A Novel Mechanism for NF-κB-activation via IκB-aggregation: Implications for Hepatic Mallory-Denk-Body Induced Inflammation
Bibliographic record
Abstract
Mallory-Denk-bodies (MDBs) are hepatic protein aggregates associated with inflammation both clinically and in MDB-inducing models. Similar protein aggregation in neurodegenerative diseases also triggers inflammation and NF-κB activation. However, the precise mechanism that links protein aggregation to NF-κB-activation and inflammatory response remains unclear. Herein we find that treating primary hepatocytes with MDB-inducing agents (N-methylprotoporphyrin (NMPP), protoporphyrin IX (PPIX), or Zinc-protoporphyrin IX (ZnPP)) elicited an IκBα-loss with consequent NF-κB activation. Four known mechanisms of IκBα-loss i.e. the canonical ubiquitin-dependent proteasomal degradation (UPD), autophagic-lysosomal degradation, calpain degradation and translational inhibition, were all probed and excluded. Immunofluorescence analyses of ZnPP-treated cells coupled with 8 M urea/CHAPS-extraction revealed that this IκBα-loss was due to its sequestration along with IκBβ into insoluble aggregates, thereby releasing NF-κB. Through affinity pulldown, proximity biotinylation by antibody recognition, and other proteomic analyses, we verified that NF-κB subunit p65, which stably interacts with IκBα under normal conditions, no longer binds to it upon ZnPP-treatment. Additionally, we identified 10 proteins that interact with IκBα under baseline conditions, aggregate upon ZnPP-treatment, and maintain the interaction with IκBα after ZnPP-treatment, either by cosequestering into insoluble aggregates or through a different mechanism. Of these 10 proteins, the nucleoporins Nup153 and Nup358/RanBP2 were identified through RNA-interference, as mediators of IκBα-nuclear import. The concurrent aggregation of IκBα, NUP153, and RanBP2 upon ZnPP-treatment, synergistically precluded the nuclear entry of IκBα and its consequent binding and termination of NF-κB activation. This novel mechanism may account for the protein aggregate-induced inflammation observed in liver diseases, thus identifying novel targets for therapeutic intervention. Because of inherent commonalities this MDB cell model is a bona fide protoporphyric model, making these findings equally relevant to the liver inflammation associated with clinical protoporphyria.
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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 | 0.001 |
| 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".