Comparison of Phenotypic and Genotypic Approaches to Capsule Typing of Neisseria meningitidis by Use of Invasive and Carriage Isolate Collections
Bibliographic record
Abstract
Neisseria meningitidis serogroup B (MnB) is a leading cause of bacterial meningitis; however, MnB is most commonly associated with asymptomatic carriage in the nasopharyngeal cavity, as opposed to the disease state. Two vaccines are now licensed for the prevention of MnB disease; a possible additional benefit of these vaccines could be to protect against disease indirectly by disrupting nasopharyngeal carriage (e.g., herd protection). To investigate this possibility, accurate diagnostic approaches to characterize MnB carriage isolates are required. In contrast to invasive meningococcal disease (IMD) isolates, which can be readily serogrouped, carriage isolates often lack capsule expression, making standard phenotypic assays unsuitable for strain characterization. Several antibody-based methods were evaluated for their abilities to serogroup isolates and were compared with two genotyping methods (real-time PCR [rt-PCR] and whole-genome sequencing [WGS]) to identify which approach would most accurately ascertain the polysaccharide groups associated with carriage isolates. WGS and rt-PCR were in agreement for 99% of IMD isolates, including those with coding sequences for MnB, MnC, MnW, and MnY, and the phenotypic methods correctly identified serogroups for 69 to 98% of IMD isolates. In contrast, only 47% of carriage isolates were groupable by genotypic methods, due to mutations within the capsule operon; of the isolates identified by genotypic methods, ≤43% were serogroupable with any of the phenotypic methods tested. These observations highlight the difficulties in the serogrouping and capsular genogrouping of meningococcal carriage isolates. Based on our findings, WGS is the most suitable approach for the characterization of meningococcal carriage isolates.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".