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Zhang, Qing

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Zhang

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Qing

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Zhang, Qing
Author's Publications: search.filters.isAuthorOfPublication.b707bfad-753c-4c53-8a42-17e00ec0a307

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Now showing 1 - 5 of 5
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    The Roles of VHL-Dependent Ubiquitination in Signaling and Cancer
    (Frontiers Research Foundation, 2012) Zhang, Qing; Yang, Haifeng
    The function of tumor suppressor VHL is compromised in the vast majority of clear cell renal cell carcinoma, and its mutations or loss of expression was causal for this disease. pVHL was found to be a substrate recognition subunit of an E3 ubiquitin ligase, and most of the tumor-derived mutations disrupt this function. pVHL was found to bind to the alpha subunits of hypoxia-inducible factor (HIF) and promote their ubiquitination and proteasomal degradation. Proline hydroxylation on key sites of HIFα provides the binding signal for pVHL E3 ligase complex. Beside HIFα, several other VHL targets have been identified, including activated epidermal growth factor receptor (EGFR), RNA polymerase II subunits RPB1 and hsRPB7, atypical protein kinase C (PKC), Sprouty2, β-adrenergic receptor II, and Myb-binding protein p160. HIFα is the most well studied substrate and has been proven to be critical for pVHL’s tumor suppressor function, but the activated EGFR and PKC and other pVHL substrates might also be important for tumor growth and drug response. Their regulations by pVHL and their relevance to signaling and cancer are discussed.
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    Postmenopausal Estrogen and Progestin Effects on the Serum Proteome
    (BioMed Central, 2009) Pitteri, Sharon J; Hanash, Samir M; Aragaki, Aaron; Amon, Lynn M; Busald Buson, Tina; Paczesny, Sophie; Katayama, Hiroyuki; Johnson, Melissa M; McIntosh, Martin; Wang, Pei; Kooperberg, Charles; Rossouw, Jacques E; Jackson, Rebecca D; Hsia, Judith; Liu, Simin; Martin, Lisa; Prentice, Ross L; Manson, JoAnn; Zhang, Qing; Chen, Lin; Wang, Hong
    Background: Women's Health Initiative randomized trials of postmenopausal hormone therapy reported intervention effects on several clinical outcomes, with some important differences between estrogen alone and estrogen plus progestin. The biologic mechanisms underlying these effects, and these differences, have yet to be fully elucidated. Methods: Baseline serum samples were compared with samples drawn 1 year later for 50 women assigned to active hormone therapy in both the estrogen-plus-progestin and estrogen-alone randomized trials, by applying an in-depth proteomic discovery platform to serum pools from 10 women per pool. Results: In total, 378 proteins were quantified in two or more of the 10 pooled serum comparisons, by using strict identification criteria. Of these, 169 (44.7%) showed evidence (nominal P less than 0.05) of change in concentration between baseline and 1 year for one or both of estrogen-plus-progestin and estrogen-alone groups. Quantitative changes were highly correlated between the two hormone-therapy preparations. A total of 98 proteins had false discovery rates less than 0.05 for change with estrogen plus progestin, compared with 94 for estrogen alone. Of these, 84 had false discovery rates less than 0.05 for both preparations. The observed changes included multiple proteins relevant to coagulation, inflammation, immune response, metabolism, cell adhesion, growth factors, and osteogenesis. Evidence of differential changes also was noted between the hormone preparations, with the strongest evidence in growth factor and inflammation pathways. Conclusions: Serum proteomic analyses yielded a large number of proteins similarly affected by estrogen plus progestin and by estrogen alone and identified some proteins and pathways that appear to be differentially affected between the two hormone preparations; this may explain their distinct clinical effects.
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    A Mouse to Human Search for Plasma Proteome Changes Associated with Pancreatic Tumor Development
    (Public Library of Science, 2008) Faca, Vitor M; Song, Kenneth S; Krasnoselsky, Alexei L; Newcomb, Lisa F; Plentz, Ruben R; Redston, Mark S; Pitteri, Sharon J; Pereira-Faca, Sandra R; Ireton, Renee C; Katayama, Hiroyuki; Glukhova, Veronika; Phanstiel, Douglas; Brenner, Dean E; Anderson, Michelle A; Misek, David; Scholler, Nathalie; Urban, Nicole D; Barnett, Matt J; Edelstein, Cim; Goodman, Gary E; Thornquist, Mark D; McIntosh, Martin W; Bardeesy, Nabeel; Hanash, Samir M; Wang, Hong; Zhang, Qing; Gurumurthy, Sushma; DePinho, Ronald A.
    Background: The complexity and heterogeneity of the human plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. Refined genetically engineered mouse (GEM) models of human cancer have been shown to faithfully recapitulate the molecular, biological, and clinical features of human disease. Here, we sought to exploit the merits of a well-characterized GEM model of pancreatic cancer to determine whether proteomics technologies allow identification of protein changes associated with tumor development and whether such changes are relevant to human pancreatic cancer. Methods and Findings: Plasma was sampled from mice at early and advanced stages of tumor development and from matched controls. Using a proteomic approach based on extensive protein fractionation, we confidently identified 1,442 proteins that were distributed across seven orders of magnitude of abundance in plasma. Analysis of proteins chosen on the basis of increased levels in plasma from tumor-bearing mice and corroborating protein or RNA expression in tissue documented concordance in the blood from 30 newly diagnosed patients with pancreatic cancer relative to 30 control specimens. A panel of five proteins selected on the basis of their increased level at an early stage of tumor development in the mouse was tested in a blinded study in 26 humans from the CARET (Carotene and Retinol Efficacy Trial) cohort. The panel discriminated pancreatic cancer cases from matched controls in blood specimens obtained between 7 and 13 mo prior to the development of symptoms and clinical diagnosis of pancreatic cancer. Conclusions: Our findings indicate that GEM models of cancer, in combination with in-depth proteomic analysis, provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection.
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    A Mouse Plasma Peptide Atlas as a Resource for Disease Proteomics
    (BioMed Central, 2008) Menon, Rajasree; Deutsch, Eric W; Pitteri, Sharon J; Faca, Vitor M; Newcomb, Lisa F; Bardeesy, Nabeel; Hung, Kenneth E; Jacks, Tyler; Politi, Katerina; Aebersold, Ruedi; Omenn, Gilbert S; States, David J; Hanash, Samir M; Zhang, Qing; Wang, Hong; DePinho, Ronald A.; Dinulescu, Daniela; Kucherlapati, Raju
    We present an in-depth analysis of mouse plasma leading to the development of a publicly available repository composed of 568 liquid chromatography-tandem mass spectrometry runs. A total of 13,779 distinct peptides have been identified with high confidence. The corresponding approximately 3,000 proteins are estimated to span a 7 logarithmic range of abundance in plasma. A major finding from this study is the identification of novel isoforms and transcript variants not previously predicted from genome analysis.
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    Integrated Proteomic Analysis of Human Cancer Cells and Plasma from Tumor Bearing Mice for Ovarian Cancer Biomarker Discovery
    (Public Library of Science, 2009) Pitteri, Sharon J.; JeBailey, Lellean; Faça, Vitor M.; Thorpe, Jason D.; Silva, Melissa A.; Ireton, Reneé C.; Horton, Marc B.; Pruitt, Liese C.; Urban, Nicole; Hanash, Samir M.; Nordheim, Alfred; Wang, Hong; Zhang, Qing; Cheng, Yu-Kang; Dinulescu, Daniela
    Background: The complexity of the human plasma proteome represents a substantial challenge for biomarker discovery. Proteomic analysis of genetically engineered mouse models of cancer and isolated cancer cells and cell lines provide alternative methods for identification of potential cancer markers that would be detectable in human blood using sensitive assays. The goal of this work is to evaluate the utility of an integrative strategy using these two approaches for biomarker discovery. Methodology/Principal Findings: We investigated a strategy that combined quantitative plasma proteomics of an ovarian cancer mouse model with analysis of proteins secreted or shed by human ovarian cancer cells. Of 106 plasma proteins identified with increased levels in tumor bearing mice, 58 were also secreted or shed from ovarian cancer cells. The remainder consisted primarily of host-response proteins. Of 25 proteins identified in the study that were assayed, 8 mostly secreted proteins common to mouse plasma and human cancer cells were significantly upregulated in a set of plasmas from ovarian cancer patients. Five of the eight proteins were confirmed to be upregulated in a second independent set of ovarian cancer plasmas, including in early stage disease. Conclusions/Significance: Integrated proteomic analysis of cancer mouse models and human cancer cell populations provides an effective approach to identify potential circulating protein biomarkers.