Inverse association of repressor growth factor independent-1 with CD8 T cell interleukin (IL)-7 receptor α expression and limited signal transducers and activators of transcription signaling in response to IL-7 among γ-chain cytokines in HIV patients
Bibliographic record
Abstract
BACKGROUND: CD8 T lymphocytes from chronically infected HIV-positive patients degenerate into a preapoptotic state and exhibit impaired functionality. Particularly in viremic patients, this was associated with an increased proportion of interleukin-7 receptor-alpha low-expressing (IL-7Ralpha(low)) effector-like CD8 T cells. As cytokine signaling through signal transducers and activators of transcription (STAT) is essential for cellular function, we hypothesized that activation of this pathway may be impaired in these cells. OBJECTIVES: To evaluate cytokine-induced STAT activation in IL-7Ralpha(low) and IL-7Ralpha(high) CD8 T cells from chronically infected HIV-positive patients and investigate the potential molecular mechanism involved in the reduced IL-7Ralpha expression. METHODS: CD8 T cells from HIV-positive patients on and off antiretroviral therapy were assayed respectively for STAT activation, cytokine receptor, and transcription factor expression by flow cytometry and real-time PCR. RESULTS: IL-7 stimulation failed to activate STAT5 in a substantial proportion of patient CD8 T cells. This correlated with reduced IL-7Ralpha mRNA and surface protein expression. Interestingly, IL-7Ralpha(low) cells appeared to be fully capable of recruiting the STAT pathway in response to IL-2, IL-4, IL-10, and IL-15. mRNA expression suggested a potential role for growth factor independent (Gfi)-1 as an IL-7Ralpha transcriptional repressor, but not that of other transcriptional regulators studied, including Gfi-1B and GA-binding protein alpha. Programmed death-1 inhibitory receptor, though upregulated in CD8 T cells from HIV-positive patients, appeared unrelated to IL-7Ralpha expression and STAT signaling capacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".