HIV-1-Specific Memory CD4+ T Cells Are Phenotypically Less Mature Than Cytomegalovirus-Specific Memory CD4+ T Cells
Bibliographic record
Abstract
HIV-1-specific CD4(+) T cells are qualitatively dysfunctional in the majority of HIV-1-infected individuals and are thus unable to effectively control viral replication. The current study extensively details the maturational phenotype of memory CD4(+) T cells directed against HIV-1 and CMV. We find that HIV-1-specific CD4(+) T cells are skewed to an early central memory phenotype, whereas CMV-specific CD4(+) T cells generally display a late effector memory phenotype. These differences hold true for both IFN-gamma- and IL-2-producing virus-specific CD4(+) T cells, are present during all disease stages, and persist even after highly active antiretroviral therapy (HAART). In addition, after HAART, HIV-1-specific CD4(+) T cells are enriched for CD27(+)CD28(-)-expressing cells, a rare phenotype, reflecting an early intermediate stage of differentiation. We found no correlation between differentiation phenotype of HIV-1-specific CD4(+) T cells and HIV-1 plasma viral load or HIV-1 disease progression. Surprisingly, HIV-1 viral load affected the maturational phenotype of CMV-specific CD4(+) T cells toward an earlier, less-differentiated state. In summary, our data indicate that the maturational state of HIV-1-specific CD4(+) T cells cannot be a sole explanation for loss of containment of HIV-1. However, HIV-1 replication can affect the phenotype of CD4(+) T cells of other specificities, which might adversely affect their ability to control those pathogens. The role for HIV-1-specific CD4(+) T cells expressing CD27(+)CD28(-) after HAART remains to be determined.
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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 | 0.001 | 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".