Dicer Deficiency Reveals MicroRNAs Predicted to Control Gene Expression in the Developing Adrenal Cortex
ABSTRACT MicroRNAs (miRNAs) are small, endogenous, non-protein coding RNAs that are an important means of post-transcriptional gene regulation. Deletion of Dicer, a key miRNA processing enzyme, is embryonic lethal in mice, and tissue-specific Dicer deletion results in developmental defects. Using a conditional knockout model, we generated mice lacking Dicer in the adrenal cortex. These Dicer knockout (KO) mice exhibited perinatal mortality and failure of the adrenal cortex during late gestation between embryonic day 16.5 (E16.5) and E18.5. Further study of Dicer KO adrenals demonstrated a significant loss of Sf1 expressing cortical cells that was histologically evident as early as E16.5 coincident with an increase in p21 and cleaved-caspase 3 staining in the cortex. However, peripheral cortical proliferation persisted in KO adrenals as assessed by anti-PCNA staining. To further characterize the embryonic adrenals from Dicer KO mice, we performed microarray analyses for both gene expression and miRNA on purified RNA isolated from control and KO adrenals of E15.5 and E16.5 embryos. Consistent with the absence of Dicer and the associated loss of miRNA-mediated mRNA degradation, we observed an up-regulation of a small subset of adrenal transcripts in Dicer KO mice, most notably the transcripts coded by the genes Nr6a1 and Acvr1c. Indeed, several miRNAs, including let-7, miR-34c, and miR-21 that are predicted to target these genes for degradation, were also markedly down-regulated in Dicer KO adrenals. Together these data suggest a role for miRNA mediated regulation of a subset of genes that are essential for normal adrenal growth and homeostasis.
SourceAvailable from: Liyang Diao[Show abstract] [Hide abstract]
ABSTRACT: microRNAs (miRNAs) are a class of ∼22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. miRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3' UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models, which are widely used in genome-wide association studies (GWASs). Essentially we use 3' UTR sequence similarity in place of population cryptic relatedness in the GWAS problem. Compared to similar methods such as miReduce, Sylamer and cWords, we found that MixMir performed better at discovering true miRNA motifs in three mouse Dicer-knockout experiments from different tissues, two of which were collected by our group. We confirmed these results on protein and mRNA expression data obtained from miRNA transfection experiments in human cell lines. MixMir can be freely downloaded from https://github.com/ldiao/MixMir.Nucleic Acids Research 07/2014; 42(17). DOI:10.1093/nar/gku672 · 8.81 Impact Factor
The Journal of Clinical Endocrinology and Metabolism 04/2014; 99(4):1152-6. DOI:10.1210/jc.2014-1062 · 6.31 Impact Factor
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ABSTRACT: The nuclear receptor superfamily includes many receptors identified based on their similarity to steroid hormone receptors but without a known ligand. The study of how these receptors are diversely regulated to interact with genomic regions to control a plethora of biological processes has provided critical insight into development, physiology and the molecular pathology of disease. Here we provide a compendium of these so-called 'orphan receptors', and focus on what has been learned about their modes of action, physiological functions, and therapeutic promise.Journal of Molecular Endocrinology 10/2013; DOI:10.1530/JME-13-0212 · 3.62 Impact Factor