Memory Phenotypes of HIV-Specific CD8+ T Cell Responses Are Independent of Functional Activity as Defined by Cytokine Output
Bibliographic record
Abstract
Objectives: The definition of CD8+ T cell attributes that mediate protective immunity in HIV dis-ease progression has not been clearly defined. Although our ability to characterize these cells continues to improve, the extent to which specific memory phenotypic categories of CD8+ T cells reliably represent their functional attributes remains controversial. Methods: We simultaneously assessed surface phenotype and functionality of HIV-specific CD8+ T cells by multiparametric flow cytometry, measuring five CD8+ T cell functions (CD107a, IFNγ, MIP-1β, TNFα and IL2) and phenotypic markers CCR7, CD45RA, and CD27, in parallel in 24 HIV-infected individuals. Results: Virus-specific responses were contained within all eight phenotypic categories defined using CCR7, CD45RA, and CD27. Phenotypic profiles of HIV-specific cells differed from CEF-specific cells, with HIV-specific cells having higher levels of CD45RA (p = 0.008). Interestingly a large portion of CEF and HIV-specific cells were found within previously undefined phenotypes CCR7+CD27－CD45RA+ (14.6% and 17.2%, respectively) and CCR7+CD27－CD45RA－(14.8% and 15.8%, respectively). In addition, up to 10% - 20% of responding cells were phenotypically “naive”. Additionally, memory phenotypes of cells exhibiting monofunctional and polyfunctional responses frequently differed, and failed to associate with a consistent phenotype representing functionally active cells. Conclusion: These data suggest that particularly after antigen stimulation, that surface phenotypes defined by CCR7, CD27 and CD45RA expression on antigen-specific CD8+ T cells, reflect a wide range of immunological functions, and that no single phenotype defined by memory marker expression can reliably be used to identify functional capacity.
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) | 0.001 | 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".