HIV exposed seronegative (HESN) compared to HIV infected individuals have higher frequencies of telomeric Killer Immunoglobulin-like Receptor (KIR) B motifs; Contribution of KIR B motif encoded genes to NK cell responsiveness
Bibliographic record
Abstract
Previously, we showed that Killer Immunoglobulin-like Receptor (KIR)3DS1 homozygotes (hmz) are more frequent in HIV exposed seronegative (HESN) than in recently HIV infected (HIV+) individuals. KIR3DS1 encodes an activating Natural Killer (NK) cell receptor (NKR). The link between KIR genotype and HIV outcomes likely arises from the function that NK cells acquire through expression of particular NKRs. An initial screen of 97 HESN and 123 HIV+ subjects for the frequency of KIR region gene carriage observed between-group differences for several telomeric KIR region loci. In a larger set of up to 106 HESN and 439 HIV+ individuals, more HESN than HIV+ subjects were KIR3DS1 homozygotes, lacked a full length KIR2DS4 gene and carried the telomeric group B KIR haplotype motif, TB01. TB01 is characterized by the presence of KIR3DS1, KIR2DL5A, KIR2DS3/5 and KIR2DS1, in linkage disequilibrium with each other. We assessed which of the TB01 encoded KIR gene products contributed to NK cell responsiveness by stimulating NK cells from 8 HIV seronegative KIR3DS1 and TB01 motif homozygotes with 721.221 HLA null cells and evaluating the frequency of KIR3DS1+/-KIR2DL5+/-, KIR3DS1+/-KIR2DS1+/-, KIR3DS1+/-KIR2DS5+/- NK cells secreting IFN-γ and/or expressing CD107a. A higher frequency of NK cells expressing, versus not, KIR3DS1 responded to 721.221 stimulation. KIR2DL5A+, KIR2DS1+ and KIR2DS5+ NK cells did not contribute to 721.221 responses or modulate those by KIR3DS1+ NK cells. Thus, of the TB01 KIR gene products, only KIR3DS1 conferred responsiveness to HLA-null stimulation, demonstrating its ligation can activate ex vivo NK cells.
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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) | 0.005 | 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".