Jingchun Sun

TSGene: a web resource for tumor suppressor genes

Tumor suppressor genes (TSGs) are guardian genes that play important roles in controlling cell proliferation processes such as cell-cycle checkpoints and inducing apoptosis. Identification of these genes and understanding their functions are critical for further investigation of tumorigenesis. So far, many studies have identified numerous TSGs and illustrated their functions in various types of tumors or normal samples. Furthermore, accumulating evidence has shown that non-coding RNAs can act as TSGs to prevent the tumorigenesis processes. Therefore, there is a growing demand to integrate TSGs with large-scale experimental evidence (e.g. gene expression and epigenetic signatures) to provide a comprehensive resource for further investigation of TSGs and their molecular mechanisms in cancer. To achieve this goal, we first developed a comprehensive literature-based database called TSGene (tumor suppressor gene database), freely available at http://bioinfo.mc.vanderbilt.edu/TSGene/. In the current release, TSGene contains 716 human (637 protein-coding and 79 non-coding genes), 628 mouse and 567 rat TSGs curated from UniProtKB, the Tumor Associated Gene database and 5795 PubMed abstracts. Additionally, the TSGene provides detailed annotations for each TSG, such as cancer mutations, gene expressions, methylation sites, TF regulations and protein–protein interactions.

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

Objective Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Methods Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drugs chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. Results This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. Conclusion The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.

A Novel microRNA and transcription factor mediated regulatory network in schizophrenia

BackgroundSchizophrenia is a complex brain disorder with molecular mechanisms that have yet to be elucidated. Previous studies have suggested that changes in gene expression may play an important role in the etiology of schizophrenia, and that microRNAs (miRNAs) and transcription factors (TFs) are primary regulators of this gene expression. So far, several miRNA-TF mediated regulatory modules have been verified. We hypothesized that miRNAs and TFs might play combinatory regulatory roles for schizophrenia genes and, thus, explored miRNA-TF regulatory networks in schizophrenia.ResultsWe identified 32 feed-forward loops (FFLs) among our compiled schizophrenia-related miRNAs, TFs and genes. Our evaluation revealed that these observed FFLs were significantly enriched in schizophrenia genes. By converging the FFLs and mutual feedback loops, we constructed a novel miRNA-TF regulatory network for schizophrenia. Our analysis revealed EGR3 and hsa-miR-195 were core regulators in this regulatory network. We next proposed a model highlighting EGR3 and miRNAs involved in signaling pathways and regulatory networks in the nervous system. Finally, we suggested several single nucleotide polymorphisms (SNPs) located on miRNAs, their target sites, and TFBSs, which may have an effect in schizophrenia gene regulation.ConclusionsThis study provides many insights on the regulatory mechanisms of genes involved in schizophrenia. It represents the first investigation of a miRNA-TF regulatory network for a complex disease, as demonstrated in schizophrenia.

Schizophrenia Gene Networks and Pathways and Their Applications for Novel Candidate Gene Selection

Background Schizophrenia (SZ) is a heritable, complex mental disorder. We have seen limited success in finding causal genes for schizophrenia from numerous conventional studies. Protein interaction network and pathway-based analysis may provide us an alternative and effective approach to investigating the molecular mechanisms of schizophrenia. Methodology/Principal Findings We selected a list of schizophrenia candidate genes (SZGenes) using a multi-dimensional evidence-based approach. The global network properties of proteins encoded by these SZGenes were explored in the context of the human protein interactome while local network properties were investigated by comparing SZ-specific and cancer-specific networks that were extracted from the human interactome. Relative to cancer genes, we observed that SZGenes tend to have an intermediate degree and an intermediate efficiency on a perturbation spreading throughout the human interactome. This suggested that schizophrenia might have different pathological mechanisms from cancer even though both are complex diseases. We conducted pathway analysis using Ingenuity System and constructed the first schizophrenia molecular network (SMN) based on protein interaction networks, pathways and literature survey. We identified 24 pathways overrepresented in SZGenes and examined their interactions and crosstalk. We observed that these pathways were related to neurodevelopment, immune system, and retinoic X receptor (RXR). Our examination of SMN revealed that schizophrenia is a dynamic process caused by dysregulation of the multiple pathways. Finally, we applied the network/pathway approach to identify novel candidate genes, some of which could be verified by experiments. Conclusions/Significance This study provides the first comprehensive review of the network and pathway characteristics of schizophrenia candidate genes. Our preliminary results suggest that this systems biology approach might prove promising for selection of candidate genes for complex diseases. Our findings have important implications for the molecular mechanisms for schizophrenia and, potentially, other psychiatric disorders.

Candidate genes for schizophrenia: a survey of association studies and gene ranking.

More than 500 genes have been reported with positive or negative association with schizophrenia. The wealth of this information, along with the complex nature of psychiatric disorders, provides a challenging but also unique opportunity for the investigation of molecular and cellular mechanisms in schizophrenia. In this study, we performed a comprehensive survey of the published association studies collected in the SchizophreniaGene database. We observed over time a strong trend for increases in the number of published reports, the number of studied genes, and the sample size of the studies. We also examined the studies, genes, and sample sizes in different ethnic populations and the distribution of these association studies and their employed markers among these susceptibility genes. We then selected and ranked candidate genes using a combined odds ratio method. The evaluation of this candidate gene set against sets selected by other methods suggested its utility in follow‐up association studies and in further bioinformatics analysis. We also examined the functional biases of the selected genes.

The dystrobrevin-binding protein 1 gene: features and networks

The dystrobrevin-binding protein 1 (DTNBP1) gene has been one of the most studied and promising schizophrenia susceptibility genes since it was first reported to be associated with schizophrenia in the Irish Study of High Density Schizophrenia Families (ISHDSF). Although many studies have been performed both at the functional level and in association with psychiatric disorders, there has been no systematic review of the features of the DTNBP1 gene, protein or the relationship between function and phenotype. Using a bioinformatics approach, we identified the DTNBP1 gene in 13 vertebrate species. The comparison of these genes revealed a conserved gene structure, protein-coding sequence and dysbindin domain, but a diverse noncoding sequence. The molecular evolutionary analysis suggests the DTNBP1 gene probably originated in chordates and matured in vertebrates. No signature of recent positive selection was seen in any primate lineage. The DTNBP1 gene likely has many more alternative transcripts than the current three major isoforms annotated in the NCBI database. Our examination of risk haplotypes revealed that, although the frequency of a single nucleotide polymorphism (SNP) or haplotype might be significantly different in cases from controls, difference between major geographic populations was even larger. Finally, we constructed the first DTNBP1 interactome and explored its network features. Besides the biogenesis of lysosome-related organelles complex 1 and dystrophin-associated protein complex, several molecules in the DTNBP1 network likely provide insight into the role of DTNBP1 in biological systems: retinoic acid, β-estradiol, calmodulin and tumour necrosis factor. Studies of these subnetworks and pathways may provide opportunities to deepen our understanding of the mechanisms of action of DTNBP1 variants.

IL-15 Regulates Homeostasis and Terminal Maturation of NKT Cells

Semi-invariant NKT cells are thymus-derived innate-like lymphocytes that modulate microbial and tumor immunity as well as autoimmune diseases. These immunoregulatory properties of NKT cells are acquired during their development. Much has been learned regarding the molecular and cellular cues that promote NKT cell development, yet how these cells are maintained in the thymus and the periphery and how they acquire functional competence are incompletely understood. We found that IL-15 induced several Bcl-2 family survival factors in thymic and splenic NKT cells in vitro. Yet, IL-15–mediated thymic and peripheral NKT cell survival critically depended on Bcl-xL expression. Additionally, IL-15 regulated thymic developmental stage 2 to stage 3 lineage progression and terminal NKT cell differentiation. Global gene expression analyses and validation revealed that IL-15 regulated Tbx21 (T-bet) expression in thymic NKT cells. The loss of IL-15 also resulted in poor expression of key effector molecules such as IFN-γ, granzyme A and C, as well as several NK cell receptors, which are also regulated by T-bet in NKT cells. Taken together, our findings reveal a critical role for IL-15 in NKT cell survival, which is mediated by Bcl-xL, and effector differentiation, which is consistent with a role of T-bet in regulating terminal maturation.

Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma

Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. Recent studies revealed that patterns of microRNA (miRNA) expression in GBM tissue samples are different from those in normal brain tissues, suggesting that a number of miRNAs play critical roles in the pathogenesis of GBM. However, little is yet known about which miRNAs play central roles in the pathology of GBM and their regulatory mechanisms of action. To address this issue, in this study, we systematically explored the main regulation format (feed-forward loops, FFLs) consisting of miRNAs, transcription factors (TFs) and their impacting GBM-related genes, and developed a computational approach to construct a miRNA-TF regulatory network. First, we compiled GBM-related miRNAs, GBM-related genes, and known human TFs. We then identified 1,128 3-node FFLs and 805 4-node FFLs with statistical significance. By merging these FFLs together, we constructed a comprehensive GBM-specific miRNA-TF mediated regulatory network. Then, from the network, we extracted a composite GBM-specific regulatory network. To illustrate the GBM-specific regulatory network is promising for identification of critical miRNA components, we specifically examined a Notch signaling pathway subnetwork. Our follow up topological and functional analyses of the subnetwork revealed that six miRNAs (miR-124, miR-137, miR-219-5p, miR-34a, miR-9, and miR-92b) might play important roles in GBM, including some results that are supported by previous studies. In this study, we have developed a computational framework to construct a miRNA-TF regulatory network and generated the first miRNA-TF regulatory network for GBM, providing a valuable resource for further understanding the complex regulatory mechanisms in GBM. The observation of critical miRNAs in the Notch signaling pathway, with partial verification from previous studies, demonstrates that our network-based approach is promising for the identification of new and important miRNAs in GBM and, potentially, other cancers.

A multi-dimensional evidence-based candidate gene prioritization approach for complex diseases–schizophrenia as a case

MOTIVATION During the past decade, we have seen an exponential growth of vast amounts of genetic data generated for complex disease studies. Currently, across a variety of complex biological problems, there is a strong trend towards the integration of data from multiple sources. So far, candidate gene prioritization approaches have been designed for specific purposes, by utilizing only some of the available sources of genetic studies, or by using a simple weight scheme. Specifically to psychiatric disorders, there has been no prioritization approach that fully utilizes all major sources of experimental data. RESULTS Here we present a multi-dimensional evidence-based candidate gene prioritization approach for complex diseases and demonstrate it in schizophrenia. In this approach, we first collect and curate genetic studies for schizophrenia from four major categories: association studies, linkage analyses, gene expression and literature search. Genes in these data sets are initially scored by category-specific scoring methods. Then, an optimal weight matrix is searched by a two-step procedure (core genes and unbiased P-values in independent genome-wide association studies). Finally, genes are prioritized by their combined scores using the optimal weight matrix. Our evaluation suggests this approach generates prioritized candidate genes that are promising for further analysis or replication. The approach can be applied to other complex diseases. AVAILABILITY The collected data, prioritized candidate genes, and gene prioritization tools are freely available at http://bioinfo.mc.vanderbilt.edu/SZGR/.

SZGR: a comprehensive schizophrenia gene resource

Schizophrenia is a major debilitating psychiatric disorder affecting ∼1% of the population worldwide. A tremendous amount of effort has been expended in the last two decades to identify genes influencing susceptibility to this disorder. Although there is a strong trend toward integrating data obtained from various genetic studies and their related biological information into a comprehensive resource for many complex diseases, we were unable to find such an effort for schizophrenia or for any other psychiatric disorder yet. In this study, we present Schizophrenia gene resource (SZGR), a comprehensive database with user-friendly web interface. SZGR deposits genetic data from all available sources, including those from association studies, linkage scans, gene expression, literature, gene ontology (GO) annotations, gene networks, cellular and regulatory pathways, as well as microRNAs and their target sites. Moreover, SZGR provides online tools for data browse and search, data integration, custom gene ranking and graphical presentation. This system can be easily applied to other complex diseases, especially to other psychiatric disorders. The SZGR database is available at http://bioinfo.mc.vanderbilt.edu/SZGR/.