Genetic Association of PPARGC1A Gene Single Nucleotide Polymorphism with Milk Production Traits in Italian Mediterranean Buffalo

Regulatory impacts of PPARGC1A gene expression on milk production and cellular metabolism in buffalo mammary epithelial cells

The PPARGC1A gene plays a fundamental role in regulating cellular energy metabolism, including adaptive thermogenesis, mitochondrial biogenesis, adipogenesis, gluconeogenesis, and glucose/fatty acid metabolism. In a previous study, our group investigated seven SNPs in Mediterranean buffalo associated with milk production traits, and the current study builds on this research by exploring the regulatory influences of the PPARGC1A gene in buffalo mammary epithelial cells (BuMECs). Our findings revealed that knockdown of PPARGC1A gene expression significantly affected the growth of BuMECs, including proliferation, cell cycle, and apoptosis. Additionally, we observed downregulated triglyceride secretion after PPARGC1A knockdown. Furthermore, the critical genes related to milk production, including the STATS, BAD, P53, SREBF1, and XDH genes were upregulated after RNAi, while the FABP3 gene, was downregulated. Moreover, Silencing the PPARGC1A gene led to a significant downregulation of β-casein synthesis in BuMECs. Our study provides evidence of the importance of the PPARGC1A gene in regulating cell growth, lipid, and protein metabolism in the buffalo mammary gland. In light of our previous research, the current study underscores the potential of this gene for improving milk production efficiency and overall dairy productivity in buffalo populations.

CSN1S1, CSN3 and LPL: Three Validated Gene Polymorphisms Useful for More Sustainable Dairy Production in the Mediterranean River Buffalo

The search for DNA polymorphisms useful for the genetic improvement of dairy farm animals has spanned more than 40 years, yielding relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing has been found in casein genes and in DGAT1. Nowadays, similar results have not yet been reached in river buffaloes, despite the availability of advanced genomic technologies and accurate phenotype records. The aim of the present study was to investigate and validate the effect of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes on seven milk traits in a larger buffalo population. These SNPs have previously been reported to be associated with, or affect, dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. The following traits were individually recorded, monthly, throughout each whole lactation period from 2010 to 2021: daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yield (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103 cell/mL) and urea (mg/dL). A total of 15,742 individual milk test day records (2496 lactations) were available for 680 buffalo cows, with 3.6 ± 1.7 parities (from 1 to 13) and an average of 6.1 ± 1.2 test day records per lactation. Three out four SNPs in the CSN1S1, CSN3 and LPL genes were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C>T) gave favorable associations with all yield traits (dMY, p = 0.022; dPY, p = 0.014; dFY, p = 0.029) and somatic cell score (SCS, p = 0.032). The CSN3 (HQ677596: c.536C>T) was positively associated with SCS (p = 0.005) and milk urea (p = 0.04). Favorable effects on daily milk yield (dMY, p = 0.028), fat (dFP, p = 0.027) and protein (dPP, p = 0.050) percentages were observed for the LPL. Conversely, the SCD did not show any association with milk traits. This is the first example of a confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in the dairy field, and it represents a very important indication for the preselection of young bulls destined for breeding programs aimed at more sustainable dairy production.

Novel polymorphisms of the KCNB1 gene and their association with production traits in Indian Sahiwal cattle

Mastitis in cattle is a prevalent mammary gland disease that contributes significantly to the increase in veterinary expenditures in the dairy sector. KCNB1 (Potassium voltage-gated channel, subfamily B member 1) gene is involved in regulating apoptosis, cell proliferation and differentiation, udder epithelial tissue maintenance and repair, mammary gland development and recommended as a candidate gene for production related traits in cattle. The purpose of this research was to detect the genetic variants of KCNB1 gene in Sahiwal cattle and to analyze the association between polymorphisms with milk production traits, udder traits, and teat traits in Sahiwal cattle. A total of 87 cattle were genotyped by polymerase chain reaction-restriction fragment length polymorphism technique. Two single nucleotide polymorphisms within the non-coding sequence of KCNB1 gene were identified (g.78216220G>A and g.78216335A>G). Analysis of productivity traits within the genotyped animals revealed that the SNP1-Msp1 locus (g.78216220G>A) located at intron 1 was associated with milk production traits, but the SNP2-BspHI locus (g.78216335A>G) had no association with milk production. Significant associations were also observed between SNP1-Msp1 and SNP2-BspHI loci with both udder and teat traits. Our results demonstrate that polymorphisms in the cattle KCNB1 gene were associated with milk production, udder and teat traits and might be utilized as a genetic marker for marker-assisted selection in cattle breeding programs.

Weighted Single-Step Genome-Wide Association Study Uncovers Known and Novel Candidate Genomic Regions for Milk Production Traits and Somatic Cell Score in Valle del Belice Dairy Sheep

Simple Summary

Milk production is the most economically crucial dairy sheep trait and constitutes the major genetic enhancement purpose via selective breeding. Also, mastitis is one of the most frequently encountered diseases, having a significant impact on animal welfare, milk yield, and quality. The aim of this study was to identify genomic region(s) associated with the milk production traits and somatic cell score (SCS) in Valle del Belice sheep using single-step genome-wide association (ssGWA) and genotyping data from medium density SNP panels. We identified several genomic regions (OAR1, OAR2, OAR3, OAR4, OAR6, OAR9, and OAR25) and candidate genes implicated in milk production traits and SCS. Our findings offer new insights into the genetic basis of milk production traits and SCS in dairy sheep.

Abstract

The objective of this study was to uncover genomic regions explaining a substantial proportion of the genetic variance in milk production traits and somatic cell score in a Valle del Belice dairy sheep. Weighted single-step genome-wide association studies (WssGWAS) were conducted for milk yield (MY), fat yield (FY), fat percentage (FAT%), protein yield (PY), protein percentage (PROT%), and somatic cell score (SCS). In addition, our aim was also to identify candidate genes within genomic regions that explained the highest proportions of genetic variance. Overall, the full pedigree consists of 5534 animals, of which 1813 ewes had milk data (15,008 records), and 481 ewes were genotyped with a 50 K single nucleotide polymorphism (SNP) array. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. The results showed that top ranked genomic windows (1 Mb windows) explained 3.49, 4.04, 5.37, 4.09, 3.80, and 5.24% of the genetic variances for MY, FY, FAT%, PY, PROT%, and total SCS, respectively. Among the candidate genes found, some known associations were confirmed, while several novel candidate genes were also revealed, including PPARGC1A, LYPLA1, LEP, and MYH9 for MY; CACNA1C, PTPN1, ROBO2, CHRM3, and ERCC6 for FY and FAT%; PCSK5 and ANGPT1 for PY and PROT%; and IL26, IFNG, PEX26, NEGR1, LAP3, and MED28 for SCS. These findings increase our understanding of the genetic architecture of six examined traits and provide guidance for subsequent genetic improvement through genome selection.