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Protein sets define disease states and predict in vivo effects of drug treatment.

ABSTRACT: Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousands of proteins and observed significant changes in protein pathways that were (dys-) regulated in diet-induced obesity mice. Analysis of the expression and post-translational modifications of proteins in various peripheral metabolic target tissues including adipose, heart, and liver tissue generated functional insights in the regulation of cell and tissue homeostasis during high-fat diet feeding and medication with two antidiabetic compounds. Protein set analyses singled out pathways for functional characterization, and indicated, for example, early-on potential cardiovascular complication of the diabetes drug rosiglitazone. In vivo protein set detection can provide new avenues for monitoring complex disease processes, and for evaluating preclinical drug candidates.

SUBMITTER: Meierhofer D

PROVIDER: S-EPMC3708179 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Protein sets define disease states and predict in vivo effects of drug treatment.

Meierhofer David D Weidner Christopher C Hartmann Ludger L Mayr Johannes A JA Han Chung-Ting CT Schroeder Frank C FC Sauer Sascha S

Molecular & cellular proteomics : MCP 20130411 7

Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousands of proteins and observed significant changes in protein pathways that were (dys-) regulated in diet-induced obesity mice. Anal ...[more]

PMID: 23579186

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Project description:Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousands of proteins and observed significant changes in protein pathways that were (dys-) regulated in diet-induced obesity mice. Analysis of the expression and post-translational modifications of proteins in various peripheral metabolic target tissues including adipose, heart, and liver tissue generated functional insights in the regulation of cell and tissue homeostasis during high-fat diet feeding and medication with two antidiabetic compounds. Protein set analyses singled out pathways for functional characterization, and indicated, for example, early-on potential cardiovascular complication of the diabetes drug rosiglitazone. In vivo protein set detection can provide new avenues for monitoring complex disease processes, and for evaluating preclinical drug candidates. Male C57BL/6 mice (age 6 wks) were fed for 12 weeks with high-fat diet (HFD) and than distributed into 3 groups. Mice were than fed over 3 weeks with HFD without compound ('HFD'), HFD with 4 mg/kg/d rosiglitazone ('HFD_RSG') or with 100 mg/kg/d amorfrutin 1 ('HFD_A1'). In parallel [GSE38854; Liver B], mice were fed for 15 weeks with low-fat diet (LFD) as healthy control subjects. In addition, a group of mice was treated with 37 mg/kg/d amorfrutin 1 during the whole 15 weeks of HFD feeding ('HFD+A1prev'). Finally, LIVER tissue was harvested and RNA extracted. --> 3-4 biological replicates (2 mice per each replicate, RNA of 2 mice always pooled). liver_HFD_*A and _*B samples are from the same biological source material, but the cRNA preparation and hybridization to Illumina arrays were performed at different days (i.e. technical repeats).

Project description:Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousands of proteins and observed significant changes in protein pathways that were (dys-) regulated in diet-induced obesity mice. Analysis of the expression and post-translational modifications of proteins in various peripheral metabolic target tissues including adipose, heart, and liver tissue generated functional insights in the regulation of cell and tissue homeostasis during high-fat diet feeding and medication with two antidiabetic compounds. Protein set analyses singled out pathways for functional characterization, and indicated, for example, early-on potential cardiovascular complication of the diabetes drug rosiglitazone. In vivo protein set detection can provide new avenues for monitoring complex disease processes, and for evaluating preclinical drug candidates. Male C57BL/6 mice (age 6 wks) were fed for 12 weeks with high-fat diet (HFD) and than distributed into 3 groups. Mice were than fed over 3 weeks with HFD without compound ('HFD'), HFD with 4 mg/kg/d rosiglitazone ('HFD_RSG') or with 100 mg/kg/d amorfrutin 1 ('HFD_A1') [GSE38853; Liver A]. In parallel, mice were fed for 15 weeks with low-fat diet (LFD) as healthy control subjects. In addition, a group of mice was treated with 37 mg/kg/d amorfrutin 1 during the whole 15 weeks of HFD feeding ('HFD+A1prev'). Finally, LIVER tissue was harvested and RNA extracted. --> 3-4 biological replicates (2 mice per each replicate, RNA of 2 mice always pooled). liver_HFD_*A and _*B samples are from the same biological source material, but the cRNA preparation and hybridization to Illumina arrays were performed at different days (i.e. technical repeats).

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Protein sets define disease states and predict in vivo effects of drug treatment.

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Protein sets define disease states and predict in vivo effects of drug treatment.

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