Expression Profiles of Ligands for Activating Natural Killer Cell Receptors on HIV Infected and Uninfected CD4+ T Cells
Bibliographic record
Abstract
Natural Killer (NK) cell responses to HIV-infected CD4 T cells (iCD4) depend on the integration of signals received through inhibitory (iNKR) and activating NK receptors (aNKR). iCD4 activate NK cells to inhibit HIV replication. HIV infection-dependent changes in the human leukocyte antigen (HLA) ligands for iNKR on iCD4 are well documented. By contrast, less is known regarding the HIV infection related changes in ligands for aNKR on iCD4. We examined the aNKR ligand profiles HIV p24+ HIV iCD4s that maintained cell surface CD4 (iCD4+), did not maintain CD4 (iCD4−) and uninfected CD4 (unCD4) T cells for expression of unique long (UL)-16 binding proteins-1 (ULBP-1), ULBP-2/5/6, ULBP-3, major histocompatibility complex (MHC) class 1-related (MIC)-A, MIC-B, CD48, CD80, CD86, CD112, CD155, Intercellular adhesion molecule (ICAM)-1, ICAM-2, HLA-E, HLA-F, HLA-A2, HLA-C, and the ligands to NKp30, NKp44, NKp46, and killer immunoglobulin-like receptor 3DS1 (KIR3DS1) by flow cytometry on CD4 T cells from 17 HIV-1 seronegative donors activated and infected with HIV. iCD4+ cells had higher expression of aNKR ligands than did unCD4. However, the expression of aNKR ligands on iCD4 where CD4 was downregulated (iCD4−) was similar to (ULBP-1, ULBP-2/5/6, ULBP-3, MIC-A, CD48, CD80, CD86 and CD155) or significantly lower than (MIC-B, CD112 and ICAM-2) what was observed on unCD4. Thus, HIV infection can be associated with increased expression of aNKR ligands or either baseline or lower than baseline levels of aNKR ligands, concomitantly with the HIV-mediated downregulation of cell surface CD4 on infected cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".