Skip to content
#protein folding Review Open access

Chaperone Networks at the Intersection of the Unfolded Protein Response and Oxidative Stress in Caenorhabditis elegans

Sep 2026 · Stresses · 0 citations · 68 references
Endoplasmic Reticulum Stress and Disease

Abstract

Caenorhabditis elegans has served as a model for the unfolded protein response of the endoplasmic reticulum (UPR-ER) and, separately, the oxidative stress response coordinated by SKN-1/Nrf. The oxidative protein folding in the ER generates reactive oxygen species, which is one reason why these systems are frequently studied together. Additionally, numerous stressors activate both pathways simultaneously. Although these molecular chaperones are critical to both processes, current research rarely considers them as an integrative concept, instead categorizing the interaction strictly within different pathway mechanisms. This review focuses on the chaperone systems themselves, including the HSP-4 and protein disulfide isomerases that are located in the ER, the HSP-70 family that resides in the cytoplasm and is controlled by HSF-1, and HSP-6 and HSP-60 that function in the mitochondria and are regulated by ATFS-1. Oxidative stress interacts with each compartment in a manner that is mechanistically distinct. This corresponds to direct redox chemistry in the ER, transcriptional coupling by SKN-1, and compartment-specific ROS-sensing evidence in the mitochondria and cytoplasm. Oxidative stress and chaperone induction converge through multiple independent pathways rather than a single sequential pathway, and this convergence deteriorates with age in a pathway- and tissue-specific manner, as shown by studies focusing on translational chaperone loss, particulate matter exposure, and proteasome-mediated degradation. Finally, we address some outstanding issues, such as the translational relevance to human diseases associated with ER stress and the functioning of redox-regulated chaperone mechanisms in C. elegans.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

Google DeepMind Blog Nov 25, 2025

AlphaFold: Five years of impact

Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.