Journal
JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 17, Issue 169, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsif.2020.0500
Keywords
stem cell; gene regulatory circuit; random circuit perturbation; hierarchical structure; systems biology
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Funding
- National Science Foundation (NSF) Center for Theoretical Biological Physics [NSF PHY-2019745]
- NSF [PHY-1522550, DMS-1361411, CHE-1614101]
- Cancer Prevention and Research Institute of Texas (CPRIT)
- Gulf Coast Consortia, on the Computational Cancer Biology Training Program(CPRIT) [RP170593]
- Jackson Laboratory
- National Cancer Institute of the National Institutes of Health [P30CA034196]
- National Institute of General Medical Sciences of the National Institutes of Health [R35GM128717]
- NSF GRFP [1842494]
- BigData Private-Cloud Research Cyberinfrastructure MRI-award - NSF [CNS-1338099]
- Rice University
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Stem cells can precisely and robustly undergo cellular differentiation and lineage commitment, referred to as stemness. However, how the gene network underlying stemness regulation reliably specifies cell fates is not well understood. To address this question, we applied a recently developed computational method,randomcircuitperturbation (RACIPE), to a nine-component gene regulatory network (GRN) governing stemness, from which we identified robust gene states. Among them, four out of the five most probable gene states exhibit gene expression patterns observed in single mouse embryonic cells at 32-cell and 64-cell stages. These gene states can be robustly predicted by the stemness GRN but not by randomized versions of the stemness GRN. Strikingly, we found a hierarchical structure of the GRN with the Oct4/Cdx2 motif functioning as the first decision-making module followed by Gata6/Nanog. We propose that stem cell populations, instead of being viewed as all having a specific cellular state, can be regarded as a heterogeneous mixture including cells in various states. Upon perturbations by external signals, stem cells lose the capacity to access certain cellular states, thereby becoming differentiated. The new gene states and key parameters regulating transitions among gene states proposed by RACIPE can be used to guide experimental strategies to better understand differentiation and design reprogramming. The findings demonstrate that the functions of the stemness GRN is mainly determined by its well-evolved network topology rather than by detailed kinetic parameters.
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