Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics
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 i...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-11-01
|
| Series: | Metabolites |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-1989/7/4/58 |
| _version_ | 1819110083551821824 |
|---|---|
| author | Gautham Vivek Sridharan Bote Gosse Bruinsma Shyam Sundhar Bale Anandh Swaminathan Nima Saeidi Martin L. Yarmush Korkut Uygun |
| author_facet | Gautham Vivek Sridharan Bote Gosse Bruinsma Shyam Sundhar Bale Anandh Swaminathan Nima Saeidi Martin L. Yarmush Korkut Uygun |
| author_sort | Gautham Vivek Sridharan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T03:36:05Z |
| format | Article |
| id | doaj.art-2974eadb832c48b5b4b648371a189e7c |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-12-22T03:36:05Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-2974eadb832c48b5b4b648371a189e7c2022-12-21T18:40:22ZengMDPI AGMetabolites2218-19892017-11-01745810.3390/metabo7040058metabo7040058Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion DynamicsGautham Vivek Sridharan0Bote Gosse Bruinsma1Shyam Sundhar Bale2Anandh Swaminathan3Nima Saeidi4Martin L. Yarmush5Korkut Uygun6Center for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USACenter for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USACenter for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USADepartment of Control and Dynamic Systems, California Institute of Technology, Pasadena, CA 91125, USACenter for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USACenter for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USACenter for Engineering in Medicine, Harvard Medical School, Massachusetts General Hospital & Shriners Hospital for Children, 51 Blossom Street, Boston, MA 02114, USALarge-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.https://www.mdpi.com/2218-1989/7/4/58metabolic networkslivercofactorsmodularity |
| spellingShingle | Gautham Vivek Sridharan Bote Gosse Bruinsma Shyam Sundhar Bale Anandh Swaminathan Nima Saeidi Martin L. Yarmush Korkut Uygun Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics Metabolites metabolic networks liver cofactors modularity |
| title | Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics |
| title_full | Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics |
| title_fullStr | Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics |
| title_full_unstemmed | Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics |
| title_short | Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics |
| title_sort | metabolomic modularity analysis mma to quantify human liver perfusion dynamics |
| topic | metabolic networks liver cofactors modularity |
| url | https://www.mdpi.com/2218-1989/7/4/58 |
| work_keys_str_mv | AT gauthamviveksridharan metabolomicmodularityanalysismmatoquantifyhumanliverperfusiondynamics AT botegossebruinsma metabolomicmodularityanalysismmatoquantifyhumanliverperfusiondynamics AT shyamsundharbale metabolomicmodularityanalysismmatoquantifyhumanliverperfusiondynamics AT anandhswaminathan metabolomicmodularityanalysismmatoquantifyhumanliverperfusiondynamics AT nimasaeidi metabolomicmodularityanalysismmatoquantifyhumanliverperfusiondynamics AT martinlyarmush metabolomicmodularityanalysismmatoquantifyhumanliverperfusiondynamics AT korkutuygun metabolomicmodularityanalysismmatoquantifyhumanliverperfusiondynamics |