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Updated: Dec 25, 2025

Quantifying Tissue-Specific Proteostatic Decline in Caenorhabditis elegans
Published on: September 7, 2021
Protein level variability determines phenotypic heterogeneity in proteotoxic stress response
Marie Guilbert1, François Anquez1, Alexandra Pruvost1
1UMR 8523, PhLAM - Physique des Lasers Atomes et Molécules, CNRS, Université de Lille, France.
Cellular stress responses show significant variability, hindering predictive disease models. This study reveals that variations in chaperone expression and network ultrasensitivity drive this heterogeneity in the heat shock response.
Area of Science:
- Molecular Biology
- Systems Biology
- Biophysics
Background:
- Cell-to-cell variability in stress response impedes accurate predictive models for diseases like cancer.
- Phenotypic heterogeneity can result in treatment resistance and cell population persistence.
- The heat shock response network is crucial for proteome protection against cellular injury.
Purpose of the Study:
- To investigate the molecular origins of phenotypic variability within the heat shock response network.
- To understand how single-cell dynamics contribute to heterogeneity in cellular stress responses.
Main Methods:
- Utilized high-throughput measurements to capture single-cell dynamics.
- Employed mathematical modeling to predict the sources of variability.
- Experimentally validated theoretical predictions through gene expression analysis.
Main Results:
- Observed broad diversity in single-cell heat shock response dynamics, including response amplitudes and temporal shapes.
- Confirmed that network ultrasensitivity and variations in heat shock factor 1 (HSF1)-controlled chaperone expression cause phenotypic heterogeneity.
- Mapped response amplitude directly to chaperone and HSF1 expression levels.
Conclusions:
- Cellular heterogeneity in heat shock response is significantly influenced by molecular mechanisms like network ultrasensitivity and gene expression variability.
- Understanding these molecular origins is key to developing more accurate predictive models for disease treatment and clinical diagnosis.
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