Animal GeneticsGenomicsPeer-reviewed journal article
Identification of differentially expressed small RNAs and prediction of target genes in Italian Large White pigs with divergent backfat deposition
R. DavoliiD, Enrico GaffoiD, Martina ZappaterraiD, Stefania BortoluzziiD, Paolo ZambonelliiD
Abstract
The identification of the molecular mechanisms regulating pathways associated to the potential of fat deposition in pigs can lead to the detection of key genes and markers for the genetic improvement of fat traits. MicroRNAs (miRNAs) interactions with target RNAs regulate gene expression and modulate pathway activation in cells and tissues. In pigs, miRNA discovery is well far from saturation and the knowledge of miRNA expression in backfat tissue and particularly of the impact of miRNA variations are still fragmentary. We characterized by RNAseq the small RNAs (sRNAs) expression profiles in Italian Large White pig backfat tissue. Comparing two groups of pigs divergent for backfat deposition, we detected 31 significant differentially expressed (DE) sRNAs, 14 up-regulated (including ssc-miR-132, ssc-miR-146b, sscmiR-221-5p, ssc-miR-365-5p, and the moRNA ssc-moR-21-5p) and 17 down-regulated (including ssc-miR-136, ssc-miR-195, ssc-miR-199a-5p, and ssc-miR-335). To understand the biological impact of the observed miRNA expression variations, we used the expression correlation of DE miRNA target transcripts expressed in the same samples to define a regulatory network of 193 interactions between DE miRNAs and 40 DE target transcripts showing opposite expression profiles and being involved in specific pathways. Several miRNAs and mRNAs in the network resulted to be expressed from backfat related pig QTLs. These results are informative on the complex mechanisms influencing fat traits, shed light on a new aspect of the genetic regulation of fat deposition in pigs, and facilitate the perspective implementation of innovative strategies of pig genetic improvement based on genomic markers.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 28
- FWCI
- 2.61
- Field percentile
- top 9%
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