A Higher Frequency of NKG2A <sup>+</sup> than of NKG2A <sup>−</sup> NK Cells Responds to Autologous HIV-Infected CD4 Cells irrespective of Whether or Not They Coexpress KIR3DL1
Bibliographic record
Abstract
UNLABELLED: Epidemiological and functional studies implicate NK cells in HIV control. However, there is little information available on which NK cell populations, as defined by the inhibitory NK cell receptors (iNKRs) they express, respond to autologous HIV-infected CD4(+) (iCD4) T cells. NK cells acquire antiviral functions through education, which requires signals received from iNKRs, such as NKG2A and KIR3DL1 (here, 3DL1), engaging their ligands. NKG2A interacts with HLA-E, and 3DL1 interacts with HLA-A/B antigens expressing the Bw4 epitope. HIV-infected cells downregulate HLA-A/B, which should interrupt negative signaling through 3DL1, leading to NK cell activation, provided there is sufficient engagement of activating NKRs. We examined the functionality of NK cells expressing or not NKG2A and 3DL1 stimulated by HLA-null and autologous iCD4 cells. Flow cytometry was used to gate on each NKG2A(+)/NKG2A(-) 3DL1(+)/3DL1(-) (NKG2A(+/-) 3DL1(+/-)) population and to measure the frequency of all possible combinations of CD107a expression and gamma interferon (IFN-γ) and CCL4 secretion. The highest frequency of functional NK cells responding to HLA-null cell stimulation was the NKG2A(+) 3DL1(+) NK cell population. The highest frequencies of functional NK cells responding to autologous iCD4 cells were those expressing NKG2A; coexpression of 3DL1 did not further modulate responsiveness. This was the case for the functional subsets characterized by the sum of all functions tested (total responsiveness), as well as by the trifunctional CD107a(+) IFN-γ(+) CCL4(+), CD107a(+) IFN-γ(+), total CD107a(+), and total IFN-γ(+) functional subsets. These results indicate that the NKG2A receptor has a role in NK cell-mediated anti-HIV responses. IMPORTANCE: HIV-infected CD4 (iCD4) cells activate NK cells, which then control HIV replication. However, little is known regarding which NK cell populations iCD4 cells stimulate to develop antiviral activity. Here, we examine the frequency of NK cell populations, defined by the presence/absence of the NK cell receptors (NKRs) NKG2A and 3DL1, that respond to iCD4 cells. NKG2A and 3DL1 are involved in priming NK cells for antiviral functions upon encountering virus-infected cells. A higher frequency of NKG2A(+) than NKG2A(-) NK cells responded to iCD4 cells by developing antiviral functions such as CD107a expression, which correlates with NK cell killing, and secretion of gamma interferon and CCL4. Coexpression of 3DL1 on the NKG2A(+) and NKG2A(-) NK cells did not modulate responses to iCD4 cells. Understanding the mechanisms underlying the interaction of NK cells with iCD4 cells that lead to HIV control may contribute to developing strategies that harness NK cells for preventing or controlling HIV infection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".