Integrating extrinsic and intrinsic cues into a minimal model of lineage commitment for hematopoietic progenitors.
Autoregulation of transcription factors and cross-antagonism between lineage-specific transcription factors are a recurrent theme in cell differentiation. An equally prevalent event that is frequently overlooked in lineage commitment models is the upregulation of lineage-specific receptors, often through lineage-specific transcription factors. Here, we use a minimal model that combines cell-extrinsic and cell-intrinsic elements of regulation in order to understand how both instructive and stochastic events can inform cell commitment decisions in hematopoiesis. Our results suggest that cytokine-mediated positive receptor feedback can induce a "switch-like" response to external stimuli during multilineage differentiation by providing robustness to both bipotent and committed states while protecting progenitors from noise-induced differentiation or decommitment. Our model provides support to both the instructive and stochastic theories of commitment: cell fates are ultimately driven by lineage-specific transcription factors, but cytokine signaling can strongly bias lineage commitment by regulating these inherently noisy cell-fate decisions with complex, pertinent behaviors such as ligand-mediated ultrasensitivity and robust multistability. The simulations further suggest that the kinetics of differentiation to a mature cell state can depend on the starting progenitor state as well as on the route of commitment that is chosen. Lastly, our model shows good agreement with lineage-specific receptor expression kinetics from microarray experiments and provides a computational framework that can integrate both classical and alternative commitment paths in hematopoiesis that have been observed experimentally.
Palani
Santhosh
S
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sarkar
Casim A
CA
D000465:Algorithms; D002454:Cell Differentiation / Q000502:physiology*; D019070:Cell Lineage; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D025461:Feedback, Physiological; D006410:Hematopoiesis / Q000502:physiology*; D006412:Hematopoietic Stem Cells / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D011956:Receptors, Cell Surface / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction; D013269:Stochastic Processes; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
D011956:Receptors, Cell Surface; D014157:Transcription Factors