HIV-1-Specific T Cell Precursors with High Proliferative Capacity Correlate with Low Viremia and High CD4 Counts in Untreated Individuals
Bibliographic record
Abstract
Evidences have recently suggested that the preservation of vaccine-induced memory rather than effector T cells is essential for better outcome and survival following pathogenic SIV challenge in macaques. However, an equivalent demonstration in humans is missing, and the immune correlates of HIV-1 control have been only partially characterized. We focused on the quantification of Ag-specific T cell precursors with high proliferative capacity (PHPC) using a peptide-based cultured IFN-gamma ELISPOT assay (PHPC assay), which has been shown to identify expandable memory T cells. To determine which responses correlate with viral suppression and positive immunologic outcome, PBMC from 32 chronically untreated HIV-1-infected individuals were evaluated in response to peptide pools, representing the complete HIV-1 Gag, Nef, and Rev proteins, by PHPC and IFN-gamma ELISPOT assay, which instead identifies effector T cells with low proliferative capacity. High magnitude of Gag-specific PHPC, but not ELISPOT, responses significantly correlated with low plasma viremia, due to responses directed toward p17 and p15 subunits. Only Gag p17-specific PHPC response significantly correlated with high CD4 counts. Analysis of 20 additional PBMC samples from an independent cohort of chronically untreated HIV-1-infected individuals confirmed the correlation between Gag p17-specific PHPC response and either plasma viremia (inverse correlation) or CD4 counts (direct correlation). Our results indicate that the PHPC assay is quantitatively and qualitatively different from the ELISPOT assay, supporting that different T cell populations are being evaluated. The PHPC assay might be an attractive option for individual patient management and for the design and testing of therapeutic and prophylactic vaccines.
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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 | 0.000 | 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".