- Research article
- Open Access
Pangenome and immuno-proteomics analysis of Acinetobacter baumannii strains revealed the core peptide vaccine targets
© The Author(s). 2016
- Received: 12 February 2016
- Accepted: 19 July 2016
- Published: 15 September 2016
Acinetobacter baumannii has emerged as a significant nosocomial pathogen during the last few years, exhibiting resistance to almost all major classes of antibiotics. Alternative treatment options such as vaccines tend to be most promising and cost effective approaches against this resistant pathogen. In the current study, we have explored the pan-genome of A. baumannii followed by immune-proteomics and reverse vaccinology approaches to identify potential core vaccine targets.
The pan-genome of all available A. baumannii strains (30 complete genomes) is estimated to contain 7,606 gene families and the core genome consists of 2,445 gene families (~32 % of the pan-genome). Phylogenetic tree, comparative genomic and proteomic analysis revealed both intra- and inter genomic similarities and evolutionary relationships. Among the conserved core genome, thirteen proteins, including P pilus assembly protein, pili assembly chaperone, AdeK, PonA, OmpA, general secretion pathway protein D, FhuE receptor, Type VI secretion system OmpA/MotB, TonB dependent siderophore receptor, general secretion pathway protein D, outer membrane protein, peptidoglycan associated lipoprotein and peptidyl-prolyl cis-trans isomerase are identified as highly antigenic. Epitope mapping of the target proteins revealed the presence of antigenic surface exposed 9-mer T-cell epitopes. Protein-protein interaction and functional annotation have shown their involvement in significant biological and molecular processes. The pipeline is validated by predicting already known immunogenic targets against Gram negative pathogen Helicobacter pylori as a positive control.
The study, based upon combinatorial approach of pan-genomics, core genomics, proteomics and reverse vaccinology led us to find out potential vaccine candidates against A. baumannii. The comprehensive analysis of all the completely sequenced genomes revealed thirteen putative antigens which could elicit substantial immune response. The integration of computational vaccinology strategies would facilitate in tackling the rapid dissemination of resistant A.baumannii strains. The scarcity of effective antibiotics and the global expansion of sequencing data making this approach desirable in the development of effective vaccines against A. baumannii and other bacterial pathogens.
- A. baumannii
- Core genome
Acinetobacter baumannii is a Gram-negative coccobacillus responsible for nosocomial outbreaks and healthcare associated infections such as septicemia and pneumonia in the immuno-compromised patients . During the last three decades, A. baumannii has emerged as one of the most difficult super-bug to treat in hospitals, worldwide . It accounts for causing infections in about 12,000 patients annually and approximately 500 deaths in the United States, only . The alarming characteristic of this pathogen is its ability to develop diverse mechanisms of resistance to existing drugs ultimately leading to therapeutic failures . Recently, Infectious Diseases Society of America (IDSA) has included A. baumannii in the hit-list of top six priority pathogens, thus requiring a prompt response by the healthcare community to curb this pathogen [5, 6]. The massive challenge posed by multi and pan-drug resistant A. baumannii in the current post antibiotic era requires aggressive exploration of other solutions .
Besides active infection control measures and antibiotic stewardship programs, vaccination approach is predominantly reckoned as an alternative therapy to reduce the burden of infections caused by this pathogen . Nevertheless, sequence- based reverse vaccinology approaches presented a more rational methodology to vaccine design since the discovery of universal vaccine against serogroup B meningococcal (menB) disease in 2000 [9, 10]. These in silico strategies led to comprehensive analysis of the pathogen genomics, proteomics, essential molecular pathways, virulence factors and host-pathogen interactions .
Selection of genomes and genes predictions
Genome statistics of completely sequenced A. baumannii strains
Pan- and Comparative Genomics of Acinetobacter baumannii
A. baumannii BJAB07104
A. baumannii MDR-TJ
A. baumannii MDR-ZJ06
A. baumann;ii TYTH-1
A. baumannii AC30
A. baumannii BJAB0715
A. baumannii BJAB0868
A. baumannii ZW85-1
A. baumannii AC29
A. baumannii AB031
A. baumannii AB030
A. baumannii Abh12 OA2
A. baumannii LAC-4
A. baumannii 6411
NZ_CP010368.1/p6411-66.409 kb:NZ_CP010903.1;p6411-89.111 kb:NZ_CP010369.1;p6411-9.012 kb:NZ_CP010370.2
A. baumannii 6200
NZ_CP010397.1/p6200-114.848 kb:NZ_CP010398.1;p6200-47.274 kb:NZ_CP010399.1; p6200-9.327 kb:NZ_CP010400.1
A. baumannii NCGM 237
A. baumannii IOMTU433
A. baumannii A1
A. baumannii AB5075-UW
A. baumannii XH386
A. baumannii Ab04- MFF
A. baumannii ACICU
A. baumannii AtCC MFF
A. baumannii AB307-0294
A. baumannii AYE
NC_010410.1/p1ABAYE:NC_010401.1; p2ABAYE:NC_010402.1; p3ABAYE:NC_010404.1; p4ABAYE:NC_010403.1
A. baumannii D1279779
A. baumannii 1656-2
A. baumannii TCDC-AB0715
A.baumannii ATCC 17978
Genome organization and pathogenomics
To explore the genetic relatedness and evolutionary relationships, the selected strains were evaluated for their respective clonal lineage through previously published literature and phylogentic analysis [16, 17]. Clonal studies play a vital role to get insight about epidemiology of isolates and aid to control outbreaks both at the institutional level and global level . Drug resistant strains of A. baumannii categorized as international clone (IC) I, II and III resulted in devastating outbreaks at global level [19, 20]. According to publicly available Pasteur’s MLST scheme (http://pubmlst.org/abaumannii/), IC II is considered as the largest clone in the global population, followed by I CI with distribution in more than 30 countries, worldwide . The included strains were analyzed for the clonal distribution of virulence factors through Virulence factor database (VFDB) RRID:SCR_007969 . Acquisition of virulence factors supports the bacteria to invade the host, evade the host defenses and persist in the host environment [23, 24]. With the aim to get insight into the virulence and pathogenic potential of A. baumannii, the virulence factors present in core genome were explored by employing VFDB and MvirDB [22, 25].
To better understand the evolutionary relationships and genomic variations, the A. baumannii genomes were analyzed through phylogenetic tree. The 16SrRNA genes were predicted by program RNAmmer . Since 16SrRNA gene exhibit slower rate of evolution and are considered as hotspot for mutation, therefore frequently targeted for estimation of phylogenetic relationships between bacterial strains. Those sequences with reliable score of more than 1700 were considered for further evaluation . ClustalW (RRID:SCR_002909)  was used for the multiple alignment of 16SrRNA sequences. MEGA6  was used to create the phylogenetic tree based on Neighbor-Joining method . 1000 bootstrap re-samplings were performed to estimate the consensus tree.
Pan- and Core genome estimation
Pan-genome represents the entire gene repertoire and encompasses core genome and dispensable genome. Core genome comprised of genes that are present in all genomes of a given species, and are essential for bacterial growth, while dispensable sequences do not necessarily present in all genomes, and may be responsible for strain specific functions like pathogenicity, stress, resistance etc.  A. baumannii conserved core genome was estimated based on BLAST similarities between the genomes following previously established 50/50 rule [32–34]. Blast hit was considered significant when 50 % of alignment (amino acid) was identical where the length of alignment was 50 % of the longest gene in the comparison. According to this criterion, genes were clustered together in gene families if their amino acid sequences were at least 50 % identical. Multiple genes may also make a single gene family if they follow the same 50/50 rule. Similarly, all genes were grouped into gene families. The genes that did not fall in any gene family were assigned to their own unique gene family. Gene families having at least one gene in common among them were gathered into the core genome. Rest of the genes that did not fit into criteria may go to the species pan-genome [35–37].
Intra- and inter-proteome comparisons
All of the selected genomes were translated into their proteomes to explore proteomic conservation among the strains. Pair-wise comparison of proteins was performed and visualized in the form of a matrix . BLASTp comparison was carried out for all the proteins in one strain to all the proteins in other strains included in the dataset; and the association was estimated based on 50/50 rule as previously employed [32, 33]. Each corresponding box in the matrix represents the number of shared proteins and percent homology. For the comparison of any two genomes, protein families were built through single linkage clustering, so that each shared connection must be between sequences from different genomes (dark green color represents more homology between proteomes as shown in Fig. 4). In the matrix, an internal hit significantly similar to query protein was grouped into the same gene family. The bottom row in the matrix depicts the number of proteins that have homologous hits within the proteome itself (internal paralogs, shaded in red color, from 2.9 % least to 7.0 % highest illustrated in Fig. 4). Different levels of homology between the sequences were represented by different color intensities in the matrix (Fig. 4). Pan-core tree was generated based upon shared gene families between the strains. The relative Manhattan distance showed the evolutionary distance between the strains.
Functional categorization of the core proteome
To prioritize potential core vaccine candidates, the core genome of all A. baumannii (30 genomes) strains was explored for the presence of essential genes using Database for Essential genes (DEG) . Parameters set for BLAST against DEG were: E-value cut off 1e-10 and minimum bit score 100. Essential genes of bacteria are an effective therapeutic target, especially when bacteria confer multidrug resistance . The core proteome was aligned with human proteome to pool out the human homologs, so as to eliminate the chances of autoimmunity . Proteins having percentage identity <35 % and E-value <0.005 were considered as non-host bacterial proteins. Subsequently, virulent proteins were also estimated within core proteome by using Virulence factor database (VfDB)  and microbial virulence database (MvirDB). Blastp search was carried out against all the virulence associated protein by employing the following parameters: Bit Score > 100, E-Value < 1.0 e-5 and percentage Identity > 35 %. The database VFDB contains substantial information of virulence factors from 30 important bacterial pathogens (including Acinetobacter species), virulence associated genes, pathogenecity islands, protein structure and function characteristics. MvirDB is another comprehensive database for the identification of virulence factors, antibiotic resistance genes and protein toxins. It retrieves data from eight public access sequence databases and facilitates in rapid characterization of sequences related to virulence and pathogenesis . PsortB was used to analyze the sub-cellular localization of proteins . PsortB helps in sorting out the bacterial proteins as cytoplasmic, extracellular, periplasmic, outer and inner membrane. Proteins located in extracellular, periplasmic and outer membranes were preferred as effective vaccine candidates . Topology Data Bank of Transmembrane Proteins, RRID:SCR_007964 (HMMTOP) was used to predict the localization of helical trans-membrane segments and topology of transmembrane proteins . Proteins with ≤1 trans-membrane helices were selected as appropriate vaccine target as it is easy to purify, clone and express them . Eventually, those proteins which were essential, virulent, non-host homologs, exposed or secreted with ≤1 trans-membrane helices prioritized as vaccine candidates. The molecular weight of the prioritized proteins was estimated by employing Expasy PI/MW tool (RRID:SCR_012880) . Those proteins having molecular weight of <110KDa were finally designated as potential vaccine candidates as they can be easily purified and can be effectively used for vaccine development .
Epitope mapping of the targeted proteins
The concept of peptide vaccines is based on identification and synthesis of B and T-cell epitopes which are immune-dominant and can generate significant specific immune responses . Immuno-informatics involves various combinatorial computational approaches to predict B and T-cell epitopes . The prioritized proteins obtained by following the above mentioned strategy were subjected to sequential epitope mapping steps. Selection of candidate immunogenic MHC class I and II restricted peptides from characterized proteins is the key to successful vaccine development . ABCpred was employed (threshold value >0.6) to retrieve the B-cell epitopes from the prioritized proteins. ABCpred predicted the B-cell epitopes based on artificial neural networks . The 20-merB-cell epitopes were subsequently analyzed for T-cell epitopes for binding with MHC I and MHC II class molecules by using Proped1 and Proped servers, respectively . The capability of an antigen to generate immune response depends upon its recognition and binding with MHC alleles from both classes I and II . Surface exposure of T-cell epitopes was estimated by using NetSurfP . Vaxijen v2.0 was used to check the antigenicity of the epitopes (threshold value >0.4) . The epitopes having values more than 0.4 were considered potentially antigenic. MHCpred was used to calculate the half maximal inhibition concentration (IC50) score for DRB1*0101 (a common and prevalent allele in worldwide population) [55, 56]. Various in silico reverse vaccinology studies targeting bacterial infections, including A. baumannii, preferred the epitopes which bind with DRB1*0101 allele, as this will lead to strong antigen recognition and immune response [41, 57, 58]. IC50 values are binding affinity measures calculated from a competitive binding assay . Prediction of MHC binding is a prerequisite to the prediction of T-cell epitopes. Estimation of binding peptides is simplified by following a classification scheme, dividing the peptides into non-binders, low affinity binders (>500nM), medium affinity binders (50–500nM) and high affinity binders (<50nM) . Virulence of the candidate epitopes was estimated by using VirulentPred .
Epitope conservation analysis
A conservation analysis is performed to estimate the level of sequence conservation in the epitopic regions across the included strains of A. baumannii (prioritized proteins). The conservation analysis of these epitopes was performed by using CLC main workbench . This user-friendly, graphically based software performs the multiple sequence alignment, and consensus sequence was derived for the epitopes. The overall conservation of the sequences was predicted and displayed with the alignment (Fig. 7). The degree of similarity or variability of a specific protein or epitopic region may provide significant information regarding structural, functional, evolutionary and immunological correlates.
Protein structure and comparative modeling
Visualization of 3D structure of proteins facilitate in understanding the sequence patterns, functional sites, binding sites and interactions of candidate proteins with other targets . Protein Databank (PDB, RRID:SCR_012820) and Swiss model (SWISS-MODEL Repository, RRID:SCR_013032) were employed to explore 3D structures of the query proteins by comparative modeling [63, 64]. Comparative modeling method make use of experimental protein structures (templates) to build models for target proteins [63, 65]. Swiss model was employed for each of the query protein and among the number of templates generated by the server (BLAST), one with highest sequence identity/similarity was selected. For example, the query sequence adeK exhibited 45.85 % sequence similarity with outer membrane protein OprM (PDB id-3d5k.1.A) which was further assessed for epitope visualization by pepitope. Similarly, for query proteins P pilus assembly protein, pili assembly chaperone, PonA, general secretion pathway protein D (HMPREF0010_02518), FhuE receptor, Type VI secretion system OmpA/MotB, Ton B dependent siderophore receptor, general secretion pathway protein D (HMPREF0010_01958), outer membrane protein, peptidoglycan associated lipoprotein and peptidyl-prolyl cis-trans isomerase, PDB structures with highest sequence similarity were selected, i.e., PDB ID: 3rfz.1.B, 1ze3.1.A, 3udi.1.B,4av2.1.A,2w77.1.A, 2n48.1.A, 1fep.1.A, 4e9j.1.A, 3d5k.1.A, 4g4v.1.A and 1q6u.1.A, respectively. Surface exposure of candidate epitopes on the proteins facilitates in generation of strong immunogenic response . Pepitope was employed to reveal the topology of epitopes within the protein structures . It is essential to ensure the arrangement of the epitopes so that the immunogenic part does not get folded within the globular protein .
Functional annotation of the predicted proteins
Functional analysis assists in studying the biological, molecular and biochemical behavior of the proteins. Here the Blast2GO (RRID:SCR_005828) was employed for functional annotation of the prioritized proteins . Cluster of orthologous groups (COG) and biological/molecular pathways were explored for the prioritized proteins as reported in Kyoto encyclopedia of genes and genomes (KEGG) database (http://weizhongli-lab.org/metagenomic-analysis/server) .
Targeted protein-protein interaction (PPI) analysis
Interaction analysis of prioritized proteins was executed by using Search tool for the retrieval of interacting genes (STRING, RRID:SCR_005223) . The association of query proteins with the predicted functional partners is represented by thick blue lines. Colored nodes represent that the interacting partners directly link to the targeted proteins with optimal score of 0.9. Interactions among the proteins constitute backbone of cellular function and the study of such interactions helps in understanding molecular mechanism and biological processes, elucidating molecular basis of diseases and identifying potential therapeutic targets .
Validation of pipeline through positive control
To test the functionalities of the pipeline and validation of the predictions, additional potential vaccine candidates are predicted against another Gram negative bacterium as a positive control such as Helicobacter pylori. The same methodology and pipeline is employed to analyze the genome of H. pylori strain reference strain 26695 to find the vaccine targets against this important organism, so as to limit its incidence and eventually the associated gastric cancer.
Genome organization and statistics
The drug resistant A. baumannii has increasingly become a cause for serious concern with regard to both nosocomial and community acquired infections . Due to next generation sequencing technology, there is wide-ranging availability of genome data on international databases. To date, 30 complete genome sequences of A. baumannii are available on GenBank/NCBI, which were all analyzed for potential vaccine candidates in our study (Table 1). The total number of proteins in all of the available complete genomes (30 A. baumannii genomes) was calculated as 114,872 and an average A. baumannii genome/proteome contain 3,829 proteins. An average GC content is observed around 39.03 %. The average gene count was found out to be 3829 genes (3702 proteins), with lowest number of genes reported in a 50-year old isolate A. baumannii ATCC 17978 (3,469) and highest number of genes were reported in a newly sequenced extremely drug resistant A. baumannii AB030 (4,336). As a consequence of strong antibiotic selective pressure, the bacteria have acquired significant antibiotic resistance and virulence genes for their survival over the period of time . To get a consistency in the genomic and proteomic data all the sequences were assessed by a single gene prediction program Prodigal which predicted more number of genes in analyzed genome sequences when compared with that of NCBI (Table 1). However, the overall numbers are comparable, this increase in prediction by the Prodigal may be due to its improved translation initiation site recognition .
Virulence potential of A. baumannii
Relative to other gram negative pathogens, very little is known in relation to the pathogenic potential and virulence repertoire of A. baumannii . To get insight into the virulence potential of A. baumannii, virulent genes present in core conserved genome were explored. Interestingly, the bacteria harbor a significant number of virulent genes (295) as part of its core genome which facilitates the organism in pathogenesis and probably survival in adverse conditions (Additional file 1). In the conserved virulome of A. baumannii genus, the major virulence factors and associated mechanisms are identified which include OmpA, pili assembly proteins, superoxide dismutase, phospholipases, siderophore dependent iron acquisition proteins, multidrug efflux proteins, and penicillin binding proteins. Among them, the significant protein OmpA is involved in the process of epithelial invasion and apoptosis . Similarly, pili assembly proteins play vital role in bacterial adherence and biofilm formation; thus imperative for pathogenesis of A. baumannii . Another important factor, superoxide dismutase found to be present in the core genome assist bacteria by detoxifying reactive oxygen species released in the course of host defense reactions . Like OmpA, the phospholipase C are responsible for survival in human serum and epithelial cell invasion . Upregulated iron acquisition systems and antibiotic efflux pumps further enhance the virulence potential of A. baumannii .
Significant virulence factors in A. baumannii strains
International Clone I
International Clone II
Heat shock protein60
Type4 pilus assembly protein
Tetracycline resistance protein
Phylogenetic analysis of A. baumannii strains
Evolution of genome composition and pangenome
Proteome comparison analysis (Intra- and Inter proteome conservation)
The proteome of all the A. baumannii strains was compared to estimate the amount of proteins they share. The result was expressed in the form of matrix showing the pair-wise comparison of proteomes (Fig. 4). The number and percentage of shared proteome was shown in corresponding box of the matrix (Fig. 4). The maximum protein conservation i.e., 95.8 %, was found among two carbapenem resistant strains, A. baumannii AC29 and A. baumannii AC30 strains, both isolated from tertiary care hospital in Malaysia. These two strains belonged to international clone II and had identical Apa I pulsotype . Minimum protein conservation was 60.8 % between A. baumannii 6411 and A. baumannii AB030 strains, both exhibiting diverse antibiotic resistance genes. A. baumannii 6411 possessed blaNDM-1 gene which conferred resistance to carbapenems and cephalosporin, and A. baumannii AB030 categorized as extremely drug resistant pathogen (showing resistant to all groups of antibiotics except colistin) . There are certain homology blocks in figure shaded dark green suggest more homology. Block1 (marked in the Fig. 4 as blue colored box) showed homology of more than 80 % between strains A. baumannii TCDC and A. baumannii 1656-2 with A. baumannii BJAB7104, MDR-TJ, MDR-Z, TYTH-1 and AC30, all belonging to IC II. Similarly, in block 2, three strains of IC I (A. baumannii AYE, AB0057, AB307-0294) showed more than 83 % homology with other IC I strains, A. baumannii A1 and A. baumannii AB5075 (marked in the Fig. 4 as a red colored box). The results of the BLAST matrix were found interesting as it can be compared to phylogenetic tree. For example, it can be seen that A. baumannii strain TYTH-1 exhibits more than 70 % homology with rest of A. baumannii strains and it is seen from the distance tree that these strains have evolved from a common ancestor and share common proteomes. Highest internal homology within proteome was seen in A. baumannii strain TCDC and A. baumannii AB030 i.e., 7 and 6.7 % respectively (Fig. 4). The relatively high proteome conservation in A. baumannii strains, for example 95.8 % homology in two carbapenem resistant strains clinical isolates, make these organism suitable to be targeted for broad spectrum therapeutics and there is a need to estimate the size of the pan-genome of the genus (closely related species).
Core proteome estimation for essentiality and non-host homologs
Vaccine candidates identification
The presence of core genes in the bacterial genomes is an evidence of conservative nature of evolution . It represents an ideal dataset for the exploration of suitable vaccine candidates against A. baumannii. It was subjected to sequential steps in order to find proteins which are at surface or secreted, and at the same time essential, virulent and non host homologs. Proteins prioritized after passing through these criteria were taken as suitable vaccine candidates and were further explored for their epitopes, protein-protein interactions, surface topology, comparative homology modeling and comprehensive functional analysis. The detailed methodology is described in the subsequent sections.
Epitope mapping of prioritized proteins
Prioritized core vaccine candidates against Acinetobacter baumannii
T Cell Epitope
Total number of MHC binding alleles
VaxiJen Score (Antigenicity) (Cut-off value 0.4)
Virulence score (Threshold 0.5)
P pilus assembly protein (HMPREF0010-00599)
P pilus assembly protein, porin PapC
Multidrug resistance outer membrane protein
ompF-ompA porin family
General secretion pathway protein D (HMPREF0010_01958)
Bacterial secretion system
FhuE receptor (HMPREF0010-00709)
Inorganic ion transport and metabolism
General Secretion pathway protein D (HMPREF0010_02518)
Type II secretory pathway
Type VI secretion system OmpA/MotB (HMPREF0010_01378)
ompF-ompA porin family
Ton B dependent siderophore receptor (HMPREF0010_01517)
Iron complex Outer membrane receptor
Outer membrane protein (29_170)
Outer membrane protein
Peptidyl-prolyl cis-trans isomerase (HMPREF0010_03292)
FKBP-type peptidyl-prolyl cis-trans isomerase
Peptidoglycan-associated lipoprotein (HMPREF0010_02142)
ompF-ompA porin family
Pilus assembly chaperone (HMPREF0010_002598)
Pilus assembly protein
ponA/Penicillin binding protein 1a
Cell wall/membrane biogenesis
The 9-mer sequence YQQVPSGGK from general secretion pathway protein D (HMPREF0010_01958) is surface exposed T-cell eptiope which exhibited antigenicity score of 0.64. The T-cell sequence YNVDASRLS from outer membrane protein OmpA can bind with total of 12 MHC 1 and MHC 11 class molecules, and is highly antigenic (1.61, Vaxijen score) and exhibited the lowest IC50 value for DRB1*0101 allele (1.99nM).The epitope YSGDSQLNA from Ton B dependent siderophore receptor fall in the category of medium affinity binder with seven out of nine surface exposed epitopes and Vaxigen antigenicity score of 1.42. Penicillin binding protein PonA revealed 9-mer completely surface exposed epitope FLIIIIILV with highest anitgenicity score of 2.12 and also bind with maximum number of MHC I and II  class molecules. The 9-mer sequence YNSASGTSI from outer membrane protein AdeK can bind with total of 30 MHC class I and II alleles, and is antigenic (1.93, Vaxijen score) and exhibited the lowest IC50 value for DRB1*0101 allele (12nM). Outer membrane protein P pilus assembly protein revealed an epitope WGDESNERC which has Vaxigen score of 1.85, virulence score 1.02, total number of MHC binding alleles 18. Likewise, 9-mer surface exposed T-cell epitopes of pilus assembly chaperone, general secretion pathway protein D (HMPREF0010_02518), Ton B dependent siderophore receptor, outer membrane protein, peptidoglycan associated lipoprotein showed appropriate antigenicity and virulence scores and fulfill all the criteria to be characterized as immunogenic.
Epitope conservation analysis
Prioritized proteins structure analysis
Functional annotation of predicted proteins
Protein-protein interactions (PPI) analysis of the candidate proteins
Validation of pipeline through positive control
H. pylori genome subjected to the same prediction process revealed five significant vaccine candidates which includes vacA, babA, sabA, fecA and omp16. The pipeline successfully identified already known immunogenic targets (vacA)  along with novel ones (babA, sabA, fecA and omp16)  against this Gram negative pathogen.
A. baumannii is an emerging MDR pathogen which is responsible for 2–10 % of all Gram negative hospital infections . The devastating healthcare and economic impact of A. baumannii infections in hospitals worldwide emphasize on the imperative need to exploit new approaches to confront the said pathogen. We have employed the pan-genomics and reverse vaccinology approaches which have revolutionized the understanding and tackling bacteria over the recent years. In this study, 30 complete genomes of A. baumannii were analyzed within the framework of pan-genomics, comparative genomics and proteomics. Previously, A. baumannii comparative studies employed varying sample sizes to study the pathogen genomics. Sahl et al. executed pan-genome analysis of six complete genomes ; Di Nocera et al. compared seven strains responsible for nosocomial outbreaks in Mediterranean hospitals . A wider and comprehensive analysis based on all completely sequenced genomes would provide a better framework for comparison than few organisms. This study showed the subsequent increase in pan-genome size with the addition of new genomes which is suggestive of an open pan-genome in A. baumannii and emphasizes on the presence of gene acquisition and loss events in the evolution, adaptation and persistence of this human pathogen . The pan-genome comprised of 7,606 coding sequences, of which 2,445 represent core and 5,161 were dispensable genome. The pan-genome appeared to be remarkably large because of presence of expansive pool of dispensable genes. The diverse pan-genome is suggestive of frequent horizontal gene transfer events . The evolution of pan-genome is largely affected by the process of gene conservation and transfer. Various studies have debated on the large and open pan-genome of A. baumannii [120, 121]. Few comparative genomic studies targeted the pan-genome of A. baumannii and subsequently the core genes have explored 1,455 and 2,688 coding sequences depending on the number and identity of strains analysis [82, 118, 122]. The open pan-genome suggests that the said pathogen has the remarkable propensity for gene gain and gene loss, which could help in its survival in diverse ecological niches, and could evolve with enhanced pathogenicity .
The A. baumannii strains have been classified based upon their clonal lineage to get better insights into epidemiology of this human pathogen . The rationale for exploring the clonal lineage and sequence typing facilitate in characterization of multidrug resistance phenotype among various strains. Secondly, these clonal studies assist in understanding the phenomenon of horizontal gene transfer in the A. baumannii strains, persuading in adaptation of this bacterium in diverse ecological environments . For instance, A. baumannii AYE belonging to clonal complex I had acquired an 86-kb resistance island (AbaR) under the selective pressure of broad spectrum antibiotics. AbaR is absent from first sequenced A. baumannii ATCC 17978, isolated before the development of new generation antibiotics . The phylogenetic analysis based on 16srRNA sequences and on shared gene families showed that strains belonging to same clonal lineages are closely related to each other and they exhibit strong homology in their proteome. The comprehensive whole proteome pair wise comparison led us to similar findings. The presence of common proteins among the strains convincingly supports that peptide vaccines can be efficiently developed against A. baumannii .
The success of A. baumannii can be attributed to its remarkable ability to harbor significant virulence factors as part of its core conserved genome; aiding it to adhere, colonize, form biofilms and escape antibiotics . Existence of certain virulence factors in the core genome like peroxiredoxin, high temperature protein G (HtpG), thioredoxin disulfide reductase (TrxB), superoxide dismutase, chaperone protein DnaJ, support A. baumannii to survive in stressful conditions. Furthermore, it was observed that there is spread of similar virulence factors and among a single clonal complex, thus aiding in the persistence of this bacterium in a particular geographical location. The existence and expansion of homogenous clonal lineages, whose main difference from the non-clonal A. baumannii appears to be their antimicrobial resistance, credibly suggests that there is horizontal acquisition of resistance genes from other nosocomial pathogens . Virulence factors comprised of 12 % of the core genome, which suggests that acquisition or existence of virulence factors is not likely a predominant factor in the recent nosocomial spread of A. baumannii clones. Other factors should be considered for the evolution of this pathogen as global superbug. One possibility is its innate notorious ability to adapt to its adverse environmental conditions and nosocomial settings. Another likelihood could be the differential regulation of conserved set of virulence genes according to the strain . Synergistic effect of multiple genes or polymorphic differences in shared virulence genes may play a part in diverse virulence potential of said pathogen .
Due to its remarkable ability to develop antimicrobial resistance, vaccination is considered a reliable alternative strategy to prevent infections caused by this drug resistant bacterium . Few conventional approaches have previously been used to identify potential vaccine candidates. Outer membrane and whole cell preparations have been employed in murine models of sepsis and generated active and passive immune response [129, 130]. But endotoxin contamination limited these conventional approaches to be developed or used in humans. In contrast, development of subunit peptide vaccines based on in silico reverse vaccinology (RV) approaches offer a feasible alternative, as these can induce efficient immunogenic response and can be obtained on a large scale . The use of sequence based approaches ensures the reliable vaccine candidates, and is strengthened by the employment of various search tools and filtering steps . In this study, we have adopted combinatory approach to identify putative vaccine candidates against A. baumannii, and we deduce that this will be more efficient than data obtained from each method as a standalone technique. Core protein of all the 30 included strains was considered as it would lead to a more representative and conserved set of targets. The strains employed covered representatives from the international A. baumannii clones currently circulating the globe. Appropriate location of proteins aid in effective recognition by MHC molecules and in inducing strong immunogenic responses . Among the core proteins, we have selected those proteins which are on cell surface or in periplasmic or extracellular space. Parameters of essentiality, virulent, non-host homologs, molecular weight and trans-membrane helices were summed up and thirteen core proteins have been found out to be potential vaccine candidates. This included OmpA which have been previously examined as vaccine candidate against A. baumannii. OmpA is an important virulence factor in the pathogenesis of A. baumannii infections . OmpA is highly conserved among the clinical strains, but share minimal homology to human proteome . In vitro studies have shown that it induces phenotypic maturation of dendritic cells (DC) and promote Th1 immune responses, and in vivo murine melanoma models, it stimulates maturation of murine splenic DCs and associate with enhanced surface expression of co-stimulatory molecules CD 80 and CD 86 and MHC class I and II molecules of dendritic cells . In another in vivo testing of OmpA antigen with aluminium hydroxide adjuvant in diabetic mice resulted in production of high anti-OmpA antibody titers and also improved survival of mice following intravenous infection with A. baumannii [134, 136]. In another study, OmpA was found to induce little protection in a mouse model of A. baumannii pneumonia infection . The difference in these observations could be due to route of challenge, immunization strategies, OmpA refolding, and animal models. Besides, OmpA few other antigens have been identified which could elicit immune response. By combining in silico comparative genome analysis with proteomic approaches, Moriel et al. identified 42 surface-exposed and secreted antigens from A. baumannii that could be used as potential vaccine targets . Chiang et al. analyzed 14 complete genomes of A.baumannii and identified 13 genes from 2752 homologous core genes of A. baumannii as potential vaccine candidate antigens. Peptidoglycan associated lipoprotein identified from our analysis of 30 complete genomes was also identified as potential antigenic candidate by Chiang et al. .
It is analyzed in this study that two of the prioritized core proteins PonA and general secretion pathway protein D interact with three TFP proteins naming Tfp pilus assembly protein PilP, type 4 fimbrial biogenesis protein PilO and type IV pilus assembly protein PilM. These three TFP proteins are bacterial surface appendages and participate in natural transformation, twitching motility and adherence of bacteria . This suggests that bacteria possess significant surface core proteins which assist with TFP proteins to help bacteria to exhibit motility. This property helps bacteria to adhere to biotic and abiotic surfaces and facilitate in virulence, colonization and subsequently cause the infection. Peleg et al. recently studied the virulence behavior of six A. baumannii strains and interestingly found presence of TFP proteins in its core proteome, facilitating in survival of bacteria in divers ecological niches . Recently, Eijekelkamp et al. observed that all the A. baumannii strains that belong to clonal lineage I were capable of twitching motility, indicating that they may produce TFP . AdeK, another core vaccine target in this study, belongs to AdeIJK resistance nodulation cell division (RND) family efflux pump reported in A. baumannii . This tripartite pump comprised of AdeI, AdeJ and AdeK genes which encode membrane fusion protein, transporter and outer membrane component of the pump, respectively . This type of pumps contributes to resistance to β -lactams, chloramphenicol, tetracycline, erythromycin, flouroquinolones, fusidic acid, trimethoprim, rifampin, pyronine and sodium dodecyl sulphate . Presence of AdeK in core proteome of thirty A. baumannii strains suggests that bacteria have acquired robust mechanisms in its core genome to exhibit resistance to almost all major antibiotics. PPI showed that this core protein interacts with its accomplices (AdeI, AdeJ, AdeK) and with multidrug transporter proteins enabling it to successfully efflux antibiotics, leading to its survival in hospital settings. TonB dependent siderophore receptor found out as an essential core protein in our study. Cells growing in aerobic conditions have adapted complex strategies to overcome scarcity of iron, an essential element. Outer membrane protein localized complexes like TonB proteins bind with iron chelates at the cell surface and promote their uptake . We have observed that A. baumannii correlates with FhuE receptor for maximal transport efficiency of iron, coprogen and ferric-rhodotorulic acid across the membrane . Fajardo Bonin et al. used human sera from A. baumannii infected patients to screen against A. baumannii outer membrane proteins and identified six immune-reactive proteins including ferric siderophore receptor protein, OmpA, Omp34kDa, OprC, OprB-like, OXA-23 [146, 147]. Further in vivo studies are required to validate the immunogenicity of these candidates.
The conserved regions within a protein are considered to evolve slowly. These regions usually considered vital for function and believed to be associated with lower variability and are highly conserved among different strains, additionally, these regions are often represent reliable targets for the development of epitope-based vaccines . Likewise, the predicted vaccine candidates containing conserved (100 %) epitopes such as IQSSGSYEY, YSGDSQLNA might be effective in providing broad-spectrum protection. The three epitopes (WGDESNERC, IKEDANLAA and IKLYDSNVN) showed 80–90 % conservations which is an indication of a slight variability in parent proteins and hence the rapid evolution the regions. As these epitopes are found with less than the identity level threshold and should be critically re-considered as they may suggest the uniqueness of the specific epitope. As the different strains of A. baumannii included in this study belong to different geographical locations they might have adapted diverse virulence mechanism and pathogenesis, such as proteins P pilus assembly protein, pilus assembly chaperone and FhuE receptor. These proteins and associated epitopes may be effectively considered for designing strain-specific vaccines.
The pipeline is validated by predicting the potential vaccine candidates against H. pylori. Studies on large and small animal models have already been carried out to demonstrate the efficacy of vacA and cagA against H. pylori through vaccination [149, 150]. In addition, promising findings were also observed in preclinical trials as a result of vaccination, which includes the induction of T-cell mediated immunity (local gastric Th1 and Th17 responses) . Two recently conducted phase I clinical trials in human volunteers based on three recombinant antigens (cagA, vacA and NAP) induced T-cell responses . Our computational pipeline successfully identified already known immunogenic targets (vacA) [149, 153] and along with novel ones (babA, sabA, fecA and omp16)  against the H. pylori. This serves as positive control for our proposed pipeline to identify potential vaccine targets in H. pylori. Thus, we are confident about the predictions by employed methodology and the identified vaccine candidates against A. baumannii and hence the functionalities of the proposed pipeline.
All the theoretical approaches have advantages and limitations in general, regarding the methodology adopted in this manuscript; a possible challenge is the predicted prioritized protein “PonA (PBP1a)” which is found to be attached with the cytoplasmic membrane and hence, may not be effectively generate immune response (antibodies). Basically, parameters were set to include all the outer membrane proteins, extracellular and periplasmic proteins. Among the predicted proteins, ponA is found out as extracellular protein by the sub-cellular localization program “PsortB”. This could be considered as a limitation of the tool itself, not with the overall pipeline; therefore it is suggested that the researcher should cross-check the findings with other subcellular localization tools such as Cello, MetaLocGramN etc. [93, 154]. In this manuscript, a universal pipeline is proposed which could be applied on both the Gram positive and Gram negative bacteria in general; however, it is strongly suggested that the parameters may be carefully monitored and adjusted while dealing with Gram negative bacteria due to compositions of cell wall.
Similarly, the peptidoglycan associated lipoprotein which is anchored in the outer membrane of Gram negative bacteria through an N-terminal hydrophobic domain and interacts with the cell wall peptidoglycan by C-terminal OmpA-like domain. This protein is classified as an outer membrane by the program (PsortB) employed for subcellular localization and validated by another program (Cello) as well. Previously, the peptidoglycan associated lipoproteins have been reported to be potential vaccine candidates in few Gram negative bacteria e.g., Haemophilus influenzae, Haemophilus ducreyi, Legionella pneumophila and Campylobacter jejuni. Quite recently, in a study conducted by Huang et al. in 2016, Omp22 from A. baumannii efficiently elicited high titers of specific IgG in mice . Domain analysis revealed that Omp22 might be an “outer membrane protein-related peptidoglycan-associated (lipo) protein” . This in vivo study further strengthens the role of this protein in generating substantial immune response against A. baumannii.
Considerable progresses have been made recently in the development of an effective vaccine to combat infections due to MDR A. baumannii. Several experimental vaccines have been evaluated and induced satisfactory antibody responses . However, substantial methodical challenges remain in the development of safe and effective vaccines for A. baumannii. The role of antibodies in vaccine induced protection has already been elucidated in immunization studies but thorough advanced specifics like antibody mediated opsonization, bactericidal studies, mucosal IgA antibody response, T cell response need further research. These understandings will likely to accelerate the vaccine development. Based upon the rapid course of A. baumannii infection, it is imperative to have an efficient vaccine which can control bacterial replication at an early stage of disease. Compared to other pathogens, relatively smaller numbers of antigen candidates have currently been identified for A. baumannii vaccines. However, recent enhanced exploration of pan-genomics, virulence factors, antibiotic resistant islands, pathogenicity islands of A. baumannii are likely to expedite the identification and development of effective vaccine candidate against said pathogen. Large scale studies like ours encompassing the pan-genomics, comparative genomics, reverse vaccinology, immunoproteomics would contribute to rational design of vaccine against A. baumannii.
The study, based upon combinatorial approach of pan-genomics, core genomics, proteomics and reverse vaccinology led us to find out potential vaccine candidates against A. baumannii. The comprehensive analysis of all the completely sequenced genomes revealed thirteen putative antigens naming P pilus assembly protein, pili assembly chaperone, AdeK, PonA, OmpA, general secretion pathway protein D, FhuE receptor, Type VI secretion system OmpA/MotB, TonB dependent siderophore receptor, general secretion pathway protein D, outer membrane protein, peptidoglycan associated lipoprotein and peptidyl-prolyl cis-trans isomerase;which could elicit substantial immune response. The integration of computational vaccinology strategies would facilitate in tackling the rapid dissemination of resistant A.baumannii strains. Future work is suggested towards the characterization of the candidate proteins and immunology followed by evaluation in animal models.
A. baumannii, Acinetobacter baumannii; BLAST, Basic Local Alignment Search Tool; BLASTp, Protein BLAST; COG, Cluster of Orthologous groups; DEG, Database for Essential genes; IC50, Half maximal inhibition concentration; IDSA, Infectious Diseases Society of America; KEGG, Kyoto encyclopedia of genes and genomes; MHC, Major histocompatibility complex; MLST, Multi locus sequence typing; MvirDB, Microbial virulence data base; NCBI, National Center for Biotechnology Information; PDB, Protein databank; VFDB, Virulence factor database
Availability of data and materials
The datasets generated during the current study are available in the DRYAD digital repository (http://dx.doi.org/10.5061/dryad.k44f0).
AH carried out the systematic methodology; analyzed and interpreted the data; prepared the manuscript. AN participated in analysis of results and drafting of the manuscript. AO participated in analysis and drafting of the manuscript. RZP participated in drafting of the manuscript. KN participated in the analysis and interpretation of the results. FMA participated in drafting of the manuscript. SAM helped in drafting of the manuscript. HAJ contributed in the critical revision of the manuscript. JA participated in drafting of the manuscript. AA conceived of the study, and participated in its design and helped to draft the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Standards of reporting
Resource identifiers (RRIDs) to identify the tools employed in this study are mentioned in methods section.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Fournier PE, Richet H, Weinstein RA. The epidemiology and control of Acinetobacter baumannii in health care facilities. Clin Infect Dis. 2006;42(5):692–9.PubMedView ArticleGoogle Scholar
- Cisneros JM, Reyes MJ, Pachon J, Becerril B, Caballero FJ, Garmendia JLG, et al. Bacteremia due to Acinetobacter baumannii: epidemiology, clinical findings, and prognostic features. Clin Infect Dis. 1996;22(6):1026–32.PubMedView ArticleGoogle Scholar
- Perez F, Bonomo RA. Vaccines for Acinetobacter baumannii: Thinking “out of the box”. Vaccine. 2014;32(22):2537–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Munoz-Price LS, Weinstein RA. Acinetobacter infection. N Engl J Med. 2008;358(12):1271–81.PubMedView ArticleGoogle Scholar
- Iwen PC. Acinetobacter baumannii: A Dangerous Pathogen Emerges.Google Scholar
- Talbot GH. What is in the pipeline for Gram-negative pathogens? 2008.Google Scholar
- Jones LS, Howe RA. Microbial Resistance and Superbugs. Biofilms in Infection Prevention and Control: A Healthcare Handbook. 2014:257.Google Scholar
- Laxminarayan R, Duse A, Wattal C, Zaidi AK, Wertheim HF, Sumpradit N, et al. Antibiotic resistance—the need for global solutions. Lancet Infect Dis. 2013;13(12):1057–98.PubMedView ArticleGoogle Scholar
- Rappuoli R. Reverse vaccinology, a genome-based approach to vaccine development. Vaccine. 2001;19(17):2688–91.PubMedView ArticleGoogle Scholar
- Giuliani MM, Adu-Bobie J, Comanducci M, Aricò B, Savino S, Santini L, et al. A universal vaccine for serogroup B meningococcus. Proc Natl Acad Sci U S A. 2006;103(29):10834–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Mora M, Donati C, Medini D, Covacci A, Rappuoli R. Microbial genomes and vaccine design: refinements to the classical reverse vaccinology approach. Curr Opin Microbiol. 2006;9(5):532–6.PubMedView ArticleGoogle Scholar
- Antunes LC, Visca P, Towner KJ. Acinetobacter baumannii: evolution of a global pathogen. Pathogens and disease. 2014;71(3):292–301.PubMedView ArticleGoogle Scholar
- Vivona S, Bernante F, Filippini F. NERVE: new enhanced reverse vaccinology environment. BMC Biotechnol. 2006;6(1):35.PubMedPubMed CentralView ArticleGoogle Scholar
- Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics. 2007;8(1):4.PubMedPubMed CentralView ArticleGoogle Scholar
- Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC bioinformatics. 2010;11(1):119.PubMedPubMed CentralView ArticleGoogle Scholar
- Grissa I, Vergnaud G, Pourcel C. The CRISPRdb database and tools to display CRISPRs and to generate dictionaries of spacers and repeats. BMC bioinformatics. 2007;8(1):172.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y, et al. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 2005;33 suppl 1:D325–8.PubMedGoogle Scholar
- Van Belkum A, Struelens M, de Visser A, Verbrugh H, Tibayrenc M. Role of genomic typing in taxonomy, evolutionary genetics, and microbial epidemiology. Clin Microbiol Rev. 2001;14(3):547–60.PubMedPubMed CentralView ArticleGoogle Scholar
- Diancourt L, Passet V, Nemec A, Dijkshoorn L, Brisse S. The population structure of Acinetobacter baumannii: expanding multiresistant clones from an ancestral susceptible genetic pool. PLoS One. 2010;5(4):e10034.PubMedPubMed CentralView ArticleGoogle Scholar
- Dijkshoorn L, Nemec A, Seifert H. An increasing threat in hospitals: multidrug-resistant Acinetobacter baumannii. Nat Rev Microbiol. 2007;5(12):939–51.PubMedView ArticleGoogle Scholar
- Karah N, Sundsfjord A, Towner K, Samuelsen Ø. Insights into the global molecular epidemiology of carbapenem non-susceptible clones of Acinetobacter baumannii. Drug Resist Updat. 2012;15(4):237–47.PubMedView ArticleGoogle Scholar
- Chen L, Xiong Z, Sun L, Yang J, Jin Q. VFDB 2012 update: toward the genetic diversity and molecular evolution of bacterial virulence factors. Nucleic Acids Res. 2012; 40(Database issue):D641–D645.Google Scholar
- Wilson BA, Salyers AA, Whitt DD, Winkler ME. Bacterial pathogenesis: a molecular approach. Washington, D. C.: American Society for Microbiology (ASM); 2011.Google Scholar
- Makarova KS, Haft DH, Barrangou R, Brouns SJ, Charpentier E, Horvath P, et al. Evolution and classification of the CRISPR–Cas systems. Nat Rev Microbiol. 2011;9(6):467–77.PubMedView ArticleGoogle Scholar
- Zhou C, Smith J, Lam M, Zemla A, Dyer MD, Slezak T. MvirDB—a microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications. Nucleic Acids Res. 2007;35 suppl 1:D391–4.PubMedView ArticleGoogle Scholar
- Lagesen K, Hallin P, Rødland EA, Stærfeldt H-H, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35(9):3100–8.PubMedPubMed CentralView ArticleGoogle Scholar
- Ali A, Naz A, Soares SC, Bakhtiar M, Tiwari S, Hassan SS, et al. Pan-Genome Analysis of Human Gastric Pathogen H. pylori: Comparative Genomics and Pathogenomics Approaches to Identify Regions Associated with Pathogenicity and Prediction of Potential Core Therapeutic Targets. BioMed Res Int. 2015;2015:17. Article ID 139580. doi:10.1155/2015/139580.
- Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22(22):4673–80.PubMedPubMed CentralView ArticleGoogle Scholar
- Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30(12):2725–29.PubMedPubMed CentralView ArticleGoogle Scholar
- Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4(4):406–25.PubMedGoogle Scholar
- Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. 2005;15(6):589–94.PubMedView ArticleGoogle Scholar
- Ali A, Soares SC, Santos AR, Guimarães LC, Barbosa E, Almeida SS, et al. Campylobacter fetus subspecies: Comparative genomics and prediction of potential virulence targets. Gene. 2012;508(2):145–56.PubMedView ArticleGoogle Scholar
- Lukjancenko O, Ussery DW, Wassenaar TM. Comparative genomics of Bifidobacterium, Lactobacillus and related probiotic genera. Microb Ecol. 2012;63(3):651–73.PubMedView ArticleGoogle Scholar
- Ussery DW, Wassenaar TM, Borini S. Microbial communities: core and pan-genomics. Computing for Comparative Microbial Genomics. London: Springer-Verlag; 2009. p. 213-28.Google Scholar
- Trost E, Blom J, de Castro Soares S, Huang I-H, Al-Dilaimi A, Schröder J, et al. Pangenomic study of Corynebacterium diphtheriae that provides insights into the genomic diversity of pathogenic isolates from cases of classical diphtheria, endocarditis, and pneumonia. J Bacteriol. 2012;194(12):3199–215.PubMedPubMed CentralView ArticleGoogle Scholar
- Ali A, Soares SC, Barbosa E, Santos AR, Barh D, et al. Microbial Comparative Genomics: An Overview of Tools and Insights Into The Genus Corynebacterium. J Bacteriol Parasitol. 2013;4:167. doi:10.4172/2155-9597.1000167.
- Snipen L, Almøy T, Ussery DW. Microbial comparative pan-genomics using binomial mixture models. BMC Genomics. 2009;10(1):385.PubMedPubMed CentralView ArticleGoogle Scholar
- Binnewies TT, Hallin PF, Stærfeldt H-H, Ussery DW. Genome update: proteome comparisons. Microbiology. 2005;151(1):1–4.PubMedView ArticleGoogle Scholar
- Luo H, Lin Y, Gao F, Zhang C-T, Zhang R. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res. 2014;42(D1):D574–80.PubMedView ArticleGoogle Scholar
- Galperin MY, Koonin EV. Searching for drug targets in microbial genomes. Curr Opin Biotechnol. 1999;10(6):571–8.PubMedView ArticleGoogle Scholar
- Naz A, Awan FM, Obaid A, Muhammad SA, Paracha RZ, Ahmad J, et al. Identification of putative vaccine candidates against Helicobacter pylori. Helicobacter. 2015;3:1.Google Scholar
- Gardy JL, Laird MR, Chen F, Rey S, Walsh C, Ester M, et al. PSORTb v. 2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics. 2005;21(5):617–23.PubMedView ArticleGoogle Scholar
- Zagursky RJ, Olmsted SB, Russell DP, Wooters JL. Bioinformatics: how it is being used to identify bacterial vaccine candidates. 2003Google Scholar
- Tusnady GE, Simon I. The HMMTOP transmembrane topology prediction server. Bioinformatics. 2001;17(9):849–50.PubMedView ArticleGoogle Scholar
- Krogh A, Larsson B, Von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305(3):567–80.PubMedView ArticleGoogle Scholar
- Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. Springer; 2005.Google Scholar
- Barh D, Barve N, Gupta K, Chandra S, Jain N, Tiwari S, et al. Exoproteome and secretome derived broad spectrum novel drug and vaccine candidates in Vibrio cholerae targeted by Piper betel derived compounds. PLoS One. 2013;8(1):e52773.PubMedPubMed CentralView ArticleGoogle Scholar
- Sette A, Vitiello A, Reherman B, Fowler P, Nayersina R, Kast WM, et al. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J Immunol. 1994;153(12):5586–92.PubMedGoogle Scholar
- Brusic V, Petrovsky N. Immunoinformatics and its relevance to understanding human immune disease. 2005.Google Scholar
- Provenzano M, Panelli MC, Mocellin S, Bracci L, Sais G, Stroncek DF, et al. MHC–peptide specificity and T-cell epitope mapping: where immunotherapy starts. Trends Mol Med. 2006;12(10):465–72.PubMedView ArticleGoogle Scholar
- Saha S, Raghava GP. Prediction methods for B-cell epitopes. Immunoinformatics: Springer; 2007. p. 387–94.Google Scholar
- Singh H, Raghava G. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics. 2003;19(8):1009–14.PubMedView ArticleGoogle Scholar
- Chaplin DD. 1. Overview of the immune response. J Allergy Clin Immunol. 2003;111(2):S442–59.PubMedView ArticleGoogle Scholar
- Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C. A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct Biol. 2009;9(1):51.PubMedPubMed CentralView ArticleGoogle Scholar
- Guan P, Doytchinova IA, Zygouri C, Flower DR. MHCPred: a server for quantitative prediction of peptide–MHC binding. Nucleic Acids Res. 2003;31(13):3621–4.PubMedPubMed CentralView ArticleGoogle Scholar
- Southwood S, Sidney J, Kondo A, del Guercio M-F, Appella E, Hoffman S, et al. Several common HLA-DR types share largely overlapping peptide binding repertoires. J Immunol. 1998;160(7):3363–73.PubMedGoogle Scholar
- Hosseingholi EZ, Rasooli I, Gargari SLM. In silico analysis of Acinetobacter baumannii phospholipase D as a subunit vaccine candidate. Acta Biotheor. 2014;62(4):455–78.PubMedView ArticleGoogle Scholar
- Rakesh S, Pradhan D, Umamaheswari A. In silico approach for future development of subunit vaccines against Leptospira interrogans serovar Lai. Int J Bioinformatics Res. 2009;1:85–92.View ArticleGoogle Scholar
- Blythe MJ, Doytchinova IA, Flower DR. JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics. 2002;18(3):434–9.PubMedView ArticleGoogle Scholar
- Garg A, Gupta D. VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens. BMC bioinformatics. 2008;9(1):1.View ArticleGoogle Scholar
- Workbench CG. v3. 6.(2010). Now new version can be available at http://www.clcbio.com/products/clcgenomicsworkbench.
- Gabdoulline RR, Hoffmann R, Leitner F, Wade RC. ProSAT: functional annotation of protein 3D structures. Bioinformatics. 2003;19(13):1723–5.PubMedView ArticleGoogle Scholar
- Maddison W, Maddison D, Team D. SWISS-MODEL. Evolution. 2008;62(5):1103–18.View ArticleGoogle Scholar
- Sussman JL, Lin D, Jiang J, Manning NO, Prilusky J, Ritter O, et al. Protein Data Bank (PDB): database of three-dimensional structural information of biological macromolecules. Acta Crystallogr D Biol Crystallogr. 1998;54(6):1078–84.PubMedView ArticleGoogle Scholar
- Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res. 2003;31(13):3381–5.PubMedPubMed CentralView ArticleGoogle Scholar
- Dormitzer PR, Ulmer JB, Rappuoli R. Structure-based antigen design: a strategy for next generation vaccines. Trends Biotechnol. 2008;26(12):659–67.PubMedView ArticleGoogle Scholar
- Mayrose I, Penn O, Erez E, Rubinstein ND, Shlomi T, Freund NT, et al. Pepitope: epitope mapping from affinity-selected peptides. Bioinformatics. 2007;23(23):3244–6.PubMedView ArticleGoogle Scholar
- Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–6.PubMedView ArticleGoogle Scholar
- Wu S, Zhu Z, Fu L, Niu B, Li W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genomics. 2011;12(1):444.PubMedPubMed CentralView ArticleGoogle Scholar
- Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011;39 suppl 1:D561–8.PubMedView ArticleGoogle Scholar
- Bultinck J, Lievens S, Tavernier J. Protein-protein interactions: network analysis and applications in drug discovery. Curr Pharm Des. 2012;18(30):4619–29.PubMedView ArticleGoogle Scholar
- Alonso A, Sanchez P, Martinez JL. Environmental selection of antibiotic resistance genes. Environ Microbiol. 2001;3(1):1–9.PubMedView ArticleGoogle Scholar
- Howard A, O’Donoghue M, Feeney A, Sleator RD. Acinetobacter baumannii: an emerging opportunistic pathogen. Virulence. 2012;3(3):243–50.PubMedPubMed CentralView ArticleGoogle Scholar
- Gaddy JA, Tomaras AP, Actis LA. The Acinetobacter baumannii 19606 OmpA protein plays a role in biofilm formation on abiotic surfaces and in the interaction of this pathogen with eukaryotic cells. Infect Immun. 2009;77(8):3150–60.PubMedPubMed CentralView ArticleGoogle Scholar
- Cerqueira GM, Peleg AY. Insights into Acinetobacter baumannii pathogenicity. IUBMB Life. 2011;63(12):1055–60.PubMedView ArticleGoogle Scholar
- Heindorf M, Kadari M, Heider C, Skiebe E, Wilharm G. Impact of Acinetobacter baumannii superoxide dismutase on motility, virulence, oxidative stress resistance and susceptibility to antibiotics. 2014.Google Scholar
- Fiester SE, Actis LA. Stress responses in the opportunistic pathogen Acinetobacter baumannii. Future Microbiol. 2013;8(3):353–65.PubMedPubMed CentralView ArticleGoogle Scholar
- Hornef MW, Wick MJ, Rhen M, Normark S. Bacterial strategies for overcoming host innate and adaptive immune responses. Nat Immunol. 2002;3(11):1033–40.PubMedView ArticleGoogle Scholar
- Liebert CA, Hall RM, Summers AO. Transposon Tn21, flagship of the floating genome. Microbiol Mol Biol Rev. 1999;63(3):507–22.PubMedPubMed CentralGoogle Scholar
- Koonin EV, Makarova KS, Aravind L. Horizontal gene transfer in prokaryotes: quantification and classification 1. Annual Reviews in Microbiology. 2001;55(1):709–42.View ArticleGoogle Scholar
- Zarrilli R, Pournaras S, Giannouli M, Tsakris A. Global evolution of multidrug-resistant Acinetobacter baumannii clonal lineages. Int J Antimicrob Agents. 2013;41(1):11–9.PubMedView ArticleGoogle Scholar
- Farrugia DN, Elbourne L, Hassan KA, Eijkelkamp BA, Tetu SG, Brown MH, et al. The complete genome and phenome of a community-acquired Acinetobacter baumannii. PLoS One. 2013;8(3):e58628.PubMedPubMed CentralView ArticleGoogle Scholar
- Loewen PC, Alsaadi Y, Fernando D, Kumar A. Genome sequence of a tigecycline-resistant clinical isolate of Acinetobacter baumannii strain AB031 obtained from a bloodstream infection. Genome Announc. 2014;2(5):e01036-14. doi:10.1128/genomeA.01036-14.
- Tada T, Miyoshi-Akiyama T, Shimada K, Nga TTT, Son NT, Ohmagari N, et al. Dissemination of clonal complex 2 Acinetobacter baumannii strains co-producing carbapenemases and 16S rRNA methylase ArmA in Vietnam. BMC Infect Dis. 2015;15(1):1.View ArticleGoogle Scholar
- Martínez JL, Baquero F, Andersson DI. Predicting antibiotic resistance. Nat Rev Microbiol. 2007;5(12):958–65.PubMedView ArticleGoogle Scholar
- Peleg AY, Seifert H, Paterson DL. Acinetobacter baumannii: emergence of a successful pathogen. Clin Microbiol Rev. 2008;21(3):538–82.PubMedPubMed CentralView ArticleGoogle Scholar
- Lean S-S, Yeo CC, Suhaili Z, Thong K-L. Comparative genomics of two ST 195 carbapenem-resistant Acinetobacter baumannii with different susceptibility to polymyxin revealed underlying resistance mechanism. Front Microbiol. 2015;6:1445.Google Scholar
- Loewen PC, Alsaadi Y, Fernando D, Kumar A. Genome sequence of an extremely drug-resistant clinical isolate of Acinetobacter baumannii strain AB030. Genome Announc. 2014;2(5):e01035-14.Google Scholar
- Zhang R, Ou HY, Zhang CT. DEG: a database of essential genes. Nucleic Acids Res. 2004;32 suppl 1:D271–2.PubMedPubMed CentralView ArticleGoogle Scholar
- Judson N, Mekalanos JJ. Transposon-based approaches to identify essential bacterial genes. Trends Microbiol. 2000;8(11):521–6.PubMedView ArticleGoogle Scholar
- Butt AM, Nasrullah I, Tahir S, Tong Y. Comparative genomics analysis of Mycobacterium ulcerans for the identification of putative essential genes and therapeutic candidates. PLoS One. 2012;7(8):e43080.PubMedPubMed CentralView ArticleGoogle Scholar
- Lapierre P, Gogarten JP. Estimating the size of the bacterial pan-genome. Trends Genet. 2009;25(3):107–10.PubMedView ArticleGoogle Scholar
- Yu CS, Lin CJ, Hwang JK. Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions. Protein Sci. 2004;13(5):1402–6.PubMedPubMed CentralView ArticleGoogle Scholar
- Shanmugham B, Pan A. Identification and characterization of potential therapeutic candidates in emerging human pathogen Mycobacterium abscessus: a novel hierarchical in silico approach. PLoS One. 2013;8(3):e59126.Google Scholar
- Dahlback M, Rask TS, Andersen PH, Nielsen MA, Ndam NT, Resende M, et al. Epitope mapping and topographic analysis of VAR2CSA DBL3X involved in P. falciparum placental sequestration. PLoS Pathog. 2006;2(11):e124.PubMedPubMed CentralView ArticleGoogle Scholar
- Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36(10):3420–35.PubMedPubMed CentralView ArticleGoogle Scholar
- Choi CH, Lee EY, Lee YC, Park TI, Kim HJ, Hyun SH, et al. Outer membrane protein 38 of Acinetobacter baumannii localizes to the mitochondria and induces apoptosis of epithelial cells. Cell Microbiol. 2005;7(8):1127–38.PubMedView ArticleGoogle Scholar
- Rahman MM, McFadden G. Modulation of tumor necrosis factor by microbial pathogens. PLoS Pathog. 2006;2(2):e4.PubMedPubMed CentralView ArticleGoogle Scholar
- Hultgren SJ, Normark S, Abraham SN. Chaperone-assisted assembly and molecular architecture of adhesive pili. Annual Reviews in Microbiology. 1991;45(1):383–415.View ArticleGoogle Scholar
- Wu H, Fives-Taylor PM. Molecular strategies for fimbrial expression and assembly. Critical Reviews in Oral Biology & Medicine. 2001;12(2):101–15.View ArticleGoogle Scholar
- Nicholas RA, Davies C. Structural Mechanisms of β-Lactam Antibiotic Resistance in Penicillin-Binding Proteins. Antibiotic Discovery and Development: Springer; 2012. p. 397–425.Google Scholar
- Stathopoulos C, Hendrixson DR, Thanassi DG, Hultgren SJ, Geme III JWS, Curtiss III R. Secretion of virulence determinants by the general secretory pathway in gram-negative pathogens: an evolving story. Microbes Infect. 2000;2(9):1061–72.PubMedView ArticleGoogle Scholar
- Moon DC, Choi CH, Lee JH, Choi C-W, Kim H-Y, Park JS, et al. Acinetobacter baumannii outer membrane protein A modulates the biogenesis of outer membrane vesicles. J Microbiol. 2012;50(1):155–60.PubMedView ArticleGoogle Scholar
- Funahashi T, Tanabe T, Mihara K, Miyamoto K, Tsujibo H, Yamamoto S. Identification and characterization of an outer membrane receptor gene in Acinetobacter baumannii required for utilization of desferricoprogen, rhodotorulic acid, and desferrioxamine B as xenosiderophores. Biol Pharm Bull. 2012;35(5):753–60.PubMedView ArticleGoogle Scholar
- Shaw PE. Peptidyl‐prolyl isomerases: A new twist to transcription. EMBO Rep. 2002;3(6):521–6.PubMedPubMed CentralView ArticleGoogle Scholar
- Wua S, Zhua Z, Fua L, Niua B, Lib W. Customizable Web Server for Fast Metagenomic Sequence Analysis. 2014.Google Scholar
- Pe’er D, Regev A, Elidan G, Friedman N. Inferring subnetworks from perturbed expression profiles. Bioinformatics. 2001;17 suppl 1:S215–24.PubMedView ArticleGoogle Scholar
- Harshey RM. Bacterial motility on a surface: many ways to a common goal. Annual Reviews in Microbiology. 2003;57(1):249–73.View ArticleGoogle Scholar
- Schneiders T, Findlay J, Amyes SG. Efflux Pumps in Acinetobacter baumannii. Acinetobacter Biology and Pathogenesis: Springer; 2008. p. 105–27.Google Scholar
- Chakraborty R, Braun V, Hantke K, Cornelis P. Iron Uptake in Bacteria with Emphasis on E. coli and Pseudomonas. Netherlands: Springer Science & Business Media; 2013.Google Scholar
- Braun V, Günter K, Hantke K. Transport of iron across the outer membrane. Biol Met. 1991;4(1):14–22.PubMedView ArticleGoogle Scholar
- Gonzalez MW, Kann MG. Chapter 4: Protein interactions and disease. PLoS Comput Biol. 2012;8(12):002819.View ArticleGoogle Scholar
- Prinz C, Hafsi N, Voland P. Helicobacter pylori virulence factors and the host immune response: implications for therapeutic vaccination. Trends Microbiol. 2003;11(3):134–8.PubMedView ArticleGoogle Scholar
- Oleastro M, Ménard A. The role of Helicobacter pylori outer membrane proteins in adherence and pathogenesis. Biology. 2013;2(3):1110–34.PubMedPubMed CentralView ArticleGoogle Scholar
- Richet H, Fournier PE. Nosocomial Infections Caused by Acinetobacter baumannii A Major Threat Worldwide. Infect Control. 2006;27(07):645–6.Google Scholar
- Sahl JW, Johnson JK, Harris AD, Phillippy AM, Hsiao WW, Thom KA, et al. Genomic comparison of multi-drug resistant invasive and colonizing Acinetobacter baumannii isolated from diverse human body sites reveals genomic plasticity. BMC Genomics. 2011;12(1):291.PubMedPubMed CentralView ArticleGoogle Scholar
- Di Nocera PP, Rocco F, Giannouli M, Triassi M, Zarrilli R. Genome organization of epidemic Acinetobacter baumannii strains. BMC Microbiol. 2011;11(1):224.PubMedPubMed CentralView ArticleGoogle Scholar
- Imperi F, Antunes L, Blom J, Villa L, Iacono M, Visca P, et al. The genomics of Acinetobacter baumannii: insights into genome plasticity, antimicrobial resistance and pathogenicity. IUBMB Life. 2011;63(12):1068–74.PubMedView ArticleGoogle Scholar
- Antunes L, Visca P, Towner KJ. Acinetobacter baumannii: evolution of a global pathogen. Pathogens and disease. 2014;71(3):292–301.PubMedView ArticleGoogle Scholar
- Rouli L, Merhej V, Fournier P-E, Raoult D. The bacterial pangenome as a new tool for analysing pathogenic bacteria. New microbes and new infections. 2015;7:72–85.PubMedPubMed CentralView ArticleGoogle Scholar
- Chan AP, Sutton G, DePew J, Krishnakumar R, Choi Y, Huang X-Z, et al. A novel method of consensus pan-chromosome assembly and large-scale comparative analysis reveal the highly flexible pan-genome of Acinetobacter baumannii. Genome Biol. 2015;16(1):1–28.View ArticleGoogle Scholar
- Adams MD, Goglin K, Molyneaux N, Hujer KM, Lavender H, Jamison JJ, et al. Comparative genome sequence analysis of multidrug-resistant Acinetobacter baumannii. J Bacteriol. 2008;190(24):8053–64.PubMedPubMed CentralView ArticleGoogle Scholar
- Struelens M. Consensus guidelines for appropriate use and evaluation of microbial epidemiologic typing systems. Clin Microbiol Infect. 1996;2(1):2–11.PubMedView ArticleGoogle Scholar
- García-Quintanilla M, Pulido MR, López-Rojas R, Pachón J, McConnell MJ. Emerging therapies for multidrug resistant Acinetobacter baumannii. Trends Microbiol. 2013;21(3):157–63.PubMedView ArticleGoogle Scholar
- Beceiro A, Tomás M, Bou G. Antimicrobial resistance and virulence: a successful or deleterious association in the bacterial world? Clin Microbiol Rev. 2013;26(2):185–230.PubMedPubMed CentralView ArticleGoogle Scholar
- Ochman H, Lawrence JG, Groisman EA. Lateral gene transfer and the nature of bacterial innovation. Nature. 2000;405(6784):299–304.PubMedView ArticleGoogle Scholar
- Groisman EA, Ochman H. Pathogenicity islands: bacterial evolution in quantum leaps. Cell. 1996;87(5):791–4.PubMedView ArticleGoogle Scholar
- Harbarth S, Samore MH. Antimicrobial resistance determinants and future control. Emerg Infect Dis. 2005;11(6):794–801.PubMedPubMed CentralView ArticleGoogle Scholar
- McConnell MJ, Pachón J. Active and passive immunization against Acinetobacter baumannii using an inactivated whole cell vaccine. Vaccine. 2010;29(1):1–5.PubMedView ArticleGoogle Scholar
- McConnell MJ, Rumbo C, Bou G, Pachón J. Outer membrane vesicles as an acellular vaccine against Acinetobacter baumannii. Vaccine. 2011;29(34):5705–10.PubMedView ArticleGoogle Scholar
- Sette A, Rappuoli R. Reverse vaccinology: developing vaccines in the era of genomics. Immunity. 2010;33(4):530–41.PubMedPubMed CentralView ArticleGoogle Scholar
- Rey S, Acab M, Gardy JL, Laird MR, Lambert C, Brinkman FS. PSORTdb: a protein subcellular localization database for bacteria. Nucleic Acids Res. 2005;33 suppl 1:D164–8.PubMedGoogle Scholar
- Choi CH, Lee JS, Lee YC, Park TI, Lee JC. Acinetobacter baumannii invades epithelial cells and outer membrane protein A mediates interactions with epithelial cells. BMC Microbiol. 2008;8(1):216.PubMedPubMed CentralView ArticleGoogle Scholar
- Luo G, Lin L, Ibrahim AS, Baquir B, Pantapalangkoor P, Bonomo RA, et al. Active and passive immunization protects against lethal, extreme drug resistant-Acinetobacter baumannii infection. PLoS One. 2012;7(1):e29446.PubMedPubMed CentralView ArticleGoogle Scholar
- Jang MJ, Kim J-E, Chung YH, Lee WB, Shin YK, Lee JS, et al. Dendritic cells stimulated with outer membrane protein A (OmpA) of Salmonella typhimurium generate effective anti-tumor immunity. Vaccine. 2011;29(13):2400–10.PubMedView ArticleGoogle Scholar
- Gaddy JA, Arivett BA, McConnell MJ, López-Rojas R, Pachón J, Actis LA. Role of acinetobactin-mediated iron acquisition functions in the interaction of Acinetobacter baumannii strain ATCC 19606 T with human lung epithelial cells, Galleria mellonella caterpillars, and mice. Infect Immun. 2012;80(3):1015–24.PubMedPubMed CentralView ArticleGoogle Scholar
- McConnell MJ, Actis L, Pachón J. Acinetobacter baumannii: human infections, factors contributing to pathogenesis and animal models. FEMS Microbiol Rev. 2013;37(2):130–55.PubMedView ArticleGoogle Scholar
- Moriel DG, Beatson SA, Wurpel DJ, Lipman J, Nimmo GR, Paterson DL, et al. Identification of novel vaccine candidates against multidrug-resistant Acinetobacter baumannii. PLoS One. 2013;8(10):e77631.PubMedPubMed CentralView ArticleGoogle Scholar
- Chiang M-H, Sung W-C, Lien S-P, Chen Y-Z, Lo AF-Y, Huang J-H, et al. Identification of novel vaccine candidates against Acinetobacter baumannii using reverse vaccinology. Hum Vaccin Immunother 2015(just-accepted):00-.Google Scholar
- Shi W, Sun H. Type IV pilus-dependent motility and its possible role in bacterial pathogenesis. Infect Immun. 2002;70(1):1–4.PubMedPubMed CentralView ArticleGoogle Scholar
- Peleg AY, de Breij A, Adams MD, Cerqueira GM, Mocali S, Galardini M, et al. The success of Acinetobacter species; genetic, metabolic and virulence attributes. 2012.Google Scholar
- Eijkelkamp BA, Stroeher UH, Hassan KA, Papadimitrious MS, Paulsen IT, Brown MH, et al. Adherence and motility characteristics of clinical Acinetobacter baumannii isolates. FEMS Microbiol Lett. 2011;323(1):44–51.PubMedView ArticleGoogle Scholar
- Damier-Piolle L, Magnet S, Brémont S, Lambert T, Courvalin P. AdeIJK, a resistance-nodulation-cell division pump effluxing multiple antibiotics in Acinetobacter baumannii. Antimicrob Agents Chemother. 2008;52(2):557–62.PubMedView ArticleGoogle Scholar
- Rosenfeld N, Bouchier C, Courvalin P, Périchon B. Expression of the resistance-nodulation-cell division pump AdeIJK in Acinetobacter baumannii is regulated by AdeN, a TetR-type regulator. Antimicrob Agents Chemother. 2012;56(5):2504–10.PubMedPubMed CentralView ArticleGoogle Scholar
- Moeck GS, Coulton JW. TonB‐dependent iron acquisition: mechanisms of siderophore‐mediated active transport†. Mol Microbiol. 1998;28(4):675–81.PubMedView ArticleGoogle Scholar
- Chen W. Current advances and challenges in the development of Acinetobacter vaccines. Human vaccines & immunotherapeutics. 2015;11(10):2495–500.View ArticleGoogle Scholar
- Fajardo Bonin R, Chapeaurouge A, Perales J, Silva JG, Do Nascimento HJ, D’Alincourt Carvalho Assef AP, et al. Identification of immunogenic proteins of the bacterium Acinetobacter baumannii using a proteomic approach. PROTEOMICS-Clinical Applications. 2014;8(11-12):916–23.PubMedView ArticleGoogle Scholar
- Bui H-H, Sidney J, Li W, Fusseder N, Sette A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC bioinformatics. 2007;8(1):1.View ArticleGoogle Scholar
- Rossi G, Ruggiero P, Peppoloni S, Pancotto L, Fortuna D, Lauretti L, et al. Therapeutic vaccination against Helicobacter pylori in the beagle dog experimental model: safety, immunogenicity, and efficacy. Infect Immun. 2004;72(6):3252–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Czinn SJ, Blanchard T. Vaccinating against Helicobacter pylori infection. Nature Reviews Gastroenterology and Hepatology. 2011;8(3):133–40.PubMedView ArticleGoogle Scholar
- Mueller A, Solnick JV. Inflammation, immunity, and vaccine development for Helicobacter pylori. Helicobacter. 2011;16(s1):26–32.View ArticleGoogle Scholar
- Malfertheiner P, Schultze V, Rosenkranz B, Kaufmann SH, Ulrichs T, Novicki D, et al. Safety and immunogenicity of an intramuscular Helicobacter pylori vaccine in noninfected volunteers: a phase I study. Gastroenterology. 2008;135(3):787–95.PubMedView ArticleGoogle Scholar
- Salama NR, Hartung ML, Müller A. Life in the human stomach: persistence strategies of the bacterial pathogen Helicobacter pylori. Nat Rev Microbiol. 2013;11(6):385–99.PubMedPubMed CentralView ArticleGoogle Scholar
- Magnus M, Pawlowski M, Bujnicki JM. MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria. Biochimica ET Biophysica Acta (BBA)-Proteins and Proteomics. 2012;1824(12):1425–33.View ArticleGoogle Scholar
- Huang W, Yao Y, Wang S, Xia Y, Yang X, Long Q, et al. Immunization with a 22-kDa outer membrane protein elicits protective immunity to multidrug-resistant Acinetobacter baumannii. Sci Rep. 2016;6:20724.Google Scholar
- Perez F, Hujer AM, Hujer KM, Decker BK, Rather PN, Bonomo RA. Global challenge of multidrug-resistant Acinetobacter baumannii. Antimicrob Agents Chemother. 2007;51(10):3471–84.PubMedPubMed CentralView ArticleGoogle Scholar