CD4 receptor diversity in chimpanzees protects against SIV infection
Bibliographic record
Abstract
Human and simian immunodeficiency viruses (HIV/SIVs) use CD4 as the primary receptor to enter target cells. Here, we show that the chimpanzee CD4 is highly polymorphic, with nine coding variants present in wild populations, and that this diversity interferes with SIV envelope (Env)–CD4 interactions. Testing the replication fitness of SIVcpz strains in CD4 + T cells from captive chimpanzees, we found that certain viruses were unable to infect cells from certain hosts. These differences were recapitulated in CD4 transfection assays, which revealed a strong association between CD4 genotypes and SIVcpz infection phenotypes. The most striking differences were observed for three substitutions (Q25R, Q40R, and P68T), with P68T generating a second N-linked glycosylation site (N66) in addition to an invariant N32 encoded by all chimpanzee CD4 alleles. In silico modeling and site-directed mutagenesis identified charged residues at the CD4–Env interface and clashes between CD4- and Env-encoded glycans as mechanisms of inhibition. CD4 polymorphisms also reduced Env-mediated cell entry of monkey SIVs, which was dependent on at least one D1 domain glycan. CD4 allele frequencies varied among wild chimpanzees, with high diversity in all but the western subspecies, which appeared to have undergone a selective sweep. One allele was associated with lower SIVcpz prevalence rates in the wild. These results indicate that substitutions in the D1 domain of the chimpanzee CD4 can prevent SIV cell entry. Although some SIVcpz strains have adapted to utilize these variants, CD4 diversity is maintained, protecting chimpanzees against infection with SIVcpz and other SIVs to which they are exposed.
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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) | 0.002 | 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".