Abstract
The analysis of the interaction between Helicobacter pylori (HP) and the host in vivo is an extremely informative way to enlighten the molecular mechanisms behind the persistency/latency of the bacterium as well as in the progression of the infection. An important source of information is represented by circulating antibodies targeting the bacteria that define a specific “disease signature” with prospective diagnostic implications. The diagnosis of some of the HP induced diseases such as gastric cancer (GC), MALT lymphoma (MALT), and autoimmune gastritis (AIG) is not easy because patients do not show symptoms of illness in early-onset stages, at the same time they progress rapidly. The possibility of identifying markers able to provide an early diagnosis would be extremely beneficial since a late diagnosis results in a delay in undergoing active therapy and reduces the survival rate of patients. With the aim to identify the HP antigens recognized during the host immune-response to the infection and possibly disease progression, we applied a discovery-driven approach, that combines “phage display” and deep sequencing. The procedure is based on the selection of ORF phage libraries, specifically generated from the pathogen’s genome, with sera antibodies from patients with different HP-related diseases. To this end two phage display libraries have been constructed starting from genomic DNA from the reference HP 26695 and the pathogenic HP B128 strains; libraries were filtered for ORFs by using an ORF selection vector developed by our group (Di Niro et al., 2005; Soluri et al., 2018), selected with antibodies from patients affected by GC, MALT, and AIG and putative HP antigens/epitopes were identified after Sequencing and ranking. The results show that individual selection significantly reduced the library diversity and comparison of individual ranks for each condition allowed us to highlight a pattern of putative antigens specific for the different pathological outcomes or common for all of them. Within the putative antigens enriched after selection, we have validated protein CagY/Cag7 by ELISA assay as a marker of HP infection and progression. Overall, we have defined HP antigenic repertoire and identified a panel of putative specific antigens/epitopes for three different HP infection pathological outcomes that could be validated in the next future.
Highlights
Helicobacter pylori (HP) infects more than 50% of the world’s population (Parsonnet, 1995; Goh et al, 2011; Peleteiro et al, 2014; Hooi et al, 2017) being the most prevalent human pathogen worldwide
The identification and characterization of H. pylori proteins or protein domains able to evoke an immune response could be of great help to enhance understanding of how bacterium and host interact one with each other
In the last years many attempts to identify novel biomarkers for Gastric Cancer (GC) early diagnosis have been made and several studies have been published reporting the evaluation of antibody titer against different H. pylori antigens in different outcomes of infection In the majority of cases these antigens have been chosen based on previous evidence of their pathological role in H. pylori infection
Summary
Helicobacter pylori (HP) infects more than 50% of the world’s population (Parsonnet, 1995; Goh et al, 2011; Peleteiro et al, 2014; Hooi et al, 2017) being the most prevalent human pathogen worldwide. The most important virulence factor of HP is the cag pathogenicity island (cagPAI), a genetic locus of about 40 kb that contains 31 genes (Tomb et al, 1997; Alm et al, 1999) and encodes for the so-called type IV secretion system (T4SS). This forms a syringe-like structure that injects bacterial components (mainly peptidoglycan and the oncoprotein cagA) into the host target cell (Rohde et al, 2003). HP strains that harbor the cagPAI (cagPAI+) pathogenicity locus show a significantly increased ability to induce severe pathological outcomes in infected individuals, compared to cagPAI− strains (Blaser et al, 1995; Kuipers et al, 1995; Noto and Peek, 2012; TakahashiKanemitsu et al, 2020)
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