Advertisement

Diversity and functional profile of bacterial communities at Lancaster acid mine drainage dam, South Africa as revealed by 16S rRNA gene high-throughput sequencing analysis

  • Thabile Lukhele
  • Ramganesh Selvarajan
  • Hlengilizwe Nyoni
  • Bheki Brilliance Mamba
  • Titus Alfred Makudali MsagatiEmail author
Original Paper

Abstract

This study surveyed physicochemical properties and bacterial community structure of water and sediments from an acid mine drainage (AMD) dam in South Africa. High-throughput sequence analysis revealed low diversity bacterial communities affiliated within 8 dominant phyla; Acidobacteria, Actinobacteria, Chloroflexi, Firmicutes, Nitrospirae, Proteobacteria, Saccharibacteria, and ca. TM6_(Dependentiae). Acidiphilium spp. which are common AMD inhabitants but rarely occur as dominant taxa, were the most abundant in both AMD water and sediments. Other groups making up the community are less common AMD inhabitants; Acidibacillus, Acidibacter, Acidobacterium, Acidothermus, Legionella, Metallibacterium, Mycobacterium, as well as elusive taxa (Saccharibacteria, ca. TM6_(Dependentiae) and ca. JG37-AG-4). Although most of the taxa are shared between sediment and water communities, alpha diversity indices indicate a higher species richness in the sediments. From canonical correspondence analysis, DOC, Mn, Cu, Cr, Al, Fe, Ca were identified as important determinants of community structure in water, compared to DOC, Ca, Cu, Fe, Zn, Mg, K, Mn, Al, sulfates, and nitrates in sediments. Predictive functional profiling recovered genes associated with bacterial growth and those related to survival and adaptation to the harsh environmental conditions. Overall, the study reports on a distinct AMD bacterial community and highlights sediments as microhabitats with higher species richness than water.

Keywords

Acid mine drainage 16S rRNA gene High-throughput sequencing Bacterial diversity 

Notes

Acknowledgements

The work was supported by the University of South Africa and the National Research Foundation of South Africa through a Grant (SFH170705248759), also authors would like to acknowledge Center for High Performance (CHPC), Pretoria for providing computational support for Metagenomic analysis.

Supplementary material

792_2019_1130_MOESM1_ESM.docx (116 kb)
Supplementary material 1 (DOCX 115 kb)

References

  1. Johnson DB, Aguilera A (2016) The microbiology of extremely acidic environments. In: Yates M, Nakatsu C, Miller R, Pillai S (eds) Manual of environmental microbiology, 4th edn. ASM Press, Washington, DC, pp 4.3.1-1-4.3.1-24.  https://doi.org/10.1128/9781555818821.ch4.3.1.
  2. Akcil A, Koldas S (2006) Acid mine drainage (AMD): causes, treatment and case studies. J Clean Prod 14:1139–1145.  https://doi.org/10.1016/j.jclepro.2004.09.006 Google Scholar
  3. Alvarez S, Jerez CA (2004) Copper ions stimulate polyphosphate degradation and phosphate efflux in Acidithiobacillus ferrooxidans. Appl Environ Microbiol 70:5177–5182.  https://doi.org/10.1128/AEM.70.9.5177-5182.2004 Google Scholar
  4. Auld RR, Myre M, Mykytczuk NCS et al (2013) Characterization of the microbial acid mine drainage microbial community using culturing and direct sequencing techniques. J Microbiol Methods 93:108–115.  https://doi.org/10.1016/j.mimet.2013.01.023 Google Scholar
  5. Auld R, Mykytczuk N, Leduc L, Merritt T (2017) Seasonal variation in an acid mine drainage microbial community. Can J Microbiol 63:137–152.  https://doi.org/10.1139/cjm-2016-0215 Google Scholar
  6. Baker BJ, Banfield JF (2003) Microbial communities in acid mine drainage. FEMS Microbiol Ecol 44:139–152.  https://doi.org/10.1016/S0168-6496(03)00028-X Google Scholar
  7. Balintova M, Petrilakova A, Singovszka E (2012) Study of metals distribution between water and sediment in the Smolnik Creek (Slovakia) contaminated by acid mine drainage. Chem Eng Trans 28:73–78.  https://doi.org/10.3303/CET1228013 Google Scholar
  8. Barabote RD, Xie G, Leu DH et al (2009) Complete genome of the cellulolytic thermophile Acidothermus cellulolyticus IIB provides insights into its ecophysiological and evolutionary adaptations. Genome Res 19:1033–1042.  https://doi.org/10.1101/gr.084848.108 Google Scholar
  9. Brantner JS, Senko JM (2014) Response of soil-associated microbial communities to intrusion of coal mine-derived acid mine drainage. Environ Sci Technol 48:8556–8563.  https://doi.org/10.1021/es502261u Google Scholar
  10. Brantner JS, Haake ZJ, Burwick JE et al (2014) Depth-dependent geochemical and microbiological gradients in Fe(III) deposits resulting from coal mine-derived acid mine drainage. Front Microbiol 5:1–15.  https://doi.org/10.3389/fmicb.2014.00215 Google Scholar
  11. Bruneel O, Duran R, Casiot C et al (2006) Diversity of microorganisms in Fe-As-rich acid mine drainage waters of Carnoulès, France. Appl Environ Microbiol 72:551–556.  https://doi.org/10.1128/AEM.72.1.551-556.2006 Google Scholar
  12. Caporaso JG, Lauber CL, Walters WA et al (2011) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci 108:4516–4522.  https://doi.org/10.1073/pnas.1000080107 Google Scholar
  13. Chen L, Huang L, Méndez-García C et al (2016) Microbial communities, processes and functions in acid mine drainage ecosystems. Curr Opin Biotechnol 38:150–158.  https://doi.org/10.1016/j.copbio.2016.01.013 Google Scholar
  14. Crossman L, Holden M, Pain A, Parkhill J (2004) Genomes beyond compare. Nat Rev Microbiol 2:616–617.  https://doi.org/10.1038/nrmicro961 Google Scholar
  15. De Mandal S, Panda AK, Bisht SS, Kumar NS (2015) Microbial ecology in the era of next generation sequencing. J Next Gener Seq Appl 01:1–6.  https://doi.org/10.4172/2469-9853.S1-001 Google Scholar
  16. Denef VJ, Mueller RS, Banfield JF (2010) AMD biofilms: using model communities to study microbial evolution and ecological complexity in nature. ISME J 4:599–610.  https://doi.org/10.1038/ismej.2009.158 Google Scholar
  17. Dopson M, Baker-Austin C, Koppineedi PR, Bond PL (2003) Growth in sulfidic mineral environments: metal resistance mechanisms in acidophilic micro-organisms. Microbiology 149:1959–1970.  https://doi.org/10.1099/mic.0.26296-0 Google Scholar
  18. Dopson M, Ossandon FJ, Lövgren L, Holmes DS (2014) Metal resistance or tolerance? Acidophiles confront high metal loads via both abiotic and biotic mechanisms. Front Microbiol 5:10–13.  https://doi.org/10.3389/fmicb.2014.00157 Google Scholar
  19. Druschel GK, Baker BJ, Gihring TM, Banfield JF (2004) Acid mine drainage biogeochemistry at Iron Mountain, California. Geochem Trans 5:13–32.  https://doi.org/10.1063/1.1769131 Google Scholar
  20. Edgar RC, Haas BJ, Clemente JC et al (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200.  https://doi.org/10.1093/bioinformatics/btr381 Google Scholar
  21. Edwards KJ, Bond PL, Druschel GK et al (2000) Geochemical and biological aspects of sulfide mineral dissolution: lessons from Iron Mountain, California. Chem Geol 169:383–397.  https://doi.org/10.1016/S0009-2541(00)00216-3 Google Scholar
  22. Escudero LV, Casamayor EO, Chong G et al (2013) Distribution of microbial arsenic reduction, oxidation and extrusion genes along a wide range of environmental arsenic concentrations. PLoS One 8:e78890.  https://doi.org/10.1371/journal.pone.0078890 Google Scholar
  23. Falagán C, Sánchez-España J, Johnson DB (2014) New insights into the biogeochemistry of extremely acidic environments revealed by a combined cultivation-based and culture-independent study of two stratified pit lakes. FEMS Microbiol Ecol 87:231–243Google Scholar
  24. Falkinham JO (2002) Nontuberculous Mycobacteria in the environment. Clin Chest Med 23(3):529–551.  https://doi.org/10.1016/S0272-5231(02)00014-X Google Scholar
  25. Falteisek L, Duchoslav V, Čepička I (2016) Substantial variability of multiple microbial communities collected at similar acidic mine water outlets. Microb Ecol 72:163–174.  https://doi.org/10.1007/s00248-016-0760-6 Google Scholar
  26. Ferrari B, Winsley T, Ji M, Neilan B (2014) Insights into the distribution and abundance of the ubiquitous candidatus Saccharibacteria phylum following tag pyrosequencing. Sci Rep 4:1–9.  https://doi.org/10.1038/srep03957 Google Scholar
  27. Fischer CR, Wilmes P, Bowen BP et al (2012) Deuterium-exchange metabolomics identifies N-methyl lyso phosphatidylethanolamines as abundant lipids in acidophilic mixed microbial communities. Metabolomics 8:566–578.  https://doi.org/10.1007/s11306-011-0344-x Google Scholar
  28. García-Moyano A, González-Toril E, Aguilera Á, Amils R (2012) Comparative microbial ecology study of the sediments and the water column of the Río Tinto, an extreme acidic environment. FEMS Microbiol Ecol 81:303–314.  https://doi.org/10.1111/j.1574-6941.2012.01346.x Google Scholar
  29. García-Moyano A, Austnes A, Lanzén A et al (2015) Novel and unexpected microbial diversity in acid mine drainage in Svalbard (78° N), revealed by culture-independent approaches. Microorganisms 3:667–694.  https://doi.org/10.3390/microorganisms3040667 Google Scholar
  30. González-Toril E, Llobet-Brossa E, Casamayor EO et al (2003) Microbial ecology of an extreme acidic environment, the Tinto River. Appl Environ Microbiol 69:4853–4865.  https://doi.org/10.1128/AEM.69.8.4853-4865.2003 Google Scholar
  31. González-Toril E, Santofimia E, López-Pamo E et al (2014) Comparative microbial ecology of the water column of an extreme acidic pit lake, Nuestra Señora del Carmen, and the Río Tinto basin (Iberian Pyrite Belt). Int Microbiol 17:225–233.  https://doi.org/10.2436/20.1501.01.225 Google Scholar
  32. Gupta A, Dutta A, Sarkar J et al (2017) Metagenomic exploration of microbial community in mine tailings of Malanjkhand copper project, India. Genom Data 12:11–13.  https://doi.org/10.1016/j.gdata.2017.02.004 Google Scholar
  33. Hallberg KB (2010) New perspectives in acid mine drainage microbiology. Hydrometallurgy 104:448–453.  https://doi.org/10.1016/j.hydromet.2009.12.013 Google Scholar
  34. Hammer Ø, Harper DAT, Ryan PD (2001) PAST: paleontological statistics software package. Palaeontol Electron 4:1–9.  https://doi.org/10.1016/j.bcp.2008.05.025 Google Scholar
  35. Hao C, Wang L, Gao Y et al (2010) Microbial diversity in acid mine drainage of Xiang Mountain sulfide mine, Anhui Province, China. Extremophiles 14:465–474.  https://doi.org/10.1007/s00792-010-0324-5 Google Scholar
  36. Hao C, Zhang L, Wang L et al (2012) Microbial community composition in acid mine drainage lake of Xiang Mountain Sulfide Mine in Anhui Province, China. Geomicrobiol J 29:886–895.  https://doi.org/10.1080/01490451.2011.635762 Google Scholar
  37. Hao C, Wei P, Pei L et al (2017) Significant seasonal variations of microbial community in an acid mine drainage lake in Anhui Province, China. Environ Pollut 223:507–516.  https://doi.org/10.1016/j.envpol.2017.01.052 Google Scholar
  38. He Z, Xiao S, Xie X et al (2007) Molecular diversity of microbial community in acid mine drainages of Yunfu sulfide mine. Extremophiles 11:305–314.  https://doi.org/10.1007/s00792-006-0044-z Google Scholar
  39. Huang LN, Kuang JL, Shu WS (2016) Microbial ecology and evolution in the acid mine drainage model system. Trends Microbiol 24:581–593.  https://doi.org/10.1016/j.tim.2016.03.004 Google Scholar
  40. Huse SM, Welch DM, Morrison HG, Sogin ML (2010) Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ Microbiol 12:1889–1898.  https://doi.org/10.1111/j.1462-2920.2010.02193.x Google Scholar
  41. Iivanainen EK, Martikainen PJ, Räisänen ML, Katila ML (1997) Mycobacteria in boreal coniferous forest soils. FEMS Microbiol Ecol 23:325–332.  https://doi.org/10.1016/S0168-6496(97)00040-8 Google Scholar
  42. Johnson DB, Hallberg KB (2003) The microbiology of acidic mine waters. Res Microbiol 154:466–473.  https://doi.org/10.1016/S0923-2508(03)00114-1 Google Scholar
  43. Johnson DB, Hallberg KB (2005) Acid mine drainage remediation options: a review. Sci Total Environ 338:3–14.  https://doi.org/10.1016/j.scitotenv.2004.09.002 Google Scholar
  44. Johnson DB, Hallberg KB (2007) Techniques for detecting and identifying acidophilic mineral-oxidizing microorganisms. Biomining.  https://doi.org/10.1007/978-3-540-34911-2_12 Google Scholar
  45. Johnson DB, Okibe N, Hallberg KB (2005) Differentiation and identification of iron-oxidizing acidophilic bacteria using cultivation techniques and amplified ribosomal DNA restriction enzyme analysis. J Microbiol Methods 60:299–313.  https://doi.org/10.1016/j.mimet.2004.10.002 Google Scholar
  46. Johnson M, Zaretskaya I, Raytselis Y et al (2008) NCBI BLAST: a better web interface. Nucleic Acids Res 36:5–9.  https://doi.org/10.1093/nar/gkn201 Google Scholar
  47. Kamika I, Momba MNB (2014) Microbial diversity of Emalahleni mine water in South Africa and tolerance ability of the predominant organism to vanadium and nickel. PLoS One 9:e86189.  https://doi.org/10.1371/journal.pone.0086189 Google Scholar
  48. Kamika I, Azizi S, Tekere M (2016) Microbial profiling of South African acid mine water samples using next generation sequencing platform. Appl Microbiol Biotechnol 100:6069–6079.  https://doi.org/10.1007/s00253-016-7428-5 Google Scholar
  49. Kay C, Rowe O, Rocchetti L et al (2013) Evolution of microbial “streamer” growths in an acidic, metal-contaminated stream draining an abandoned underground copper mine. Life 3:189–211.  https://doi.org/10.3390/life3010189 Google Scholar
  50. Kay CM, Haanela A, Johnson DB (2014) Microorganisms in subterranean acidic waters within Europe’s deepest metal mine. Res Microbiol 165:705–712.  https://doi.org/10.1016/j.resmic.2014.07.007 Google Scholar
  51. Keshri J, Mankazana BBJ, Momba MNB (2015) Profile of bacterial communities in South African mine-water samples using Illumina next-generation sequencing platform. Appl Microbiol Biotechnol 99:3233–3242.  https://doi.org/10.1007/s00253-014-6213-6 Google Scholar
  52. Kuang JL, Huang LN, Chen LX et al (2013) Contemporary environmental variation determines microbial diversity patterns in acid mine drainage. ISME J 7:1038–1050.  https://doi.org/10.1038/ismej.2012.139 Google Scholar
  53. Kuang J, Huang L, He Z et al (2016) Predicting taxonomic and functional structure of microbial communities in acid mine drainage. ISME J 10:1527–1539.  https://doi.org/10.1038/ismej.2015.201 Google Scholar
  54. Kuyucak N (2002) Role of microorganisms in mining: generation of acid rock drainage and its mitigation and treatment. Eur J Miner Process Environ Prot 2:179–196Google Scholar
  55. Langille MGI, Zaneveld J, Caporaso JG et al (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31:814–821.  https://doi.org/10.1038/nbt.2676 Google Scholar
  56. Liang J-L, Li X-J, Shu H-Y et al (2017) Fine-scale spatial patterns in microbial community composition in an acid mine drainage. FEMS Microbiol Ecol 93:1–8.  https://doi.org/10.1093/femsec/fix124 Google Scholar
  57. Liu J, Hua ZS, Chen LX et al (2014) Correlating microbial diversity patterns with geochemistry in an extreme and heterogeneous environment of mine tailings. Appl Environ Microbiol 80:3677–3686.  https://doi.org/10.1128/AEM.00294-14 Google Scholar
  58. Logares R, Haverkamp THA, Kumar S et al (2012) Environmental microbiology through the lens of high-throughput DNA sequencing: synopsis of current platforms and bioinformatics approaches. J Microbiol Methods 91:106–113.  https://doi.org/10.1016/j.mimet.2012.07.017 Google Scholar
  59. Lozupone CA, Knight R (2005) UniFrac: a new phylogenetic method for omparing microbial communities. Appl Environ Microbiol 71:8228–8235.  https://doi.org/10.1128/AEM.71.12.8228 Google Scholar
  60. McLean JS, Lombardo M-J, Badger JH et al (2013) Candidate phylum TM6 genome recovered from a hospital sink biofilm provides genomic insights into this uncultivated phylum. Proc Natl Acad Sci 110:E2390–E2399.  https://doi.org/10.1073/pnas.1219809110 Google Scholar
  61. Méndez-García C, Mesa V, Sprenger RR et al (2014) Microbial stratification in low pH oxic and suboxic macroscopic growths along an acid mine drainage. ISME J 8:1259–1274.  https://doi.org/10.1038/ismej.2013.242 Google Scholar
  62. Méndez-García C, Peláez AI, Mesa V et al (2015) Microbial diversity and metabolic networks in acid mine drainage habitats. Front Microbiol 6:1–17.  https://doi.org/10.3389/fmicb.2015.00475 Google Scholar
  63. Mesa V, Gallego JLR, González-Gil R et al (2017) Bacterial, archaeal, and eukaryotic diversity across distinct microhabitats in an acid mine drainage. Front Microbiol 8:1–17.  https://doi.org/10.3389/fmicb.2017.01756 Google Scholar
  64. Michels M, Bakker EP (1985) Generation of a large, protonophore-sensitive proton motive force and pH difference in the acidophilic bacteria Thermoplasma acidophilum and Bacillus acidocaldarius. J Bacteriol 161:231–237Google Scholar
  65. Mosier AC, Justice NB, Bowen BP et al (2013) Metabolites associated with adaptation of microorganisms to an acidophilic, metal-rich environment identified by stable-isotope-enabled metabolomics. MBio 4:1–8.  https://doi.org/10.1128/mBio.00484-12 Google Scholar
  66. Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:590–596.  https://doi.org/10.1093/nar/gks1219 Google Scholar
  67. Ram RJ, Verberkmoes NC, Thelen MP et al (2005) Community proteomics of a natural microbial biofilm. Science 308:1915–1920.  https://doi.org/10.1126/science.1109070 Google Scholar
  68. Rzhepishevska OI, Lindström EB, Tuovinen OH, Dopson M (2005) Bioleaching of sulfidic tailing samples with a novel, vacuum-positive pressure driven bioreactor. Biotechnol Bioeng 92:559–567.  https://doi.org/10.1002/bit.20609 Google Scholar
  69. Sajjad W, Zheng G, Zhang G et al (2018) Diversity of prokaryotic communities indigenous to acid mine drainage and related rocks from Baiyin Open-Pit Copper Mine Stope, China. Geomicrobiol J 35:580–600.  https://doi.org/10.1080/01490451.2018.1430873 Google Scholar
  70. Sánchez-Andrea I, Rodríguez N, Amils R, Sanz JL (2011) Microbial diversity in anaerobic sediments at Río Tinto, a naturally acidic environment with a high heavy metal content. Appl Environ Microbiol 77:6085–6093.  https://doi.org/10.1128/aem.00654-11 Google Scholar
  71. Sanz JL, Rodríguez N, Díaz EE, Amils R (2011) Methanogenesis in the sediments of Rio Tinto, an extreme acidic river. Environ Microbiol 13:2336–2341.  https://doi.org/10.1111/j.1462-2920.2011.02504.x Google Scholar
  72. Schloss PD, Westcott SL, Ryabin T et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541.  https://doi.org/10.1128/AEM.01541-09 Google Scholar
  73. Selenska-Pobell S, Kampf G, Flemming K et al (2001) Bacterial diversity in soil samples from two uranium waste piles as determined by rep-APD, RISA and 16S rDNA retrieval. Antonie van Leeuwenhoek. Int J Gen Mol Microbiol 79:149–161.  https://doi.org/10.1023/A:1010237711077 Google Scholar
  74. Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol 26:1135–1145.  https://doi.org/10.1038/nbt1486 Google Scholar
  75. Sibanda T, Selvarajan R, Msagati T et al (2019) Defunct gold mine tailings are natural reservoir for unique bacterial communities revealed by high-throughput sequencing analysis. Sci Total Environ 650:2199–2209.  https://doi.org/10.1016/j.scitotenv.2018.09.380 Google Scholar
  76. Slonczewski JL, Fujisawa M, Dopson M, Krulwich TA (2009) Cytoplasmic pH measurement and homeostasis in bacteria and archaea. Adv Microb Physiol 55:1–317.  https://doi.org/10.1016/S0065-2911(09)05501-5 Google Scholar
  77. Streten-Joyce C, Manning J, Gibb KS et al (2013) The chemical composition and bacteria communities in acid and metalliferous drainage from the wet-dry tropics are dependent on season. Sci Total Environ 443:65–79.  https://doi.org/10.1016/j.scitotenv.2012.10.024 Google Scholar
  78. Sun W, Xiao T, Sun M et al (2015) Diversity of the sediment microbial community in the Aha watershed (Southwest China) in response to acid mine drainage pollution gradients. Appl Environ Microbiol 81:4874–4884.  https://doi.org/10.1128/AEM.00935-15 Google Scholar
  79. Sun W, Xiao E, Krumins V et al (2016) Characterization of the microbial community composition and the distribution of Fe-metabolizing bacteria in a creek contaminated by acid mine drainage. Appl Microbiol Biotechnol 100:8523–8535.  https://doi.org/10.1007/s00253-016-7653-y Google Scholar
  80. Tittel J, Bissinger V, Gaedke U, Kamjunke N (2005) Inorganic carbon limitation and mixotrophic growth in Chlamydomonas from an acidic mining lake. Protist 156:63–75.  https://doi.org/10.1016/j.protis.2004.09.001 Google Scholar
  81. Tyson GW, Chapman J, Hugenholtz P et al (2004) Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428:37–43.  https://doi.org/10.1038/nature02340 Google Scholar
  82. Volant A, Bruneel O, Desoeuvre A et al (2014) Diversity and spatiotemporal dynamics of bacterial communities: physicochemical and other drivers along an acid mine drainage. FEMS Microbiol Ecol 90:247–263.  https://doi.org/10.1111/1574-6941.12394 Google Scholar
  83. Wakao N, Yasuda T, Jojima Y et al (2002) Enhanced growth of Acidocella facilis and related acidophilic bacteria at high concentrations of aluminum. Microbes Environ 17:98–104.  https://doi.org/10.1264/jsme2.2002.98 Google Scholar
  84. Walker JJ, Spear JR, Pace NR (2005) Geobiology of a microbiol endolithic community in the Yellowstone geothermal environment. Nature 434:1011–1014.  https://doi.org/10.1038/nature03447 Google Scholar
  85. Winsley TJ, Snape I, McKinlay J et al (2014) The ecological controls on the prevalence of candidate division TM7 in polar regions. Front Microbiol 5:1–10.  https://doi.org/10.3389/fmicb.2014.00345 Google Scholar
  86. Yang Y, Li Y, Sun QY (2014) Archaeal and bacterial communities in acid mine drainage from metal-rich abandoned tailing ponds, Tongling, China. Trans Nonferrous Met Soc China (English Ed) 24:3332–3342.  https://doi.org/10.1016/s1003-6326(14)63474-9 Google Scholar
  87. Yeoh YK, Sekiguchi Y, Parks DH, Hugenholtz P (2016) Comparative genomics of candidate phylum TM6 suggests that parasitism is widespread and ancestral in this lineage. Mol Biol Evol 33:915–927.  https://doi.org/10.1093/molbev/msv281 Google Scholar
  88. Yergeau E, Bokhorst S, Kang S et al (2012) Shifts in soil microorganisms in response to warming are consistent across a range of Antarctic environments. ISME J 6:692–702.  https://doi.org/10.1038/ismej.2011.124 Google Scholar
  89. Youssef N, Steidley BL, Elshahed MS (2012) Novel high-rank phylogenetic lineages within a sulfur spring (Zodletone Spring, Oklahoma), revealed using a combined pyrosequencing-Sanger approach. Appl Environ Microbiol 78:2677–2688.  https://doi.org/10.1128/aem.00002-12 Google Scholar
  90. Zakrzewski M, Proietti C, Ellis JJ et al (2017) Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions. Bioinformatics 33:782–783.  https://doi.org/10.1093/bioinformatics/btw725 Google Scholar
  91. Ziegler S, Waidner B, Itoh T et al (2013) Metallibacterium scheffleri gen. nov., sp. nov., an alkalinizing gammaproteobacterium isolated from an acidic biofilm. Int J Syst Evol Microbiol 63:1499–1504.  https://doi.org/10.1099/ijs.0.042986-0 Google Scholar

Copyright information

© Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Nanotechnology and Water Sustainability Research Unit, College of Science Engineering and TechnologyUniversity of South AfricaJohannesburgSouth Africa
  2. 2.College of Agriculture and Environmental SciencesUniversity of South AfricaJohannesburgSouth Africa
  3. 3.State Key Laboratory of Separation and Membranes, Membrane ProcessesNational Center for International Joint Research on Membrane Science and TechnologyTianjinPeople’s Republic of China

Personalised recommendations