bioRxivBiophysicsPreprint
Integrating cell-state and enhancer constraints discriminates models of PU.1 regulation during hematopoietic differentiation
Abstract
Distinct PU.1 levels are associated with progenitor, B-cell, and macrophage states during hematopoietic differentiation. A previous dynamical-systems model attributed these levels to multistability generated by PU.1 autoregulation coupled to feedback between PU.1 abundance and cell-cycle progression, but its predictions were not quantitatively evaluated against the motivating measurements. Here, we test this mechanism using reported measurements of PU.1 abundance, its synthesis rate, and cell-cycle duration, and develop progressively more mechanistic alternatives. The original growth-feedback model neither represents the two reported progenitor groups nor reproduces the nonmonotonic relationship between PU.1 abundance and cell-cycle length. Even with experimentally measured cell-type-specific growth rates, PU.1 autoregulation alone does not reproduce the observed PU.1 abundance and synthesis rates across cell states. Reproducing the cell-state measurements requires an additional cell-state-dependent input to PU.1 production. Guided by enhancer regulation, we model positive feedback between PU.1 and C/EBP and constrain it with reporter measurements, but this architecture cannot jointly reproduce the reporter and cell-state datasets. Adding RUNX1 permits data-consistent stable states when high effective cooperativity in PU.1-dependent C/EBP activation is allowed. Thus, combined dynamical and cis-regulatory constraints support additional regulation of PU.1 production beyond autoregulation, cell-type-specific dilution, and C/EBP feedback. The RUNX1-extended architecture provides one biologically grounded realization and generates experimentally testable hypotheses for differentiation networks.
- organism: mouse
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: benchmarking
- approach: statistical model
- approach: diffusion model
- Citations
- 0
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