Late Expression of Granulysin by Microbicidal CD4+ T Cells Requires PI3K- and STAT5-Dependent Expression of IL-2Rβ That Is Defective in HIV-Infected Patients
Bibliographic record
Abstract
Granulysin is a cytolytic effector molecule used by lymphocytes to kill tumor and microbial cells. Regulation of granulysin production is complex. A significant delay (5 days) following stimulation of CD4(+) T cells with IL-2 occurs before granulysin is produced. Unfortunately, the mechanisms responsible for this delay are unknown. We have recently demonstrated that granulysin-mediated killing of Cryptococcus neoformans by CD4(+) T cells is defective during HIV infection. This is because CD4(+) T cells from HIV-infected patients fail to produce granulysin in response to IL-2 activation. The present studies examined the mechanism of delayed production of granulysin and the mechanism of the defect in HIV patients. We demonstrate that IL-2 initially requires both STAT5 and PI3K activation to increase expression of IL-2Rbeta, produce granulysin, and kill C. neoformans. The increased expression of IL-2Rbeta precedes granulysin, and preventing the increased expression of IL-2Rbeta using small interfering RNA knockdown abrogates granulysin expression. Moreover, following the increased expression of IL-2Rbeta, blocking subsequent signaling by IL-2 using IL-2Rbeta-specific blocking Abs abrogates expression of granulysin. Finally, CD4(+) T cells from HIV-infected patients, who are defective in both STAT5 and PI3K signaling, fail to express IL-2Rbeta and fail to produce granulysin. These results suggest that IL-2 signals via PI3K and STAT5 to increase expression of IL-2Rbeta, which in turn is required for production of granulysin. These results provide a mechanism to explain the "late" production of granulysin during normal T cell responses, as well as for defective granulysin production by CD4(+) T cells in HIV-infected patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".