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Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics

Sridharan, Gautham Vivek and Bruinsma, Bote Gosse and Bale, Shyam Sundhar and Swaminathan, Anandh and Saeidi, Nima and Yarmush, Martin L. and Uygun, Korkut (2017) Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics. Metabolites, 7 (4). Art. No. 58. ISSN 2218-1989. PMCID PMC5746738.

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Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Item Type:Article
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Swaminathan, Anandh0000-0001-9935-6530
Additional Information:© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license( Received: 18 August 2017; Revised: 24 October 2017; Accepted: 8 November 2017; Published: 13 November 2017. Funding from the US National Institutes of Health (Grants R01EB008678, R01DK096075, R01DK084053, F32 DK103500 and R21EB020819), CIMIT Project No. 12-1732. We also acknowledge Shriners Children Hospital and the New England Organ Bank (NEOB) for supporting this work. Author Contributions: G.V.S. conceived of the MMA concept, implanted algorithm, collected and analyzed data, and wrote the manuscript. B.G.B. designed and led all human liver perfusion experiments. S.S. edited the manuscripts, figures, and performed data analysis. A.S. and N.S. contributed to the development of M.M.A. as a metabolomics data analysis tool. M.Y. and K.U. provided guidance on data analysis and helped edit the manuscript. The authors declare no conflict of interest.
Funding AgencyGrant Number
NIHF32 DK103500
Consortia for Improving Medicine with Innovation and Technology (CIMIT)12-1732
Shriners Children HospitalUNSPECIFIED
New England Organ BankUNSPECIFIED
Subject Keywords:metabolic networks; liver; cofactors; modularity
Issue or Number:4
PubMed Central ID:PMC5746738
Record Number:CaltechAUTHORS:20171117-075633850
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Official Citation:Sridharan, G.V.; Bruinsma, B.G.; Bale, S.S.; Swaminathan, A.; Saeidi, N.; Yarmush, M.L.; Uygun, K. Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics. Metabolites 2017, 7, 58.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:83278
Deposited By: Tony Diaz
Deposited On:20 Nov 2017 19:42
Last Modified:15 Apr 2020 00:23

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