Biology subjects

Trottier, C.

Publications and source records attributed to Trottier, C..

7 recordsLinked to original sources

bioRxiv2026

pyreml: a general-purpose, GPU-accelerated mixed model solver in Python

Linear mixed models (LMM) are a popular modeling and inference framework, widely used in bioinformatics and other scientific fields. To obtain an unbiased estimation of the variance components, an LMM has to be fitted using restricted maximum likelihood (REML), a computationally intensive step. Most REML software is closed source, and currently there is no Python library offering complete LMM functionalities, such as the specification of spatial and genomic variance structures, multivariate analyses, random regression and factors, and heteroscedastic residuals. Here we propose pyreml, an LMM solver that fills this gap, comes with a user-centered interface and speeds up calculations by running on GPU. We show that pyreml fits a genomic best linear unbiased prediction (GBLUP) model more than one order of magnitude (>12\times) faster than the current state-of-the-art, closed source REML software. We also show that \pyreml can estimate a latent factor-analytic variance structure, a challenging feature that no current open source REML solution implements. Finally, we illustrate the use of pyreml to analyze longitudinal data, and to perform spatial analysis. Anchored by 500+ functional tests assessing the equivalence of its outputs to those from reference software (lme4, rrBLUP and BLUPF90), we demonstrate pyreml to be a performant, reliable and open-source solution available for academic, research and industrial LMM users.

bioinformatics↗

bioRxiv2026

Fecal extracellular vesicles from adults with autism spectrum disorder affect mouse behavior and gut motility by targeting the neuronal nitrergic pathway

Beside behavioral symptoms, many patients with autism spectrum disorder (ASD) present gastrointestinal disorders and gut microbiota dysbiosis. In order to better understand the mechanisms underlying microbiota-gut-brain interactions in ASD, we isolated fecal extracellular vesicles (EVs), as a source of microbiota-derived EVs, from control individuals (f-EV-CTL) and patients with ASD (f-EV-ASD) to study in mice their impact on brain and gut. The f-EV-ASD induced hypersocial and anxiolytic-like behavior, and reduced gastrointestinal transit time compared to mice treated with f-EV-CTL. In the colon, f-EV-ASD altered motility through the enteric neuronal nitric oxide (NO)-sensitive component. f-EV-ASD potentiated NO signaling and disrupted intracellular calcium dynamics in enteric neurons. This effect was mediated by butyrate and valerate through FFAR3 receptor. Our findings establish fecal EVs as critical mediators of microbiota-host communication, linking microbial metabolites to altered behavior and gut function and identifying a potential mechanism underlying ASD pathophysiology.

physiology↗

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

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 large phylogenetic distances. Identified co-active communities included species displaying smaller genomes but encoding a higher potential for quorum sensing, biofilm formation, and secondary metabolism. Community metabolic modelling revealed a higher potential for interaction within co-active communities and pointed towards conserved metabolic cross-feedings, in particular of specific amino acids and group B vitamins. Our integrated ecological and metabolic modelling approach indicates genome streamlining and metabolic auxotrophies as central joint mechanisms shaping bacterioplankton community assembly in the surface global ocean.

ecology↗

bioRxiv2022

Combined in vivo and in situ genome-resolved metagenomics reveals novel symbiotic nitrogen fixing interactions between non-cyanobacterial diazotrophs and microalgae

BackgroundNon-cyanobacteria diazotrophs (NCDs) were shown to dominate in surface waters shifting the long-held paradigm of cyanobacteria dominance and raising fundamental questions on how these putative heterotrophic bacteria thrive in sunlit oceans. The absence of laboratory cultures of these bacteria significantly limits our ability to understand their behavior in natural environments and, consequently, their contribution to the marine nitrogen cycle. ResultsHere, we used a multidisciplinary approach and report an unprecedented finding in the diatom Phaeodactylum tricornutum (Pt) of NCDs in the phycosphere or the pelagic community sustaining its survival in the absence of bioavailable nitrogen. We sequenced the bacterial metacommunity associated with Pt and assembled several bacterial genomes, identifying multiple NCDs from the Rhizobiales order, including Bradyrhizobium, Mesorhizobium, Georhizobium and Methylobacterium. We demonstrated the nitrogen-fixing ability of PtNCDs through in silico identification of nitrogen fixation genes, or by using PCR, acetylene reduction, or 15N incorporation. We showed the wide occurrence of this type of interactions with the isolation of NCDs from other microalgae, their identification in the environment, and their predicted associations with photosynthetic microalgae. ConclusionsOur study underscores the importance of microalgae interactions with NCDs to permit and support nitrogen fixation. This work provides a unique model Pt-NCDs to study the ecology of this interaction advancing our understanding of the key drivers of global marine nitrogen fixation.

microbiology↗

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↗