Activation of the Akt-NF-κB Pathway by Subtilase Cytotoxin through the ATF6 Branch of the Unfolded Protein Response
Bibliographic record
Abstract
Shiga toxin has the potential to induce expression of inflammation-associated genes, although the underlying mechanisms are not well understood. We examined the effects of subtilase cytotoxin (SubAB), an AB(5) toxin produced by some Shiga toxigenic Escherichia coli, on the activation of NF-kappaB. SubAB is known to be a protease which selectively degrades GRP78/Bip. Treatment of NRK-52E cells with SubAB caused rapid cleavage of GRP78. Following the degradation of GRP78, transient activation of NF-kappaB was observed with a peak at 6-12 h; the activation subsided within 24 h despite the continuous absence of intact GRP78. The activation of NF-kappaB was preceded by transient phosphorylation of Akt. Treatment of the cells with a selective inhibitor of Akt1/2 or an inhibitor of PI3K attenuated SubAB-induced NF-kappaB activation, suggesting that activation of Akt is an event upstream of NF-kappaB. Degradation of GRP78 caused the unfolded protein response (UPR), and inducers of the UPR mimicked the stimulatory effects of SubAB on Akt and NF-kappaB. SubAB triggered the three major branches of the UPR including the IRE1-XBP1, PERK, and ATF6 pathways. Dominant-negative inhibition of IRE1alpha, XBP1, or PERK did not attenuate activation of NF-kappaB by SubAB. In contrast, genetic and pharmacological inhibition of ATF6 significantly suppressed SubAB-triggered Akt phosphorylation and NF-kappaB activation. These results suggested that loss of GRP78 by SubAB leads to transient phosphorylation of Akt and consequent activation of NF-kappaB through the ATF6 branch of the UPR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".