Phylogenomic analysis of Neisseria gonorrhoeae: a promising tool for tracking putative gonococcal sexual networks
Bibliographic record
Abstract
In The Lancet Infectious Diseases, Katy Town and colleagues 1 Town K Field N Harris SR et al. Phylogenomic analysis of Neisseria gonorrhoeae transmission to assess sexual mixing and HIV transmission risk in England: a cross-sectional, observational, whole-genome sequencing study. Lancet Infect Dis. 2020; (published online Jan 21.)https://doi.org/10.1016/S1473-3099(19)30610-3 Summary Full Text Full Text PDF PubMed Scopus (10) Google Scholar report findings of the first large-scale study in which phylogenomic, whole-genome sequencing (WGS) analysis of Neisseria gonorrhoeae isolates was used to identify molecular networks of transmission. This information was linked with epidemiological data, including gender, sexual orientation, and HIV status. The authors sequenced 1277 gonococcal isolates from five geographically dispersed clinics in England (two in London and one each in Bristol, Birmingham, and Liverpool) that had participated in the British Gonococcal Resistance to Antimicrobials Surveillance Programme (GRASP). These isolates comprised 21% of all isolates collected by GRASP and about 9% of all gonorrhoea diagnoses from the five clinics during the study period (2013–16). Although clinical, demographic, and biological information on partners was unavailable, the relative sizes of the molecular transmission clusters identified by this single nucleotide polymorphism-based WGS analysis resembled empirical data of sexual networks constructed from sex-partner data alone. 2 Trecker MA Dillon JR Lloyd K Hennink M Jolly A Waldner C Can social network analysis help address the high rates of bacterial sexually transmitted infections in Saskatchewan?. Sex Transm Dis. 2017; 44: 338-343 Crossref PubMed Scopus (3) Google Scholar , 3 De P Singh AEE Wong T Yacoub W Jolly AMM Sexual network analysis of a gonorrhoea outbreak. Sex Transm Infect. 2004; 80: 280-285 Crossref PubMed Scopus (107) Google Scholar The study showed that the majority of isolates were linked in dyads (63% of the clusters), triads, or as singletons, with very few large clusters of isolates (the two largest clusters comprised 21 and 11 isolates), consistent with the findings of sex-partner studies. 2 Trecker MA Dillon JR Lloyd K Hennink M Jolly A Waldner C Can social network analysis help address the high rates of bacterial sexually transmitted infections in Saskatchewan?. Sex Transm Dis. 2017; 44: 338-343 Crossref PubMed Scopus (3) Google Scholar , 3 De P Singh AEE Wong T Yacoub W Jolly AMM Sexual network analysis of a gonorrhoea outbreak. Sex Transm Infect. 2004; 80: 280-285 Crossref PubMed Scopus (107) Google Scholar Phylogenomic analysis of Neisseria gonorrhoeae transmission to assess sexual mixing and HIV transmission risk in England: a cross-sectional, observational, whole-genome sequencing studyN gonorrhoeae molecular data can provide information indicating risk of HIV or other sexually transmitted infections for some individuals for whom such risk might not be known from clinical history. These findings have implications for sexual health care, including offering testing, prevention advice, and preventive treatment, such as HIV pre-exposure prophylaxis. Full-Text PDF Open Access
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".