Neurotransmitter signalling via<scp>NMDA</scp>receptors leads to decreased T helper type 1‐like and enhanced T helper type 2‐like immune balance in humans
Bibliographic record
Abstract
Summary Given the pivotal roles thatCD4+T cell imbalance plays in human immune disorders, much interest centres on better understanding influences that regulate human helper T‐cell subset dominancein vivo. Here, using primaryCD4+T cells and short‐term T helper type 1 (Th1) and Th2‐like lines, we investigated roles and mechanisms by which neurotransmitter receptors may influence human type 1 versus type 2 immunity. We hypothesized thatN‐methyl‐d‐aspartate receptors (NMDA‐R), which play key roles in memory and learning, can also regulate humanCD4+T cell function through induction of excitotoxicity. Fresh primaryCD4+T cells from healthy donors express functionalNMDA‐R that are strongly up‐regulated upon T cell receptor (TCR) mediated activation. Synthetic and physiologicalNMDA‐R agonists elicited Ca2+flux and led to marked inhibition of type 1 but not type 2 or interleukin‐10 cytokine responses. AmongCD4+lines,NMDAand quinolinic acid preferentially reduced cytokine production, Ca2+flux, proliferation and survival of Th1‐like cells through increased induction of cell death whereas Th2‐like cells were largely spared. Collectively, the findings demonstrate that (i)NMDA‐R is rapidly up‐regulated uponCD4+T cell activation in humans and (ii) Th1 versus Th2 cell functions such as proliferation, cytokine production and cell survival are differentially affected byNMDA‐R agonists. Differential cytokine production and proliferative capacity of Th1 versus Th2 cells is attributable in part to increased physiological cell death among fully committed Th1 versus Th2 cells, leading to increased Th2‐like dominance. Hence, excitotoxicity, beyond its roles in neuronal plasticity, may contribute to ongoing modulation of human T cell responses.
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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 | 0.000 |
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".