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Functional metabolomics: from biomarker discovery to metabolome reprogramming.

ABSTRACT: Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabolomics here. The striking feature on the similarity of the basic metabolic pathways and components among vastly different species makes the reprogramming metabolomics possible when the engineered metabolites play biological roles in cellular activity as a substrate of enzymes and a regulator to other molecules including proteins. The reprogramming metabolomics approach can be used to clarify metabolic mechanisms of responding to changed internal and external environmental factors and to establish a framework to develop targeted tools for dealing with the changes such as controlling and/or preventing infection with pathogens and enhancing host immunity against pathogens. This review introduces the current state and trends of discovery metabolomics and reprogramming metabolomics and highlights the importance of reprogramming metabolomics.

SUBMITTER: Peng B

PROVIDER: S-EPMC4537470 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Functional metabolomics: from biomarker discovery to metabolome reprogramming.

Peng Bo B Li Hui H Peng Xuan-Xian XX

Protein & cell 20150702 9

Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabolomics here. The striking feature on the similarity of the basic me ...[more]

PMID: 26135925

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Project description:Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start "speaking the same language" in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multi-metabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.

Project description:Bi-allelic mutations in the dedicator of cytokinesis 8 (DOCK8) are responsible for a rare autosomal recessive primary combined immunodeficiency syndrome, characterized by atopic dermatitis, elevated serum Immunoglobulin E (IgE) levels, recurrent severe cutaneous viral infections, autoimmunity, and predisposition to malignancy. The molecular link between DOCK8 deficiency and atopic skin inflammation remains unknown. Severe atopic dermatitis (AD) and DOCK8 deficiency share some clinical symptoms, including eczema, eosinophilia, and increased serum IgE levels. Increased serum IgE levels are characteristic of, but not specific to allergic diseases. Herein, we aimed to study the metabolomic profiles of DOCK8-deficient and AD patients for potential disease-specific biomarkers using chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS). Serum samples were collected from DOCK8-deficient (n = 10) and AD (n = 9) patients. Metabolomics profiling using CIL LC-MS was performed on patient samples and compared to unrelated healthy controls (n = 33). Seven metabolites were positively identified, distinguishing DOCK8-deficient from AD patients. Aspartic acid and 3-hydroxyanthranillic acid (3HAA, a tryptophan degradation pathway intermediate) were up-regulated in DOCK8 deficiency, whereas hypotaurine, leucyl-phenylalanine, glycyl-phenylalanine, and guanosine were down-regulated. Hypotaurine, 3-hydroxyanthranillic acid, and glycyl-phenyalanine were identified as potential biomarkers specific to DOCK8 deficiency. Aspartate availability has been recently implicated as a limiting metabolite for tumour growth and 3HAA; furthermore, other tryptophan metabolism pathway-related molecules have been considered as potential novel targets for cancer therapy. Taken together, perturbations in tryptophan degradation and increased availability of aspartate suggest a link of DOCK8 deficiency to oncogenesis. Additionally, perturbations in taurine and dipeptides metabolism suggest altered antixidation and cell signaling states in DOCK8 deficiency. Further studies examining the mechanisms underlying these observations are necessary.

Dataset Information

Functional metabolomics: from biomarker discovery to metabolome reprogramming.

Publications

Functional metabolomics: from biomarker discovery to metabolome reprogramming.

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