Co-expression network analysis predicts a key role of microRNAs in the adaptation of the porcine skeletal muscle to nutrient supply

Abstract

Background: The role of non-coding RNAs in the porcine muscle metabolism is poorly understood, with few studies investigating their expression patterns in response to nutrient supply. Therefore, we aimed to investigate the changes in microRNAs (miRNAs), long intergenic non-coding RNAs (lincRNAs) and mRNAs muscle expression before and after food intake.

Results: We measured the miRNA, lincRNA and mRNA expression levels in the gluteus medius muscle of 12 gilts in a fasting condition (AL-T0) and 24 gilts fed ad libitum during either 5 h. (AL-T1, N = 12) or 7 h. (AL-T2, N = 12) prior to slaughter. The small RNA fraction was extracted from muscle samples retrieved from the 36 gilts and sequenced, whereas lincRNA and mRNA expression data were already available. In terms of mean and variance, the expression profiles of miRNAs and lincRNAs in the porcine muscle were quite different than those of mRNAs. Food intake induced the differential expression of 149 (AL-T0/AL-T1) and 435 (AL-T0/AL-T2) mRNAs, 6 (AL-T0/AL-T1) and 28 (AL-T0/AL-T2) miRNAs and none lincRNAs, while the number of differentially dispersed genes was much lower. Among the set of differentially expressed miRNAs, we identified ssc-miR-148a-3p, ssc-miR-22-3p and ssc-miR-1, which play key roles in the regulation of glucose and lipid metabolism. Besides, co-expression network analyses revealed several miRNAs that putatively interact with mRNAs playing key metabolic roles and that also showed differential expression before and after feeding. One case example was represented by seven miRNAs (ssc-miR-148a-3p, ssc-miR-151-3p, ssc-miR-30a-3p, ssc-miR-30e-3p, ssc-miR-421-5p, ssc-miR-493-5p and ssc-miR-503) which putatively interact with the PDK4 mRNA, one of the master regulators of glucose utilization and fatty acid oxidation.

Conclusions: As a whole, our results evidence that microRNAs are likely to play an important role in the porcine skeletal muscle metabolic adaptation to nutrient availability.

Keywords: Co-expression analysis; Pig; Regulatory impact factor; Skeletal muscle; lincRNAs; microRNAs.

Fig. 1

Depiction of the experimental design used in our study. Gilts were fed ad libitum (N = 36, N = 12 per group) with a commercial feeding diet during the whole growth period. Prior to slaughter, the 36 gilts were fasted for 12 h. The day of slaughter, 12 gilts (AL-T0) were killed under fasting conditions. The remaining 24 gilts were fed during 5 h. (AL-T1) and 7 h. (AL-T2) and they were subsequently slaughtered

Fig. 2

Expression variability and quantification of expression levels of mRNAs, microRNAs and lincRNAs. a Biological Coefficient of Variation (BCV) distribution across transcript types within each analyzed group. b DESeq2 regularized log₂ mean expression (rlog) values across transcript types within each analyzed group

Fig. 3

Biological Coefficient of Variation (BCV) vs. DESeq2 regularized log₂ mean expression (Rlog) of (a) mRNAs, (b) microRNAs and (c) lincRNAs in each of the analyzed groups (AL-T0, AL-T1 and AL-T2)

Fig. 4

Log₂ Fold change (FC) of the dispersion values estimated with MDSeq tools vs. log₂ mean expression (counts-per-million, CPM) of (a) mRNAs, (b) microRNAs and (c) lincRNAs expression patterns in the AL-T0/AL-T1 (left column) and AL-T0/AL-T2 contrasts (right column)

Fig. 5

Principal Component Analysis (PCA) clustering of gluteus medius skeletal muscle samples (11 AL-T0, 12 AL-T1 and 12 AL-T2 gilts) according to the expression profiles of (a) mRNAs, (b) microRNAs and (c) lincRNAs

Fig. 6

Selected miRNA-to-mRNA and mRNA-to-mRNA co-expression network according to the PCIT algorithm in the AL-T0/AL-T2 contrast. Differentially expressed miRNAs and mRNAs were considered. Only significant correlations below − 0.5 for miRNA-to-mRNA and above |0.7| for mRNA-to-mRNA interactions where selected. Red and blue edges indicate negative and positive correlations in the co-expression network, respectively