Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

Abstract Bacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the...

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Autores principales:	Danijela Šantić, Kasia Piwosz, Frano Matić, Ana Vrdoljak Tomaš, Jasna Arapov, Jason Lawrence Dean, Mladen Šolić, Michal Koblížek, Grozdan Kušpilić, Stefanija Šestanović
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d95352bcc9f84a6193e5c3a908299892
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spelling	oai:doaj.org-article:d95352bcc9f84a6193e5c3a9082998922021-12-02T15:00:59ZArtificial neural network analysis of microbial diversity in the central and southern Adriatic Sea10.1038/s41598-021-90863-72045-2322https://doaj.org/article/d95352bcc9f84a6193e5c3a9082998922021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90863-7https://doaj.org/toc/2045-2322Abstract Bacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.Danijela ŠantićKasia PiwoszFrano MatićAna Vrdoljak TomašJasna ArapovJason Lawrence DeanMladen ŠolićMichal KoblížekGrozdan KušpilićStefanija ŠestanovićNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Danijela Šantić Kasia Piwosz Frano Matić Ana Vrdoljak Tomaš Jasna Arapov Jason Lawrence Dean Mladen Šolić Michal Koblížek Grozdan Kušpilić Stefanija Šestanović Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea
description	Abstract Bacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.
format	article
author	Danijela Šantić Kasia Piwosz Frano Matić Ana Vrdoljak Tomaš Jasna Arapov Jason Lawrence Dean Mladen Šolić Michal Koblížek Grozdan Kušpilić Stefanija Šestanović
author_facet	Danijela Šantić Kasia Piwosz Frano Matić Ana Vrdoljak Tomaš Jasna Arapov Jason Lawrence Dean Mladen Šolić Michal Koblížek Grozdan Kušpilić Stefanija Šestanović
author_sort	Danijela Šantić
title	Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea
title_short	Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea
title_full	Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea
title_fullStr	Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea
title_full_unstemmed	Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea
title_sort	artificial neural network analysis of microbial diversity in the central and southern adriatic sea
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d95352bcc9f84a6193e5c3a908299892
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Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

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