Transcriptome-based screening in TARDBP/TDP-43 knock-in motor neurons identifies the NEDD8-activating enzyme inhibitor MLN4924
Bibliographic record
Abstract
A growing body of knowledge implicates perturbed RNA homeostasis in amyotrophic lateral sclerosis (ALS), a neurodegenerative disease that currently has no cure and few available treatments. Dysregulation of the multifunctional RNA-binding protein TDP-43 is increasingly regarded as a convergent feature of this disease, evidenced at the neuropathological level by the detection of TDP-43 pathology in most patient tissues, and at the genetic level by the identification of disease-associated mutations in its coding gene TARDBP. To characterize the transcriptional landscape induced by TARDBP mutations, we performed whole-transcriptome profiling of motor neurons (MNs) differentiated from two knock-in iPSC lines expressing the ALS-linked TDP-43 variants p.A382T or p.G348C. Our results show that the TARDBP mutations significantly altered the expression profiles of mRNAs and microRNAs of the 14q32 cluster in MNs. Using mutation-induced gene signatures and the Connectivity Map database, we identified compounds predicted to restore gene expression toward wild-type levels. Among top-scoring compounds selected for further investigation, the NEDD8-activating enzyme inhibitor MLN4924 effectively improved cell viability and neuronal activity, highlighting a possible role for protein post-translational modification via NEDDylation in the pathobiology of TDP-43 in ALS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".