LXRα gene expression, genetic variation and association analysis between novel SNPs and growth traits in Chinese native cattle

Abstract

Liver X receptor α (LXRα) has emerged as an important regulator of lipid and energy metabolism. In this study, to better understand the effects of LXRα gene on growth traits in cattle, the mRNA tissue expression patterns and the polymorphisms of some exons of LXRα were revealed. The expression profile of the bovine LXRα gene was detected by quantitative real-time polymerase chain reaction (qRT-PCR) in 11 different Jiaxian cattle tissues and was found mainly expressed in spleen, liver, fat tissue, kidney, muscle, and lung. Meanwhile, it showed that four single nucleotide polymorphisms (SNPs), named g.1028 T>C, g.1514 T>C, g.2929G>A, and g.3493 T>C, were detected and 12 different haplotypes were constructed. Haplotype with CCGT was dominant with frequency of 40.8 %. There was a strong link between g.1028 T>C and g.1514 T>C (r (2) = 0.374). Association analysis of SNPs with growth- and body-related traits was carried out in 445 Chinese native cattle. The results displayed that the heterozygous genotypes of g.1028 T>C and g.1514 T>C showed a molecular heterosis on four performance traits related to body size: height at withers, body length, hipbone width, and hip width (P < 0.05). The multiple effects of four sites showed that the height at withers, body length, hipbone width, and hip width of individuals of TC-TC-GG-TT combined genotypes were significantly higher than other genotypes (P < 0.05). The effects of the four loci genotype combination on conformation traits were consistent with the effects of g.1028 T>C and g.1514 T>C loci. The SNPs of g.1028 T>C and g.1514 T>C of the bovine LXRα gene could be potential genetic markers for growth traits in cattle. These results suggest that LXRα gene is expressed in many tissues and may provide primary molecular information for further studies on body size traits in Chinese indigenous cattle.

Tissue expression and predicted protein structures of the bovine ANGPTL3 and association of novel SNPs with growth and meat quality traits

“…The ANGPTL4 rs4076317 polymorphism was associated with a higher percentage of body fat in adolescents as well as a higher waist-to-hip ratio in adults (Legry et al, 2009). In cattle, previous research into the expression profiles and SNPs of the bovine LXRα, ANGPTL4 and ANGPTL6 also indicated that these genes were mainly expressed in liver and adipose tissue and that they were significantly associated with growth and slaughter traits (height at withers, body length, hip width, height at hip cross and CW) of cattle (Mamedova et al, 2010;Li et al, 2012;Ma et al, 2014). Considering the similar functions of these new lipid and energy metabolism modulators, we hypothesized that ANGPTL3 may also be relevant to growth development of cattle.…”

“…Animals in every breeding farm were under the same forage and feeding management conditions. The animals were weaned at an average of 6 months age and raised on a corn-corn silage diet, according to nutrient requirements of growing heifers (NRC, 2000;Ma et al, 2014). The NY animals were randomly chosen from the breeding center of Nanyang cattle (Nanyang city, Henan Province, P.R.…”

“…The expression levels of genes were quantified by SYBR Green I assay and qRT-PCR reactions were performed as two-step reactions in ABI 7300 (Applied Biosystems, Foster City, CA, USA; Ma et al, 2014). Primers for ANGPTL3 were designed with Primer Premier 5.0 software (GenBank number NM_001080345.1).…”

“…Three-microliters PCR products were separated by 13% PAGE in 1 × TBE buffer under constant voltage (130 V) for 12 h at room temperature. The gels were stained with 0.1% silver nitrate and the genotypes were estimated based on different electrophoretic patterns (Ma et al, 2014). SNPs, expression and protein structure of ANGPTL3 gene Predicted protein structures To predict protein structures, we retrieved mRNA sequence of the bovine ANGPTL3 from NCBI Genbank database (GenBank number NM_001080345.1) and then introduced three missense mutations identified in our populations, SNP2 (rs451104723: g.104A > T), SNP3 (rs482516226: g.509A > G) and SNP4 (rs477165942: g.8661T > C), using BioEdit version 7.0 software.…”

“…The SPSS software was used to analyze the relationship between the genotypes and traits in cattle (Ma et al, 2014). The linear model was: Y ijkl = μ + B i + G j + A k + e ijkl , where Y ijk is the phenotype of the animal, μ the mean of the population, B i the effect of breed, G j the effect of genotype, A k the effect of age and e ijkl the random error.…”