Genome-wide computational analyses of microRNAs and their targets from Canis familiaris

By computational analyses, we identified 357 miRNA candidates from Canis familiaris genome, among which 300 are homology of characterized human miRNAs, the remains are not reported in any other animal. Of the 357 miRNA genes, 142 are organized into 53 clusters, and two clusters locate in the paternally imprinted region. These dog miRNAs may regulate more than 800 possible targets, which are involved in a wide range of cellular processes. Remarkably, miR-186 resides in the eighth intron of its target gene in the same orientation, suggesting a feedback regulation of miRNA on its host gene.     

Genome-wide computational identification of microRNAs and their targets in the deep-branching eukaryote Giardia lamblia

Using a combined computational program, we identified 50 potential microRNAs (miRNAs) in Giardia lamblia, one of the most primitive unicellular eukaryotes. These miRNAs are unique to G. lamblia and no homologues have been found in other organisms; miRNAs, currently known in other species, were not found in G. lamblia. This suggests that miRNA biogenesis and miRNA-mediated gene regulation pathway may evolve independently, especially in evolutionarily distant lineages. A majority (43) of the predicted miRNAs are located at one single locus; however, some miRNAs have two or more copies in the genome. Among the 58 miRNA genes, 28 are located in the intergenic regions whereas 30 are present in the anti-sense strands of the protein-coding sequences. Five predicted miRNAs are expressed in G. lamblia trophozoite cells evidenced by expressed sequence tags or RT-PCR. Thirty-seven identified miRNAs may target 50 protein-coding genes, including seven variant-specific surface proteins (VSPs). Our findings provide a clue that miRNA-mediated gene regulation may exist in the early stage of eukaryotic evolution, suggesting that it is an important regulation system ubiquitous in eukaryotes.     

A computational model for non-conserved mature miRNAs from the rice genome

Several computational approaches employ the high complementarity of plant miRNAs to target mRNAs as a filter to recognize miRNA. Numerous non-conserved miRNAs are known with more recent evolutionary origin as a result of target gene duplication events. We present here a computational model with knowledge inputs from reported non-conserved mature miRNAs of Oryza sativa (rice). Sequence- and structure-based approaches were used to retrieve miRNA features based on rice Argonaute protein and develop a multiple linear regression (MLR) model (r² = 0.996, q²_cv = 0.989) which scored mature miRNAs as predicted by the MaturePred program. The model was validated by scoring test set (q² = 0.990) and computationally predicted mature miRNAs as external test set (q²_test = 0.895). This strategy successfully enhanced the confidence of retrieving most probable non-conserved miRNAs from the rice genome. We anticipate that this computational model would recognize unknown non-conserved miRNA candidates and nurture the current mechanistic understanding of miRNA sorting to unveil the role of non-conserved miRNAs in gene silencing.     

Computational identification of novel microRNA homologs in the chimpanzee genome

MicroRNAs are important negative regulators of gene expression in higher eukaryotes. The miRNA repertoire of the closest human animal relative, the chimpanzee (Pan troglodytes), is largely unknown. In this study, we focused on computational search of novel miRNA homologs in chimpanzee. We have searched and analyzed the chimp homologs of the human pre-miRNA and mature miRNA sequences. Based on a homology search of the chimpanzee genome with human miRNA precursor sequences as queries, we identified 639 chimp miRNA genes, including 529 novel chimp miRNAs. 91.8% of chimp mature miRNAs and 60.3% of precursors are 100% identical to their human orthologs. The pre-miRNA secondary structures, miRNA families, and clusters are also highly conserved. We also found certain sequence differences in pre-miRNAs and even mature miRNAs that occurred after the divergence of the two species. Some of these differences (especially in mature miRNAs) could have caused species-specific changes in the expression levels of their target genes which in turn could have resulted in phenotypic variation between human and chimp.     

Identification of evolutionarily conserved Momordica charantia microRNAs using computational approach and its utility in phylogeny analysis

Momordica charantia (bitter gourd, bitter melon) is a monoecious Cucurbitaceae with anti-oxidant, anti-microbial, anti-viral and anti-diabetic potential. Molecular studies on this economically valuable plant are very essential to understand its phylogeny and evolution. MicroRNAs (miRNAs) are conserved, small, non-coding RNA with ability to regulate gene expression by bind the 3′ UTR region of target mRNA and are evolved at different rates in different plant species. In this study we have utilized homology based computational approach and identified 27 mature miRNAs for the first time from this bio-medically important plant. The phylogenetic tree developed from binary data derived from the data on presence/absence of the identified miRNAs were noticed to be uncertain and biased. Most of the identified miRNAs were highly conserved among the plant species and sequence based phylogeny analysis of miRNAs resolved the above difficulties in phylogeny approach using miRNA. Predicted gene targets of the identified miRNAs revealed their importance in regulation of plant developmental process. Reported miRNAs held sequence conservation in mature miRNAs and the detailed phylogeny analysis of pre-miRNA sequences revealed genus specific segregation of clusters.     

Analysis of the NCI-60 dataset for cancer-related microRNA and mRNA using expression profiles

BackgroundRecent studies have indicated that microRNA (miRNA) may play an oncogenic or tumor suppressor role in human cancer. To study the regulatory role of miRNAs in tumorigenesis, an integrated platform has been set up to provide a user friendly interface for query. The main advantage of the present platform is that all the miRNA target genes’ information and disease records are drawn from experimentally verified or high confidence records.ResultsMiRNA target gene results are annotated with reference to the disease gene as well as the pathway database. The correlation strength between miRNA and target gene expression profile is quantified by computing the correlation coefficient using the NCI-60 expression profiling data. Comprehensive analysis of the NCI-60 data found that the cumulative percentage of negative correlation coefficients for cleavage regulation is slightly higher than its positive counterpart; which indicated that the mRNA degradation mechanism is slightly dominant. In addition, the RNAHybrid and TargetScans scores are computed which potentially served as quantitative estimators for miRNA–mRNA binding events.Three scores are defined for each miRNA–mRNA pair, which are based on the disease gene and pathway information. These three scores allow user to sort out high confidence cancer-related miRNA–mRNA pairs.Statistical tests were applied to investigate the relations of three chromosomal features, i.e., CpG island, fragile site, and miRNA cluster, with cancer-related miRNAs. A web-based interface has been set up for query, which can be accessed at: http://ppi.bioinfo.asia.edu.tw/mirna_target/ConclusionsThe main advantage of the present platform on miRNA–mRNA targeting information is that all the target genes’ information and disease records are experimentally verified. Although this may limit the number of miRNA–mRNA relationships, the results provided here are more solid and have fewer false positive events. Certain novel cancer-related miRNA–mRNA pairs are identified and confirmed in the literature. Fisher's exact test suggests that CpG island and fragile site associated miRNAs tend to associate with cancer formation. In summary, the present platform provides an easy means of investigating cancer-related miRNAs.