A multiplex platform to identify mechanisms and modulators of proteotoxicity in neurodegeneration
Bibliographic record
Abstract
Abstract Neurodegenerative disorders are a family of diseases that remain poorly treated despite their growing global health burden. A shared feature of many neurodegenerative disorders is the accumulation of toxic misfolded proteins. To gain insight into the mechanisms and modulators of protein misfolding, we developed a multiplex reverse genetics platform. Using this novel platform 29 cell-based models expressing proteins that undergo misfolding in neurodegeneration were probed against more than a thousand genetic modifiers. The resulting data provide insight into the nature of modifiers that act on multiple misfolded proteins as compared to those that show activity on only one. To illustrate the utility of this platform, we extensively characterized a potent hit from our screens, the human chaperone DNAJB6. We show that DNAJB6 is a general modifier of the toxicity and solubility of multiple amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD)-linked RNA-binding proteins (RBPs), including FUS, TDP-43, and hnRNPA1. Biophysical examination of DNAJB6 demonstrated that it co-phase separates with, and alters the behavior of FUS containing condensates by locking them into a loose gel-like state which prevents their fibrilization. Domain mapping and a deep mutational scan of DNAJB6 support the critical importance for DNAJB6 phase separation in its effects on multiple RNA-binding proteins. Crucially, these studies also suggest that this property can be further tuned to generate novel variants with enhanced activity that might illuminate potential avenues for clinical translation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".