Activation of p38 and ERK Signaling during Adenovirus Vector Cell Entry Lead to Expression of the C-X-C Chemokine IP-10
Bibliographic record
Abstract
The use of adenovirus vectors for human gene therapy is limited by potent inflammatory responses that result in significant morbidity. In kidney-derived epithelial cells (REC), activation of extracellular signal-regulated kinase 1/2 (ERK) and p38 kinase (p38) pathways occurred within 20 min of transduction with the serotype 5 adenovirus vector AdCMV beta gal. Inhibition of ERK and p38 with U0126 and SB203580, respectively, reduced the expression of IP-10 mRNA following transduction with AdCMV beta gal. To determine the role of the coxsackievirus-adenovirus receptor (CAR) or alpha(v) integrins in the activation of ERK and p38 and the expression of IP-10, REC cells were transduced with the fiber-modified and RGD-deleted adenovirus vectors AdL.F(RAEK-HA) and AdL.PB(HA), respectively. Compared with the wild-type capsid vector Ad5Luc, transduction with AdL.F(RAEK-HA) and AdL.PB(HA) resulted in reduced ERK-p38 activation and less IP-10 mRNA expression. The decreased IP-10 expression induced by the tropism-modified vectors was due to diminished transduction, since increasing multiplicity of infection resulted in increased IP-10 expression. Inhibition of adenovirus penetration with bafilomycin A1 or ammonium chloride attenuated the activation of ERK-p38 and IP-10 mRNA expression following infection, suggesting that endosomal escape was required to trigger these pathways. In vivo, direct inhibition of ERK and p38 signaling pathways inhibited adenovirus vector-induced IP-10 expression in mouse liver 1 h following transduction. These results demonstrate the importance of signaling via ERK and p38 in the early host response to adenovirus vectors and will permit the development of novel strategies to improve the safety and efficacy of these agents in human gene therapy.
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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) | 0.002 | 0.001 |
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".