Cytotoxic CD4 <sup>+</sup> T cells driven by T-cell intrinsic IL-18R/MyD88 signaling predominantly infiltrate <i>Trypanosoma cruzi</i> -infected hearts
Bibliographic record
Abstract
Abstract Increasing attention has been directed to cytotoxic CD4 + T cells (CD4CTLs) in different pathologies, both in humans and mice. The impact of CD4CTLs in immunity and the mechanisms controlling their generation, however, remain poorly understood. Here, we show that CD4CTLs abundantly differentiate during mouse infection with the intracellular parasite Trypanosoma cruzi . CD4CTLs display parallel kinetics to Th1 cells in the spleen, mediate specific cytotoxicity against cells presenting pathogen-derived antigens and express immunoregulatory and/or exhaustion markers. We demonstrate that CD4CTL absolute numbers and activity are severely reduced in both Myd88 -/- and Il18ra -/- mice. Of note, the infection of mixed-bone marrow chimeras revealed that WT but not Myd88 -/- cells transcribe the CD4CTL gene signature and that Il18ra -/- and Myd88 -/- CD4 + T cells phenocopy each other. Moreover, adoptive transfer of WT CD4 + GzB + T cells to infected Il18ra -/- mice extended their survival. Importantly, cells expressing the CD4CTL phenotype predominate among CD4 + T cells infiltrating the infected mouse cardiac tissue and are increased in the blood of Chagas patients, in which the frequency of CD4CTLs correlates with the severity of cardiomyopathy. Our findings describe CD4CTLs as a major player in immunity to a relevant human pathogen and disclose T-cell intrinsic IL-18R/MyD88 signaling as a key pathway controlling the magnitude of the CD4CTL response.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".