Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach

Liu, Limin; Jiang, Hao; Qiu, Yin Long; Ching, Wai‐Ki; Vassiliadis, Vassilios S.

doi:10.1186/1752-0509-7-s2-s13

“…For instance, constraint‐based modeling‐based analyses have predicted biomarkers of inborn errors in human metabolism, with 77% accuracy in the case of Recon2 . The human reconstruction was also applied to predicting novel biomarkers of type 2 diabetes and Alzheimer's disease . Linking Recon2 with a microbial community could be applied to predicting the effects of the microbes on disease‐associated biomarkers in humans.…”

Section: Future Perspectivesmentioning

confidence: 99%

Systems biology of host–microbe metabolomics

Heinken

¹

,

Thiele

²

2015

WIREs Mechanisms of Disease

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The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301For further resources related to this article, please visit the WIREs website.Conflict of interest: The authors have declared no conflicts of interest for this article.

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“…This is largely due to a lack of understanding about the precise roles of and the interrelationships between markers, and an insufficient grasp of how related markers associate across different biologic levels (eg, genetic, transcription, protein) within and between individuals. Big data using new analytical approaches and standards will assist with addressing this, and new methodologies are being proposed; one example is the development of a statistical approach grounded in flux-based analysis to discover new potential metabolic markers based on their reactions between networks and integrate gene expression with metabolite data 171. Machine learning techniques are already being applied and will assist with models using biomarker data to predict treatment outcomes in studies with big data 172…”

Section: Introductionmentioning

confidence: 99%

Biomarkers for depression: recent insights, current challenges and future prospects

Strawbridge¹,

Young²,

Cleare³

2017

NDT

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A plethora of research has implicated hundreds of putative biomarkers for depression, but has not yet fully elucidated their roles in depressive illness or established what is abnormal in which patients and how biologic information can be used to enhance diagnosis, treatment and prognosis. This lack of progress is partially due to the nature and heterogeneity of depression, in conjunction with methodological heterogeneity within the research literature and the large array of biomarkers with potential, the expression of which often varies according to many factors. We review the available literature, which indicates that markers involved in inflammatory, neurotrophic and metabolic processes, as well as neurotransmitter and neuroendocrine system components, represent highly promising candidates. These may be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomic and neuroimaging assessments. The use of novel approaches and systematic research programs is now required to determine whether, and which, biomarkers can be used to predict response to treatment, stratify patients to specific treatments and develop targets for new interventions. We conclude that there is much promise for reducing the burden of depression through further developing and expanding these research avenues.

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“…Big data using new analytical approaches and standards will assist with addressing this, and new methodologies are being proposed; one example is the development of a statistical approach grounded in flux-based analysis to discover new potential metabolic markers based on their reactions between networks and integrate gene expression with metabolite data. 171 Machine learning techniques are already being applied and will assist with models using biomarker data to predict treatment outcomes in studies with big data. 172 …”

Section: Technologymentioning

confidence: 99%

Biomarkers for Depression: Recent Insights, Current Challenges and Future Prospects

Strawbridge¹,

Young

²

,

Cleare³

2018

FOC

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A plethora of research has implicated hundreds of putative biomarkers for depression, but has not yet fully elucidated their roles in depressive illness or established what is abnormal in which patients and how biologic information can be used to enhance diagnosis, treatment and prognosis. This lack of progress is partially due to the nature and heterogeneity of depression, in conjunction with methodological heterogeneity within the research literature and the large array of biomarkers with potential, the expression of which often varies according to many factors. We review the available literature, which indicates that markers involved in inflammatory, neurotrophic and metabolic processes, as well as neurotransmitter and neuroendocrine system components, represent highly promising candidates. These may be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomic and neuroimaging assessments. The use of novel approaches and systematic research programs is now required to determine whether, and which, biomarkers can be used to predict response to treatment, stratify patients to specific treatments and develop targets for new interventions. We conclude that there is much promise for reducing the burden of depression through further developing and expanding these research avenues.

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Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach

Cited by 11 publications

References 41 publications

Systems biology of host–microbe metabolomics

Systems biology of host–microbe metabolomics

Biomarkers for depression: recent insights, current challenges and future prospects

Biomarkers for Depression: Recent Insights, Current Challenges and Future Prospects

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