Biology subjects

Frainay, C.

Publications and source records attributed to Frainay, C..

9 recordsLinked to original sources

bioRxiv2026

Deciphering the links between metabolism and health by building small-scale knowledge graphs: application to endometriosis and persistent pollutants

MotivationKnowledge graphs (KGs) are a robust formalism for structuring biomedical knowledge, but large-scale KGs often require complex queries, are difficult for non-experts to explore, and lack real-world context (such as experimental data, clinical conditions, patients symptoms). This limits their usability for addressing specific research questions. ResultsWe present Kg4j, a computational framework built on FORVM (a large-scale KG containing 82 million compound-biological concept associations), that constructs local, keyword-based sub-graphs tailored to address biomedical research questions. Resulting graphs support hypothetical relationships and can integrate experimental datasets, enabling the discovery of plausible but yet unknown connections. Starting from a conceptual definition of a research field of interest (e.g., disease, symptoms, exposure), the framework extracts relevant associations from FORVM and identifies potential biological mechanisms and chemical compounds. We applied this approach to endometriosis, exploring links between exposure to Persistent Organic Pollutants (POPs) and disease risk. We propose a novel validation strategy comparing the resulting sub-graph (2,706 nodes and 23,243 edges, 0.002% of FORVM) with recent scientific literature, showing consistency with known findings while also revealing new hypothetical associations requiring further investigation.We also showed that removing duplicated nodes and edges from the KG improves the proportion of validated nodes (from 8.4% to 16%), doubles the precision (from 0.085 to 0.197) while maintaining the recall (0.954 to 0.952), illustrating a trade-off between the loss of potentially relevant but redundant information and the reliability of remaining associations. By combining automated knowledge mining with experimental data integration, this framework supports reproducible, context-based exploration of biomedical knowledge and systematic hypothesis generation. Applied to endometriosis, it highlights potential mechanisms linking exposure to POPs to the aetiology of the disease, offering a scalable strategy for constructing disease-specific KGs. AvailabilityThe code and data underlying this article are available in the MetExplore repository at https://forge.inrae.fr/metexplore/kg4j. Contactclement.frainay@inrae.fr Key MessagesO_LIKg4 builds targeted knowledge maps from large biomedical databases using simple keywords. C_LIO_LIKeyword-driven exploration reveals the most relevant disease-exposure relationships without navigating millions of connections. C_LIO_LIApplied to endometriosis, the method recovered known links with persistent organic pollutant exposure. C_LIO_LIRemoving redundant information and formatting Knowledge graph as Labeled Property Graph improves the reliability of extracted knowledge. C_LI

bioinformatics↗

bioRxiv2025

Simulated metabolic profiles reveal biases in pathway analysis methods

Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where exported metabolites may be far from internal disruptions in the organism. Experimentally evaluating PA methods is practically impossible when the samples "true" metabolic disruption is unknown. Using in silico metabolic modelling, we simulated metabolic profiles for entire pathway knockouts, providing both a known disruption site as well as a simulated metabolic profile for use in PA methods. PA should be able to detect the known disrupted pathway among the significantly enriched pathways for that profile. Through network-level statistics, visualisation, and graph-based metrics, we show that even when a given pathway is completely blocked, it may not be detected as significantly enriched with PA. This work highlights how some metabolomics data may not be suited to typical PA methods, and serves as a benchmark for analysing, improving and developing new PA tools.

bioinformatics↗

bioRxiv2024

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate. Author summaryOmics data, which provides a readout of the levels of molecules such as genes, proteins, and metabolites in a sample, is frequently generated to study biological processes and perturbations within an organism. Combining multiple omics data types can provide a more comprehensive understanding of the underlying biology, making it possible to piece together how different molecules interact. There exist many software packages designed to integrate multi-omics data, but interpreting the resulting outputs remains a challenge. Placing molecules into the context of biological pathways enables us to better understand their collective functions and understand how they may contribute to the condition under study. We have developed PathIntegrate, a pathway-based multi-omics integration tool which helps integrate and interpret multi-omics data in a single step using machine learning. By integrating data at the pathway rather than the molecular level, the relationships between molecules in pathways can be strengthened and more readily identified. PathIntegrate is demonstrated on Chronic Obstructive Pulmonary Disease and COVID-19 metabolomics, proteomics, and transcriptomics datasets, showcasing its ability to efficiently extract perturbed multi-omics pathways from large-scale datasets.

bioinformatics↗

bioRxiv2023

Genome scale metabolic network modelling for metabolic profile predictions

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design more quickly and efficiently metabolomics studies. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses. Author summaryAssociating diseases and other metabolic disruptions with physiological markers is key for diagnostic and personalised medicine. These markers can be metabolites - small molecules involved in every living beings metabolism, and can be measured in biofluids such as blood or urine using metabolic profiling (metabolomics). Nevertheless, this experimental metabolomics design needs to be tailor made for each disease to ensure that most relevant metabolites will be detected. The selection of metabolites to analyse for future experiments can be time-consuming and expensive. In this paper, we build upon an existing computational method for simulating metabolite changes in a human model. This provides a prediction of the change in biofluid abundance of every known metabolite involved in human metabolism in a potentially large number of metabolic situations. The newly introduced method produces a change score and a rank for each metabolite in each condition. We show the strong potential of the approach by comparing predictions with experimental results.

systems biology↗

bioRxiv2023

New genome scale network modeling and mining workflow for detecting metabolic changes induced by exposure to chemicals

BackgroundThe growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. ResultsTo address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated conditions-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes (PHH) transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone) or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. ConclusionThe proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. All code is freely available as well as data to reproduce results.

bioinformatics↗

bioRxiv2022

Suggesting disease associations for overlooked metabolites using literature from metabolic neighbours

In human health research, metabolic signatures extracted from metabolomics data are a strong-added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that a large fraction of metabolites are rarely or never mentioned in the literature. Consequently, these overlooked metabolites are often set aside and the interpretation of metabolic signatures is restricted to a subset of the significant metabolites. To suggest potential pathological phenotypes related to these understudied metabolites, we extend the guilt by association principle to literature information by using a Bayesian framework. With this approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (See information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation.).

bioinformatics↗

bioRxiv2021

Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention in the field. We developed in-silico simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases: KEGG, Reactome, and BioCyc, led to vastly different results in both the number and function of significantly enriched pathways. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics. Author summaryMetabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biological status (such as health or disease) on cellular biochemistry, and has wide-ranging applications, from biomarker discovery and personalised medicine in healthcare to crop protection and food security in agriculture. Pathway analysis helps to understand which biological pathways, representing collections of molecules performing a particular function, are involved in response to a disease phenotype, or drug treatment, for example. Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community. However, ORA can give drastically different results depending on the input data and parameters used. In this work, we have established the effects of these factors on ORA results using computational simulations applied to five real-world datasets. Based on our results, we offer the research community a set of best-practice recommendations applicable not only to ORA but also to other pathway analysis methods to help ensure the reliability and reproducibility of results.

bioinformatics↗

bioRxiv2021

FORUM: Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases

Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. Overcoming this challenge is critical when aiming to associate metabolic signatures with potential pathological outcomes. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. An important number of scientific articles discuss relations between chemical compounds and biomedical concepts in various contexts, from biomarkers to therapeutic uses. The extraction of these statements and their interconnection in a graph structure can thus allow us to identify and explore relations strongly supported in the scientific literature. The use of a Semantic Web framework on biological data allows us to apply ontological based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. Beyond this result, FORUM can also provide insights into complex biological questions and the extracted information could then be used for further developments. Containing more than 8 billion triples and providing more than 8 million relations, FORUM leverages the increasing availability of linked datasets in life science and is built in agreement with FAIR principles. A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM knowledge graph, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem.

bioinformatics↗

bioRxiv2018

Integrated transcriptomics and metabolomics reveal signatures of lipid metabolism dysregulation in HepaRG liver cells exposed to PCB 126

Chemical pollutant exposure is a risk factor contributing to the growing epidemic of nonalcoholic fatty liver disease (NAFLD) affecting human populations that consume a Western diet. Although it is recognized that intoxication by chemical pollutants can lead to NAFLD, there is limited information available regarding the mechanism by which typical environmental levels of exposure can contribute to the onset of this disease. Here we describe the alterations in gene expression profiles and metabolite levels in the human hepatocyte HepaRG cell line, a validated model for cellular steatosis, exposed to the polychlorinated biphenyl (PCB) 126, one of the most potent chemical pollutants that can induce NAFLD. Sparse partial least squares classification of the molecular profiles revealed that exposure to PCB 126 provoked a decrease in polyunsaturated fatty acids as well as an increase in sphingolipid levels, concomitant with a decrease in the activity of genes involved in lipid metabolism. This was associated with an increased oxidative stress reflected by marked disturbances in taurine metabolism. A gene ontology analysis showed hallmarks of an activation of the AhR receptor by dioxin-like compounds. These changes in metabolome and transcriptome profiles were observed even at the lowest concentration (100 pM) of PCB 126 tested. A decrease in docosatrienoate levels was the most sensitive biomarker. Overall, our integrated multi-omics analysis provides mechanistic insight into how this class of chemical pollutant can cause NAFLD. Our study lays the foundation for the development of molecular signatures of toxic effects of chemicals causing fatty liver diseases to move away from a chemical risk assessment based on in vivo animal experiments.

pharmacology and toxicology↗