Resumen de Unravelling the environmental variance of litter size through the genome and gut microbiome
Disclosing the biological mechanisms of the VE can help to gain some insight into the biological basics of animal resilience. In this thesis, genomic, metagenomic, and metabolomic analyses were performed on rabbit lines divergently selected for high and low VE of litter size (LS). These animals showed differences in their resilience potential. Thus, these divergent populations are an excellent biological material for studying animal resilience through the VE.
Genome-wide association studies (GWAS) were performed using single marker regression, and Bayesian multiple marker regression approaches. Four genomic regions were associated with the VE in the Oryctolagus cuniculus chromosome (OCU) 3, OCU7, OCU10, and OCU14, explaining 8.6% of the total genetic variance for the VE. In addition, the signature of selection (SS) study identified 134 genomic regions which could be under selection for VE. Overlapping between both studies was placed in the OCU3, where functional mutations for the DOCK2, INSYN2B and FOXI1 genes were also found. Candidate genes from GWAS and SS were those with functional mutations identified using whole genome sequencing (WGS) analysis with pools of DNA. Highlighted candidate genes showed biological functions related to the development of sensory structures, the immune response, the stress response, and the nervous system. All of them are relevant functions to modulate animal resilience. On the other hand, metagenomic and metabolomic studies showed that the selection for VE modified the gut microbiome and metabolome composition. Beneficial microbial species such as Alistipes prutedinis, Alistipes shahii, Odoribacter splanchnicus and Limosilactobacillus fermentum were more abundant in the resilient population. In contrast, harmful microbial species such as Acetatifactor muris and Eggerthella sp were more abundant in the non-resilient animals. Genes related to biofilm formation, aromatic amino acid metabolism (Phenylalanine, tryptophan, and tyrosine), and glutamate metabolism were also differentially expressed between the rabbit populations. Furthermore, 15 gut metabolites were identified as potential biomarkers to properly discriminate and predict between the resilient and non-resilient rabbit populations. Five of them, the equol, 3-(4-hydroxyphenyl)lactate, 5-aminovalerate, N6-acetyllisine, and serine were microbial-derived metabolites.
This is the first study unravelling important biological mechanisms under the animal resilience generated by VE of LS selection. Genome and gut microbiome and metabolome composition were modified throughout the selection process, affecting the immune and stress response. Overlapping results were found between the metagenomic and metabolome studies. On the other hand, in this thesis, we developed a flexible tool for simulating the coevolution of the genome and microbiome across a selection process for the first time. The key of this tool was the implementation of the microbiome inheritance. It is constructed in R and based on AlphaSimR so the user can modify the code and implement different scenarios. This thesis is the first step to develop future strategies and further research to improve animal resilience. A selection combining genomic and metagenomic information may improve the selection response. Moreover, gut-derived metabolites with evidence of crosstalk can be used as biomarkers to identify resilient animals by plasma, avoiding the extraction of faecal samples to determine the microbiome composition. If these studies suceed, these strategies could improve animal resilience with the aim of search a more sustainable livestock system. Lastly, the simulation tool developed could help unravel the microbiome's implications in animal breeding programs.
