Abstract
Transcription initiates the cascade of gene expression and is often assumed to play a predominant role in determining how much gene products are ultimately expressed. The relationship between mRNA levels and protein levels has been studied extensively to reveal the degrees of transcriptional and post-transcriptional regulation of protein expression. The extent to which transcription globally controls the differential expression of non-coding RNAs, however, is poorly defined. MicroRNAs (miRNAs) are a class of small, non-coding RNAs whose biogenesis involves transcription followed by extensive processing. Here, using hundreds of datasets produced from the ENCODE (Encyclopedia of DNA Elements) project we calculated the correlations between transcriptional activity and mature miRNA expression in diverse human cells, human tissues, and mouse tissues. While correlations vary among samples, most correlation coefficients are small. Interestingly, excluding miRNAs that were discovered later or weighting miRNA expression improves the correlations. Our results suggest that transcription contributes only modestly to differential miRNA expression at the genome-wide scale in mammals.
Highlights
How gene expression is regulated at the global scale is among the most intensely studied subjects in genomics (Vogel and Marcotte, 2012; Liu et al, 2016)
Most miRNAs are transcribed by Pol2, so Pol2 binding as determined by ChIP-seq experiments approximates transcriptional activity in miRNA genes
Unless specified otherwise, mRNAs referred hereafter include non-coding RNAs, e.g., long noncoding RNAs (lncRNAs), many of which have already been annotated as pri-miRNAs in ENCODE datasets
Summary
How gene expression is regulated at the global scale is among the most intensely studied subjects in genomics (Vogel and Marcotte, 2012; Liu et al, 2016). In contrast to protein expression, how transcription regulates non-coding RNA levels at the genome-wide scale has not been examined in detail. This is paradoxical, as some of the RNA species have been well characterized, and it is easier to quantify RNAs than proteins. One is that certain RNA classes are encoded by multiple genes, sometimes with complex genomic structures. Another is that prevailing RNAseq techniques typically yield short sequence reads that often do not adequately distinguish between RNAs such as small nucleolar RNAs and lncRNAs and their initial transcripts or processed intermediates. The global regulatory mechanisms of other RNAs such as miRNAs remain to be elucidated
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