Abstract

Vaccination is the most effective method to prevent infectious diseases. However, approaches to identify novel vaccine candidates are commonly laborious and protracted. While surface proteins are suitable vaccine candidates and can elicit antibacterial antibody responses, systematic approaches to define surfomes from gram-negatives have rarely been successful. Here we developed a combined discovery-driven mass spectrometry and computational strategy to identify bacterial vaccine candidates and validate their immunogenicity using a highly prevalent gram-negative pathogen, Helicobacter pylori, as a model organism. We efficiently isolated surface antigens by enzymatic cleavage, with a design of experiment based strategy to experimentally dissect cell surface-exposed from cytosolic proteins. From a total of 1,153 quantified bacterial proteins, we thereby identified 72 surface exposed antigens and further prioritized candidates by computational homology inference within and across species. We next tested candidate-specific immune responses. All candidates were recognized in sera from infected patients, and readily induced antibody responses after vaccination of mice. The candidate jhp_0775 induced specific B and T cell responses and significantly reduced colonization levels in mouse therapeutic vaccination studies. In infected humans, we further show that jhp_0775 is immunogenic and activates IFNγ secretion from peripheral CD4+ and CD8+ T cells. Our strategy provides a generic preclinical screening, selection and validation process for novel vaccine candidates against gram-negative bacteria, which could be employed to other gram-negative pathogens.

Highlights

  • Vaccination is the most effective method to prevent infectious diseases

  • We established a combined experimental and computational vaccinology pipeline for gram-negative bacteria - using H. pylori as a model organism - that enables the identification of few promising vaccine candidates from thousands of expressed bacterial proteins

  • We analyzed the abundance of the annotated H. pylori proteomes comprehensively and account close to 1,200 expressed proteins, which is, to our knowledge, the most comprehensive protein inventory of this pathogen to date (Fig. 2A, Supplementary Fig. 1)

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Summary

Introduction

Vaccination is the most effective method to prevent infectious diseases. approaches to identify novel vaccine candidates are commonly laborious and protracted. We developed a combined discovery-driven mass spectrometry and computational strategy to identify bacterial vaccine candidates and validate their immunogenicity using a highly prevalent gram-negative pathogen, Helicobacter pylori, as a model organism. Among the key challenges in vaccine design is the selection of antigens that are capable of inducing protective immunity against the pathogen. Outer membrane proteins (OMPs) are considered most promising targets to select vaccine candidates, especially due to their physical accessibility on the bacterial surface, and ability to be bound by opsonizing antibodies[1,2,3,4]. State of the art approaches identify OMPs by time consuming membrane extraction or labeling strategies and subsequent mass spectrometry (MS) While these strategies delivered novel vaccine candidates, they are inherently limited in their capacity to differentiate between inner or outer membrane localization and probe actual surface accessibility. We further computationally prioritized proteins by high homology within the pathogenic bacterial genus and low homology to other bacterial species and the host

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