Chun-Chi Liu

miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

MicroRNAs (miRNAs) are small non-coding RNA molecules capable of negatively regulating gene expression to control many cellular mechanisms. The miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) provides the most current and comprehensive information of experimentally validated miRNA-target interactions. The database was launched in 2010 with data sources for >100 published studies in the identification of miRNA targets, molecular networks of miRNA targets and systems biology, and the current release (2013, version 4) includes significant expansions and enhancements over the initial release (2010, version 1). This article reports the current status of and recent improvements to the database, including (i) a 14-fold increase to miRNA-target interaction entries, (ii) a miRNA-target network, (iii) expression profile of miRNA and its target gene, (iv) miRNA target-associated diseases and (v) additional utilities including an upgrade reminder and an error reporting/user feedback system.

miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database

MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides, which negatively regulate the gene expression at the post-transcriptional level. This study describes an update of the miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) that provides information about experimentally validated miRNA-target interactions (MTIs). The latest update of the miRTarBase expanded it to identify systematically Argonaute-miRNA-RNA interactions from 138 crosslinking and immunoprecipitation sequencing (CLIP-seq) data sets that were generated by 21 independent studies. The database contains 4966 articles, 7439 strongly validated MTIs (using reporter assays or western blots) and 348 007 MTIs from CLIP-seq. The number of MTIs in the miRTarBase has increased around 7-fold since the 2014 miRTarBase update. The miRNA and gene expression profiles from The Cancer Genome Atlas (TCGA) are integrated to provide an effective overview of this exponential growth in the miRNA experimental data. These improvements make the miRTarBase one of the more comprehensively annotated, experimentally validated miRNA-target interactions databases and motivate additional miRNA research efforts.

Joint Genome-Wide Profiling of miRNA and mRNA Expression in Alzheimer's Disease Cortex Reveals Altered miRNA Regulation

Although microRNAs are being extensively studied for their involvement in cancer and development, little is known about their roles in Alzheimers disease (AD). In this study, we used microarrays for the first joint profiling and analysis of miRNAs and mRNAs expression in brain cortex from AD and age-matched control subjects. These data provided the unique opportunity to study the relationship between miRNA and mRNA expression in normal and AD brains. Using a non-parametric analysis, we showed that the levels of many miRNAs can be either positively or negatively correlated with those of their target mRNAs. Comparative analysis with independent cancer datasets showed that such miRNA-mRNA expression correlations are not static, but rather context-dependent. Subsequently, we identified a large set of miRNA-mRNA associations that are changed in AD versus control, highlighting AD-specific changes in the miRNA regulatory system. Our results demonstrate a robust relationship between the levels of miRNAs and those of their targets in the brain. This has implications in the study of the molecular pathology of AD, as well as miRNA biology in general.

Polycomb-Repressed Genes Have Permissive Enhancers that Initiate Reprogramming

Key regulatory genes, suppressed by Polycomb and H3K27me3, become active during normal differentiation and induced reprogramming. Using the well-characterized enhancer/promoter pair of MYOD1 as a model, we have identified a critical role for enhancers in reprogramming. We observed an unexpected nucleosome-depleted region (NDR) at the H3K4me1-enriched enhancer at which transcriptional regulators initially bind, leading to subsequent changes in the chromatin at the cognate promoter. Exogenous Myod1 activates its own transcription by binding first at the enhancer, leading to an NDR and transcription-permissive chromatin at the associated MYOD1 promoter. Exogenous OCT4 also binds first to the permissive MYOD1 enhancer but has a different effect on the cognate promoter, where the monovalent H3K27me3 marks are converted to the bivalent state characteristic of stem cells. Genome-wide, a high percentage of Polycomb targets are associated with putative enhancers in permissive states, suggesting that they may provide a widespread avenue for the initiation of cell-fate reprogramming.

Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called ‘multi-dimensional genomic data’. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional ‘omic’ data.

CircNet: a database of circular RNAs derived from transcriptome sequencing data

Circular RNAs (circRNAs) represent a new type of regulatory noncoding RNA that only recently has been identified and cataloged. Emerging evidence indicates that circRNAs exert a new layer of post-transcriptional regulation of gene expression. In this study, we utilized transcriptome sequencing datasets to systematically identify the expression of circRNAs (including known and newly identified ones by our pipeline) in 464 RNA-seq samples, and then constructed the CircNet database (http://circnet.mbc.nctu.edu.tw/) that provides the following resources: (i) novel circRNAs, (ii) integrated miRNA-target networks, (iii) expression profiles of circRNA isoforms, (iv) genomic annotations of circRNA isoforms (e.g. 282 948 exon positions), and (v) sequences of circRNA isoforms. The CircNet database is to our knowledge the first public database that provides tissue-specific circRNA expression profiles and circRNA–miRNA-gene regulatory networks. It not only extends the most up to date catalog of circRNAs but also provides a thorough expression analysis of both previously reported and novel circRNAs. Furthermore, it generates an integrated regulatory network that illustrates the regulation between circRNAs, miRNAs and genes.

Anticancer effects of tanshinone I in human non-small cell lung cancer

Tanshinones are the major bioactive compounds of Salvia miltiorrhiza Bunge (Danshen) roots, which are used in many therapeutic remedies in Chinese traditional medicine. We investigated the anticancer effects of tanshinones on the highly invasive human lung adenocarcinoma cell line, CL1-5. Tanshinone I significantly inhibited migration, invasion, and gelatinase activity in macrophage-conditioned medium-stimulated CL1-5 cells in vitro and also reduced the tumorigenesis and metastasis in CL1-5-bearing severe combined immunodeficient mice. Unlike tanshinone IIA, which induces cell apoptosis, tanshinone I did not have direct cytotoxicity. Real-time quantitative PCR, luciferase reporter assay, and electrophoretic mobility shift assay revealed that tanshinone I reduces the transcriptional activity of interleukin-8, the angiogenic factor involved in cancer metastasis, by attenuating the DNA-binding activity of activator protein-1 and nuclear factor-κB in conditioned medium-stimulated CL1-5 cells. Microarray and pathway analysis of tumor-related genes identified the differentially expressed genes responding to tanshinone I, which may be associated with the Ras-mitogen-activated protein kinase and Rac1 signaling pathways. These results suggest that tanshinone I exhibits anticancer effects both in vitro and in vivo and that these effects are mediated at least partly through the interleukin-8, Ras-mitogen-activated protein kinase, and Rac1 signaling pathways. Although tanshinone I has a remarkable anticancer action, its potential anticoagulant effect should be noted and evaluated. [Mol Cancer Ther 2008;7(11):3527–38]

Integrative analysis of many weighted co-expression networks using tensor computation.

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks.

Identifying multi-layer gene regulatory modules from multi-dimensional genomic data

Motivation: Eukaryotic gene expression (GE) is subjected to precisely coordinated multi-layer controls, across the levels of epigenetic, transcriptional and post-transcriptional regulations. Recently, the emerging multi-dimensional genomic dataset has provided unprecedented opportunities to study the cross-layer regulatory interplay. In these datasets, the same set of samples is profiled on several layers of genomic activities, e.g. copy number variation (CNV), DNA methylation (DM), GE and microRNA expression (ME). However, suitable analysis methods for such data are currently sparse. Results: In this article, we introduced a sparse Multi-Block Partial Least Squares (sMBPLS) regression method to identify multi-dimensional regulatory modules from this new type of data. A multi-dimensional regulatory module contains sets of regulatory factors from different layers that are likely to jointly contribute to a local ‘gene expression factory’. We demonstrated the performance of our method on the simulated data as well as on The Cancer Genomic Atlas Ovarian Cancer datasets including the CNV, DM, ME and GE data measured on 230 samples. We showed that majority of identified modules have significant functional and transcriptional enrichment, higher than that observed in modules identified using only a single type of genomic data. Our network analysis of the modules revealed that the CNV, DM and microRNA can have coupled impact on expression of important oncogenes and tumor suppressor genes. Availability and implementation: The source code implemented by MATLAB is freely available at: http://zhoulab.usc.edu/sMBPLS/. Contact: xjzhou@usc.edu Supplementary information: Supplementary material are available at Bioinformatics online.

DiseaseConnect: a comprehensive web server for mechanism-based disease–disease connections

The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover disease–disease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drug–disease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments.