Myeloid-Derived Suppressor Cells Contribute to Susceptibility to <i>Trypanosoma congolense</i> Infection by Suppressing CD4+ T Cell Proliferation and IFN-γ Production
Bibliographic record
Abstract
Abstract Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of bone marrow–derived myeloid cells that have immune-suppressive activities. These cells have been reported to suppress T cell immunity against tumors as well as in some parasitic and bacterial infections. However, their role during Trypanosoma congolense infection has not been studied. Given that immunosuppression is a hallmark of African trypanosomiasis, we investigated the role of MDSCs in immunity to T. congolense infection. We found increased numbers of MDSCs in the spleen and liver of infected mice, which correlated with increased parasitemia. Depletion of MDSCs significantly increased the percentage of proliferating and IFN-γ–producing CD4+ T cells from the spleen of T. congolense–infected mice. Furthermore, MDSCs from T. congolense–infected mice directly suppressed CD4+ T cell proliferation in a coculture setting. This suppressive effect was abolished by the arginase-1 inhibitor, Nω-hydroxy-nor-l-arginine (nor-NOHA), indicating that MDSCs suppress CD4+ T cell proliferation and function in an arginase-1–dependent manner. Indeed, depletion of MDSCs during infection led to control of the first wave of parasitemia and prolonged survival of infected mice. This was also associated with increased CD4+ T cell proliferation and IFN-γ production. Taken together, our findings identify an important role of MDSCs in the pathogenesis of experimental T. congolense infection via suppression of T cell proliferative and effector cytokine responses in an arginase-1–dependent manner.
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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".