Suchergebnisse

12 pages, 5 figures, 1 table, supplementary information https://doi.org/10.1038/s41396-021-01053-2 ; Bacteria display dynamic abundance fluctuations over time in marine environments, where they play key biogeochemical roles. Here, we characterized the seasonal dynamics of marine bacteria in a coastal oligotrophic time series station, tested how similar the temporal niche of closely related taxa is, and what are the environmental parameters modulating their seasonal abundance patterns. We further explored how conserved the niche is at higher taxonomic levels. The community presented recurrent patterns of seasonality for 297 out of 6825 amplicon sequence variants (ASVs), which constituted almost half of the total relative abundance (47%). For certain genera, niche similarity decreased as nucleotide divergence in the 16S rRNA gene increased, a pattern compatible with the selection of similar taxa through environmental filtering. Additionally, we observed evidence of seasonal differentiation within various genera as seen by the distinct seasonal patterns of closely related taxa. At broader taxonomic levels, coherent seasonal trends did not exist at the class level, while the order and family ranks depended on the patterns that existed at the genus level. This study identifies the coexistence of closely related taxa for some bacterial groups and seasonal differentiation for others in a coastal marine environment subjected to a strong seasonality ; This research was funded by grants REMEI (CTM2015-70340-R), MIAU (RTI2018-101025-B-I00), and ECLIPSE (PID2019-110128RB-I00) from the Spanish Ministry of Science and Innovation, by the Grup de Recerca de la Generalitat de Catalunya (2017SGR/1568) and received support from the Spanish government through the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S). Adrià Auladell was supported by a Spanish FPI grant and IF received the support of the Fundación BBVA through the 'Becas Leonardo a Investigadores y Creadores Culturales' 2019 Program ; Peer reviewed

AbstractOne of the biggest challenges in molecular biology is bridging the gap between the known and the unknown coding sequence space. This challenge is especially extreme in microbial systems, where between 40% and 60% of the predicted genes are of unknown function. Discarding this uncharacterized fraction should not be an option anymore. Here, we present a conceptual framework and a computational workflow that bridges this gap and provides a powerful strategy to contextualize the investigations of genes of unknown function. Our approach partitions the coding sequence space removing the known-unknown dichotomy, unifies genomic and metagenomic data and provides a framework to expand those investigations across environments and organisms. By analyzing 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes we showcase our approach and its application in ecological, evolutionary and biotechnological investigations. As a result, we put into perspective the extent of the unknown fraction, its diversity, and its relevance in genomic and environmental contexts. By identifying a target gene of unknown function for antibiotic resistance, we demonstrate how a contextualized unknown coding sequence space enables the generation of hypotheses that can be used to augment experimental data. ; The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (RES-AECT-2014-2-0085), the BMBF795 funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) (031A537B, 031A533A, 031A538A, 031A533B, 031A535A, 031A537C, 031A534A, 031A532B), the University of Oxford Advanced Research Computing (http://dx.doi.org/10.5281/zenodo.22558) and the MARBITS bioinformatics core at ICM-CSIC. CV was supported by the Max Planck Society. AFG received funding from the European Union's Horizon 2020 research and innovation program Blue Growth: Unlocking the potential of Seas and Oceans under grant agreement no. 634486 (project acronym INMARE). AM was supported by the Biotechnology and Biological Sciences Research Council [BB/M011755/1, BB/R015228/1] and RDF by the European Molecular Biology Laboratory core funds. EOC was supported by project INTERACTOMA RTI2018-101205-B-I00 from the Spanish Agency of Science MICIU/AEI. SGA and PS received additional funding by the project MAGGY (CTM2017-87736-R) from the Spanish Ministry of Economy and Competitiveness. The Malaspina 2010 Expedition was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the Consolider-Ingenio program (ref. CSD2008-00077). The authors thank Johannes Söding and Alex Bateman for helpful discussions.