miRTar Hunter: A prediction system for identifying human microRNA target sites

MicroRNAs (miRNAs) are important regulators of gene expression and play crucial roles in many biological processes including apoptosis, differentiation, development, and tumorigenesis. Recent estimates suggest that more than 50% of human protein coding genes may be regulated by miRNAs and that each miRNA may bind to 300–400 target genes. Approximately 1,000 human miRNAs have been identified so far with each having up to hundreds of unique target mRNAs. However, the targets for a majority of these miRNAs have not been identified due to the lack of large-scale experimental detection techniques. Experimental detection of miRNA target sites is a costly and time-consuming process, even though identification of miRNA targets is critical to unraveling their functions in various biological processes. To identify miRNA targets, we developed miRTar Hunter, a novel computational approach for predicting target sites regardless of the presence or absence of a seed match or evolutionary sequence conservation. Our approach is based on a dynamic programming algorithm that incorporates more sequence-specific features and reflects the properties of various types of target sites that determine diverse aspects of complementarities between miRNAs and their targets. We evaluated the performance of our algorithm on 532 known human miRNA:target pairs and 59 experimentally-verified negative miRNA:target pairs, and also compared our method with three popular programs for 481 miRNA:target pairs. miRTar Hunter outperformed three popular existing algorithms in terms of recall and precision, indicating that our unique scheme to quantify the determinants of complementary sites is effective at detecting miRNA targets. miRTar Hunter is now available at http://203.230.194.162/~kbkim.     

An oligonucleotide microarray for microRNA expression analysis based on labeling RNA with quantum dot and nanogold probe

MicroRNAs (miRNAs) play important regulatory roles in animals and plants by targeting mRNAs for cleavage or translational repression. They have diverse expression patterns and might regulate various developmental and physiological processes. Profiling miRNA expression is very helpful for studying biological functions of miRNAs. We report a novel miRNA profiling microarray, in which miRNAs were directly labeled at the 3′ terminus with biotin and hybridized with complementary oligo-DNA probes immobilized on glass slides, and subsequently detected by measuring fluorescence of quantum dots labeled with streptavidin bound to miRNAs through streptavidin–biotin interaction. The detection limit of this microarray for miRNA was ~0.4 fmol, and the detection dynamic range spanned about 2 orders of magnitude. We made a model microarray to profile 11 miRNAs from leaf and root of rice (Oryza sativa L. ssp. indica) seedlings. The analysis results of the miRNAs had a good reproducibility and were consistent with the northern blot result. To avoid using high-cost detection equipment, colorimetric detection, a method based on nanogold probe coupled with silver enhancement, was also successfully introduced into miRNA profiling microarray detection.     

Prediction and validation of potential pathogenic microRNAs involved in Phytophthora infestans infection

Being one kind of approximately 22nt long small RNA, miRNA has shown its roles in host-pathogen interaction, providing one possible way for pathogen infection. Though Phytophthora infestans is a major pathogen that causes devastating late blight of potato, tomato and so on, so far there have not been any systematic researches on miRNAs and even pathogenic miRNAs in P. infestans. Here, for the first time we comprehensively predicted and identified pathogenic miRNAs that may exist in P. infestans. First, a total of 128 putative miRNAs belonging to 66 miRNA family were identified by bioinformatic approaches. Then, 33 vital pathogenic miRNAs were screened by constructing miRNA–miRNA relationship networks. Finally, four potential pathogenic miRNAs were chosen for detection, two of which are chosen for validation. The expression quantity of pi-miR466 and pi-miR1918 changed dramatically during incubation of tomato leaves, implying that they are potential pathogenic miRNAs.     

Mirna expression profiles identify drivers in colorectal and pancreatic cancers

Background and Aim

Altered expression of microRNAs (miRNAs) hallmarks many cancer types. The study of the associations of miRNA expression profile and cancer phenotype could help identify the links between deregulation of miRNA expression and oncogenic pathways.

Methods

Expression profiling of 866 human miRNAs in 19 colorectal and 17 pancreatic cancers and in matched adjacent normal tissues was investigated. Classical paired t-test and random forest analyses were applied to identify miRNAs associated with tissue-specific tumors. Network analysis based on a computational approach to mine associations between cancer types and miRNAs was performed.

Results

The merge between the two statistical methods used to intersect the miRNAs differentially expressed in colon and pancreatic cancers allowed the identification of cancer-specific miRNA alterations. By miRNA-network analysis, tissue-specific patterns of miRNA deregulation were traced: the driving miRNAs were miR-195, miR-1280, miR-140-3p and miR-1246 in colorectal tumors, and miR-103, miR-23a and miR-15b in pancreatic cancers.

Conclusion

MiRNA expression profiles may identify cancer-specific signatures and potentially useful biomarkers for the diagnosis of tissue specific cancers. miRNA-network analysis help identify altered miRNA regulatory networks that could play a role in tumor pathogenesis.     

MicroRNA-target gene responses to root knot nematode (Meloidogyne incognita) infection in cotton (Gossypium hirsutum L.)

MicroRNAs (miRNAs) are a large class of small regulatory RNA molecules, however no study has been performed to elucidate the role of miRNAs in cotton (Gossypium hirsutum) response to the root knot nematode (RKN, Meloidogyne incognita) infection. We selected 28 miRNAs and 8 miRNA target genes to investigate the miRNA-target gene response to M. incognita infection. Our results show that RKN infection significantly affected the expression of several miRNAs and their targeted genes. After 10 days of RKN infection, expression fold changes on miRNA expressions ranged from down-regulated by 33% to upregulated by 406%; meanwhile the expression levels of miRNA target genes were 45.8% to 231%. Three miRNA-target pairs, miR159-MYB, miR319-TCP4 and miR167-ARF8, showed inverse expression patterns between gene targets and their corresponded miRNAs, suggesting miRNA-mediated gene regulation in cotton roots in response to RKN infection.     

Recovery of dicer-like 1-late flowering phenotype by miR172 expressed by the noncanonical DCL4-dependent biogenesis pathway

MicroRNAs (miRNAs) act as down-regulators of gene expression, and play a dominant role in eukaryote development. In Arabidopsis thaliana, DICER-LIKE 1 (DCL1) is the main processor in miRNA biogenesis, and dcl1 mutants show various developmental defects at the early stage of embryogenesis or at gamete formation. However, miRNAs responsible for the respective developmental stages of the dcl1 defects have not been identified. Here, we developed a DCL1-independent miRNA expression system using the unique DCL4-dependent miRNA, miR839. By replacing the mature sequence in the miR839 precursor sequence with that of miR172, one of the most widely conserved miRNAs in angiosperms, we succeeded in expressing miR172 from a chimeric miR839 precursor in dcl1-7 plants and observed the repression of miR172 target gene expression. In parallel, the DCL4-dependent miR172 expression rescued the late flowering phenotype of dcl1-7 by acceleration of flowering. We established the DCL1-independent miRNA expression system, and revealed that the reduction of miR172 expression is responsible for the dcl1-7 late flowering phenotype.     

Comparison of Microarray Platforms for Measuring Differential MicroRNA Expression in Paired Normal/Cancer Colon Tissues

Background

Microarray technology applied to microRNA (miRNA) profiling is a promising tool in many research fields; nevertheless, independent studies characterizing the same pathology have often reported poorly overlapping results. miRNA analysis methods have only recently been systematically compared but only in few cases using clinical samples.

Methodology/Principal Findings

We investigated the inter-platform reproducibility of four miRNA microarray platforms (Agilent, Exiqon, Illumina, and Miltenyi), comparing nine paired tumor/normal colon tissues. The most concordant and selected discordant miRNAs were further studied by quantitative RT-PCR. Globally, a poor overlap among differentially expressed miRNAs identified by each platform was found. Nevertheless, for eight miRNAs high agreement in differential expression among the four platforms and comparability to qRT-PCR was observed. Furthermore, most of the miRNA sets identified by each platform are coherently enriched in data from the other platforms and the great majority of colon cancer associated miRNA sets derived from the literature were validated in our data, independently from the platform. Computational integration of miRNA and gene expression profiles suggested that anti-correlated predicted target genes of differentially expressed miRNAs are commonly enriched in cancer-related pathways and in genes involved in glycolysis and nutrient transport.

Conclusions

Technical and analytical challenges in measuring miRNAs still remain and further research is required in order to increase consistency between different microarray-based methodologies. However, a better inter-platform agreement was found by looking at miRNA sets instead of single miRNAs and through a miRNAs – gene expression integration approach.