Rescaling of spatio-temporal sensing in eukaryotic chemotaxis

TL;DR

This paper proposes that the scale invariance in eukaryotic chemotaxis signaling, explained through a LEGI model, accounts for the system's ability to respond to relative gradient steepness and fold changes in stimuli, unifying static and dynamic responses.

Contribution

It introduces a hypothesis that scale invariance underpins key chemotactic response properties, supported by analysis of a LEGI-based model and its general applicability.

Findings

The model explains how cells detect relative gradient steepness.

The model accounts for transient responses to fold changes in stimuli.

Scale invariance can be implemented in various network topologies.

Abstract

Eukaryotic cells respond to a chemoattractant gradient by forming intracellular gradients of signaling molecules that reflect the extracellular chemical gradient - an ability called directional sensing. Quantitative experiments have revealed two characteristic input-output relations of the system: First, in a static chemoattractant gradient, the shapes of the intracellular gradients of the signaling molecules are determined by the relative steepness, rather than the absolute concentration, of the chemoattractant gradient along the cell body. Second, upon a spatially homogeneous temporal increase in the input stimulus, the intracellular signaling molecules are transiently activated such that the response magnitudes are dependent on fold changes of the stimulus, not on absolute levels. However, the underlying mechanism that endows the system with these response properties remains elusive. Here, by adopting a widely used modeling framework of directional sensing, local excitation and global inhibition (LEGI), we propose a hypothesis that the two rescaling behaviors stem from a single design principle, namely, invariance of the governing equations to a scale transformation of the input level. Analyses of the LEGI-based model reveal that the invariance can be divided into two parts, each of which is responsible for the respective response properties. Our hypothesis leads to an experimentally testable prediction that a system with the invariance detects relative steepness even in dynamic gradient stimuli as well as in static gradients. Furthermore, we show that the relation between the response properties and the scale invariance is general in that it can be implemented by models with different network topologies.