Unmasking Upstream Gene Expression Regulators with miRNA-corrected mRNA Data

Abstract

Expressed micro-RNA (miRNA) affects messenger RNA (mRNA) abundance, hindering the accuracy of upstream regulator analysis. Our objective was to provide an algorithm to correct such bias. Large mRNA and miRNA analyses were performed on RNA extracted from bovine liver and mammary tissue. Using four levels of target scores from TargetScan (all miRNA:mRNA target gene pairs or only the top 25%, 50%, or 75%). Using four levels of target scores from TargetScan (all miRNA:mRNA target gene pairs or only the top 25%, 50%, or 75%) and four levels of the magnitude of miRNA effect (ME) on mRNA expression (30%, 50%, 75%, and 83% mRNA reduction), we generated 17 different datasets (including the original dataset). For each dataset, we performed upstream regulator analysis using two bioinformatics tools. We detected an increased effect on the upstream regulator analysis with larger miRNA:mRNA pair bins and higher ME. The miRNA correction allowed identification of several upstream regulators not present in the analysis of the original dataset. Thus, the proposed algorithm improved the prediction of upstream regulators.

The Experimental and In Silico-Based Evaluation of NRF2 Modulators, Sulforaphane and Brusatol, on the Transcriptome of Immortalized Bovine Mammary Alveolar Cells

“…For DIA, the analyses of KEGG pathways and the upstream transcription factor [68] were performed. The functional analysis in DAVID was performed with all DEGs and upand downregulated DEGs in each comparison.…”