Genome-scale community modelling reveals conserved metabolic cross-feedings in epipelagic bacterioplankton communities.

Marine microorganisms form complex communities of interacting organisms that influence central ecosystem functions in the ocean such as primary production and nutrient cycling. Identifying the mechanisms controlling their assembly and activities is a major challenge in microbial ecology. Here, we integrated Tara Oceans meta-omics data to predict genome-scale community interactions within prokaryotic assemblages in the euphotic ocean. A global genome-resolved co-activity network revealed a significant number of inter-lineage associations across diverse phylogenetic distances. Identified co-active communities include species displaying smaller genomes but encoding a higher potential for quorum sensing, biofilm formation, and secondary metabolism. Community metabolic modelling reveals a higher potential for interaction within co-active communities and points towards conserved metabolic cross-feedings, in particular of specific amino acids and group B vitamins. Our integrated ecological and metabolic modelling approach suggests that genome streamlining and metabolic auxotrophies may act as joint mechanisms shaping bacterioplankton community assembly in the global ocean surface.

Damage Localization on Composite Structures Based on the Delay-and-Sum Algorithm Using Simulation and Experimental Methods.

Damage detection and localization based on ultrasonic guided waves revealed to be promising for structural health monitoring and nondestructive testing. However, the use of a piezoelectric sensor's network to locate and image damaged areas in composite structures requires a number of precautions including the consideration of anisotropy and baseline signals. The lack of information related to these two parameters drastically deteriorates the imaging performance of numerous signal processing methods. To avoid such deterioration, the present contribution proposes different methods to build baseline signals in different types of composites. Baseline signals are first constructed from a numerical simulation model using the previously determined elasticity tensor of the structure. Since the latter tensor is not always easy to obtain especially in the case of anisotropic materials, a second PZT network is used in order to obtain signals related to Lamb waves propagating in different directions. Waveforms are then translated according to a simplified theoretical propagation model of Lamb waves in homogeneous structures. The application of the different methods on transversely isotropic, unidirectional and quasi-transversely isotropic composites allows to have satisfactory images that well represent the damaged areas with the help of the delay-and-sum algorithm.

Gut Microbiota Remodeling and Intestinal Adaptation to Lipid Malabsorption After Enteroendocrine Cell Loss in Adult Mice.

BACKGROUND & AIMS: Enteroendocrine cells (EECs) and their hormones are essential regulators of whole-body energy homeostasis. EECs sense luminal nutrients and microbial metabolites and subsequently secrete various hormones acting locally or at a distance. Impaired development of EECs during embryogenesis is life-threatening in newborn mice and humans due to compromised nutrient absorption. However, the physiological importance of the EEC system in adult mice has yet to be directedly studied. Herein, we aimed to determine the long-term consequences of a total loss of EECs in healthy adults on energy metabolism, intestinal transcriptome, and microbiota. METHODS: We depleted intestinal EECs by tamoxifen treatment of adult Neurog3fl/fl; Villin-CreERT2 male mice. We studied intestinal cell differentiation, food efficiency, lipid absorption, microbiota composition, fecal metabolites, and transcriptomic responses in the proximal and distal small intestines of mice lacking EECs. We also determined the high-fat diet-induced transcriptomic changes in sorted Neurog3eYFP/+ EECs. RESULTS: Induction of EEC deficiency in adults is not life-threatening unless fed with a high-fat diet. Under a standard chow diet, mice lose 10% of weight due to impaired food efficiency. Blood concentrations of cholesterol, triglycerides, and free fatty acids are reduced, and lipid absorption is impaired and delayed in the distal small intestine. Genes controlling lipogenesis, carbohydrate metabolism, and neoglucogenesis are upregulated. Microbiota composition is rapidly altered after EECs depletion and is characterized by decreased α-diversity. Bacteroides and Lactobacillus were progressively enriched, whereas Lachnospiraceae declined without impacting fecal short-chain fatty acid concentrations. CONCLUSIONS: EECs are dispensable for survival in adult male mice under a standard chow diet. The absence of EECs impairs intestinal lipid absorption, leading to transcriptomic and metabolic adaptations and remodeling of the gut microbiota.

Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway Drift.

Inferring genome-scale metabolic networks in emerging model organisms is challenged by incomplete biochemical knowledge and partial conservation of biochemical pathways during evolution. Therefore, specific bioinformatic tools are necessary to infer biochemical reactions and metabolic structures that can be checked experimentally. Using an integrative approach combining genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterols or mycosporine-like amino acid synthesis pathways, undergo substantial turnover. This phenomenon, here formally defined as "metabolic pathway drift," is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We present a proof of concept with a methodological approach to formalize the logical reasoning necessary to infer reactions and molecular structures, abstracting molecular transformations based on previous biochemical knowledge.

The genome of Ectocarpus subulatus - A highly stress-tolerant brown alga.

Brown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kützing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies.

Scalable and exhaustive screening of metabolic functions carried out by microbial consortia.

Motivation: The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far. Results: We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria. Availability and implementation: Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.

Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models.

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

BACKGROUND: The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. RESULTS: We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. CONCLUSION: The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Transcription factors in microalgae: genome-wide prediction and comparative analysis.

BACKGROUND: Studying transcription factors, which are some of the key players in gene expression, is of outstanding interest for the investigation of the evolutionary history of organisms through lineage-specific features. In this study we performed the first genome-wide TF identification and comparison between haptophytes and other algal lineages. RESULTS: For TF identification and classification, we created a comprehensive pipeline using a combination of BLAST, HMMER and InterProScan software. The accuracy evaluation of the pipeline shows its applicability for every alga, plant and cyanobacterium, with very good PPV and sensitivity. This pipeline allowed us to identify and classified the transcription factor complement of the three haptophytes Tisochrysis lutea, Emiliania huxleyi and Pavlova sp.; the two stramenopiles Phaeodactylum tricornutum and Nannochloropsis gaditana; the chlorophyte Chlamydomonas reinhardtii and the rhodophyte Porphyridium purpureum. By using T. lutea and Porphyridium purpureum, this work extends the variety of species included in such comparative studies, allowing the detection and detailed study of lineage-specific features, such as the presence of TF families specific to the green lineage in Porphyridium purpureum, haptophytes and stramenopiles. Our comprehensive pipeline also allowed us to identify fungal and cyanobacterial TF families in the algal nuclear genomes. CONCLUSIONS: This study provides examples illustrating the complex evolutionary history of algae, some of which support the involvement of a green alga in haptophyte and stramenopile evolution.