Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling
Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a c...
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| Format: | Article |
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Elsevier
2018
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| Online Access: | http://hdl.handle.net/1721.1/113270 https://orcid.org/0000-0002-6897-2135 https://orcid.org/0000-0002-5560-8246 |
| _version_ | 1811080405945679872 |
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| author | Chandrasekaran, Sriram Zhang, Jin Sun, Zhen Zhang, Li Ross, Christian A. Huang, Yu-Chung Asara, John M. Li, Hu Daley, George Q. Collins, James J. |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science Chandrasekaran, Sriram Zhang, Jin Sun, Zhen Zhang, Li Ross, Christian A. Huang, Yu-Chung Asara, John M. Li, Hu Daley, George Q. Collins, James J. |
| author_sort | Chandrasekaran, Sriram |
| collection | MIT |
| description | Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate. Chandrasekaran et al. use computational modeling, metabolomics, and metabolic inhibitors to discover metabolic differences between various pluripotent stem cell states and infer their impact on stem cell fate decisions. Keywords: systems biology; stem cell biology; metabolism; genome-scale modeling; pluripotency; histone methylation; naive (ground) state; primed state; cell fate; metabolic network |
| first_indexed | 2024-09-23T11:31:08Z |
| format | Article |
| id | mit-1721.1/113270 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:31:08Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1132702022-09-27T20:04:12Z Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling Chandrasekaran, Sriram Zhang, Jin Sun, Zhen Zhang, Li Ross, Christian A. Huang, Yu-Chung Asara, John M. Li, Hu Daley, George Q. Collins, James J. Institute for Medical Engineering and Science Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Synthetic Biology Center Chandrasekaran, Sriram Collins, James J. Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate. Chandrasekaran et al. use computational modeling, metabolomics, and metabolic inhibitors to discover metabolic differences between various pluripotent stem cell states and infer their impact on stem cell fate decisions. Keywords: systems biology; stem cell biology; metabolism; genome-scale modeling; pluripotency; histone methylation; naive (ground) state; primed state; cell fate; metabolic network 2018-01-23T15:33:20Z 2018-01-23T15:33:20Z 2017-12 2017-05 2018-01-19T19:07:34Z Article http://purl.org/eprint/type/JournalArticle 2211-1247 http://hdl.handle.net/1721.1/113270 Chandrasekaran, Sriram et al. “Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling.” Cell Reports 21, 10 (December 2017): 2965–2977 © 2017 The Authors https://orcid.org/0000-0002-6897-2135 https://orcid.org/0000-0002-5560-8246 http://dx.doi.org/10.1016/j.celrep.2017.07.048 Cell Reports Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0 application/pdf Elsevier Cell Reports |
| spellingShingle | Chandrasekaran, Sriram Zhang, Jin Sun, Zhen Zhang, Li Ross, Christian A. Huang, Yu-Chung Asara, John M. Li, Hu Daley, George Q. Collins, James J. Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling |
| title | Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling |
| title_full | Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling |
| title_fullStr | Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling |
| title_full_unstemmed | Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling |
| title_short | Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling |
| title_sort | comprehensive mapping of pluripotent stem cell metabolism using dynamic genome scale network modeling |
| url | http://hdl.handle.net/1721.1/113270 https://orcid.org/0000-0002-6897-2135 https://orcid.org/0000-0002-5560-8246 |
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