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Protectome Analysis: A New Selective Bioinformatics Tool for Bacterial Vaccine Candidate Discovery[S]

Open AccessPublished:November 03, 2014DOI:https://doi.org/10.1074/mcp.M114.039362
      New generation vaccines are in demand to include only the key antigens sufficient to confer protective immunity among the plethora of pathogen molecules. In the last decade, large-scale genomics-based technologies have emerged. Among them, the Reverse Vaccinology approach was successfully applied to the development of an innovative vaccine against Neisseria meningitidis serogroup B, now available on the market with the commercial name BEXSERO® (Novartis Vaccines). The limiting step of such approaches is the number of antigens to be tested in in vivo models. Several laboratories have been trying to refine the original approach in order to get to the identification of the relevant antigens straight from the genome. Here we report a new bioinformatics tool that moves a first step in this direction. The tool has been developed by identifying structural/functional features recurring in known bacterial protective antigens, the so called “Protectome space,” and using such “protective signatures” for protective antigen discovery. In particular, we applied this new approach to Staphylococcus aureus and Group B Streptococcus and we show that not only already known protective antigens were re-discovered, but also two new protective antigens were identified.
      Although vaccines based on attenuated pathogens as pioneered by Luis Pasteur have been shown to be extremely effective, safety and technical reasons recommend that new generation vaccines include few selected pathogen components which, in combination with immunostimulatory molecules, can induce long lasting protective responses. Such approach implies that the key antigens sufficient to confer protective immunity are singled out among the plethora of pathogen molecules. As it turns out, the search for such protective antigens can be extremely complicated.
      Genomic technologies have opened the way to new strategies in vaccine antigen discovery (
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      • et al.
      Whole-genome random sequencing and assembly of Haemophilus influenzae Rd.
      ,
      • Pizza M.
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      • Baldi L.
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      • Grandi G.
      • Rappuoli R.
      • et al.
      Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing.
      ,
      • Maione D.
      • Margarit I.
      • Rinaudo C.D.
      • Masignani V.
      • Mora M.
      • et al.
      Identification of a universal Group B streptococcus vaccine by multiple genome screen.
      ). Among them, Reverse Vaccinology (RV)

      The abbreviations used are:

      RV
      Reverse Vaccinology
      5′ NT
      5′ nucleotidase
      AMP
      adenosine monophosphate
      BCA
      Bicinchoninic acid
      CFU
      Colony Forming Unit
      DUF
      Domain of Unknown Function
      fHbp
      Meningococcal Factor H binding protein
      GAS
      Group A Streptococcus
      GBS
      Group B Streptococcus
      IPTG
      Isopropyl β-D-1-thiogalactopyranoside
      LD90
      90% lethal dosage
      MenB
      Neisseria meningitidis serogroup B, Meningococcus B
      ORF
      Open Reading Frame
      PA
      Protective Antigen
      PCR
      Polymerase Chain Reaction
      PS
      Protective Signature
      PT
      Pertussis Toxin
      SLO
      Streptolysin O from Streptococcus pyogenes.
      has proved to be highly effective, as demonstrated by the fact that a new Serogroup B Neisseria meningitidis (MenB) vaccine, incorporating antigens selected by RV, is now available to defeat meningococcal meningitis (
      • Giuliani M.M.
      • Adu-Bobie J.
      • Comanducci M.
      • Aricò B.
      • Savino S.
      • Santini L.
      • Brunelli B.
      • Bambini S.
      • Biolchi A.
      • Capecchi B.
      • Cartocci E.
      • Ciucchi L.
      • Di Marcello F.
      • Ferlicca F.
      • Galli B.
      • Luzzi E.
      • Masignani V.
      • Serruto D.
      • Veggi D.
      • Contorni M.
      • Morandi M.
      • Bartalesi A.
      • Cinotti V.
      • Mannucci D.
      • Titta F.
      • Ovidi E.
      • Welsch J.A.
      • Granoff D.
      • Rappuoli R.
      • Pizza M.
      A universal vaccine for serogroup B meningococcus.
      ,
      • Serruto D.
      • Bottomley M.J.
      • Ram S.
      • Giuliani M.M.
      • Rappuoli R.
      The new multicomponent vaccine against meningococcal serogroup B, 4CMenB: immunological, functional, and structural characterization of the antigens.
      ). In essence, RV is based on the simple assumption that cloning all annotated proteins/genes and screening them against a robust and reliable surrogate-of-protection assay must lead to the identification of all protective antigens. Because most of the assays available for protective antigen selection involve animal immunization and challenge, the number of antigens to be tested represents a severe bottleneck of the entire process. For this reason, despite the fact that RV is a brute force, inclusive approach (“test-all-to-lose-nothing” type of approach) in their pioneered work of MenB vaccine discovery, Pizza and co-workers did not test the entire collection of MenB proteins but rather restricted their analysis to the ones predicted to be surface-localized. This was based on the evidence that for an anti-MenB vaccine to be protective bactericidal antibodies must be induced, a property that only surface-exposed antigens have. For the selection of surface antigens Pizza and co-workers mainly used PSORT and other available tools like MOTIFS and FINDPATTERNS to find proteins carrying localization-associated features such as transmembrane domains, leader peptides, and lipobox and outer membrane anchoring motifs. At the end, 570 proteins were selected and entered the still very labor intensive screening phase. Over the last few years, our laboratories have been trying to move to more selective strategies. Our ultimate goal, we like to refer to as the “Holy Grail of Vaccinology,” is to identify protective antigens by “simply” scanning the genome sequence of any given pathogen, thus avoiding time consuming “wet science” and “move straight from genome to the clinic” (
      • Grandi G.
      Bacterial vaccine discovery: from “brute force” to high selectivity.
      ).
      With this objective in mind, we have developed a series of proteomics-based protocols that, in combination with bioinformatics tools, have substantially reduced the number of antigens to be tested in the surrogate-of-protection assays (
      • Rodríguez-Ortega M.J.
      • Norais N.
      • Bensi G.
      • Liberatori S.
      • Capo S.
      • Mora M.
      • Scarselli M.
      • Doro F.
      • Ferrari G.
      • Garaguso I.
      • Maggi T.
      • Neumann A.
      • Covre A.
      • Telford J.L.
      • Grandi G.
      Characterization and identification of vaccine candidate proteins through analysis of the group A Streptococcus surface proteome.
      ,
      • Doro F.
      • Liberatori S.
      • Rodríguez-Ortega M.J.
      • Rinaudo C.D.
      • Rosini R.
      • et al.
      Surfome analysis as a fast track to vaccine discovery: identification of a novel protective antigen for Group B Streptococcus hypervirulent strain COH1.
      ). In particular, we have recently described a three-technology strategy that allows to narrow the number of antigens to be tested in the animal models down to less than ten (
      • Bensi G.
      • Mora M.
      • Tuscano G.
      • Biagini M.
      • Chiarot E.
      • Bombaci M.
      • Capo S.
      • Falugi F.
      • Manetti A.G.
      • Donato P.
      • Swennen E.
      • Gallotta M.
      • Garibaldi M.
      • Pinto V.
      • Chiappini N.
      • Musser J.M.
      • Janulczyk R.
      • Mariani M.
      • Scarselli M.
      • Telford J.L.
      • Grifantini R.
      • Norais N.
      • Margarit I.
      • Grandi G.
      Multi high-throughput approach for highly selective identification of vaccine candidates: the Group A Streptococcus case.
      ). However, this strategy still requires high throughput experimental activities. Therefore, the availability of in silico tools that selectively and accurately single out relevant categories of antigens among the complexity of pathogen components would greatly facilitate the vaccine discovery process.
      In the present work, we describe a new bioinformatics approach that brings an additional contribution to our “from genome to clinic” goal. The approach has been developed on the basis of the assumption that protective antigens are protective in that they have specific structural/functional features (“protective signatures”) that distinguish them from immunologically irrelevant pathogen components. These features have been identified by using existing databases and prediction tools, such as PFam and SMART. Our approach focuses on protein biological role rather than its localization: it is completely protein localization unbiased, and lead to the identification of both surface-exposed and secreted antigens (which are the majority in extracellular bacteria) as well as cytoplasmic protective antigens (for instance, antigens that elicit interferon γ producing CD4+ T cells, thus potentiating the killing activity of phagocytic cells toward intracellular pathogens). Should these assumptions be valid, PS could be identified if: (1) all known protective antigens are compiled to create what we refer to as “the Protectome space,” and (2) Protectome is subjected to computer-assisted scrutiny using selected tools. Once signatures are identified, novel protective antigens of a pathogen of interest should be identifiable by scanning its genome sequence in search for proteins that carry one or more protective signatures. A similar attempt has been reported (
      • Doytchinova I.A.
      • Flower D.R.
      VaxiJen: a server for prediction of protective antigens, tumor antigens, and subunit vaccines.
      ), where the discrimination of protective antigens versus nonprotective antigens was tried using statistical methods based on amino acid compositional analysis and auto cross-covariance. This model was implemented in a server for the prediction of vaccine candidates, that is, Vaxijen (www.darrenflower.info/Vaxijen); however, the selection criteria applied are still too general leading to a list of candidates that include ca. 30% of the total genome ORFs very similarly to the number of antigens predicted by classical RV based on the presence of localization signals.
      Here we show that Protectome analysis unravels specific signatures embedded in protective antigens, most of them related to the biological role/function of the proteins. These signatures narrow down the candidate list to ca. 3% of the total ORFs content and can be exploited for protective antigen discovery. Indeed, the strategy was validated by demonstrating that well characterized vaccine components could be identified by scanning the genome sequence of the corresponding pathogens for the presence of the PS. Furthermore, when the approach was applied to Staphylococcus aureus and Streptococcus agalactiae (Group B Streptococcus, GBS) not only already known protective antigens were rediscovered, but also two new protective antigens were identified.

      DISCUSSION

      The “Holy Grail” of Vaccinology is the knowledge to select PAs from genome sequence. With this power at their disposal, vaccinologists would dramatically shorten the time to vaccine development, would restrict animal use to toxicology, and would test vaccine efficacy directly in humans.
      The Holy Grail is not a reality yet because we are still not capable of recognizing a protective antigen from its primary and/or tertiary structure. At present, the best we can achieve is to define criteria that make a protective antigen protective and then to use high throughput technologies to identify from all pathogen components those fulfilling such criteria. Two different criteria have been extensively applied. The first, the “immunogenicity criterion,” braces the idea that PAs must be immunogenic during natural infection. With this assumption, a number of experimental strategies have been developed to identify pathogen-associated proteins that induce antibody and cell-mediated responses (
      • Meinke A.
      • Henics T.
      • Hanner M.
      • Minh D.B.
      • Nagy E.
      Antigenome technology: a novel approach for the selection of bacterial vaccine candidate antigens.
      ,
      • Bombaci M.
      • Grifantini R.
      • Mora M.
      • Reguzzi V.
      • Petracca R.
      • et al.
      Protein array profiling of tic patient sera reveals a broad range and enhanced immune response against Group A Streptococcus antigens.
      ,
      • Finco O.
      • Frigimelica E.
      • Buricchi F.
      • Petracca R.
      • Galli G.
      • Faenzi E.
      • Meoni E.
      • Bonci A.
      • Agnusdei M.
      • Nardelli F.
      • Bartolini E.
      • Scarselli M.
      • Caproni E.
      • Laera D.
      • Zedda L.
      • Skibinski D.
      • Giovinazzi S.
      • Bastone R.
      • Ianni E.
      • Cevenini R.
      • Grandi G.
      • Grifantini R.
      Approach to discover T- and B-cell antigens of intracellular pathogens applied to the design of Chlamydia trachomatis vaccines.
      ). The second, the “compartmentalization criterion,” sustains that if antibodies are mediating the protective response, PAs are either secreted or surface-associated. Therefore, proteomics analyses have been used to experimentally identify this category of antigens (
      • Rodríguez-Ortega M.J.
      • Norais N.
      • Bensi G.
      • Liberatori S.
      • Capo S.
      • Mora M.
      • Scarselli M.
      • Doro F.
      • Ferrari G.
      • Garaguso I.
      • Maggi T.
      • Neumann A.
      • Covre A.
      • Telford J.L.
      • Grandi G.
      Characterization and identification of vaccine candidate proteins through analysis of the group A Streptococcus surface proteome.
      ,
      • Doro F.
      • Liberatori S.
      • Rodríguez-Ortega M.J.
      • Rinaudo C.D.
      • Rosini R.
      • et al.
      Surfome analysis as a fast track to vaccine discovery: identification of a novel protective antigen for Group B Streptococcus hypervirulent strain COH1.
      ,
      • Solis N.
      • Larsen M.R.
      • Cordwell S.J.
      Improved accuracy of cell surface shaving proteomics in Staphylococcus aureus using a false-positive control.
      ,
      • Ventura C.L.
      • Malachowa N.
      • Hammer C.H.
      • Nardone G.A.
      • Robinson M.A.
      • Kobayashi S.D.
      • DeLeo F.R.
      Identification of a novel Staphylococcus aureus two-component leukotoxin using cell surface proteomics.
      ,
      • Dreisbach A.
      • Hempel K.
      • Buist G.
      • Hecker M.
      • Becher D.
      • van Dijl J.M.
      Profiling the surfacome of Staphylococcus aureus.
      ,
      • Berlanda Scorza F.
      • Doro F.
      • Rodríguez-Ortega M.J.
      • Stella M.
      • Liberatori S.
      • Taddei A.R.
      • Serino L.
      • Gomes Moriel D.
      • Nesta B.
      • Fontana M.R.
      • Spagnuolo A.
      • Pizza M.
      • Norais N.
      • Grandi G.
      Proteomics characterization of outer membrane vesicles from the extraintestinal pathogenic Escherichia coli DeltatolR IHE3034 mutant.
      ). More recently, a third criterion has been proposed that essentially combines the immunogenicity and compartmentalization hypotheses: to be PAs they must be: (1) conserved, (2) well expressed, (3) immunogenic during natural infection, and (4) secreted and/or surface associated. By this way, the number of vaccine candidates to be tested in the animal models of protection, the most demanding and critical part of the whole vaccine discovery process, is remarkably reduced: from a few thousand (the average number of bacterial proteins each of which potentially being a protective antigen) to a few tens and, in the case of the “immunogenic/compartmentalization combined criterion,” to less than ten (
      • Bensi G.
      • Mora M.
      • Tuscano G.
      • Biagini M.
      • Chiarot E.
      • Bombaci M.
      • Capo S.
      • Falugi F.
      • Manetti A.G.
      • Donato P.
      • Swennen E.
      • Gallotta M.
      • Garibaldi M.
      • Pinto V.
      • Chiappini N.
      • Musser J.M.
      • Janulczyk R.
      • Mariani M.
      • Scarselli M.
      • Telford J.L.
      • Grifantini R.
      • Norais N.
      • Margarit I.
      • Grandi G.
      Multi high-throughput approach for highly selective identification of vaccine candidates: the Group A Streptococcus case.
      ). However, regardless the criterion applied, the identification of PAs still requires a substantial amount of experimental work and it is biased toward the selection of antigens providing antibody-mediated protection. In the present work, we have moved one step forward toward the from-genome-to-vaccine goal. The Protectome approach reaches a substantial filtering of the bacterial proteome without the need of any experimental work and performs an unbiased selection of new vaccine candidates, because all known bacterial PAs were initially included in the Proteoctome space regardless of their mechanism of protection.
      Starting from the assumption that bacterial PAs must have specific structural/functional signatures that make them protective, we created the “Protectome” database including all PAs described so far and we have used different tools to identify common motifs within the Protectome space. As predicted, the bioinformatics analysis has revealed that PAs can be grouped in families that share “protective signatures” that classify antigens based on their function/biological role (toxins, iron-uptake systems, adhesins, etc.) and/or based on their structural organization (for instance, multiple internal structural motifs of bacterial adhesins). Finally, we have scanned the genomes of different pathogens in search of conserved proteins carrying protective signatures. When applied to different pathogens the approach not only allowed to rediscover already known PAs but also to identify new vaccine candidates that deserve future attention.
      Bioinformatics has been the first filtering strategy applied in genome-based vaccine discovery projects to reduce the number of antigens to be tested in animal models. Starting from the assumption that antigens that induce bactericidal antibodies must be surface-exposed, in their pivotal work Pizza and co-workers used PSORT to identify meningococcal genes encoding proteins carrying leader sequence for secretion. In this way ∼70% of the genome was excluded from subsequent high throughput analysis but still 600 proteins had to be tested for bactericidal activity to ultimately identify the five PAs currently included in the commercialized vaccine (
      • Pizza M.
      • Scarlato V.
      • Masignani V.
      • Giuliani M.M.
      • Aricó B.
      • Comanducci M.
      • Jennings G.T.
      • Baldi L.
      • Bartolini E.
      • Capecchi B.
      • Galeotti C.L.
      • Luzzi E.
      • Manetti R.
      • Marchetti E.
      • Mora M.
      • Nuti S.
      • Ratti G.
      • Santini L.
      • Savino S.
      • Scarselli M.
      • Storni E.
      • Zuo P.
      • Broeker M.
      • Hundt E.
      • Knapp B.
      • Blair E.
      • Mason T.
      • Tettelin H.
      • Hood D.W.
      • Jeffries A.C.
      • Saunders N.J.
      • Granoff D.M.
      • Venter J.C.
      • Moxon E.R
      • Grandi G.
      • Rappuoli R.
      • et al.
      Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing.
      ,
      • Giuliani M.M.
      • Adu-Bobie J.
      • Comanducci M.
      • Aricò B.
      • Savino S.
      • Santini L.
      • Brunelli B.
      • Bambini S.
      • Biolchi A.
      • Capecchi B.
      • Cartocci E.
      • Ciucchi L.
      • Di Marcello F.
      • Ferlicca F.
      • Galli B.
      • Luzzi E.
      • Masignani V.
      • Serruto D.
      • Veggi D.
      • Contorni M.
      • Morandi M.
      • Bartalesi A.
      • Cinotti V.
      • Mannucci D.
      • Titta F.
      • Ovidi E.
      • Welsch J.A.
      • Granoff D.
      • Rappuoli R.
      • Pizza M.
      A universal vaccine for serogroup B meningococcus.
      ). An even higher number of proteins were selected for subsequent testing in the cumbersome active maternal immunization mouse model to select GBS PAs (
      • Maione D.
      • Margarit I.
      • Rinaudo C.D.
      • Masignani V.
      • Mora M.
      • et al.
      Identification of a universal Group B streptococcus vaccine by multiple genome screen.
      ). With the bioinformatics approach described here the pool of protective candidates is further reduced down 50–70, thus less than 5% of the total predicted ORFs. Considering that retrospective analyses on a number different pathogens, including B. pertussis, H. pylori, MenB, GBS, GAS, and S. aureus, have shown that the approach would not have missed a single antigen, we believe that the result is remarkable. Furthermore, as we demonstrated for GBS and S. aureus, new promising PAs that had not been selected using previous genomic approaches have been identified.
      The Protectome method described here has been generated including all bacterial PAs regardless their mechanism of protection (antibody mediated or cell mediated), their compartmentalization (secreted, surface-associated, and cytoplasmic), the type of biological assay used for establishing protective activity (in vitro versus in vivo), level of protection, formulation (adjuvants) used for inducing protective immune responses, etc. We believe that this unbiased approach is particularly useful when little is known about the type of immune response needed to protect the pathogen of interest. However, in a number of cases, the type of immune response the vaccine should elicit is known. Typical examples are MenB and GBS. In the case of MenB, to be effective vaccination has to induce high bactericidal antibody titers in infants and adolescents, the main vaccine target population. As far as GBS is concerned, the vaccine should protect newborns within their first 90 days of life from delivery. Protection is exclusively antibody-mediated and the vaccine should be administered to women to allow passive transfer of opsonophagocytic antibodies to the fetus. Therefore, for both pathogens, the only antigens that can induce protective antibodies are those that are surface-associated; secreted toxins, cytoplasmic proteins, and proteins inducing T cell responses cannot elicit the proper immune response. Indeed, the newly identified antigen 5′ NT is a surface-associated protein. Although we have not done this analysis, we expect that by using “subprotectomes” tailored on the basis of the immune responses needed for protection, for example, T-cell versus antibody-mediated, the number of selected vaccine candidates would be further reduced.
      The animal models used to test protection of the vaccine candidates deserve a comment. It is still not clear how many PAs exist for each pathogen. In the case of pathogens whose pathogenicity is mediated by secreted toxins, single inactivated toxins have been shown to be sufficient to prevent disease. Likewise, one antigen abundantly expressed on the bacterial surface can be sufficient to induce excellent bactericidal/opsonophagocytic antibodies; typical examples are polysaccharides constituting glycoconjugate vaccines, and fHbp of MenB (
      • Giuliani M.M.
      • Adu-Bobie J.
      • Comanducci M.
      • Aricò B.
      • Savino S.
      • Santini L.
      • Brunelli B.
      • Bambini S.
      • Biolchi A.
      • Capecchi B.
      • Cartocci E.
      • Ciucchi L.
      • Di Marcello F.
      • Ferlicca F.
      • Galli B.
      • Luzzi E.
      • Masignani V.
      • Serruto D.
      • Veggi D.
      • Contorni M.
      • Morandi M.
      • Bartalesi A.
      • Cinotti V.
      • Mannucci D.
      • Titta F.
      • Ovidi E.
      • Welsch J.A.
      • Granoff D.
      • Rappuoli R.
      • Pizza M.
      A universal vaccine for serogroup B meningococcus.
      ,
      • Dull P.M.
      • McIntosh E.D.
      Meningococcal vaccine development–from glycoconjugates against MenACWY to proteins against MenB–potential for broad protection against meningococcal disease.
      ,
      • Edwards M.S.
      • Gonik B.
      Preventing the broad spectrum of perinatal morbidity and mortality through group B streptococcal vaccination.
      ). For other pathogens with a much more complex mechanism of pathogenesis, such as GAS and S. aureus, effective vaccines are expected to require cocktails of several antigens in order to neutralize different bacterial virulence factors. However, in many cases the animal models used to screen for PAs do not mimic human infection. These models are biased by the fact that animals are infected with large quantities of bacteria and the only antigens that result protective are those that block the bacteremia induced by the challenge. Other antigens that might play important role in inducing protective responses in humans are completely lost because of inadequacy of the models. This aspect has to be constantly kept in mind before excluding or including antigens identified by genomic approaches.
      Our bioinformatics approach described here and based on the identification of antigens carrying protective signatures, appears to be the most selective in silico strategy for vaccine candidate discovery reported so far. The optimization of the “Protectome space” to be used for protective signature identification and an optimization of the tools used for protective signature selection are expected to bring the candidates down to a number that allow their direct testing in the human model. For instance we have observed that some PS, that is, those associated to specific Pfam domains such as Peptidase_S8 and Pertactin, are highly selective. On the contrary, PS exist that identify large families of proteins that include both protective and nonprotective antigens. In some cases, these discrepancies can be caused by technical artifacts or use of nonappropriate animal models. However, in other cases, it could be a consequence of the lack of a complete knowledge about protein function and of its associated structural/functional features. A significant step forward would be achieved when a detailed biological characterization of these protective antigens is carried out, leading to a further refinement of PS that would greatly improve the specificity of the Protectome prediction.

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