Abstract
Co-expression network analyses provide insights into the molecular interactions underlying complex traits and diseases. In this study, co-expression network analysis was performed to detect expression patterns (modules or clusters) of microRNAs (miRNAs) during lactation, and to identify miRNA regulatory mechanisms for milk yield and component traits (fat, protein, somatic cell count (SCC), lactose, and milk urea nitrogen (MUN)) via miRNA target gene enrichment analysis. miRNA expression (713 miRNAs), and milk yield and components (Fat%, Protein%, lactose, SCC, MUN) data of nine cows at each of six different time points (day 30 (D30), D70, D130, D170, D230 and D290) of an entire lactation curve were used. Four modules or clusters (GREEN, BLUE, RED and TURQUOISE) of miRNAs were identified as important for milk yield and component traits. The GREEN and BLUE modules were significantly correlated (|r| > 0.5) with milk yield and lactose, respectively. The RED and TURQUOISE modules were significantly correlated (|r| > 0.5) with both SCC and lactose. In the GREEN module, three abundantly expressed miRNAs (miR-148a, miR-186 and miR-200a) were most significantly correlated to milk yield, and are probably the most important miRNAs for this trait. DDR1 and DDHX1 are hub genes for miRNA regulatory networks controlling milk yield, while HHEX is an important transcription regulator for these networks. miR-18a, miR-221/222 cluster, and transcription factors HOXA7, and NOTCH 3 and 4, are important for the regulation of lactose. miR-142, miR-146a, and miR-EIA17-14144 (a novel miRNA), and transcription factors in the SMAD family and MYB, are important for the regulation of SCC. Important signaling pathways enriched for target genes of miRNAs of significant modules, included protein kinase A and PTEN signaling for milk yield, eNOS and Noth signaling for lactose, and TGF β, HIPPO, Wnt/β-catenin and cell cycle signaling for SCC. Relevant enriched gene ontology (GO)-terms related to milk and mammary gland traits included cell differentiation, G-protein coupled receptor activity, and intracellular signaling transduction. Overall, this study uncovered regulatory networks in which miRNAs interacted with each other to regulate lactation traits.
Highlights
MicroRNAs are small noncoding RNA molecules about 22 nucleotides long which regulate gene expression post-transcriptionally, and play key roles in a wide range of biological processes
Important signaling pathways enriched for target genes of miRNAs of significant modules, included protein kinase A and PTEN signaling for milk yield, eNOS and Noth signaling for lactose, and TGF β, HIPPO, Wnt/β-catenin and cell cycle signaling for somatic cell count (SCC)
There was no clear trend for milk urea nitrogen (MUN) content, since it had highest numerical increase on D170 (p < 0.05) but remained similar across all other time points throughout lactation
Summary
MicroRNAs (miRNAs) are small noncoding RNA molecules about 22 nucleotides long which regulate gene expression post-transcriptionally, and play key roles in a wide range of biological processes. Several approaches have been proposed to explore these relationships, such as miRNA–miRNA synergistic network [5] and co-expression analyses [6,7]. The first approach is based on the downstream study of miRNA target genes through construction of networks based on different weighted methods on common target genes, as well as the interactions among them [3,5]. Weighted co-expression network is based on construction of interaction networks (modules) of miRNAs with similar expression patterns, whereby miRNAs in the same module interact with one another to regulate the same or similar biological processes [3,8]. The hub genes of each module points to the most active miRNAs in each network, which are potentially the most important miRNAs regulating the transcriptomic mechanisms underlying the traits
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