TRIBUNE. Les pouvoirs publics seuls sont en mesure de favoriser un changement profond et généralisé dans les habitudes des citoyens par la promotion de solutions de remplacement à l’alimentation carné...

INFO TÉLÉRAMA — Le journaliste nous annonce son retour à l’antenne à la rentrée fin août, avec “Recto verso”, une toute nouvelle émission en deux parties le week-end. Florence Paracuellos garde le fau...

YouTube video by Echange Climatique

CR26-GA-1 - You will join the Genetics, Physiology and Livestock Systems (GenPhyse, about 150 permanent staff) joint research unit, where researchers aim to contribute to the agroecological transition of livestock systems through better understanding of livestock biology, genetic bases of traits, and selection schemes to achieve resilient populations. The unit brings together skills in biology, physiology, genomics, genetics, statistics, and bioinformatics. Within the unit, you will join the Chamade team (Characterization and management of genetic diversity) of the Diversity and Selection group comprising methodologists in quantitative genetics (genomic prediction, selection and evolution) and population genomics, as well as statisticians. Within your research team, you will be in charge of developing a new research programme in applied statistics to integrate high-throughput, heterogenous, and multiscale data into genetic and genomic evaluation methods. You will conduct your research to improve genomic prediction models by integrating new information relative to genome function (e.g. functional annotation), molecular phenotypes (e.g. transcriptomics, methylation), and high-throughput or intermediate phenotypes (e.g. longitudinal data, high throughput sensors).To integrate different types of data into current genomic prediction models, you will draw on a variety of statistical modelling, for example modelling SNP effects according to their functional annotation category, or including random effects capturing the inter-individual covariance for intermediate phenotypes. The modelling could draw, for example, on hierarchical models, meta-analysis methods, mediation analysis or machine learning, possibly simulation-based. In addition, as new high-throughput data may not necessarily be available on the same individuals as traditional data, their integration will require the implementation of suitable statistical techniques. The predictive performance of the developed models will be evaluated using numerical simulations, for instance based on real breeding programmes. The models will also be tested on real data from experimental and commercial programmes of livestock species. These data will be available through existing projects and partnerships within the unit and the division to initiate your research project. Computational efficiency must be taken into consideration in your developments to ultimately ensure their practical use in genetic and genomic evaluation. To develop your research, you will benefit from the proximity of experts in statistics, computer science, quantitative, molecular and population genetics within GenPhySE. In accordance with INRAE's policy for open science, in addition to scientific publications, you will promote your work by distributing free software implementing the new methods developed to ensure their wide dissemination to the international community.

Move comes after Roger Innes complained about the government’s prosecution of Chinese postdocs

Biology department chair Armin Moczek said experiments are being aborted every day during the shutdown, including projects months in the testing.

Longtemps invisibles, les échanges entre plantes et micro-organismes du sol jouent un rôle essentiel dans l’agriculture. Au cœur de ces interactions, des molécules clés, les facteurs Nod et Myc, contr...

Siencesconf.org

Plants rely on specialized metabolites to defend against herbivores, yet how root-associated symbiotic microbes influence their regulation, coordination, and overall contribution to defense remains widely unknown. We hypothesized that symbiotic microbes can reprogram specialized metabolic networks, thereby enhancing herbivore resistance, with a particular focus here on polyamine metabolic network. Using the fungal symbiotic endophyte Trichoderma harzianum, tomato, and the herbivore Spodoptera exigua, we combined greenhouse bioassays with molecular, genetic, and metabolomic analyses to investigate how Trichoderma symbiosis reshapes the plant polyamine metabolic network, and affects plant-herbivore interactions. We found that in the absence of the root symbiosis, herbivory primarily activated polyamine uptake transport and catabolism, reflecting a stress-driven turnover response. Trichoderma symbiosis markedly reconfigured the polyamine network. Symbiosis primed uptake transport and catabolic responses, enhanced polyamine flux through activation of the ornithine decarboxylase pathway, and redirected polyamines into conjugated metabolites with anti-herbivore activity. Genetic analyses confirmed that this metabolic rewiring contributes to Trichoderma-induced resistance to herbivory, linking primary metabolic routes to the accumulation of specialized defense compounds. Our study highlights root symbionts as key modulators of plant metabolism, showing that specialized metabolite diversity can be shaped by symbiotic interactions.