Feed efficiency (FE) in dairy cattle needs to be improved for sustainable (milk) production. The understanding of the biological mechanisms and the role of gene expression patterns across the whole genome (transcriptomics) may be helpful in order to identify and select animals with high FE. In the present study, the high throughput of RNA sequencing was used to detect genes that are differentially expressed (DE) between two groups of extreme cows (high and low FE), to detect molecular pathways for FE attributes. Ten Jersey and 9 Holstein cows were used in the experiment. These two groups of cows were divided into two FE groups depending on their FE status. The two FE groups also received two different diets (Low Concentrate (LC) and High Concentrate (HC)) which is contained 70:30 and 40:60 of forage:concentrate, respectively. This enabled us to compare the interaction between FE status and two different diets. mRNA from liver biopsies of the cows analyzed by paired end RNA sequencing resulted in an average of 57 million reads per sample. The RNAseq gene expression data were then analyzed using statistical-bioinformatics pipelines to identify DE genes. On average, 91% of reads per sample were mapped with STAR aligner. The expression of 24616 genes annotated from Ensembl was quantified with HTSeq-count and analyzed with DESeq2 R package. We compared gene expressions of the FE groups, and identified 70 and 19 DE genes in Holstein and Jersey, respectively. An interaction term (high vs low FE x LC vs HC) detected two significantly DE genes in Jersey cows. The biological function of the DE genes was analyzed using the Gene Set Enrichment Analysis (GSEA). In conclusion, the transcriptomics approach was effective in detecting enrichment of pathways for primary immunodeficiency, and metabolism of lipid, sugar and protein that are different between High and Low FE cows.
