Pervasive convergent evolution and extreme phenotypes define chaperone requirements of protein homeostasis
Bibliographic record
Abstract
Abstract Maintaining protein homeostasis is an essential requirement for cell and organismal viability. An elaborate regulatory system within cells, the protein homeostasis network, safeguards that proteins are correctly folded and functional. At the heart of this regulatory system lies a class of specialized protein quality control enzymes called chaperones that are tasked with assisting proteins in their folding, avoiding aggregation, and degradation. Failure and decline of protein homeostasis are directly associated with conditions of aging and aging-related neurodegenerative diseases such as Alzheimer’s and Parkinson’s. However, it is not clear what tips the balance of protein homeostasis and leads to onset of aging and diseases. Here, we present a comparative genomics analysis of protein homeostasis in eukaryotes and report general principles of maintaining protein homeostasis across the eukaryotic tree of life. Expanding a previous analysis of 16 eukaryotes to 216 eukaryotic genomes, we find a strong correlation between the size of eukaryotic chaperone networks and size of the genomes that is distinct for different species kingdoms. Importantly, organisms with pronounced phenotypes clearly buck this trend. Northobranchius furzeri , the shortest-lived vertebrate and widely used model for fragile protein homeostasis is found to be chaperone limited. Heterocephalus glaber as the longest-lived rodent thus especially robust organism is characterized by above average numbers of chaperones. Our work thus indicates that the balance in protein homeostasis may be a key variable in explaining organismal robustness. Finally, our work provides an elegant example of harnessing the power of evolution and comparative genomics to address fundamental open questions in biology with direct relevance to human diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".