Landscape of epithelial–mesenchymal plasticity as an emergent property of coordinated teams in regulatory networks

Abstract

Elucidating the design principles of regulatory networks driving cellular decision-making has fundamental implications in mapping and eventually controlling cell-fate decisions. Despite being complex, these regulatory networks often only give rise to a few phenotypes. Previously, we identified two 'teams' of nodes in a small cell lung cancer regulatory network that constrained the phenotypic repertoire and aligned strongly with the dominant phenotypes obtained from network simulations (Chauhan et al., 2021). However, it remained elusive whether these 'teams' exist in other networks, and how do they shape the phenotypic landscape. Here, we demonstrate that five different networks of varying sizes governing epithelial-mesenchymal plasticity comprised of two 'teams' of players - one comprised of canonical drivers of epithelial phenotype and the other containing the mesenchymal inducers. These 'teams' are specific to the topology of these regulatory networks and orchestrate a bimodal phenotypic landscape with the epithelial and mesenchymal phenotypes being more frequent and dynamically robust to perturbations, relative to the intermediary/hybrid epithelial/ mesenchymal ones. Our analysis reveals that network topology alone can contain information about corresponding phenotypic distributions, thus obviating the need to simulate them. We propose 'teams' of nodes as a network design principle that can drive cell-fate canalization in diverse decision-making processes.

Epigenetic memory acquired during long-term EMT induction governs the recovery to the epithelial state

“…The relative rates of cell-state transitions define the population distribution of cells along the E-M axis, as evident from dynamics of isolated subpopulations in vitro and in vivo [4,5,11]. These rates of transition, and thus the equilibrium state distribution, are determined by the relative stability of each state [51].…”

Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis

“…To understand the emergence and frequency of hybrid E/M phenotypes, we simulated the dynamics of 13 different GRNs of varying sizes reported to be underlying EMP [11,22,24,30,31,[36][37][38][39]. These networks can be categorized into three classes based on their size (number of nodes and edges): (i) four small-sized networks (Figs 1A i and S1).…”

“…For a given steady state, we calculate the frequency of a phenotype as the fraction of parameter sets (respectively, initial conditions) that converge to the corresponding steady state in RACIPE (respectively, Boolean) simulations. We then characterize these steadystates into hybrid (E/M) or "terminal" (referring cumulatively to epithelial and mesenchymal states) based on an EMT score [39] and define the 'hybridness' of a network as the sum of the frequencies of the hybrid (E/M) steady states (Fig 1C, Methods).…”

“…The tendency of hybrid E/M phenotypes to be more "plastic" in wild-type network topologies can be possibly attributed to them being more "frustrated" than terminal phenotypes [39]. Frustration measures the disagreement of a given steady state with the network topology [43].…”

“…Frustrated phenotypes can be the outcome of gene expression patterns resulting from conflicting signals [43]. Recent computational analysis of large EMP networks showed high frustration for hybrid phenotypes compared to terminal phenotypes [39]. We then estimated network-level frustration (F N ) using the following three metrics: minimum, maximum, and mean frustration, and compared these against the hybridness of the networks.…”