Naslov
Artificial neural network analysis of microbial diversity in the Central and Southern Adriatic Sea

Autori
Danijela Šantić, Kasia Piwosz, Frano Matić, Ana Vrdoljak Tomaš, Jasna Arapov, Jason Lawrence Dean, Mladen Šolić1, Michal Koblížek, Grozdan Kušpilić, Stefanija Šestanović

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Skup
18th International Symposium on Microbial Ecology (ISME18)

Mjesto i datum
Lausanne, Švicarska, 14.08.2022. - 19.08.2022

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
16S rRNA ; the Neural gas algorithm

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Interdisciplinarne prirodne znanosti

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