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Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks

Interactions between microalgae and bacteria can directly influence the global biogeochemical cycles but the majority of such interactions remain unknown. 16S rRNA gene-based co-occurrence networks have potential to help identify microalgal-bacterial interactions. Here, we used data from 10 Earth mi...

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Autores principales: Pushpakumara, B. L. D. Uthpala, Tandon, Kshitij, Willis, Anusuya, Verbruggen, Heroen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935533/
https://www.ncbi.nlm.nih.gov/pubmed/36797257
http://dx.doi.org/10.1038/s41598-023-27816-9
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author Pushpakumara, B. L. D. Uthpala
Tandon, Kshitij
Willis, Anusuya
Verbruggen, Heroen
author_facet Pushpakumara, B. L. D. Uthpala
Tandon, Kshitij
Willis, Anusuya
Verbruggen, Heroen
author_sort Pushpakumara, B. L. D. Uthpala
collection PubMed
description Interactions between microalgae and bacteria can directly influence the global biogeochemical cycles but the majority of such interactions remain unknown. 16S rRNA gene-based co-occurrence networks have potential to help identify microalgal-bacterial interactions. Here, we used data from 10 Earth microbiome projects to identify potential microalgal-bacterial associations in aquatic ecosystems. A high degree of clustering was observed in microalgal-bacterial modules, indicating densely connected neighbourhoods. Proteobacteria and Bacteroidetes predominantly co-occurred with microalgae and represented hubs of most modules. Our results also indicated that species-specificity may be a global characteristic of microalgal associated microbiomes. Several previously known associations were recovered from our network modules, validating that biologically meaningful results can be inferred using this approach. A range of previously unknown associations were recognised such as co-occurrences of Bacillariophyta with uncultured Planctomycetes OM190 and Deltaproteobacteria order NB1-j. Planctomycetes and Verrucomicrobia were identified as key associates of microalgae due to their frequent co-occurrences with several microalgal taxa. Despite no clear taxonomic pattern, bacterial associates appeared functionally similar across different environments. To summarise, we demonstrated the potential of 16S rRNA gene-based co-occurrence networks as a hypothesis-generating framework to guide more focused research on microalgal-bacterial associations.
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spelling pubmed-99355332023-02-18 Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks Pushpakumara, B. L. D. Uthpala Tandon, Kshitij Willis, Anusuya Verbruggen, Heroen Sci Rep Article Interactions between microalgae and bacteria can directly influence the global biogeochemical cycles but the majority of such interactions remain unknown. 16S rRNA gene-based co-occurrence networks have potential to help identify microalgal-bacterial interactions. Here, we used data from 10 Earth microbiome projects to identify potential microalgal-bacterial associations in aquatic ecosystems. A high degree of clustering was observed in microalgal-bacterial modules, indicating densely connected neighbourhoods. Proteobacteria and Bacteroidetes predominantly co-occurred with microalgae and represented hubs of most modules. Our results also indicated that species-specificity may be a global characteristic of microalgal associated microbiomes. Several previously known associations were recovered from our network modules, validating that biologically meaningful results can be inferred using this approach. A range of previously unknown associations were recognised such as co-occurrences of Bacillariophyta with uncultured Planctomycetes OM190 and Deltaproteobacteria order NB1-j. Planctomycetes and Verrucomicrobia were identified as key associates of microalgae due to their frequent co-occurrences with several microalgal taxa. Despite no clear taxonomic pattern, bacterial associates appeared functionally similar across different environments. To summarise, we demonstrated the potential of 16S rRNA gene-based co-occurrence networks as a hypothesis-generating framework to guide more focused research on microalgal-bacterial associations. Nature Publishing Group UK 2023-02-16 /pmc/articles/PMC9935533/ /pubmed/36797257 http://dx.doi.org/10.1038/s41598-023-27816-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Pushpakumara, B. L. D. Uthpala
Tandon, Kshitij
Willis, Anusuya
Verbruggen, Heroen
Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks
title Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks
title_full Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks
title_fullStr Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks
title_full_unstemmed Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks
title_short Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks
title_sort unravelling microalgal-bacterial interactions in aquatic ecosystems through 16s rrna gene-based co-occurrence networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935533/
https://www.ncbi.nlm.nih.gov/pubmed/36797257
http://dx.doi.org/10.1038/s41598-023-27816-9
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