Proteomic insights into Helicobacter pylori infection in stomach cells, revealing host response and host-targeted therapeutics repurposing

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Authors: Aditya Shetty, Tejaswi Lakshman, Rajesh Raju, Poornima Ramesh
Cited by: 0

Abstract

Helicobacter pylori (H. pylori) is a globally prevalent gastric pathogen strongly associated with chronic gastritis, peptic ulcers, and gastric cancer. While bacterial factors have been extensively studied, host proteomic responses and their therapeutic potential remain largely underexplored. Current analyses employed a systematic proteomics-based data integration and harmonization approach (retrospective qualitative cohort study) to identify important differentially regulated host proteins. Proteomic datasets were curated from in vitro studies and analyzed for functional enrichment, protein-protein interaction networks, and hub protein identification. To explore therapeutic repurposing, drug repositioning was performed using the DrugBank database. Data summation describing protein differential regulation in human gastric cells as a result of the infection revealed 1672 perturbed host proteins. Bioinformatics analysis revealed 11 proteins including CSK, MET, RELA, MARK2, GRB2, FTO, PLCG1, CRKL, RPS5, RPS9, and RPS27A to be ideal host targets for therapeutic repurposing. Clinically approved drugs such as Dasatinib (targeting CSK) and Crizotinib (targeting MET) emerged as promising candidates due to favorable pharmacokinetics and known bioactivity. Host-directed therapeutics could offer alternative strategies to conventional antibiotic therapy, addressing challenges such as resistance and infection recurrence, providing a foundation for future experimental validation and development of host-targeted interventions for infection control.

Fields of Study & Subjects

Helicobacter pylori-related gastroenterology studies Computational Drug Discovery Methods Gastric Cancer Management and Outcomes

Publication details

Journal / source
Figshare
Publication date
1 January 2026
Language
EN
Document type
Dataset