Local and global features of genetic networks supporting a phenotypic switch

Abstract

Phenotypic switches are associated with alterations in the cell's gene expression profile and are vital to many aspects of biology. Previous studies have identified local motifs of the genetic regulatory network that could underlie such switches. Recent advancements allowed the study of networks at the global, many-gene, level; however, the relationship between the local and global scales in giving rise to phenotypic switches remains elusive. In this work, we studied the epithelial-mesenchymal transition (EMT) using a gene regulatory network model. This model supports two clusters of stable steady-states identified with the epithelial and mesenchymal phenotypes, and a range of intermediate less stable hybrid states, whose importance in cancer has been recently highlighted. Using an array of network perturbations and quantifying the resulting landscape, we investigated how features of the network at different levels give rise to these landscape properties. We found that local connectivity patterns affect the landscape in a mostly incremental manner; in particular, a specific previously identified double-negative feedback motif is not required when embedded in the full network, because the landscape is maintained at a global level. Nevertheless, despite the distributed nature of the switch, it is possible to find combinations of a few local changes that disrupt it. At the level of network architecture, we identified a crucial role for peripheral genes that act as incoming signals to the network in creating clusters of states. Such incoming signals are a signature of modularity and are expected to appear also in other biological networks. Hybrid states between epithelial and mesenchymal arise in the model due to barriers in the interaction between genes, causing hysteresis at all connections. Our results suggest emergent switches can neither be pinpointed to local motifs, nor do they arise as typical properties of random network ensembles. Rather, they arise through an interplay between the nature of local interactions, and the core-periphery structure induced by the modularity of the cell.

Hybrid E/M Phenotype(s) and Stemness: A Mechanistic Connection Embedded in Network Topology

“…Mechanism-based models of individual axes of plasticity are the building blocks required to answer this question from a dynamical systems perspective. There has been increasing interest in dynamical modeling of EMP [ 82 , 83 , 84 , 85 , 86 ]; similar efforts to model related aspects of EMP, such as metabolic reprogramming [ 87 ], autophagy [ 88 , 89 ], therapy resistance [ 90 , 91 ], and immune evasion [ 92 ], are being attempted too. Coupling these modules to decode the interconnections among different axes of plasticity can help unravel the survival strategies of metastasis-initiating cells and eventually contribute to designing new clinical strategies.…”

Classification of triple-negative breast cancers through a Boolean network model of the epithelial-mesenchymal transition

“…Recent work has corroborated our results showing that the fundamental property allowing gene regulatory networks to establish robust cell phenotypes is their state of minimal frustration (Tripathi et al, 2020), which is well captured by Boolean networks like the one we used to described the EMT. Furthermore, the presence of hybrid states is due to a complex interplay between local and global interactions within the network (Shomar et al, 2020). Boolean networks simplify regulatory interactions between genes using discrete variables, but they reproduce faithfully the states obtained with more realistic continuum reaction kinetics models (Hari et al, 2020).…”