1,807,270 research outputs found
MMpred: functional miRNA – mRNA interaction analyses by miRNA expression prediction
Get PDFBackground: MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional
regulation. Comprehensive analyses of how microRNA influence biological processes requires paired
miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few
such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of
interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding
genes (host genes).
Results: We developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two
miRNA expression prediction methods – a scaling function and a linear model. Additionally, miRNA-mRNA anticorrelation
analyses are used to determine the most probable miRNA gene targets (i.e. the differentially
expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy
of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted
targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the
predicted interactions.
Conclusions: The MMpred pipeline requires only mRNA expression data as input and is independent of third
party miRNA target prediction methods. The method passed extensive numerical validation based on the
binding energy between the mature miRNA and 3’ UTR region of the target gene. We report that MMpred is
capable of generating results similar to that obtained using paired datasets. For the reported test cases we
generated consistent output and predicted biological relationships that will help formulate further testable
hypotheses
<it>In silico </it>miRNA prediction in metazoan genomes: balancing between sensitivity and specificity
Get PDFAbstract Background MicroRNAs (miRNAs), short ~21-nucleotide RNA molecules, play an important role in post-transcriptional regulation of gene expression. The number of known miRNA hairpins registered in the miRBase database is rapidly increasing, but recent reports suggest that many miRNAs with restricted temporal or tissue-specific expression remain undiscovered. Various strategies for in silico miRNA identification have been proposed to facilitate miRNA discovery. Notably support vector machine (SVM) methods have recently gained popularity. However, a drawback of these methods is that they do not provide insight into the biological properties of miRNA sequences. Results We here propose a new strategy for miRNA hairpin prediction in which the likelihood that a genomic hairpin is a true miRNA hairpin is evaluated based on statistical distributions of observed biological variation of properties (descriptors) of known miRNA hairpins. These distributions are transformed into a single and continuous outcome classifier called the L score. Using a dataset of known miRNA hairpins from the miRBase database and an exhaustive set of genomic hairpins identified in the genome of Caenorhabditis elegans, a subset of 18 most informative descriptors was selected after detailed analysis of correlation among and discriminative power of individual descriptors. We show that the majority of previously identified miRNA hairpins have high L scores, that the method outperforms miRNA prediction by threshold filtering and that it is more transparent than SVM classifiers. Conclusion The L score is applicable as a prediction classifier with high sensitivity for novel miRNA hairpins. The L-score approach can be used to rank and select interesting miRNA hairpin candidates for downstream experimental analysis when coupled to a genome-wide set of in silico-identified hairpins or to facilitate the analysis of large sets of putative miRNA hairpin loci obtained in deep-sequencing efforts of small RNAs. Moreover, the in-depth analyses of miRNA hairpins descriptors preceding and determining the L score outcome could be used as an extension to miRBase entries to help increase the reliability and biological relevance of the miRNA registry.</p
miRNA-Seq
No full textTo determine if HuR regulates miRNA biogenesis, we used miRNA-seq for genome-wide miRNA profiling to compare the miRNA profiles in HuR small interfering RNA (siRNA) targeting HuR mRNA (siHuR) transfected IEC-6 intestinal epithelial cells. Interestingly, almost all the mature miRNAs (370 of 372) were downregulated in the HuR-silenced IEC-6 cells with the remaining two being upregulated. The raw data are provided in detail in this table
MiRNA-21 in osteoclastogenes.
No full textBackgroundMany researchers focused on diverse miRNAs in the progression of osteoporosis in postmenopausal women. This study aimed to evaluate the association between plasma miRNA-21-5p and miRNA-422a with osteoporosis in postmenopausal women.MethodsThis cross-sectional comparative study was performed on 126 randomly selected postmenopausal women aged 50–65, including 65 osteoporotic and 61 normal-bone mineral density (BMD) women. miRNA-21 and miRNA-422a were identified using qRT-PCR in these women. BMD was evaluated by the dual-energy X-ray absorptiometry method. A binary logistic regression model adjusted for confounders was used to evaluate the associations between plasma miRNAs’ expression levels and osteoporosis. The Area Under Curve (AUC) was calculated to differentiate low BMD in the postmenopausal period using Receiver-Operator Characteristic (ROC) curves.ResultsmiRNA-21 and miRNA-422a were significantly up-regulated in osteoporotic compared to non-osteoporotic postmenopausal women. The expression levels of miRNA-21 and miRNA-422a indicated a significant reverse correlation with both lumbar spine and femoral neck density. After adjusting the confounders, the likelihood of osteoporosis in the postmenopausal women with under the median plasma levels of miRNA-21 (OR = 0.025; 95% CI: 0.003 to 0.198, ppppppConclusionsThis study indicated that the odds of osteoporosis in postmenopausal women increased with the higher expression of plasma miRNA-21 and miRNA-422a.</div
MiRNA-21 in osteoblastogenes.
No full textBackgroundMany researchers focused on diverse miRNAs in the progression of osteoporosis in postmenopausal women. This study aimed to evaluate the association between plasma miRNA-21-5p and miRNA-422a with osteoporosis in postmenopausal women.MethodsThis cross-sectional comparative study was performed on 126 randomly selected postmenopausal women aged 50–65, including 65 osteoporotic and 61 normal-bone mineral density (BMD) women. miRNA-21 and miRNA-422a were identified using qRT-PCR in these women. BMD was evaluated by the dual-energy X-ray absorptiometry method. A binary logistic regression model adjusted for confounders was used to evaluate the associations between plasma miRNAs’ expression levels and osteoporosis. The Area Under Curve (AUC) was calculated to differentiate low BMD in the postmenopausal period using Receiver-Operator Characteristic (ROC) curves.ResultsmiRNA-21 and miRNA-422a were significantly up-regulated in osteoporotic compared to non-osteoporotic postmenopausal women. The expression levels of miRNA-21 and miRNA-422a indicated a significant reverse correlation with both lumbar spine and femoral neck density. After adjusting the confounders, the likelihood of osteoporosis in the postmenopausal women with under the median plasma levels of miRNA-21 (OR = 0.025; 95% CI: 0.003 to 0.198, ppppppConclusionsThis study indicated that the odds of osteoporosis in postmenopausal women increased with the higher expression of plasma miRNA-21 and miRNA-422a.</div
miREE: miRNA Recognition Elements Ensemble
Get PDFAbstract Background Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. Results The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. Conclusions The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/</p
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Determination of how miRNAs mediate repression in Drosophila and the essential role of the oskar mRNA in egg chamber development
Get PDFtextmiRNAs are important regulators of gene expression. These small RNAs function throughout development and regulate translation of a number mRNAs. miRNAs exert their affect on translation as part of the RNP complex RISC. RISC can affect translation of transcripts at both the level of translation initiation, and post-initiation. Although mechanisms of repression mediated by miRNAs have been intensively studied, repression is not well characterized. In order to understand how miRNAs regulate translation in Drosophila, we first characterized miRNA-mediated repression in the ovary. We developed an ovarian assay sensitive to regulation by miRNAs and found that regulated transcripts localize to cytoplasmic puncta distinct from sponge bodies, cytoplasmic RNP structures consisting of proteins implicated in miRNA-mediated regulation. In addition, we devised a genetic screen to identify genes involved in miRNA-mediated regulation. Seven mutants were isolated from the screen, and two mutants were subsequently mapped to separate 1Mb genomic regions. Both these regions are devoid of genes implicated in miRNA-mediated regulation, suggesting our mutants identify novel components involved in repression. The oskar mRNA encodes for the Oskar protein, which is vital in establishing the posterior axis of the Drosophila embryo. In addition to its protein coding function, the osk mRNA has another essential role: it is required for egg chamber progression through oogenesis. This role of oskar is mediated by its 3ʼ UTR, but how it functions in this role is unknown. Here, we investigate the function of the 3ʼ UTR and discover that the well-defined BRE sequences are required for egg chamber progression through oogenesis. The BREs mediate translational repression of the highly regulated oskar mRNA and were previously defined by their ability to bind Bruno, which represses translation of the oskar mRNA. We also provide evidence that the osk BREs sequester Bruno, potentially inhibiting Bruno from binding and misregulating other mRNAs. Our results suggest a novel regulatory loop, where oskar sequesters and inhibits Bruno from misregulating mRNAs, and Bruno, in turn, regulates translation of the oskar mRNA.Cellular and Molecular Biolog
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