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Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies.

ABSTRACT: To enhance the therapeutic efficacy and reduce the adverse effects of traditional Chinese medicine, practitioners often prescribe combinations of plant species and/or minerals, called formulae. Unfortunately, the working mechanisms of most of these compounds are difficult to determine and thus remain unknown. In an attempt to address the benefits of formulae based on current biomedical approaches, we analyzed the components of Yinchenhao Tang, a classical formula that has been shown to be clinically effective for treating hepatic injury syndrome. The three principal components of Yinchenhao Tang are Artemisia annua L., Gardenia jasminoids Ellis, and Rheum Palmatum L., whose major active ingredients are 6,7-dimethylesculetin (D), geniposide (G), and rhein (R), respectively. To determine the mechanisms underlying the efficacy of this formula, we conducted a systematic analysis of the therapeutic effects of the DGR compound using immunohistochemistry, biochemistry, metabolomics, and proteomics. Here, we report that the DGR combination exerts a more robust therapeutic effect than any one or two of the three individual compounds by hitting multiple targets in a rat model of hepatic injury. Thus, DGR synergistically causes intensified dynamic changes in metabolic biomarkers, regulates molecular networks through target proteins, has a synergistic/additive effect, and activates both intrinsic and extrinsic pathways.

SUBMITTER: Wang X

PROVIDER: S-EPMC3650334 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies.

Wang Xijun X Zhang Aihua A Wang Ping P Sun Hui H Wu Gelin G Sun Wenjun W Lv Haitao H Jiao Guozheng G Xu Hongying H Yuan Ye Y Liu Lian L Zou Dixin D Wu Zeming Z Han Ying Y Yan Guangli G Dong Wei W Wu Fangfang F Dong Tianwei T Yu Yang Y Zhang Shuxiang S Wu Xiuhong X Tong Xin X Meng Xiangcai X

Molecular & cellular proteomics : MCP 20130129 5

To enhance the therapeutic efficacy and reduce the adverse effects of traditional Chinese medicine, practitioners often prescribe combinations of plant species and/or minerals, called formulae. Unfortunately, the working mechanisms of most of these compounds are difficult to determine and thus remain unknown. In an attempt to address the benefits of formulae based on current biomedical approaches, we analyzed the components of Yinchenhao Tang, a classical formula that has been shown to be clinic ...[more]

PMID: 23362329

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Project description:BackgroundBreast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.ResultsIn this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 "activated" plasma proteins were present in several cancer pathways, including 'Complement and coagulation cascades', 'Regulation of actin cytoskeleton', and 'Focal adhesion', and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.ConclusionsWe presented a 'systems biology' method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

Project description:The urgent need for diagnostics and therapeutics against the COVID-19 pandemic has shown the great potential of antibodies, proteomics and metabolomics in this direction. Several clinical trials are underway using antibodies from COVID-19 patients that show very specific and strong binding to viral proteins leading to neutralization. On the other hand, proteomic and metabolomic profiles of COVID-19 patients present novel diagnostic biomarkers to predict patient outcomes and enable the development of personalized therapeutics to target the dysregulated pathways, as revealed by those profiles. Here, we discuss how studies based on antibodies, proteomics and metabolomics contribute to the development of diagnostics and therapeutics against COVID-19. The elegant technology can extend to high-throughput, rapid and reliable drug discovery strategies of the future. Lay abstract In order to prevent and treat the ongoing COVID-19 pandemic, several groups around the world are focused on a detailed understanding of the biology of SARS-CoV-2 infection, the biological events occurring inside the patients and the response of the patients to the infection. SARS-CoV-2 is the coronavirus that causes COVID-19. Some of the approaches to combat the pandemic have provided important results that can help toward therapeutic developments against COVID-19. This review discusses three such areas – antibody treatment to prevent COVID-19 infection, analysis of changes in protein profiles of COVID-19 patients, and analysis of metabolism or energy-related changes in COVID-19 patients. Antibodies are molecules produced in the host’s body as a defense response to infection. Antibodies extracted from patients who recovered from COVID-19 have been used to successfully manufacture large amounts of antibodies to treat COVID-19 patients. The analysis of protein and metabolism profiles of COVID-19 patients has shown that several proteins and metabolism-related entities in the body are either upregulated or downregulated in COVID-19. These abnormal levels can either be attenuated by medical intervention or can be monitored as indicators of COVID-19 diagnosis. Overall, antibodies, protein and metabolism profiles are three important tools, among several others, that are helpful in combating the COVID-19 pandemic.

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Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies.

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Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies.

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