Towards omics-based predictions of planktonic functional composition from environmental data

Abstract

Marine microbes play a crucial role in climate regulation, biogeochemical cycles, and trophic networks. Unprecedented amounts of data on planktonic communities were recently collected, sparking a need for innovative data-driven methodologies to quantify and predict their ecosystemic functions. We reanalyze 885 marine metagenome-assembled genomes through a network-based approach and detect 233,756 protein functional clusters, from which 15% are functionally unannotated. We investigate all clusters’ distributions across the global ocean through machine learning, identifying biogeographical provinces as the best predictors of protein functional clusters’ abundance. The abundances of 14,585 clusters are predictable from the environmental context, including 1347 functionally unannotated clusters. We analyze the biogeography of these 14,585 clusters, identifying the Mediterranean Sea as an outlier in terms of protein functional clusters composition. Applicable to any set of sequences, our approach constitutes a step towards quantitative predictions of functional composition from the environmental context.

Multitrait diversification in marine diatoms in constant and warmed environments

“…Furthermore, adaptation that produced novel trait combinations was not uncommon. This has ramifications for how we link lineage diversity and functional trait diversity [21,25,27]. Our results show the existence of connections between local optima, revealing that associations between lineage identity and multitrait phenotype can be flexible in the face of demographic and environmental perturbations.…”

Genomic differentiation of three pico‐phytoplankton species in the Mediterranean Sea

“…However, most seascape genetic studies rely on the study of pluricellular, macroscopic organisms (Selkoe et al, 2016). High-throughput metagenomics now enable to investigate highly abundant components of the ecosystems (e.g., the Oithona nana copepod, Madoui et al, 2017; Oithona similis, Laso-Jadart et al, 2020) involving more and more microbes (Delmont et al, 2019;Vannier et al, 2016;Seeleuthner et al, 2018;Leconte et al, 2020;Faure et al, 2021;Laso-Jadart et al, 2021). By analogy, we conducted a comparative study of three picoeukaryotes.…”

Large-Data Omics Approaches in Modern Remediation

“…The generation of vast data sets from studies that utilize omics approaches and the associated environmental metadata mandates us to harness computational methods that can help predict the biogeochemical parameters of contaminated sites based on the genomic information available (Faure et al 2021;Smith et al 2015). Metabolite profiling and proteome assessments provide information that is vital to understanding and developing biodegradation pathways and models.…”