Understanding the molecular basis of differentiation programs is one of the grand unmet challenges facing modern cell-biology. In this study, we integrated computational and experimental network analysis to unravel the response of HL-60 human myeloblastic leukemia cells to Retinoic Acid (RA) stimulation. HL-60, because of its defined but limited ability to differentiate, is an archetype model to study the molecular architecture of human differentiation programs. HL-60 differentiates down two possible lineages depending upon external stimuli such as Retinoic Acid (RA) or Vitamin D3 (D3). Our initial studies have focused on the role of the BLR1 receptor in the transduction of RA signals. BLR1, a Gq-protein coupled receptor expressed following RA exposure, is required for RA-induced cell-cycle arrest and differentiation and leads to atypical persistent MAPK signaling. A dynamic mathematical model of RA induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt), knock-out and knock-in HL-60 cell-lines with and without RA. The current HL-60 model architecture described the dynamics of 729 proteins and protein complexes interconnected by 1356 interactions. An ensemble strategy was used to compensate for uncertain model parameters. Consistent with previous experimental studies, the initial HL-60 model showed up-regulation of BLR1 expression following RA exposure along with sustained MAPK activation. The model also captured the feedback between BLR1 expression and MAPK activation in BLR1 knock-out and Raf-knock-down cell-lines. Lastly, the model predicted the direction of RA-induced expression shifts for a panel of proteins not used in model training. Taken together, our modeling efforts have established a prototype organization for the differentiation program in HL-60. BLR1 acts as a component of a feed-forward control element which drives its own expression and that of other proteins required for differentiation. The initial simulation studies have also led to testable structural linkages between BLR1 expression and MAPK activation that we are exploring experimentally. More broadly, we have demonstrated that modeling of molecular programs can prioritize experimental directions and expand current biological knowledge despite model uncertainty.
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