HAL ENVT (Ecole Nationale Vétérinaire de Toulouse)
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Leveraging digital tools to improve the diagnosis of bovine respiratory diseases
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The genomic history of Iberian horses since the last Ice Age
No full textInternational audienceHorses have inhabited Iberia (present-day Spain and Portugal) since the Middle Pleistocene, shaping a complex history in the region. Iberia has been proposed as a potential domestication centre and is renowned for producing world-class bloodlines. Here, we generate genome-wide sequence data from 87 ancient horse specimens (median coverage = 0.97X) from Iberia and the broader Mediterranean to reconstruct their genetic history over the last similar to 26,000 years. Here, we report that wild horses of the divergent IBE lineage inhabited Iberia from the Late Pleistocene, while domesticated DOM2 horses, native from the Pontic-Caspian steppes, already arrived similar to 1850 BCE. Admixture dating suggests breeding practices involving continued wild restocking until at least similar to 350 BCE, with IBE disappearing shortly after. Patterns of genetic affinity highlight the far-reaching influence of Iberian bloodlines across Europe and north Africa during the Iron Age and Antiquity, with continued impact extending thereafter, particularly during the colonization of the Americas
MilkOligoCorpus: A semantically annotated resource for knowledge extraction on mammalian milk oligosaccharides
No full textInternational audienceMilk oligosaccharides are bioactive components that regulate the composition of the neonatal microbiota and exert immunomodulatory functions. Their beneficial effects depend on their structure. Numerous studies have shown intra- and inter-species variation in the structural composition and concentration of these compounds in mammalian milk, yet the biological significance of such variation remains poorly understood. Automated natural language processing methods are promising tools for extracting and gathering structured data from unstructured texts to get insight into the biological significance of milk oligosaccharide variation across mammals. These methods require training and evaluation on manually annotated text corpora. While annotated corpora exist for chemical substances, none are specifically designed for training natural language processing models to extract information on milk oligosaccharides. To this end, we propose MilkOligoCorpus, a new gold standard for milk oligosaccharide composition in mammalian species. MilkOligoCorpus’ annotation scheme is a rich entity/relation model designed to describe the diversity pattern of milk oligosaccharides according to female factor variability and to help better understand the structure-related function of milk oligosaccharides. MilkOligoCorpus consists of abstracts (15) and extracts (15) from 20 full text articles indexed by PubMed annotated with entities related to individuals, samples, oligosaccharides and oligosaccharide quantification linked by binary and n-ary relationships. To address data interoperability across disparate publications and databases, four terminological resources were also developed to assign unique identifiers to the entities, supported by external ontologies. This paper presents the creation of the MilkOligoCorpus and its associated schema, along with the development of annotation guidelines and terminological resources. We also present experimental results obtained by baseline information extraction models on the corpus
Novel drugs approved by the EMA, the FDA and the MHRA in 2024: A year in review
No full textInternational audienceIn the past year, the European Medicines Agency (EMA), the Food and Drug Administration (FDA) and the Medicines and Healthcare Products Regulatory Agency (MHRA) authorised 53 novel drugs. While the 2024 harvest is not as rich as in 2023, when 70 new chemical entities were approved, the number of ‘orphan’ drug authorisations in 2024 (21) is similar to that of 2023 (24), illustrating the dynamic development of therapeutics in areas of unmet need. The 2024 approvals of novel protein therapeutics (15) and advanced therapy medicinal products (ATMPs, 6) indicate a sustained trend also noticeable in the 2023 new drugs reviewed in this journal last year (16 and 11, respectively). Clearly, the most striking characteristic of the 2024 drug yield is the creative pharmacological design, which allows these medicines to employ a novel approach to target a disease. Some notable examples are the first drug successfully using a ‘dock‐and‐block’ mechanism of inhibition (zenocutuzumab), the first approved drug for schizophrenia designed as an agonist of M 1 /M 4 muscarinic receptors (xanomeline), the first biparatopic antibody (zanidatamab), binding two distinct epitopes of the same molecule, the first haemophilia therapy that instead of relying on external supplementation of clotting factors, restores Factor Xa activity by inhibiting TFPI (marstacimab), or the first ever authorised direct telomerase inhibitor (imetelstat) that reprogrammes the oncogenic drive of tumour cells. In addition, an impressive percentage of novel drugs were first in class (28 out of 53 or 53% of the total) and a substantial number can be considered disease agnostic, indicating the possibility of future approved extensions of their use for additional indications. The 2024 harvest demonstrates the therapeutic potential of innovative pharmacological design, which allows the effective targeting of intractable disorders and addresses crucial, unmet therapeutic needs
Cats as sentinels of mammal exposure to H5Nx avian influenza viruses: a seroprevalence study, France, December 2023 to January 2025
No full textInternational audienceCirculation of clade 2.3.4.4b highly pathogenic avian influenza H5Nx viruses has intensified in recent years, increasing epizootics and mammalian exposure. Cats, bridging wild and domestic environments, are key for studying cross-species transmission. To assess their exposure in France, we screened 728 outdoor cats (December 2023–January 2025). Seropositivity was 2.6% (19/728), with an estimated seroprevalence at 1.8%. Absence of hunting behaviour was a significant protective factor. These findings highlight high recent exposure and the need for targeted surveillance in cats
Widening exposome exploration with a novel multiplexed HRMS analytical approach : a case study on pesticide internal exposure
No full textInternational audienceIntroduction : Human exposure to food and environmental chemical contaminants including pesticides is generally assessed by indirect (e.g. questionnaires) and/or targeted methods focusing on a limited number of selected compounds. These methods often require a large amount of sample for analyses as complete and sensitive as possible. Thus, human health risks associated with multi-exposure to complex mixtures currently remain underexplored. Based on the exposomics concept and previous studies (1,2), we propose an innovative global chemical profiling approach integrating three complementary HRMS platforms (LC-HILIC-HRMS, LC-C18-HRMS and GC-HRMS), for capturing an extended range of contaminants and related metabolites from a unique urine sample.Methods : Fractionation of reduced-volume urine samples (0.5 mL) was preformed using Strata-X® SPE cartridges. HRMS experiments were conducted on GC-Orbitrap q-Exactive (GC-MS), Sciex X-500-R Q-ToF (C18-LC-MS) and LTQ-Orbitrap XL (HILIC-LC-MS) instruments. Data processing was carried out on both commercial and open source softwares (Trace Finder, W4M, Scannotation, …). A set of 187 standard compounds (parent compounds + metabolites) covering both a large range of molecular weights (72 g/mol1000) from a single sample, provides valuable data for assessing associations with health endpoints for epidemiological studies. Novelty : innovative multiplexed HRMS methodology for wide exposomics from a unique sample. References1 E. L. Jamin et al. Anal. Bioanal. Chem., 2014, 406, 1149-1161.2 N. Bonvallot et al. Sci. Tot. Environ., 2021, 786, 147499.3 T. Moufawad et al. In preparatio
High-throughput Plant Metabolomics and Predictive Modelling
No full textInternational audienceThis chapter explores advances and methodologies in high-throughput metabolic phenotyping through metabolomics and predictive modeling to enhance the understanding of plant metabolism. Key techniques, data analysis tools, and applications in plant science research are discussed. The potential of predictive modeling to identify new metabolic pathways and markers associated with plant performance and improve crop traits is highlighted. Future directions and challenges in the field are also examined
Efficiency of genomic and phenomic selection using mid-infrared milk spectra for milk production, somatic cell count, and udder type traits in French Lacaune dairy sheep
No full textInternational audienceGenomic selection uses molecular and pedigree information to accurately estimate genomic breeding values of animals from birth for traits in selection. Recent research in phenomic selection in plant production is opening up new opportunities in animal breeding. The approach of phenomic selection has been little studied in animal production. Here, we evaluate the efficiency of phenomic selection to estimate the phenomic values of phenotype-free females using mid-infrared spectral (MIRS) data from their milk samples. The phenotypes of 1,531 first-lactation French Lacaune dairy ewes were considered for traits included classically in the breeding goals, such as milk production and functional traits (SCS and udder type traits). The inclusion of standardized raw MIRS data instead of SNPs led to very low phenomic predictive abilities for udder type traits (Pearson correlations between phenotype and phenomic values from-0.08 to 0.07). For milk production traits, the phenomic predictions were superior to the genomic ones, in particular for lactation SCS (LSCS), with a predictive ability at 0.49 instead of 0.04. Overall, random regression-BLUP and Bayesian reproducing kernel Hilbert space methods gave equivalent results on phenomic predictions across all traits, with no impact from spectral data preprocessing. Finally, the efficiency of the combination of SNPs and milk MIRS in prediction models was low (average +3.8% for milk production and LSCS traits). Phenomic predictions could open up new prospects especially for the selection of nongenotyped females
A comprehensive review and benchmark of differential analysis tools for Hi-C data
No full textInternational audienceThe three-dimensional conformation of the genome has a key role in multiple biological processes such as gene expression regulation. Hi-C is a sequencing technique used to profile 3D chromosomal conformation. The data are summarized in a symmetric matrix, where each pixel (i,j) (or (j,i)) represents the interaction frequency between genomic positions i and j, estimating their spatial proximity. The objective of Hi-C data differential analysis is to identify genomic regions that display significant changes of interactions between two biological conditions. Several tools have been proposed to address this question and most propose to test each pixel of the matrix independently. Here, we present a review and a thorough statistical benchmark of these tools. We first focused on describing the tools on multiple aspects. First, we gave a technical description of the tools' implementations, specifically of their usability. Then, we focused on a statistical description, highlighting the different preprocessings, modelling choices (replicates and covariates, spatial dependency awareness, etc.) and multiple testing corrections proposed. In a second part, we performed an evaluation of the tools on simulations based on real data (mimicking a H0 and a H1 settings with no and controlled signal respectively), which led us to assess proper control of the Type-I error and power of the tools. Then, we evaluated tools to recover biological signal on a auxin/no auxin dataset validated through CTCF external data.Our experiments highlighted the strong impact of data preprocessings, the fact that tools did not control the FDR, and showed the superiority of diffHic
Evaluation of the toxic effects of food additives, alone or in mixture, in four human cell models
No full textInternational audienceFood additives are present in more than 50% of food products. Several studies have suggested a link between the consumption of certain food additives and an increased risk of developing cancer. This study aimed to evaluate the genotoxicity of 32 additives and six mixtures identified by the NutriNet-Santé cohort as the most widely consumed. Genotoxicity screening was conducted using the γH2AX (for clastogenic compounds) and pH3 (for aneugenic compounds) biomarkers in four human cell models (colon, liver, kidney, and neurons) representing the target organs of food contaminants. The 32 compounds were categorized into five groups based on their toxicological profiles. Eight additives were cytotoxic, four promoted cell proliferation, two were genotoxic with a clastogenic mode of action, and the remaining 19 were neither cytotoxic nor genotoxic at the concentration tested. Among the six mixtures tested, three were neither cytotoxic nor genotoxic, one was cytotoxic, and two were genotoxic at the highest tested concentrations. The observed genotoxicity of the mixtures could not be attributed to the relative concentrations of the individual additives. These findings suggest the possibility of toxic synergies in mixtures and highlight the challenges of studying the combined effects of multiple substances