Identification of Differentially Expressed miRNAs in Porcine Adipose Tissues and Evaluation of Their Effects on Feed Efficiency

Abstract

Feed efficiency (FE) is a very important trait affecting the economic benefits of pig breeding enterprises. Adipose tissue can modulate a variety of processes such as feed intake, energy metabolism and systemic physiological processes. However, the mechanism by which microRNAs (miRNAs) in adipose tissues regulate FE remains largely unknown. Therefore, this study aimed to screen potential miRNAs related to FE through miRNA sequencing. The miRNA profiles in porcine adipose tissues were obtained and 14 miRNAs were identified differentially expressed in adipose tissues of pigs with extreme differences in FE, of which 9 were down-regulated and 5 were up-regulated. GO and KEGG analyses indicated that these miRNAs were significantly related to lipid metabolism and these miRNAs modulated FE by regulating lipid metabolism. Subsequently, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) of five randomly selected DEMs was used to verify the reliability of miRNA-seq data. Furthermore, 39 differentially expressed target genes of these DEMs were obtained, and DEMs-target mRNA interaction networks were constructed. In addition, the most significantly down-regulated miRNAs, ssc-miR-122-5p and ssc-miR-192, might be the key miRNAs for FE. Our results reveal the mechanism by which adipose miRNAs regulate feed efficiency in pigs. This study provides a theoretical basis for the further study of swine feed efficiency improvement.

Keywords: adipose tissues; feed efficiency; microRNA; pig; residual feed intake.

Figure 1

Summary of RFI in the populations of Yorkshire pigs. (A) The distribution of RFI in the populations of Yorkshire pigs. (B) The RFI of the sequencing individual. *** represent p ≤ 0.001, meaning the RFI between High-FE and Low-FE significant differences.

Figure 2

Volcano plot of DEMs in adipose tissues between high-FE and low-FE pigs. The blue dots represent the DEMs; the red dots represent the miRNAs with no significant expression differences.

Figure 3

Hierarchically cluster heat map of DEMs. Red represents up-DEMs in adipose tissue, and blue represents down-DEMs in adipose tissue. Color intensity represents the degree of regulation (up- or down-regulation).

Figure 4

Validation of the RNA sequencing using qRT-PCR. U6 snRNA was used for samples standardization. (A) qRT-PCR validation of five DEMs in adipose tissues. (B) Line-fit plot of qRT-PCR results and RNA-sequencing data for selected DEMs. Mark * in (A) represent the expression level significantly different between two group (p < 0.05).

Figure 5

GO enrichment analysis of target genes of DEMs.

Figure 6

Pathway enrichment of target genes of DEMs.

Figure 7

Cluster heat map of DEMs and enrichment pathways associated with target gene. The miRNAs enriched in the similar pathways were clustered together, and the pathways enriched with similar miRNAs were clustered together. Since porcine miRNAs were not available to the DIANA miRPath, the prediction used human miRNAs instead.

Figure 8

DEMs–target mRNA interaction network. The network of 11 DEMs and 39 target genes of miRNA were analyzed using Cytoscape.