Hepatic Transcriptome Analysis Reveals Genes, Polymorphisms, and Molecules Related to Lamb Tenderness

Abstract

Tenderness is a key meat quality trait that determines the public acceptance of lamb consumption, so genetic improvement toward lamb with higher tenderness is pivotal for a sustainable sheep industry. However, unravelling the genomics controlling the tenderness is the first step. Therefore, this study aimed to identify the transcriptome signatures and polymorphisms related to divergent lamb tenderness using RNA deep sequencing. Since the molecules and enzymes that control muscle growth and tenderness are metabolized and synthesized in the liver, hepatic tissues of ten sheep with divergent phenotypes: five high- and five low-lamb tenderness samples were applied for deep sequencing. Sequence analysis identified the number of reads ranged from 21.37 to 25.37 million bases with a mean value of 22.90 million bases. In total, 328 genes are detected as differentially expressed (DEGs) including 110 and 218 genes that were up- and down-regulated, respectively. Pathway analysis showed steroid hormone biosynthesis as the dominant pathway behind the lamb tenderness. Gene expression analysis identified the top high (such as TP53INP1, CYP2E1, HSD17B13, ADH1C, and LPIN1) and low (such as ANGPTL2, IGFBP7, FABP5, OLFML3, and THOC5) expressed candidate genes. Polymorphism and association analysis revealed that mutation in OLFML3, ANGPTL2, and THOC5 genes could be potential candidate markers for tenderness in sheep. The genes and pathways identified in this study cause variation in tenderness, thus could be potential genetic markers to improve meat quality in sheep. However, further validation is needed to confirm the effect of these markers in different sheep populations so that these could be used in a selection program for lamb with high tenderness.

Keywords: genetic marker; meat quality; next generation genome sequencing; sheep; single nucleotide polymorphism.

Figure 1

Volcano plot. The 110 differentially expressed protein coding genes (represented in blue) were plotted as a volcano with a fold change of 1.5 and a p-value of 0.05. The x-axis values are the base mean expression values, and the y-axis values are the log2 expression values (fold change).

Figure 2

A heatmap depicting the expression of hepatic genes in sheep. The red blocks are overexpressed genes, while the green blocks are underexpressed genes. HT 1–5 sheep have high meat tenderness, and LT 1–5 sheep have low meat tenderness.

Figure 3

Illustration of a histogram of GO classification in Garut composite sheep. The findings were divided into three categories: biological process, cellular component, and molecular function. The number of genes involved is indicated on the y-axis.

Figure 4

KEGG pathway histogram illustration in Garut composite sheep. The number of genes involved is indicated on the x-axis.

Figure 5

Protein–protein interaction network of hepatic transcriptomes from Garut composite sheep with divergent tenderness. (A) The PPI network showing the hub genes and their connections. (B) The table illustrates the network centrality measures (degree and betweenness) and expression values of potential hub genes.

Figure 6

Co-expression network of hepatic transcriptomes from Garut composite sheep with divergent tenderness. (A) The co-expression network showing the hub genes and their connections. (B) The table illustrated the network centrality measures (degree and betweenness) and expression values of potential hub genes.

Figure 7

Validation of ten hepatic DEGs in sheep with varying meat tenderness using qRT–PCR. The validation was carried out using the same RNA samples as the RNA deep sequencing.

Figure 8

The distribution of the number of SNPs found in DEGs. (A) Number of SNPs; (B) Gene Name.