Abstract
Ras is a plasma membrane (PM)-associated signaling hub protein that interacts with its partners (effectors) in a mutually exclusive fashion. We have shown earlier that competition for binding and hence the occurrence of specific binding events at a hub protein can modulate the activation of downstream pathways. Here, using a mechanistic modeling approach that incorporates high-quality proteomic data of Ras and 56 effectors in 29 (healthy) human tissues, we quantified the amount of individual Ras-effector complexes, and characterized the (stationary) Ras “wiring landscape” specific to each tissue. We identified nine effectors that are in significant amount in complex with Ras in at least one of the 29 tissues. We simulated both mutant- and stimulus-induced network re-configurations, and assessed their divergence from the reference scenario, specifically discussing a case study for two stimuli in three epithelial tissues. These analyses pointed to 32 effectors that are in significant amount in complex with Ras only if they are additionally recruited to the PM, e.g. via membrane-binding domains or domains binding to activated receptors at the PM. Altogether, our data emphasize the importance of tissue context for binding events at the Ras signaling hub.
Highlights
Protein interactions and signaling networks critically coordinate many cellular functions, such as proliferation, differentiation, survival, migration, and apoptosis
We have shown earlier that if a hub protein at a critical network branch point is present at a limiting concentration, the formation of a specific protein complex is determined by the concentrations of the binding partners that, in their turn, shape the way the flow of information is further transmitted along the numerous downstream signaling pathways[8]
We have shown that the subunits that are competing for associating to the same hub, tend to be the ones that undergo a dynamical change during differentiation from one cell type to another[9]
Summary
Protein interactions and signaling networks critically coordinate many cellular functions, such as proliferation, differentiation, survival, migration, and apoptosis. Networks often operate in a cell/tissue-specific manner[2,3,4,5], and the need for quantitative data related to gene and protein expression levels (both cell- and tissue-dependent) has led to several initiatives aiming at a universal standardized database (e.g. the Human Protein Atlas[6] and the Human Cell Atlas[7]). Incorporating these data into mechanistic and quantitative mathematical models, in order to predict cell/tissue- and context-specific (e.g. microenvironmental) signaling responses, is of crucial importance to understand cell behavior in health and disease. We have shown that the subunits that are competing for associating to the same hub, tend to be the ones that undergo a dynamical change during differentiation from one cell type to another[9]
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