miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets

Background

Many prediction tools for microRNA (miRNA) targets have been developed, but inconsistent predictions were observed across multiple algorithms, which can make further analysis difficult. Moreover, the nomenclature of human miRNAs changes rapidly. To address these issues, we developed a web-based system, miRSystem, for converting queried miRNAs to the latest annotation and predicting the function of miRNA by integrating miRNA target gene prediction and function/pathway analyses.

Results

First, queried miRNA IDs were converted to the latest annotated version to prevent potential conflicts resulting from multiple aliases. Next, by combining seven algorithms and two validated databases, potential gene targets of miRNAs and their functions were predicted based on the consistency across independent algorithms and observed/expected ratios. Lastly, five pathway databases were included to characterize the enriched pathways of target genes through bootstrap approaches. Based on the enriched pathways of target genes, the functions of queried miRNAs could be predicted.

Conclusions

MiRSystem is a user-friendly tool for predicting the target genes and their associated pathways for many miRNAs simultaneously. The web server and the documentation are freely available at http://mirsystem.cgm.ntu.edu.tw/.     

Investigating microRNA-Target Interaction-Supported Tissues in Human Cancer Tissues Based on miRNA and Target Gene Expression Profiling

Recent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.

Availability and Implementation

Our analysis results are available at http://tsmti.mbc.nctu.edu.tw/ and http://www.stat.nctu.edu.tw/hwang/tsmti.html.     

Purification and characterization of a serine protease (CESP) from mature coconut endosperm

Background

Identifying the endogenous RNA induced silencing complex(RISC)-associated RNAs is essential for understanding the cellular regulatory networks by miRNAs. Recently, isolation of RISC-associated mRNAs using antibody was reported, but their method needs a large amount of initial materials. We tried to improve the protocol and constructed an efficient and convenient system for analyzing miRNA and mRNA contents in RISC.

Findings

With our protocol, it is possible to clone both miRNAs and mRNAs from the endogenous RISC-associated RNAs immunoprecipitated from less than 10⁷ cells, and we show the ability of our system to isolate the particular target mRNAs for a specific miRNA from the RISC-associated mRNAs using well-characterized miR-122 as an example. After introduction of miR-122 into HepG2 cells, we found several cDNA clones that have miR-122 target sequences. Four of these clones that were concentrated in RISC but decreased in total RNA fraction are expected to be miR-122 target candidates. Interestingly, we found substantial amounts of Alu-related sequences, including both free Alu RNA and Alu-embedded mRNA, which might be one of the general targets for miRNA, in the cDNA clones from the RISC-associated mRNAs.

Conclusion

Our method thus enables us to examine not only dynamic changes in miRNA and mRNA contents in RISC but also the relationship of miRNA and target mRNA. We believe that our method can contribute to understanding cellular regulatory networks by miRNAs.     

Should Informed Consent for Cancer Treatment Include a Discussion about Hospital Outcome Disparities?

Background to the debate: Several studies have found disparities in the outcome of medical procedures across different hospitals—better outcomes have been associated with higher procedure volume. An Institute of Medicine workshop found such a “volume–outcome relationship” for two types of cancer surgery: resection of the pancreas and esophagus (http://www.iom.edu/?id=31508). This debate examines whether physicians have an ethical obligation to inform patients of hospital outcome disparities for these cancers.     

MiRComb: An R Package to Analyse miRNA-mRNA Interactions. Examples across Five Digestive Cancers

MicroRNAs (miRNAs) are small RNAs that regulate the expression of target mRNAs by specific binding on the mRNA 3''UTR and promoting mRNA degradation in the majority of cases. It is often of interest to know the specific targets of a miRNA in order to study them in a particular disease context. In that sense, some databases have been designed to predict potential miRNA-mRNA interactions based on hybridization sequences. However, one of the main limitations is that these databases have too many false positives and do not take into account disease-specific interactions. We have developed an R package (miRComb) able to combine miRNA and mRNA expression data with hybridization information, in order to find potential miRNA-mRNA targets that are more reliable to occur in a specific physiological or disease context. This article summarizes the pipeline and the main outputs of this package by using as example TCGA data from five gastrointestinal cancers (colon cancer, rectal cancer, liver cancer, stomach cancer and esophageal cancer). The obtained results can be used to develop a huge number of testable hypotheses by other authors. Globally, we show that the miRComb package is a useful tool to deal with miRNA and mRNA expression data, that helps to filter the high amount of miRNA-mRNA interactions obtained from the pre-existing miRNA target prediction databases and it presents the results in a standardised way (pdf report). Moreover, an integrative analysis of the miRComb miRNA-mRNA interactions from the five digestive cancers is presented. Therefore, miRComb is a very useful tool to start understanding miRNA gene regulation in a specific context. The package can be downloaded in http://mircomb.sourceforge.net.     

Complexity of the microRNA repertoire revealed by next-generation sequencing

MicroRNAs (miRNAs) have been implicated to play key roles in normal physiological functions, and altered expression of specific miRNAs has been associated with a number of diseases. It is of great interest to understand their roles and a prerequisite for such study is the ability to comprehensively and accurately assess the levels of the entire repertoire of miRNAs in a given sample. It has been shown that some miRNAs frequently have sequence variations termed isomirs. To better understand the extent of miRNA sequence heterogeneity and its potential implications for miRNA function and measurement, we conducted a comprehensive survey of miRNA sequence variations from human and mouse samples using next generation sequencing platforms. Our results suggest that the process of generating this isomir spectrum might not be random and that heterogeneity at the ends of miRNA affects the consistency and accuracy of miRNA level measurement. In addition, we have constructed a database from our sequencing data that catalogs the entire repertoire of miRNA sequences (http://galas.systemsbiology.net/cgi-bin/isomir/find.pl). This enables users to determine the most abundant sequence and the degree of heterogeneity for each individual miRNA species. This information will be useful both to better understand the functions of isomirs and to improve probe or primer design for miRNA detection and measurement.     

Identifying Cancer Specific Functionally Relevant miRNAs from Gene Expression and miRNA-to-Gene Networks Using Regularized Regression

Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNA''s differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network. Our method is based on a linear regression with an elastic net regularization. Our simulations show that, with our method, the active miRNAs can be detected with high accuracy and our approach is robust to high levels of noise and missing information. Furthermore, our results on real datasets for glioblastoma and prostate cancer are confirmed by microRNA expression measurements. Our method leads to the selection of potentially functionally important microRNAs. The associations of some of our identified miRNAs with cancer mechanisms are already confirmed in other studies (hypoxia related hsa-mir-210 and apoptosis-related hsa-mir-296-5p). We have also identified additional miRNAs that were not previously studied in the context of cancer but are coherently predicted as active by our method and may warrant further investigation. The code is available in Matlab and R and can be downloaded on http://www.cs.toronto.edu/goldenberg/Anna_Goldenberg/Current_Research.html.     

Sequence Maneuverer: tool for sequence extraction from genomes

The availability of genomic sequences of many organisms has opened new challenges in many aspects particularly in terms of genome analysis. Sequence extraction is a vital step and many tools have been developed to solve this issue. These tools are available publically but have limitations with reference to the sequence extraction, length of the sequence to be extracted, organism specificity and lack of user friendly interface. We have developed a java based software package having three modules which can be used independently or sequentially. The tool efficiently extracts sequences from large datasets with few simple steps. It can efficiently extract multiple sequences of any desired length from a genome of any organism. The results are crosschecked by published data.

Availability

URL 1: http://ww3.comsats.edu.pk/bio/ResearchProjects.aspxURL 2: http://ww3.comsats.edu.pk/bio/SequenceManeuverer.aspx     

miRdentify: high stringency miRNA predictor identifies several novel animal miRNAs

During recent years, miRNAs have been shown to play important roles in the regulation of gene expression. Accordingly, much effort has been put into the discovery of novel uncharacterized miRNAs in various organisms. miRNAs are structurally defined by a hairpin-loop structure recognized by the two-step processing apparatus, Drosha and Dicer, necessary for the production of mature ∼22-nucleotide miRNA guide strands. With the emergence of high-throughput sequencing applications, tools have been developed to identify miRNAs and profile their expression based on sequencing reads. However, as the read depth increases, false-positive predictions increase using established algorithms, underscoring the need for more stringent approaches. Here we describe a transparent pipeline for confident miRNA identification in animals, termed miRdentify. We show that miRdentify confidently discloses more than 400 novel miRNAs in humans, including the first male-specific miRNA, which we successfully validate. Moreover, novel miRNAs are predicted in the mouse, the fruit fly and nematodes, suggesting that the pipeline applies to all animals. The entire software package is available at www.ncrnalab.dk/mirdentify.