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Identification of miRNAs in sorghum by using bioinformatics approach.

ABSTRACT: MicroRNAs (miRNAs) regulate gene expression mainly by post-transcriptional gene silencing (PTGS) and in some cases by transcriptional genes silencing (TGS). miRNAs play critical roles in developmental processes, nutrient homeostasis, abiotic stress and pathogen responses of plants. In contrast to the large number of miRNAs predicted in cereal model plant rice, only 148 miRNAs were predicted in sorghum till date (miRBase release 17). This suggested that miRNAs identified in sorghum is far from saturation. Hence, we developed a bioinformatics pipeline using an in-house PERL script and publicly available structure prediction tools to identify miRNAs and their target genes from publically available Expressed Sequence Tags (EST) and Genomic Survey Sequence (GSS). About 1379 known and unique plant miRNAs from 33 different crops were used to predict new miRNAs in sorghum. We identified 31 new miRNAs belonging to 10 different miRNA families. We predicted 72 potential target genes for 31 miRNAs, and most of these target genes are predicted to be involved in plant growth and development.These newly identified miRNAs add to the growing database of miRNA and lay the foundation for further understanding of miRNA function in sorghum plant development.

SUBMITTER: Katiyar A

PROVIDER: S-EPMC3405690 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Identification of miRNAs in sorghum by using bioinformatics approach.

Katiyar Amit A Smita Shuchi S Chinnusamy Viswanathan V Pandey Dev Mani DM Bansal Kailash K

Plant signaling & behavior 20120201 2

MicroRNAs (miRNAs) regulate gene expression mainly by post-transcriptional gene silencing (PTGS) and in some cases by transcriptional genes silencing (TGS). miRNAs play critical roles in developmental processes, nutrient homeostasis, abiotic stress and pathogen responses of plants. In contrast to the large number of miRNAs predicted in cereal model plant rice, only 148 miRNAs were predicted in sorghum till date (miRBase release 17). This suggested that miRNAs identified in sorghum is far from sa ...[more]

PMID: 22415044

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Project description:Osteoporosis (OP) is an age-related disease, and osteoporotic fracture is one of the major causes of disability and mortality in elderly patients (>70 years old). As the pathogenesis and molecular mechanism of OP remain unclear, the identification of disease biomarkers is important for guiding research and providing therapeutic targets. In the present study, core genes and microRNAs (miRNAs) associated with OP were identified. Differentially expressed genes (DEGs) between human mesenchymal stem cell specimens from normal osseous tissues and OP tissues were detected using the GEO2R tool of the Gene Expression Omnibus database and Morpheus. Network topological parameters were determined using NetworkAnalyzer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, and ClueGO. Cytoscape with the Search Tool for the Retrieval of Interacting Genes and Molecular Complex Detection plug-in was used to visualize protein-protein interactions (PPIs). Additionally, miRNA-gene regulatory modules were predicted using CyTargetLinker in order to guide future research. In total, 915 DEGs were identified, including 774 upregulated and 141 downregulated genes. Enriched GO terms and pathways were determined, including 'nervous system development', 'regulation of molecular function', 'glutamatergic synapse pathway' and 'pathways in cancer'. The node degrees of DEGs followed power-law distributions. A PPI network with 541 nodes and 1,431 edges was obtained. Overall, 3 important modules were identified from the PPI network. The following 10 genes were identified as core genes based on high degrees of connectivity: Albumin, PH domain leucine-rich repeat-containing protein phosphatase 2 (PHLPP2), DNA topoisomerase 2-?, kininogen 1 (KNG1), interleukin 2 (IL2), leucine-rich repeats and guanylate kinase domain containing, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit ? (PIK3CG), leptin, transferrin and RNA polymerase II subunit A (POLR2A). Additionally, 15 miRNA-target interactions were obtained using CyTargetLinker. Overall, 7 miRNAs co-regulated IL2, 3 regulated PHLPP2, 3 regulated KNG1, 1 regulated PIK3CG and 1 modulated POLR2A. These results indicate potential biomarkers in the pathogenesis of OP and therapeutic targets.

Project description:BackgroudIt has been shown that aberrant expression of microRNAs (miRNAs) and transcriptional factors (TFs) is tightly associated with the development of HCC. Therefore, in order to further understand the pathogenesis of HCC, it is necessary to systematically study the relationship between the expression of miRNAs, TF and genes. In this study, we aim to identify the potential transcriptomic markers of HCC through analyzing common microarray datasets, and further establish the differential co-expression network of miRNAs-TF-mRNA to screen for key miRNAs as candidate diagnostic markers for HCC.MethodWe first downloaded the mRNA and miRNA expression profiles of liver cancer from the GEO database. After pretreatment, we used a linear model to screen for differentially expressed genes (DEGs) and miRNAs. Further, we used weighed gene co-expression network analysis (WGCNA) to construct the differential gene co-expression network for these DEGs. Next, we identified mRNA modules significantly related to tumorigenesis in this network, and evaluated the relationship between mRNAs and TFs by TFBtools. Finally, the key miRNA was screened out in the mRNA-TF-miRNA ternary network constructed based on the target TF of differentially expressed miRNAs, and was further verified with external data set.ResultsA total of 465 DEGs and 215 differentially expressed miRNAs were identified through differential genes expression analysis, and WGCNA was used to establish a co-expression network of DEGs. One module that closely related to tumorigenesis was obtained, including 33 genes. Next, a ternary network was constructed by selecting 256 pairs of mRNA-TF pairs and 100 pairs of miRNA-TF pairs. Network mining revealed that there were significant interactions between 18 mRNAs and 25 miRNAs. Finally, we used another independent data set to verify that miRNA hsa-mir-106b and hsa-mir-195 are good classifiers of HCC and might play key roles in the progression of HCC.ConclusionOur data indicated that two miRNAs-hsa-mir-106b and hsa-mir-195-are identified as good classifiers of HCC.

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Identification of miRNAs in sorghum by using bioinformatics approach.

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Identification of miRNAs in sorghum by using bioinformatics approach.

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