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Evolution of NK receptors: a single Ly49 and multiple KIR genes in the cow

2002· article· en· W2011259604 on OpenAlexaff
Karina L. McQueen, Brian T. Wilhelm, Kristin D. Harden, Dixie L. Mager

Bibliographic record

VenueEuropean Journal of Immunology · 2002
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of British ColumbiaTerry Fox Research InstituteBC Cancer Agency
Fundersnot available
KeywordsBiologyGeneComplementary DNAGeneticsOpen reading frameReceptorGene familySouthern blotGenomePeptide sequence

Abstract

fetched live from OpenAlex

Natural killer (NK) cell receptors for classical MHC class I molecules are encoded by the killer Ig-like receptor (KIR) multigene family in humans and other primates. Mouse NK cells, however, employ a completely different multigene family, the C-type lectin-like Ly49 genes, to perform the same function. This example of functional convergent evolution raises the question of what type of receptors are found in non-primate and non-rodent mammals. By screening a bovine spleen cDNA library, we isolated an Ly49 gene from the cow (Bos Taurus) and show by genomic Southern blotting that it is likely a single copy gene in this species. The coding region is intact and has an immunoreceptor tyrosine-based inhibition motif (ITIM) in the cytoplasmic domain, suggesting a role as an inhibitory receptor. We have also identified several bovine cDNA clones related to KIR and show that at least one has an intact open reading frame with two ITIM. Evidence for multiple KIR-like genes in the cow was obtained by Southern blotting and we found that at least two of these genes contain an ancient retro-element present in all human KIR genes. These results suggest that the cow and primate KIRgene families arose from a common ancestral gene but amplified independently. Furthermore, these findings indicate that the existence of multiple Ly49 genes may be a phenomenon unique to rodents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.197
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations82
Published2002
Admission routes1
Has abstractyes

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