Expression profile of microRNAs in young stroke patients

10.1371/journal.pone.0007689PLoS ONE41

MicroRNAs in hyperglycemia induced endothelial cell dysfunction

Hyperglycemia is closely associated with prediabetes and Type 2 Diabetes Mellitus. Hyperglycemia increases the risk of vascular complications such as diabetic retinopathy, diabetic nephropathy, peripheral vascular disease and cerebro/cardiovascular diseases. Under hyperglycemic conditions, the endothelial cells become dysfunctional. In this study, we investigated the miRNA expression changes in human umbilical vein endothelial cells exposed to different glucose concentrations (5, 10, 25 and 40 mM glucose) and at various time intervals (6, 12, 24 and 48 h). miRNA microarray analyses showed that there is a correlation between hyperglycemia induced endothelial dysfunction and miRNA expression. In silico pathways analyses on the altered miRNA expression showed that the majority of the affected biological pathways appeared to be associated to endothelial cell dysfunction and apoptosis. We found the expression of ten miRNAs (miR-26a-5p, -26b-5p, 29b-3p, -29c-3p, -125b-1-3p, -130b-3p, -140-5p, -192-5p, -221-3p and -320a) to increase gradually with increasing concentration of glucose. These miRNAs were also found to be involved in endothelial dysfunction. At least seven of them, miR-29b-3p, -29c-3p, -125b-1-3p, -130b-3p, -221-3p, -320a and -192-5p, can be correlated to endothelial cell apoptosis

Overview of integrated network of genes regulated by both lncRNAs and miRNAs, that are crucial for precise neuronal development.

<p>Upregulated mRNAs/lncRNAs/miRNAs are shown in red font. Green font represents downregulated mRNAs/lncRNAs/miRNAs. LncRNAs associated with the gene are underlined. Black solid feedback arrows (→) on the gene indicate lncRNAs associated with the gene with synergistic expression. Black inhibitory arrows (⊥) indicate lncRNAs associated with the gene with reciprocal expression. Ten differentially expressed miRNAs targeting the respective genes are indicated. Solid lines (–) emerging from the miRNAs indicate validated targets. Dashed lines (----) indicate predicted targets as determined by TargetScan and microRNA.org. miRNAs shaded in grey (miR-329, -377, -495) belong to the miR-379-410 cluster.</p

Transcriptome of maturing neurons.

<p>(<b>A</b>) Hierarchical clustering analyses of mRNAs in maturing cortical neurons. Clusters of highly down- and up-regulated mRNAs were identified. <b>Bi)</b> lncRNAs in maturing neurons. Clusters of highly down- and up-regulated lncRNAs with their orientation to the genome or proximal gene loci were identified. (<b>Bii</b>) Subgroup analyses of altered lncRNAs in relation to their nearby coding genes. (<b>C</b>) miRNAs in maturing cortical neurons. Clusters of highly down- and up-regulated miRNAs were identified. Total RNA from 4 separate experiments (n = 4) carried out in triplicates were pooled for each time points. The microarray analyses were carried out for each time point on the pooled RNA. The average signal intensities were 369.03, 501.12, 429.58 and 455.03 for the lncRNA and mRNA microarray for Day 2, Day 4, Day 6 and Day 8 respectively. For miRNA microarray, the average signal intensities were 1673.43, 1783.01, 1385.10 and 1698.38 for Day 2, Day 4, Day 6 and Day 8 respectively. Hierarchical clusters were constructed out using average linkage and Euclidean distance as the similarity measure. Green rectangle indicates downregulation and red, upregulation.</p

Validation and quantification of mRNA, lncRNA and miRNA expression by qPCR.

<p>(<b>A</b>) <i>Ncam1</i> mRNA (NM_010875) and lncRNA (AK156022), (<b>B</b>) <i>Negr1</i> mRNA (NM_001039094) and lncRNA (uc008rva.1), (<b>C</b>) <i>Ncam1</i> and <i>Negr1</i> mRNA-lncRNA pairs in neurons subjected to 2 hr OGD. (<b>D</b>) Stem-loop PCR quantification of miR-377 in maturing neurons. Expression of GAPDH was used as a control/housekeeping gene to normalize mRNA, lncRNA and miRNA expression. Statistically significant differences were tested using the Student’s <i>t-test</i> (*<i>p&lt;0.05</i>, **<i>p&lt;0.01</i>).</p

Pathways implicated during neuronal maturation.

<p>Genes identified had differentially expressed mRNAs and lncRNAs associated with them. mRNA of genes in bold were predicted to be targets of the altered miRNAs as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103525#pone.0103525.s007" target="_blank">Table S4</a>.</p

Expression level of miR-124, miR-143 and miR-223, in primary cultures.

<p>qPCR was carried out on 10 ng RNA from maturing neurons and neurons subjected to OGD to determine the purity of the primary cortical neuronal cultures. qPCR amplification was carried out for a maximum of 40 cycles. miR-124 was found to be significantly expressed in neuronal cultures +/− OGD. miR-143 and miR-223 were found to be significantly expressed only in astrocyte enriched cultures. Expression is shown as mean C_T±SD. Three technical replicates and 3 biological replicates were used as described in the methods section. Statistical significance were evaluated using the Student’s <i>t-test</i> (**<i>p&lt;0.01</i>).</p

Supporting data for characterization of non-coding RNAs associated with the Neuronal growth regulator 1 (NEGR1) adhesion protein

Long non-coding RNAs and microRNAs control gene expression to determine central nervous system development and function. Neuronal growth regulator 1 (NEGR1) is a cell adhesion molecule that plays an important role in neurite outgrowth during neuronal development and its precise expression is crucial for correct brain development. The data described here is related to the research article titled “A long non-coding RNA, BC048612 and a microRNA, miR-203 coordinate the gene expression of Neuronal growth regulator 1 (NEGR1) adhesion protein” [1]. This data article contains detailed bioinformatics analysis of genetic signatures at the Negr1 gene locus retrieved from the UCSC genome browser. This approach could be adopted to identify putative regulatory non-coding RNAs in other tissues and diseases. Keywords: Bioinformatics, Long non-coding RNA, MicroRNA, Negr1, Regulatio

MicroRNA 144 impairs insulin signaling by inhibiting the expression of insulin receptor substrate 1 in type 2 diabetes mellitus.

BACKGROUND: Dysregulation of microRNA (miRNA) expression in various tissues and body fluids has been demonstrated to be associated with several diseases, including Type 2 Diabetes mellitus (T2D). Here, we compare miRNA expression profiles in different tissues (pancreas, liver, adipose and skeletal muscle) as well as in blood samples from T2D rat model and highlight the potential of circulating miRNAs as biomarkers of T2D. In parallel, we have examined the expression profiles of miRNAs in blood samples from Impaired Fasting Glucose (IFG) and T2D male patients. METHODOLOGY/PRINCIPAL FINDINGS: Employing miRNA microarray and stem-loop real-time RT-PCR, we identify four novel miRNAs, miR-144, miR-146a, miR-150 and miR-182 in addition to four previously reported diabetes-related miRNAs, miR-192, miR-29a, miR-30d and miR-320a, as potential signature miRNAs that distinguished IFG and T2D. Of these microRNAs, miR-144 that promotes erythropoiesis has been found to be highly up-regulated. Increased circulating level of miR-144 has been found to correlate with down-regulation of its predicted target, insulin receptor substrate 1 (IRS1) at both mRNA and protein levels. We could also experimentally demonstrate that IRS1 is indeed the target of miR-144. CONCLUSION: We demonstrate that peripheral blood microRNAs can be developed as unique biomarkers that are reflective and predictive of metabolic health and disorder. We have also identified signature miRNAs which could possibly explain the pathogenesis of T2D and the significance of miR-144 in insulin signaling