RNA-Seq explores the functional role of the FGF10 gene in bovine adipocytes differentiation.

Objective: The present study was executed to explore the molecular mechanism of FGF10 gene in bovine adipogenesis. Methods: The bovine FGF10 gene was overexpressed through Ad-FGF10 or inhibited through siFGF10 and their NCs in bovine adipocytes, and the MOI, transfection efficiency, interference efficiency were evaluated through qRT-PCR, western blotting and fluorescence microscopy. The lipid droplets, TG content and the expression levels of adipogenic marker genes were measured during preadipocytes differentiation. The differentially expressed genes were explored through deep RNA sequencing. Results: The highest mRNA level was found in omasum, subcutaneous fat, and intramuscular fat. Moreover, the highest mRNA level was found in adipocytes at day 4 of differentiation. The results of red-oil o staining showed that overexpression (Ad-FGF10) of the FGF10 gene significantly (p<0.05) reduced the lipid droplets and TG content, and their down-regulation (siFGF10) increased the measurement of lipid droplets and TG in differentiated bovine adipocytes. Furthermore, the overexpression of the FGF10 gene down regulated the mRNA levels of adipogenic marker genes such as C/EBPa, FABP4, PPARy, LPL, and FAS, similarly, down-regulation of the FGF10 gene enriched the mRNA levels of C/EBPa, PPARy, FABP4, and LPL genes (P˂0.01). Additionally, the protein levels of PPARγ and FABP4 were reduced (p<0.05) in adipocytes infected with Ad-FGF10 gene and enriched in adipocytes transfected with siFGF10. Moreover, a total of 1774 DEGs (differentially expressed genes) including 157 up regulated and 1617 down regulated genes were explored in adipocytes infected with Ad-FGF10 or Ad-NC through deep RNA-sequencing. The top KEGG pathways regulated through DEGs were the PPAR signaling pathway, cell cycle, base excision repair, DNA replication, apoptosis, and regulation of lipolysis in adipocytes. Conclusion: Therefore, we can conclude that the FGF10 gene is a negative regulator of bovine adipogenesis and could be used as a candidate gene in marker-assisted selection.

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed.

The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. In conclusion, the usage of CART procedure may be worthy of reflection for identifying breed standards and choosing superior sheep for meat yield in France.