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EnrichMiR predicts functionally relevant microRNAs based on target collections.

ABSTRACT: MicroRNAs (miRNAs) are small non-coding RNAs that are among the main post-transcriptional regulators of gene expression. A number of data collections and prediction tools have gathered putative or confirmed targets of these regulators. It is often useful, for discovery and validation, to harness such collections to perform target enrichment analysis in given transcriptional signatures or gene-sets in order to predict involved miRNAs. While several methods have been proposed to this end, a flexible and user-friendly interface for such analyses using various approaches and collections is lacking. enrichMiR (https://ethz-ins.org/enrichMiR/) addresses this gap by enabling users to perform a series of enrichment tests, based on several target collections, to rank miRNAs according to their likely involvement in the control of a given transcriptional signature or gene-set. enrichMiR results can furthermore be visualised through interactive and publication-ready plots. To guide the choice of the appropriate analysis method, we benchmarked various tests across a panel of experiments involving the perturbation of known miRNAs. Finally, we showcase enrichMiR functionalities in a pair of use cases.

SUBMITTER: Soutschek M

PROVIDER: S-EPMC9252831 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Long-term opioid treatment results in reduced therapeutic efficacy and in turn leads to an increase in the dose required to produce equivalent pain relief and alleviate break-through or insurmountable pain. Altered gene expression is a likely means for inducing long-term neuroadaptations responsible for tolerance. Studies conducted by our laboratory (Tapocik et al., 2009) revealed a network of gene expression changes occurring in canonical pathways involved in neuroplasticity, and uncovered miRNA processing as a potential mechanism. In particular, the mRNA coding the protein responsible for processing miRNAs, Dicer1, was positively correlated with the development of analgesic tolerance. The purpose of the present study was to test the hypothesis that miRNAs play a significant role in the development of analgesic tolerance as measured by thermal nociception. Dicer1 knockdown, miRNA profiling, bioinformatics, and confirmation of high value targets were used to test the proposition. Regionally targeted Dicer1 knockdown (via shRNA) had the anticipated consequence of eliminating the development of tolerance in C57BL/6J (B6) mice, thus supporting the involvement of miRNAs in the development of tolerance. MiRNA expression profiling identified a core set of chronic morphine-regulated miRNAs (miR's 27a, 9, 483, 505, 146b, 202). Bioinformatics approaches were implemented to identify and prioritize their predicted target mRNAs. We focused our attention on miR27a and its predicted target serpin peptidase inhibitor clade I (Serpini1) mRNA, a transcript known to be intricately involved in dendritic spine density regulation in a manner consistent with chronic morphine's consequences and previously found to be correlated with the development of analgesic tolerance. In vitro reporter assay confirmed the targeting of the Serpini1 3'-untranslated region by miR27a. Interestingly miR27a was found to positively regulate Serpini1 mRNA and protein levels in multiple neuronal cell lines. Lastly, Serpini1 knockout mice developed analgesic tolerance at a slower rate than wild-type mice thus confirming a role for the protein in analgesic tolerance. Overall, these results provide evidence to support a specific role for miR27a and Serpini1 in the behavioral response to chronic opioid administration (COA) and suggest that miRNA expression and mRNA targeting may underlie the neuroadaptations that mediate tolerance to the analgesic effects of morphine.

Project description:BackgroundUnigene sequences constitute a rich source of functionally relevant microsatellites. The present study was undertaken to mine the microsatellites in the available unigene sequences of sugarcane for understanding their constitution in the expressed genic component of its complex polyploid/aneuploid genome, assessing their functional significance in silico, determining the extent of allelic diversity at the microsatellite loci and for evaluating their utility in large-scale genotyping applications in sugarcane.ResultsThe average frequency of perfect microsatellite was 1/10.9 kb, while it was 1/44.3 kb for the long and hypervariable class I repeats. GC-rich trinucleotides coding for alanine and the GA-rich dinucleotides were the most abundant microsatellite classes. Out of 15,594 unigenes mined in the study, 767 contained microsatellite repeats and for 672 of these putative functions were determined in silico. The microsatellite repeats were found in the functional domains of proteins encoded by 364 unigenes. Its significance was assessed by establishing the structure-function relationship for the beta-amylase and protein kinase encoding unigenes having repeats in the catalytic domains. A total of 726 allelic variants (7.42 alleles per locus) with different repeat lengths were captured precisely for a set of 47 fluorescent dye labeled primers in 36 sugarcane genotypes and five cereal species using the automated fragment analysis system, which suggested the utility of designed primers for rapid, large-scale and high-throughput genotyping applications in sugarcane. Pair-wise similarity ranging from 0.33 to 0.84 with an average of 0.40 revealed a broad genetic base of the Indian varieties in respect of functionally relevant regions of the large and complex sugarcane genome.ConclusionMicrosatellite repeats were present in 4.92% of sugarcane unigenes, for most (87.6%) of which functions were determined in silico. High level of allelic diversity in repeats including those present in the functional domains of proteins encoded by the unigenes demonstrated their use in assay of useful variation in the genic component of complex polyploid sugarcane genome.

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EnrichMiR predicts functionally relevant microRNAs based on target collections.

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