Expression of Interleukin-7 Receptor and Activation Markers on Central Memory CD8 Cells in HIV-Infected Individuals Treated with Antiretrovirals.
Bibliographic record
Abstract
Abstract We have recently shown that high levels of interleukin-7 (IL-7) predict poor virological response in HIV-1-infected patients. To understand the underlying mechanisms associated with increase in IL-7 levels, we assess the expression of IL-7 receptor and activation markers on different CD8 subsets in there well defined groups of patients including successfully treated (ST; n=8), untreated (UT; n=6), failing HAART due to drug resistance (FT; n=12) and healthy subjects (n=5). The expression of IL-7/IL-2 receptors (CD127/CD25) and activation markers (CD38/HLA-DR) on CD8 naïve cells (CD45RA+CCR7+CD27+), central memory (TCM; CD45RA−CCR7+CD27+), pre-terminal effector memory (TPEM; CD45RA−CCR7−CD27−) and terminal effector memory cells (TTEM; CD45RA+CCR7−CD27−) was measured in fresh blood samples using 8-color flow cytometry. Mann Whitney U test and Spearman correlation were used. Although there was a trend toward a decrease in naïve, TCM, TPEM and TTEM cells from ST to UT and then to FT, this did not reach significance (P=0.09). FT and UT subjects showed higher number of CD8 activated cells than ST or controls (22%, 20%, 3% and 4% respectively; P=0.07). Similarly, the percentages of TCM activated cells were significantly higher in FT and UT than ST or controls (28%, 27%, 7% and 0.07%; P=0.02), whereas naïve and TTEM activated cells were similar in all groups. Interestingly, the percentage of TCM cells expressing CD127+CD25+ was lower in FT compared to UT or ST (0.7%, 2.1 and 7.4%; P=0.02). A strong positive correlation was observed between viral load and CD8+ or TCM activated cells (r=0.46, P=0.02; r=0.65, P=0.004 respectively) but not with naïve CD8+ cells. These data suggest that a substantial proportion of TCM but not naïve, TPEM and TTEM cells expressing immune activation markers with lower IL-7 receptor expression is present in subjects with persistent antigen stimulation, thus providing an explanation for increasing circulating IL-7 levels.
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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) | 0.002 | 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".