The TLR7/IRF-5 axis sensitizes memory CD4+ T cells to Fas-mediated apoptosis during HIV-1 infection
Bibliographic record
Abstract
HIV-1 infection is characterized by inflammation and a progressive decline in CD4+ T cell count. Despite treatment with antiretroviral therapy (ART), the majority of people living with HIV (PLWH) maintain residual levels of inflammation, a low degree of immune activation, and higher sensitivity to cell death in their memory CD4+ T cell compartment. To date, the mechanisms responsible for this high sensitivity remain elusive. We have identified the transcription factor IRF-5 to be involved in impairing the maintenance of murine CD4+ T cells during chronic infection. Here, we investigate whether IRF-5 also contributes to memory CD4+ T cell loss during HIV-1 infection. We show that TLR7 and IRF-5 were upregulated in memory CD4+ T cells from PLWH, when compared with naturally protected elite controllers and HIVfree participants. TLR7 was upstream of IRF-5, promoting Caspase 8 expression in CD4+ T cells from ART HIV-1+ but not from HIVfree donors. Interestingly, the TLR7/IRF-5 axis acted synergistically with the Fas/FasL pathway, suggesting that TLR7 and IRF-5 expression in ART HIV-1+ memory CD4+ T cells represents an imprint that predisposes cells to Fas-mediated apoptosis. This predisposition could be blocked using IRF-5 inhibitory peptides, suggesting IRF-5 blockade as a possible therapy to prevent memory CD4+ T cell loss in PLWH.
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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".