<i>Neisseria gonorrhoeae</i>Sequence Typing for Antimicrobial Resistance (NG-STAR) clonal complexes are consistent with genomic phylogeny and provide simple nomenclature, rapid visualization and antimicrobial resistance (AMR) lineage predictions
Bibliographic record
Abstract
OBJECTIVES: Surveillance of antimicrobial resistance (AMR) in Neisseria gonorrhoeae, supported by molecular typing, ideally through genome sequencing, is imperative. We defined N. gonorrhoeae Sequence Typing for Antimicrobial Resistance (NG-STAR) clonal complexes (CCs) and validated their usefulness in gonococcal AMR surveillance. METHODS: All NG-STAR alleles and STs available in the public database (https://ngstar.canada.ca/) were analysed using PHYLOViZ 2.0 to define CCs according to the closest founder ST with ≥5 identical alleles and founding ST with the highest number of links. The published 2013 European gonococcal dataset (n = 1054), the 2016 WHO reference strain panel (n = 14) and N. gonorrhoeae isolates with ceftriaxone resistance determinant penA-60.001 (n = 7) from several countries were used for validation. RESULTS: The majority of the isolates (n = 1063) were designated to 71 CCs. The most common CC was CC90 (n = 194), followed by CC63 (n = 166), CC139 (n = 73), CC158 (n = 73) and CC127 (n = 62). CC90 included isolates belonging to the internationally spread MDR clone N. gonorrhoeae Multi-Antigen Sequence Typing (NG-MAST) G1407 (predominantly MLST ST1901). The ceftriaxone-resistant isolates with penA-60.001 (n = 7) belonged to CC73 or STs linking between CC90 and CC73 (ST233 and ST1133). Phylogenomic analysis revealed that NG-STAR CCs more appropriately correlated to phylogenomic AMR clusters compared with MLST STs, NG-MAST STs, NG-MAST genogroups and NG-STAR STs. CONCLUSIONS: NG-STAR CCs: are consistent with the gonococcal genome phylogeny; allow rapid visualizations with limited computational requirements; provide a simple, reproducible and portable nomenclature (for WGS and conventional Sanger sequencing data); and predict AMR lineages. Phenotypic AMR surveillance, supplemented with WGS, is imperative and NG-STAR CCs can effectively support this.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".