The majority of infiltrating CD8 T lymphocytes in multiple sclerosis lesions is insensitive to enhanced PD‐L1 levels on CNS cells
Bibliographic record
Abstract
Central nervous system (CNS) cells locally modulate immune responses using numerous molecules that are not fully elucidated. Engagement of programmed death-1 (PD-1), expressed on activated T cells, by its ligands (PD-L1 or PD-L2) suppresses T-cell responses. Enhanced CNS PD-1 and PD-L1 expression has been documented in inflammatory murine models; however, human CNS data are still incomplete. We determined that human primary cultures of astrocytes, microglia, oligodendrocytes, or neurons expressed low or undetectable PD-L1 under basal conditions, but inflammatory cytokines significantly induced such expression, especially on astrocytes and microglia. Blocking PD-L1 expression in astrocytes using specific siRNA led to significantly increased CD8 T-cell responses (proliferation, cytokines, lytic enzyme). Thus, our results establish that inflamed human glial cells can express sufficient and functional PD-L1 to inhibit CD8 T cell responses. Extensive immunohistochemical analysis of postmortem brain tissues demonstrated a significantly greater PD-L1 expression in multiple sclerosis (MS) lesions compared with control tissues, which colocalized with astrocyte or microglia/macrophage cell markers. However, more than half of infiltrating CD8 T lymphocytes in MS lesions did not express PD-1, the cognate receptor. Thus, our results demonstrate that inflamed human CNS cells such as in MS lesions express significantly elevated PD-L1, providing a means to reduce CD8 T cell responses, but most of these infiltrating immune cells are devoid of PD-1 and thus insensitive to PD-L1/L2. Strategies aimed at inducing PD-1 on deleterious activated human CD8 T cells that are devoid of this receptor could provide therapeutic benefits since PD-L1 is already increased in the target organ.
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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 | 0.000 |
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".