Biology subjects

Eveillard, D.

Publications and source records attributed to Eveillard, D..

17 recordsLinked to original sources

bioRxiv2026

Satellite-gene abundance fusion reveals global hotspots of ocean ecosystem services

The Ocean sustains planetary health through climate regulation, biodiversity, and essential ecosystem services. Plankton underpins these processes by driving primary production, sustaining food webs, and mediating carbon sequestration. Yet the spatial organization of plankton-based ecosystem services remains poorly understood. The High Seas Treaty and the 30% Ocean protection target by 2030 create an urgent need for science-based guidance for global ocean governance. Here, we aim to identify key planktonic areas associated with functional diversity, net primary productivity, and carbon export globally. We used machine learning to integrate omics-derived plankton functions and satellite observations with diversity metric and ecosystem services. Leveraging metagenomic data, we identified molecular functions strongly linked to ecosystem functioning and project their global distributions across seasons. We reveal putative hotspots of functional diversity, net primary productivity, and carbon export. Our analysis pinpoints areas where all three indicators converge synchronously, predominantly along oceanographic structures such as polar fronts and principal current systems. By relying on satellite data as core input, our approach captures the temporal variability of these hotspots, providing a global, dynamic protocol for projections of plankton-based ecosystem services. These findings establish a foundation for developing integrative conservation tools based on ecosystem functioning and inform adaptive management under climate change.

ecology↗

bioRxiv2025

MIMEco: Multi-objective metabolic modeling to predict and explain pairwise interactions

Genome-scale metabolic models (GEMs) provide a mechanistic framework for quantitatively exploring an organisms physiology and its interactions with the environment. However, their use in microbial community modeling has been limited since it usually relies on community-wide objective functions or the integration of community abundance data. Nevertheless, predicting microbial interactions is crucial for understanding ecosystem dynamics, and thus requires dedicated methods. Here, we introduce MIMEco (Metabolic Interaction Modeling in Ecosystems), an open-source Python package that predicts pairwise microbial interactions through multi-objective optimization. MIMEco does not require abundance data, provides a user-friendly workflow, and predicts interaction types, strengths, and exchanged metabolites directly from Pareto fronts. In this study, MIMEco is validated by replicating experimental results of an in vitro co-culture of two auxotrophic Escherichia coli strains. By combining mechanistic inference with usability, MIMEco makes metabolic interaction modeling accessible to non-specialists while remaining flexible for experts, establishing a versatile platform for advancing metabolic ecology across biomedical and environmental fields. IMPORTANCEDeciphering microbial interactions through in vitro experiments is often costly and limited in scope, while existing bioinformatics tools can be complex to use or limited by actual sample data. MIMEco offers an open-source and user-friendly multi-objective framework for predicting and quantifying pairwise microbial ecosystem interactions and nutrient exchanges. We demonstrate its reliability by replicating a well-characterized laboratory experiment, showing that MIMEco can accurately reflects real biological behavior. This tool improves the communitys ability to explore and understand metabolic interaction mechanisms underpinning microbial ecosystems.

bioinformatics↗

bioRxiv2025

HUMESS: Integrating Quantitative Transcriptomic Analysis and Metabolic Modeling to Unveil Condition-Specific Gene Signatures

Transcriptomic analysis is a key tool for exploring gene expression, but the complexity of biological systems often limits its insights. In particular, the lack of intermodal or multi-layered analysis hinders the ability to fully capture key cellular functions such as metabolism from transcriptomic data alone. Here, we introduce a novel approach that integrates transcriptomic data with metabolic network modeling to address this. Unlike traditional methods, HUMESS prioritizes genes based on their metabolic significance, offering a deeper understanding of condition-specific gene expression. Our computational pipeline, supported by a user-friendly Rshiny application, enhances gene expression analysis by uncovering metabolic phenotypic signatures.

systems biology↗

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

Community metabolic modeling of host-microbiota interactions through multi-objective optimization

The human gut microbiota comprises various microorganisms engaged in intricate interactions among themselves and with the host, affecting its health. While advancements in omics technologies have led to the inference of clear associations between microbiome composition and health conditions, we usually lack a causal and mechanistic understanding of these associations. For modeling mechanisms driving the interactions, we simulated the organisms metabolism using in silico Genome-Scale Metabolic Models (GEMs). We used multi-objective optimization to predict and explain metabolic interactions among gut microbes and an intestinal epithelial cell. We developed a score integrating model simulation results to predict the type (competition, neutralism, mutualism) and quantify the interaction between several organisms. This framework uncovered a potential cross-feeding for choline, explaining the predicted mutualism between Lactobacillus rhamnosus GG and the epithelial cell. Finally, we analyzed a five-organism ecosystem, revealing that a minimal microbiota can favor the epithelial cells maintenance.

systems biology↗

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↗

bioRxiv2023

Unveiling abundance-dependent metabolic phenotypes of microbial communities

Constraint-based modeling has risen as an alternative for characterizing the metabolism of communities. Adaptations of Flux Balance Analysis have been proposed to model metabolic interactions in most cases, considering a unique optimal flux distribution derived from the maximization of biomass production. However, these approaches do not consider the development of other potentially novel essential functions not directly related to cell growth which forces them to display suboptimal growth rates in nature. Additionally, suboptimal states allow a degree of plasticity in the metabolism, thus allowing quick shifts between alternative flux distributions as an initial response to environmental changes. In this work, we present a method to explore the abundance-growth space as a representation of metabolic flux distributions of a community. This space is defined by the composition of a community, represented by its members relative abundance and their growth rate. The analysis of this space allows us to represent the whole set of feasible fluxes without needing a complete description of the solution space unveiling abundance-dependent metabolic phenotypes displayed in a given environment. As an illustration, we consider a community composed of two bioleaching bacteria, Acidithiobacillus ferrooxidans Wenelen and Sulfobacillus thermosulfidooxidans Cutipay, finding that changes in the composition of their available resources significantly affects their metabolic plasticity. IMPORTANCEIn nature, organisms live in communities and not as isolated species. Their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach which, only using genomics information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding the light of the degree of metabolic plasticity inherent to the community.

bioinformatics↗

bioRxiv2022

Differential global distribution of marine picocyanobacteria gene clusters reveals distinct niche-related adaptive strategies

The ever-increasing number of available microbial genomes and metagenomes provide new opportunities to investigate the links between niche partitioning and genome evolution in the ocean, notably for the abundant and ubiquitous marine picocyanobacteria Prochlorococcus and Synechococcus. Here, by combining metagenome analyses of the Tara Oceans dataset with comparative genomics, including phyletic patterns and genomic context of individual genes from 256 reference genomes, we first showed that picocyanobacterial communities thriving in different niches possess distinct gene repertoires. We then managed to identify clusters of adjacent genes that display specific distribution patterns in the field (CAGs) and are thus potentially involved in the adaptation to particular environmental niches. Several CAGs are likely involved in the uptake or incorporation of complex organic forms of nutrients, such as guanidine, cyanate, cyanide, pyrimidine or phosphonates, which might be either directly used by cells, for e.g. the biosynthesis of proteins or DNA, or degraded into inorganic nitrogen and/or phosphorus forms. We also highlight the frequent presence of CAGs involved in polysaccharide capsule biosynthesis in Synechococcus populations thriving in both nitrogen- and phosphorus-depleted areas, which are absent in low-iron regions, suggesting that the complexes they encode may be too energy-consuming for picocyanobacteria thriving in these areas. In contrast, Prochlorococcus populations thriving in iron-depleted areas specifically possess an alternative respiratory terminal oxidase, potentially involved in the reduction of Fe(III) into Fe(II). Together, this study provides insights into how these key members of the phytoplankton community might behave in response to ongoing global change. Significance StatementPicocyanobacteria face various environmental conditions in the ocean and numerous studies have shown that genetically distinct ecotypes colonize different niches. Yet the functional basis of their adaptation remains poorly known, essentially due to the large number of genes of yet unknown function, many of which have little or no beneficial effect on fitness. Here, by combining comparative genomics and metagenomics approaches, we have identified not only single genes but also entire gene clusters, potentially involved in niche adaptation. Although being sometimes present in only one or a few sequenced strains, they occur in a large part of the population in specific ecological niches and thus constitute precious targets for elucidating the biochemical function of yet unknown niche-related genes.

microbiology↗

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↗

bioRxiv2021

Disentangling temporal associations in marine microbial networks

BackgroundMicrobial interactions are fundamental for Earths ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (temporal) network from a single static network. ResultsA single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. For the decade, we aimed to identify persistent, seasonal, and temporary microbial associations by determining a temporal network that captures the interactome of each individual sample. We found that the temporal network appears to follow an annual cycle, collapsing and reassembling when transiting between colder and warmer waters. We observed higher association repeatability in colder than in warmer months. Only 16 associations could be validated using observations reported in literature, underlining our knowledge gap in marine microbial ecological interactions. ConclusionsOur results indicate that marine microbial associations follow recurrent temporal dynamics in temperate zones, which need to be accounted for to better understand the functioning of the ocean microbiome. The constructed marine temporal network may serve as a resource for testing season-specific microbial interaction hypotheses. The applied approach can be transferred to microbiome studies in other ecosystems.

microbiology↗

bioRxiv2021

Disentangling marine microbial networks across space

Microbial interactions underpin ocean ecosystem function, but they remain barely known. Multiple studies have analyzed microbial interactions using static association networks based on omics data, yet microbial interactions are dynamic and can change across spatiotemporal scales. Understanding the dynamics of microbial interactions is needed for a better comprehension of ocean ecosystems. Here, we explored associations between archaea, bacteria, and picoeukaryotes along the water column, from the surface to the deep ocean, across the northern subtropical to the southern temperate ocean and the Mediterranean Sea by defining sample-specific subnetworks, which allowed us to examine changes in microbial associations across space. We found that associations tend to change with depth as well as with geographical scale, with a few associations being global (i.e., present across regions within the same depth layer) and 11-36% being regional within specific water layers. The lowest fraction of global associations was found in the bathypelagic zone, while associations restricted to certain regions increased with depth. The majority of associations observed in surface waters disappeared with depth, suggesting that surface ocean associations are not transferred to the deep sea, despite microbial sinking. Altogether, our results suggest that microbial associations have highly heterogeneous distributions in the horizontal and vertical dimensions of the ocean and that such distributions do not mirror taxonomic distributions. Our work contributes to better understand the dynamics of microbial interactions in the global ocean, which is urgently needed in a context of global change.

microbiology↗

bioRxiv2021

Insights into biotic and abiotic modulation of ocean mesopelagic communities

For decades, marine plankton have been investigated for their capacity to modulate biogeochemical cycles and provide fishery resources. Between the sunlit (epipelagic) layer and the deep dark waters, lies a vast and heterogeneous part of the ocean: the mesopelagic zone. How plankton composition is shaped by environment has been well-explored in the epipelagic but much less in the mesopelagic ocean. Here, we conducted comparative analyses of trans-kingdom community assemblages thriving in the mesopelagic oxygen minimum zone (OMZ), mesopelagic oxic, and their epipelagic counterparts. We identified nine distinct types of intermediate water masses that correlate with variation in mesopelagic community composition. Furthermore, oxygen, NO - and particle flux together appeared as the main drivers governing these communities. Novel taxonomic signatures emerged from OMZ while a global co-occurrence network analysis showed that about 70% of the abundance of mesopelagic plankton groups is organized into three community modules. One module gathers prokaryotes, pico-eukaryotes and Nucleo-Cytoplasmic Large DNA Viruses (NCLDV) from oxic regions, and the two other modules are enriched in OMZ prokaryotes and OMZ pico-eukaryotes, respectively. We hypothesize that OMZ conditions led to a diversification of ecological niches, and thus communities, due to selective pressure from limited resources. Our study further clarifies the interplay between environmental factors in the mesopelagic oxic and OMZ, and the compositional features of communities.

ecology↗

bioRxiv2020

Environmental vulnerability of the global ocean plankton community interactome

Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles, and help regulate climate. Though global surveys are starting to reveal ecological drivers underlying planktonic community structure, and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here we leveraged Tara Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network - the community interactome - and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar), and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change, and forecasted most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios, while identifying plausible plankton bioindicators for ocean monitoring of climate change.

ecology↗

bioRxiv2020

Ecogenomics of key prokaryotes in the Arctic Ocean

The Arctic Ocean is a key player in the regulation of climate and at the same time is under increasing pressure as a result of climate change. Predicting the future of this ecosystem requires understanding of the responses of Arctic microorganisms to environmental change, as they are the main drivers of global biogeochemical cycles. However, little is known about the ecology and metabolic potential of active Arctic microbes. Here, we reconstructed a total of 3,550 metagenomic bins from 41 seawater metagenomes collected as part of the Tara Oceans expedition, covering five different Arctic Ocean regions as well as the sub-Arctic North Atlantic Ocean and including various depths and different seasons (spring to autumn). Of these bins, 530 could be classified as Metagenome Assembled Genomes (MAGs) and over 75% of them represented novel species. We describe their habitat range and environmental preferences, as well as their metabolic capabilities, building the most comprehensive dataset of uncultured bacterial and archaeal genomes from the Arctic Ocean to date. We found a prevalence of mixotrophs, while chemolithoautotrophs were mostly present in the mesopelagic Arctic Ocean during spring and autumn. Finally, the catalogue of Arctic MAGs was complemented with metagenomes and metatranscriptomes from the global ocean to identify the most active MAGs present exclusively in polar metagenomes. These polar MAGs, which display a range of metabolic strategies, might represent Arctic sentinels of climate change and should be considered in prospective studies of the future state of the Arctic Ocean.

ecology↗

bioRxiv2020

Dissecting the microbiome of a polyketide-producing ascidian across the Anvers Island archipelago, Antarctica

Polar marine ecosystems hold the potential for bioactive compound biodiscovery, based on their untapped macro- and microorganismal diversity. Characterization of polar benthic marine invertebrate-associated microbiomes is limited to few studies. This study was motivated by our interest in better understanding the microbiome structure and composition of the ascidian, Synoicum adareanum, in which the bioactive macrolide that has specific activity to melanoma, palmerolide A (PalA), was found. PalA bears structural resemblance to a combined nonribosomal peptide polyketide, that has similarities to microbially-produced macrolides. We conducted a spatial survey to assess both PalA levels and microbiome composition in S. adareanum in a region of the Antarctic Peninsula near Anvers Island (64{degrees} 46'S, 64{degrees} 03'W). PalA was ubiquitous and abundant across a collection of 21 ascidians (3 subsamples each) sampled from seven sites across the Anvers Island archipelago. The microbiome composition (V3-V4 16S rRNA gene sequence variants) of these 63 samples revealed a core suite of 21 bacteria, 20 of which were distinct from regional bacterioplankton. Co-occurrence analysis yielded several potentially interacting subsystems and, although the levels of PalA detected were not found to correlate with specific sequence variants, the core members appeared to occur in a preferred optimum and tolerance range of PalA levels. Taking these results together with an analysis of biosynthetic potential of related microbiome taxa indicates a core microbiome with substantial promise for natural product biosynthesis that likely interact with the host and with each other.

microbiology↗

bioRxiv2019

Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems

Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. Here we assessed the global structure of plankton geography and its relation to the biological, chemical and physical context of the ocean (the seascape) by analyzing metagenomes of plankton communities sampled across oceans during the Tara Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity. We found robust evidence for a basin-scale impact of transport by ocean currents on plankton biogeography, and on a characteristic timescale of community dynamics going beyond simple seasonality or life history transitions of plankton.

ecology↗