668120 (2023).
Sun, M. et al. Microbial communities involved in electricity generation from sulfide oxidation in a microbial fuel cell. Biosens. Bioelectron. 26, 470–476 (2010).
de Rink, R. et al. Continuous electron shuttling by sulfide oxidizing bacteria as a novel strategy to produce electric current. J. Hazard. Mater. 424, 127358 (2022).
Ter Heijne, A., de Rink, R., Liu, D., Klok, J. B. M. & Buisman, C. J. N. Bacteria as an electron shuttle for sulfide oxidation. Environ. Sci. Technol. Lett. 5, 495–499 (2018).
Jung, H., Baek, G. & Lee, C. Magnetite-assisted in situ microbial oxidation of H2S to S0 during anaerobic digestion: A new potential for sulfide control. Chem. Eng. J. 397, 124982 (2020).
Jung, H., Yu, H. & Lee, C. Direct interspecies electron transfer enables anaerobic oxidation of sulfide to elemental sulfur coupled with CO2-reducing methanogenesis. iScience 26, 107504 (2023).
Holmkvist, L., Ferdelman, T. G. & Jørgensen, B. B. A cryptic sulfur cycle driven by iron in the methane zone of marine sediment (Aarhus Bay, Denmark). Geochim. Cosmochim. Acta 75, 3581–3599 (2011).
Mikucki, J. A. et al. A contemporary microbially maintained subglacial ferrous ‘ocean’. Science 324, 397–400 (2009).
Och, L. M. et al. New insights into the formation and burial of Fe/Mn accumulations in Lake Baikal sediments. Chem. Geol. 330-331, 244–259 (2012).
Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M. & Bairoch, A. UniProtKB/Swiss-Prot. Methods Mol. Biol. 406, 89–112 (2007).
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Marcia, M., Ermler, U., Peng, G. & Michel, H. A new structure-based classification of sulfide:quinone oxidoreductases. Proteins 78, 1073–1083 (2010).
Orellana, L. H., Rodriguez-R, L. M. & Konstantinidis, K. T. ROCker: Accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores. Nucleic Acids Res. 45, e14 (2017).
Eddy, S. R., Wheeler, T. J. & the HMMER development team. HMMER User’s Guide Version 3.1b2, 108 (Howard Hughes Medical Institute, 2015); http://eddylab.org/software/hmmer3/3.1b2/Userguide.pdf.
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).
Haft, D. H. et al. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).
Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).
McDaniel, E. A., Anantharaman, K. & McMahon, K. D. metabolisHMM: Phylogenomic analysis for exploration of microbial phylogenies and metabolic pathways. Preprint at bioRxiv https://doi.org/10.1101/2019.12.20.884627 (2019).
Neukirchen, S. & Sousa, F. L. DiSCo: a sequence-based type-specific predictor of Dsr-dependent dissimilatory sulphur metabolism in microbial data. Microb. Genomics 7, 000603 (2021).
Teng, Z.-J. et al. Biogeographic traits of dimethyl sulfide and dimethylsulfoniopropionate cycling in polar oceans. Microbiome 9, 207 (2021).
Tanabe, T. S. & Dahl, C. HMS-S-S: A tool for the identification of sulphur metabolism-related genes and analysis of operon structures in genome and metagenome assemblies. Mol. Ecol. Resour. 22, 2758–2774 (2022).
Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T.-Y. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).
Gupta, D., Chen, K., Elliott, S. J. & Nayak, D. D. MmcA is an electron conduit that facilitates both intracellular and extracellular electron transport in Methanosarcina acetivorans. Nat. Commun. 15, 3300 (2024).
Leu, A. O. et al. Lateral gene transfer drives metabolic flexibility in the anaerobic methane-oxidizing archaeal family Methanoperedenaceae. mBio 11, e01325 (2020).
Garber, A. I., Nealson, K. H. & Merino, N. Large-scale prediction of outer-membrane multiheme cytochromes uncovers hidden diversity of electroactive bacteria and underlying pathways. Front. Microbiol. 15, 1448685 (2024).
Moreno, J., Nielsen, H., Winther, O. & Teufel, F. Predicting the subcellular location of prokaryotic proteins with DeepLocPro. Bioinformatics 40, btae677 (2024).
Huerta-Cepas, J. et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).
Yin, Y. et al. dbCAN: A web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, W445–W451 (2012).
Khot, V. et al. CANT-HYD: A curated database of phylogeny-derived Hidden Markov Models for annotation of marker genes involved in hydrocarbon degradation. Front. Microbiol. 12, 764058 (2021).
Boeuf, D., Audic, S., Brillet-Guéguen, L., Caron, C. & Jeanthon, C. MicRhoDE: A curated database for the analysis of microbial rhodopsin diversity and evolution. Database 2015, bav080 (2015).
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).
Teufel, F. et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40, 1023–1025 (2022).
Woodcroft, B. J. et al. Comprehensive taxonomic identification of microbial species in metagenomic data using SingleM and Sandpiper. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02738-1 (2025).
Amend, J. P. & LaRowe, D. E. Minireview: Demystifying microbial reaction energetics. Environ. Microbiol. 21, 3539–3547 (2019).
Cornell, R. M. & Schwertmann, U. The Iron Oxides: Structure, Properties, Reactions, Occurrence and Uses (Wiley-VCH, 2003).
Rickard, D. & Morse, J. W. Acid volatile sulfide (AVS). Mar. Chem. 97, 141–197 (2005).
Majzlan, J., Navrotsky, A. & Schwertmann, U. Thermodynamics of iron oxides: part III. Enthalpies of formation and stability of ferrihydrite (∼Fe(OH)3), schwertmannite (∼FeO(OH)3/4(SO4)1/8), and ε-Fe2O3. Geochim. Cosmochim. Acta 68, 1049–1059 (2004).
Ritz, C., Baty, F., Streibig, J. C. & Gerhard, D. Dose–response analysis using R. PLoS ONE 10, e0146021 (2015).
Poulton, S. W., Krom, M. D. & Raiswell, R. A revised scheme for the reactivity of iron (oxyhydr)oxide minerals towards dissolved sulfide. Geochim. Cosmochim. Acta 68, 3703–3715 (2004).
Cline, J. D. Spectrophotometric determination of hydrogen sulfide in natural waters. Limnol. Oceanogr. 14, 454–458 (1969).
Wan, M., Shchukarev, A., Lohmayer, R., Planer-Friedrich, B. & Peiffer, S. Occurrence of surface polysulfides during the interaction between ferric (hydr)oxides and aqueous sulfide. Environ. Sci. Technol. 48, 5076–5084 (2014).
Ehmann, T. et al. Modification and validation of the pyromellitic acid electrolyte for the capillary electrophoretic determination of anions. J. Chromatogr. A 995, 217–226 (2003).
Thamdrup, B., Finster, K., Hansen, J. W. & Bak, F. Bacterial disproportionation of elemental sulfur coupled to chemical reduction of iron or manganese. Appl. Environ. Microbiol. 59, 101–108 (1993).
Hausmann, B. et al. Consortia of low-abundance bacteria drive sulfate reduction-dependent degradation of fermentation products in peat soil microcosms. ISME J. 10, 2365–2375 (2016).
Griffiths, R. I., Whiteley, A. S., O’Donnell, A. G. & Bailey, M. J. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl. Environ. Microbiol. 66, 5488–5491 (2000).
Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Haynes, W. in Encyclopedia of Systems Biology (eds Dubitzky, W. et al.) 78–78 (Springer, 2013).
Ye, J. et al. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics 13, 134 (2012).
Deng, X., Dohmae, N., Nealson, K. H., Hashimoto, K. & Okamoto, A. Multi-heme cytochromes provide a pathway for survival in energy-limited environments. Sci. Adv. 4, eaao5682 (2018).
Yu, N. Y. et al. PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).
Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).
Notredame, C., Higgins, D. G. & Heringa, J. T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302, 205–217 (2000).
Holm, L. & Park, J. DaliLite workbench for protein structure comparison. Bioinformatics 16, 566–567 (2000).
Holm, L. Benchmarking fold detection by DaliLite v.5. Bioinformatics 35, 5326–5327 (2019).
Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).
Morris, G. M. et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009).
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J. Chem. Inf. Model. 61, 3891–3898 (2021).
Letunic, I. & Bork, P. Interactive Tree of Life (iTOL) v6: Recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 52, W78–W82 (2024).
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
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).
Cai, L. et al. Tad pilus-mediated twitching motility is essential for DNA uptake and survival of Liberibacters. PLoS ONE 16, e0258583 (2021).
Melville, S., & Craig, L. Type IV pili in Gram-positive bacteria. Microbiol. Mol. Biol. Rev. 77, 323–341 (2013).
نشر لأول مرة على: www.nature.com
تاريخ النشر: 2025-08-27 03:00:00
الكاتب: Song-Can Chen
تنويه من موقع “بتوقيت بيروت”:
تم جلب هذا المحتوى بشكل آلي من المصدر:
www.nature.com
بتاريخ: 2025-08-27 03:00:00.
الآراء والمعلومات الواردة في هذا المقال لا تعبر بالضرورة عن رأي موقع “بتوقيت بيروت”، والمسؤولية الكاملة تقع على عاتق المصدر الأصلي.
ملاحظة: قد يتم استخدام الترجمة الآلية في بعض الأحيان لتوفير هذا المحتوى.
ظهرت المقالة Microbial iron oxide respiration coupled to sulfide oxidation أولاً على بتوقيت بيروت | اخبار لبنان والعالم لحظة بلحظة 24/24 تابعونا.
