Damien Eveillard

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.

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.

REVIEW: Predictive ecology in a changing world

1. In a rapidly changing world, ecology has the potential to move from empirical and conceptual stages to application and management issues. It is now possible to make large-scale predictions up to continental or global scales, ranging from the future distribution of biological diversity to changes in ecosystem functioning and services. With these recent developments, ecology has a historical opportunity to become a major actor in the development of a sustainable human society. With this opportunity, however, also comes an important responsibility in developing appropriate predictive models, correctly interpreting their outcomes and communicating their limitations. There is also a danger that predictions grow faster than our understanding of ecological systems, resulting in a gap between the scientists generating the predictions and stakeholders using them (conservation biologists, environmental managers, journalists, policymakers). 2. Here, we use the context provided by the current surge of ecological predictions on the future of biodiversity to clarify what prediction means, and to pinpoint the challenges that should be addressed in order to improve predictive ecological models and the way they are understood and used. 3. Synthesis and applications. Ecologists face several challenges to ensure the healthy development of an operational predictive ecological science: (i) clarity on the distinction between explanatory and anticipatory predictions; (ii) developing new theories at the interface between explanatory and anticipatory predictions; (iii) open data to test and validate predictions; (iv) making predictions operational; and (v) developing a genuine ethics of prediction.

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.

Seasonal and annual reoccurrence in betaproteobacterial ammonia‐oxidizing bacterial population structure

Microbes exhibit remarkably high genetic diversity compared with plant and animal species. Many phylogenetically diverse but apparently functionally redundant microbial taxa are detectable within a cubic centimetre of mud or a millilitre of water, and the significance of this diversity, in terms of ecosystem function, has been difficult to understand. Thus it is not known whether temporal and spatial differences in microbial community composition are linked to particular environmental factors or might modulate ecosystem response to environmental change. Fifty-three water and sediment samples from upper and lower Chesapeake Bay were analysed in triplicate arrays to determine temporal and spatial patterns and relationships between ammonia-oxidizing bacterial (AOB) communities and environmental variables. Thirty-three water samples (three depths) collected during April, August and October, 2001-2004, from the oligohaline upper region of the Bay were analysed to investigate temporal patterns in archetype distribution. Using a combination of a non-weighted discrimination analysis and principal components analysis of community composition data obtained from functional gene microarrays, it was found that co-varying AOB assemblages reoccurred seasonally in concert with specific environmental conditions, potentially revealing patterns of niche differentiation. Among the most notable patterns were correlations of AOB archetypes with temperature, DON and ammonium concentrations. Different AOB archetypes were more prevalent at certain times of the year, e.g. some were more abundant every autumn and others every spring. This data set documents the successional return to an indigenous community following massive perturbation (hurricane induced flooding) as well as the seasonal reoccurrence of specific lineages, identified by key functional genes, associated with the biogeochemically important process nitrification.

Beyond the Black Queen Hypothesis.

The Black Queen Hypothesis, recently proposed to explain an evolution of dependency based on gene loss, is gaining ground. This paper focuses on how the evolution of dependency transforms interactions and the community. Using agent-based modeling we suggest that species specializing in the consumption of a common good escape competition and therefore favor coexistence. This evolutionary trajectory could open the way for novel long-lasting interactions and a need to revisit the classically accepted assembly rules. Such evolutionary events also reshape the structure and dynamics of communities, depending on the spatial heterogeneity of the common good production. Let Black be the new black!

A shift in the archaeal nitrifier community in response to natural and anthropogenic disturbances in the northern Gulf of Mexico

The Gulf of Mexico is affected by hurricanes and suffers seasonal hypoxia. The Deepwater Horizon oil spill impacted every trophic level in the coastal region. Despite their importance in bioremediation and biogeochemical cycles, it is difficult to predict the responses of microbial communities to physical and anthropogenic disturbances. Here, we quantify sediment ammonia-oxidizing archaeal (AOA) community diversity, resistance and resilience, and important geochemical factors after major hurricanes and the oil spill. Dominant AOA archetypes correlated with different geochemical factors, suggesting that different AOA are constrained by distinct parameters. Diversity was lowest after the hurricanes, showing weak resistance to physical disturbances. However, diversity was highest during the oil spill and coincided with a community shift, suggesting a new alternative stable state sustained for at least 1 year. The new AOA community was not significantly different from that at the spill site 1 year after the spill. This sustained shift in nitrifier community structure may be a result of oil exposure.

Time course gene expression in the one-carbon metabolism network using HepG2 cell line grown in folate-deficient medium

The integrated view of the expression of genes involved in folate-dependent one-carbon metabolism (FOCM) under folate deficiency remains unknown. Dynamics of changes in the transcriptional expression of 28 genes involved in the FOCM network were evaluated at different time points (0, 2, 4, 6, 12, 24 and 48 h) in human hepatoma HepG2 cell line. Combined experimental and computational approaches were conducted for emphasizing characteristic patterns in the gene expression changes produced by cellular folate deficiency. Bivariate analysis showed that folate deficiency (0.3 nmol/L of folate vs. 2.27 mumol/L in control medium) displayed rapid and coordinated regulation during the first 2 h with differential expression for hRfc1 (increased by 69%) and Ahcy (decreased by 437%). Density analysis through the time points gave evidence of differential expression for five genes (Ahcy, Cth, Gnmt, Mat1A, Mtrr and hRfc1). Differential expression of Ahcy, Gnmt, Mat1A and Mtrr was confirmed by time-series analysis gene expression. We also found a marked differential expression of Mtrr. Qualitative analysis of genes allowed identifying four clusters of gene that was coexpressed. Two of these clusters were consistent with specific metabolic functions as they associated genes involved in the remethylation (Mthfr and Mtrr) and in the transmethylation (Dnmt1and Dnmt3B) pathways. The study shows a strong influence of folate status on Mtrr transcription in HepG2 cells. It suggests also that folate deficiency produces transcription changes that particularly involve the clusters of genes related with the remethylation and the transmethylation pathways.

Temporal constraints of a gene regulatory network: Refining a qualitative simulation.

The modelling of gene regulatory networks (GRNs) has classically been addressed through very different approaches. Among others, extensions of Thomass asynchronous Boolean approach have been proposed, to better fit the dynamics of biological systems: genes may reach different discrete expression levels, depending on the states of other genes, called the regulators: thus, activations and inhibitions are triggered conditionally on the proper expression levels of these regulators. In contrast, some fine-grained propositions have focused on the molecular level as modelling the evolution of biological compound concentrations through differential equation systems. Both approaches are limited. The first one leads to an oversimplification of the system, whereas the second is incapable to tackle large GRNs. In this context, hybrid paradigms, that mix discrete and continuous features underlying distinct biological properties, achieve significant advances for investigating biological properties. One of these hybrid formalisms proposes to focus, within a GRN abstraction, on the time delay to pass from a gene expression level to the next. Until now, no research work has been carried out, which attempts to benefit from the modelling of a GRN by differential equations, converting it into a multi-valued logical formalism of Thomas, with the aim of performing biological applications. This paper fills this gap by describing a whole pipelined process which orchestrates the following stages: (i) model conversion from a piece-wise affine differential equation (PADE) modelization scheme into a discrete model with focal points, (ii) characterization of subgraphs through a graph simplification phase which is based on probabilistic criteria, (iii) conversion of the subgraphs into parametric linear hybrid automata, (iv) analysis of dynamical properties (e.g. cyclic behaviours) using hybrid model-checking techniques. The present work is the outcome of a methodological investigation launched to cope with the GRN responsible for the reaction of Escherichia coli bacterium to carbon starvation. As expected, we retrieve a remarkable cycle already exhibited by a previous analysis of the PADE model. Above all, hybrid model-checking enables us to infer temporal properties, whose biological signification is then discussed.

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.