The differential impact of natural killer (NK) cell education via KIR2DL3 and KIR3DL1 on CCL4 secretion in the context of<i>in-vitro</i>HIV infection
Bibliographic record
Abstract
Summary Carriage of certain inhibitory natural killer (NK) cell receptor (iNKR)/HLA ligand pairs is associated with protection from infection and slow time to AIDS implicating NK cells in HIV control. NK cells acquire functional potential through education, which requires the engagement of iNKRs by their human leucocyte antigen (HLA) ligands. HIV infection down-regulates cell surface HLA-A/B, but not HLA-C/E. We investigated how NK cell populations expressing combinations of the iNKRs NKG2A, KIR2DL3 (2DL3) and KIR3DL1 (3DL1) responded to autologous HIV infected CD4 (iCD4) cells. Purified NK cells from HIV-uninfected individuals were stimulated with autologous HIV iCD4 or uninfected CD4 T cells. Using flow cytometry we gated on each of the 8 NKG2A+/–2DL3+/–3DL1+/- populations and analysed all possible combinations of interferon (IFN)-γ, CCL4 and CD107a functional subsets responding to iCD4 cells. Infected CD4 cells induced differential frequencies of NKG2A+/–2DL3+/–3DL1+/– populations with total IFN-γ+, CCL4+ and CD107a+ functional profiles. 2DL3+NKG2A+ NK cells had a higher frequency of responses to iCD4 than other populations studied. A higher frequency of 2DL3+ NK cells responded to iCD4 from individuals that were not HLA-C1 homozygotes. These results show that 2DL3+ NK cells are mediators of HIV-specific responses. Furthermore, responses of NK cell populations to iCD4 are influenced not only by NK cell education through specific KIR/HLA pairs, but also by differential HIV-mediated changes in HLA expression.
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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) | 0.001 | 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".