TDP-43 Inhibits NF-κB Activity by Blocking p65 Nuclear Translocation
Bibliographic record
Abstract
TDP-43 (TAR DNA binding protein 43) is a heterogeneous nuclear ribonucleoprotein (hnRNP) that has been found to play an important role in neurodegenerative diseases. TDP-43's involvement in nuclear factor-kappaB pathways has been reported in both neurons and microglial cells. The NF-κB pathway targets hundreds of genes, many of which are involved in inflammation, immunity and cancer. p50/p65 (p50/RelA) heterodimers, as the major Rel complex in the NF-κB family, are induced by diverse external physiological stimuli and modulate transcriptional activity in almost all cell types. Both p65 and TDP-43 translocation occur through the classic nuclear transportation system. In this study, we report that TDP-43 overexpression prevents TNF-α induced p65 nuclear translocation in a dose dependent manner, and that this further inhibits p65 transactivation activity. The inhibition by TDP-43 does not occur through preventing IκB degradation but probably by competing for the nuclear transporter-importin α3 (KPNA4). This competition is dependent on the presence of the nuclear localization signal (NLS) in TDP-43. Silencing TDP-43 using a specific siRNA also increased p65 nuclear localization upon TNF-α stimulation, suggesting that endogenous TDP-43 may be a default suppressor of the NF-κB pathway. Our results indicate that TDP-43 may play an important role in regulating the levels of NF-κB activity by controlling the nuclear translocation of p65.
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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 | 0.001 |
| 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".