Different selective pressures shape the evolution of Toll-like receptors in human and African great ape populations
Bibliographic record
Abstract
The study of the genetic and selective landscape of immunity genes across primates can provide insight into the existing differences in susceptibility to infection observed between human and non-human primates. Here, we explored how selection has driven the evolution of a key family of innate immunity receptors, the Toll-like receptors (TLRs), in African great ape species. We sequenced the 10 TLRs in various populations of chimpanzees and gorillas, and analysed these data jointly with a human data set. We found that purifying selection has been more pervasive in great apes than in humans. Furthermore, in chimpanzees and gorillas, purifying selection has targeted TLRs irrespectively of whether they are endosomal or cell surface, in contrast to humans where strong selective constraints are restricted to endosomal TLRs. These observations suggest important differences in the relative importance of TLR-mediated pathogen sensing, such as that of recognition of flagellated bacteria by TLR5, between humans and great apes. Lastly, we used a population genetics-phylogenetics method that jointly analyses polymorphism and divergence data to detect fine-scale variation in selection pressures at specific codons within TLR genes. We identified different codons at different TLRs as being under positive selection in each species, highlighting that functional variation at these genes has conferred a selective advantage in immunity to infection to specific primate species. Overall, this study showed that the degree of selection driving the evolution of TLRs has largely differed between human and non-human primates, increasing our knowledge on their respective biological contribution to host defence in the natural setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".