N-BLR, a primate-specific non-coding transcript leads to colorectal cancer invasion and migration

Background: non-coding RNAs have been drawing increasing attention in recent years as functional data suggest that they play important roles in key cellular processes. N-BLR is a primate-specific long non-coding RNA that modulates the epithelial-to-mesenchymal transition, facilitates cell migration, and increases colorectal cancer invasion. Results: we performed multivariate analyses of data from two independent cohorts of colorectal cancer patients and show that the abundance of N-BLR is associated with tumor stage, invasion potential, and overall patient survival. Through in vitro and in vivo experiments we found that N-BLR facilitates migration primarily via crosstalk with E-cadherin and ZEB1. We showed that this crosstalk is mediated by a pyknon, a short ~20 nucleotide-long DNA motif contained in the N-BLR transcript and is targeted by members of the miR-200 family. In light of these findings, we used a microarray to investigate the expression patterns of other pyknon-containing genomic loci. We found multiple such loci that are differentially transcribed between healthy and diseased tissues in colorectal cancer and chronic lymphocytic leukemia. Moreover, we identified several new loci whose expression correlates with the colorectal cancer patients' overall survival. Conclusions: the primate-specific N-BLR is a novel molecular contributor to the complex mechanisms that underlie metastasis in colorectal cancer and a potential novel biomarker for this disease. The presence of a functional pyknon within N-BLR and the related finding that many more pyknon-containing genomic loci in the human genome exhibit tissue-specific and disease-specific expression suggests the possibility of an alternative class of biomarkers and therapeutic targets that are primate-specific

MicroRNA expression differences in human hematopoietic cell lineages enable regulated transgene expression.

Blood microRNA (miRNA) levels have been associated with and shown to participate in disease pathophysiology. However, the hematopoietic cell of origin of blood miRNAs and the individual blood cell miRNA profiles are poorly understood. We report the miRNA content of highly purified normal hematopoietic cells from the same individuals. Although T-cells, B-cells and granulocytes had the highest miRNA content per cell, erythrocytes contributed more cellular miRNA to the blood, followed by granulocytes and platelets. miRNA profiling revealed different patterns and different expression levels of miRNA specific for each lineage. miR-30c-5p was determined to be an appropriate reference normalizer for cross-cell qRT-PCR comparisons. miRNA profiling of 5 hematopoietic cell lines revealed differential expression of miR-125a-5p. We demonstrated endogenous levels of miR-125a-5p regulate reporter gene expression in Meg-01 and Jurkat cells by (1) constructs containing binding sites for miR-125a-5p or (2) over-expressing or inhibiting miR-125a-5p. This quantitative analysis of the miRNA profiles of peripheral blood cells identifies the circulating hematopoietic cellular miRNAs, supports the use of miRNA profiles for distinguishing different hematopoietic lineages and suggests that endogenously expressed miRNAs can be exploited to regulate transgene expression in a cell-specific manner

Circulating microRNAs in patients with immune thrombocytopenia before and after treatment wirh thrombopoietin-receptor agonists

MicroRNAs (miRNAs) are small non-coding RNAs involved in the regulation of gene expression. Dysregulated expression of several miRNAs has been found in primary immune thrombocytopenia (ITP) suggesting that miRNAs are likely involved in the pathogenesis of ITP. We aimed to explore the differential expression of miRNAs in patients with ITP before and after starting treatment with thrombopoietin-receptor agonists (TPO-RAs) to clarify their roles in the pathophysiology of ITP, and as potential diagnostic and prognostic markers of this disorder. We performed a profiling study where 179 miRNAs were analyzed in eight ITP patients before and during treatment with TPO-RAs and in eight controls using miRNA PCR panel; 81 miRNAs were differentially expressed in ITP patients compared to controls, and 14 miRNAs showed significant changes during TPO-RA-treatment. Ten miRNAs were selected for validation that was performed in 23 patients and 22 controls using droplet digital PCR. Three miRNAs were found to be differentially expressed in ITP patients before TPO-RA-treatment compared to controls: miR-199a-5p was down-regulated (p = 0.0001), miR-33a-5p (p = 0.0002) and miR-195-5p (p = 0.035) were up-regulated. Treatment with TPO-RAs resulted in changes in six miRNAs including miR-199a-5p (p = 0.001), miR-33a-5p (p = 0.003), miR-382-5p (p = 0.004), miR-92b-3p (p = 0.005), miR-26a-5p (p = 0.008) and miR-221-3p (p = 0.023); while miR-195-5p remained unchanged and significantly higher than in controls, despite the increase in the platelet count, which may indicate its possible role in the pathophysiology of ITP. Regression analysis revealed that pre-treatment levels of miR-199a-5p and miR-221-3p could help to predict platelet response to TPO-RA-treatment. ROC curve analysis showed that the combination of miR-199a-5p and miR-33a-5p could distinguish patients with ITP from controls with AUC of 0.93. This study identifies a number of differentially expressed miRNAs in ITP patients before and after initiation of TPO-RAs with potential roles in the pathophysiology, as well as with a possible utility as diagnostic and prognostic biomarkers. These interesting findings deserve further exploration and validation in future studies

Peripheral blood cells miRNA profiles.

<p>(A) Pie graph representation of the top 10 most abundant miRNAs in each cell type. (B) Unsupervised hierarchical clustering of miRNA expression profiles. The dendrogram was generated using the union of the 20 most abundant miRNAs (39 in total) in each cell type. Data from 24 samples was used in these analyses (one granulocyte sample was not analyzed due to technical issues). (C) The left heatmap is derived from the average of the miRNAs shown in panel B. For ease of comparison, the right set of heatmaps display primary miRNA profiles adjacent to corresponding profiles from transformed cell lines, Jurkat (T-lymphoblastic), Raji (B-lymphoblastic), U937 (monocytic), Meg-01 (megakaryoblastic) and K562 (erythroleukemic) cell lines. Each column in the heatmaps indicates the average log-ratio intensity data. * indicates probes with similar and indistinguishable sequence with the nCounter platform.</p

Exploiting endogenous miRNAs to modify exogenous gene expression.

<p>(A) Illustration that <i>miR-125a-5p</i> was selectively reduced in the lymphocytic cell lines, Jurkat and Raji. (B) Schematic of reporter constructs used to assess effect of endogenous levels of <i>miR-125a-5p</i>. Constructs were engineered to contain 1, 2 or 4 <i>miR-125a-5p</i> binding sites or scrambled sequence controls. (C) Meg-01 cells were transfected with the indicated constructs. Luciferase repression was enhanced with more <i>miR-125a-5p</i> binding sites. (D) Meg-01, Raji, Jurkat and K562 cells were transfected with the 4xSC and 4x125 constructs. Luciferase was quantified and normalized to GFP for transfection efficiency. Data plotted as fold-expression compared to constructs with scrambled sequence. (E) Meg-01 cells were co-transfected with Luc-4x125 construct and control locked nucleic acid (LNA) or LNA that specifically inhibits <i>miR-125a-5p</i>. Jurkat cells were co-transfected with Luc-4x125 construct and control pre-miRNA or pre-<i>miR-125a-5p.</i> Data in panels C-E are mean ± SD of at least three independent experiments with two replicates each.</p

miRNAs DE by cell type.

<p>(A) The 10 most stable miRNAs across all cell types are shown for both NormFinder (left) and Coefficient of Variation (CV) methods (right). (B) Validation of NanoString-derived data for <i>miR-301a-3p</i>by qRT-PCR data. The X-axis represents the expression level of each miRNA normalized to <i>miR-30c-3p</i> using 2^−ΔCt method. Each point represents the mean ± SEM of 5 subjects for each cell type. (C,D) Validation of microRNAs DE by cell type. The NanoString-derived data for the indicated miRNAs was validated by qRT-PCR using RNA isolated from the 5 hematopoietic cell types from two different preparations of cells and RNA. qRT-PCR data was normalized and presented as in Figure 4B.P, platelets; T, T-cells; B, B-cells; G, granulocytes; E, erythrocytes.</p

Peripheral blood cells miRNA profiles.

<p>(A) Pie graph representation of the top 10 most abundant miRNAs in each cell type. (B) Unsupervised hierarchical clustering of miRNA expression profiles. The dendrogram was generated using the union of the 20 most abundant miRNAs (39 in total) in each cell type. Data from 24 samples was used in these analyses (one granulocyte sample was not analyzed due to technical issues). (C) The left heatmap is derived from the average of the miRNAs shown in panel B. For ease of comparison, the right set of heatmaps display primary miRNA profiles adjacent to corresponding profiles from transformed cell lines, Jurkat (T-lymphoblastic), Raji (B-lymphoblastic), U937 (monocytic), Meg-01 (megakaryoblastic) and K562 (erythroleukemic) cell lines. Each column in the heatmaps indicates the average log-ratio intensity data. * indicates probes with similar and indistinguishable sequence with the nCounter platform.</p

Quantification of total RNA and miRNA in platelets, T-Cells, B-Cells, granulocytes and erythrocytes.

<p>(A) Average yield of total RNA from each cell type. (B) Average of miRNA fraction in the total RNA from each cell type. (C) Average miRNA content of each cell type. In (A–C) the box represents the 25th to 75th percentiles, the line in the box is the median and the whiskers represent minimum and maximum values. (E) Summary of comparisons across cell types in panels A–C (one tail t-test). P, platelets; T, T-cells; B, B-cells; G, granulocytes; E, erythrocytes. N = 5 for each of the 5 cell types.</p

Contribution of hematopoietic cell type to total miRNA content per volume of blood.

<p>Contribution of hematopoietic cell type to total miRNA content per volume of blood.</p