Therapeutic advantage of anti-VAP-1 over anti-α4 integrin antibody in concanavalin a-induced hepatitis
Bibliographic record
Abstract
UNLABELLED: Hepatitis induced by concanavalin A (Con A) in mice is well known to be a T-lymphocyte-mediated injury. It has been reported that T helper (Th)1 and Th2 lymphocytes use α4 integrin and vascular adhesion protein (VAP)-1, respectively, to adhere within the hepatic sinusoids. Therefore, we investigated whether inhibition of these molecules ameliorates or worsens the Con A-induced hepatic injury in vivo. Vehicle or antibody to α4 integrin or VAP-1 was intravenously administered 30 minutes before Con A administration. In control mice Con A markedly increased the serum alanine aminotransferase (ALT) level in a dose-dependent manner, and induced a massive infiltration of CD3, particularly interleukin (IL)-4 producing CD4 T cells and liver injury. Both parameters were reduced by anti-VAP-1 antibody despite antibody only blocking the adhesion, not the amine oxidase activity of VAP-1. Both activities of VAP-1 were eliminated in VAP-1-deficient mice and both Con A-induced liver injury and CD4 T-cell infiltration were eradicated. In contrast to anti-VAP-1, anti-α4 integrin antibody reduced interferon-gamma (IFN-γ)-producing CD3 T cells but this worsened Con A hepatitis, suggesting inhibition of a suppressor cell. Con A induced the recruitment of CD49d(+) monocytic myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs) into the liver. Anti-α4 integrin dramatically blocked the influx of MDSCs but not Tregs. CONCLUSION: Our findings show that VAP-1 and α4 integrin have opposing effects in Con A-induced hepatic injury, which is associated with blocking the recruitment of CD4 lymphocytes and monocytic MDSCs, respectively. Moreover, these data provide the rationale for a potential therapeutic approach to target adhesion molecules in autoimmune hepatitis.
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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".