miR-10a is aberrantly overexpressed in Nucleophosmin1 mutated acute myeloid leukaemia and its suppression induces cell death

<p>Abstract</p> <p>Background</p> <p>Acute myeloid leukaemia (AML) with nucleophosmin-1 (<it>NPM1</it>) mutation is a major subtype of AML. The <it>NPM1 </it>mutation induces a myeloproliferative disorder, but evidence indicates that other insults are necessary for the development of AML. We utilised microRNA microarrays and functional assays to determine if microRNA dysregulation could be involved in the pathogenesis of in <it>NPM1 </it>mutated (<it>NPM1^mut</it>)-AML.</p> <p>Results</p> <p>We used a stringent locked nucleic acid (LNA) based microRNA microarray platform to profile bone marrow samples of patients with normal karyotype AML. A panel of five microRNAs dichotomised AML patients according to their <it>NPM1 </it>mutational status. miR-10a, let-7b and let-7c were significantly over-expressed, while miR-130a and miR-335 were under-expressed in <it>NPM1^mut</it>-AML when compared to <it>NPM1^wildtype</it>-AML. Of these, miR-10a is the most differentially expressed in <it>NPM1^mut</it>-AML versus <it>NPM1^wildtype</it>-AML (> 10 fold higher as confirmed by qRT-PCR). To investigate the functions of miR-10a, the OCI-AML3 cell line was utilised, which is the only commercially available cell line bearing <it>NPM1^mut</it>. OCI-AML3 cells were firstly demonstrated to have a similarly high miR-10a expression to primary <it>NPM1^mut</it>-AML patient samples. Inhibition of miR-10a expression by miRCURY LNA Inhibitors (Exiqon) in these cells resulted in increased cell death as assessed by MTS, cell cycle and Annexin-V assays and reduced clonogenic capacity, indicative of an involvement in leukaemic cell survival. <it>In silico </it>filtering of bioinformatically predicted targets of miR-10a identified a number of potential mRNA targets with annotated functions in haematopoiesis, cell growth and apoptosis. Lucferase reporter assays confirmed a number of these putative tumorogenic genes that are miR-10a suppressible including <it>KLF4 </it>and <it>RB1CC1</it>. This provides a potential mechanism for the pathogenic role of miR-10a in <it>NPM1^mut</it>-AML.</p> <p>Conclusions</p> <p>This study provides, for the first time, <it>in vitro </it>evidence of a pro-survival role of miR-10a in <it>NPM1^mut</it>-AML, that it may contribute to the pathogenesis of <it>NPM1^mut</it>-AML and identifies putative tumorogenic targets.</p

Identification of microRNA-mRNA modules using microarray data

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are post-transcriptional regulators of mRNA expression and are involved in numerous cellular processes. Consequently, miRNAs are an important component of gene regulatory networks and an improved understanding of miRNAs will further our knowledge of these networks. There is a many-to-many relationship between miRNAs and mRNAs because a single miRNA targets multiple mRNAs and a single mRNA is targeted by multiple miRNAs. However, most of the current methods for the identification of regulatory miRNAs and their target mRNAs ignore this biological observation and focus on miRNA-mRNA pairs.</p> <p>Results</p> <p>We propose a two-step method for the identification of many-to-many relationships between miRNAs and mRNAs. In the first step, we obtain miRNA and mRNA clusters using a combination of miRNA-target mRNA prediction algorithms and microarray expression data. In the second step, we determine the associations between miRNA clusters and mRNA clusters based on changes in miRNA and mRNA expression profiles. We consider the miRNA-mRNA clusters with statistically significant associations to be potentially regulatory and, therefore, of biological interest.</p> <p>Conclusions</p> <p>Our method reduces the interactions between several hundred miRNAs and several thousand mRNAs to a few miRNA-mRNA groups, thereby facilitating a more meaningful biological analysis and a more targeted experimental validation.</p

Measures of Association for Identifying MicroRNA-mRNA Pairs of Biological Interest

MicroRNAs are a class of small non-protein coding RNAs that play an important role in the regulation of gene expression. Most studies on the identification of microRNA-mRNA pairs utilize the correlation coefficient as a measure of association. The use of correlation coefficient is appropriate if the expression data are available for several conditions and, for a given condition, both microRNA and mRNA expression profiles are obtained from the same set of individuals. However, there are many instances where one of the requirements is not satisfied. Therefore, there is a need for new measures of association to identify the microRNA-mRNA pairs of interest and we present two such measures. The first measure requires expression data for multiple conditions but, for a given condition, the microRNA and mRNA expression may be obtained from different individuals. The new measure, unlike the correlation coefficient, is suitable for analyzing large data sets which are obtained by combining several independent studies on microRNAs and mRNAs. Our second measure is able to handle expression data that correspond to just two conditions but, for a given condition, the microRNA and mRNA expression must be obtained from the same set of individuals. This measure, unlike the correlation coefficient, is appropriate for analyzing data sets with a small number of conditions. We apply our new measures of association to multiple myeloma data sets, which cannot be analyzed using the correlation coefficient, and identify several microRNA-mRNA pairs involved in apoptosis and cell proliferation

Estimation of Phylogeny Using a General Markov Model

The non-homogeneous model of nucleotide substitution proposed by Barry and Hartigan (Stat Sci, 2: 191-210) is the most general model of DNA evolution assuming an independent and identical process at each site. We present a computational solution for this model, and use it to analyse two data sets, each violating one or more of the assumptions of stationarity, homogeneity, and reversibility. The log likelihood values returned by programs based on the F84 model (J Mol Evol, 29: 170-179), the general time reversible model (J Mol Evol, 20: 86-93), and Barry and Hartigan’s model are compared to determine the validity of the assumptions made by the first two models. In addition, we present a method for assessing whether sequences have evolved under reversible conditions and discover that this is not so for the two data sets. Finally, we determine the most likely tree under the three models of DNA evolution and compare these with the one favoured by the tests for symmetry

Nonadaptive molecular evolution of seminal fluid proteins in Drosophila

Seminal fluid proteins (SFPs) are a group of reproductive proteins that are among the most evolutionarily divergent known. As SFPs can impact male and female fitness, these proteins have been proposed to evolve under postcopulatory sexual selection (PCSS). However, the fast change of the SFPs can also result from nonadaptive evolution, and the extent to which selective constraints prevent SFPs rapid evolution remains unknown. Using intra- and interspecific sequence information, along with genomics and functional data, we examine the molecular evolution of approximately 300 SFPs in Drosophila. We found that 50-57% of the SFP genes, depending on the population examined, are evolving under relaxed selection. Only 7-12% showed evidence of positive selection, with no evidence supporting other forms of PCSS, and 35-37% of the SFP genes were selectively constrained. Further, despite associations of positive selection with gene location on the X chromosome and protease activity, the analysis of additional genomic and functional features revealed their lack of influence on SFPs evolving under positive selection. Our results highlight a lack of sufficient evidence to claim that most SFPs are driven to evolve rapidly by PCSS while identifying genomic and functional attributes that influence different modes of SFPs evolution

Identification of significant miRNA-mRNA pairs using association measures based on unmatched and matched data.

<p>Identification of significant miRNA-mRNA pairs using association measures based on unmatched and matched data.</p

Relative expression levels of hsa-miR-320 and two of its predicted targets in samples with RB deletion.

<p>Relative expression levels of hsa-miR-320 and two of its predicted targets in samples with RB deletion.</p