Proteostasis signatures in human diseases

The protein homeostasis (proteostasis) network maintains the proteome in a functional state. Although this network has been comprehensively mapped, its perturbations in disease remain incompletely characterised. To address this problem, here we define the proteostasis signatures, which represent the characteristic patterns of change in the proteostasis network associated with disease. We performed a large-scale, pan-disease analysis across 32 human diseases spanning 7 disease types. We first identified unique proteostasis perturbations in specific disease states. We then uncovered distinctive signatures differentiating disease types, pointing to a range of proteostasis mechanisms in disease development. Next, we tracked the temporal evolution of proteostasis signatures, revealing shifts in proteostasis disruption over the course of disease progression. Finally, we demonstrated how smoking, a major risk factor for many diseases, impairs proteostasis in a manner similar to disease, potentially creating a predisposed environment for disease onset. These results illustrate the opportunities offered by the study of human diseases from the perspective of proteostasis signatures.

Authors

Institutions

Publication Details

Journal
PLoS Computational Biology
Published
2025-06-17
DOI
https://doi.org/10.1371/journal.pcbi.1013155
Citations
3
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
2.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Proteostasis signatures in human diseases

Michele Vendruscolo, Christine Lim
3 citations
PLoS Computational Biology
Bioinformatics and Genomic Networks
2.42
article

Proteostasis signatures in human diseases

Michele Vendruscolo, Christine Lim
article en
3 citations

Abstract

The protein homeostasis (proteostasis) network maintains the proteome in a functional state. Although this network has been comprehensively mapped, its perturbations in disease remain incompletely characterised. To address this problem, here we define the proteostasis signatures, which represent the characteristic patterns of change in the proteostasis network associated with disease. We performed a large-scale, pan-disease analysis across 32 human diseases spanning 7 disease types. We first identified unique proteostasis perturbations in specific disease states. We then uncovered distinctive signatures differentiating disease types, pointing to a range of proteostasis mechanisms in disease development. Next, we tracked the temporal evolution of proteostasis signatures, revealing shifts in proteostasis disruption over the course of disease progression. Finally, we demonstrated how smoking, a major risk factor for many diseases, impairs proteostasis in a manner similar to disease, potentially creating a predisposed environment for disease onset. These results illustrate the opportunities offered by the study of human diseases from the perspective of proteostasis signatures.

PLoS Computational BiologyVol. 21(6)
University of Cambridge (GB)
UK Research and Innovation
Good health and well-being
Openalex Percentile: Top 11%
Bioinformatics and Genomic Networks
2.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.