Biology subjects

Regimbeau, A.

Publications and source records attributed to Regimbeau, A..

4 recordsLinked to original sources

bioRxiv2025

Planetary-scale marine community modeling predicts metabolic synergy and viral impacts

The oceans buffer against climate change via biogeochemical cycles underpinned by microbial metabolic activities. While planetary-scale surveys provide baseline microbiome data, inferring metabolic and biogeochemical impacts remains challenging. Genome-scale modeling has addressed analogous issues at the cellular level, highlighting key metabolic reactions contingent upon specific environmental conditions. Here we adapt this mechanistic modeling framework towards analyzing global ocean microbial communities to reveal metabolic processes predicted to maintain ecosystem functioning. To achieve this, we developed a genome-scale superorganism metabolic model for each TARA Ocean metagenome or metatranscriptome (i.e., limited to reactions known from heterotrophic prokaryotes and viruses), and evaluated these models to establish a community-wide metabolic phenotype for each sample. To validate, we showed that even with reaction-mappable genes only ([~]1/4 of the total genes), model composition revealed metabolism-inferred ecological zones that matched taxonomy-inferred zones. Model inferred metabolic phenotypes revealed reaction cooperation associated with microbial metabolism and organism diversity. These phenotypes also suggest elevated ecological roles for viruses as model predictions suggest they genomically target community-critical metabolic reactions that underpin metabolic phenotype stability, and also demonstrate that, as metabolites are better understood, immediate estimates could be made for where viruses remineralize versus sink carbon. While this new constraints-based, agile, and mechanistic modeling framework is highly upgradable, it already begins to convert molecular-scale environmental omics data to ecological and even planetary-scale biogeochemical features that will better bring microbes and their viruses into Earth system and climate models.

systems biology↗

bioRxiv2023

Towards modeling genome-scale knowledge in the global ocean

Earth System Models (ESMs) highly simplify their representation of biological processes, leading to major uncertainty in climate change impacts. Despite a growing understanding of molecular networks from genomic data, describing how changing phytoplankton physiology affects the production of key metabolites remains elusive. Here we embed a genome-scale model within a state-of-the-art ESM to deliver an integrated understanding of how gradients of nutritional constraints modulate metabolic reactions and molecular physiology. Applied to the prevalent marine cyanobacteria Prochlorococcus, we find that glycogen and lipid storage can be understood as a consequence of acclimation to environmental gradients. Given the pressing need to assess how biological diversity influences biogeochemical functions, genome-enabled ESMs allow the quantification of the contribution of modeled organisms to the production of dissolved organic carbon and its molecular composition.

bioinformatics↗

bioRxiv2023

PhotoEukStein: Towards an omics-based definition of unicellular eukaryote phototrophs functional traits via metabolic modelling

Defining biological functional traits for unicellular organisms relies on comprehending the set and combination of the biochemical reactions their genomes encode for. This network of biochemical reactions defines the metabolic strategy organisms and communities use. Understanding the functional traits of unicellular organisms involves studying the combination of biochemical reactions encoded in their genomes. These reactions determine the metabolic strategy that organisms and communities use to grow in a specific environment. While prokaryotes have been extensively studied for their metabolic networks, eukaryotes have lagged behind due to the complexity of their genomes and the need for a better understanding of their metabolism. We have created PhotoEukstein, a meta-metabolic model for unicellular phototrophic eukaryotes. This meta-model enables quick and automated derivation of Genome-Scale Metabolic models directly from genomes. We have used it to analyse 533 environmental genomes and marine eukaryotic unicellular plankton transcriptomes. These models can help predict functional traits that cannot be purely deducted from taxonomic information or the listing of metabolic reactions encoded by the genome. They provide the opportunity to build connections with Earth System Models to pinpoint environmental parameters to capture specific functional traits.

systems biology↗

bioRxiv2021

Contribution of genome scale metabolic modeling to niche theory

Standard niche modeling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome-scale descriptions of these organisms. This study over-comes this shortcoming by proposing a new conceptual niche that encompasses the whole metabolic capabilities of an organism. The so-called metabolic niche resumes well-known traits such as nutrient needs and their dependencies for survival. Despite the computational challenge, its implementation allows the detection of traits and the formal comparison of niches of different organisms, emphasizing that the presence-absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organisms niche sheds light on genes essential for the metabolic niche and their role in understanding various biological experiments, such as transcriptomics, paving the way for incorporating better the genome-scale description in ecological studies.

ecology↗