BackgroundMicroRNAs are a class of small non-coding RNAs that regulate mRNA expression at the post - transcriptional level and thereby many fundamental biological processes. A number of methods, such as multiplex polymerase chain reaction, microarrays have been developed for profiling levels of known miRNAs. These methods lack the ability to identify novel miRNAs and accurately determine expression at a range of concentrations. Deep or massively parallel sequencing methods are providing suitable platforms for genome wide transcriptome analysis and have the ability to identify novel transcripts.ResultsThe results of analysis of small RNA sequences obtained by Solexa technology of normal peripheral blood mononuclear cells, tumor cell lines K562 and HL60 are presented. In general K562 cells displayed overall low level of miRNA population and also low levels of DICER. Some of the highly expressed miRNAs in the leukocytes include several members of the let-7 family, miR-21, 103, 185, 191 and 320a. Comparison of the miRNA profiles of normal versus K562 or HL60 cells revealed a specific set of differentially expressed molecules. Correlation of the miRNA with that of mRNA expression profiles, obtained by microarray, revealed a set of target genes showing inverse correlation with miRNA levels. Relative expression levels of individual miRNAs belonging to a cluster were found to be highly variable. Our computational pipeline also predicted a number of novel miRNAs. Some of the predictions were validated by Real-time RT-PCR and or RNase protection assay. Organization of some of the novel miRNAs in human genome suggests that these may also be part of existing clusters or form new clusters.ConclusionsWe conclude that about 904 miRNAs are expressed in human leukocytes. Out of these 370 are novel miRNAs. We have identified miRNAs that are differentially regulated in normal PBMC with respect to cancer cells, K562 and HL60. Our results suggest that post - transcriptional processes may play a significant role in regulating levels of miRNAs in tumor cells. The study also provides a customized automated computation pipeline for miRNA profiling and identification of novel miRNAs; even those that are missed out by other existing pipelines. The Computational Pipeline is available at the website: http://mirna.jnu.ac.in/deep_sequencing/deep_sequencing.html

Chronic myeloid leukemia patients with different BCR-ABL transcripts might respond differently to Imatinib mesylate. This prompted us to study BCR-ABL transcripts in chronic myeloid leukemia (CML) patients and their correlation with response to Imatinib. Eighty-seven chronic phase CML patients (median age, 35 years; range, 13-62 years; M/F, 59:28) were included in this study; 57 patients had received interferon-alpha and/or hydroxyurea, and 30 were previously untreated. All patients received Imatinib mesylate (Gleevec) 400 mg daily. Complete hematological remission rate and major cytogenetic response (CGR) rates were 99% and 72%, respectively. B3a2 transcript was present in 53% of patients, b2a2 in 39%, and both transcripts in 8% of patients. Twenty of 34(59%) patients with b2a2 type achieved complete CGR compared to 15 of 53 (28%) patients with b3a2, p = 0.04. Among 24 patients with minor or no CGR, six (25%) had b2a2 compared to 18 (75%) b3a2 type, p = 0.04. Expression of BCR-ABL/ABL% was higher in b3a2 patients compared to b2a2, p = 0.120. Pre-treatment characteristics-mean spleen size (6.6 vs. 6.4 cm, p = 0.868), mean hemoglobin (G/dl; 12.0 vs. 11.8, p = 0.690), mean WBC count (83 x 10(9) vs. 77 x 10(9)/L, p = 0.923), and mean platelets counts (360x10(9) vs. 340 x 10(9)/L, p = 0.712)-were not significantly different in the b3a2 vs. b2a2 transcripts groups. Our preliminary findings suggest that CML patients with b2a2 BCR-ABL transcript might have higher CGRs to Imatinib mesylate (Gleevec).

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

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