TAR DNA-binding protein 43 (TDP-43) regulates stress granule dynamics via differential regulation of G3BP and TIA-1
Bibliographic record
Abstract
TAR deoxyribonucleic acid-binding protein 43 (TDP-43) is a multifunctional protein with roles in transcription, pre-messenger ribonucleic acid (mRNA) splicing, mRNA stability and transport. TDP-43 interacts with other heterogeneous nuclear ribonucleoproteins (hnRNPs), including hnRNP A2, via its C-terminus and several hnRNP family members are involved in the cellular stress response. This relationship led us to investigate the role of TDP-43 in cellular stress. Our results demonstrate that TDP-43 and hnRNP A2 are localized to stress granules (SGs), following oxidative stress, heat shock and exposure to thapsigargin. TDP-43 contributes to both the assembly and maintenance of SGs in response to oxidative stress and differentially regulates key SGs components, including TIA-1 and G3BP. The controlled aggregation of TIA-1 is disrupted in the absence of TDP-43 resulting in slowed SG formation. In addition, TDP-43 regulates the levels of G3BP mRNA, a SG nucleating factor. The disease-associated mutation TDP-43(R361S) is a loss-of-function mutation with regards to SG formation and confers alterations in levels of G3BP and TIA-1. In contrast, a second mutation TDP-43(D169G) does not impact this pathway. Thus, mutations in TDP-43 are mechanistically divergent. Finally, the cellular function of TDP-43 extends beyond splicing and places TDP-43 as a participant of the central cellular response to stress and an active player in RNA storage.
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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 | 0.000 | 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".