Detecting microRNA binding and siRNA off-target effects from expression data.

Sylamer is a method for detecting microRNA target and small interfering RNA off-target signals in 3' untranslated regions from a ranked gene list, sorted from upregulated to downregulated, after a microRNA perturbation or RNA interference experiment. The output is a landscape plot that tracks occurrence biases using hypergeometric P-values for all words across the gene ranking. We demonstrated the utility, speed and accuracy of this approach on several datasets

Preferential regulation of stably expressed genes in the human genome suggests a widespread expression buffering role of microRNAs

In this study, we comprehensively explored the stably expressed genes (SE genes) and fluctuant genes (FL genes) in the human genome by a meta-analysis of large scale microarray data. We found that these genes have distinct function distributions. miRNA targets are shown to be significantly enriched in SE genes by using propensity analysis of miRNA regulation, supporting the hypothesis that miRNAs can buffer whole genome expression fluctuation. The expression-buffering effect of miRNA is independent of the target site number within the 3'-untranslated region. In addition, we found that gene expression fluctuation is positively correlated with the number of transcription factor binding sites in the promoter region, which suggests that coordination between transcription factors and miRNAs leads to balanced responses to external perturbations

Accurate microRNA target prediction correlates with protein repression levels

MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and diseas

The antiinflammatory potential of phenolic compounds from Emblica officinalis L. in rat

Antiinflammatory effects of phenolic compounds from Emblica officinalis were evaluated in carrageenan and cotton pellet induced acute and chronic inflammatory animal model. Fractions of E. officinalis containing free (FPEO) and bounded (BPEO) phenolic compounds were assessed by HPLC technique. The free and bound phenolic compounds were studied for their acute and chronic antiinflammatory activity at dose level of 20 and 40 mg/kg. The carrageenan induced acute inflammation was assessed by measuring rat paw volume at different time of intervals. Further, cotton pellet induced chronic inflammation was assessed by granulomatous tissue mass estimation along with the estimation of tissue biomarker changes (i.e. lipid peroxidation, reduced glutathione, myeloperoxidase and plasma extravasation). The results indicated that in both acute and chronic inflammation, FPEO and BPEO show reduction in the inflammation, but significant effects was observed only at high doses of both fractions which was comparable to diclofenac treated group. In conclusion, phenolic compounds of E. officinalis may serve as potential herbal candidate for amelioration of acute and chronic inflammation due to their modulatory action of free radicals

Removal of AU Bias from Microarray mRNA Expression Data Enhances Computational Identification of Active MicroRNAs

Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3′-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3′-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3′-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3′-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions

Profiling and Functional Analyses of MicroRNAs and Their Target Gene Products in Human Uterine Leiomyomas

Human uterine leiomyomas (ULM) are characterized by dysregulation of a large number of genes and non-coding regulatory microRNAs. In order to identify microRNA::mRNA associations relevant to ULM pathogenesis, we examined global correlation patterns between the altered microRNA expression and the predicted target genes in ULMs and matched myometria.Patterns of inverse association of microRNA with mRNA expression in ULMs revealed an involvement of multiple candidate pathways, including extensive transcriptional reprogramming, cell proliferation control, MAP kinase, TGF-beta, WNT, JAK/STAT signaling, remodeling of cell adhesion, and cell-cell and cell-matrix contacts. We further examined the correlation between the expression of the selected target gene protein products and microRNAs in thirty-six paired sets of leiomyomas and matched myometria. We found that a number of dysregulated microRNAs were inversely correlated with their targets at the protein level. The comparative genomic hybridization (CGH) in eight ULM patients revealed that partially shared deletions of two distinct chromosomal regions might be responsible for loss of cancer-associated microRNA expression and could thus contribute to the ULM pathogenesis via deregulation of target mRNAs. Last, we functionally tested the repressor effects of selected cancer-related microRNAs on their predicted target genes in vitro.We found that some but not all of the predicted and inversely correlated target genes in ULMs can be directly regulated by microRNAs in vitro. Our findings provide a broad overview of molecular events underlying the tumorigenesis of uterine ULMs and identify select genetic and regulatory events that alter microRNA expression and may play important roles in ULM pathobiology by positively regulating tumor growth while maintaining the non-invasive character of ULMs

Detection of a MicroRNA Signal in an In Vivo Expression Set of mRNAs

Background. microRNAs (miRNAs) are approximately 21 nucleotide non-coding transcripts capable of regulating gene expression. The most widely studied mechanism of regulation involves binding of a miRNA to the target mRNA. As a result, translation of the target mRNA is inhibited and the mRNA may be destabilized. The inhibitory effects of miRNAs have been linked to diverse cellular processes including malignant proliferation, apoptosis, development, differentiation, and metabolic processes. We asked whether endogenous fluctuations in a set of mRNA and miRNA profiles contain correlated changes that are statistically distinguishable from the many other fluctuations in the data set. Methodology/Principal Findings. RNA was extracted from 12 human primary brain tumor biopsies. These samples were used to determine genome-wide mRN

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012