Session Type: ePosters
Session Title: ePosters
Authors(s): M.T. Badr (1), M. Omar (2), G. Häcker (1)
Authors Affiliations(s): (1) Uniklinik Freiburg, Germany, (2) Weill Cornell Medicine, United States
Background:
Helicobacter pylori is a gram negative bacterium that frequently colonizes the mucus layer of human stomach, where some bacteria will attach to the gastric epithelial cells triggering an immune response recruiting neutrophils and macrophages, which may lead to gastritis. This inflammatory environment can in some cases advance to peptic ulcers and gastric cancer. Here we harness in silico analysis models of published gastric biopsies gene expression data for H. pylori infected patients in different disease stages to identify novel unique markers for disease development, relevant signaling pathways, and potential targets for therapy.
Methods:Gene expression data were retrieved from public databases such as NCBI GEO. 98 samples were included for discovery analysis from studies where gene expression of gastric biopsies of patients in various stages of H. pylori gastric disease had been compared to healthy controls. Initial signature was curated using the MetaIntegrator pipeline and refined using the Random Effect Model in NetworkAnalyst tool. These signatures were used to predict disease status in gastric adenocarcinoma datasets and H. pylori cell line infection models. Genetic variants were mined through GWA-studies to perform a gene-gene network analysis using NetworkAnalyst. Possible drug targets mimicking or reversing the gene signature were predicted using the broad institute connectivity map tool.
Results:This analysis revealed a core signature of 55 differentially expressed genes (DEGs) that were shared between patients at different stages of the pathology, with TLR8, CASP1, and TNFRSF10B among the most significantly upregulated genes and PPP1R16B/TIMAP, SIAH1, and NIPSNAP3B among the most significantly downregulated genes. Pathway enrichment analysis revealed ‘cytokine-mediated signaling pathway’, ‘interferon-gamma-mediated signaling pathway’ and ‘Mineral absorption’ among the most strongly associated common pathways. Dihomo-gamma-linolenic acid (DGLA) was one of agents inducing the highest reverse signatures in our connectivity map analysis.
Conclusions:Our observations provide valuable data about the underlying biology of the host response to H. pylori and shed light to importance of early screening in various other diseases as metabolic syndrome. This work could guide the substantial effort being exerted in finding new therapeutic agents to gastric ulcer and cancer especially when H. pylori antibiotic resistance is alarmingly on the rise.
Keyword(s): Helicobacter pylori, Gastric cancer, Multi-cohort analysisSession Type: ePosters
Session Title: ePosters
Authors(s): M.T. Badr (1), M. Omar (2), G. Häcker (1)
Authors Affiliations(s): (1) Uniklinik Freiburg, Germany, (2) Weill Cornell Medicine, United States
Background:
Helicobacter pylori is a gram negative bacterium that frequently colonizes the mucus layer of human stomach, where some bacteria will attach to the gastric epithelial cells triggering an immune response recruiting neutrophils and macrophages, which may lead to gastritis. This inflammatory environment can in some cases advance to peptic ulcers and gastric cancer. Here we harness in silico analysis models of published gastric biopsies gene expression data for H. pylori infected patients in different disease stages to identify novel unique markers for disease development, relevant signaling pathways, and potential targets for therapy.
Methods:Gene expression data were retrieved from public databases such as NCBI GEO. 98 samples were included for discovery analysis from studies where gene expression of gastric biopsies of patients in various stages of H. pylori gastric disease had been compared to healthy controls. Initial signature was curated using the MetaIntegrator pipeline and refined using the Random Effect Model in NetworkAnalyst tool. These signatures were used to predict disease status in gastric adenocarcinoma datasets and H. pylori cell line infection models. Genetic variants were mined through GWA-studies to perform a gene-gene network analysis using NetworkAnalyst. Possible drug targets mimicking or reversing the gene signature were predicted using the broad institute connectivity map tool.
Results:This analysis revealed a core signature of 55 differentially expressed genes (DEGs) that were shared between patients at different stages of the pathology, with TLR8, CASP1, and TNFRSF10B among the most significantly upregulated genes and PPP1R16B/TIMAP, SIAH1, and NIPSNAP3B among the most significantly downregulated genes. Pathway enrichment analysis revealed ‘cytokine-mediated signaling pathway’, ‘interferon-gamma-mediated signaling pathway’ and ‘Mineral absorption’ among the most strongly associated common pathways. Dihomo-gamma-linolenic acid (DGLA) was one of agents inducing the highest reverse signatures in our connectivity map analysis.
Conclusions:Our observations provide valuable data about the underlying biology of the host response to H. pylori and shed light to importance of early screening in various other diseases as metabolic syndrome. This work could guide the substantial effort being exerted in finding new therapeutic agents to gastric ulcer and cancer especially when H. pylori antibiotic resistance is alarmingly on the rise.
Keyword(s): Helicobacter pylori, Gastric cancer, Multi-cohort analysis