MicroRNA-mediated up-regulation of an alternatively polyadenylated variant of the mouse cytoplasmic β-actin gene

Ghosh, Tanay; Soni, Kartik; Scaria, Vinod; Halimani, Mahantappa; Bhattacharjee, Chaitali; Pillai, Beena

doi:10.1093/nar/gkn624

Cited by 88 publications

(81 citation statements)

References 51 publications

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“…We were able to analyze >4200 genes with such transcript isoforms and expression in CGAP, and to find differential expression of isoforms between cancer and normal states. Intriguingly, we found an enrichment of transcripts harboring miRNA targeting sites in the sequence unique to one of two differentially expressed isoforms (Hirst et al 2007;Ghosh et al 2008), implicating their regulation in cancer biology.…”

Section: Discussionmentioning

confidence: 81%

Next-generation tag sequencing for cancer gene expression profiling

Morrissy¹,

Morin²,

Delaney³

et al. 2009

Genome Res.

294

259

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We describe a new method, Tag-seq, which employs ultra high-throughput sequencing of 21 base pair cDNA tags for sensitive and cost-effective gene expression profiling. We compared Tag-seq data to LongSAGE data and observed improved representation of several classes of rare transcripts, including transcription factors, antisense transcripts, and intronic sequences, the latter possibly representing novel exons or genes. We observed increases in the diversity, abundance, and dynamic range of such rare transcripts and took advantage of the greater dynamic range of expression to identify, in cancers and normal libraries, altered expression ratios of alternative transcript isoforms. The strand-specific information of Tag-seq reads further allowed us to detect altered expression ratios of sense and antisense (S-AS) transcripts between cancer and normal libraries. S-AS transcripts were enriched in known cancer genes, while transcript isoforms were enriched in miRNA targeting sites. We found that transcript abundance had a stronger GC-bias in LongSAGE than Tagseq, such that AT-rich tags were less abundant than GC-rich tags in LongSAGE. Tag-seq also performed better in gene discovery, identifying >98% of genes detected by LongSAGE and profiling a distinct subset of the transcriptome characterized by AT-rich genes, which was expressed at levels below those detectable by LongSAGE. Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.

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Section: Discussionmentioning

confidence: 81%

Next-generation tag sequencing for cancer gene expression profiling

Morrissy¹,

Morin²,

Delaney³

et al. 2009

Genome Res.

294

259

View full text Add to dashboard Cite

show abstract

“…Similarly, expression of the actin-bundling molecule fascin-1 can be suppressed by both miR-133a and miR-145 (Chiyomaru et al, 2010). Moreover, it appears that the miRNA miR-34 can regulate the mRNA encoding -actin itself, although this modulatory interaction might be quite complex and seems to involve the miRNA-mediated stabilization of an alternatively polyadenylated -actin mRNA species (Ghosh et al, 2008). When assessed collectively, the above-cited studies clearly indicate that miRNAs play a pervasive role in regulating biochemical pathways relevant to cell adhesion through their capacity to control the expression levels of numerous cytoskeletal regulatory proteins ( Fig.…”

Section: Cytoskeletal Regulatory Proteinsmentioning

confidence: 99%

Roles for microRNAs in the regulation of cell adhesion molecules

Valastyan

Weinberg

2011

Journal of Cell Science

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SummaryMaintenance of appropriate cell adhesion is crucial for normal cellular and organismal homeostasis. Certain microRNAs have recently been found capable of regulating molecules that oversee the fundamental cell biological events that drive cellular adhesion. It is now apparent that microRNAs play crucial roles in the great majority of biochemical pathways that contribute to normal cell adhesion. In this Commentary, we describe the latest advances within this still-emerging field, and highlight connections between the deregulation of microRNAs that affect cell-adhesion-associated molecules and the pathogenesis of several human diseases. Current evidence suggests that the ability of certain microRNAs -notably miR-17, miR-29, miR-31, miR-124 and miR-200 -to pleiotropically regulate multiple molecular components of the cell adhesion machinery endows these microRNAs with the capacity to function as key modulators of adhesion-associated processes. This, in turn, holds important implications for our understanding of both the basic biology of cell adhesion and the etiology of multiple pathological conditions.

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“…Overexpression of miR-223 leads to increased expression of its target GLUT4 protein expression thereby leading to increased glucose uptake . miR-34 binding to the 3 ′ UTR has been reported to lead to the up-regulation of expression of its target (Ghosh et al 2008). The miRNA X1miR16 mediates translational activation of Myt1 gene leading to maintenance of immature state of the oocyte (Mortensen et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of combinatorial regulation suggests that apparent positive regulation of targets by miRNA could be an artifact resulting from competition for mRNA

Nyayanit

Gadgil

2015

RNA

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MicroRNAs bind to and regulate the abundance and activity of target messenger RNA through sequestration, enhanced degradation, and suppression of translation. Although miRNA have a predominantly negative effect on the target protein concentration, several reports have demonstrated a positive effect of miRNA, i.e., increase in target protein concentration on miRNA overexpression and decrease in target concentration on miRNA repression. miRNA-target pair-specific effects such as protection of mRNA degradation owing to miRNA binding can explain some of these effects. However, considering such pairs in isolation might be an oversimplification of the RNA biology, as it is known that one miRNA interacts with several targets, and conversely target mRNA are subject to regulation by several miRNAs. We formulate a mathematical model of this combinatorial regulation of targets by multiple miRNA. Through mathematical analysis and numerical simulations of this model, we show that miRNA that individually have a negative effect on their targets may exhibit an apparently positive net effect when the concentration of one miRNA is experimentally perturbed by repression/overexpression in such a multi-miRNA multitarget situation. We show that this apparent unexpected effect is due to competition and will not be observed when miRNA interact noncompetitively with the target mRNA. This result suggests that some of the observed unusual positive effects of miRNA may be due to the combinatorial complexity of the system rather than due to any inherently unusual positive effect of the miRNA on its target.

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MicroRNA-mediated up-regulation of an alternatively polyadenylated variant of the mouse cytoplasmic β-actin gene

Cited by 88 publications

References 51 publications

Next-generation tag sequencing for cancer gene expression profiling

Next-generation tag sequencing for cancer gene expression profiling

Roles for microRNAs in the regulation of cell adhesion molecules

Mathematical modeling of combinatorial regulation suggests that apparent positive regulation of targets by miRNA could be an artifact resulting from competition for mRNA

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