miRNomics—The bioinformatics of microRNA genes

Ghosh, Zhumur; Chakrabarti, Jayprokas; Mallick, Bibekanand

doi:10.1016/j.bbrc.2007.08.030

Cited by 50 publications

(41 citation statements)

References 50 publications

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“…A family of 20-to 22-nucleotide (nt) noncoding RNAs termed microRNAs (miRNAs) has been identified in eukaryotic organisms ranging from nematodes to humans (5)(6)(7). After maturation, these small RNAs are incorporated into the RNA-induced silencing complex (RISC), through which they mediate post-transcriptional gene silencing of specific mRNA targets (8).…”

Section: Introductionmentioning

confidence: 99%

miRNA-regulated expression of oncogenes and tumor suppressor genes in the cisplatin-inhibited growth of K562 cells

Xie¹

2010

Oncol Rep

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Abstract.To explore the mechanism of apoptosis induced by cisplatin, the expression of microRNAs (miRNAs) and regulating genes in K562 cells was analyzed using reverse transcription PCR, quantitative real-time PCR and enzymelinked immunosorbent assays. Our results showed that miR-16, miR-34a-c, miR-17-5p and miR-125 were upregulated, and their associated oncogenes (BCL2, E2F1 and E2F3, respectively) were down-regulated after cisplatin treatment. We also showed that miR-106 and miR-150 were down-regulated while their target genes (RB1 and P53, respectively) were up-regulated after cisplatin treatment. Moreover, miR-16, miR-34a-c and miR-17-5p proved to be upstream factors, regulating the expression of BCL2, E2F1 and E2F3, respectively. The oncogene E2F3 was downregulated when RB1 expression was increased after treatment with antisense oligonucleotides (ASO). Similarly, BCL2 and E2F3 were down-regulated when P53 expression was elevated by ASO treatment. The study demonstrated that cisplatin induces K562 cells to apoptosis by reducing miR-106 which up-regulates RB1 or by inhibiting miR-150 which increases P53 expression. IntroductionCisplatin [cis-diamminedichloroplatinum (II), CDDP] has been used in numerous studies to induce the apoptosis of carcinoma cells. Cells, growth-arrested by cisplatin treatment, showed a higher spontaneous cell death rate than that of untreated proliferating cells. Cisplatin is an effective chemotherapeutic agent that elicits its antineoplastic activity by binding to DNA and disrupting template functions (1). Studies have shown that cisplatin strongly inhibits the decatenation activity of topoisomerase II (2), which is critical for relieving the torsional stress that occurs during replication and transcription and for daughter strand separation during mitosis. Cisplatin can also regulate the expression of oncogenes or tumor suppressor genes to induce tumor cell apoptosis. Likewise, these genes can predict the cisplatin sensitivity of cancer cell lines. Cisplatin was found to induce p53-dependent Fas-associated death domain-like interleukin-1ß-converting enzyme (FLICE)-like inhibitory protein (FLIP) degradation in chemosensitive ovarian cancer cells. A recent study demonstrated that cisplatin induces the PIDD (p53-induced protein with a death domain)-dependent activation of caspase-2. In turn, caspase-2 cleaves and activates Bid, resulting in the oligomerization of Bak and the release of cytochrome c, resulting in a synergistic induction of cisplatininduced cytotoxicity (3). Farnebo et al found that single nucleotide polymorphisms in the DNA repair genes XRCC3241 and XPD751 influence the intrinsic cisplatin sensitivity and that XRCC3241, XPD751, EGFR, Hsp70, Bax and Bcl-2 predict the cisplatin sensitivity of head and neck cancer cell lines (4).A family of 20-to 22-nucleotide (nt) noncoding RNAs termed microRNAs (miRNAs) has been identified in eukaryotic organisms ranging from nematodes to humans (5-7). After maturation, these small RNAs are incorporated into the RNA-induced silencing comp...

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Section: Introductionmentioning

confidence: 99%

miRNA-regulated expression of oncogenes and tumor suppressor genes in the cisplatin-inhibited growth of K562 cells

Xie¹

2010

Oncol Rep

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“…Accurate prediction of miRNA-target mRNAs is important in order to capture their full regulatory and fine tuning capacity. 20 A number of miRNA and miRNA-target-prediction algorithms are already available, however, different algorithms provide varying predictions and the overlap between alternative outputs is often scant. 21 Recent endeavours in bioinformatics are directed towards the increase of prediction specificity and the handling of complex data obtained from high-throughput methods in order to enable for efficient biological validation.…”

Section: Bioinformatics Of Small Rnasmentioning

confidence: 99%

“…Biological validation on the other hand often depends on computational techniques resulting in a symbiotic process. 20 One example of this symbiosis was given by Ute Bissels (Miltenyi Biotec, Bergisch Gladbach, Germany). She presented the novel miRNA microarray platform (miRXplore TM ).…”

Section: Innovative Techniques For the Identification/ Validation Of mentioning

confidence: 99%

miRNA, siRNA, piRNA and Argonautes: News in small matters

Riedmann

Schwentner²

2010

RNA Biology

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“…Thus, extracted precursor miRNA sequences were submitted to Mfold (http://mfold.rna.albany.edu/?q=mfold/RNA-Folding-Form), which will calculate the free energy (∆G) for various possible miRNA hairpin structures. The criteria employed for selection of pre-miRNA structures were: 1) the minimal folding free energy (MFE) ≤ -18 kcal/mol; 2) mature miRNA on the stem region of the hairpin structure; 3) the A + U content should be in the range of 30 too 70%; 4) the bulge size in the mature miRNA region should not exceed 7 bases (Singh and Nagaraju, 2008;Ghosh et al, 2007). Pre-miRNAs that satisfied all the aforementioned criteria was selected for naming and target prediction.…”

Section: Secondary Structure Predictionmentioning

confidence: 99%

“…These pri-miRNAs are then cleaved into 60 to 80 nt long precursor miRNA sequences (pre-miRNAs) by the Drosha, Pasha (Denli et al, 2004;Landthaler et al, 2004). These hairpin premiRNAs were further transported by Exportin-5 in the presence of cofactor, Ran-GTP from nucleus to cytoplasm and cleaved by Dicer (Bartel, 2004) and thus produces approximately 19 to 22 bp miRNA:miRNA duplex (Denli et al, 2004;Ghosh et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

In silico mining of micro-RNAs from Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae)

Asokan¹,

Roopa²,

Rebijith³

et al. 2014

Afr. J. Biotechnol.

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MicroRNAs (miRNAs) are small, endogenously, non-coding genes that regulate protein production either by mRNA cleavage or by translational repression in eukaryotes and viruses. miRNAs plays a key role in biological processes including growth, development and physiology of an organism. In this study, we employed insilico approaches to identify the miRNAs from Spodoptera frugiperda, a major pest of small grain crops. A total of seven miRNAs were identified and characterized from 67,360 expressed sequence tags (ESTs) of S. frugiperda with: 1) mature and pre-miRNAs sizes vary from 19 to 25 ans 61 to 95 nucleotides respectively; 2) minimum free energy ranged from -31.70 to -21.00 kcal/mol; and 3) (A + U) content varied from 27 to 60. The functional annotation of these miRNAs were identified as regulation of transcription factors, catalytic activities and signal transduction pathways. Further studies of these miRNAs will help to carryout functional analyses, which promises more towards insect pest management free of insecticides and pesticides.

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miRNomics—The bioinformatics of microRNA genes

Cited by 50 publications

References 50 publications

miRNA-regulated expression of oncogenes and tumor suppressor genes in the cisplatin-inhibited growth of K562 cells

miRNA-regulated expression of oncogenes and tumor suppressor genes in the cisplatin-inhibited growth of K562 cells

miRNA, siRNA, piRNA and Argonautes: News in small matters

In silico mining of micro-RNAs from Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae)

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