Cellular dynamics of immune evasion during Leishmania major infection
Bibliographic record
Abstract
Despite the generation of a strong T cell response, clearance of Leishmania major is incomplete and leaves a pool of chronically infected cells. Understanding of the persistence mechanisms is lacking, but Leishmania major driven induction of the immunosuppressive microenvironment through recruitment of regulatory T cells at the site of infection has been proposed to prevent parasite clearance in vivo. In the presented thesis, I used a novel TCR transgenic mouse model, where CD4+ T cells recognize an immunodominant peptide derived from Leishmania- glycosomal phosphoenolpyruvate carboxykinase (PEPCK), as a sensitive tool to characterize the dynamics of anti-L. major CD4+ T cell responses and to characterize mechanisms which restrain their effector function. Intravital microscopy studies characterizing L. major-specific CD4+ T cell migration dynamics within skin lesions directly in live mice show a significant recruitment of adoptively transferred effector T cells to the lesion site in vivo, displaying cellular behaviors consistent with antigen recognition at early and late stages of infection. However, cellular dynamics are augmented at the healed stage, indicating a fundamentally altered environment. I show that Leishmania-specific Tregs display higher suppressive activity compared to polyclonal control Tregs, and that this suppression is mediated through IL-10 and not through disrupting cell-cell contacts or antigen presentation. Challenge of healed mice with L. major antigen results in expansion of endogenous Leishmania-specific Tregs that lead to loss of lesion control in an IL-10 dependent manner. Lack of PEPCK antigen during challenge does not supress effector Th1 response and parasite control. My data proposes a stochastic model of parasite survival, where inflammatory factors that control parasite numbers are counterbalanced by Leishmania-specific immunosuppressive factors that facilitate parasite persistence.
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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".