Helicobacter pylori (H. pylori) is a Gram-negative, microaerophilic, spiral bacterium, which is able to colonize the human stomach. Over 50% of people worldwide are infected with Helicobacter pylori. Recently, stomach microbiome studies revealed that the interaction of H. pylori with gastric microbes may play a role in H. pylori-associated disorders (He et al. 2016).

Gastrointestinal microbiota influences variety of physiological and pathologic processes, such as pathogen colonization, regulation of the immune response. Thus, dysbiosis of gastric microbiota can lead to the onset and progression of certain diseases. A healthy stomach is prominently colonized by phyla Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria. H. pylori infection significantly alters the gastric microbial composition in infected patients. Study of Ozbey et al. showed that H. pylori becomes dominant species, resulting in lower complexity and diversity of gastric microbiota in infected than uninfected individuals (Ozbey et al. 2020). There are currently no studies that address the structure of the gastric microbial community and the association of H. pylori with the gastric microbiota in Vietnamese. In this study, we aim to describe the changes in gastrointestinal microbiota composition between H. pylori-infected and non-infected patients.

Gastric biopsies were collected during endoscopic examination from patients with gastric symptoms. Candidate participants had not received antibiotic treatment during 30 days prior to the exploration. CLO-test was performed to determine the biopsy tissue's H. pylori status. DNA from each sample was extracted and sequenced on the DNBSEQ platform.

Shotgun metagenomics analysis results are strongly dependent on the used bioinformatics software, databases and parameters. We also developed an analysis pipeline by using several simulated datasets specific to human microbiome. Our simulated datasets comprised of top species that are present in the human stomach with an abundance of even or log distribution. Besides, the published simulated datasets with high complexity were also used to assess the pipeline. All datasets consist of sequencing error profile of next-generation sequencing technology and human contamination. The result showed that the kmer-based metagenomic pipeline using Kraken (Wood et al. 2019) performed most robustly across the microbiome datasets in our experiment. By using the established pipeline, we explored the alterations in the composition and diversity of gastric microbiota in the infected patients.

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