Analysis of MicroRNA-mRNA Interactions in Stem Cell-Enriched Fraction of Oral Squamous Cell Carcinoma.

This study is an integrated analysis of the transcriptome profile microRNA (miRNA) and its experimentally validated mRNA targets differentially expressed in the tumorigenic stem-like fraction of oral squamous cell carcinoma (OSCC). We had previously reported the coexistence of multiple drug-resistant tumorigenic fractions, termed side population (SP1, SP2, and MP2), and a nontumorigenic fraction, termed main population (MP1), in oral cancer. These fractions displayed a self-renewal, regenerative potential and expressed known stemness-related cell surface markers despite functional differences. Flow cytometrically sorted pure fractions of SP1 and MP1 cells were subjected to differential expression analysis of both mRNAs and miRNAs. A significant upregulation of genes associated with inflammation, cell survival, cell proliferation, drug transporters, and antiapoptotic pathways, in addition to enhanced transcriptome reprogramming mediated by DNA-histone binding proteins and pattern recognition receptor-mediated signaling, was found to play a crucial role in the transformation of the nontumorigenic MP1 fraction to the tumorigenic SP1 fraction. We also identified several differentially expressed miRNAs that specifically target genes distinctive of tumorigenic SP1 fraction. miRNA-mediated downregulation of stemness-associated markers CD44 and CD147 and upregulation of CD151 may also account for the emergence and persistence of multiple tumorigenic stem cell fractions with varying degrees of malignancy. The phenotypic switch of cancer cells to stem-like OSCC cells mediated by transcriptomal regulation is effectual in addressing biological tumor heterogeneity and subsequent therapeutic resistance leading to a minimal residual disease (MRD) condition in oral cancer. A detailed study of the interplay of miRNAs, mRNA, and the cellular phases involved in the gradual transition of nontumorigenic cancer cells to tumorigenic stem-like cells in solid tumors would enable detection and development of a treatment regimen that targets and successfully eliminates multiple, drug-resistant fractions of cancer cells.

Differential ligand-signaling network of CCL19/CCL21-CCR7 system.

Chemokine (C-C motif) receptor 7 (CCR7), a class A subtype G-Protein Coupled Receptor (GPCR), is involved in the migration, activation and survival of multiple cell types including dendritic cells, T cells, eosinophils, B cells, endothelial cells and different cancer cells. Together, CCR7 signaling system has been implicated in diverse biological processes such as lymph node homeostasis, T cell activation, immune tolerance, inflammatory response and cancer metastasis. CCL19 and CCL21, the two well-characterized CCR7 ligands, have been established to be differential in their signaling through CCR7 in multiple cell types. Although the differential ligand signaling through single receptor have been suggested for many receptors including GPCRs, there exists no resource or platform to analyse them globally. Here, first of its kind, we present the cell-type-specific differential signaling network of CCL19/CCL21-CCR7 system for effective visualization and differential analysis of chemokine/GPCR signaling. Database URL: http:// www. netpath. org/ pathways? path_ id= NetPath_ 46.

MethFinder - A software package for prediction of human tissue-specific methylation status of CpG islands.

UNLABELLED: DNA methylation, the highly studied epigenetic mechanism which is involved in the regulatory events of various cellular processes like chromatin structure modifications, chromosomal inactivation, gene expressional patterns, embriyonic developments and transcriptional modification etc. Various high throughput techniques evolved for direct detection of methylation actions as well as information across the entire region. However, despite high throughput technological advances in experimental field, the development of software tools that has been dedicated to the prediction of epigenetic information from specific genome sequences is warranted. To this end we developed a tissue specific classifier MethFinder based on the frequency of novel sequence patterns across nine human tissues that was capable of discriminating methylation prone and methylation resistant CpG islands with an overall accuracy of 93%. AVAILABILITY: MethFinder is freely available at www.rgcb.res.in/methfinder.

IntmiR: a complete catalogue of intronic miRNAs of human and mouse.

UNLABELLED: IntmiR is a manually curated database of published intronic miRNAs of Human and Mouse genome. Each entry in the database, aims at providing a complete resource of intronic miRNA with their target gene and deregulation in various diseases with related tissues and pathways. The current release contains 426 intronic miRNA loci from human and 76 from mouse, expressing distinct target mRNA sequences. Database gives information on an intronic miRNA-disease relationship, including miRNA ID, pathaway connected and related tissues. All entries can be retrieved by miRNA ID or target gene. IntmiR is freely available at rgcb.res.in/intmir. AVAILABILITY: The database is available for free at rgcb.res.in/intmir.

MTar: a computational microRNA target prediction architecture for human transcriptome.

BACKGROUND: MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs. RESULTS: We present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone. CONCLUSION: MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.