Genomic determinants of antibiotic resistance for Helicobacter pylori treatment: a retrospective phenotypic and genotypic observational study
Bibliographic record
Abstract
BACKGROUND: Rising antimicrobial resistance of Helicobacter pylori is a public health challenge. Genomic-based susceptibility testing allows for the identification of resistance-associated mutations, complementing conventional diagnostics and advancing towards pathogen-based personalised therapies. Our study aimed to identify genes and mutations involved in antimicrobial resistance in H pylori and evaluate the extent to which these markers can be used as predictors of phenotypic resistance against clarithromycin and levofloxacin. METHODS: In this retrospective phenotypic and genotypic observational study, we included 1011 H pylori whole-genome sequences and strains of known geographical origin from the H pylori Genome Project (HpGP) collection. We performed phenotypic clarithromycin and levofloxacin susceptibility testing on a subset of 419 HpGP strains using Etest at a centralised laboratory. A genomic analysis was conducted to identify 23S rRNA and gyrA variants and build a curated catalogue of mutations associated with resistance to clarithromycin (ie, 23S rRNA 2142A→G, 2142A→C, and 2143A→G) and levofloxacin (ie, gyrA A88V or A88P, N87K or N87I, and D91G, D91N, or D91Y). Genotype-phenotype concordance was assessed to estimate sensitivity and specificity, and the curated catalogue of resistance-associated mutations was applied to the complete HpGP set. Region-specific prevalence of resistance-associated mutations was calculated for a combined dataset including the HpGP genomes and 768 whole-genome sequences retrieved from the US National Center for Biotechnology Information Sequence Read Archive repository. Associations between resistance genotypes, H pylori subpopulations, and minimum inhibitory concentrations (MICs) were tested. FINDINGS: Clarithromycin-resistant and levofloxacin-resistant HpGP strains were estimated with a sensitivity and specificity of 100%, with all confidence intervals ranging from 96% to 100%. The combined analysis (n=1779) found the highest prevalence of clarithromycin resistance in the western Pacific region (173 [51·2%] of 338 in southeast Asia and 75 [29·8%] of 252 in eastern Asia), north African region (seven [38·9%] of 18), and western Asian region (12 [31·6%] of 38), whereas the highest prevalence of levofloxacin resistance was found in south Asia (14 [51·85%] of 27), Central America (48 [38·7%] of 124), eastern Europe (four [36·4%] of 11), and southern Africa (three [33·3%] of nine). Similarly, 23S rRNA and gyrA genotypes are variable across H pylori subpopulations. MIC values changed depending on the specific mutation in 23S rRNA (mean clarithromycin MIC 24·61 mg/L [95% CI 12·27-36·96] for 2143A→G and 142·25 mg/L [95% CI 77·88-206·61] for 2142A→G) and gyrA (mean levofloxacin MIC 9·66 mg/L [95% CI 6·75-12·56] for mutations on codon 91, and 27·97 mg/L [95% CI 25·82-30·11] for mutations on codon 87). INTERPRETATION: Mutations in specific genes are reliable indicators to clarithromycin and levofloxacin resistance in H pylori, making them useful markers for the development of diagnostic assays and molecular monitoring. Our results suggest that using clarithromycin and levofloxacin empirically, without previous susceptibility testing, is unsuitable in all geographical regions covered by this study. FUNDING: Intramural Research Program of the US National Cancer Institute, the European Research Council, and the Spanish Ministry of Science and Innovation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".