Designing Multi-Antigen Vaccines Against Acinetobacter baumannii Using Systemic Approaches

Abstract

Vaccines and monoclonal antibodies are promising approaches for preventing and treating infections caused by multidrug resistant Acinetobacter baumannii. However, only partial protection has been achieved with many previously tested protein antigens, which suggests that vaccines incorporating multiple antigens may be necessary in order to obtain high levels of protection. Several aspects that use the wealth of omic data available for A. baumannii have not been fully exploited for antigen identification. In this study, the use of fractionated proteomic and computational data from ~4,200 genomes increased the number of proteins potentially accessible to the humoral response to 8,824 non-redundant proteins in the A. baumannii panproteome. Among them, 59% carried predicted B-cell epitopes and T-cell epitopes recognized by two or more alleles of the HLA class II DP supertype. Potential cross-reactivity with human proteins was detected for 8.9% of antigens at the protein level and 2.7% at the B-cell epitope level. Individual antigens were associated with different infection types by genomic, transcriptomic or functional analyses. High intra-clonal genome density permitted the identification of international clone II as a "vaccitype", in which 20% of identified antigens were specific to this clone. Network-based centrality measurements were used to identify multiple immunologic nodes. Data were formatted, unified and stored in a data warehouse database, which was subsequently used to identify synergistic antigen combinations for different vaccination strategies. This study supports the idea that integration of multi-omic data and fundamental knowledge of the pathobiology of drug-resistant bacteria can facilitate the development of effective multi-antigen vaccines against these challenging infections.