As the first available prebiotics for neonates, milk oligosaccharides regulate gut microbial composition and modulate host immune response, playing a crucial role in the holobiont assembly. By using two livestock models (pigs and rabbits) with different maturity levels at birth, HoloOLIGO aims to decipher causal links between milk oligosaccharidesstructures, the offspring microbiota and immune system. We will create a database using data mining of the literature to find, visualise and analyse milk oligosaccharidesstructure diversity patterns within and between mammalian species. We will produce the first MO data in rabbits and expand them in pigs. To understand structure importance of milk oligosaccharides, we will undertake in vitro functional analyses in both species on commensal bacterial strains and intestinal immune cells and further validate results in vivo. Finally, we will evaluate, via in silico analyses (pig) and in vivo (rabbit), the existing genetic variability and assess the genetic determinism of milk oligosaccharidescomposition.

Aim is to develop climate smart cattle farming systems reducing GHG and ammonia emissions while maintaining the social-economic outlook of the farm business. Key words are efficiency of production and care for climate. Central in the approach are innovative housing and manure handling systems in reducing emmissions, like use of composted bedding material, separation of faeces and urine, artificial floor constructions, manure cleaning robots, cow toilet, virtual fencing and ICT data collection techniques, and precision crop fertilization. Promising feeding, breeding and grassland mitigation practices are examined to contribute to the integrated systems approach. Our study will deliver an assessment of the environmental performance of a network of study field farms in eight EU-countries on basis of NPC balance tools and simple emission measurement methods. Researcher–farmer interaction is meant to improve performance. Expertise groups in each country evaluate the outcomes.

The overall goal of this project is to help improving the efficiency and sustainability of the egg production sector. The approach consists in extending the laying cycle up to 90 weeks from 60-70 weeks currently and improving hen feed efficiency (FE) and laying rate at these advanced ages, all while maintaining good egg quality, both on the nutritional and food safety plans. Eggs are a valuable and cheap source of nutrients for human alimentation all over the world. Their production is steadily increasing worldwide. Extending the laying cycle will ensure sustainability of the sector: i) economically, by reducing the per-egg cost, ii) socially, by reducing the flocks size, their turnover and the number of one-day males killed, iii) environmentally, by reducing the feed inputs. However, the current understanding of the genetic basis of egg productions at these advanced ages is scarce if anything, since almost all the studies are conducted around 30 to 60 weeks. Hence, this project aims at (a) providing a comprehensive view of the genetic basis of the laying rate, feed efficiency and egg quality in layers at a production stage never studied before (90 weeks of age) and (b) evaluating the age effect (60 weeks versus 90 weeks) on this genetic architecture and on the cis-regulation of gene expression in liver at the adult stage in relation to liver-related trait QTL refining. The egg quality will be studied under both the nutritional (yolk lipid quantity and composition) and the food safety (antimicrobial ability of the white against Salmonella enterica serovar Enteritidis, a bacterium of great concern in food safety) aspects. The project is organized in two Parts and 6 working tasks (WT), in addition to a management / dissemination WT). Part 1 (4 tasks) is aimed at gaining genetic knowledge on all the traits of interest at an advanced age. To this end, we will estimate the genetic parameters and map by GWAS the QTL regions for FE and egg rate (WT1), egg nutritional traits (WT3) and antimicrobial ability (WT4). We will also develop a genomic evaluation of FE, trait very costly to measure (WT2). Part 2 (2 tasks) is dedicated at going further into the QTL region characterization, an endeavor known to be challenging. First, in WT5, we will gain knowledge on gene expression regulation in adult liver and its variation between 60 and 90 weeks by detecting cis-eQTLs and new regulatory regions, using cutting-edge genomic methods (ATAC-, ChIP-, DNA-, RNA-seq, RRBS). In particular, we will be able to study a class of regulatory genes, called long noncoding RNA genes (LNC) using a genome annotation enriched in LNC that we recently generated. This task should facilitate the identification of causative genes and perhaps variants (SNPs or INDEL), underlying QTL, notably those related to the egg nutritional quality (in particular lipid content), since the liver is the tissue where egg yolk’s lipids and proteins synthesis takes place. Finally, in WT6 we will prioritize a few regions highlighted in WT5, that we will select for being involved in a trait of interest, affected by the age, and with an effect observable in cell lines for molecular biology validations. From a practical viewpoint, this project will contribute to identify new targeted selection criteria for strategical traits such as FE, egg laying rate, egg nutritional quality and egg safety. From an academic viewpoint, it will contribute to provide a cis-regulation map of gene expression in adult liver and its variation between 60 and 90 weeks, and to identify causative genes and variants controlling QTL detected in Part 1, with a particular attention to yolk lipid related traits. The consortium is composed of 5 INRAE groups specialized in chicken genetics, genomics, bioinformatics, feed efficiency, food quality and security, one French international chicken breeding company and one platform specialized in lipidomic, thus gathering the expertise required for this project.

European cattle farmers are facing increased demand for pasture-based and environmentally friendly products. Although feeding strategies to reduce Greenhouse Gas (GHG) emissions have been studied intensively, strategies for grazing systems are under-researched. The lack of easy-to-implement technologies for methane measurement with grazing cattle complicates the necessary large-scale studies. GrASTech will develop an animal-mounted sensor platform for methane measurement in grazing cattle and validate using established techniques (Respiration chambers, LaserGun and Greenfeed). Additionally, herd productivity, which has a major impact on GHG emission intensity (per kg product), will be improved using a wide range of precision livestock farming technologies. All strategies will be evaluated using life cycle assessment in order to find net positive effects. GrASTech will provide important advances towards achieving the challenging goals of the climate action plan.