Biology subjects

Belcour, A.

Publications and source records attributed to Belcour, A..

9 recordsLinked to original sources

bioRxiv2025

Predicting coarse-grained representations of biogeochemical cycles from metabarcoding data

MotivationTaxonomic analysis of environmental microbial communities is now routinely performed thanks to advances in DNA sequencing. Determining the role of these communities in global biogeochemical cycles requires the identification of their metabolic functions, such as hydrogen oxidation, sulfur reduction, and carbon fixation. These functions can be directly inferred from metagenomics data, but in many environmental applications metabarcoding is still the method of choice. The reconstruction of metabolic functions from metabarcoding data and their integration into coarse-grained representations of geobiochemical cycles remains a difficult bioinformatics problem today. ResultsWe developed a pipeline, called Tabigecy, which exploits taxonomic affiliations to predict metabolic functions constituting biogeochemical cycles. In a first step, Tabigecy uses the tool EsMeCaTa to predict consensus proteomes from input affiliations. To optimise this process, we generated a precomputed database containing information about 2,404 taxa from UniProt. The consensus proteomes are searched using bigecyhmm, a newly developed Python package relying on Hidden Markov Models to identify key enzymes involved in metabolic function of biogeochemical cycles. The metabolic functions are then projected on coarse-grained representation of the cycles. We applied Tabigecy to two salt cavern datasets and validated its predictions with microbial activity and hydrochemistry measurements performed on the samples. The results highlight the utility of the approach to investigate the impact of microbial communities on geobiochemical processes.

bioinformatics↗

bioRxiv2024

SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects.

The composition of the gut microbiota is a known factor in various diseases, and has proven to be a strong basis for automatic classification of disease state. A need for a better understanding of this community on the functional scale has since been voiced, as it would enhance these approaches biological interpretability. In this paper, we have developed a computational pipeline for integrating the functional annotation of the gut microbiota to an automatic classification process, and facilitating downstream interpretation of its results. The process takes as input taxonomic composition data (such as tables of Operational Taxonomic Unit (OTU) or Amplicon Sequence Variant (ASV) abundances), and links each component to its functional annotations through interrogation of the UniProt database. A functional profile of the gut microbiota is built from this basis. Both profiles, microbial and functional, are used to train Random Forest classifiers to discern unhealthy from control samples. An automatic variable selection is then performed on the basis of variable importance, and the method can be iterated until classification performances diminish. This process shows that the translation of the microbiota into functional profiles gives comparable, albeit slightly inferior performances when compared to microbial profiles. Through repetition, it also outputs a robust subset of discriminant variables. These selections were shown to be more reliable than those obtained by a state of the art method, and its contents were validated through a manual bibliographic research. The interconnections between selected OTUs and functional annotations were also analyzed, and revealed that important annotations emerge from the cumulated influence of non-selected OTUs.

bioinformatics↗

bioRxiv2024

Evolutionary genomics of the emergence of brown algae as key components of coastal ecosystems

Brown seaweeds are keystone species of coastal ecosystems, often forming extensive underwater forests, that are under considerable threat from climate change. Despite their ecological and evolutionary importance, this phylogenetic group, which is very distantly related to animals and land plants, is still poorly characterised at the genome level. Here we analyse 60 new genomes that include species from all the major brown algal orders. Comparative analysis of these genomes indicated the occurrence of several major events coinciding approximately with the emergence of the brown algal lineage. These included marked gain of new orthologous gene families, enhanced protein domain rearrangement, horizontal gene transfer events and the acquisition of novel signalling molecules and metabolic pathways. The latter include enzymes implicated in processes emblematic of the brown algae such as biosynthesis of the alginate-based extracellular matrix, and halogen and phlorotannin biosynthesis. These early genomic innovations enabled the adaptation of brown algae to their intertidal habitats. The subsequent diversification of the brown algal orders tended to involve loss of gene families, and genomic features were identified that correlated with the emergence of differences in life cycle strategy, flagellar structure and halogen metabolism. Analysis of microevolutionary patterns within the genus Ectocarpus indicated that deep gene flow between species may be an important factor in genome evolution on more recent timescales. Finally, we show that integration of large viral genomes has had a significant impact on brown algal genome content and propose that this process has persisted throughout the evolutionary history of the lineage.

genomics↗

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↗

bioRxiv2022

EsMeCaTa: Estimating metabolic capabilities from taxonomic affiliations

PurposeMetabarcoding, and metagenomic sequencing have enabled the characterization of highly diverse environmental communities. The challenge of estimating the metabolic functions carried out by these communities has led to the development of several state-of-the-art methods, most of which are tailored to a specific gene marker. However, the increasing diversity of approaches resulting from advances in sequencing technologies drives the need for methods capable of handling heterogeneous microbial community data. Moreover, predictions often depend on their internal analysis pipelines and are influenced by the underlying databases, which link marker genes to specific functional annotations. This limits users ability to evaluate the quality of predictions by tracing internal data and processes. Finally, users are constrained by the specific annotations provided by these methods (e.g. EC numbers), limiting their ability to conduct further specialized analyses based on intermediate results. MethodsEsMeCaTa predicts consensus proteomes and their associated functions from taxonomic affiliations. A key feature of EsMeCaTa is its explainability and flexibility. To support the flexible integration of heterogeneous sequencing data, EsMeCaTa utilizes taxonomic affiliations obtained through analyses of diverse sequencing datasets. To provide insight into the knowledge available for each taxonomic affliation and to interpret the relevance of predicted functions, EsMeCaTa identifies a taxonomic rank within a given affliation that is suffciently represented by documented proteomes in the UniProt database. The proteins of the UniProt proteomes are clustered and filtered according to a threshold to create consensus proteomes. These consensus proteomes are automatically annotated with functional information (e.g., EC numbers, GO terms) but they are also designed to be used in further customized annotation workflows. Functional annotations are reported in a functional table, which can be enriched with taxon abundances to generate comprehensive functional profiles. ResultsEsMeCaTa predictions have been validated using multiple datasets and compared to a state-of-the-art method. Additionally, it was applied to a novel metabarcoding dataset from a methanogenic reactor, characterizing the microbial community and biogas production across different time points and intake condition. Our results demonstrate the link between biogas production, intake condition and the dynamics of the metabolic functions predicted by EsMeCaTa in the microbial communities.

bioinformatics↗

bioRxiv2020

Soil microbiota influences clubroot disease by modulating Plasmodiophora brassicae and Brassica napus transcriptomes

The contribution of surrounding plant microbiota to disease development has led to the postulation of the pathobiome concept, which represents the interaction between the pathogen, the host-plant, and the associated biotic microbial community, resulting or not in plant disease. The structure, composition and assembly of different plant-associated microbial communities (soil, rhizosphere, leaf, root) are more and more described, both in healthy and infected plants. A major goal is now to shift from descriptive to functional studies of the interaction, in order to gain a mechanistic understanding of how microbes act on plant growth and defense, and/or on pathogen development and pathogenicity. The aim herein is to understand how the soil microbial environment may influence the functions of a pathogen and its pathogenesis, as well as the molecular response of the plant to the infection, with a dual-RNAseq transcriptomics approach. We address this question using Brassica napus and Plasmodiophora brassicae, the pathogen responsible for clubroot. A time-course experiment was conducted to study interactions between P. brassicae, two B. napus genotypes, and three soils harboring High (H), Medium (M) or Low (L) microbiota diversities and displaying different levels of richness and diversity. The soil microbial diversity levels had an impact on disease development (symptom levels and pathogen quantity). The P. brassicae and B. napus transcriptional patterns were modulated by these microbial diversities, and the modulations were dependent of the host genotype plant and the kinetic time. The functional analysis of gene expressions allowed the identification of pathogen and plant-host functions potentially involved in the change of plant disease level, such as pathogenicity-related genes (NUDIX effector) in P. brassicae and plant defense-related genes (glucosinolate metabolism) in B. napus. Author summaryThe untapped soil microbiota diversity can influence plant tolerance and resistance to several pests. A better understanding of the mechanisms underlying the plant / pests / microbiota interaction is required to contribute to the improvement of new plant protection methods taking into account sustainability, respect for the environment, and low input utilization. Our work showed that in the Plasmodiophora brassicae / Brassica napus pathosystem, the soil microbiota diversity modulated the disease symptom level and the pathogen development. We discovered that soil microbial composition modulated both the pathogen and the plant expression genes profiles. On one hand, the pathogen transcriptome was mainly modulated by the microbial communities at the end of infection, when the pathogen infects a susceptible plant genotype, and the expression of genes potentially involved in growth and pathogenicity was affected. On the other hand, the plant transcriptome was more modulated by the microbial communities at the early step of infection, in the most resistant genotype and the expression of genes potentially involved in defense was affected. This study provides new insights into the molecular basis of soil microbiota-mediated modulation of plant pest diseases.

pathology↗

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

Computational analysis of the Plasmodiophora brassicae genome: mitochondrial sequence description and metabolic pathway database design

Plasmodiophora brassicae is an obligate biotrophic pathogenic protist responsible for clubroot, a root gall disease of Brassicaceae species. In addition to the reference genome of the P. brassicae European e3 isolate and the draft genomes of Canadian or Chinese isolates, we present the genome of eH, a second European isolate. Refinement of the annotation of the eH genome led to the identification of the mitochondrial genome sequence, which was found to be bigger than that of Spongospora subterranea, another plant parasitic Plasmodiophorid phylogenetically related to P. brassicae. New pathways were also predicted, such as those for the synthesis of spermidine, a polyamine up-regulated in clubbed regions of roots. A P. brassicae pathway genome database was created to facilitate the functional study of metabolic pathways in transcriptomics approaches. These available tools can help in our understanding of the regulation of P. brassicae metabolism during infection and in response to diverse constraints.

genomics↗