Functional expression of HLA-E by human oligodendrocytes (101.40)
Bibliographic record
Abstract
Abstract Immune cells infiltrating the central nervous system (CNS) of Multiple Sclerosis (MS) patients contribute to the destruction of oligodendrocytes. We aim at characterizing mechanisms implicated in such CNS damage. HLA-E, a non-classical MHC class I molecule, is recognized by either the NKG2C or NKG2A receptors expressed on subsets of T cells and NK cells. While NKG2C is an activating receptor, NKG2A is described as an inhibitory receptor. Activated human immune effectors including NK and T cells can kill human oligodendrocytes in vitro, yet whether HLA-E could modulate such cytotoxicity is unresolved. We observed that human adult oligodendrocytes express basal HLA-E levels, which are upregulated upon pro-inflammatory cytokine treatments, mimicking the inflamed CNS observed in MS lesions. We performed in vitro functional assays using activated immune cells bearing either the NKG2A (NK cells) or NKG2C (CD4 T cells) receptor co-cultured with human oligodendrocytes. We observed that addition of blocking antibodies specific for NKG2A on NK cells increased the killing of oligodendrocytes compared to an isotype control. In contrast, we detected a decreased cytotoxicity against oligodendrocytes when we block the activating receptor NKG2C on a subset of CD56-positive CD4 T cells, which we previously identified as carrying cytotoxic properties. Our results support that human oligodendrocytes modulate via the expression of HLA-E, cytotoxic attacks mediated by several immune effectors.
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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) | 0.002 | 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".