CD16+ γδ T cells mediate antibody dependent cellular cytotoxicity: Potential mechanism in the pathogenesis of multiple sclerosis
Bibliographic record
Abstract
Our overall objective is to understand the role of gammadelta T cells in the pathogenesis of the central nervous system (CNS) autoimmune disease multiple sclerosis (MS). We have demonstrated that gammadelta T cells are directly cytotoxic to CNS cells in vitro. Although the exact mechanism of damage in MS is unknown, recent evidence suggests a role for B cells and antibodies to myelin. We were therefore interested in examining whether gammadelta T cells can injure CNS cells via an indirect mechanism involving antibody dependent cellular cytotoxicity. To study this we developed an in vitro flow cytometric cellular cytotoxicity assay (called "FC(3)A") to quantitate the amount of cytotoxicity. We utilized known target cells (Burkitt's B lymphoma) that express CD20, together with a monoclonal antibody (mAb) to CD20, rituximab, that is being studied as a potential treatment for MS. Target cells are first coated with rituximab followed by co-culture with gammadelta T cells derived from patients with MS. Specific lysis of target cells was determined by quantitation of 7-AAD (which increases only upon nuclear disruption indicating cell death). We determined that this lysis was due to gammadelta T cells that express CD16 (Fc gamma receptor) and were therefore capable of binding the rituximab and mediating cytolysis via ADCC. This specific cell lysis correlated with rituximab concentration, E:T ratio, and the surface expression of CD16 on gammadelta T cells. These findings provide a new perspective with regards to the role of gammadelta T cells in the immunopathogenesis of MS and an insight into one of the potential therapeutic effects of rituximab in the treatment of MS. In addition, this new FC(3)A method we developed could readily be adapted to study other types of immune cells suspected of ADCC-type killing.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".