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Abstract：
Helicobacter pylori (H.pylori, Hp) affects billions of people worldwide. However, the emerging resistance of Hp to antibiotics challenges the effectiveness of current treatments. Investigating the genotype-phenotype connection for Hp using next-generation sequencing could enhance our understanding of this resistance.
In this study, we analyzed 52 Hp strains collected from various hospitals. The susceptibility of these strains to five antibiotics was assessed using the agar dilution assay. Whole-genome sequencing was then performed to screen the antimicrobial resistance (AMR) genotypes of these Hp strains. To model the relationship between drug resistance and genotype, we employed univariate statistical tests, unsupervised machine learning, and supervised machine learning techniques, including the development of support vector machine models.
Our models for predicting Amoxicillin resistance demonstrated 66% sensitivity and 100% specificity, while those for Clarithromycin resistance showed 100% sensitivity and 100% specificity. These results outperformed the known resistance sites for Amoxicillin (A1834G) and Clarithromycin (A2147), which had sensitivities of 22.2% and 87%, and specificities of 100% and 96%, respectively.
Our study demonstrates that predictive modeling using supervised learning algorithms with feature selection can yield diagnostic models with higher predictive power compared to models relying on single single-nucleotide polymorphism (SNP) sites. This approach significantly contributes to enhancing the precision and effectiveness of antibiotic treatment strategies for Hp infections. The application of whole-genome sequencing for Hp presents a promising pathway for advancing personalized medicine in this context.
In this study, we analyzed 52 Hp strains collected from various hospitals. The susceptibility of these strains to five antibiotics was assessed using the agar dilution assay. Whole-genome sequencing was then performed to screen the antimicrobial resistance (AMR) genotypes of these Hp strains. To model the relationship between drug resistance and genotype, we employed univariate statistical tests, unsupervised machine learning, and supervised machine learning techniques, including the development of support vector machine models.
Our models for predicting Amoxicillin resistance demonstrated 66% sensitivity and 100% specificity, while those for Clarithromycin resistance showed 100% sensitivity and 100% specificity. These results outperformed the known resistance sites for Amoxicillin (A1834G) and Clarithromycin (A2147), which had sensitivities of 22.2% and 87%, and specificities of 100% and 96%, respectively.
Our study demonstrates that predictive modeling using supervised learning algorithms with feature selection can yield diagnostic models with higher predictive power compared to models relying on single single-nucleotide polymorphism (SNP) sites. This approach significantly contributes to enhancing the precision and effectiveness of antibiotic treatment strategies for Hp infections. The application of whole-genome sequencing for Hp presents a promising pathway for advancing personalized medicine in this context.
摘要：
■幽门螺杆菌(H.pylori,Hp）影响全球数十亿人。然而,出现的Hp对抗生素的耐药性挑战了当前治疗的有效性.使用下一代测序研究Hp的基因型-表型连接可以增强我们对这种抗性的理解。
■在这项研究中，我们分析了从不同医院收集的52株Hp。使用琼脂稀释测定法评估这些菌株对五种抗生素的敏感性。然后进行全基因组测序以筛选这些Hp菌株的抗微生物抗性(AMR)基因型。为了建立耐药性与基因型之间的关系模型，我们采用单变量统计检验，无监督机器学习，和监督机器学习技术，包括支持向量机模型的开发。
■我们预测阿莫西林耐药性的模型显示出66%的敏感性和100%的特异性，而对克拉霉素耐药的患者表现出100%的敏感性和100%的特异性。这些结果优于阿莫西林（A1834G）和克拉霉素（A2147）的已知耐药位点，敏感度分别为22.2%和87%，100%和96%的特异性，分别。
■我们的研究表明，与依赖单个单核苷酸多态性（SNP）位点的模型相比，使用带有特征选择的监督学习算法进行预测建模可以产生具有更高预测能力的诊断模型。这种方法大大有助于提高Hp感染的抗生素治疗策略的准确性和有效性。在这种情况下，Hp全基因组测序的应用为推进个性化医疗提供了有希望的途径。
■在这项研究中，我们分析了从不同医院收集的52株Hp。使用琼脂稀释测定法评估这些菌株对五种抗生素的敏感性。然后进行全基因组测序以筛选这些Hp菌株的抗微生物抗性(AMR)基因型。为了建立耐药性与基因型之间的关系模型，我们采用单变量统计检验，无监督机器学习，和监督机器学习技术，包括支持向量机模型的开发。
■我们预测阿莫西林耐药性的模型显示出66%的敏感性和100%的特异性，而对克拉霉素耐药的患者表现出100%的敏感性和100%的特异性。这些结果优于阿莫西林（A1834G）和克拉霉素（A2147）的已知耐药位点，敏感度分别为22.2%和87%，100%和96%的特异性，分别。
■我们的研究表明，与依赖单个单核苷酸多态性（SNP）位点的模型相比，使用带有特征选择的监督学习算法进行预测建模可以产生具有更高预测能力的诊断模型。这种方法大大有助于提高Hp感染的抗生素治疗策略的准确性和有效性。在这种情况下，Hp全基因组测序的应用为推进个性化医疗提供了有希望的途径。
