Biology subjects

Delmas, M.

Publications and source records attributed to Delmas, M..

7 recordsLinked to original sources

bioRxiv2026

Viability of engineered AAVs via protein language models

Capsid engineering has greatly improved the performance of recombinant AAV vectors used for gene therapy. One commonly used strategy is the insertion of a short, 7-mer, peptide into surface-exposed loops to modify receptor interactions and enhance cell entry. While effective in receptor retargeting and improved transduction, these insertions might destabilize the capsid protein, hinder assembly, and thus limit production. While previous attempts have used deep mutational scanning and AI to predict which insertions are viable, there is lack in understanding the structural consequences of these peptide insertions at the amino-acid level. Here we combined experiments, deep sequencing and large protein language models to gain insight on the impact of 7-mer insertions on the VR-VIII region. We first characterize the biochemical properties of viable insertions, thus identifying which residues are well tolerated, and which should instead be avoided. We then focus on the nearby context of those insertions, by studying the effect of the linkers, either for highly diverse libraries or for individual variants known for their efficiency. Next, we study the broader context, by extending our analysis to the whole capsid sequence, and identifying regions that can tolerate insertions without long-ranged structural deformations that could affect capsid functionality. We conclude with a cross-serotype comparison and a viability analysis of tens of previously engineered variants. Our work showcases how AI can uncover structure-function rules governing the success of engineered AAV capsids.

bioinformatics↗

bioRxiv2026

Promfusion: a synthetic fusion promoter enabling enhanced and balanced photoreceptor transgene expression

Achieving efficient and balanced transgene expression in both rods and cones remains a major challenge in retinal gene therapy. Current promoters either lack specificity or fail to provide sufficient cellular coverage and expression level. To address this limitation, we developed and evaluated two fusion promoters, Pikali and Nocchu, by combining PR1.7, a cone-specific promoter and GRK1, a promoter most active in rods. Here, we show that Pikali and Nocchu outperform their parental promoters, driving broader and more balanced GFP expression in rods and cones of human iPSC-derived retinal organoids. These constructs achieved transduction in 30% to 45% of photoreceptors, with higher expression levels than GRK1 and broader cellular coverage than PR1.7. Our findings establish Pikali and Nocchu as excellent candidates for retinal gene therapy, overcoming the limitations of existing promoters. By combining specificity, efficiency, and extensive photoreceptor targeting, these fusion constructs represent a novel and promising strategy for next-generation gene therapy vectors, addressing inherited retinal dystrophies and advancing clinical translation.

genetics↗

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↗

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↗

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

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↗

bioRxiv2020

Circular ANRIL isoforms switch from repressors to activators of p15/CDKN2B expression during RAF1 oncogene-induced senescence

Long non-coding RNAs (ncRNAs) are major regulators of gene expression and cell fate. The INK4 locus encodes the tumour suppressor proteins p15INK4b, p16INK4a and p14ARF required for cell cycle arrest and whose expression increases during senescence. ANRIL is a ncRNA antisense to the p15 gene. In proliferative cells, ANRIL prevents senescence by repressing INK4 genes through the recruitment of Polycomb-group proteins. In models of replicative and RASval12 oncogene-induced senescence (OIS), the expression of ANRIL and Polycomb proteins decreases, thus allowing INK4 derepression. Here, we found in a model of RAF1 OIS that ANRIL expression rather increases, due in particular to an increased stability. This led us to search for circular ANRIL isoforms, as circular RNAs are rather stable species. We found that the expression of two circular ANRIL increases in several OIS models (RAF1, MEK1 and BRAF). In proliferative cells, they repress p15 expression, while in RAF1 OIS, they promote full induction of p15, p16 and p14ARF expression. Further analysis of one of these circular ANRIL shows that it interacts with Polycomb proteins and decreases EZH2 Polycomb protein localization and H3K27me3 at the p15 and p16 promoters, respectively. We propose that changes in the ratio between Polycomb proteins and circular ANRIL isoforms allow these isoforms to switch from repressors of p15 gene to activators of all INK4 genes in RAF1 OIS. Our data reveal that regulation of ANRIL expression depends on the senescence inducer and underline the importance of circular ANRIL in the regulation of INK4 gene expression and senescence.

molecular biology↗