Deciphering Complex Antibiotic Resistance Patterns in Helicobacter pylori Through Whole Genome Sequencing and Machine Learning

Helicobacter pylori affects billions of people worldwide. Despite the availability of different antibiotics, emerging resistance of H. pylori renders antibiotic treatment ineffective. Next generation sequencing provides a powerful technology to investigate the genotype-phenotype connection for H. pylori. However, the prediction of antibiotic resistance using whole genome sequencing data remains a formidable challenge. Here we conducted a comprehensive investigation into the antibiotic resistance profiles of H. pylori strains against five distinct antibiotics, alongside assessing clinical treatment outcomes for Amoxicillin and Clarithromycin combination therapy. Concurrently, we performed whole-genome sequencing on a collection of H. pylori isolates. We rigorously evaluated the potential for predicting antibiotic resistance through univariate statistical tests, multivariate unsupervised and supervised machine learning. Our study contributes valuable insights towards enhancing precision and effectiveness in antibiotic treatment strategies for H. pylori infections with the application of whole-genome sequencing for H. pylori.