+Advanced Search
Volume 2 Issue 1
Feb.  2020
Article Contents


Microbial diversity of sediments from an inactive hydrothermal vent field, Southwest Indian Ridge

  • Corresponding author: Yu Zhang, zhang.yusjtu@sjtu.edu.cn
  • Received Date: 2019-08-05
    Accepted Date: 2019-09-01
    Published online: 2019-10-18
  • Electronic supplementary material The online version of this article (https://doi.org/10.1007/s42995-019-00007-0) contains supplementary material, which is available to authorized users.
  • Edited by Chengchao Chen.
  • The Southwest Indian Ridge, which is the slowest-spreading of the main ridges, separates the African and Antarctic plates. The slow expanding rate is associated with less density of hydrothermal vent fields, shorter longevity of hydrothermal activity, cold mantle temperatures and thick lithosphere. However, the microbial communities adapting to such specific characteristics of this area have remained largely unexplored. To study the microbial diversity at the Southwest Indian Ridge, we sampled three sediment cores in a newly found inactive vent field, the Tianzuo field, and used high-throughput sequencing of 16S rRNA genes to reveal the microbial composition. Microbial communities of three sampling sites were very similar at the surface, and underwent a gradient change along depth. Gammaproteobacteria, namely Alteromonadaceae, Nitrosococcus and the JTB255 marine benthic group, were the most dominant bacterial taxa. Marine Group Ⅰ was the dominant archaeal taxon in our samples. In addition, microbial populations capable of ammonia oxidation, nitrite oxidation, sulfur oxidation and manganese oxidation were detected to be the main chemolithoautotrophs. The enrichment of sulfur-oxidizing and manganese-oxidizing bacteria was observed in deep layers. When compared with other vent fields along different ocean ridges, the Tianzuo field showed distinct composition in both archaeal and bacterial communities. These results provide the first view of microbial communities of the Tianzuo field at the Southwest Indian Ridge, and give a better understanding of metabolic potential possessed by the microbial populations.
  • 加载中
  • Attard E, Poly F, Commeaux C, Laurent F, Terada A, Smets BF, Recous S, Roux XL (2010) Shifts between Nitrospira- and Nitrobacter-like nitrite oxidizers underlie the response of soil potential nitrite oxidation to changes in tillage practices. Environ Microbiol 12:315-326 doi: 10.1111/j.1462-2920.2009.02070.x
    Bachraty C, Legendre P, Desbruyères D (2009) Biogeographic relationships among deep-sea hydrothermal vent faunas at global scale. Deep Sea Res Part Ⅰ Oceanogr Res Pap 56:1371-1378 doi: 10.1016/j.dsr.2009.01.009
    Baker ET (2017) Exploring the ocean for hydrothermal venting: new techniques, new discoveries, new insights. Ore Geol Rev 86:55-69 doi: 10.1016/j.oregeorev.2017.02.006
    Bohu T, Akob DM, Abratis M, Lazar CS, Küsel K (2016) Biological low-pH Mn(Ⅱ) oxidation in a manganese deposit influenced by metal-rich groundwater. Appl Environ Microbiol 8:3009-3021. 
    Borcard D, Gillet F, Legendre P (2011) Numerical ecology with R. Springer, New York
    Campbell BJ, Polson SW, Zeigler Allen L, Williamson SJ, Lee CK, Wommack KE, Cary SC (2013) Diffuse flow environments within basalt- and sediment-based hydrothermal vent ecosystems harbor specialized microbial communities. Front Microbiol 4:182 
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335-336 doi: 10.1038/nmeth.f.303
    Cerqueira T, Pinho D, Egas C, Froufe H, Altermark B, Candeias C, Santos RS, Bettencourt R (2015) Microbial diversity in deep-sea sediments from the Menez Gwen hydrothermal vent system of the Mid-Atlantic Ridge. Mar Genomics 24:343-355 doi: 10.1016/j.margen.2015.09.001
    Cerqueira T, Pinho D, Froufe H, Santos RS, Bettencourt R, Egas C (2017) Sediment microbial diversity of three deep-sea hydrothermal vents southwest of the Azores. Microb Ecol 74:332-349 doi: 10.1007/s00248-017-0943-9
    Cerqueira T, Barroso C, Froufe H, Egas C, Bettencourt R (2018) Metagenomic signatures of microbial communities in deep-sea hydrothermal sediments of Azores vent fields. Microb Ecol 76:387-403 doi: 10.1007/s00248-018-1144-x
    Chao A, Shen TJ (2003) Nonparametric estimation of Shannon's index of diversity when there are unseen species in sample. Environ Ecol Stat 10:429-443 doi: 10.1023/A:1026096204727
    Chen P, Zhang L, Guo X, Dai X, Liu L, Xi L, Wang J, Song L, Wang Y, Zhu Y, Huang L, Huang Y (2016) Diversity, Biogeography, and Biodegradation Potential of Actinobacteria in the Deep-Sea sediments along the Southwest Indian Ridge. Front Microbiol 7:1340 
    Chen J, Tao C, Liang J, Liao S, Dong C, Li H, Li W, Wang Y, Yue X, He Y (2018) Newly discovered hydrothermal fields along the ultraslow-spreading Southwest Indian Ridge around 63°E. Acta Oceanol Sin 37:61-67 
    Clarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Aust J Ecol 18:117-143 doi: 10.1111/j.1442-9993.1993.tb00438.x
    Copley JT, Marsh L, Glover AG, Hühnerbach V, Nye VE, Reid WDK, Sweeting CJ, Wigham BD, Wiklund H (2016) Ecology and biogeography of megafauna and macrofauna at the first known deep-sea hydrothermal vents on the ultraslow-spreading Southwest Indian Ridge. Sci Rep 6:39158 doi: 10.1038/srep39158
    D'Hondt S, Inagaki F, Zarikian CA, Abrams LJ, Dubois N, Engelhardt T, Evans H, Ferdelman T, Gribsholt B, Harris RN, Hoppie BryceW, Hyun J-H, Kallmeyer J, Kim J, Lynch JE, McKinley Claire C, Mitsunobu S, Morono Y, Murray RW, Pockalny R et al (2015) Presence of oxygen and aerobic communities from sea floor to basement in deep-sea sediments. Nat Geosci 8:299-304 doi: 10.1038/ngeo2387
    Dahle H, Okland I, Thorseth IH, Pederesen RB, Steen IH (2015) Energy landscapes shape microbial communities in hydrothermal systems on the Arctic Mid-Ocean Ridge. ISME J 9:1593-1606 doi: 10.1038/ismej.2014.247
    Daims H, Lebedeva EV, Pjevac P, Han P, Herbold C, Albertsen M, Jehmlich N, Palatinszky M, Vierheilig J, Bulaev A (2015) Complete nitrification by Nitrospira bacteria. Nature 528:504 doi: 10.1038/nature16461
    Dick GJ, Lee YE, Tebo BM (2006) Manganese(Ⅱ)-oxidizing Bacillus spores in Guaymas Basin hydrothermal sediments and plumes. Appl Environ Microbiol 72:3184-3190 doi: 10.1128/AEM.72.5.3184-3190.2006
    Dick GJ, Torpey JW, Beveridge TJ, Tebo BM (2008) Direct identification of a bacterial manganese(Ⅱ) oxidase, the multicopper oxidase MnxG, from spores of several different marine Bacillus species. Appl Environ Microbiol 74:1527-1534 doi: 10.1128/AEM.01240-07
    Ding J, Zhang Y, Wang H, Jian H, Leng H, Xiao X (2017) Microbial community structure of deep-sea hydrothermal vents on the ultraslow spreading Southwest Indian Ridge. Front Microbiol 8:1012 doi: 10.3389/fmicb.2017.01012
    Djurhuus A, Read JF, Rogers AD (2017) The spatial distribution of particulate organic carbon and microorganisms on seamounts of the South West Indian Ridge. Deep Res Part Ⅱ Topical Stud Oceanogr 136:73-84 doi: 10.1016/j.dsr2.2015.11.015
    Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI (2019) PICRUSt2: an improved and extensible approach for metagenome inference. bioRxiv 1:672295. https://doi.org/10.1101/672295
    Edlund A, Hardeman F, Jansson JK, Sjoling S (2008) Active bacterial community structure along vertical redox gradients in Baltic Sea sediment. Environ Microbiol 10:2051-2063. doi: 10.1111/j.1462-2920.2008.01624.x
    Fisher CR, Takai K, Le Bris N (2007) Hydrothermal vent ecosystems vol 20. In: Oceanography, vol 1. Oceanography Society, Rockville. https://www.jstor.org/stable/24859970
    Flores GE, Shakya M, Meneghin J, Yang ZK, Seewald JS, Geoff Wheat C, Podar M, Reysenbach AL (2012) Inter-field variability in the microbial communities of hydrothermal vent deposits from a back-arc basin. Geobiology 10:333-346 doi: 10.1111/j.1472-4669.2012.00325.x
    Fukunaga Y, Kurahashi M, Sakiyama Y, Ohuchi M, Yokota A, Harayama S (2009) Phycisphaera mikurensis gen. nov., sp. nov., isolated from a marine alga, and proposal of Phycisphaeraceae fam. nov., Phycisphaerales ord. nov. and Phycisphaerae classis nov. in the phylum Planctomycetes. J Gen Appl Microbiol 55:267-275 doi: 10.2323/jgam.55.267
    Füssel J, Lücker S, Yilmaz P, Nowka B, van Kessel MAHJ, Bourceau P, Hach PF, Littmann S, Berg J, Spieck E, Daims H, Kuypers MMM, Lam P (2017) Adaptability as the key to success for the ubiquitous marine nitrite oxidizer Nitrococcus. Sci Adv 3:e1700807 doi: 10.1126/sciadv.1700807
    German CR (2010) Diverse styles of submarine venting on the ultraslow spreading Mid-Cayman Rise. Proc Natl Acad Sci 107(32):14020-14025 doi: 10.1073/pnas.1009205107
    German CR, Baker ET, Mevel C, Tamaki K, FUJI Science Team (1998) Hydrothermal activity along the southwest Indian ridge. Nature 395:490-493 doi: 10.1038/26730
    Handley KM, Boothman C, Mills RA, Pancost RD, Lloyd JR (2010) Functional diversity of bacteria in a ferruginous hydrothermal sediment. ISME J 4:1193-1205 doi: 10.1038/ismej.2010.38
    Hawley AK, Nobu MK, Wright JJ, Durno WE, Morgan-Lang C, Sage B, Schwientek P, Swan BK, Rinke C, Torres-Beltran M, Mewis K, Liu WT, Stepanauskas R, Woyke T, Hallam SJ (2017) Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients. Nat Commun 8:1507 doi: 10.1038/s41467-017-01376-9
    Heip CH, Herman PM, Soetaert K (1998) Indices of diversity and evenness. Oceanis 24:61-88
    Henry S, Bru D, Stres B, Hallet S, Philippot L (2006) Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ genes in soils. Appl Environ Microbiol 72:5181-5189 doi: 10.1128/AEM.00231-06
    Holmes AJ, Costello A, Lidstrom ME, Murrell JC (1995) Evidence that participate methane monooxygenase and ammonia monooxygenase may be evolutionarily related. FEMS Microbiol Lett 132:203-208 doi: 10.1111/j.1574-6968.1995.tb07834.x
    Horz HP, Yimga MT, Liesack W (2001) Detection of methanotroph diversity on roots of submerged rice plants by molecular retrieval of pmoA, mmoX, mxaF, and 16S rRNA and ribosomal DNA, including pmoA-based terminal restriction fragment length polymorphism profiling. Appl Environ Microbiol 67:4177-4185 doi: 10.1128/AEM.67.9.4177-4185.2001
    Inagaki F, Hinrichs K-U, Kubo Y, Bowles MW, Heuer VB, Hong W-L, Hoshino T, Ijiri A, Imachi H, Ito M, Kaneko M, Lever MA, Lin Y-S, Methé BA, Morita S, Morono Y, Tanikawa W, Bihan M, Bowden SA, Elvert M et al (2015) Exploring deep microbial life in coal-bearing sediment down to ~ 2.5 km below the ocean floor. Science 349:420-424 doi: 10.1126/science.aaa6882
    Ivanova EP, Mikhailov VV (2001) A new family, Alteromonadaceae fam. nov., including marine proteobacteria of the genera Alteromonas, Pseudoalteromonas, Idiomarina, and Colwellia. Microbiology 70:10-17 doi: 10.1023/A:1004876301036
    Jahnke RA (1996) The global ocean flux of particulate organic carbon: a real distribution and magnitude. Global Biogeochem Cycles 10:71-88 doi: 10.1029/95GB03525
    Kemp PF, Aller JY (2004) Estimating prokaryotic diversity: when are 16S rDNA libraries large enough? Limnol Oceanogr Methods 2:114-125 doi: 10.4319/lom.2004.2.114
    Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glockner FO (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 41:e1 doi: 10.1093/nar/gks808
    Koch H, Lucker S, Albertsen M, Kitzinger K, Herbold C, Spieck E, Nielsen PH, Wagner M, Daims H (2015) Expanded metabolic versatility of ubiquitous nitrite-oxidizing bacteria from the genus Nitrospira. Proc Natl Acad Sci 112:11371-11376 doi: 10.1073/pnas.1506533112
    Koops HP, Pommerening-Röser A (2015) Nitrosococcus. In: Whitman WB, Rainey F, Kämpfer P, Trujillo M, Chun J, DeVos P, Hedlund B, Dedysh S (eds) Bergey's manual of systematics of archaea and bacteria. Wiley, Hoboken
    Kovaleva OL, Merkel AY, Novikov AA, Baslerov RV, Toshchakov SV, Bonch-Osmolovskaya EA (2015) Tepidisphaera mucosa gen. nov., sp. nov., a moderately thermophilic member of the class Phycisphaerae in the phylum Planctomycetes, and proposal of a new family, Tepidisphaeraceae fam. nov., and a new order, Tepidisphaerales ord. nov. Int J Syst Evol Microbiol 65:549-555 doi: 10.1099/ijs.0.070151-0
    Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31:814-821 doi: 10.1038/nbt.2676
    Li J, Zhou H, Fang J, Wu Z, Peng X (2015) Microbial distribution in a hydrothermal plume of the Southwest Indian Ridge. Geomicrobiol J 33:401-415 
    Liu S, Hu J-J, Shen J-X, Chen S, Tian G-M, Zheng P, Lou L-P, Ma F, Hu B-L (2017) Potencial correlate environmental factors leading to the niche segregation of ammonia-oxidizing archaea and ammonia-oxidizing bacteria: a review. Appl Environ Biotechnol 2:11-19
    Louca S, Parfrey LW, Doebeli M (2016) Decoupling function and taxonomy in the global ocean microbiome. Science 353:1272 doi: 10.1126/science.aaf4507
    Magoč T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:2957-2963 doi: 10.1093/bioinformatics/btr507
    Mahmoudi N, Robeson MS, 2nd, Castro HF, Fortney JL, Techtmann SM, Joyner DC, Paradis CJ, Pfiffner SM, Hazen TC (2015) Microbial community composition and diversity in Caspian Sea sediments. FEMS Microbiol Ecol 91: 1-11
    Meyer S, Wegener G, Lloyd KG, Teske A, Boetius A, Ramette A (2013) Microbial habitat connectivity across spatial scales and hydrothermal temperature gradients at Guaymas Basin. Front Microbiol 4:207 
    Münch U, Lalou C, Halbach P, Fujimoto H (2001) Relict hydrothermal events along the super-slow Southwest Indian spreading ridge near 63°56′E—mineralogy, chemistry and chronology of sulfide samples. Chem Geol 177:341-349 doi: 10.1016/S0009-2541(00)00418-6
    Mussmann M, Pjevac P, Kruger K, Dyksma S (2017) Genomic repertoire of the Woeseiaceae/JTB255, cosmopolitan and abundant core members of microbial communities in marine sediments. ISME J 11:1276-1281 doi: 10.1038/ismej.2016.185
    Naeem S (2009) Gini in the bottle. Nature 458:579 doi: 10.1038/458579a
    Nunoura T, Oida H, Nakaseama M, Kosaka A, Ohkubo SB, Kikuchi T, Kazama H, Hosoi-Tanabe S, Nakamura K, Kinoshita M, Hirayama H, Inagaki F, Tsunogai U, Ishibashi J, Takai K (2010) Archaeal diversity and distribution along thermal and geochemical gradients in hydrothermal sediments at the Yonaguni Knoll Ⅳ hydrothermal field in the Southern Okinawa trough. Appl Environ Microbiol 76:1198-1211 doi: 10.1128/AEM.00924-09
    Opatkiewicz AD, Butterfield DA, Baross JA (2009) Individual hydrothermal vents at Axial Seamount harbor distinct subseafloor microbial communities. FEMS Microbiol Ecol 70:413-424 doi: 10.1111/j.1574-6941.2009.00747.x
    Pachiadaki MG, Sintes E, Bergauer K, Brown JM, Record NR, Swan BK, Mathyer ME, Hallam SJ, Lopez-Garcia P, Takaki Y (2017) Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science 358:1046-1051 doi: 10.1126/science.aan8260
    Peng X, Chen S, Zhou H, Zhang L, Wu Z, Li J, Li J, Xu H (2011) Diversity of biogenic minerals in low-temperature Si-rich deposits from a newly discovered hydrothermal field on the ultraslow spreading Southwest Indian Ridge. J Geophys Res Biogeosci 116(G3):G03030 
    Penn K, Jenkins C, Nett M, Udwary DW, Gontang EA, McGlinchey RP, Foster B, Lapidus A, Podell S, Allen EE, Moore BS, Jensen PR (2009) Genomic islands link secondary metabolism to functional adaptation in marine Actinobacteria. ISME J 3:1193-1203 doi: 10.1038/ismej.2009.58
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41(D1):D590-D596 
    Ricotta C, Avena G (2003) On the relationship between Pielou's evenness and landscape dominance within the context of Hill's diversity profiles. Ecol Ind 2:361-365 doi: 10.1016/S1470-160X(03)00005-0
    Rognes T, Flouri T, Nichols B, Quince C, Mahé F (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584 doi: 10.7717/peerj.2584
    Roussel EG, Konn C, Charlou JL, Donval JP, Fouquet Y, Querellou J, Prieur D, Bonavita MA (2011) Comparison of microbial communities associated with three Atlantic ultramafic hydrothermal systems. FEMS Microbiol Ecol 77:647-665 doi: 10.1111/j.1574-6941.2011.01161.x
    Sabirova JS, Cloetens L, Vanhaecke L, Forrez I, Verstraete W, Boon N (2008) Manganese-oxidizing bacteria mediate the degradation of 17α-ethinylestradiol. Microb Biotechnol 1:507-512 doi: 10.1111/j.1751-7915.2008.00051.x
    Sauter D, Cannat M, Rouméjon S, Andreani M, Birot D, Bronner A, Brunelli D, Carlut J, Delacour A, Guyader V, MacLeod CJ, Manatschal G, Mendel V, Ménez B, Pasini V, Ruellan E, Searle R (2013) Continuous exhumation of mantle-derived rocks at the Southwest Indian Ridge for 11 million years. Nat Geosci 6:314-320 doi: 10.1038/ngeo1771
    Sinha RK, Krishnan KP, Thomas FA, Binish MB, Mohan M, Kurian PJ (2019) Polyphasic approach revealed complex bacterial community structure and function in deep sea sediment of ultra-slow spreading Southwest Indian Ridge. Ecol Ind 96:40-51 doi: 10.1016/j.ecolind.2018.08.063
    Storesund JE, Ovreas L (2013) Diversity of Planctomycetes in iron-hydroxide deposits from the Arctic Mid Ocean Ridge (AMOR) and description of Bythopirellula goksoyri gen. nov., sp. nov., a novel Planctomycete from deep sea iron-hydroxide deposits. Antonie Van Leeuwenhoek 104:569-584 doi: 10.1007/s10482-013-0019-x
    Storesund JE, Lanzen A, Garcia-Moyano A, Reysenbach AL, Ovreas L (2018) Diversity patterns and isolation of Planctomycetes associated with metalliferous deposits from hydrothermal vent fields along the Valu Fa Ridge (SW Pacific). Antonie Van Leeuwenhoek 111:841-858 doi: 10.1007/s10482-018-1026-8
    Tao C, Wu G, Ni J, Zhao H, Su X, Zhou N, Li J, Chen YJ, Cui R, Deng X, Egorov I, Dobretsova IG, Sun G, Qiu Z, Deng X, Zhou J, Gu C, Li J, Yang J, Zhang K et al (2009) New hydrothermal fields found along the SWIR during the Legs 5-7 of the Chinese DY115-20 expedition. In: AGU fall meeting, San Francisco
    R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
    Tebo BM, Johnson HA, McCarthy JK, Templeton AS (2005) Geomicrobiology of manganese (Ⅱ) oxidation. Trends Microbiol 13:421-428 doi: 10.1016/j.tim.2005.07.009
    Tourna M, Maclean P, Condron L, O'Callaghan M, Wakelin SA (2014) Links between sulphur oxidation and sulphur-oxidising bacteria abundance and diversity in soil microcosms based on soxB functional gene analysis. FEMS Microbiol Ecol 88:538-549 doi: 10.1111/1574-6941.12323
    Voordouw G (1992) Evolution of hydrogenase genes. In: Advances in inorganic chemistry, vol 38. Academic Press, New York, pp 397-422
    Vuillemin A, Ariztegui D, Horn F, Kallmeyer J, Orsi WD, Team PS (2018) Microbial community composition along a 50, 000-year lacustrine sediment sequence. FEMS Microbiol Ecol 94:fiy029 
    Wang L, Cheung MK, Kwan HS, Hwang JS, Wong CK (2015) Microbial diversity in shallow-water hydrothermal sediments of Kueishan Island, Taiwan as revealed by pyrosequencing. J Basic Microbiol 55:1308-1318 doi: 10.1002/jobm.201400811
    Yamamoto M, Takai K (2011) Sulfur metabolisms in epsilon- and gamma-proteobacteria in deep-sea hydrothermal fields. Front Microbiol 2:192 
    Zhang L, Kang M, Xu J, Xu J, Shuai Y, Zhou X, Yang Z, Ma K (2016) Bacterial and archaeal communities in the deep-sea sediments of inactive hydrothermal vents in the Southwest India Ridge. Sci Rep 6:25982 doi: 10.1038/srep25982
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索


Article Metrics

Article views(325) PDF downloads(12) Cited by()

Proportional views

Microbial diversity of sediments from an inactive hydrothermal vent field, Southwest Indian Ridge

    Corresponding author: Yu Zhang, zhang.yusjtu@sjtu.edu.cn
  • 1. School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China
  • 2. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • 3. State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract: The Southwest Indian Ridge, which is the slowest-spreading of the main ridges, separates the African and Antarctic plates. The slow expanding rate is associated with less density of hydrothermal vent fields, shorter longevity of hydrothermal activity, cold mantle temperatures and thick lithosphere. However, the microbial communities adapting to such specific characteristics of this area have remained largely unexplored. To study the microbial diversity at the Southwest Indian Ridge, we sampled three sediment cores in a newly found inactive vent field, the Tianzuo field, and used high-throughput sequencing of 16S rRNA genes to reveal the microbial composition. Microbial communities of three sampling sites were very similar at the surface, and underwent a gradient change along depth. Gammaproteobacteria, namely Alteromonadaceae, Nitrosococcus and the JTB255 marine benthic group, were the most dominant bacterial taxa. Marine Group Ⅰ was the dominant archaeal taxon in our samples. In addition, microbial populations capable of ammonia oxidation, nitrite oxidation, sulfur oxidation and manganese oxidation were detected to be the main chemolithoautotrophs. The enrichment of sulfur-oxidizing and manganese-oxidizing bacteria was observed in deep layers. When compared with other vent fields along different ocean ridges, the Tianzuo field showed distinct composition in both archaeal and bacterial communities. These results provide the first view of microbial communities of the Tianzuo field at the Southwest Indian Ridge, and give a better understanding of metabolic potential possessed by the microbial populations.


  • Deep-sea hydrothermal vents are commonly distributed near the mid-ocean ridge, ocean floor subduction zones, back-arc basins, and ocean volcanoes. The seawater infiltrated through the seabed cracks is gradually mixed with the mantle material, generating the hydrothermal fluid bursting from the vent. Reduced materials rich in the hydrothermal fluid, such as Fe(Ⅱ), sulfur, and methane, support the growth of autotrophic microorganisms, which are the main drivers of the hydrothermal vent ecosystem (Fisher et al. 2007). Since the discovery of hydrothermal vents in the late 1970s, the bloom and distribution of the microbial communities under the influence of hydrothermal activity have been a long-standing research hotspot.

    Microbial populations of a diverse range of taxa are widely distributed in various ecological niches in sediments near hydrothermal vents (Fisher et al. 2007). In sediments of an inactive hydrothermal vent field, the bacterial communities were dominated by Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes whereas Thaumarchaeota and Euryarchaeota represented the most dominant archaeal taxa (Zhang et al. 2016). In addition, the cosmopolitan OTUs in all sediments of two vent fields along Mid-Atlantic Ridge were affiliated with the bacterial clades JTB255, Sh765B-TzT-29, Rhodospirillaceae and the OCS155 marine group and with the archaeal Marine Group Ⅰ (Cerqueira et al. 2017). Another study of a vent field along the Mid-Atlantic Ridge revealed that specific mesophilic, thermophilic and hyperthermophilic archaeal (e.g., Archaeoglobus, ANME-1) and bacterial (e.g., Caldithrix, Thermodesulfobacteria) taxa were highly abundant near the vent chimney (Cerqueira et al. 2015). Epsilonproteobacteria belonging to the mesophilic chemolithoautotrophic genera Sulfurovum and Sulfurimonas was the most abundant group in shallow-water hydrothermal sediments (Wang et al. 2015). Nevertheless, in a research on hydrothermal sediments covering a wide range of temperatures and depths, more than 80% of all detected OTUs were shared among different temperature realms and sediment depths, suggesting a connectivity between distinct hydrothermal habitats (Meyer et al. 2013). The environmental gradients generated by hydrothermal activities give rise to the specific populations inhabiting hydrothermal sediments and make the deep-sea hydrothermal field a highly diverse ecosystem.

    Ultraslow oceanic spreading ridges cover over 15, 000 km of divergent plate boundaries, and an assessment of their role in participating global biogeochemical cycle requires an intensive survey of the microbial community. The Southwest Indian Ridge (SWIR) was the slowest expanding edge in the world, spreading at a full rate of 14 mm year−1 (German et al. 1998). Also, the SWIR produced more sparse hydrothermal vents as a result of the slow spreading rate (Baker 2017). Besides, the slow expanding rate was associated with shorter longevity of hydrothermal activity, cold mantle temperatures and a thick lithosphere at individual vent fields (Copley et al. 2016; German et al. 1998). Microbial communities surviving there should have shaped specific structures to adapt to these traits of SWIR. There were 26 vent fields found in SWIR whereas only one, named Longqi, was confirmed with activity according to the InterRidge Database (http://vents-data.interridge.org). Studies on microbial communities in SWIR revealed the diversity and abundance of microbial communities inhabiting the vent chimney (Ding et al. 2017), ridge-flank (Sinha et al. 2019), seamounts (Djurhuus et al. 2017), hydrothermal plume (Li et al. 2015) and sediments (Chen et al. 2016). Further work on microbial diversity in SWIR will enhance our apprehension of the ecosystem in this largely unexplored area.

    During the investigation of deep-sea hydrothermal fields in the Southwest Indian Ocean as part of the Chinese DY115-20th expedition, abnormal values of methane, hydrogen sulfide, reduction potential and temperature were detected and associated with hydrothermal activity in the Tianzuo hydrothermal field (63.541°E, 27.951°S) (Chen et al. 2018; Tao et al. 2009). Subsequent investigations have revealed remarkable long lasting, i.e., over 50, 000 years, hydrothermal activity, which is caused by the slow spreading rate and a weak thermal budget in the area, being based on geochemical and mineral analyses (Sauter et al. 2013; Chen et al. 2018; Münch et al. 2001). In this study, we tried to understand the ecological features, which have been impacted by the thousands of years' of hydrothermal activity. Therefore, the microbial communities as well as their metabolic potential in the Tianzuo field have been analyzed and compared with those of other vents located along ultraslow-spreading and slow-spreading ocean ridges.


    Overview of the sampling sites

  • The Tianzuo hydrothermal field (63.541°E, 27.951°S) is located at the easternmost ultraslow-spreading Southwest Indian Ridge. It is an inactive sulfide field, which is hosted by ultramafic rocks and controlled by detachment faults, and is covered by thick sediments, indicating that the ancient hydrothermal activity has ceased for a long time (Chen et al. 2018). Three sediment columns were sampled at south (63.53909°E, 27.9535°S), west (63.53422°E, 27.9466°S) and north (63.53896°E, 27.9391°S) of Tianzuo, which were divided into several layers as described in the Methods. The location of the Tianzuo field and the sampling sites as well as two adjacent hydrothermal vents have been shown in Fig. S1. The water depth of the sampling sites ranged from 3618.83 to 3759.47 m whereas the temperature range was from 1.52 to 1.54 ℃. In addition, the salinity was 34.7‰ for all three sites.

  • Alpha diversity of bacterial and archaeal communities

  • The bacterial and archaeal sequencing quality data have been included in Tables S1 and S2. 13, 417 OTUs were identified from a total of 468, 205 bacterial 16S rRNA gene sequences. In addition, 13, 540 OTUs were identified from a total of 743, 580 archaeal 16S rRNA gene sequences. The rarefaction curve of bacterial and archaeal sequences suggested that the sequencing depth was close to saturation. Therefore, the sequences could recover most species in the original communities (Fig. S2). The reads of all samples were rarified to an even depth (bacteria: 19, 453, archaea: 21, 215) for alpha and beta diversity analyses. The richness of bacterial communities (the number of total OTUs in each bacterial community) was similar at the surface among three sampling sites (south, west and north) and continued to decrease until the depth of 15 cm at the south sample (Fig. 1a). Similarly, the evenness of bacterial communities (opposed to the Gini unevenness index) was close at the same depth among three sampling sites, and rapidly decreased in the south sample until 13 cm (Fig. 1b). Conversely, the number of total OTUs and evenness of the archaeal communities varied greatly at three locations, and decreased until depth of 7 cm and 13 cm, respectively (Fig. 1c, d). The Shannon diversity index, Pielou's evenness index and Chao1 diversity index showed similar patterns (Tables S3, S4).

    Figure 1.  Alpha diversity of bacterial and archaeal communities along depth at three sites in the Tianzuo hydrothermal fields. (a) Bacterial richness estimated by total number of OTUs; (b) bacterial unevenness estimated by Gini index.; (c) archaeal richness; (d) archaeal unevenness

  • Bacterial and archaeal community structure

  • The relative abundance of the bacterial class with average percentages over 1% has been summarized in Fig. 2a. Generally, the surface bacterial composition was relatively similar across south, west and north sampling sites, whereas a greater variation was detected along the depth of the south sediment. The most abundant bacterial class retrieved from all sites was Gammaproteobacteria with an average percentage of 22.2%. Actinobacteria, representing 11.0% of the whole bacterial communities, was the second abundant class. A clear decrease of Gammaproteobacteria (33.5%-4.5%) from sediment surface to deep layers was observed in the south sediment. Actinobacteria were distributed mostly in the deep layer, reaching a peak abundance (56.9%) in the 24-26 cm layer of the south sediment. Similarly, the relative abundance of bacilli increased from 1.3% to 20.6% with increasing depth of layers. Furthermore, Alphaproteobacteria consisted a relatively stable part of the total communities with a mean abundance of 9.7% and a standard deviation of 2.3% (Fig. 2a). In addition, the bacterial classes with less than 1% relative abundance have been shown in Fig. S3. The surface communities showed similar composition for these classes whereas the communities of deep layers were relatively variable. Zetaproteobacteria were found at up to 0.4% in surface layers but hardly detected at all in deep layers. In contrast, Marinimicrobia (SAR406 clade) was distributed mostly in the deep layers, with a peak percentage of 0.3% in the south 10-12 cm layer (Fig. S3).

    Figure 2.  Relative abundance of bacterial (a) and archaeal (b) classes in each sample. The bacterial classes with percentages over 1% were shown while the other minor populations below 1% were summed as "Others" at the plot. The sample name describes the sampling location (S: south, W: west, N: north) and its layer depth (cm)

    The archaeal community structure was much simpler than that of bacteria. Thus, the relative abundance of archaeal members at the level of classes has been included in Fig. 2b. Marine Group Ⅰ, which included mainly ammonia-oxidizing archaea, was dominant at all vent sites with percentages from 87.7% to 98.6%. Woesearchaeota uncultured group represented the second most abundant archaea, accounting for 0.9%-9.4% of all recovered archaeal sequences (Fig. 2b).

  • Beta diversity of bacterial and archaeal communities

  • To see the gradient change of the community composition (beta diversity) among all samples, a non-metric multidimensional scaling (NMDS) plot was applied to express the community variation into two orthogonal directions, NMDS1 and NMDS2 (Fig. 3). The distance of two samples at the NMDS plot represents the dissimilarity of their community composition. Thus, the bacterial community structure was similar at the surface of three sampling sites, and showed a gradient change within 15 cm depth, which is along the direction of NMDS1 (Fig. 3). At a depth of over 15 cm, the bacterial community showed a random change along the NMDS1 direction, but a gradient change along the NMDS2 direction. Furthermore, the main microbial clades were joined at the NMDS plot to show the distribution of clades among samples. Any clade that lies close to a sample point is more likely to be found in that sample. At the bacterial phylum level, the composition of bacterial communities in the NMDS1 direction changed from Proteobacteria, Acidobacteria and Planctomycetes to Bacteroidetes, Gemmatimonadetes, Nitrospirae and then Actinobacteria (Fig. 3a). Specially, in the main phylum Proteobacteria, Zetaproteobacteria was shifted to Gammaproteobacteria, Deltaproteobactera, JTB23, SPOTSOCT00m83 and then Alphaproteobacteria, Betaproteobacteria and Epsilonproteobacteria in the NMDS1 direction. In the NMDS2 direction, Epsilonproteobacteria replaced other classes (Fig. 3b). Also, the archaeal community structure was similar at the surface of three sampling sites. Moreover, the archaeal community showed a depth-dependent change in the NMDS1 direction above 17 cm depth. Being different from the bacteria, a random change occurred in the NMDS2 direction (Fig. 3c). At the phylum level, Euryarchaeota, Thaumarchaeota and Woesearchaeota changed to the Miscellaneous Crenarchaeotic Group in the NMDS1 direction (Fig. 3c).

    Figure 3.  NMDS plot of bacterial communities and archaeal communities along depth at three sites. (a), (b) Beta diversity of bacterial community which shows shift of phylum or classes in Proteobacteria. (c) Beta diversity of archaeal community which shows shift of phylum. The number indicates the depth of each community while its color differs for three sampling sites. The red text is the acronym of taxonomy: Proteobacteria: PRO, Acidobacteria: ACI, Actinobacteria: ACT, Chloroflexi: CHL, Gemmatimonadetes: GEM, Planctomycetes: PLA, Firmicutes: FIR, Bacteroidetes: BAC, Nitrospirae: NIT. Euryarchaeota: EUR, Miscellaneous Crenarchaeotic Group: MCG, Thaumarchaeota: THA, Woesearchaeota: WOE, Alphaproteobacteria: ALP, Betaproteobacteria: BET, Deltaproteobacteria: DEL, Epsilonproteobacteria: EPS, Gammaproteobacteria: GAM, JTB23: JTB, SPOTSOCT00m83: SPO, Zetaproteobacteria: ZET

    The Venn plot of microbial communities of the surface layer (south: 0-2 cm, west: 0-2 cm, north: 0-5 cm) showed that there were 883 bacterial OTUs and 440 archaeal OTUs shared by three sites (Fig. S4). Moreover, these shared bacterial OTUs accounted for 30%-34% of total bacterial OTUs whereas the shared archaeal OTUs accounted for 22%-30% of total archaeal OTUs at each site (Fig. S4). Also, the shared bacterial OTUs made up 78%-82% reads of each bacterial community whereas the shared archaeal OTUs made up 85%-91% reads of each archaeal community. Furthermore, these results confirmed the similarity between the surface communities.

  • Bacterial metabolic potentials

  • According to confirmed characteristics of isolated and cultured bacteria, the relative abundance of bacteria capable of hydrogen oxidation, sulfur compound oxidation, manganese oxidation, ammonia oxidation, nitrite oxidation, and methane oxidation was estimated at sediment in the south sample using Functional Annotation of Prokaryotic Taxa (FAPROTAX) (Fig. 4a). Among 13, 417 bacterial OTUs, 2870 OTUs were assigned to 67 functional groups, accounting for 24%±5% reads in each sample. Among all types of bacteria, bacteria with functions of ammonia oxidation and nitrite oxidation were the most abundant chemoautotrophic types over 20 cm. These two types of bacteria had the highest relative abundance in the shallow layer of 5-7 cm, accounting for 7.8% and 6.4% of all bacteria, respectively. Also, there was a local peak at the depth of 21-23 cm, which accounted for 1.9% and 3.9% of all bacteria, respectively. Sulfur-compound-oxidizing bacteria comprised the third most common autotrophic bacteria, showing a maximum abundance of 4.9% at 21 cm and a minimum of 0.4%. Manganese-oxidizing bacteria were relatively low in abundance, but reached peaks of 0.25% at 19 cm. The relative abundance of methane-oxidizing bacteria and hydrogen-oxidizing bacteria was less than 0.03%, and they were not detected in nearly half of the layers (Fig. 4a).

    Figure 4.  Function potential of bacterial communities. Relative abundance of the bacteria able to oxidize hydrogen, sulfur compound, nitrite, ammonium, Mn and methane along depth of the south sediment (a) and functional gene copies per 16S rRNA gene of bacterial communities along depth of the south sediment (b)

    In addition, we applied Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) to predict the average functional gene copies per 16S rRNA gene in the samples (Fig. 4b). Functional genes pmoA/amoA, narG/nxrA, soxB, mnxG, mmoX and hyaB gene were used to quantify the bacteria capable of methane/ammonium oxidation (Holmes et al. 1995), nitrite reduction/oxidation (Attard et al. 2010; Henry et al. 2006), sulfur oxidation (Tourna et al. 2014), manganese oxidation (Dick et al. 2008), methane oxidation (Horz et al. 2001) and H2 metabolism (H2 producing or H2 oxidation) (Voordouw 1992). mnxG and mmoX were not detected in any sample. The pmoA/amoA and narG/nxrA genes, which are phylogenically similar, are unable to be distinguished by PICRUSt. The average copies of four detected functional genes fluctuated along all depths, whereas the narG/nxrA gene was the most abundant. The narG/nxrA gene showed the highest copies per 16S rRNA gene at the 25 cm layer (Fig. 4b). Due to the small number of isolates but much more genomic information on archaea, which is insufficiently recognized by FAPROTAX, we applied only PICRUSt rather than both methods to the archaeal communities. As Thaumarchaeota is the main archaeal group, the pmoA/amoA gene copies per 16S rRNA gene were calculated for archaeal communities which indicates the ammonia oxidizing potential (Fig. S5). The archaeal communities were predicted to harbor 0.44-1.61 copies pmoA/amoA gene per 16S rRNA gene. However, the weighted nearest sequenced taxon index (NSTI) (bacteria: 0.10-0.37; archaea: 0.17-0.22) for PICRUSt indicated long phylogenic distances between our OTUs with the reference genomes.

  • Specificity of microbial communities in the Tianzuo field

  • The microbial communities in the Tianzuo field were compared with those in hydrothermal vent fields along the Mid-Atlantic Ridge (Menez Gwen, Lucky Strike and Rainbow vents) (Cerqueira et al. 2017), Arctic Mid-Ocean Ridge (Loki's Castle and Soria Moria vents) (Dahle et al. 2015) and another inactive hydrothermal vent field of west SWIR (Zhang et al. 2016). Sediments from Lucky Strike and Rainbow vents were far from active vents whereas sediments of Menez Gwen were close to an active vent. Biofilms of Loki's Castle and Soria Moria were located at the surface of active chimneys. Also, sediments from another inactive hydrothermal vent field of west SWIR were far away from active vents. The research area that we selected was either from ultraslow-spreading (West SWIR, Loki's Castle and Soria Moria vents) or slow-spreading ridges (Menez Gwen, Lucky Strike and Rainbow vents). The microbial communities of the Tianzuo field are significantly different from other five hydrothermal fields according to the NMDS plot (Fig. 5). The Analysis of Similarities (ANOSIM) test for bacterial communities is 0.991 (P=0.001) and the ANOSIM test for archaeal communities is 0.943 (P=0.001). Moreover, the microbial communities of the Tianzuo field were more similar to Lucky Strike and Rainbow as well as another inactive hydrothermal vent of west SWIR. Actinobacteria and Phycisphaerae were the second and eighth most abundant bacterial classes in the Tianzuo field whereas they were hardly detected at all in other fields (Fig. 6a). Besides, Marine Group Ⅰ was the main archaeal class of sediments from Tianzuo and other three vent fields away from hydrothermal activity (Fig. 6b). In contrast, although biofilms from Arctic Mid-Ocean Ridge and sediments from Menez Gwen were both influenced by intensive hydrothermal activity, their community similarity is extremely low (Fig. 6).

    Figure 5.  NMDS plot of bacterial communities (a) and archaeal communities (b) at different hydrothermal vent fields. Arctic represents sediments from Loki's Castle and Soria Moria at the Arctic Mid-Ocean Ridge; Menez Gwen, Lucky Strike and Rainbow represents sediments sampled from these three vent fields at the Mid-Atlantic Ridge; West SWIR represents sediments (50.9277°E, 37.6251°S and 50.9643°E, 37.6174°S) sampled from a vent field at west SWIR

    Figure 6.  Relative abundance of bacterial (a) and archaeal classes (b) with percentages over 1% in different vent fields. The relative abundance of classes in each vent field was the average relative abundance of classes in samples belonging to that vent field

  • In this article, we present the first report of microbial diversity at three sediments in the Tianzuo field of SWIR, which was recently determined to be an inactive hydrothermal vent field. Based on the results of alpha diversity and community structure (Figs. 1, 2, 3, Figs. S3, S4), the microbial communities of top layers at different sites were very similar. This result indicated that no strong environmental gradient drove the turnover of microbial communities in our sampling area. It is realized that temperature (Meyer et al. 2013; Nunoura et al. 2010), organic concentration (Mahmoudi et al. 2015), metal composition (Cerqueira et al. 2017) and reduction potential (Edlund et al. 2008) are the common environmental factors driving community change in hydrothermal sediments. Considering the low density of hydrothermal vents and weak hydrothermal activities caused by the ultraslow spreading rate of SWIR, it is not surprising that the plume or fluids from distant active vents (about 40 km) (Fig. S1) could not build sufficiently strong environmental gradients to influence overall communities. Moreover, the inactive state of the Tianzuo vent field may have lasted long enough to eliminate the environmental gradient caused by ancient hydrothermal activities.

  • Dominant taxa and their ecological functions

  • In the sediment samples examined in this study, we found a series of bacterial populations associated with hydrothermal activities, including Epsilonproteobacteria (0.27%±0.43%) (Fig. 3b) and bacteria capable of hydrogen oxidation, sulfur compound oxidation, and manganese oxidation (Fig. 4a). Similar populations taking part in N cycling, S oxidation, metal oxidation and methane oxidation were detected in limited abundance in another inactive vent of SWIR (Zhang et al. 2016). As a common group of hydrothermal vent communities, Epsilonproteobacteria is widely distributed in sulfide deposits (Flores et al. 2012) and diffuse flow (Campbell et al. 2013), where this group is involved in sulfur metabolism and associated with a high concentration of hydrogen sulfide. As hydrothermal plumes could spread out over a few hundred kilometers away from the vent source (German 2010), the reduced compound in deposits from the plume may fuel these autotrophic bacteria to a limited abundance (Flores et al. 2012). Within 40 km from the Tianzuo field, the newly discovered Tiancheng hydrothermal field was identified as an active low-temperature diffuse flow field with potential high-temperature vents nearby (Chen et al. 2018). This could be the source of some sulfur compounds for the Tianzuo field (Fig. S1).

    The enrichment of autotrophic species may serve also as an indicator for past hydrothermal activity. There is a potential that reduced materials from past active vents may be able to sustain these microbial groups to exist even in lower populations. In addition, iron-oxidizing and sulfur-oxidizing bacteria of hydrothermal sediments were enriched in incubation without adding organic matter (Handley et al. 2010) showing that autotrophic microorganisms may be adapted to oligotrophic conditions and survive for a long time. The sudden enrichment of sulfur-oxidizing bacteria at the depth of 21 cm indicates a sulfur-rich layer (Fig. 4a). Similarly, although the population of manganese-oxidizing bacteria was quite small, its abundance was much higher at 19 cm (0.25%) relative to the surface layer. The enrichment of sulfur-oxidizing and manganese-oxidizing bacteria in the adjacent deep layers may be attributed to supplementation of sulfur and Mn(Ⅱ) by metalliferous sediments from past hydrothermal plumes of Tianzuo rather than distant vents. By contrast, iron-oxidizing bacteria were not found in the sediments. In comparison with iron-oxidizing bacteria, most manganese-oxidizing bacteria are heterotrophic (Bohu et al. 2016; Dick et al. 2006; Tebo et al. 2005). This heterotrophic trait makes it possible for manganese-oxidizing bacteria to survive for a long time without Mn(Ⅱ) (Sabirova et al. 2008).

    In addition to Epsilonproteobacteria, chemoautotrophic Gammaproteobacteria is the predominant primary producer of both free-living and symbiotic microbial communities in deep-sea hydrothermal fields (Yamamoto and Takai 2011). However, most Gammaproteobacteria in our sediments are not attributed to sulfur-oxidizing species, which were merely detected in our sediments (Fig. 4a). Instead, they are attributed to species of the genera Alteromonadaceae, Nitrosococcus and the JTB255 marine benthic group; these three groups are commonly distributed in diverse marine environments (Ivanova and Mikhailov 2001; Koops and Pommerening-Röser 2015; Mussmann et al. 2017). A metagenomic analysis revealed that the JTB255 marine benthic group is heterogeneous and covers a broad physiological spectrum, ranging from facultative sulfur- and hydrogen-based chemolithoautotrophy to obligate chemorganoheterotrophy (Mussmann et al. 2017). However, it is not clear if the JTB255 marine benthic group in our sediments was able to oxidize sulfur, because of the lack of both isolates and metagenomic analysis in our study. The ammonia-oxidizing bacteria genus Nitrosococcus represented 3.1% in total communities and accounted for most bacterial ammonia oxidizers in our sediments (3.5%), whereas this genus comprised 4.7% of populations in bathyal plain sediments from the Menez Gwen hydrothermal vent system of the Mid-Atlantic Ridge (Cerqueira et al. 2015). In contrast, the ammonia-oxidizing archaea Marine Group Ⅰ remained as a relatively stable population among all sites (Fig. 2b). Despite the competition over ammonia, there was not any significant negative correlation between the abundances of these two groups (Pearson's r=− 0.12, p value=0.6392) because of the complex effect of multiple environmental factors—the interaction of ammonia concentration, pH, organic matter, and temperature (Liu 2017). Besides, the fluctuation of pmoA/amoA gene copies per 16S rRNA gene of archaea suggested a shift of subgroups occupying individual niches (Fig. S5).

    The microbial communities of Tianzuo harbored a high proportion of bacteria, which were mainly attributed to the class Nitrospira, being able to oxidize nitrite (Fig. 4a). Compared with Thaumarchaeota, nitrite oxidizers of this group were important contributors to global dark carbon fixation with a greater metabolic efficiency, owing to their less costly carbon fixation through the rTCA cycle (Pachiadaki et al. 2017). In addition to its ability of oxidizing nitrite, Nitrospira exhibited diverse metabolic potential, such as nitrate reduction, ureolysis, ammonia oxidation and sulfide oxidation, giving rise to its worldwide success (Daims et al. 2015; Füssel et al. 2017; Koch et al. 2015). Although the database of FAPROTAX assigns Nitrospira nitrosa, Nitrospira nitrificans and Nitrospira inopinata as ammonia-oxidizers, OTUs in our samples did not belong to these three species. Another metagenomic analysis of microbial communities in Rainbow hydrothermal vent fields verified this group as a potential nitrite-oxidizer, as suggested by the presence of genes encoding enzymes of the nitrification process (Cerqueira et al. 2018). Similarly, the sequences retrieved from the vent chimney of SWIR included diverse populations taking part in the N cycle, and included Nitrosococcus, Nitrospira, Nitratifractor, Nitrosomonas, and Rhizobiales (Ding et al. 2017). So, our results confirmed the importance of nitrification in the hydrothermal vent field of SWIR. Moreover, it was interesting to find that the narG/nxrA genes were much higher in deep layers, indicating a bloom of denitrifiers therein (Fig. 4b). In contrast to our sediments samples, the communities inhabiting the chimney were mainly governed by sulfur oxidizers other than ammonia oxidizers (Ding et al. 2017). For lack of sufficient sulfur flux from the active vent, the easily available nitrogen compounds would be a better choice for chemolithotrophic bacteria in our sediments.

  • Spatial patterns of the microbial communities

  • Organic flux to the sea floor broadly co-varies with the sedimentation rate. As an example, at very low sediment accumulation rate of SWIR (1 g/cm2 per thousand years), most organic matter is consumed at or near the sea floor (D'Hondt et al. 2015; Jahnke 1996). Thus, the intense change of microbial communities along quite a short depth of 26 cm in our sediments (Fig. 3a, c) could result from a decreasing flux rate of organic carbon along depth. After a certain depth, the species gradually adapts to the local environmental stress, commonly coupled with a relatively stable alpha diversity in sediments (Mahmoudi et al. 2015; Vuillemin et al. 2018). Although similar stable states of microbial communities were observed in our sediments, the fluctuation of alpha and beta diversity was somewhat larger than other samples (Figs. 1, 3). Specially, it was the class Actinobacteria that mainly contributed to the community variation in deep layers, and was even more abundant in deep layers, unlike most clades (Fig. 2a). Besides, this class appeared to be a special group when compared with other vents (Fig. 6a). The strong adaptation ability of Actinobacteria to the harsh environment of deep layers may be attributed to several reasons. For example, the genomic islands in its genome comprise gene clusters that could produce secondary metabolites to increase adaptation ability. The gene clusters are dynamic entities that are readily acquired, rearranged and fragmented in the context of genomic islands (Penn et al. 2009). As a result, fitness or niche utilization could be formed quickly through shifting the ability of producing diverse natural products. In addition, most of isolated Actinobacteria from the sediments of Southwest Indian Ocean showed the activity of using refractory organic carbon, which could fuel this group in energy-limited condition (Chen et al. 2016). In our work, sequences assigned to Actinobacteria were mostly affiliated with the order Corynebacteriales. This order was also the dominant Actinobacteria in sediments from an active hydrothermal vent field of SWIR, which may benefit from its ability in degrading polycyclic aromatic hydrocarbons, a commonly found substrate in hydrothermal vents (Chen et al. 2016). For the rare class (less than 1% of the total), Marinimicrobia (SAR406 clade) contributed a lot to the variation of communities (Fig. S3). This group was reported to possess variable energy metabolism and conservation strategies including utilization of nitrogen oxide, sulfur compounds, and methanol (Hawley et al. 2017), which may account for its enrichment in deep layers of our sediments.

    Microbial communities in the Tianzuo field had shaped specific microbial community structure compared with hydrothermal vent fields in the Mid-Atlantic Ridge, Arctic Mid-Ocean Ridge and west SWIR (Fig. 5). Although both Arctic Mid-Ocean Ridge and SWIR are ultraslow-spreading ridges, their microbial communities were not more similar compared with vent fields in the Mid-Atlantic Ridge, which is a slow-spreading ridge. This result suggested no overwhelming impact of spreading speed on microbial communities whereas local environmental characteristics should have played a more important role in shaping specific community structure. Similarly, microbial communities in the Juan de Fuca Ridge showed a distinct structure among six vents while Epsilonproteobacteria was important in distinguishing the community structure (Opatkiewicz et al. 2009). Besides, the distinct pattern revealed by both functional genes and 16S rRNA genes was observed in Atlantic hydrothermal systems whereas the similar pattern of most vents was the detection of abundant populations participating in H2 and methane oxidation (Roussel et al. 2011). The global distribution and comparation of microbial communities along ocean ridges remained unclear. Bachraty's research on deep-sea hydrothermal vent faunas delineated six major hydrothermal provinces and possible dispersal pathways (Bachraty et al. 2009), which provided a good research direction for the global study on microbial communities in vent fields. Also, our results identified Actinobacteria and Phycisphaerae in distinguishing the microbial communities of the Tianzuo field from other fields (Fig. 6a). Similar with Actinobacteria, isolates of Phycisphaerae have been shown to be capable of degrading a variety of different polysaccharides (Fukunaga et al. 2009; Kovaleva et al. 2015). Additionally, Phycisphaerae was observed as one of the most abundant classes in hydrothermal vent fields along the Valu Fa Ridge in southwestern Pacific and iron-hydroxide deposits from the Arctic Mid-Ocean Ridge (Storesund et al. 2018; Storesund and Ovreas 2013). Our results suggested Phycisphaerae may be restricted in distribution in hydrothermal systems, for which the reason requires intensive research.

  • Limitation of methods

  • In our study, FAPROTAX supplied a fast and reliable method to access the functional groups according to the confirmed characteristics of the isolates. Compared with another widely used function prediction method PICRUSt with genomes as reference (Douglas et al. 2019; Langille et al. 2013), FAPROTAX was more reliable and direct owing to experimental verification. Furthermore, PICRUSt assumes the close relatives of published genomes share partial genes, whereas FAPROTAX is more conserved to assume the close relatives of published isolates share similar functions only if all isolates in this taxon possess the function. As a result of predicting the function of phylogenic distant OTUs, the high weighted NSTI of PICRUST for our samples suggested a low accuracy. However, FAPROTAX is supposed to underestimate the abundance of each group due to relatively fewer isolates compared with the huge number of uncultured species. Indeed, our result showed that only 2870 OTUs could be assigned to functional groups out of a total of 13, 417 OTUs. As an additional method for groups with few isolates, PICRUSt could be applied to predict the functional genes in archaea. It was quite difficult to calculate the percentages of OTUs for each function using PICRUSt like FAPROTAX. Instead, we calculated the functional gene copies per 16S rRNA gene in our samples, which provided an indirect estimation of functional groups (Fig. 4b, Fig. S5). So, an integrated method combining both characteristics of cultured species and genome information is required for a more detailed and reliable analysis based on the phylogenic information, which would be helpful in filling the gap between phylogenic diversity and function diversity. In addition, considering the continuous change of microbial community along depth, our samples as well as those of other previous work in SWIR (Ding et al. 2017; Peng et al. 2011; Zhang et al. 2016) were mostly restricted to the surface part of this area, which were far from revealing the great diversity below the sea floor. In contrast, the Integrated Ocean Drilling Program (IODP) Expedition revealed that microbial communities buried deeply below the sea floor differed markedly from shallower sub-seafloor communities and instead resembled organotrophic communities in forest soils (Inagaki et al. 2015). As SWIR is associated with cold mantle temperatures and thick lithosphere (German et al. 1998), the microorganisms may inhabit and bloom in deeper layers than at the fast-spreading edge, due to the modest temperature boundary of SWIR for microbes to survive. Therefore, a deeper sampling depth below the sea floor and better annotation to the retrieved sequences from samples may be required for a clear and full comprehension on the role and diversity of microbial communities in SWIR.

  • High-throughput sequencing revealed a total of 13, 417 bacterial OTUs and 13, 540 archaeal OTUs from the sediments of the Tianzuo hydrothermal field. Both bacterial and archaeal compositions were similar at the surface layer of three sampling sites around Tianzuo, whereas the deep layers of the south sampling site showed a gradient change along depth. Gammaproteobacteria and Actinobacteria were the main bacteria classes, whereas Marine group Ⅰ accounted for at least 87.7% of all archaea among all samples. The bacteria capable of ammonia oxidation and nitrite oxidation were the most abundant chemolithoautotrophic groups. In addition, sulfur-oxidizing bacteria, such as Epsilonproteobacteria and Mn(Ⅱ)-oxidizing bacteria, occurred as enriched populations in deep layers of the sediments. Through comparison of microbial communities with other vent fields, the Tianzuo field showed a significant distinct composition of microbial communities. In addition, Actinobacteria and Phycisphaerae played an important role in distinguishing microbial communities of Tianzuo from other vents. To our knowledge, this provides the first view of microbial diversity and the metabolic characteristics of sediments in the Tianzuo hydrothermal vent field along the Southwest Indian Ridge.

Materials and methods

    Study site

  • In December, 2014 during the Dayang 35 cruise to the Tianzuo field, three sediment cores were sampled at the south, north, west of the Tianzuo hydrothermal field (63.541°E, 27.951°S) using the manned submersible Jiao Long. Temperature and depth of the sampling sites were measured using CTD. Soon after the sediments were acquired on board, the sediment core of 26 cm depth sampled at the south was immediately divided into 13 layers every 2 cm, and the sediment core of 8 cm depth sampled at the west was immediately divided into 4 layers every 2 cm. The sediment core of 5 cm depth sampled at the north was not divided. Then, the sediments were fully mixed with 2 volumes of RNAlater (Thermo Fisher, USA) and stored at 4 ℃ on board. Finally, the material was transported and stored at -80 ℃ in the laboratory until processing.

  • DNA extracting and sequencing

  • After being fully mixed, ~0.5 g sediments were used to extract environmental DNA using the FastDNA SPIN Kit for Soil (MP Biomedicals, USA) following manufacturer's instructions. The DNA concentration was then measured by a Nanodrop 2000 (Thermo Scientific, USA). The V4 region of the bacterial 16S rRNA gene was amplified using the primers 533F (5′-TGCCAGCAGCCGCGGTAA-3 and Bact 806R (5′-G GACTACCAGGGTATCTAATCCTGTT-3′) (Klindworth et al. 2013) and V4-V5 region of the archaeal 16S rRNA gene was amplified using the primer Arch516F (5′-TGYCAGCCGCCGCGGTAAHACCVGC-3′) and Arch855R (5′-TCCCCCGCCAATTCCTTTAA-3′) (Klindworth et al. 2013) with a 8 bp unique barcode at 5′ end of forward primer. PCR was carried out in triplicate 50 μl reactions using 10-50 ng of DNA. Thermal cycling conditions for bacterial 16S rRNA gene consisted of initial denaturation at 94 ℃ for 5 min followed by 25 cycles of denaturation at 94 ℃ for 30 s, annealing at 58 ℃ for 40 s, and extension at 72 ℃ for 30 s with a final extension at 72 ℃ for 10 min. The thermal cycling conditions for archaeal 16S rRNA gene consisted of initial denaturation at 94 ℃ for 5 min followed by 35 cycles of denaturation at 94 ℃ for 30 s, annealing at 58 ℃ for 40 s, and extension at 72 ℃ for 45 s with a final extension at 72 ℃ for 10 min. PCR products were gel-purified using an EZNA Gel Extraction Kit (Omega Bio-Tek, Inc., USA). The purified DNA was sequenced on the Illumina MiSeq platform by Personalbio Biotechnology company (Shanghai, China). All sequences have been deposited in NCBI SRA under the BioProject accession number PRJNA558519.

  • Sequence processing

  • The paired-end FASTQ reads were quality-filtered by sliding windows of 5 bp with 1 bp per step, and the remaining bases had an average quality of more than Q20 for each window. Filtered reads over 150 bp remained, and no N (ambiguous base) was included in the reads. FLASH was then used to merge the paired-end reads with an overlap of more than 10 bp (Magoč and Salzberg 2011). Then, we used QIIME (Caporaso et al. 2010) and VSEARCH (Rognes et al. 2016) to eliminate chimeric sequences and pick operational taxonomic units (OTUs) for 97% similarity from the reads. Then, the OTUs were assigned a taxonomy according to the SILVA database (123 release) (Quast et al. 2013). Non-specific amplification of OTUs was eliminated from the reads. The rarefaction curve of sequences was plotted using alpha_rarefaction.py script in QIIME. Then, we randomly sampled 19453 reads per sample for bacteria and 21215 reads per sample for archaea to eliminate the effect of reads number when comparing alpha diversity and beta diversity between samples.

  • Data analysis

  • Most data analysis and visualization was done using R language (R Core Team 2019). The alpha diversity of archaeal and bacterial communities is estimated by calculating the total number of OTUs per sample, Shannon diversity index, Pielou's evenness index, the Gini unevenness index, and Chao1 diversity index (Chao and Shen 2003; Heip et al. 1998; Kemp and Aller 2004; Naeem 2009; Ricotta and Avena 2003). Due to the high diversity of bacterial classes, the relative abundance of bacterial classes in different samples was plotted to show the classes over 1% and below 1%, respectively. For limited number of archaeal classes, we plotted the relative abundance of all archaeal classes. The Bray-Curtis distance was calculated to show the beta diversity among communities through NMDS (Borcard et al. 2011).

    The relative abundance of species possessing the metabolic potential of oxidizing hydrogen, methane, nitrogen compounds, sulfur compounds, and manganese in each sample was estimated using FAPROTAX (version 1.1) (Louca et al. 2016). FAPROTAX is a manually constructed database that maps prokaryotic taxa (e.g., genera or species) to metabolic or other ecologically relevant functions (e.g., nitrification, denitrification or fermentation), based on the literature on cultured representatives. For example, if all cultured species within a bacterial genus (or more precisely, all type strains of species) have been identified as denitrifiers, FAPROTAX assumes that all uncultured members of that genus are also denitrifiers. And the database in FAPROTAX contained 7820 functional annotations covering 4724 taxa, which would facilitate our analysis of potential function and should be more comprehensive than one-by-one annotation by ourselves. PICRUSt (PICRUSt2: a new version) was also applied to predict the abundance of functional genes in bacterial communities and archaeal communities (Douglas et al. 2019). Considering that the predicted read counts for each sample did not represent the abundance of functional groups, we divided the predicted counts of each functional gene by the total 16S rRNA gene counts. Thus, the average copies per 16S rRNA gene of functional genes could be then used to compare the weight of functional genes in different communities.

    To compare the community composition of Tianzuo field with other hydrothermal vent fields, we downloaded the sequencing data of biofilms or sediments in six hydrothermal vents located at Mid-Atlantic Ridge (Cerqueira et al. 2017), Arctic Mid-Ocean Ridge (Dahle et al. 2015) and west SWIR (Zhang et al. 2016). The quality filter was processed following similar methods for our samples. Due to the difference of primer sets used in different researches, when clustering the OTUs for 97% similarity, we used SILVA database as the reference through the pick_close_otus.py script in QIIME rather than clustering our sequences against each other. The samples with over 500 reads clustered into OTUs remained for later analysis. Then, we could compare the OTUs between samples with enough reads and consistent OTU names. Other steps followed the methods we used for our data. NMDS plot was also applied to show the community variation between different vent fields. Besides, ANOSIM was applied to test statistically whether there is a significant difference between communities of six locations (Clarke 1993). The relative abundance of bacterial classes and archaeal classes over 1% was also plotted to show if there were special populations in the Tianzuo field.

  • This work was supported by the National Science Foundation of China (41530967, 41776173, 41576129) and the National Key Research and Development Program of China (2016YFC03007). We acknowledge the crew of the Dayang 35th cruise.

Author contributions
  • XX and YZ designed the experiments and collected the samples. ZY performed the experiments and analyzed the data. ZY, XX and YZ wrote the paper. The final manuscript was approved by all the authors

Compliance with ethical standards

    Conflicts of interest

  • The authors declare that they have no conflict of interest.

  • Animal and human rights statement

  • This article does not contain any studies with human participants or animals performed by any of the authors.

Reference (77)



DownLoad:  Full-Size Img  PowerPoint