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author:("Wu, xiaoyan")
1.  Development of a Blocking ELISA for Detection of Serum Neutralizing Antibodies against Newly Emerged Duck Tembusu Virus 
PLoS ONE  2012;7(12):e53026.
Background
Since April 2010, domesticated ducks in China have been suffering from an emerging infectious disease characterized by retarded growth, high fever, loss of appetite, decline in egg production, and death. The causative agent was identified as a duck Tembusu virus (DTMUV), a member of the Ntaya virus (NTAV) group within the genus Flavivirus, family Flaviviridae. DTMUV is highly contagious and spreads rapidly in many species of ducks. More than 10 million shelducks have been infected and approximately 1 million died in 2010. The disease remains a constant threat to the duck industry; however, it is not known whether DTMUV can infect humans or other mammalians, despite the fact that the virus has spread widely in southeast China, one of the most densely populated areas in the world. The lack of reliable methods to detect the serum antibodies against DTMUV has limited our ability to conduct epidemiological investigations in various natural hosts and to evaluate the efficiency of vaccines to DTMUV.
Methodology/Principal Findings
A neutralizing monoclonal antibody (mAb) 1F5 binding specifically to the E protein was developed. Based on the mAb, a blocking enzyme-linked immunosorbent assay (ELISA) was developed for the detection of neutralizing antibodies against DTMUV. The average value of percent inhibition (PI) of 350 duck serum samples obtained from DTMUV-free farms was 1.0% ±5.8% (mean ± SD). The selected cut-off PI values for negative and positive sera were 12.6% (mean +2SD) and 18.4% (mean +3SD), respectively. When compared with a serum neutralizing antibody test (SNT) using chicken embryonated eggs, the rate of coincidence was 70.6% between the blocking ELISA and SNT, based on the titration of 20 duck DTMUV-positive serum samples.
Conclusions/Significance
The blocking ELISA based on a neutralizing mAb allowed rapid, sensitive, and specific detection of neutralization-related antibodies against DTMUV.
doi:10.1371/journal.pone.0053026
PMCID: PMC3534074  PMID: 23300851
2.  Multiple-Level Regulation of 2,4-Diacetylphloroglucinol Production by the Sigma Regulator PsrA in Pseudomonas fluorescens 2P24 
PLoS ONE  2012;7(11):e50149.
Background
Pseudomonas fluorescens 2P24 is a rhizospheric bacterium that aggressively colonizes the plant roots. It produces the antibiotic 2,4-diacetylphoroglucinol (2,4-DAPG), which contributes to the protection of various crop plants against soil borne diseases caused by bacterial and fungal pathogens. The biosynthesis of 2,4-DAPG is regulated at the transcriptional level in the expression of the phlACBD operon as well as at the posttranscriptional level by the Gac/Rsm signal transduction pathway. However, the detailed mechanism of such regulation is not clear.
Methodology/Principal Findings
In this study, we identified a binding site for the sigma regulator PsrA in the promoter region of the phlA gene. Electrophoretic mobility shift experiments revealed direct and specific binding of PsrA to the phlA promoter region. Consistent with the fact that its binding site locates within the promoter region of phlA, PsrA negatively regulates phlA expression, and its inactivation led to significant increase in 2,4-DAPG production. Interestingly, PsrA also activates the expression of the sigma factor RpoS, which negatively regulates 2,4-DAPG production by inducing the expression of the RNA-binding protein RsmA.
Conclusions/Significance
These results suggest that PsrA is an important regulator that modulates 2,4-DAPG biosynthesis at both transcriptional and posttranscriptional levels.
doi:10.1371/journal.pone.0050149
PMCID: PMC3510223  PMID: 23209661
3.  C2Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships 
BMC Genomics  2012;13(Suppl 6):S17.
Background
Network pharmacology has emerged as a new topic of study in recent years. It aims to study the myriad relationships among proteins, drugs, and disease phenotypes. The concept of molecular connectivity maps has been proposed to establish comprehensive knowledge links between molecules of interest in a given biological context. Molecular connectivity maps between drugs and genes/proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic profiles and toxicological profiles of candidate drugs.
Methods
To assess drug pharmacological effect, we assume that "ideal" drugs for a patient can treat or prevent the disease by modulating gene expression profiles of this patient to the similar level with those in healthy people. Starting from this hypothesis, we build comprehensive disease-gene-drug connectivity relationships with drug-protein directionality (inhibit/activate) information based on a computational connectivity maps (C2Maps) platform. An interactive interface for directionality annotation of drug-protein pairs with literature evidences from PubMed has been added to the new version of C2Maps. We also upload the curated directionality information of drug-protein pairs specific for three complex diseases - breast cancer, colorectal cancer and Alzheimer disease.
Results
For relevant drug-protein pairs with directionality information, we use breast cancer as a case study to demonstrate the functionality of disease-specific searching. Based on the results obtained from searching, we perform pharmacological effect evaluation for two important breast cancer drugs on treating patients diagnosed with different breast cancer subtypes. The evaluation is performed on a well-studied breast cancer gene expression microarray dataset to portray how useful the updated C2Maps is in assessing drug efficacy and toxicity information.
Conclusions
The C2Maps platform is an online bioinformatics resource that provides biologists with directional relationships between drugs and genes/proteins in specific disease contexts based on network mining, literature mining, and drug effect annotating. A new insight to assess overall drug efficacy and toxicity can be provided by using the C2Maps platform to identify disease relevant proteins and drugs. The case study on breast cancer correlates very well with the existing pharmacology of the two breast cancer drugs and highlights the significance of C2Maps database.
doi:10.1186/1471-2164-13-S6-S17
PMCID: PMC3481399  PMID: 23134618
4.  PAGED: a pathway and gene-set enrichment database to enable molecular phenotype discoveries 
BMC Bioinformatics  2012;13(Suppl 15):S2.
Background
Over the past decade, pathway and gene-set enrichment analysis has evolved into the study of high-throughput functional genomics. Owing to poorly annotated and incomplete pathway data, researchers have begun to combine pathway and gene-set enrichment analysis as well as network module-based approaches to identify crucial relationships between different molecular mechanisms.
Methods
To meet the new challenge of molecular phenotype discovery, in this work, we have developed an integrated online database, the Pathway And Gene Enrichment Database (PAGED), to enable comprehensive searches for disease-specific pathways, gene signatures, microRNA targets, and network modules by integrating gene-set-based prior knowledge as molecular patterns from multiple levels: the genome, transcriptome, post-transcriptome, and proteome.
Results
The online database we developed, PAGED http://bio.informatics.iupui.edu/PAGED is by far the most comprehensive public compilation of gene sets. In its current release, PAGED contains a total of 25,242 gene sets, 61,413 genes, 20 organisms, and 1,275,560 records from five major categories. Beyond its size, the advantage of PAGED lies in the explorations of relationships between gene sets as gene-set association networks (GSANs). Using colorectal cancer expression data analysis as a case study, we demonstrate how to query this database resource to discover crucial pathways, gene signatures, and gene network modules specific to colorectal cancer functional genomics.
Conclusions
This integrated online database lays a foundation for developing tools beyond third-generation pathway analysis approaches on for discovering molecular phenotypes, especially for disease-associated pathway/gene-set enrichment analysis.
doi:10.1186/1471-2105-13-S15-S2
PMCID: PMC3439733  PMID: 23046413
5.  Systems Biology Approach to Identify Gene Network Signatures for Colorectal Cancer 
In this work, we integrated prior knowledge from gene signatures and protein interactions with gene set enrichment analysis (GSEA), and gene/protein network modeling together to identify gene network signatures from gene expression microarray data. We demonstrated how to apply this approach into discovering gene network signatures for colorectal cancer (CRC) from microarray datasets. First, we used GSEA to analyze the microarray data through enriching differential genes in different CRC-related gene sets from two publicly available up-to-date gene set databases – Molecular Signatures Database (MSigDB) and Gene Signatures Database (GeneSigDB). Second, we compared the enriched gene sets through enrichment score, false-discovery rate, and nominal p-value. Third, we constructed an integrated protein–protein interaction (PPI) network through connecting these enriched genes by high-quality interactions from a human annotated and predicted protein interaction database, with a confidence score labeled for each interaction. Finally, we mapped differential gene expressions onto the constructed network to build a comprehensive network model containing visualized transcriptome and proteome data. The results show that although MSigDB has more CRC-relevant gene sets than GeneSigDB, the integrated PPI network connecting the enriched genes from both MSigDB and GeneSigDB can provide a more complete view for discovering gene network signatures. We also found several important sub-network signatures for CRC, such as TP53 sub-network, PCNA sub-network, and IL8 sub-network, corresponding to apoptosis, DNA repair, and immune response, respectively.
doi:10.3389/fgene.2012.00080
PMCID: PMC3354560  PMID: 22629282
network biology; gene set enrichment analysis; gene expression signatures; microarray analysis; colorectal cancer
6.  Reordering based integrative expression profiling for microarray classification 
BMC Bioinformatics  2012;13(Suppl 2):S1.
Background
Current network-based microarray analysis uses the information of interactions among concerned genes/gene products, but still considers each gene expression individually. We propose an organized knowledge-supervised approach - Integrative eXpression Profiling (IXP), to improve microarray classification accuracy, and help discover groups of genes that have been too weak to detect individually by traditional ways. To implement IXP, ant colony optimization reordering (ACOR) algorithm is used to group functionally related genes in an ordered way.
Results
Using Alzheimer's disease (AD) as an example, we demonstrate how to apply ACOR-based IXP approach into microarray classifications. Using a microarray dataset - GSE1297 with 31 samples as training set, the result for the blinded classification on another microarray dataset - GSE5281 with 151 samples, shows that our approach can improve accuracy from 74.83% to 82.78%. A recently-published 1372-probe signature for AD can only achieve 61.59% accuracy in the same condition. The ACOR-based IXP approach also has better performance than the IXP approach based on classic network ranking, graph clustering, and random-ordering methods in an overall classification performance comparison.
Conclusions
The ACOR-based IXP approach can serve as a knowledge-supervised feature transformation approach to increase classification accuracy dramatically, by transforming each gene expression profile to an integrated expression files as features inputting into standard classifiers. The IXP approach integrates both gene expression information and organized knowledge - disease gene/protein network topology information, which is represented as both network node weights (local topological properties) and network node orders (global topological characteristics).
doi:10.1186/1471-2105-13-S2-S1
PMCID: PMC3583189  PMID: 22536860
7.  Predicting adverse side effects of drugs 
BMC Genomics  2011;12(Suppl 5):S11.
Background
Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology combines data from clinical observation and molecular biology. This approach is new, however, and there are few examples of how it can practically predict adverse reactions (ADRs) from an experimental drug with acceptable accuracy.
Results
We have developed a new and practical computational framework to accurately predict ADRs of trial drugs. We combine clinical observation data with drug target data, protein-protein interaction (PPI) networks, and gene ontology (GO) annotations. We use cardiotoxicity, one of the major causes for drug withdrawals, as a case study to demonstrate the power of the framework. Our results show that an in silico model built on this framework can achieve a satisfactory cardiotoxicity ADR prediction performance (median AUC = 0.771, Accuracy = 0.675, Sensitivity = 0.632, and Specificity = 0.789). Our results also demonstrate the significance of incorporating prior knowledge, including gene networks and gene annotations, to improve future ADR assessments.
Conclusions
Biomolecular network and gene annotation information can significantly improve the predictive accuracy of ADR of drugs under development. The use of PPI networks can increase prediction specificity and the use of GO annotations can increase prediction sensitivity. Using cardiotoxicity as an example, we are able to further identify cardiotoxicity-related proteins among drug target expanding PPI networks. The systems pharmacology approach that we developed in this study can be generally applicable to all future developmental drug ADR assessments and predictions.
doi:10.1186/1471-2164-12-S5-S11
PMCID: PMC3287493  PMID: 22369493
8.  A new approach to construct pathway connected networks and its application in dose responsive gene expression profiles of rat liver regulated by 2,4DNT 
BMC Genomics  2010;11(Suppl 3):S4.
Abstract
Background
Military and industrial activities have lead to reported release of 2,4-dinitrotoluene (2,4DNT) into soil, groundwater or surface water. It has been reported that 2,4DNT can induce toxic effects on humans and other organisms. However the mechanism of 2,4DNT induced toxicity is still unclear. Although a series of methods for gene network construction have been developed, few instances of applying such technology to generate pathway connected networks have been reported.
Results
Microarray analyses were conducted using liver tissue of rats collected 24h after exposure to a single oral gavage with one of five concentrations of 2,4DNT. We observed a strong dose response of differentially expressed genes after 2,4DNT treatment. The most affected pathways included: long term depression, breast cancer regulation by stathmin1, WNT Signaling; and PI3K signaling pathways. In addition, we propose a new approach to construct pathway connected networks regulated by 2,4DNT. We also observed clear dose response pathway networks regulated by 2,4DNT.
Conclusions
We developed a new method for constructing pathway connected networks. This new method was successfully applied to microarray data from liver tissue of 2,4DNT exposed animals and resulted in the identification of unique dose responsive biomarkers in regards to affected pathways.
doi:10.1186/1471-2164-11-S3-S4
PMCID: PMC2999349  PMID: 21143786
9.  Danwei Profitability and Earnings Inequality in Urban China* 
The China quarterly  2008;195:558-581.
Prior research has debated the relative importance of such factors as human capital, political capital and region in determining workers’ earnings in reform-era urban China. This article argues that a main agent of social stratification in contemporary China continues to be the danwei, the work unit. Using data from a 1999 survey we conducted in three large Chinese cities, Wuhan, Shanghai and Xi’an, we assess the extent to which workers’ earnings (including regular wages, bonuses and subsidies) depend on the profitability of their danwei. Results show that the financial situation of the danwei is one of the most important determinants of earnings in today’s urban China. Furthermore, the importance of danwei profitability does not vary by city or by employment sector.
doi:10.1017/S0305741008000775
PMCID: PMC2864038  PMID: 20445771
10.  DANWEI AND SOCIAL INEQUALITY IN CONTEMPORARY URBAN CHINA 
Prior research showed that danwei, the work unit, was very important in determining workers’ social, economic, and political lives in pre-reform urban China. In this paper, we argue that danwei continues to be an agent of social stratification in contemporary urban China. Using data from a 1999 survey we conducted in three large Chinese cities, Wuhan, Shanghai, and Xi’an, we assess the extent to which workers’ socioeconomic well-being depends on the financial conditions of their danwei. Results show that the financial situation of danwei remains one of the most important determinants of earnings and benefits. However, the explanatory power of danwei’s financial situation is much greater for earnings than for benefits.
doi:10.1108/S0277-2833(2009)0000019013
PMCID: PMC2828673  PMID: 20191102
11.  HPD: an online integrated human pathway database enabling systems biology studies 
BMC Bioinformatics  2009;10(Suppl 11):S5.
Background
Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database; however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities.
Results
We developed the Human Pathway Database (HPD) by integrating heterogeneous human pathway data that are either curated at the NCI Pathway Interaction Database (PID), Reactome, BioCarta, KEGG or indexed from the Protein Lounge Web sites. Integration of pathway data at syntactic, semantic, and schematic levels was based on a unified pathway data model and data warehousing-based integration techniques. HPD provides a comprehensive online view that connects human proteins, genes, RNA transcripts, enzymes, signaling events, metabolic reaction events, and gene regulatory events. At the time of this writing HPD includes 999 human pathways and more than 59,341 human molecular entities. The HPD software provides both a user-friendly Web interface for online use and a robust relational database backend for advanced pathway querying. This pathway tool enables users to 1) search for human pathways from different resources by simply entering genes/proteins involved in pathways or words appearing in pathway names, 2) analyze pathway-protein association, 3) study pathway-pathway similarity, and 4) build integrated pathway networks. We demonstrated the usage and characteristics of the new HPD through three breast cancer case studies.
Conclusion
HPD http://bio.informatics.iupui.edu/HPD is a new resource for searching, managing, and studying human biological pathways. Users of HPD can search against large collections of human biological pathways, compare related pathways and their molecular entity compositions, and build high-quality, expanded-scope disease pathway models. The current HPD software can help users address a wide range of pathway-related questions in human disease biology studies.
doi:10.1186/1471-2105-10-S11-S5
PMCID: PMC3226194  PMID: 19811689
12.  Pattern Discovery in Breast Cancer Specific Protein Interaction Network 
The interest in indentifying novel biomarkers for early stage breast cancer (BRCA) detection has become grown significantly in recent years. From a view of network biology, one of the emerging themes today is to re-characterize a protein’s biological functions in its molecular network. Although many methods have been presented, including network-based gene ranking for molecular biomarker discovery, and graph clustering for functional module discovery, it is still hard to find systems-level properties hidden in disease specific molecular networks. We reconstructed BRCA-related protein interaction network by using BRCA-associated genes/proteins as seeds, and expanding them in an integrated protein interaction database. We further developed a computational framework based on Ant Colony Optimization to rank network nodes. The task of ranking nodes is represented as the problem of finding optimal density distributions of “ant colonies” on all nodes of the network. Our results revealed some interesting systems-level pattern in BRCA-related protein interaction network.
PMCID: PMC3041566  PMID: 21347162

Results 1-12 (12)