The Basal Level of Gene Expression Associated with Chromatin Loosening Shapes Waddington Landscapes and Controls Cell Differentiation

Abstract

The baseline level of transcription, which is variable and difficult to quantify, seriously complicates the normalization of comparative transcriptomic data, but its biological importance remains unappreciated. We show that this currently neglected ingredient is essential for controlling gene network multistability and therefore cellular differentiation. Basal expression is correlated to the degree of chromatin loosening measured by DNA accessibility, and systematically leads to cellular dedifferentiation as assessed by transcriptomic signatures, irrespective of the molecular and cellular tools used. Modeling gene network motifs formally involved in developmental bifurcations, reveals that the epigenetic landscapes of Waddington are restructured by the level of non specific expression, such that the attractors of progenitor and differentiated cells can be mutually exclusive. This mechanism is universal and holds beyond the particular nature of the genes involved, provided the multistable circuits are correctly described with autonomous basal expression. These results explain the relationships long established between gene expression noise, chromatin decondensation and cellular dedifferentiation, and highlight how heterochromatin maintenance is essential for preventing pathological cellular reprogramming, age-related diseases and cancer.

Phenotypic plasticity during metastatic colonization

“…Whilst a CSC model in which these cells unidirectionally give rise to both CSCs and non-CSCs holds true in some neoplasias, it appears that non-CSCs can also revert to a CSC state. Indeed, the dynamic gene regulatory networks underpinning both CSCs and non-CSCs states are sustained by feedback loops, whose epigenetic dysregulation can lead to cell state conversion [28][29][30][31][32]. Additional studies have suggested that CSCs may be metastatic and resistant to therapy, which poses major therapeutic challenges [30,33].…”

Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data

“…Theoretically, cell-fate transitions and switch-like behavior has been modeled in terms of gene-regulatory networks (GRNs) and an associated system of ordinary differential equations (ODEs) that describe the dynamic changes in TF concentrations. 4 , 7 , 8 , 9 In most cases however, the full underlying GRNs are unknown, and the associated ODEs typically include many unknown parameters whose precise values may strongly affect the resulting dynamics, rendering this ODE-modeling approach impractical. In the face of these difficulties, single-cell RNA-Seq (scRNA-Seq) data 10 and other single-cell omic data types 11 offer the unprecedented opportunity to study the underlying bifurcation dynamics from a more data-driven perspective, as exemplified by the development of numerous lineage-trajectory inference algorithms 12 , 13 and statistical methods to detect TFs controlling the cell-fate commitment process.…”