CD8α is expressed by human monocytes and enhances FcγR-dependent responses
Bibliographic record
Abstract
BACKGROUND: CD8 alpha enhances the responses of antigen-specific CTL activated through TCR through binding MHC class I, favoring lipid raft partitioning of TCR, and inducing intracellular signaling. CD8 alpha is also found on dendritic cells and rat macrophages, but whether CD8 alpha enhances responses of a partner receptor, like TCR, to activate these cells is not known. TCR and FcR, use analogous or occasionally interchangeable signaling mechanisms suggesting the possibility that CD8 alpha co-activates FcR responses. Interestingly, CD8 alpha+ monocytes are often associated with rat models of disease involving immune-complex deposition and FcR-mediated pathology, such as arthritis, glomerulonephritis, ischaemia, and tumors. While rat macrophages have been shown to express CD8 alpha evidence for CD8 alpha expression by mouse or human monocytes or macrophages was incomplete. RESULTS: We detected CD8 alpha, but not CD8 beta on human monocytes and the monocytic cell line THP-1 by flow cytometry. Reactivity of anti-CD8 alpha mAb with monocytes is at least partly independent of FcR as anti-CD8 alpha mAb detect CD8 alpha by western blot and inhibit binding of MHC class I tetramers. CD8 alpha mRNA is also found in monocytes and THP-1 suggesting CD8 alpha is synthesized by monocytes and not acquired from other CD8 alpha+ cell types. Interestingly, CD8 alpha from monocytes and blood T cells presented distinguishable patterns by 2-D electrophoresis. Anti-CD8 alpha mAb alone did not activate monocyte TNF release. In comparison, TNF release by human monocytes stimulated in a FcR-dependent manner with immune-complexes was enhanced by inclusion of anti-CD8 alpha mAb in immune-complexes. CONCLUSION: Human monocytes express CD8 alpha. Co-engagement of CD8 alpha and FcR enhances monocyte TNF release, suggesting FcR may be a novel partner receptor for CD8 alpha on innate immune cells.
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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) | 0.003 | 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".