Th1 Polarization of CD4+ T Cells by Toll-Like Receptor 3-Activated Human Microglia
Bibliographic record
Abstract
Toll-like receptors (TLRs) are expressed by human microglia and translate environmental cues into distinct activation programs. We addressed the impact of TLR ligation on the capacity of human microglia to activate and polarize CD4 T cell responses. As microglia exist under distinct states of activation, we examined both ramified and ameboid microglia isolated from adult and fetal CNS, respectively. In vitro, ligation of TLR3 significantly increased major histocompatibility complex and costimulatory molecule expression on adult microglia and induced high levels of interferon-alpha, interleukin-12p40, and interleukin-23. TLR4 and, in particular, TLR2 had a more limited capacity to induce such responses. Coculturing allogeneic CD4 T cells with microglia preactivated with TLR3 did not increase T cell proliferation above basal levels but consistently led to elevated levels of interferon-gamma secretion and Th1 polarization. Fetal microglial TLR3 responses were comparable; in contrast, TLR2 and TLR4 decreased major histocompatibility complex class II expression on fetal cells and reduced CD4 T cell proliferation to levels below those found in untreated cocultures. All 3 TLRs induced comparable interleukin-6 secretion by microglia. Our findings illustrate how activation of human microglia via TLRs, particularly TLR3, can change the profile of local CNS immune responses by translating Th1 polarizing signals to CD4 T 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) | 0.001 | 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".