Proximity labeling reveals an extensive steady-state stress granule interactome and insights to neurodegeneration
Bibliographic record
Abstract
Stress granules (SGs) are transient ribonucleoprotein (RNP) aggregates that form in response to proteotoxic stress. Although SGs are distinct from aggregates observed in neurodegenerative disorders, they share protein components. We used APEX-mediated proximity labeling combined with quantitative mass spectrometry and high-throughput imaging to identify >100 previously unknown SG proteins in human cells, about 10% of which localize to SGs in a cell type- or stress type-dependent manner. Supporting a link between SG proteins and neurodegeneration, we demonstrate aberrant SG composition and subcellular distribution in iPSC-derived motor neurons from ALS patients, and identify several known and previously unidentified SG proteins that modify toxicity of mutant FUS and TDP-43 overexpression in Drosophila. We show that even in an unstressed steady-state, SG proteins form a densely-connected protein interaction network (PIN) and propose a model in which existing RNPs coalesce rapidly into microscopically visible granules that can act as gateways to pathological protein aggregation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".