Identification of Tissue Transglutaminase as a Novel Molecule Involved In Human CD8+ T Cell Transendothelial Migration
Bibliographic record
Abstract
During inflammation, T lymphocytes migrate out of the blood across the vascular endothelium in a multistep process. The receptors mediating T cell adhesion to endothelium are well characterized; however, the molecules involved in T cell transendothelial migration (TEM) subsequent to lymphocyte adhesion to the endothelium are less clear. To identify receptors mediating TEM, mAbs were produced against human blood T cells adhering to IFN-gamma-activated HUVEC in mice and tested for inhibition of lymphocyte TEM across cytokine-activated HUVEC. Most of the mAbs were against beta(1) and beta(2) integrins, but one mAb, 6B9, significantly inhibited T cell TEM across IFN-gamma, TNF-alpha, and IFN-gamma plus TNF-alpha-stimulated HUVEC, and did not react with an integrin. 6B9 mAb did not inhibit T cell adhesion to HUVEC, suggesting that 6B9 blocked a novel pathway in T cell TEM. The 6B9 Ag was 80 kDa on SDS-PAGE, and was expressed by both blood leukocytes and HUVEC. Immunoaffinity purification and mass spectrometry identified this Ag as tissue transglutaminase (tTG), a molecule not known to mediate T cell TEM. Treatment of HUVEC with 6B9 was more effective than treatment of T cells. 6B9 blockade selectively inhibited CD4(-), but not CD4(+), T cell TEM, suggesting a role for tTG in recruitment of CD8(+) T lymphocytes. Thus, 6B9 is a new blocking mAb to human tTG, which demonstrates that tTG may have a novel role in mediating CD8(+) T cell migration across cytokine-activated endothelium and infiltration of tissues during inflammation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".