Metagenomic and single-cell RNA-Seq survey of the Helicobacter pylori-infected stomach in asymptomatic individuals

JCI Insight. 2023 Feb 22;8(4):e161042. doi: 10.1172/jci.insight.161042.

Abstract

Helicobacter pylori colonization of the gastric niche can persist for years in asymptomatic individuals. To deeply characterize the host-microbiota environment in H. pylori-infected (HPI) stomachs, we collected human gastric tissues and performed metagenomic sequencing, single-cell RNA-Seq (scRNA-Seq), flow cytometry, and fluorescent microscopy. HPI asymptomatic individuals had dramatic changes in the composition of gastric microbiome and immune cells compared with noninfected individuals. Metagenomic analysis uncovered pathway alterations related to metabolism and immune response. scRNA-Seq and flow cytometry data revealed that, in contrast to murine stomachs, ILC2s are virtually absent in the human gastric mucosa, whereas ILC3s are the dominant population. Specifically, proportion of NKp44+ ILC3s out of total ILCs were highly increased in the gastric mucosa of asymptomatic HPI individuals, and correlated with the abundance of selected microbial taxa. In addition, CD11c+ myeloid cells and activated CD4+ T cells and B cells were expanded in HPI individuals. B cells of HPI individuals acquired an activated phenotype and progressed into a highly proliferating germinal-center stage and plasmablast maturation, which correlated with the presence of tertiary lymphoid structures within the gastric lamina propria. Our study provides a comprehensive atlas of the gastric mucosa-associated microbiome and immune cell landscape when comparing asymptomatic HPI and uninfected individuals.

Keywords: Bacterial infections; Cellular immune response; Immunology; Microbiology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Gastric Mucosa
  • Helicobacter Infections*
  • Helicobacter pylori*
  • Humans
  • Immunity, Innate
  • Mice
  • Plasma Cells
  • Single-Cell Gene Expression Analysis
  • Stomach

Grants and funding

J.D. was supported by the Swedish Foundation for Strategic Research (SSF) (ICA16-0050), Svenska Läkaresällskapet (SLS-960584) and Karolinska Institutet. A.T. was supported by Erling-Persson Foundation (140604) and L. E. was supported by Söderbergs foundation. E.J.V. was supported by grants from the Swedish Research Council, VR grant (K2015-68X-22765-01-6 and 2018-02533), Formas grant (FR-2016/0005), Cancerfonden (19 0395 Pj), and the Wallenberg Academy Fellow program (2019.0315). The computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at KTH partially funded by the Swedish Research Council through grant agreement no. 2018-05973.