Multi-layer omic data integration underlies candidate biomarkers that predict the response to endurance exercise in horse

Systems biology-based approaches are particularly well suited for the study of the association between the physiological status, the tissue functions, and the metabolic condition along the most important life phases of livestock animals such as lactation, weaning, or pregnancy. In the case of athletic horses, the nature of the physiologic and metabolic adaptations during the endurance rides is multi-layered and involves the synchronization of key tissues such as muscle, heart, liver, adipose tissue, gut and brain. Moreover, new insights about the energy metabolism and the immune system response during endurance ride could be provided by the study of the dynamics of the intestinal macrofauna and of its fermentation capacity. In this specific case, a key challenge is to identify specific metabolome and transcriptome profiles following endurance exercise and to integrate them with the microbial profiles in order to potentially predict the performance and health of endurance horses.
During this presentation, we will try to unravel the putative candidate molecules (e.g. genes, miRNAs, metabolites) and their interplay that ultimately result in the coordinated cellular adaptation following endurance exercise. Up to date, the use of graphical Gaussian Models (GGM) and of linear models for the analysis of transcriptomic, miRNome and metabolomic data from blood allowed the visualization and the integration of the most impacted metabolic pathways related to the adaptation to exercise. The next step will be the integration of the macrofauna profiles (e.g. bacteria, archaea, fungi and protozoa) to the complexity of the system through kernel-based methods, as well as Least-Square Vector Machine approach (ILS-VMS), thus uncovering the key molecular players involved in the adaptation of the tissues to endurance.