Biology subjects

Aite, M.

Publications and source records attributed to Aite, M..

5 recordsLinked to original sources

bioRxiv2022

AuCoMe: inferring and comparing metabolisms across heterogeneous sets of annotated genomes

Comparative analysis of Genome-Scale Metabolic Networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe - a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three datasets, one bacterial, one fungal, and one algal, and demonstrated that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared metabolic traits and divergence points among evolutionarily distant species, such as algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

systems biology↗

bioRxiv2021

Changing microbial activities during low salinity acclimation in the brown alga Ectocarpus subulatus

Ectocarpus subulatus is one of the few brown algae found in river habitats. Its ability to tolerate freshwater is due, in part, to its uncultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory-grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. The acclimation to low salinity of fresh water-tolerant and intolerant holobionts was then compared. Salinity had a significant impact on bacterial gene expression as well as the expression of algae- and bacteria-associated viruses in all holobionts, albeit in different ways for each holobiont. On the other hand, gene expression of the algal host and metabolite profiles were affected almost exclusively in the fresh water intolerant holobiont. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, we identified vitamin K synthesis as one possible bacterial service missing specifically in the fresh water-intolerant holobiont in low salinity. We also noticed an increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI-1, a compound that regulates quorum sensing. This could have caused a shift in bacterial behavior in the intolerant holobiont, resulting in virulence or dysbiosis. Originality-Significance StatementThe importance of symbiotic microbes for the health and stress resistance of multicellular eukaryotes is widely acknowledged, but understanding the mechanisms underlying these interactions is challenging. They are especially difficult to separate in systems with one or more uncultivable components. We bridge the gap between fully controlled, cultivable model systems and purely environmental studies through the use of a multi-omics approach and metabolic models on experimentally modified "holobiont" systems. This allows us to generate two promising working hypotheses on the mechanisms by which uncultivated bacteria influence their brown algal hosts fresh water tolerance.

ecology↗

bioRxiv2019

Metage2Metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species

Capturing the functional diversity of microbiotas entails identifying metabolic functions and species of interest within hundreds or thousands. Starting from genomes, a way to functionally analyse genetic information is to build metabolic networks. Yet, no method enables a functional screening of such a large number of metabolic networks nor the identification of critical species with respect to metabolic cooperation.\n\nMetage2Metabo (M2M) addresses scalability issues raised by metagenomics datasets to identify keystone, essential and alternative symbionts in large microbiotas communities with respect to individual metabolism and collective metabolic complementarity. Genome-scale metabolic networks for the community can be either provided by the user or very efficiently reconstructed from a large family of genomes thanks to a multi-processing solution to run the Pathway Tools software. The pipeline was applied to 1,520 genomes from the gut microbiota and 913 metagenome-assembled genomes of the rumen microbiota. Reconstruction of metabolic networks and subsequent metabolic analyses were performed in a reasonable time.\n\nM2M identifies keystone, essential and alternative organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

bioinformatics↗

bioRxiv2018

Inferring biochemical reactions and metabolite structures to cope with metabolic pathway drift

Inferring genome-scale metabolic networks in emerging model organisms is challenging because of incomplete biochemical knowledge and incomplete conservation of biochemical pathways during evolution. This limits the possibility to automatically transfer knowledge from well-established model organisms. Therefore, specific bioinformatic tools are necessary to infer new biochemical reactions and new metabolic structures that can be checked experimentally. Using an integrative approach combining both genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterol or mycosporine-like amino acids (MAA) synthesis pathways, undergo substantial turnover. This phenomenon, which we formally define 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 new methodological approach to formalize the logical reasoning necessary to infer new reactions and new molecular structures, based on previous biochemical knowledge. We use this approach to infer previously unknown reactions in the sterol and MAA pathways.\n\nAuthor summaryGenome-scale metabolic models describe our current understanding of all metabolic pathways occuring in a given organism. For emerging model species, where few biochemical data are available about really occurring enzymatic activities, such metabolic models are mainly based on transferring knowledge from other more studied species, based on the assumption that the same genes have the same function in the compared species. However, integration of metabolomic data into genome-scale metabolic models leads to situations where gaps in pathways cannot be filled by known enzymatic reactions from existing databases. This is due to structural variation in metabolic pathways accross evolutionary time. In such cases, it is necessary to use complementary approaches to infer new reactions and new metabolic intermediates using logical reasoning, based on available partial biochemical knowledge. Here we present a proof of concept that this is feasible and leads to hypotheses that are precise enough to be a starting point for new experimental work.

systems biology↗

bioRxiv2018

The genome of Ectocarpus subulatus highlights unique mechanisms for stress tolerance in brown algae

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 Kutzing, 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.

genomics↗