Gene conversions are frequent but not under positive selection in the Siglec gene families of primates
Bibliographic record
Abstract
Siglecs are cell surface proteins that belong to the immunoglobulin superfamily and which bind sialic acids. They are composed of two groups, the conserved Siglecs and the CD33-related Siglecs. Previous studies have reported the occurrence of gene conversions between human CD33-related Siglecs and suggested that these conversions are adaptive because they increase the diversity of these immunoglobulin-related genes. Here, we analyze the Siglec genes of five primate species and show that gene conversions are not observed between conserved Siglec genes but that they are frequent between primate CD33-related Siglecs. The gene conversions between CD33-related Siglec genes only occur between similar genes and equally frequently in sialic acid binding and nonbinding domains. Furthermore, dN/dS ratio tests show that most of the Ig-like V-type 1 and the Ig-like C2-type 1 domains of Siglec genes evolve either neutrally or under purifying selection and that gene conversions were not responsible for the positively selected regions detected in the Ig-like V-type1 domain of the human SIGLEC7 and SIGLEC9 genes. Our results suggest that the frequent gene conversions between CD33-related Siglec genes are simply a consequence of the high degree of sequence similarity of these genes and that they are not adaptive.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".