Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling
Sriram Chandrasekaran,
Jin Zhang,
Zhen Sun,
Li Zhang,
Christian A. Ross,
Yu-Chung Huang,
John M. Asara,
Hu Li,
George Q. Daley,
James J. Collins
Affiliations
Sriram Chandrasekaran
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
Jin Zhang
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
Zhen Sun
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
Li Zhang
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
Christian A. Ross
Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
Yu-Chung Huang
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
John M. Asara
Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
Hu Li
Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
George Q. Daley
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; Corresponding author
James J. Collins
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; Corresponding author
Summary: 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
