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1. Discovery of tumor-reactive T cell receptors by massively parallel library synthesis and screening

Author

Moravec, Ziva, primary, Zhao, Yue, additional, Voogd, Rhianne, additional, Cook, Danielle R., additional, Kinrot, Seon, additional, Capra, Benjamin, additional, Yang, Haiyan, additional, Raud, Brenda, additional, Ou, Jiayu, additional, Xuan, Jiekun, additional, Wei, Teng, additional, Ren, Lili, additional, Hu, Dandan, additional, Wang, Jun, additional, Haanen, John B.A.G., additional, Schumacher, Ton N., additional, Chen, Xi, additional, Porter, Ely, additional, and Scheper, Wouter, additional

Published
2024
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6. Immunomagnetic Capture of Big Six Shiga Toxin–Producing Escherichia coliStrains in Apple Juice with Detection by Multiplex Real-Time PCR Eliminates Interference from the Food Matrix

Author

Triplett, Odbert A., Xuan, Jiekun, Foley, Steven, Nayak, Rajesh, and Tolleson, William H.

Abstract

Having reliable methods for detecting Shiga toxin–producing Escherichia coli(STEC) in foods is an important food safety goal. The majority of STEC outbreaks have involved either the O157:H7 serotype or one of six non-O157 serogroups, O26, O45, O103, O111, O121, and O145, termed “The Big Six.” We have compared detection by PCR of the Shiga toxin genes stx1aand stx2afrom STEC bacteria isolated from unclarified apple juice by simple centrifugation with the use of an immunocapture technique to minimize contaminants (such as pectin and polyphenols that may copurify with DNA) that may interfere with DNA amplification efficiencies and limit sensitivity. An internal control for successful immunocapture, DNA extraction, and PCR amplification was generated by introducing the pmRaspberry plasmid into an stxnull strain, yielding an E. coliO45 pmRaspberry derivative that can be added to food samples directly. Using serial dilutions of a representative Big Six STEC in apple juice, our immunocapture method resulted in a 50% probability of detection value of 3.34, 2.25, and 4.25 CFU for detection by multiplex real-time PCR, growth on solid agar, and multiplex endpoint PCR, respectively. The time to result was 6.5 h, 9.5 h, and 1.5 days for immunocapture of Big Six STECs and detection by multiplex real-time PCR, endpoint PCR, and growth on solid agar, respectively. A set of 52 Big Six STEC isolates and 30 non–Big Six STEC strains was used to establish the inclusivity and exclusivity of the method. Finally, the ability to detect Big Six STEC contamination reliably was confirmed at 4.5 and 45 CFU/25-mL portions of refrigerated apple juice.

Published
2019
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7. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Author

Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, Fischer, Matthias, Zhang, Wenqian, Yu, Ying, Hertwig, Falk, Thierry-Mieg, Jean, Zhang, Wenwei, Thierry-Mieg, Danielle, Wang, Jian, Furlanello, Cesare, Devanarayan, Viswanath, Cheng, Jie, Deng, Youping, Hero, Barbara, Hong, Huixiao, Jia, Meiwen, Li, Li, Lin, Simon M., Nikolsky, Yuri, Oberthuer, Andre, Qing, Tao, Su, Zhenqiang, Volland, Ruth, Wang, Charles, Wang, May D., Ai, Junmei, Albanese, Davide, Asgharzadeh, Shahab, Avigad, Smadar, Bao, Wenjun, Bessarabova, Marina, Brilliant, Murray H., Brors, Benedikt, Chierici, Marco, Chu, Tzu-Ming, Zhang, Jibin, Grundy, Richard G., He, Min Max, Hebbring, Scott, Kaufman, Howard L., Lababidi, Samir, Lancashire, Lee J., Li, Yan, Lu, Xin X., Luo, Heng, Ma, Xiwen, Ning, Baitang, Noguera, Rosa, Peifer, Martin, Phan, John H., Roels, Frederik, Rosswog, Carolina, Shao, Susan, Shen, Jie, Theissen, Jessica, Tonini, Gian Paolo, Vandesompele, Jo, Wu, Po-Yen, Xiao, Wenzhong, Xu, Joshua, Xu, Weihong, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Dong, Zirui, Zhang, Ke K., Yin, Ye, Zhao, Chen, Zheng, Yuanting, Wolfinger, Russell D., Shi, Tieliu, Malkas, Linda H., Berthold, Frank, Wang, Jun, Tong, Weida, Shi, Leming, Peng, Zhiyu, and Fischer, Matthias

Abstract

Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. Results: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. Conclusions: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Published
2015

10. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Author

Zhang, Wenqian, primary, Yu, Ying, additional, Hertwig, Falk, additional, Thierry-Mieg, Jean, additional, Zhang, Wenwei, additional, Thierry-Mieg, Danielle, additional, Wang, Jian, additional, Furlanello, Cesare, additional, Devanarayan, Viswanath, additional, Cheng, Jie, additional, Deng, Youping, additional, Hero, Barbara, additional, Hong, Huixiao, additional, Jia, Meiwen, additional, Li, Li, additional, Lin, Simon M, additional, Nikolsky, Yuri, additional, Oberthuer, André, additional, Qing, Tao, additional, Su, Zhenqiang, additional, Volland, Ruth, additional, Wang, Charles, additional, Wang, May D., additional, Ai, Junmei, additional, Albanese, Davide, additional, Asgharzadeh, Shahab, additional, Avigad, Smadar, additional, Bao, Wenjun, additional, Bessarabova, Marina, additional, Brilliant, Murray H., additional, Brors, Benedikt, additional, Chierici, Marco, additional, Chu, Tzu-Ming, additional, Zhang, Jibin, additional, Grundy, Richard G., additional, He, Min Max, additional, Hebbring, Scott, additional, Kaufman, Howard L., additional, Lababidi, Samir, additional, Lancashire, Lee J., additional, Li, Yan, additional, Lu, Xin X., additional, Luo, Heng, additional, Ma, Xiwen, additional, Ning, Baitang, additional, Noguera, Rosa, additional, Peifer, Martin, additional, Phan, John H., additional, Roels, Frederik, additional, Rosswog, Carolina, additional, Shao, Susan, additional, Shen, Jie, additional, Theissen, Jessica, additional, Tonini, Gian Paolo, additional, Vandesompele, Jo, additional, Wu, Po-Yen, additional, Xiao, Wenzhong, additional, Xu, Joshua, additional, Xu, Weihong, additional, Xuan, Jiekun, additional, Yang, Yong, additional, Ye, Zhan, additional, Dong, Zirui, additional, Zhang, Ke K., additional, Yin, Ye, additional, Zhao, Chen, additional, Zheng, Yuanting, additional, Wolfinger, Russell D., additional, Shi, Tieliu, additional, Malkas, Linda H., additional, Berthold, Frank, additional, Wang, Jun, additional, Tong, Weida, additional, Shi, Leming, additional, Peng, Zhiyu, additional, and Fischer, Matthias, additional

Published
2015
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11. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Author

Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caiment, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., Shi, Leming, Su, Zhenqiang, Labaj, Pawel P., Li, Sheng, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Shi, Wei, Wang, Charles, Schroth, Gary P., Setterquist, Robert A., Thompson, John F., Jones, Wendell D., Xiao, Wenzhong, Xu, Weihong, Jensen, Roderick V., Kelly, Reagan, Xu, Joshua, Conesa, Ana, Furlanello, Cesare, Gao, Hanlin, Hong, Huixiao, Jafari, Nadereh, Letovsky, Stan, Liao, Yang, Lu, Fei, Oakeley, Edward J., Peng, Zhiyu, Praul, Craig A., Santoyo-Lopez, Javier, Scherer, Andreas, Shi, Tieliu, Smyth, Gordon K., Staedtler, Frank, Sykacek, Peter, Tan, Xin-Xing, Thompson, E. Aubrey, Vandesompele, Jo, Wang, May D., Wang, Jian, Wolfinger, Russell D., Zavadil, Jiri, Auerbach, Scott S., Bao, Wenjun, Binder, Hans, Blomquist, Thomas, Brilliant, Murray H., Bushel, Pierre R., Cain, Weimin, Catalano, Jennifer G., Chang, Ching-Wei, Chen, Tao, Chen, Geng, Chen, Rong, Chierici, Marco, Chu, Tzu-Ming, Clevert, Djork-Arne, Deng, Youping, Derti, Adnan, Devanarayan, Viswanath, Dong, Zirui, Dopazo, Joaquin, Du, Tingting, Fang, Hong, Fang, Yongxiang, Fasold, Mario, Fernandez, Anita, Fischer, Matthias, Furio-Tari, Pedro, Fuscoe, James C., Caiment, Florian, Gaj, Stan, Gandara, Jorge, Gao, Huan, Ge, Weigong, Gondo, Yoichi, Gong, Binsheng, Gong, Meihua, Gong, Zhuolin, Green, Bridgett, Guo, Chao, Guo, Lei, Guo, Li-Wu, Hadfield, James, Hellemans, Jan, Hochreiter, Sepp, Jia, Meiwen, Jian, Min, Johnson, Charles D., Kay, Suzanne, Kleinjans, Jos, Lababidi, Samir, Levy, Shawn, Li, Quan-Zhen, Li, Li, Li, Peng, Li, Yan, Li, Haiqing, Li, Jianying, Li, Shiyong, Lin, Simon M., Lopez, Francisco J., Lu, Xin, Luo, Heng, Ma, Xiwen, Meehan, Joseph, Megherbi, Dalila B., Mei, Nan, Mu, Bing, Ning, Baitang, Pandey, Akhilesh, Perez-Florido, Javier, Perkins, Roger G., Peters, Ryan, Phan, John H., Pirooznia, Mehdi, Qian, Feng, Qing, Tao, Rainbow, Lucille, Rocca-Serra, Philippe, Sambourg, Laure, Sansone, Susanna-Assunta, Schwartz, Scott, Shah, Ruchir, Shen, Jie, Smith, Todd M., Stegle, Oliver, Stralis-Pavese, Nancy, Stupka, Elia, Suzuki, Yutaka, Szkotnicki, Lee T., Tinning, Matthew, Tu, Bimeng, van Deft, Joost, Vela-Boza, Alicia, Venturini, Elisa, Walker, Stephen J., Wan, Liqing, Wang, Wei, Wang, Jinhui, Wang, Jun, Wieben, Eric D., Willey, James C., Wu, Po-Yen, Xuan, Jiekun, Yang, Yong, Ye, Zhan, Yin, Ye, Yu, Ying, Yuan, Yate-Ching, Zhang, John, Zhang, Ke K., Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Zhao, Chen, Zheng, Yuanting, Zhou, Yiming, Zumbo, Paul, Tong, Weida, Kreil, David P., Mason, Christopher E., and Shi, Leming

Abstract

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific-filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Published
2014

13. Ginkgo biloba leaf extract induces DNA damage by inhibiting topoisomerase II activity in human hepatic cells.

Author

Zhang, Zhuhong, Chen, Si, Mei, Hu, Xuan, Jiekun, Guo, Xiaoqing, Couch, Letha, Dobrovolsky, Vasily N., Guo, Lei, and Mei, Nan

Subjects
GINKGO, LIVER tumors, LABORATORY mice, BIOLOGICAL assay, QUERCETIN
Abstract

Ginkgo biloba leaf extract has been shown to increase the incidence in liver tumors in mice in a 2-year bioassay conducted by the National Toxicology Program. In this study, the DNA damaging effects of Ginkgo biloba leaf extract and many of its constituents were evaluated in human hepatic HepG2 cells and the underlying mechanism was determined. A molecular docking study revealed that quercetin, a flavonoid constituent of Ginkgo biloba, showed a higher potential to interact with topoisomerase II (Topo II) than did the other Ginkgo biloba constituents; this in silico prediction was confirmed by using a biochemical assay to study Topo II enzyme inhibition. Moreover, as measured by the Comet assay and the induction of γ-H2A.X, quercetin, followed by keampferol and isorhamnetin, appeared to be the most potent DNA damage inducer in HepG2 cells. In Topo II knockdown cells, DNA damage triggered by Ginkgo biloba leaf extract or quercetin was dramatically decreased, indicating that DNA damage is directly associated with Topo II. DNA damage was also observed when cells were treated with commercially available Ginkgo biloba extract product. Our findings suggest that Ginkgo biloba leaf extract- and quercetin-induced in vitro genotoxicity may be the result of Topo II inhibition. [ABSTRACT FROM AUTHOR]

Published
2015
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14. Ginkgo biloba leaf extract induces DNA damage by inhibiting topoisomerase II activity in human hepatic cells.

Author

Zhang, Zhuhong, Chen, Si, Mei, Hu, Xuan, Jiekun, Guo, Xiaoqing, Couch, Letha, Dobrovolsky, Vasily N., Guo, Lei, and Mei, Nan

Subjects
*GINKGO, *LIVER tumors, *LABORATORY mice, *BIOLOGICAL assay, *QUERCETIN
Abstract

Ginkgo biloba leaf extract has been shown to increase the incidence in liver tumors in mice in a 2-year bioassay conducted by the National Toxicology Program. In this study, the DNA damaging effects of Ginkgo biloba leaf extract and many of its constituents were evaluated in human hepatic HepG2 cells and the underlying mechanism was determined. A molecular docking study revealed that quercetin, a flavonoid constituent of Ginkgo biloba, showed a higher potential to interact with topoisomerase II (Topo II) than did the other Ginkgo biloba constituents; this in silico prediction was confirmed by using a biochemical assay to study Topo II enzyme inhibition. Moreover, as measured by the Comet assay and the induction of γ-H2A.X, quercetin, followed by keampferol and isorhamnetin, appeared to be the most potent DNA damage inducer in HepG2 cells. In Topo II knockdown cells, DNA damage triggered by Ginkgo biloba leaf extract or quercetin was dramatically decreased, indicating that DNA damage is directly associated with Topo II. DNA damage was also observed when cells were treated with commercially available Ginkgo biloba extract product. Our findings suggest that Ginkgo biloba leaf extract- and quercetin-induced in vitro genotoxicity may be the result of Topo II inhibition. [ABSTRACT FROM AUTHOR]

Published
2015
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15. Effects of the dopamine D3 receptor (DRD3) gene polymorphisms on risperidone response: a pharmacogenetic study.

Author

Xuan J, Zhao X, He G, Yu L, Wang L, Tang W, Li X, Gu N, Feng G, Xing Q, and He L

Subjects
Adult, Age of Onset, Antipsychotic Agents therapeutic use, DNA genetics, DNA isolation & purification, Humans, Polymerase Chain Reaction, Polymorphism, Genetic, Polymorphism, Single Nucleotide, Receptors, Dopamine D3 genetics, Risperidone therapeutic use, Schizophrenia drug therapy, Schizophrenia genetics
Abstract

Previous observations of the anatomical distribution and pharmacological profile of the dopamine D(3) receptor (DRD3) have indicated its potential role in antipsychotic drug action. Risperidone, an effective first-line atypical antipsychotic agent, exhibits a relatively high affinity for this receptor. Recent studies have reported an association of the Ser9Gly polymorphism in the DRD3 gene with therapeutic response to risperidone, but the results were inconsistent. We therefore postulated that the Ser9Gly polymorphism might be in linkage disequilibrium with an undetected variant that exerts a direct influence on risperidone efficacy. The present study genotyped eight single nucleotide polymorphisms (SNPs) distributed throughout the DRD3 gene and examined five of these for association with treatment outcome, following an 8-week period of risperidone monotherapy in 130 schizophrenic patients from mainland China. Clinical symptoms were assessed before and after the treatment period, using the Brief Psychiatry Rating Scale (BPRS). The confounding effects of non-genetic factors were estimated and the baseline symptom score was included as a covariate for adjustment. Neither was any association observed between the five polymorphisms and improvement in total BPRS scores nor was any combined effect of these variants detected in the haplotype analysis. The current results indicate that genetic variations within the DRD3 gene may not contribute significantly to interindividual differences in the therapeutic efficacy of risperidone.

Published
2008
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