Transcriptional Profiling of CD4 T Cells Identifies Distinct Subgroups of HIV-1 Elite Controllers
Bibliographic record
Abstract
Human immunodeficiency virus type 1 (HIV-1) elite controllers maintain undetectable levels of viral replication in the absence of antiretroviral therapy (ART), but their underlying immunological and virological characteristics may vary. Here, we used a whole-genome transcriptional profiling approach to characterize gene expression signatures of CD4 T cells from an unselected cohort of elite controllers. The transcriptional profiles for the majority of elite controllers were similar to those of ART-treated patients but different from those of HIV-1-negative persons. Yet, a smaller proportion of elite controllers showed an alternative gene expression pattern that was indistinguishable from that of HIV-1-negative persons but different from that of highly active antiretroviral therapy (HAART)-treated individuals. Elite controllers with the latter gene expression signature had significantly higher CD4 T cell counts and lower levels of HIV-1-specific CD8(+) T cell responses but did not significantly differ from other elite controllers in terms of HLA class I alleles, HIV-1 viral loads determined by ultrasensitive single-copy PCR assays, or chemokine receptor polymorphisms. Thus, these data identify a specific subgroup of elite controllers whose immunological and gene expression characteristics approximate those of HIV-1-negative persons.
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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 | 0.001 | 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".