Mapping and characterizing ALS-linked TDP-43 protein-protein interactions
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a moto-neuron disorder in which an RNA-binding protein, TDP-43, mislocalizes and pathologically accumulates from its normal nuclear locale to the cytosol. Given that the subcellular localization and expression of TDP-43 is tightly regulated and affected by its protein-protein interactions (PPIs), we posit that identifying novel interactors of wild-type and mutant TDP-43 could reveal insight into networks involved in driving ALS pathogenesis. Using CRISPR/Cas9, our lab has generated knockin cell lines expressing GFP-tagged wildtype (WT) and an ALS-causing mutant (Q331K) TDP-43, in the endogenous TARDBP locus (coding for TDP-43). We have shown that the Q331K mutation causes loss-of-function and mislocalization of TDP-43. We have performed immunoprecipitation-mass spectrometry (IP-MS) on these cell lines to elucidate interactors of WT- and Q331K, TDP-43. Our data has shown that there is an overall loss of interactors with the Q331K mutation. We have analyzed these data using bioinformatic approaches to shortlist and validate 14 candidates. From this, 4 interactors have shown robust interaction with TDP-43 via IP-western blot. We are using cellular and biochemical assays to assess the effects of knockdown and overexpression of these top 4 hits on TDP-43 localization and loss-of-function. Using this unbiased approach, we will identify TDP-43 PPIs and characterize their roles in cellular functions in the context of ALS, giving insight into pathways involved in driving neurodegeneration.
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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 | 0.000 |
| 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".