Immune checkpoint expression on HIV-specific CD4+ T cells and response to their blockade are dependent on lineage and function
Bibliographic record
Abstract
Background Immune checkpoint blockade (ICB) partially reverses the dysfunctional state of antigen-specific T cell in chronic infections. However, its impact on the diverse subsets of CD4+ T cells in humans is largely unknown. Methods We examined immune checkpoint (IC) expression and function in HIV-specific CD4+ T cells of viremic individuals (≥5000 vRNA cp/ml, n = 17) prior to ART and persons with spontaneous ( n = 11) or therapy-induced ( n = 16) viral suppression (<40 cp/ml). We investigated IC patterns associated with exhaustion-related transcription factors and chemokine receptors using activation-induced marker assays. We determined effector functions representative of T FH , T H 1, and T H 17/T H 22 using RNA flow cytometric fluorescence in situ hybridization (FISH). We compared increase in cytokine expression upon ICB across functions and patient status. Findings Expression of dysfunction-related molecules, such as transcription factors and ICs PD-1, TIGIT, and CD200, followed a hierarchy associated with infection status and effector profile. In vitro responsiveness to PD-L1 blockade varied with defined functions rather than IC levels: frequencies of cells with T H 1- and T H 17/T H 22-, but not T FH -related functions, increased. Cells co-expressing T H 1 and T FH functions showed response to ICB, suggesting that the cell's state rather than function dictates responsiveness to PD-L1 blockade. Response to PD-L1 blockade was strongest in viremic participants and reduced after ART initiation. Interpretation Our data highlight a polarization-specific regulation of IC expression and differing sensitivities of antigen-specific T helper subsets to PD-1-mediated inhibition. This heterogeneity may direct and constrain ICB efficacy in restoring CD4+ T cell function in HIV infection and other diseases. Funding NIH, CIHR, CFI, FRQS
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 | 0.000 |
| 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".