Explorative Meta-Analysis of 417 Extant Archaeal Genomes to Predict Their Contribution to the Total Microbiome Functionality

. 2021 Feb 13 ; 9 (2) : . [epub] 20210213

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid33668634

Grantová podpora
20-02022Y Grantová Agentura České Republiky

Odkazy

PubMed 33668634
PubMed Central PMC7918521
DOI 10.3390/microorganisms9020381
PII: microorganisms9020381
Knihovny.cz E-zdroje

Revealing the relationship between taxonomy and function in microbiomes is critical to discover their contribution to ecosystem functioning. However, while the relationship between taxonomic and functional diversity in bacteria and fungi is known, this is not the case for archaea. Here, we used a meta-analysis of 417 completely annotated extant and taxonomically unique archaeal genomes to predict the extent of microbiome functionality on Earth contained within archaeal genomes using accumulation curves of all known level 3 functions of KEGG Orthology. We found that intergenome redundancy as functions present in multiple genomes was inversely related to intragenome redundancy as multiple copies of a gene in one genome, implying the tradeoff between additional copies of functionally important genes or a higher number of different genes. A logarithmic model described the relationship between functional diversity and species richness better than both the unsaturated and the saturated model, which suggests a limited total number of archaeal functions in contrast to the sheer unlimited potential of bacteria and fungi. Using the global archaeal species richness estimate of 13,159, the logarithmic model predicted 4164.1 ± 2.9 KEGG level 3 functions. The non-parametric bootstrap estimate yielded a lower bound of 2994 ± 57 KEGG level 3 functions. Our approach not only highlighted similarities in functional redundancy but also the difference in functional potential of archaea compared to other domains of life.

Zobrazit více v PubMed

Woese C.R., Kandler O., Wheelis M.L. Towards a natural system of organisms: Proposal for the domains Archaea, Bacteria, and Eucarya. Proc. Natl. Acad. Sci. USA. 1990;87:4576–4579. doi: 10.1073/pnas.87.12.4576. PubMed DOI PMC

Delong E.F. Everything in moderation: Archaea as “non-extremophiles.”. Curr. Opin. Genet. Dev. 1998;8:649–654. doi: 10.1016/S0959-437X(98)80032-4. PubMed DOI

Timonen S., Bomberg M. Archaea in dry soil environments. Phytochem. Rev. 2009;8:505–518. doi: 10.1007/s11101-009-9137-5. DOI

DeLong E.F., Pace N.R. Environmental diversity of bacteria and archaea. Syst. Biol. 2001;50:470–478. doi: 10.1080/106351501750435040. PubMed DOI

Stoica E., Herndl G.J. Contribution of Crenarchaeota and Euryarchaeota to the prokaryotic plankton in the coastal northwestern Black Sea. J. Plankton Res. 2007;29:699–706. doi: 10.1093/plankt/fbm051. DOI

Bates S.T., Berg-Lyons D., Caporaso J.G., Walters W.A., Knight R., Fierer N. Examining the global distribution of dominant archaeal populations in soil. Isme J. 2011;5:907–917. doi: 10.1038/ismej.2010.171. PubMed DOI PMC

Korzhenkov A.A., Toshchakov S.V., Bargiela R., Gibbard H., Ferrer M., Teplyuk A.V., Jones D.L., Kublanov I.V., Golyshin P.N., Golyshina O.V. Archaea dominate the microbial community in an ecosystem with low-to-moderate temperature and extreme acidity. Microbiome. 2019;7:11–14. doi: 10.1186/s40168-019-0623-8. PubMed DOI PMC

Leininger S., Urich T., Schloter M., Schwark L., Qi J., Nicol G.W., Prosser J.I., Schuster S.C., Schleper C. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature. 2006;442:806–809. doi: 10.1038/nature04983. PubMed DOI

Bengtson P., Sterngren A.E., Rousk J. Archaeal abundance across a pH gradient in an arable soil and its relationship to bacterial and fungal growth rates. Appl. Env. Microbiol. 2012;78:5906–5911. doi: 10.1128/AEM.01476-12. PubMed DOI PMC

Hubbell S.P. Neutral theory in community ecology and the hypothesis of functional equivalence. Funct. Ecol. 2005;19:166–172. doi: 10.1111/j.0269-8463.2005.00965.x. DOI

Žifčáková L., Větrovský T., Lombard V., Henrissat B., Howe A., Baldrian P. Feed in summer, rest in winter: Microbial carbon utilization in forest topsoil. Microbiome. 2017;5:1–12. doi: 10.1186/s40168-017-0340-0. PubMed DOI PMC

Starke R., Capek P., Morais D., Jehmlich N., Baldrian P. Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions. Front. Microbiol. 2020;11:143. doi: 10.3389/fmicb.2020.00143. PubMed DOI PMC

Starke R., Capek P., Morais D., Callister S.J., Jehmlich N. The total microbiome functions in bacteria and fungi. J. Proteom. 2020;2013:103623. doi: 10.1016/j.jprot.2019.103623. PubMed DOI

Starke R., Capek P., Morais D.K., Jehmlich N., Baldrian P. The Total Fungal Microbiome Functionality. [(accessed on 17 July 2020)];2019 Available online: https://www.biorxiv.org/content/biorxiv/early/2020/08/04/2020.08.04.236075.full.pdf.

Kanehisa M., Sato Y., Kawashima M., Furumichi M., Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44:D457–D462. doi: 10.1093/nar/gkv1070. PubMed DOI PMC

Kanehisa M., Sato Y., Morishima K. BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences. J. Mol. Biol. 2016;428:726–731. doi: 10.1016/j.jmb.2015.11.006. PubMed DOI

Pham V.H.T., Kim J. Cultivation of unculturable soil bacteria. Trends Biotechnol. 2012;30:475–484. doi: 10.1016/j.tibtech.2012.05.007. PubMed DOI

Martiny A.C. High proportions of bacteria are culturable across major biomes. ISME J. 2019;13:2125–2128. doi: 10.1038/s41396-019-0410-3. PubMed DOI PMC

Starke R., Jehmlich N., Bastida F. Using proteins to study how microbes contribute to soil ecosystem services: The current state and future perspectives of soil metaproteomics. J. Proteom. 2018;198:50–58. doi: 10.1016/j.jprot.2018.11.011. PubMed DOI

Větrovský T., Kohout P., Kopecký M., Macháč A., Man M., Bahnmann B.D., Brabcová V., Choi J., Meszárošová L., Human Z.R., et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 2019;10:1–9. doi: 10.1038/s41467-019-13164-8. PubMed DOI PMC

Gotelli N.J., Colwell R.K. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 2001;4:379–391. doi: 10.1046/j.1461-0248.2001.00230.x. DOI

Thompson G.G., Withers P.C., Pianka E.R., Thompson S.A. Assessing biodiversity with species accumulation curves; inventories of small reptiles by pit-trapping in Western Australia. Austral. Ecol. 2003;28:361–383. doi: 10.1046/j.1442-9993.2003.01295.x. DOI

Oksanen J., Blanchet F.G., Friendly M., Kindt R., Legendre P., McGlinn D., Minchin P.R., O’Hara R.B., Simpson G.L., Solymos P., et al. Vegan: Community Ecology Package. [(accessed on 17 July 2020)];2015 Available online: https://cran.r-project.org/web/packages/vegan/index.html.

Čapek P., Kotas P., Manzoni S., Šantrůčková H. Drivers of phosphorus limitation across soil microbial communities. Funct. Ecol. 2016;30:1705–1713. doi: 10.1111/1365-2435.12650. DOI

Bertrand P.V., Sakamoto Y., Ishiguro M., Kitagawa G. Akaike Information Criterion Statistics. J. R. Stat. Soc. Ser. A. 2006;151:567–568. doi: 10.2307/2983028. DOI

Yarza P., Yilmaz P., Pruesse E., Glöckner F.O., Ludwig W., Schleifer K.H., Whitman W.B., Euzéby J., Amann R., Rosselló-Móra R. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 2014;12:635–645. doi: 10.1038/nrmicro3330. PubMed DOI

Spiess A.N. Propagate: Propagation of Uncertainty. [(accessed on 17 July 2020)];2018 Available online: https://cran.r-project.org/web/packages/vegan/index.html.

Chao A. Non-parametric estimation of the classes in a population. Scand. J. Stat. 1984;11:265–270. doi: 10.2307/4615964. DOI

Chao A. Estimating Population Size for Sparse Data in Capture-Recapture Experiments. Biometrics. 1989;45:427–438. doi: 10.2307/2531487. DOI

Tukey J.W. Comparing Individual Means in the Analysis of Variance. Biometrics. 1949;5:99–114. doi: 10.2307/3001913. PubMed DOI

De Mendiburu F. Agricolae: Statistical Procedures for Agricultural Research. [(accessed on 17 July 2020)];2014 Available online: https://cran.r-project.org/web/packages/agricolae/index.html.

Petrov D.A. Evolution of genome size: New approaches to an old problem. Trends Genet. 2001;17:23–28. doi: 10.1016/S0168-9525(00)02157-0. PubMed DOI

Cavalier-Smith T. Nuclear volume control by nucleoskeletal DNA, selection for cell volume and cell growth rate, and the solution of the DNA C-value paradox. J. Cell Sci. 1978;24:247–278. PubMed

Vinogradov A.E. Buffering: A possible passive-homeostasis role for redundant DNA. J. Biol. 1998;193:197–199. doi: 10.1006/jtbi.1997.0629. PubMed DOI

Wang H.Y., Guo S.Y., Huang M.R., Thorsten L.H., Wei J.C. Ascomycota has a faster evolutionary rate and higher species diversity than Basidiomycota. Sci. China Life Sci. 2010;53:1163–1169. doi: 10.1007/s11427-010-4063-8. PubMed DOI

Větrovský T., Baldrian P. The Variability of the 16S rRNA Gene in Bacterial Genomes and Its Consequences for Bacterial Community Analyses. PLoS ONE. 2013;8:e57923. doi: 10.1371/journal.pone.0057923. PubMed DOI PMC

Falkowski P.G., Fenchel T., Delong E.F. The microbial engines that drive earth’s biogeochemical cycles. Science. 2008;320:1034–1039. doi: 10.1126/science.1153213. PubMed DOI

Rineau F., Courty P.E. Secreted enzymatic activities of ectomycorrhizal fungi as a case study of functional diversity and functional redundancy. Anna. For. Sci. 2011;68:69–80. doi: 10.1007/s13595-010-0008-4. DOI

Gotelli N., Colwell R. Estimating species richness. Biol. Divers. Front. Meas. Assess. 2011;12:39–54. doi: 10.2307/3547060. DOI

Chao A., Colwell R.K., Lin C.W., Gotelli N.J. Sufficient sampling for asymptotic minimum species richness estimators. Ecology. 2009;90:1125–1133. doi: 10.1890/07-2147.1. PubMed DOI

Williams T.A., Szöllosi G.J., Spang A., Foster P.G., Heaps S.E., Boussau B., Ettema T.J.G., Martin Embley T. Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proc. Natl. Acad. Sci. USA. 2017;114:E4602–E4611. doi: 10.1073/pnas.1618463114. PubMed DOI PMC

Castelle C.J., Banfield J.F. Major New Microbial Groups Expand Diversity and Alter our Understanding of the Tree of Life. Cell. 2018;172:1181–1197. doi: 10.1016/j.cell.2018.02.016. PubMed DOI

Amann R., Rosselló-Móra R. After all, only millions? MBio. 2016;7:e00201-16. doi: 10.1128/mBio.00999-16. PubMed DOI PMC

Zhu Q., Mai U., Pfeiffer W., Janssen S., Asnicar F., Sanders J.G., Belda-Ferre P., Al-Ghalith G.A., Kopylova E., McDonald D., et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea. Nat. Commun. 2019;10:1–14. doi: 10.1038/s41467-019-13443-4. PubMed DOI PMC

Starke R., Fernandes M.L.P., Morais D.K., Odriozola I., Jehmlich N., Baldrian P. Explorative Meta-Analysis of 417 Extant Archaeal Genomes to Predict Their Contribution to the Total Microbiome Functionality. [(accessed on 17 July 2020)];2020 Available online: https://www.biorxiv.org/content/10.1101/2020.08.04.236075v1. PubMed DOI PMC

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...