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Cell Rep. 2017 Dec 5;21(10):2965-2977. doi: 10.1016/j.celrep.2017.07.048.

Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling.

Author information

1
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105, USA; Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
2
Stem Cell Transplantation Program, Division of Pediatric Hematology Oncology, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA; Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences and Institute of Hematology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
3
Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences and Institute of Hematology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
4
Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.
5
Stem Cell Transplantation Program, Division of Pediatric Hematology Oncology, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.
6
Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
7
Stem Cell Transplantation Program, Division of Pediatric Hematology Oncology, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: george.daley@childrens.harvard.edu.
8
Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA. Electronic address: jimjc@mit.edu.

Abstract

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.

KEYWORDS:

cell fate; genome-scale modeling; histone methylation; metabolic network; metabolism; naive (ground) state; pluripotency; primed state; stem cell biology; systems biology

PMID:
29212039
PMCID:
PMC5752146
DOI:
10.1016/j.celrep.2017.07.048
[Indexed for MEDLINE]
Free PMC Article

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