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Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions
Unveiling the relationship between taxonomy and function of the microbiome is crucial to determine its contribution to ecosystem functioning. However, while there is a considerable amount of information on microbial taxonomic diversity, our understanding of its relationship to functional diversity i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015973/ https://www.ncbi.nlm.nih.gov/pubmed/32117162 http://dx.doi.org/10.3389/fmicb.2020.00143 |
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author | Starke, Robert Capek, Petr Morais, Daniel Jehmlich, Nico Baldrian, Petr |
author_facet | Starke, Robert Capek, Petr Morais, Daniel Jehmlich, Nico Baldrian, Petr |
author_sort | Starke, Robert |
collection | PubMed |
description | Unveiling the relationship between taxonomy and function of the microbiome is crucial to determine its contribution to ecosystem functioning. However, while there is a considerable amount of information on microbial taxonomic diversity, our understanding of its relationship to functional diversity is still scarce. Here, we used a meta-analysis of completely annotated extant genomes of 377 taxonomically distinct fungal species to predict the total fungal microbiome functionality on Earth with accumulation curves (ACs) of all known functions from the level 3 of KEGG Orthology using both parametric and non-parametric estimates in an explorative data-mining approach. The unsaturated model extrapolating functional diversity as a function of species richness described the ACs significantly better than the saturated model that assumed a limited total number of functions, which suggested the presence of widespread and rare functions. Based on previous estimates of 3.8 million fungal species on Earth, we propagated the unsaturated model to predict a total of 42.4 ± 0.5 million KEGG level 3 functions of which only 0.06% are known today. Our approach not only highlights the presence of widespread and rare functions but points toward the necessity of novel and more sophisticated methods to unveil the entirety of functions to fully understand the involvement of the fungal microbiome in ecosystem functioning. |
format | Online Article Text |
id | pubmed-7015973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70159732020-02-28 Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions Starke, Robert Capek, Petr Morais, Daniel Jehmlich, Nico Baldrian, Petr Front Microbiol Microbiology Unveiling the relationship between taxonomy and function of the microbiome is crucial to determine its contribution to ecosystem functioning. However, while there is a considerable amount of information on microbial taxonomic diversity, our understanding of its relationship to functional diversity is still scarce. Here, we used a meta-analysis of completely annotated extant genomes of 377 taxonomically distinct fungal species to predict the total fungal microbiome functionality on Earth with accumulation curves (ACs) of all known functions from the level 3 of KEGG Orthology using both parametric and non-parametric estimates in an explorative data-mining approach. The unsaturated model extrapolating functional diversity as a function of species richness described the ACs significantly better than the saturated model that assumed a limited total number of functions, which suggested the presence of widespread and rare functions. Based on previous estimates of 3.8 million fungal species on Earth, we propagated the unsaturated model to predict a total of 42.4 ± 0.5 million KEGG level 3 functions of which only 0.06% are known today. Our approach not only highlights the presence of widespread and rare functions but points toward the necessity of novel and more sophisticated methods to unveil the entirety of functions to fully understand the involvement of the fungal microbiome in ecosystem functioning. Frontiers Media S.A. 2020-02-06 /pmc/articles/PMC7015973/ /pubmed/32117162 http://dx.doi.org/10.3389/fmicb.2020.00143 Text en Copyright © 2020 Starke, Capek, Morais, Jehmlich and Baldrian. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Starke, Robert Capek, Petr Morais, Daniel Jehmlich, Nico Baldrian, Petr Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions |
title | Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions |
title_full | Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions |
title_fullStr | Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions |
title_full_unstemmed | Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions |
title_short | Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions |
title_sort | explorative meta-analysis of 377 extant fungal genomes predicted a total mycobiome functionality of 42.4 million kegg functions |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015973/ https://www.ncbi.nlm.nih.gov/pubmed/32117162 http://dx.doi.org/10.3389/fmicb.2020.00143 |
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