Contribution of genome-scale metabolic modelling to niche theory.

Standard niche modelling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome-scale descriptions of these organisms. This study overcomes this shortcoming by proposing a new conceptual niche that resumes 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, emphasising that the presence-absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organism's 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 genome-scale description in ecological studies.

Plankton networks driving carbon export in the oligotrophic ocean.

The biological carbon pump is the process by which CO2 is transformed to organic carbon via photosynthesis, exported through sinking particles, and finally sequestered in the deep ocean. While the intensity of the pump correlates with plankton community composition, the underlying ecosystem structure driving the process remains largely uncharacterized. Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve our understanding of carbon export in the oligotrophic ocean. We show that specific plankton communities, from the surface and deep chlorophyll maximum, correlate with carbon export at 150 m and highlight unexpected taxa such as Radiolaria and alveolate parasites, as well as Synechococcus and their phages, as lineages most strongly associated with carbon export in the subtropical, nutrient-depleted, oligotrophic ocean. Additionally, we show that the relative abundance of a few bacterial and viral genes can predict a significant fraction of the variability in carbon export in these regions.

Uncovering the Core Microbiome and Distribution of Palmerolide in Synoicum adareanum Across the Anvers Island Archipelago, Antarctica.

Polar marine ecosystems hold the potential for bioactive compound biodiscovery, based on their untapped macro- and microorganism 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 palmerolide A (PalA), a bioactive macrolide with specificity against melanoma, was isolated. PalA bears structural resemblance to a hybrid 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° 46'S, 64° 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 bacterial amplicon sequence variants (ASVs)-20 of which were distinct from regional bacterioplankton. ASV co-occurrence analysis across all 63 samples yielded subgroups of taxa that may be interacting biologically (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. These results, together with an analysis of the biosynthetic potential of related microbiome taxa, describe a conserved, high-latitude core microbiome with unique composition and substantial promise for natural product biosynthesis that likely influences the ecology of the holobiont.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

Deciphering transcriptional regulations coordinating the response to environmental changes.

BACKGROUND: Gene co-expression evidenced as a response to environmental changes has shown that transcriptional activity is coordinated, which pinpoints the role of transcriptional regulatory networks (TRNs). Nevertheless, the prediction of TRNs based on the affinity of transcription factors (TFs) with binding sites (BSs) generally produces an over-estimation of the observable TF/BS relations within the network and therefore many of the predicted relations are spurious. RESULTS: We present LOMBARDE, a bioinformatics method that extracts from a TRN determined from a set of predicted TF/BS affinities a subnetwork explaining a given set of observed co-expressions by choosing the TFs and BSs most likely to be involved in the co-regulation. LOMBARDE solves an optimization problem which selects confident paths within a given TRN that join a putative common regulator with two co-expressed genes via regulatory cascades. To evaluate the method, we used public data of Escherichia coli to produce a regulatory network that explained almost all observed co-expressions while using only 19 % of the input TF/BS affinities but including about 66 % of the independent experimentally validated regulations in the input data. When all known validated TF/BS affinities were integrated into the input data the precision of LOMBARDE increased significantly. The topological characteristics of the subnetwork that was obtained were similar to the characteristics described for known validated TRNs. CONCLUSIONS: LOMBARDE provides a useful modeling scheme for deciphering the regulatory mechanisms that underlie the phenotypic responses of an organism to environmental challenges. The method can become a reliable tool for further research on genome-scale transcriptional regulation studies.

The genome-scale metabolic network of Ectocarpus siliculosus (EctoGEM): a resource to study brown algal physiology and beyond.

Brown algae (stramenopiles) are key players in intertidal ecosystems, and represent a source of biomass with several industrial applications. Ectocarpus siliculosus is a model to study the biology of these organisms. Its genome has been sequenced and a number of post-genomic tools have been implemented. Based on this knowledge, we report the reconstruction and analysis of a genome-scale metabolic network for E. siliculosus, EctoGEM (http://ectogem.irisa.fr). This atlas of metabolic pathways consists of 1866 reactions and 2020 metabolites, and its construction was performed by means of an integrative computational approach for identifying metabolic pathways, gap filling and manual refinement. The capability of the network to produce biomass was validated by flux balance analysis. EctoGEM enabled the reannotation of 56 genes within the E. siliculosus genome, and shed light on the evolution of metabolic processes. For example, E. siliculosus has the potential to produce phenylalanine and tyrosine from prephenate and arogenate, but does not possess a phenylalanine hydroxylase, as is found in other stramenopiles. It also possesses the complete eukaryote molybdenum co-factor biosynthesis pathway, as well as a second molybdopterin synthase that was most likely acquired via horizontal gene transfer from cyanobacteria by a common ancestor of stramenopiles. EctoGEM represents an evolving community resource to gain deeper understanding of the biology of brown algae and the diversification of physiological processes. The integrative computational method applied for its reconstruction will be valuable to set up similar approaches for other organisms distant from biological benchmark models.

A metabolic approach to study algal-bacterial interactions in changing environments.

Increasing evidence exists that bacterial communities interact with and shape the biology of algae and that their evolutionary histories are connected. Despite these findings, physiological studies were and still are generally carried out with axenic or at least antibiotic-treated cultures. Here, we argue that considering interactions between algae and associated bacteria is key to understanding their biology and evolution. To deal with the complexity of the resulting 'holobiont' system, a metabolism-centred approach that uses combined metabolic models for algae and associated bacteria is proposed. We believe that these models will be valuable tools both to study algal-bacterial interactions and to elucidate processes important for the acclimation of the holobiont to environmental changes.

Ecological associations distribution modelling of marine plankton at a global scale.

Marine plankton communities form intricate networks of interacting organisms at the base of the food chain, and play a central role in regulating ocean biogeochemical cycles and climate. However, predicting plankton community shifts in response to climate change remains challenging. While species distribution models are valuable tools for predicting changes in species biogeography under climate change scenarios, they generally overlook the key role of biotic interactions, which can significantly shape ecological processes and ecosystem responses. Here, we introduce a novel statistical framework, association distribution modelling (ADM), designed to model and predict ecological associations distribution in space and time. Applied on a Tara Oceans genome-resolved metagenomics dataset, the present-day biogeography of ADM-inferred marine plankton associations revealed four major biogeographic biomes organized along a latitudinal gradient. We predicted the evolution of these biome-specific communities in response to a climate change scenario, highlighting differential responses to environmental change. Finally, we explored the functional potential of impacted plankton communities, focusing on carbon fixation, outlining the predicted evolution of its geographical distribution and implications for ecosystem function.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Datascape: exploring heterogeneous dataspace.

Data science is a powerful field for gaining insights, comparing, and predicting behaviors from datasets. However, the diversity of methods and hypotheses needed to abstract a dataset exhibits a lack of genericity. Moreover, the shape of a dataset, which structures its contained information and uncertainties, is rarely considered. Inspired by state-of-the-art manifold learning and hull estimations algorithms, we propose a novel framework, the datascape, that leverages topology and graph theory to abstract heterogeneous datasets. Built upon the combination of a nearest neighbor graph, a set of convex hulls, and a metric distance that respects the shape of the data, the datascape allows exploration of the dataset's underlying space. We show that the datascape can uncover underlying functions from simulated datasets, build predictive algorithms with performance close to state-of-the-art algorithms, and reveal insightful geodesic paths between points. It demonstrates versatility through ecological, medical, and simulated data use cases.

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 organism's 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 cell's maintenance.

Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean.

Microbial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibiting highly heterogeneous distributions. A few potential interactions were global, meaning they occurred across regions at the same depth, while 11-36% were regional within specific depths. The bathypelagic zone had the lowest proportion of global associations, and regional associations increased with depth. Moreover, we observed that most surface water associations do not persist in deeper ocean layers despite microbial vertical dispersal. Our work contributes to a deeper understanding of the tropical and subtropical global ocean interactome, which is essential for addressing the challenges posed by global change.

Unveiling abundance-dependent metabolic phenotypes of microbial communities.

IMPORTANCE: In nature, organisms live in communities and not as isolated species, and 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 that, only using genomic 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 light of the degrees of metabolic plasticity inherent to the community.

Disentangling temporal associations in marine microbial networks.

BACKGROUND: Microbial interactions are fundamental for Earth's 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. RESULTS: A 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. CONCLUSIONS: Our 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. Video Abstract.

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

The ever-increasing number of available microbial genomes and metagenomes provides new opportunities to investigate the links between niche partitioning and genome evolution in the ocean, especially 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 show that picocyanobacterial communities thriving in different niches possess distinct gene repertoires. We also identify clusters of adjacent genes that display specific distribution patterns in the field (eCAGs) and are thus potentially involved in the same metabolic pathway and may have a key role in niche adaptation. Several eCAGs 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 example for the biosynthesis of proteins or DNA, or degraded to inorganic nitrogen and/or phosphorus forms. We also highlight the enrichment of eCAGs involved in polysaccharide capsule biosynthesis in Synechococcus populations thriving in both nitrogen- and phosphorus-depleted areas vs. low-iron (Fe) regions, suggesting that the complexes they encode may be too energy-consuming for picocyanobacteria thriving in the latter areas. In contrast, Prochlorococcus populations thriving in Fe-depleted areas specifically possess an alternative respiratory terminal oxidase, potentially involved in the reduction of Fe(III) to Fe(II). Altogether, this study provides insights into how phytoplankton communities populate oceanic ecosystems, which is relevant to understanding their capacity to respond to ongoing climate change.

Ocean-wide comparisons of mesopelagic planktonic community structures.

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, NO3- 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.

Environmental factors determining ammonia-oxidizing organism distribution and diversity in marine environments.

Ammonia-oxidizing bacteria (AOB) and archaea (AOA) play a vital role in bridging the input of fixed nitrogen, through N-fixation and remineralization, to its loss by denitrification and anammox. Yet the major environmental factors determining AOB and AOA population dynamics are little understood, despite both groups having a wide environmental distribution. This study examined the relative abundance of both groups of ammonia-oxidizing organisms (AOO) and the diversity of AOA across large-scale gradients in temperature, salinity and substrate concentration and dissolved oxygen. The relative abundance of AOB and AOA varied across environments, with AOB dominating in the freshwater region of the Chesapeake Bay and AOA more abundant in the water column of the coastal and open ocean. The highest abundance of the AOA amoA gene was recorded in the oxygen minimum zones (OMZs) of the Eastern Tropical South Pacific (ETSP) and the Arabian Sea (AS). The ratio of AOA : AOB varied from 0.7 in the Chesapeake Bay to 1600 in the Sargasso Sea. Relative abundance of both groups strongly correlated with ammonium concentrations. AOA diversity, as determined by phylogenetic analysis of clone library sequences and archetype analysis from a functional gene DNA microarray, detected broad phylogenetic differences across the study sites. However, phylogenetic diversity within physicochemically congruent stations was more similar than would be expected by chance. This suggests that the prevailing geochemistry, rather than localized dispersal, is the major driving factor determining OTU distribution.

Integrating quantitative knowledge into a qualitative gene regulatory network.

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments.

Microbial community functioning during plant litter decomposition.

Microbial life in soil is fueled by dissolved organic matter (DOM) that leaches from the litter layer. It is well known that decomposer communities adapt to the available litter source, but it remains unclear if they functionally compete or synergistically address different litter types. Therefore, we decomposed beech, oak, pine and grass litter from two geologically distinct sites in a lab-scale decomposition experiment. We performed a correlative network analysis on the results of direct infusion HR-MS DOM analysis and cross-validated functional predictions from 16S rRNA gene amplicon sequencing and with DOM and metaproteomic analyses. Here we show that many functions are redundantly distributed within decomposer communities and that their relative expression is rapidly optimized to address litter-specific properties. However, community changes are likely forced by antagonistic mechanisms as we identified several natural antibiotics in DOM. As a consequence, the decomposer community is specializing towards the litter source and the state of decomposition (community divergence) but showing similar litter metabolomes (metabolome convergence). Our multi-omics-based results highlight that DOM not only fuels microbial life, but it additionally holds meta-metabolomic information on the functioning of ecosystems.

Diversity and ecological footprint of Global Ocean RNA viruses.

DNA viruses are increasingly recognized as influencing marine microbes and microbe-mediated biogeochemical cycling. However, little is known about global marine RNA virus diversity, ecology, and ecosystem roles. In this study, we uncover patterns and predictors of marine RNA virus community- and "species"-level diversity and contextualize their ecological impacts from pole to pole. Our analyses revealed four ecological zones, latitudinal and depth diversity patterns, and environmental correlates for RNA viruses. Our findings only partially parallel those of cosampled plankton and show unexpectedly high polar ecological interactions. The influence of RNA viruses on ecosystems appears to be large, as predicted hosts are ecologically important. Moreover, the occurrence of auxiliary metabolic genes indicates that RNA viruses cause reprogramming of diverse host metabolisms, including photosynthesis and carbon cycling, and that RNA virus abundances predict ocean carbon export.

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.

Oceans are brimming with life invisible to our eyes, a myriad of species of bacteria, viruses and other microscopic organisms essential for the health of the planet. These 'marine plankton' are unable to swim against currents and should therefore be constantly on the move, yet previous studies have suggested that distinct species of plankton may in fact inhabit different oceanic regions. However, proving this theory has been challenging; collecting plankton is logistically difficult, and it is often impossible to distinguish between species simply by examining them under a microscope. However, within the last decade, a research schooner called Tara has travelled the globe to gather thousands of plankton samples. At the same time, advances in genomics have made it possible to identify species based only on fragments of their DNA sequence. To understand the hidden geography of plankton communities in Earth's oceans, Richter et al. pored over DNA from the Tara Oceans expedition. This revealed that, despite being unable to resist the flow of water, various planktonic species which live close to the surface manage to occupy distinct, stable provinces shaped by currents. Different sizes of plankton are distributed in different sized provinces, with the smallest organisms tending to inhabit the smallest areas. Comparing DNA similarities and speeds of currents at the ocean surface revealed how these might stretch and mix plankton communities. Plankton play a critical role in the health of the ocean and the chemical cycles of planet Earth. These results could allow deeper investigation by marine modellers, ecologists, and evolutionary biologists. Meanwhile, work is already underway to investigate how climate change might impact this hidden geography.