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

0546615 - MBÚ 2022 RIV GB eng J - Článek v odborném periodiku
Šantić, D. - Piwosz, K. - Matic, F. - Tomas, A. V. - Arapov, J. - Dean, Jason Lawrence - Šolić, M. - Koblížek, Michal - Kušpilić, G. - Šestanović, S.
Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea.
Scientific Reports. Roč. 11, č. 1 (2021), č. článku 11186. ISSN 2045-2322. E-ISSN 2045-2322
Grant CEP: GA ČR(CZ) GJ18-14095Y
Institucionální podpora: RVO:61388971
Klíčová slova: aerobic anoxygenic phototrophs * mediterranean sea * surface waters * picoplankton distribution * community composition * temporal variability * seasonal-changes * coastal waters * bacteria * bacterioplankton
Obor OECD: Microbiology
Impakt faktor: 4.997, rok: 2021
Způsob publikování: Open access
https://www.nature.com/articles/s41598-021-90863-7

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.
Trvalý link: http://hdl.handle.net/11104/0323062