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Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems

Microbes continually shape Earth’s biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucida...

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Detalles Bibliográficos
Autores principales: Amorín de Hegedüs, Rocío, Conesa, Ana, Foster, Jamie S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416242/
https://www.ncbi.nlm.nih.gov/pubmed/37577445
http://dx.doi.org/10.3389/fmicb.2023.1174685
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author Amorín de Hegedüs, Rocío
Conesa, Ana
Foster, Jamie S.
author_facet Amorín de Hegedüs, Rocío
Conesa, Ana
Foster, Jamie S.
author_sort Amorín de Hegedüs, Rocío
collection PubMed
description Microbes continually shape Earth’s biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as “microbial dark matter,” the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of “microbial dark matter” in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems.
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spelling pubmed-104162422023-08-12 Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems Amorín de Hegedüs, Rocío Conesa, Ana Foster, Jamie S. Front Microbiol Microbiology Microbes continually shape Earth’s biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as “microbial dark matter,” the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of “microbial dark matter” in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems. Frontiers Media S.A. 2023-07-28 /pmc/articles/PMC10416242/ /pubmed/37577445 http://dx.doi.org/10.3389/fmicb.2023.1174685 Text en Copyright © 2023 Amorín de Hegedüs, Conesa and Foster. https://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
Amorín de Hegedüs, Rocío
Conesa, Ana
Foster, Jamie S.
Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems
title Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems
title_full Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems
title_fullStr Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems
title_full_unstemmed Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems
title_short Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems
title_sort integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416242/
https://www.ncbi.nlm.nih.gov/pubmed/37577445
http://dx.doi.org/10.3389/fmicb.2023.1174685
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