Sun, 20 Jul 2008 21:39:10 BST CiteULike: heliopais Wu http://www.citeulike.org/user/heliopais/author/Wu CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/heliopais/article/2841685 <i>BMC Genomics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression by targeting messenger RNAs (mRNAs) and causing mRNA cleavage or translation blockage. Of the 355 Arthropod miRNAs that have been identified, only 21 are B. mori miRNAs that were predicted computationally; of these, only let-7 has been confirmed by Northern blotting.RESULTS:Combining a computational method based on sequence homology searches with experimental identification based on microarray assays and Northern blotting, we identified 46 miRNAs, an additional 21 plausible miRNAs, and a novel small RNA in B. mori. The latter, bmo-miR-100-like, was identified using the known miRNA aga-miR-100 as a probe; bmo-miR-100-like was detected by microarray assay and Northern blotting, but its precursor sequences did not fold into a hairpin structure. Among these identified miRNAs, we found 12 pairs of miRNAs and miRNA*s. Northern blotting revealed that some B. mori miRNA genes were expressed only during specific stages, indicating that B. mori miRNA genes (e.g., bmo-miR-277) have developmentally regulated patterns of expression. We identified two miRNA gene clusters in the B. mori genome. bmo-miR-2b, which is found in the gene cluster bmo-miR-2a-1 / bmo-miR-2a-1* / bmo-miR-2a-2/ bmo-miR-2b / bmo-miR-13a* / bmo-miR-13b, encodes a newly identified member of the mir-2 family. Moreover, we found that methylation can increase the sensitivity of a DNA probe used to detect a miRNA by Northern blotting. Functional analysis revealed that 11 miRNAs may regulate 13 B. mori orthologs of the 25 known Drosophila miRNA-targeted genes according to the functional conservation. We predicted the binding sites on the 1671 3'UTR of B. mori genes; 547 targeted genes, including 986 target sites, were predicted. Of these target sites, 338 had perfect base pairing to the seed region of 43 miRNAs. From the predicted genes, 61 genes, each of them with multiple predicted target sites, should be considered excellent candidates for future functional studies. Biological classification of predicted miRNA targets showed that "binding", "catalytic activity" and "physiological process" were over-represented for the predicted genes.CONCLUSIONS:Combining computational predictions with microarray assays, we identified 46 B. mori miRNAs, 13 of which were miRNA*s. We identified a novel small RNA and 21 plausible B. mori miRNAs that could not be located in the available B. mori genome, but which could be detected by microarray. Thirteen and 547 target genes were predicted according to the functional conservation and binding sites, respectively. Identification of miRNAs in B. mori, particularly those that are developmentally regulated, provides a foundation for subsequent functional studies. Identification and characteristics of microRNAs from Bombyx mori Ping He Zuoming Nie Jianqing Chen Jian Chen Zhengbing Lv Qing Sheng Songping Zhou Xiaolian Gao Lingyin Kong Xiangfu Wu Yongfeng Jin Yaozhou Zhang doi:10.1186/1471-2164-9-248 BMC Genomics, Vol. 9, No. 1. (2008) 2008-05-28T14:41:55-00:00 2008 BMC Genomics 9 1 b_mori microrna microrna_gene_finding http://www.citeulike.org/user/heliopais/article/2718410 <i>Developmental Cell, Vol. 6, No. 3. (March 2004), pp. 423-435.</i><br /><br />Runx2 is necessary and sufficient for osteoblast differentiation, yet its expression precedes the appearance of osteoblasts by 4 days. Here we show that Twist proteins transiently inhibit Runx2 function during skeletogenesis. Twist-1 and -2 are expressed in Runx2-expressing cells throughout the skeleton early during development, and osteoblast-specific gene expression occurs only after their expression decreases. Double heterozygotes for Twist-1 and Runx2 deletion have none of the skull abnormalities observed in Runx2+/- mice, a Twist-2 null background rescues the clavicle phenotype of Runx2+/- mice, and Twist-1 or -2 deficiency leads to premature osteoblast differentiation. Furthermore, Twist-1 overexpression inhibits osteoblast differentiation without affecting Runx2 expression. Twist proteins' antiosteogenic function is mediated by a novel domain, the Twist box, which interacts with the Runx2 DNA binding domain to inhibit its function. In vivo mutagenesis confirms the antiosteogenic function of the Twist box. Thus, relief of inhibition by Twist proteins is a mandatory event precluding osteoblast differentiation. A Twist Code Determines the Onset of Osteoblast Differentiation Peter Bialek Britt Kern Xiangli Yang Marijke Schrock Drazen Sosic Nancy Hong Hua Wu Kai Yu David Ornitz Eric Olson Monica Justice Gerard Karsenty doi:10.1016/S1534-5807(04)00058-9 Developmental Cell, Vol. 6, No. 3. (March 2004), pp. 423-435. 2008-04-25T13:17:16-00:00 2004 Developmental Cell 6 3 423 435 runx2 http://www.citeulike.org/user/heliopais/article/2250332 <i>Molecular Cell, Vol. 29, No. 1. (18 January 2008), pp. 1-7.</i><br /><br />The downregulation of gene expression by miRNAs and siRNAs is a complex process involving both translational repression and accelerated mRNA turnover, each of which appears to occur by multiple mechanisms. Moreover, under certain conditions, miRNAs are also capable of activating translation. A variety of cellular proteins have been implicated in these regulatory mechanisms, yet their exact roles remain largely unresolved. Let Me Count the Ways: Mechanisms of Gene Regulation by miRNAs and siRNAs Ligang Wu Joel Belasco doi:10.1016/j.molcel.2007.12.010 Molecular Cell, Vol. 29, No. 1. (18 January 2008), pp. 1-7. 2008-01-18T09:41:56-00:00 2008 Molecular Cell 29 1 1 7 microrna microrna_modes_of_action http://www.citeulike.org/user/heliopais/article/873528 <i>Genome Biology, Vol. 7 (26 September 2006), R85.</i> Comparative Sequence Analysis Reveals an Intricate Network Among REST, CREB and miRNA in Mediating Neuronal Gene Expression Jie Wu Xiaohui Xie doi:10.1186/gb-2006-7-9-r85 Genome Biology, Vol. 7 (26 September 2006), R85. 2006-09-26T12:01:07-00:00 2006 Genome Biology 1465-6906 7 R85 genetic_regulatory_network microrna http://www.citeulike.org/user/heliopais/article/520112 <i>Proc Natl Acad Sci U S A (22 February 2006)</i><br /><br />MicroRNAs (miRNAs) are ubiquitous regulators of eukaryotic gene expression. In addition to repressing translation, miRNAs can down-regulate the concentration of mRNAs that contain elements to which they are imperfectly complementary. Using miR-125b and let-7 as representative miRNAs, we show that in mammalian cells this reduction in message abundance is a consequence of accelerated deadenylation, which leads to rapid mRNA decay. The ability of miRNAs to expedite poly(A) removal does not result from decreased translation; nor does translational repression by miRNAs require a poly(A) tail, a 3' histone stem-loop being an effective substitute. These findings suggest that miRNAs use two distinct posttranscriptional mechanisms to down-regulate gene expression. MicroRNAs direct rapid deadenylation of mRNA. Ligang Wu Jihua Fan Joel G Belasco doi:10.1073/pnas.0510928103 Proc Natl Acad Sci U S A (22 February 2006) 2006-02-24T22:14:28-00:00 2006 Proc Natl Acad Sci U S A 0027-8424 deadenylation microrna http://www.citeulike.org/user/heliopais/article/453015 <i>Nat Genet (25 December 2005)</i><br /><br />Understanding the molecular mechanisms that regulate cellular proliferation and differentiation is a central theme of developmental biology. MicroRNAs (miRNAs) are a class of regulatory RNAs of approximately 22 nucleotides that post-transcriptionally regulate gene expression. Increasing evidence points to the potential role of miRNAs in various biological processes. Here we show that miRNA-1 (miR-1) and miRNA-133 (miR-133), which are clustered on the same chromosomal loci, are transcribed together in a tissue-specific manner during development. miR-1 and miR-133 have distinct roles in modulating skeletal muscle proliferation and differentiation in cultured myoblasts in vitro and in Xenopus laevis embryos in vivo. miR-1 promotes myogenesis by targeting histone deacetylase 4 (HDAC4), a transcriptional repressor of muscle gene expression. By contrast, miR-133 enhances myoblast proliferation by repressing serum response factor (SRF). Our results show that two mature miRNAs, derived from the same miRNA polycistron and transcribed together, can carry out distinct biological functions. Together, our studies suggest a molecular mechanism in which miRNAs participate in transcriptional circuits that control skeletal muscle gene expression and embryonic development. The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Jian-Fu Chen Elizabeth M Mandel J Michael Thomson Qiulian Wu Thomas E Callis Scott M Hammond Frank L Conlon Da-Zhi Wang doi:10.1038/ng1725 Nat Genet (25 December 2005) 2005-12-29T19:51:43-00:00 2005 Nat Genet 1061-4036 microrna http://www.citeulike.org/user/heliopais/article/1730440 <i>Bioinformatics, Vol. 20, No. 18. (12 December 2004), pp. 3691-3693.</i><br /><br />SUMMARY: Inferring genetic network architecture from time series data generated from high-throughput experimental technologies, such as cDNA microarray, can help us to understand the system behavior of living organisms. We have developed an interactive tool, GeneNetwork, which provides four reverse engineering models and three data interpolation approaches to infer relationships between genes. GeneNetwork enables a user to readily reconstruct genetic networks based on microarray data without having intimate knowledge of the mathematical models. A simple graphical user interface enables rapid, intuitive mapping and analysis of the reconstructed network allowing biologists to explore gene relationships at the system level. AVAILABILITY: Download from http://genenetwork.sbl.bc.sinica.edu.tw/. SUPPLEMENTARY INFORMATION: Supplement documentation of algorithms for the four approaches is downloadable at the above location. GeneNetwork: an interactive tool for reconstruction of genetic networks using microarray data. CC Wu HC Huang HF Juan ST Chen Bioinformatics, Vol. 20, No. 18. (12 December 2004), pp. 3691-3693. 2007-10-05T09:28:25-00:00 2004 Bioinformatics 1367-4803 20 18 3691 3693 genetic_regulatory_network