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author:("Qian, yiliang")
1.  Genetic amplification of PPME1 in gastric and lung cancer and its potential as a novel therapeutic target 
Cancer Biology & Therapy  2013;15(1):128-134.
Protein phosphatase methylesterase 1 (PPME1) is a protein phosphatase 2A (PP2A)-specific methyl esterase that negatively regulates PP2A through demethylation at its carboxy terminal leucine 309 residue. Emerging evidence shows that the upregulation of PPME1 is associated with poor prognosis in glioblastoma patients. By performing an array comparative genomic hybridization analysis to detect copy number changes, we have been the first to identify PPME1 gene amplification in 3.8% (5/131) of Chinese gastric cancer (GC) samples and 3.1% (4/124) of Chinese lung cancer (LC) samples. This PPME1 gene amplification was confirmed by fluorescence in situ hybridization analysis and is correlated with elevated protein expression, as determined by immunohistochemistry analysis. To further investigate the role of PPME1 amplification in tumor growth, short-hairpin RNA-mediated gene silencing was employed. A knockdown of PPME1 expression resulted in a significant inhibition of cell proliferation and induction of cell apoptosis in PPME1-amplified human cancer cell lines SNU668 (GC) and Oka-C1 (LC), but not in nonamplified MKN1 (GC) and HCC95 (LC) cells. The PPME1 gene knockdown also led to a consistent decrease in PP2A demethylation at leucine 309, which was correlated with the downregulation of cellular Erk and AKT phosphorylation. Our data indicate that PPME1 could be an attractive therapeutic target for a subset of GCs and LCs.
doi:10.4161/cbt.27146
PMCID: PMC3938515  PMID: 24253382
gastric cancer; lung cancer; PPME1 amplification; PP2A; shRNA-knockdown
2.  Using GeneReg to construct time delay gene regulatory networks 
BMC Research Notes  2010;3:142.
Background
Understanding gene expression and regulation is essential for understanding biological mechanisms. Because gene expression profiling has been widely used in basic biological research, especially in transcription regulation studies, we have developed GeneReg, an easy-to-use R package, to construct gene regulatory networks from time course gene expression profiling data; More importantly, this package can provide information about time delays between expression change in a regulator and that of its target genes.
Findings
The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. There are two parameters in the model, time delay and regulation coefficient. Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. Regulation coefficient expresses the regulation effect: a positive regulation coefficient indicates activation and negative indicates repression. GeneReg was implemented on a real Saccharomyces cerevisiae cell cycle dataset; more than thirty percent of the modeled regulations, based entirely on gene expression files, were found to be consistent with previous discoveries from known databases.
Conclusions
GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. It may be applied to study time-related biological processes such as cell cycle, cell differentiation, or causal inference.
doi:10.1186/1756-0500-3-142
PMCID: PMC2892504  PMID: 20500822
3.  The combination approach of SVM and ECOC for powerful identification and classification of transcription factor 
BMC Bioinformatics  2008;9:282.
Background
Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand.
Results
The support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine (SVM) methodology for TF classification. The overall success rates of identification and classification achieved 88.22% and 97.83% respectively. Finally, a web site was constructed to let users access our tools (see Availability and requirements section for URL).
Conclusion
The SVM method was a valid and stable means for TFs identification with protein domains and functional sites as feature vectors. Error-correcting output coding (ECOC) algorithm is a powerful method for multi-class classification problem. When combined with SVM method, it can remarkably increase the accuracy of TF classification using protein domains and functional sites as feature vectors. In addition, our work implied that ECOC algorithm may succeed in a broad range of applications in biological data mining.
doi:10.1186/1471-2105-9-282
PMCID: PMC2440765  PMID: 18554421
4.  Gene-Centric Characteristics of Genome-Wide Association Studies 
PLoS ONE  2007;2(12):e1262.
Background
The high-throughput genotyping chips have contributed greatly to genome-wide association (GWA) studies to identify novel disease susceptibility single nucleotide polymorphisms (SNPs). The high-density chips are designed using two different SNP selection approaches, the direct gene-centric approach, and the indirect quasi-random SNPs or linkage disequilibrium (LD)-based tagSNPs approaches. Although all these approaches can provide high genome coverage and ascertain variants in genes, it is not clear to which extent these approaches could capture the common genic variants. It is also important to characterize and compare the differences between these approaches.
Methodology/Principal Findings
In our study, by using both the Phase II HapMap data and the disease variants extracted from OMIM, a gene-centric evaluation was first performed to evaluate the ability of the approaches in capturing the disease variants in Caucasian population. Then the distribution patterns of SNPs were also characterized in genic regions, evolutionarily conserved introns and nongenic regions, ontologies and pathways. The results show that, no mater which SNP selection approach is used, the current high-density SNP chips provide very high coverage in genic regions and can capture most of known common disease variants under HapMap frame. The results also show that the differences between the direct and the indirect approaches are relatively small. Both have similar SNP distribution patterns in these gene-centric characteristics.
Conclusions/Significance
This study suggests that the indirect approaches not only have the advantage of high coverage but also are useful for studies focusing on various functional SNPs either in genes or in the conserved regions that the direct approach supports. The study and the annotation of characteristics will be helpful for designing and analyzing GWA studies that aim to identify genetic risk factors involved in common diseases, especially variants in genes and conserved regions.
doi:10.1371/journal.pone.0001262
PMCID: PMC2092383  PMID: 18060058
5.  The use of global transcriptional analysis to reveal the biological and cellular events involved in distinct development phases of Trichophyton rubrum conidial germination 
BMC Genomics  2007;8:100.
Background
Conidia are considered to be the primary cause of infections by Trichophyton rubrum.
Results
We have developed a cDNA microarray containing 10250 ESTs to monitor the transcriptional strategy of conidial germination. A total of 1561 genes that had their expression levels specially altered in the process were obtained and hierarchically clustered with respect to their expression profiles. By functional analysis, we provided a global view of an important biological system related to conidial germination, including characterization of the pattern of gene expression at sequential developmental phases, and changes of gene expression profiles corresponding to morphological transitions. We matched the EST sequences to GO terms in the Saccharomyces Genome Database (SGD). A number of homologues of Saccharomyces cerevisiae genes related to signalling pathways and some important cellular processes were found to be involved in T. rubrum germination. These genes and signalling pathways may play roles in distinct steps, such as activating conidial germination, maintenance of isotropic growth, establishment of cell polarity and morphological transitions.
Conclusion
Our results may provide insights into molecular mechanisms of conidial germination at the cell level, and may enhance our understanding of regulation of gene expression related to the morphological construction of T. rubrum.
doi:10.1186/1471-2164-8-100
PMCID: PMC1871584  PMID: 17428342
6.  An efficient method for statistical significance calculation of transcription factor binding sites 
Bioinformation  2007;2(5):169-174.
Various statistical models have been developed to describe the DNA binding preference of transcription factors, by which putative transcription factor binding sites (TFBS) can be identified according to scores assigned. Statistical significance of these scores, usually known as the p-value, play a critical role in identification. We developed an efficient algorithm to provide precise calculation of the statistical significance, remarkably enhancing the calculation efficiency by reducing the time complexity from an exponent scale to a linear scale, and successfully extended the application of this algorithm to a wide range of models, from the commonly used position weight matrix models to the complicated Bayesian Network models. Further, we calculated p-values of all transcription factor DNA binding sites recorded in the database, JASPAR, and based on these, we investigated some unseen properties of p-values as a whole, such as the p-value distribution of different models and the p-value variance according to changed scoring schemes. We hope that our algorithm and the result of computational experiments would offer an improved solution to the statistical significance of transcription factor binding sites. The software to implement our method can be downloaded from http://pcal.biosino.org/pCal.html.
PMCID: PMC2241927  PMID: 18305824
transcription factor; DNA; binding sites; Bayesian network
7.  Genomic characterization of ribitol teichoic acid synthesis in Staphylococcus aureus: genes, genomic organization and gene duplication 
BMC Genomics  2006;7:74.
Background
Staphylococcus aureus or MRSA (Methicillin Resistant S. aureus), is an acquired pathogen and the primary cause of nosocomial infections worldwide. In S. aureus, teichoic acid is an essential component of the cell wall, and its biosynthesis is not yet well characterized. Studies in Bacillus subtilis have discovered two different pathways of teichoic acid biosynthesis, in two strains W23 and 168 respectively, namely teichoic acid ribitol (tar) and teichoic acid glycerol (tag). The genes involved in these two pathways are also characterized, tarA, tarB, tarD, tarI, tarJ, tarK, tarL for the tar pathway, and tagA, tagB, tagD, tagE, tagF for the tag pathway. With the genome sequences of several MRSA strains: Mu50, MW2, N315, MRSA252, COL as well as methicillin susceptible strain MSSA476 available, a comparative genomic analysis was performed to characterize teichoic acid biosynthesis in these S. aureus strains.
Results
We identified all S. aureus tar and tag gene orthologs in the selected S. aureus strains which would contribute to teichoic acids sythesis.Based on our identification of genes orthologous to tarI, tarJ, tarL, which are specific to tar pathway in B. subtilis W23, we also concluded that tar is the major teichoic acid biogenesis pathway in S. aureus. Further analyses indicated that the S. aureus tar genes, different from the divergon organization in B. subtilis, are organized into several clusters in cis. Most interesting, compared with genes in B. subtilis tar pathway, the S. aureus tar specific genes (tarI,J,L) are duplicated in all six S. aureus genomes.
Conclusion
In the S. aureus strains we analyzed, tar (teichoic acid ribitol) is the main teichoic acid biogenesis pathway. The tar genes are organized into several genomic groups in cis and the genes specific to tar (relative to tag): tarI, tarJ, tarL are duplicated. The genomic organization of the S. aureus tar pathway suggests their regulations are different when compared to B. subtilis tar or tag pathway, which are grouped in two operons in a divergon structure.
doi:10.1186/1471-2164-7-74
PMCID: PMC1458327  PMID: 16595020

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