Biomarker Characterization and Prediction of Virulence and Antibiotic Resistance from Helicobacter pylori Next Generation Sequencing Data

Biomolecules. 2022 May 11;12(5):691. doi: 10.3390/biom12050691.

Abstract

The Gram-negative bacterium Helicobacter pylori colonizes c.a. 50% of human stomachs worldwide and is the major risk factor for gastric adenocarcinoma. Its high genetic variability makes it difficult to identify biomarkers of early stages of infection that can reliably predict its outcome. Moreover, the increasing antibiotic resistance found in H. pylori defies therapy, constituting a major human health problem. Here, we review H. pylori virulence factors and genes involved in antibiotic resistance, as well as the technologies currently used for their detection. Furthermore, we show that next generation sequencing may lead to faster characterization of virulence factors and prediction of the antibiotic resistance profile, thus contributing to personalized treatment and management of H. pylori-associated infections. With this new approach, more and permanent data will be generated at a lower cost, opening the future to new applications for H. pylori biomarker identification and antibiotic resistance prediction.

Keywords: H. pylori; NGS; WGS; antibiotic resistance; biomarkers; virulence.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers
  • Drug Resistance, Microbial
  • Helicobacter Infections* / drug therapy
  • Helicobacter Infections* / genetics
  • Helicobacter pylori* / genetics
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Virulence / genetics
  • Virulence Factors / genetics

Substances

  • Biomarkers
  • Virulence Factors

Grants and funding

F.F.V. is funded by Fundação para a Ciência e a Tecnologia (FCT) through an Assistant Researcher grant CEECIND/03023/2017, and a project grant (project PTDC/BTM-TEC/3238/2020) that supported this work. JSV holds a research fellowship within the scope of project PTDC/BTM-TEC/3238/2020 (FCT). The work has been partially supported by National funds from FCT, projects UIDB/04138/2020 and UIDP/04138/2020.