No abstract

G-protein-coupled receptor 120 (GPR120) is reported as a long-chain fatty acid (LCFA) receptor that elicits free fatty acid (FFA) regulation on metabolism homeostasis. The study aimed to clone the gpr120 gene of goats (g-GPR120) and subsequently investigate phylogenetic analysis and tissue distribution throughout the digestive tracts of kid goats, as well as the effect of housing versus grazing (H vs G) feeding systems on GPR120 expression. Partial coding sequence (CDS) of g-GPR120 was cloned and submitted to NCBI (accession no. KU161270 ). Phylogenetic analysis revealed that g-GPR120 shared higher homology in both mRNA and amino acid sequences for ruminants than nonruminants. Immunochemistry, real-time PCR, and Western blot analysis showed that g-GPR120 was expressed throughout the digestive tracts of goats. The expression of g-GPR120 was affected by feeding system and age, with greater expression of g-GPR120 in the G group. It was concluded that the g-GPR120-mediated LCFA chemosensing mechanism is widely present in the tongue and gastrointestinal tract of goats and that its expression can be affected by feeding system and age.

The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R2 of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system.

No abstract