PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (55)
 

Clipboard (0)
None

Select a Filter Below

Year of Publication
1.  Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of epitranscriptome 
Molecular bioSystems  2014;11(1):262-274.
Biochemical modifications to mRNA, especially N6-methyladenosine (m6A) and 5-methylcytosine (m5C), are recently shown to be associated with crucial biological functions. Despite the intriguing advancements, little is known so far about the dynamic landscape of RNA methylome across different cell types and how the epitranscriptome is regulated at system level by enzymes, i.e., RNA methyltransferases and demethylases. To investigate this issue, a meta-analysis of m6A MeRIP-Seq datasets from 10 different experimental conditions (cell type/tissue or treatment) are collected, and the combinatorial epitranscriptome, which consists of 42758 m6A sites, is extracted and divided into 3 clusters, in which the methylation sites are likely to be hyper- or hypo-methylated simultaneously (or co-methylated), indicating the sharing of a common methylation regulator. Four different clustering approaches are used, including, K-means, hierarchical clustering (HC), Bayesian factor regression model (BFRM) and nonnegative matrix factorization (NMF) to unveil the co-methylation patterns.
To validate whether the patterns are corresponding to enzymatic regulators, i.e., RNA methyltransferases or demethylases, the target sites of a known m6A regulator, fat mass and obesity-associated protein (FTO), are identified from an independent mouse MeRIP-Seq dataset and lifted to human. Our study shows that, 3 out of the 4 clustering approaches used can successfully identify a group of methylation sites overlapping with FTO target sites at significance level 0.05 (after multiple hypothesis adjustment), among which, the result of NMF is the most significant (p-value 2.81e-06). We defined a new approach evaluating the consistency between two clustering results and shows that clustering results of different methods are highly correlated indicating strongly the existence of co-methylation patterns. Consistent with recent studies, a number of cancer and neuronal disease-related bimolecular functions are enriched in the identified clusters, which are biological functions can be regulated at epitranscriptional level, indicating the pharmaceutical prospect of RNA N6-methyladenosine-related studies.
This result successfully reveals the linkage between the global RNA co-methylation patterns embedded in the epitranscriptomic data under multiple experimental conditions and the latent enzymatic regulators, suggesting a promising direction towards more comprehensive understanding of the epitranscriptome.
doi:10.1039/c4mb00604f
PMCID: PMC4253022  PMID: 25370990
3.  TraceRNA: A Web Application for ceRNAs Exploration 
doi:10.1161/CIRCGENETICS.113.000125
PMCID: PMC4560354  PMID: 25140062
microRNA; cancer and stroke; gene expression; genetic regulatory network
4.  Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model 
BioMed Research International  2015;2015:852070.
With the development of new sequencing technology, the entire N6-methyl-adenosine (m6A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single methylation site determined from MeRIP-Seq data can in practice contain multiple RNA methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential methylation regions with higher statistical significance on real dataset.
doi:10.1155/2015/852070
PMCID: PMC4537718  PMID: 26301253
5.  Viral miRNA Targeting of Bicistronic and Polycistronic Transcripts 
Successful viral infection entails a choreographic regulation of viral gene expression program. Kaposi’s sarcoma–associated herpesvirus (KSHV) encodes numerous miRNAs that regulate viral life cycle. However, few viral targets have been identified due to the lack of information on KSHV 3′ untranslated regions (3′UTRs). Recent genome-wide mapping of KSHV transcripts and 3′UTRs has revealed abundant bicistronic and polycistronic transcripts. The extended 3′UTRs of the 5′ proximal genes of bicistronic and polycistronic transcripts offer additional regulatory targets. Indeed, a genome-wide screening of KSHV 3′UTRs has identified several bicistronic and polycistronic transcripts as the novel targets of viral miRNAs. Together, these works have expanded our knowledge of the unique features of KSHV gene regulation program and provided valuable resources for the research community.
doi:10.1016/j.coviro.2014.04.004
PMCID: PMC4149931  PMID: 24821460
6.  Education, collaboration, and innovation: intelligent biology and medicine in the era of big data 
BMC Genomics  2015;16(Suppl 7):S1.
Here we present a summary of the 2014 International Conference on Intelligent Biology and Medicine (ICIBM 2014) and the editorial report of the supplement to BMC Genomics and BMC Systems Biology that includes 20 research articles selected from ICIBM 2014. The conference was held on December 4-6, 2014 at San Antonio, Texas, USA, and included six scientific sessions, four tutorials, four keynote presentations, nine highlight talks, and a poster session that covered cutting-edge research in bioinformatics, systems biology, and computational medicine.
doi:10.1186/1471-2164-16-S7-S1
PMCID: PMC4474420  PMID: 26099197
7.  Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads 
BMC Genomics  2015;16(Suppl 7):S14.
Background
RNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of them takes the misaligned reads that are mapped to non-exonic regions into account. We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.
Results
We implemented our novel XBSeq algorithm and evaluated it by using a set of simulated expression datasets under different conditions, using a combination of negative binomial and Poisson distributions with parameters derived from real RNA-seq data. We compared the performance of our method with other commonly used differential expression analysis algorithms. We also evaluated the changes in true and false positive rates with variations in biological replicates, differential fold changes, and expression levels in non-exonic regions. We also tested the algorithm on a set of real RNA-seq data where the common and different detection results from different algorithms were reported.
Conclusions
In this paper, we proposed a novel XBSeq, a differential expression analysis algorithm for RNA-seq data that takes non-exonic mapped reads into consideration. When background noise is at baseline level, the performance of XBSeq and DESeq are mostly equivalent. However, our method surpasses DESeq and other algorithms with the increase of non-exonic mapped reads. Only in very low read count condition XBSeq had a slightly higher false discovery rate, which may be improved by adjusting the background noise effect in this situation. Taken together, by considering non-exonic mapped reads, XBSeq can provide accurate expression measurement and thus detect differential expressed genes even in noisy conditions.
doi:10.1186/1471-2164-16-S7-S14
PMCID: PMC4474535  PMID: 26099631
RNA-Seq; Differential expression analysis; XBSeq; DESeq; Non-exonic mapped reads; Negative binomial distribution; Poisson distribution
8.  HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data 
BMC Genomics  2015;16(Suppl 4):S2.
Background
Methylated RNA Immunoprecipatation combined with RNA sequencing (MeRIP-seq) is revolutionizing the de novo study of RNA epigenomics at a higher resolution. However, this new technology poses unique bioinformatics problems that call for novel and sophisticated statistical computational solutions, aiming at identifying and characterizing transcriptome-wide methyltranscriptome.
Results
We developed HEP, a Hidden Markov Model (HMM)-based Exome Peak-finding algorithm for predicting transcriptome methylation sites using MeRIP-seq data. In contrast to exomePeak, our previously developed MeRIP-seq peak calling algorithm, HEPeak models the correlation between continuous bins in an m6A peak region and it is a model-based approach, which admits rigorous statistical inference. HEPeak was evaluated on a simulated MeRIP-seq dataset and achieved higher sensitivity and specificity than exomePeak. HEPeak was also applied to real MeRIP-seq datasets from human HEK293T cell line and mouse midbrain cells and was shown to be able to recapitulate known m6A distribution in transcripts and identify novel m6A sites in long non-coding RNAs.
Conclusions
In this paper, a novel HMM-based peak calling algorithm, HEPeak, was developed for peak calling for MeRIP-seq data. HEPeak is written in R and is publicly available.
doi:10.1186/1471-2164-16-S4-S2
PMCID: PMC4416174  PMID: 25917296
9.  BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data 
BMC Bioinformatics  2014;15(Suppl 12):S6.
DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer.
doi:10.1186/1471-2105-15-S12-S6
PMCID: PMC4243086  PMID: 25474268
DNA methylation; differential methylation; MBDCap-seq; Hidden Markov Model (HMM)
10.  MeT-DB: a database of transcriptome methylation in mammalian cells 
Nucleic Acids Research  2014;43(Database issue):D197-D203.
Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. The MethylTranscriptome DataBase (MeT-DB, http://compgenomics.utsa.edu/methylation/) is the first comprehensive resource for N6-methyladenosine (m6A) in mammalian transcriptome. It includes a database that records publicaly available data sets from methylated RNA immunoprecipitation sequencing (MeRIP-Seq), a recently developed technology for interrogating m6A methyltranscriptome. MeT-DB includes ∼300k m6A methylation sites in 74 MeRIP-Seq samples from 22 different experimental conditions predicted by exomePeak and MACS2 algorithms. To explore this rich information, MeT-DB also provides a genome browser to query and visualize context-specific m6A methylation under different conditions. MeT-DB also includes the binding site data of microRNA, splicing factor and RNA binding proteins in the browser window for comparison with m6A sites and for exploring the potential functions of m6A. Analysis of differential m6A methylation and the related differential gene expression under two conditions is also available in the browser. A global perspective of the genome-wide distribution of m6A methylation in all the data is provided in circular ideograms, which also act as a navigation portal. The query results and the entire data set can be exported to assist publication and additional analysis.
doi:10.1093/nar/gku1024
PMCID: PMC4383883  PMID: 25378335
11.  Genomewide Mapping and Screening of Kaposi's Sarcoma-Associated Herpesvirus (KSHV) 3′ Untranslated Regions Identify Bicistronic and Polycistronic Viral Transcripts as Frequent Targets of KSHV MicroRNAs 
Journal of Virology  2014;88(1):377-392.
Kaposi's sarcoma-associated herpesvirus (KSHV) encodes over 90 genes and 25 microRNAs (miRNAs). The KSHV life cycle is tightly regulated to ensure persistent infection in the host. In particular, miRNAs, which primarily exert their effects by binding to the 3′ untranslated regions (3′UTRs) of target transcripts, have recently emerged as key regulators of KSHV life cycle. Although studies with RNA cross-linking immunoprecipitation approach have identified numerous targets of KSHV miRNAs, few of these targets are of viral origin because most KSHV 3′UTRs have not been characterized. Thus, the extents of viral genes targeted by KSHV miRNAs remain elusive. Here, we report the mapping of the 3′UTRs of 74 KSHV genes and the effects of KSHV miRNAs on the control of these 3′UTR-mediated gene expressions. This analysis reveals new bicistronic and polycistronic transcripts of KSHV genes. Due to the 5′-distal open reading frames (ORFs), KSHV bicistronic or polycistronic transcripts have significantly longer 3′UTRs than do KSHV monocistronic transcripts. Furthermore, screening of the 3′UTR reporters has identified 28 potential new targets of KSHV miRNAs, of which 11 (39%) are bicistronic or polycistronic transcripts. Reporter mutagenesis demonstrates that miR-K3 specifically targets ORF31-33 transcripts at the lytic locus via two binding sites in the ORF33 coding region, whereas miR-K10a-3p and miR-K10b-3p and their variants target ORF71-73 transcripts at the latent locus through distinct binding sites in both 5′-distal ORFs and intergenic regions. Our results indicate that KSHV miRNAs frequently target the 5′-distal coding regions of bicistronic or polycistronic transcripts and highlight the unique features of KSHV miRNAs in regulating gene expression and life cycle.
doi:10.1128/JVI.02689-13
PMCID: PMC3911756  PMID: 24155407
12.  Exome-based analysis for RNA epigenome sequencing data 
Bioinformatics  2013;29(12):1565-1567.
Motivation: Fragmented RNA immunoprecipitation combined with RNA sequencing enabled the unbiased study of RNA epigenome at a near single-base resolution; however, unique features of this new type of data call for novel computational techniques.
Result: Through examining the connections of RNA epigenome sequencing data with two well-studied data types, ChIP-Seq and RNA-Seq, we unveiled the salient characteristics of this new data type. The computational strategies were discussed accordingly, and a novel data processing pipeline was proposed that combines several existing tools with a newly developed exome-based approach ‘exomePeak’ for detecting, representing and visualizing the post-transcriptional RNA modification sites on the transcriptome.
Availability: The MATLAB package ‘exomePeak’ and additional details are available at http://compgenomics.utsa.edu/exomePeak/.
Contact: yufei.huang@utsa.edu or jmeng@mit.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt171
PMCID: PMC3673212  PMID: 23589649
13.  Integrative Genomics and Computational Systems Medicine 
BioMed Research International  2014;2014:945253.
doi:10.1155/2014/945253
PMCID: PMC4082850  PMID: 25025078
14.  Alteration of Fractional Anisotropy and Mean Diffusivity in Glaucoma: Novel Results of a Meta-Analysis of Diffusion Tensor Imaging Studies 
PLoS ONE  2014;9(5):e97445.
Objectives
We hypothesized that a meta-analysis of existing studies may help to reveal significant changes on diffusion tensor imaging (DTI) in patients with glaucoma. Therefore, a meta-analysis was utilized to investigate the possibility that DTI can detect white matter damage in patients with glaucoma.
Methods
The study design and report adhered to the PRISMA Statement guidelines. DTI studies that compared glaucoma patients and controls were surveyed using PubMed, Web of Science and EMBASE (January 2008 to September 2013). Stata was used to analyze the decrease in fractional anisotropy (FA) and increase in mean diffusivity (MD) in the optic nerve and optic radiation in patients with glaucoma.
Results
Eleven DTI studies were identified through a comprehensive literature search, and 10 independent DTI studies of glaucoma patients were eligible for the meta-analysis. A random effects model revealed a significant FA reduction in the optic nerve and optic radiation, as well as a significant MD increase in the tracts. A heterogeneity analysis suggested that FA may be related to glaucoma severity.
Conclusions
Our findings revealed that the optic nerve and optic radiation were vulnerable regions in patients with glaucoma and that FA may be correlated with glaucoma severity and age. Furthermore, this study suggests that magnetic resonance imaging in patients with glaucoma may help to provide objective evidence to aid in the diagnosis and management of glaucoma.
doi:10.1371/journal.pone.0097445
PMCID: PMC4020845  PMID: 24828063
15.  Correction: KSHV MicroRNAs Mediate Cellular Transformation and Tumorigenesis by Redundantly Targeting Cell Growth and Survival Pathways 
PLoS Pathogens  2014;10(1):10.1371/annotation/582f0298-9999-46ea-be7e-6718608b11c5.
doi:10.1371/annotation/582f0298-9999-46ea-be7e-6718608b11c5
PMCID: PMC3899379
16.  Aging interferes central control mechanism for eccentric muscle contraction 
Previous studies report greater activation in the cortical motor network in controlling eccentric contraction (EC) than concentric contraction (CC) despite lower muscle activation level associated with EC vs. CC in healthy, young individuals. It is unknown, however, whether elderly people exhibiting increased difficulties in performing EC than CC possess this unique cortical control mechanism for EC movements. To address this question, we examined functional magnetic resonance imaging (fMRI) data acquired during EC and CC of the first dorsal interosseous (FDI) muscle in 11 young (20–32 years) and 9 old (67–73 years) individuals. During the fMRI experiment, all subjects performed 20 CC and 20 EC of the right FDI with the same angular distance and velocity. The major findings from the behavioral and fMRI data analysis were that (1) movement stability was poorer in EC than CC in the old but not the young group; (2) similar to previous electrophysiological and fMRI reports, the EC resulted in significantly stronger activation in the motor control network consisting of primary, secondary and association motor cortices than CC in the young and old groups; (3) the biased stronger activation towards EC was significantly greater in the old than the young group especially in the secondary and association cortices such as supplementary and premotor motor areas and anterior cingulate cortex; and (4) in the primary motor and sensory cortices, the biased activation towards EC was significantly greater in the young than the old group. Greater activation in higher-order cortical fields for controlling EC movement by elderly adults may reflect activities in these regions to compensate for aging-related impairments in the ability to control complex EC movements. Our finding is useful for potentially guiding the development of targeted therapies to counteract age-related movement deficits and to prevent injury.
doi:10.3389/fnagi.2014.00086
PMCID: PMC4023019  PMID: 24847261
brain activation; concentric contraction; eccentric contraction; fMRI; movement stability
17.  KSHV MicroRNAs Mediate Cellular Transformation and Tumorigenesis by Redundantly Targeting Cell Growth and Survival Pathways 
PLoS Pathogens  2013;9(12):e1003857.
Kaposi's sarcoma-associated herpesvirus (KSHV) is causally linked to several human cancers, including Kaposi's sarcoma, primary effusion lymphoma and multicentric Castleman's disease, malignancies commonly found in HIV-infected patients. While KSHV encodes diverse functional products, its mechanism of oncogenesis remains unknown. In this study, we determined the roles KSHV microRNAs (miRs) in cellular transformation and tumorigenesis using a recently developed KSHV-induced cellular transformation system of primary rat mesenchymal precursor cells. A mutant with a cluster of 10 precursor miRs (pre-miRs) deleted failed to transform primary cells, and instead, caused cell cycle arrest and apoptosis. Remarkably, the oncogenicity of the mutant virus was fully restored by genetic complementation with the miR cluster or several individual pre-miRs, which rescued cell cycle progression and inhibited apoptosis in part by redundantly targeting IκBα and the NF-κB pathway. Genomic analysis identified common targets of KSHV miRs in diverse pathways with several cancer-related pathways preferentially targeted. These works define for the first time an essential viral determinant for KSHV-induced oncogenesis and identify NF-κB as a critical pathway targeted by the viral miRs. Our results illustrate a common theme of shared functions with hierarchical order among the KSHV miRs.
Author Summary
Kaposi's sarcoma-associated herpesvirus (KSHV) is the causal agent of several human cancers. KSHV encodes over two dozen genes that regulate diverse cellular pathways. However, the molecular mechanism of KSHV-induced oncogenesis remains unknown. In this study, we determined the roles of KSHV microRNAs (miRs) in KSHV-induced oncogenesis using a recently developed KSHV cellular transformation system of primary rat mesenchymal precursor cells. A KSHV mutant with a cluster of 10 precursor miRs (pre-miRs) deleted failed to transform primary cells, and instead, caused cell cycle arrest and apoptosis. Expression of the miR cluster or several pre-miRs was sufficient to restore the oncogenicity of the mutant virus. KSHV miRs regulated cell cycle progression and inhibited apoptosis in part by redundantly targeting IκBα and the NF-κB pathway. By integrating gene expression profiling and target prediction, we identified common targets of KSHV miRs in diverse pathways. Importantly, several cancer-related pathways were preferentially targeted by KSHV miRs. These works have demonstrated for the first time the important roles of KSHV miRs in oncogenesis and identified NF-κB as a critical pathway targeted by the miRs. Our results reveal that shared function is a common theme of KSHV miRs, which manifest functional hierarchical order.
doi:10.1371/journal.ppat.1003857
PMCID: PMC3873467  PMID: 24385912
18.  Interdisciplinary dialogue for education, collaboration, and innovation: Intelligent Biology and Medicine in and beyond 2013 
BMC Genomics  2013;14(Suppl 8):S1.
The 2013 International Conference on Intelligent Biology and Medicine (ICIBM 2013) was held on August 11-13, 2013 in Nashville, Tennessee, USA. The conference included six scientific sessions, two tutorial sessions, one workshop, two poster sessions, and four keynote presentations that covered cutting-edge research topics in bioinformatics, systems biology, computational medicine, and intelligent computing. Here, we present a summary of the conference and an editorial report of the supplements to BMC Genomics and BMC Systems Biology that include 19 research papers selected from ICIBM 2013.
doi:10.1186/1471-2164-14-S8-S1
PMCID: PMC4042234  PMID: 24564388
19.  BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database 
BMC Systems Biology  2013;7(Suppl 5):S5.
Background
Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.
Method
Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.
Result
BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.
Conclusions
The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.
Website: The web based application is developed and can be access through the following link http://compgenomics.utsa.edu/BRCAMoNet/
doi:10.1186/1752-0509-7-S5-S5
PMCID: PMC4029357  PMID: 24564956
Connectivity Map; Mode of Action (MoA); Breast Cancer Mode of Action Network (BRCA-MoNet)
20.  Correction: Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors 
PLoS ONE  2013;8(9):10.1371/annotation/a076115e-dd8c-4da7-989d-c1174a8cd31e.
doi:10.1371/annotation/a076115e-dd8c-4da7-989d-c1174a8cd31e
PMCID: PMC3792147  PMID: 24116246
21.  Correction: Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors 
PLoS ONE  2013;8(8):10.1371/annotation/28592478-72f5-4937-919b-b2342d6ceda0.
doi:10.1371/annotation/28592478-72f5-4937-919b-b2342d6ceda0
PMCID: PMC3752034
22.  Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors 
PLoS ONE  2013;8(8):e70204.
Background
The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies.
Methodology/Principal Findings
It is known that miRNAs with similar functions are often associated with similar diseases and vice versa. Therefore, the functional similarity of two miRNAs has been successfully estimated by measuring the semantic similarity of their associated diseases. To effectively predict disease miRNAs, we calculated the functional similarity by incorporating the information content of disease terms and phenotype similarity between diseases. Furthermore, the members of miRNA family or cluster are assigned higher weight since they are more probably associated with similar diseases. A new prediction method, HDMP, based on weighted k most similar neighbors is presented for predicting disease miRNAs. Experiments validated that HDMP achieved significantly higher prediction performance than existing methods. In addition, the case studies examining prostatic neoplasms, breast neoplasms, and lung neoplasms, showed that HDMP can uncover potential disease miRNA candidates.
Conclusions
The superior performance of HDMP can be attributed to the accurate measurement of miRNA functional similarity, the weight assignment based on miRNA family or cluster, and the effective prediction based on weighted k most similar neighbors. The online prediction and analysis tool is freely available at http://nclab.hit.edu.cn/hdmpred.
doi:10.1371/journal.pone.0070204
PMCID: PMC3738541  PMID: 23950912
24.  A Kaposi's Sarcoma-Associated Herpesvirus MicroRNA and Its Variants Target the Transforming Growth Factor β Pathway To Promote Cell Survival 
Journal of Virology  2012;86(21):11698-11711.
Transforming growth factor β (TGF-β) signaling regulates cell growth and survival. Dysregulation of the TGF-β pathway is common in viral infection and cancer. Latent infection by Kaposi's sarcoma-associated herpesvirus (KSHV) is required for the development of several AIDS-related malignancies, including Kaposi's sarcoma and primary effusion lymphoma (PEL). KSHV encodes more than two dozen microRNAs (miRs) derived from 12 pre-miRs with largely unknown functions. In this study, we show that miR variants processed from pre-miR-K10 are expressed in KSHV-infected PEL cells and endothelial cells, while cellular miR-142-3p and its variant miR-142-3p_-1_5, which share the same seed sequence with miR-K10a_ +1_5, are expressed only in PEL cells and not in uninfected and KSHV-infected TIME cells. KSHV miR-K10 variants inhibit TGF-β signaling by targeting TGF-β type II receptor (TβRII). Computational and reporter mutagenesis analyses identified three functional target sites in the TβRII 3′ untranslated region (3′UTR). Expression of miR-K10 variants is sufficient to inhibit TGF-β-induced cell apoptosis. A suppressor of the miRs sensitizes latent KSHV-infected PEL cells to TGF-β and induces apoptosis. These results indicate that miR-K10 variants manipulate the TGF-β pathway to confer cells with resistance to the growth-inhibitory effect of TGF-β. Thus, KSHV miRs might target the tumor-suppressive TGF-β pathway to promote viral latency and contribute to malignant cellular transformation.
doi:10.1128/JVI.06855-11
PMCID: PMC3486299  PMID: 22915806
25.  De-Regulated MicroRNAs in Pediatric Cancer Stem Cells Target Pathways Involved in Cell Proliferation, Cell Cycle and Development 
PLoS ONE  2013;8(4):e61622.
Background
microRNAs (miRNAs) have been implicated in the control of many biological processes and their deregulation has been associated with many cancers. In recent years, the cancer stem cell (CSC) concept has been applied to many cancers including pediatric. We hypothesized that a common signature of deregulated miRNAs in the CSCs fraction may explain the disrupted signaling pathways in CSCs.
Methodology/Results
Using a high throughput qPCR approach we identified 26 CSC associated differentially expressed miRNAs (DEmiRs). Using BCmicrO algorithm 865 potential CSC associated DEmiR targets were obtained. These potential targets were subjected to KEGG, Biocarta and Gene Ontology pathway and biological processes analysis. Four annotated pathways were enriched: cell cycle, cell proliferation, p53 and TGF-beta/BMP. Knocking down hsa-miR-21-5p, hsa-miR-181c-5p and hsa-miR-135b-5p using antisense oligonucleotides and small interfering RNA in cell lines led to the depletion of the CSC fraction and impairment of sphere formation (CSC surrogate assays).
Conclusion
Our findings indicated that CSC associated DEmiRs and the putative pathways they regulate may have potential therapeutic applications in pediatric cancers.
doi:10.1371/journal.pone.0061622
PMCID: PMC3629228  PMID: 23613887

Results 1-25 (55)