Characterisation of Neisseria meningitidis cc11/ET-15 variant by whole genome sequencing
Bibliographic record
Abstract
BACKGROUND Neisseria meningitidis strains belonging to clonal complex 11 is the cause of numerous outbreaks and epidemics in the United States, Canada and Europe, accounting for 49.5% of cases of meningococcal disease caused by serogroup C worldwide. In Brazil, it is the second most frequent clonal complex within this serogroup. The genetic characterisation of cc11/ET-15 variants is important for the epidemiological monitoring of meningococcal disease, through the identification of circulating epidemic clones, to support specific actions of Health Surveillance aiming outbreaks control. OBJECTIVES The objective of this study was to identify features in the genome of cc11/ET-15 clones through whole-genome sequencing (WGS), that differ from cc11/non-ET-15 strains that could explain their virulence. METHODS The whole genome of three cc11/ET-15 representative strains were sequenced with a minimum coverage of 100X with the MiSeq System and compared to the genome of cc11/non-ET-15 strains. RESULTS Genome analysis of cc11/ET-15 variants showed the presence of resistance factors, mobile genetic elements and virulence factors not found in cc11/non-ET-15 strains. MAIN CONCLUSIONS Our results show that these strains carry virulence factors not identified in cc11/non-ET-15 strains, which could explain the high lethality rates attributed to this clone worldwide.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".