Dynamically adjusted cell fate decisions and resilience to mutant invasion during steady- state hematopoiesis revealed by an experimentally parameterized mathematical model

• 成果类型: Article
• 署名作者: Komarova, Natalia L.; Rignot, Chiara; Fleischman, Angela G.; Wodarz, Dominik
• 署名单位: University of California System; University of California San Diego; University of California System; University of California Irvine; University of California System; University of California Irvine; University of California System; University of California San Diego
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-15122
• DOI: 10.1073/pnas.2321525121
• 发表日期: 2024-09-17
• 关键词: stem-cells; self-renewal; clonal hematopoiesis; differentiation; niche; chemotherapy; COMPETITION; division; IMPACT; RISK

摘要

A major next step in hematopoietic stem cell (HSC) biology is to enhance our quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models have been and continue to be key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. In this paper, we use data from label propagation experiments in mice to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that nonlinear feedback control can drastically change the interpretation of kinetic estimates at homeostasis. This suggests that short- term HSC and multipotent progenitors can dynamically adjust to sustain themselves temporarily in the absence of long- term HSCs, even if they differentiate more often than they self- renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. in stem cell differentiation and evolutionary niche construction dynamics based on a mutant- associated inflammatory environment. This helps us understand the evolution of e.g., TET2 or DNMT3A mutants, and how to potentially reduce mutant burden.