Genomic and geographical structure of human cytomegalovirus
Bibliographic record
Abstract
Human cytomegalovirus (CMV) has infected humans since the origin of our species and currently infects most of the world's population. Variability between CMV genomes is the highest of any human herpesvirus, yet large portions of the genome are conserved. Here, we show that the genome encodes 74 regions of relatively high variability each with 2 to 8 alleles. We then identified two patterns in the CMV genome. Conserved parts of the genome and a minority (32) of variable regions show geographic population structure with evidence for African or European clustering, although hybrid strains are present. We find no evidence that geographic segregation has been driven by host immune pressure affecting known antigenic sites. Forty-two variable regions show no geographical structure, with similar allele distributions across different continental populations. These "nongeographical" regions are significantly enriched for genes encoding immunomodulatory functions suggesting a core functional importance. We hypothesize that at least two CMV founder populations account for the geographical differences that are largely seen in the conserved portions of the genome, although the timing of separation and direction of spread between the two are not clear. In contrast, the similar allele frequencies among 42 variable regions of the genome, irrespective of geographical origin, are indicative of a second evolutionary process, namely balancing selection that may preserve properties critical to CMV biological function. Given that genetic differences between CMVs are postulated to alter immunogenicity and potentially function, understanding these two evolutionary processes could contribute important information for the development of globally effective vaccines and the identification of novel drug targets.
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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 | 0.000 | 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) | 0.001 | 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".