Hamdy Abdel-Shafy

Understanding the genetic mechanisms underlying milk production traits contribute to improving the production potential of dairy animals. Long-chain acyl-CoA synthetase 1 (ACSL1) plays a key role in fatty acid metabolism and was highly expressed in the lactating mammary gland epithelial cells (MGECs). The objectives of the present study were to detect the polymorphisms within ACSL1 in Mediterranean buffalo, the genetic effects of these mutations on milk production traits, and understand the gene regulatory effects on MGECs. A total of twelve SNPs were identified by sequencing, including nine SNPs in the intronic region and three in the exonic region. Association analysis showed that nine SNPs were associated with one or more traits. Two haplotype blocks were identified, and among these haplotypes, the individuals carrying the H2H2 haplotype in block 1 and H5H1 in block 2 were superior to those of other haplotypes in milk production traits. Immunohistological staining of ACSL1 in buffalo mammary gland tissue indicated its expression and localization in MGECs. Knockdown of ACSL1 inhibited cell growth, diminished MGEC lipid synthesis and triglyceride secretion, and downregulated CCND1, PPARγ, and FABP3 expression. The overexpression of ACSL1 promoted cell growth, enhanced the triglyceride secretion, and upregulated CCND1, PPARγ, SREBP1, and FABP3. ACSL1 was also involved in milk protein regulation as indicated by the decreased or increased β-casein concentration and CSN3 expression in the knockdown or overexpression group, respectively. In summary, our present study depicted that ACSL1 mutations were associated with buffalo milk production performance. This may be related to its positive regulation roles on MGEC growth, milk fat, and milk protein synthesis. The current study showed the potential of the ACSL1 gene as a candidate for milk production traits and provides a new understanding of the physiological mechanisms underlying milk production regulation.

The horse strains designation system was shaped in the 7th century in the Arabian Peninsula and spread to the neighboring oriental empires and it is still used. The aim of the present study was to investigate genetic diversity among three Arabian horse strains using twelve equine microsatellite markers. The study included 84 Arabian horses comprising Arabian horses from the major Syrian strains Hamdani, Kahlawi and Saglawi. The studied horses represented five geographical regions in Syria. To determine genetic population structure within and between the three strains, we applied different methods. The selected panel of microsatellite markers confirmed a general genetic feature of the three main strains of Syrian Arabian horses suggesting low level of population differentiation. To ascertain the results, it will be necessary to collate our findings with the historical pedigrees. Furthermore, we need a genome-wide investigation of microsatellites or an analysis of strain/breed specific SNPs across the Syrian Arabian horse populations for better insight into the genetic diversity.

Breeding is a powerful science and remains a core component of most agriculture production. Although traditional selection has proven to be a successful approach for improving animal and plant production in the last decades, several studies have reported a worrying shortage between the current rates of production and expected global future demands for food. These challenges, coupled with a constant loss of suitable land for agricultural production, needs to implement new breeding strategies to significantly increase the genetic gain and meet the future requirements. Recent advances in genotyping capacity with affordable costs have allowed selection based on genomic information. These technologies would reshape the breeding programs, accelerate the genetic gain and maximize the productivity per unit. Although this concept was suggested many decades ago, the applications of marker-assisted selection were limited due to the low number of genotyped markers and low number of identified QTL. In contrast, genomic selection (GS) utilizes the dense genetic markers across the whole genome to predict the effect of all QTL that are assumed to be in linkage disequilibrium with at least one genetic marker. Nowadays, GS is widely implemented in several species for breeding purposes and attracted attention for human disease risk predictions. It has been proven for many traits to double the rate of the genetic gain and reduce the generation interval, which would efficiently contribute to more food security. Here, we introduce the general concept of GS, and briefly describe the GS models along with the recent progress in some farm animal species.