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Integrating personalized gene expression profiles into predictive disease-associated gene pools.

ABSTRACT: Gene expression data are routinely used to identify genes that on average exhibit different expression levels between a case and a control group. Yet, very few of such differentially expressed genes are detectably perturbed in individual patients. Here, we develop a framework to construct personalized perturbation profiles for individual subjects, identifying the set of genes that are significantly perturbed in each individual. This allows us to characterize the heterogeneity of the molecular manifestations of complex diseases by quantifying the expression-level similarities and differences among patients with the same phenotype. We show that despite the high heterogeneity of the individual perturbation profiles, patients with asthma, Parkinson and Huntington's disease share a broadpool of sporadically disease-associated genes, and that individuals with statistically significant overlap with this pool have a 80-100% chance of being diagnosed with the disease. The developed framework opens up the possibility to apply gene expression data in the context of precision medicine, with important implications for biomarker identification, drug development, diagnosis and treatment.

SUBMITTER: Menche J

PROVIDER: S-EPMC5445628 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Integrating personalized gene expression profiles into predictive disease-associated gene pools.

Menche Jörg J Guney Emre E Sharma Amitabh A Branigan Patrick J PJ Loza Matthew J MJ Baribaud Frédéric F Dobrin Radu R Barabási Albert-László AL

NPJ systems biology and applications 20170313

Gene expression data are routinely used to identify genes that <i>on average</i> exhibit different expression levels between a case and a control group. Yet, very few of such differentially expressed genes are detectably perturbed in individual patients. Here, we develop a framework to construct <i>personalized</i> perturbation profiles for individual subjects, identifying the set of genes that are significantly perturbed in each individual. This allows us to characterize the heterogeneity of th ...[more]

PMID: 28649437

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Project description:Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous disease, and metabolomics plays a hub role in predictive, preventive, and personalized medicine (PPPM) related to COPD. This study thus aimed to reveal the role of induced sputum metabolomics in predicting COPD severity. In this pilot study, a total of 20 COPD patients were included. The induced sputum metabolites were assayed using a liquid chromatography-mass spectrometry (LC-MS/MS) system. Five oxidative stress products (myeloperoxidase (MPO), superoxide dismutase (SOD), glutathione (GSH), neutrophil elastase (NE), and 8-iso-PGF2?) in induced sputum were measured by ELISA, and the metabolomic profiles were distinguished by principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis, and a significant difference in induced sputum metabolomics was observed between moderate and severe COPD. The KEGG analysis revealed that the glycerophospholipid metabolism pathway was downregulated in severe COPD. Due to the critical role of glycerophospholipid metabolism in oxidative stress, significant negative correlations were discovered between glycerophospholipid metabolites and three oxidative stress products (SOD, MPO, and 8-iso-PGF2?). The diagnostic values of SOD, MPO, and 8-iso-PGF2? in induced sputum were found to exhibit high sensitivities and specificities in the prediction of COPD severity. Collectively, this study provides the first identification of the association between induced sputum metabolomic profiles and COPD severity, indicating the potential value of metabolomics in PPPM for COPD management. The study also reveals the correlation between glycerophospholipid metabolites and oxidative stress products and their value for predicting COPD severity. Supplementary Information:The online version contains supplementary material available at 10.1007/s13167-020-00227-w.

Dataset Information

Integrating personalized gene expression profiles into predictive disease-associated gene pools.

Publications

Integrating personalized gene expression profiles into predictive disease-associated gene pools.

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