The p110δ Isoform of Phosphatidylinositol 3-Kinase Controls the Quality of Secondary Anti- <i>Leishmania</i> Immunity by Regulating Expansion and Effector Function of Memory T Cell Subsets
Bibliographic record
Abstract
We showed previously that mice with an inactivating knockin mutation in the p110delta isoform of PI3K (referred to as p110delta(D910A) mice) displayed enhanced primary resistance to Leishmania major despite mounting paradoxically impaired T cell responses. In this study, we show that p110delta(D910A) mice are impaired in their secondary (memory) anti-Leishmania responses in vitro and in vivo. Following secondary L. major challenge, p110delta(D910A) mice exhibited reduced delayed-type hypersensitivity response and weaker parasite control compared to wild-type mice. Using adoptive transfer experiments, we show that immune T cells from healed p110delta(D910A) mice were impaired in their proliferation and effector cytokine (IFN-gamma) responses upon L. major challenge. Interestingly, Leishmania-reactive T cells from healed p110delta(D910A) mice contain severalfold lower numbers of CD62L(lo) and CD62(hi) T cells than those from healed wild-type mice. The reduction in numbers of CD62L(lo) T cells in p110delta(D910A) mice is due to failure of their CD62L(hi) T cells to downregulate CD62L expression in response to L. major. Furthermore, although CD62L(lo) cells from p110delta(D910A) mice could home efficiently to lymphoid organs, their ability to exit these tissues and emigrate to cutaneous sites of infection was greatly impaired. Collectively, our data identify PI3K signaling as important events that control memory T cell subset differentiation, generation, effector function, and recruitment to cutaneous tissues and suggest that manipulating this pathway could provide means of enhancing desired memory T cell subset, response during vaccination, or both.
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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) | 0.001 | 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".