# Reverse vaccinology: A strategy also used for identifying potential vaccine antigens in poultry

Noémie Gloanec, Muriel Guyard-Nicodème, Marianne Chemaly, Daniel Dory

► To cite this version:

Noémie Gloanec, Muriel Guyard-Nicodème, Marianne Chemaly, Daniel Dory. Reverse vaccinology: A strategy also used for identifying potential vaccine antigens in poultry. Vaccine, 2025, 48, pp.126756. 10.1016/j.vaccine.2025.126756. anses-04933934

HAL Id: anses-04933934

https://anses.hal.science/anses-04933934v1

Submitted on 7 Feb 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Review

# Reverse vaccinology: A strategy also used for identifying potential vaccine antigens in poultry

Noémie Gloanec<sup>a,b,c</sup>, Muriel Guyard-Nicodème<sup>b,**</sup>, Marianne Chemaly<sup>b</sup>, Daniel Dory<sup>a,d,*</sup>

<figure><img src="image_2.png" /></figure>

ARTICLE INFO

Keywords:

Reverse vaccinology

Poultry

Pangenome analysis

Antigen prediction

Host pathogen interaction

ABSTRACT

Vaccination of livestock plays a major role in improving animal health, welfare and productivity, but also in public health by preventing zoonotic diseases. Advances in bioinformatics and whole-genome sequencing techniques since the 2000s have led to the development of genome-based vaccinology, called reverse vaccinology. Reverse vaccinology is a rapid and competitive strategy that uses pathogen genome sequences to screen for and identify potential vaccine antigens and, unlike conventional methods, does not require culturing the pathogenic microorganism, at least initially. Based on *in silico* approaches and dedicated software, reverse vaccinology has led to the identification of a wide range of proteins as putative vaccine candidates against human pathogens and has been applied more recently to several animal diseases. After a brief overview of the principle of the approach and its applications in human medicine, this review focuses on the use of reverse vaccinology for the development of vaccines specifically for poultry, a representative example of livestock vaccination, and discusses the important points to consider when using this method.

1. Introduction

Based on the vaccination principles discovered by Jenner at the end of the 18th century, Pasteur — nearly one century later — developed the first inactivated whole-cell vaccine. These “killed” vaccines consist in isolating, inactivating and injecting the microorganism responsible for the disease to stimulate the immune system and build a specific, protective immune response against this pathogen. This approach fostered the development of safe vaccines for fowl cholera, anthrax and rabies [1] and its use continued thereafter to develop vaccines against highly infectious diseases such as measles, for example [2]. Nevertheless, because the protective immune response induced by this latter vaccine is too weak — a common shortcoming of most killed vaccines — the inactivated whole-cell vaccine has been replaced by an attenuated live whole-cell vaccine to obtain stronger protection [2]. However, live whole-cell vaccines are sometimes less safe because they can proliferate in the vaccinated body. For example, in rare cases, the attenuated live oral polio vaccine can revert to virulence [3]. One way to overcome this drawback consists in using subunit vaccines, which requires prior

knowledge of the immunogenic properties of certain components (proteins or lipids) of the pathogen. With advances in genetic engineering, it is now easy to insert the gene(s) encoding the immunogen(s) in different vectors to either produce recombinant viral or bacterial vectors expressing the vaccine antigens, recombinant vaccine proteins or nucleic vaccines. For example, one of the first subunit vaccines was the recombinant hepatitis B vaccine, which was licensed in 1986 [4]. Since then, subunit vaccines such as Recombivax HB® or Engerix-B have been used against hepatitis B [5] and as a booster vaccination (Boostrix) against diphtheria [6]. Moreover, licensed vaccines against porcine circovirus type 2 are composed of recombinant proteins [7]. One of the latest, and perhaps most iconic, examples of subunit vaccines is the development of mRNA vaccines encoding the SARS-CoV-2 spike protein to fight COVID-19 pandemics; these include the Pfizer-BioNTech, Janssen (Johnson & Johnson) and Moderna COVID-19 vaccines [8]. All these types of subunit vaccines present great advantages: standardised methods for the large-scale production of the vectored antigens and possibility to modify the encoded antigens to render them more immunogenic and/or better adapted to different strains of the pathogen

<footnote><sup>a</sup> GVB-Viral Genetics and Biosafety Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), France</footnote>

<footnote><sup>b</sup> HQPAP-Unit of Hygiene and Quality of Poultry and Pork Products, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), France</footnote>

<footnote><sup>c</sup> UFR of Life Sciences Environment, University of Rennes 1, 35065 Rennes, France</footnote>

<footnote><sup>d</sup> VIPAC-Avian and Rabbit Virology, Immunology and Parasitology Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), France</footnote>

<footnote>* Corresponding author at: GVB-Viral Genetics and Biosafety Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), France.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: muriel.guyard@anses.fr (M. Guyard-Nicodème), marianne.chemaly@anses.fr (M. Chemaly), daniel.dory@anses.fr (D. Dory).</footnote>
[9]. Multiple antigens can be introduced in subunit vaccines, which can increase immunogenicity [10] and vaccine half-life *in vivo* [11]. However, although the subunit approach is more refined, it cannot be applied to all pathogens. Subunit vaccines require broad knowledge of the pathogenesis or immunogenicity of the pathogen to identify potential vaccine candidates. Additionally, as for all vaccines, there are cases where cross-protection among multiple pathogen serotypes cannot be obtained [12]. Therefore, it is essential to have alternative, more efficient and comprehensive approaches to identify potential vaccine antigen candidates.

Over the past decades, advances in bioinformatics and whole-genome sequencing techniques have led to genome-based identification of potential subunit vaccine antigens, called reverse vaccinology, first described in the pioneering study by Pizza et al. [13]. It is a rapid and competitive strategy that does not require culturing pathogens. Accordingly, dedicated software and web platforms have been developed and made available to the scientific community to screen for a large spectrum of putative protein vaccine candidates. The immunogenicity of these antigens must then be evaluated and validated experimentally. However, only a few of the selected potential highly immunogenic antigens are tested *in vivo*, to limit insofar as possible the use of animals in biomedical research.

Livestock vaccination is widespread practice, and necessary to protect animal species and/or prevent zoonotic diseases. Vaccinating livestock can improve overall production for primary producers and is relatively affordable. Additionally, vaccination against zoonotic microorganisms can reduce the risk to consumers [14]. Furthermore,

vaccination may help to reduce the use of antibiotics and thus limit the development of antimicrobial resistance. For example, in 2021, for swine, the European Medicines Agency (EMA) recommended two vaccines for marketing approval (CircoMax® against porcine circovirus type 2 (PCV2) and Suiseng Diff/A against Clostridioides difficile and Clostridium perfringens - EMA, Annual report 2021, https://www.ema.europa.eu/en/documents/annual-report/2021-annual-report-european-medicines-agency_en.pdf). Likewise, several vaccines have been used in fish, in particular tilapia, sea bass, yellowtail, carp and salmorns. For instance, Renogen®, developed for salmon, is a live attenuated vaccine against Renibacterium salmoninarum [15]. Finally, in poultry, vaccines are widely used on a large scale or evaluated: e.g. a live attenuated Newcastle disease virus vaccine [16] or inactivated or recombinant vaccine candidates against avian influenza A [17,18].

In this review, we briefly go over the main principles of reverse vaccinology and its use in human medicine, and then focus more specifically on the work carried out to identify vaccine antigens for poultry as a representative example of what has been done for veterinary species. We also highlight some of the important points to consider when using reverse vaccinology strategies.

## 2. Reverse vaccinology: Principle and overview of its use in humans

The first step in reverse vaccinology is the prediction of vaccine candidates based on *in silico* analysis using several different bioinformatics tools. These tools “read” the genomic sequences to identify the

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** General overview of the reverse vaccinology strategy.</figcaption></figure>
putative antigens that meet the criteria that are generally accepted as
those necessary to obtain an effective vaccine candidate. In general,
effective candidate antigens should be extracellular or exposed on the
cell surface be accessible to the immune system, are adhesins in the case
of bacteria, have few transmembrane regions (often a maximum of 2)
and/or are soluble to facilitate their production and purification, readily
elicit an immune response and/or possess B-cells and major histocom-
patibility class (MHC) I and II epitopes (important for colonisation and
protection against pathogens). In addition, homology between the
candidate antigen and host proteins should be analysed to avoid side
effects and autoimmune reactions in the host, as well as the absence of
cross-reaction with other pathogenic antigens [19]. Fig. 1 summarizes
these very general and basic criteria. Suitable candidate antigens (in
general less than 10, but it can rise to 30–50 in certain cases) are sub-
sequently produced, purified and then evaluated experimentally for
their immunogenic and protective potential. The identified protein an-
tigens can then be inoculated as subunit vaccines. Moreover, an esti-
mated 90 % of B-cell epitopes from native proteins are conformational
rather than linear [20], and host antibodies that bind to conformational
epitopes may have a greater neutralising effect than linear epitopes [21].
The reverse vaccinology tools have to take into account all the types of
epitopes. Nonetheless, this genome-based approach cannot predict
lipids or polysaccharides, although some may be effective vaccine
candidates.

This figure illustrates the basic principles of reverse vaccinology and
does not necessarily represent all the strategies published. In some cases,
authors may not perform all these steps or may use a different order.
Likewise, the steps are continually updated or additional steps are
included with advances in informatics, omics studies and systems
biology, as discussed in this review.

The principle of reverse vaccinology was first described by Pizza
et al., who developed a human vaccine against *Neisseria meningitides*
serogroup B (MenB) [13]. The conventional approach to developing a
vaccine had failed due to the low immunogenicity and autoimmune
potential of the MenB capsular polysaccharide. Moreover, vaccines
consisting of outer membrane vesicles did not induce cross-protection
against heterologous strains and exhibited antigen and sequence vari-
ability. Therefore, in an effort to develop a rational and systemic way to
potentially identify all the vaccine antigens of the pathogen and to
evaluate their cross-protective potential, a new screening strategy was
deployed based on the thorough analysis of the genome of the bacterial
pathogen to search for vaccine targets. Furthermore, by the early 2000s,
genome sequencing of bacteria had made sufficient progress and the
whole genome sequence of the virulent *N. meningitides* MC58 strain was
available. This new strategy led to the identification of 570 open reading
frames (ORF) potentially encoding 570 proteins. After an attempt to
clone and express each ORF, only 350 proteins could be successfully
expressed and produced in *Escherichia coli*. Using an enzyme-linked
immunosorbent assay (ELISA) and fluorescence-activated cell sorting
(FACS) analysis, seven of these proteins were found to be surface-
exposed, to induce an antibody response in mice, to induce a serum
bactericidal activity and to be conserved across 31 strains of
*N. meningitidis*. Based on these findings, a vaccine (first called 4CMenB;
commercial name: Bexsero®) was developed by Novartis Vaccines and
received marketing approval in Europe in 2013. This vaccine consists of
two recombinant proteins (fHbp-GNA1030 and fHbp-GNA2091) and the
NadA protein combined with a detergent-extracted outer membrane
vesicle containing PorA [22]. Since then, bioinformatics tools — with
the first being Vaxign in 2008 [23] — have been developed; they
consider the location of the proteins within the pathogen, the presence
of transmembrane domains, the adhesin potential, the exclusion of
proteins homologous to the host and the presence of putative B- and T-
cell epitopes within the proteins. This approach has been applied to
various bacterial species that infect humans such as *Streptococcus pneu-
moniae* [24], *Porphyromonas gingivalis* [25], *Staphylococcus aureus* [26],
*Mycobacterium tuberculosis* [27] and *Mycobacterium leprae* and

*Mycobacterium lepromatosis* [28]. Additionally, this strategy has also been used to identify vaccine antigens against viruses by selecting protein sequences from virus genomes, such as for herpes virus [29] and more recently, for the development of multi-epitope subunit vaccines such as SARS-CoV-2 [30–32]. Similarly, human anti-parasite vaccines have been developed, such as for *Cryptosporidium* species [33] and *Schistosoma mansoni* [34,35], and a fungal multi-epitope vaccine has been developed against *Candida* [36].

Progress has been made in identifying potential vaccine antigens through reverse vaccinology. Like for the pioneer *N. meningitides* study, reverse vaccinology studies were first performed on a single-strain-specific genome of a pathogen, because only one or very few strains were available. The list of potential vaccine antigens obtained was then used to identify shared vaccine antigens across different pathogen strains (Fig. 1). This approach was used to screen for potential vaccine antigens to protect chickens against *Campylobacter* [37]. Nevertheless, this method of searching for cross-protective vaccine antigens is not fully satisfactory, because different pathogen strains may have different antigen repertoires. Given the genomic diversity among strains, a pan-genomic reverse vaccinology model has been proposed [38,39]. This approach uses the global genetic repertoire of a species, increasing the possibility of identifying cross-protective vaccine candidates. This pan-genomic reverse vaccinology approach was first applied to eight strains of group B streptococcus (GBS), resulting in the identification of four proteins that induce protection in mice [40]. Moreover, the identification of potential vaccine antigens is even more efficient if non-pathogenic strains of the pathogen are included in the bioinformatics analysis to eliminate antigens of the list shared with non-pathogenic strains; this inclusion of non-virulent strains is the subtractive reverse vaccinology approach. Indeed, it consists in discarding the antigens identified in the virulent strains shared with the non-virulent ones. This strategy, for example, identified nine proteins that confer protection ranging from 13 % to 82 % against extra-intestinal pathogenic *E. coli* [41]. Furthermore, artificial intelligence, artificial neural networks, and other bioinformatics tools can be combined with reverse vaccinology to generate more accurate results. These tools include transcriptomics, proteomics, metabolomics, structural and systems biology (to understand the biology of both the pathogen and the host), vaccinomics (to understand how the immune response is generated in response to antigens) and quantum vaccinomics [42–45]. Only the most immunogenic parts of the selected antigens, the B- and T-cell epitopes as well as the ligands of immune receptors, can be optimally and stably combined to form a putative highly immunogenic multi-epitope recombinant protein [46]. This approach also eliminates the need for purification of the native antigen. All these advances will facilitate the rational and rapid design within a few days of putative highly efficient vaccine candidates that can be easily produced using existing vaccine platforms (recombinant proteins, recombinant vectors or nucleic acids) [44]. This is particularly important and reassuring in the context of emerging infectious disease. Finally, reverse vaccinology approaches are also being used to develop personalized vaccines against local pathogen strains [43].

Reverse vaccinology approaches have been widely applied to iden-
tify potential vaccine antigens to protect livestock against infectious
diseases, for example for pigs, bovine or fish and poultry. Poultry
vaccination has benefited from the advances made in reverse vaccinol-
ogy methodology described above. Here, we use poultry as a represen-
tative example of current progress in terms of strategy for livestock
vaccination.

3. Reverse vaccinology in poultry

In accordance with the general reverse vaccination strategy
described in Fig. 1, the main software or web-platforms used for poultry
reverse vaccinology are listed in Table 1. Several studies have used a
single pathogen genome to search for potential vaccine antigens
<table><caption>**Table 1**
main tools used for Reverse Vaccinology studies in chickens.</caption><thead><tr><th></th><th>Software or website platform</th><th>Tool's role</th></tr></thead><tbody><tr><td rowspan="3">Antigen localization</td><td>PSORTb, BUSCA, SOSUIGramN or CELLO2GO</td><td>Prediction of outer membrane proteins and extracellular proteins</td></tr><tr><td>Proteome Analyst or SecretomeP</td><td>Prediction of secreted proteins</td></tr><tr><td>SignalP</td><td>Prediction of proteins with a signal peptide (proteins potentially excreted or on the cell surface)</td></tr><tr><td rowspan="2">Antigen properties</td><td>SPAAN</td><td>Prediction of adhesins</td></tr><tr><td>HMMTOP, TMHMM</td><td>Prediction of proteins with low number of transmembrane domains</td></tr><tr><td rowspan="7">Antigen immunogenicity</td><td>SoDoPE</td><td>Prediction of the protein solubility</td></tr><tr><td>VaxiJen</td><td>Prediction of the antigen/epitope antigenicity</td></tr><tr><td>BCPreds</td><td>Prediction of B-cell epitopes</td></tr><tr><td>VacFinder®</td><td>Prediction of B-cell epitopes</td></tr><tr><td>NetCTL</td><td>Prediction of CTL epitopes</td></tr><tr><td>IEBD</td><td>Prediction of T-cell epitopes</td></tr><tr><td>C-ImmSim</td><td>Prediction of immunogenic potential</td></tr><tr><td rowspan="3">Antigen conservation</td><td>NCBI Blast</td><td>Identification of antigens conserved among various pathogen serotypes and antigens common to host proteins</td></tr><tr><td>MAFFT</td><td>Identification of proteins conserved among various pathogen serotypes</td></tr><tr><td>Clustal Omega Server, DNAstar</td><td>Identification of conserved regions among antigens</td></tr></tbody></table>

(Table 2). The reverse vaccinology strategy arose at around the same time as novel tools to sequence the whole genome of pathogens, particularly bacteria with large genome sizes [47]. Therefore, there are cases where only one genome has been sequenced and integrated in the reverse vaccinology process, like for *Pasteurella multocida*, which is responsible for fowl cholera. Here, only the complete sequence of the genome of the non-virulent *P. multocida* Pm70 strain was available at the time of the reverse vaccinology study [48], which identified a total of 129 secreted proteins (located in the outer membrane) and lipoproteins. Three others previously identified proteins, from the virulent strain x-73 [49], were also evaluated. Of these 132 proteins, 105 were cloned and expressed in *E. coli* and [48], 71 proteins were ultimately tested for their immunogenicity and protective efficacy. All proteins elicited a strong immune response in chickens, but only lipoprotein E (PlpE) protected chickens against fowl cholera induced by the virulent *P. multocida* x-73 and VP 161 strains [50]. Importantly, PlpE was identified through the reverse vaccinology analysis [48] and not in the previous study [49]. Subsequently, PlpE was evaluated as a vaccine candidate against *P. multocida* in mice [51,52]. Interestingly, although the starting point was a non-virulent pathogen strain, it was still possible to identify a relevant vaccine antigen for this model (however, this may not be the case for other pathogens). For full disclosure, we note that the same vaccine protein was identified by other teams using another strategy [53,54].

In another study, reverse vaccinology focused on a highly virulent zoonotic *Campylobacter jejuni* strain [37] responsible for severe campylobacteriosis in humans. The idea here was to vaccinate chickens, which are the main source of human campylobacteriosis and healthy carriers of this bacterial species, to reduce the risk of transmission of this pathogen to humans. At the time of the study, only 10C. jejuni genomes had been implemented in the Vaxign databank, Vaxign being one of the web-based software programmes dedicated to reverse vaccinology evaluation (https://virolinet.org/vaxign2). Among the 10 strains, C. jejuni subsp. jejuni 81–176 is one of the most virulent for humans and was therefore chosen for the study. Fourteen out of 1758 potential

proteins encoded by the bacterial genome were selected as potential vaccine antigens based on their predicted extracellular or outer membrane location, adhesin-like properties, antigenicity, B-cell epitope density and the conservation of the selected proteins among 93 strains of C. jejuni and nine strains of C. coli. [37]. This last criterion was not included in the Vaxign pipeline but was applied afterwards using BLAST® analyses. Six of the 14 vaccine candidates were then evaluated for their immunogenicity and protective potential against *Campylobacter* in broilers. Four (ShlB/FhaC/HecB family hemolysin secretion/activation protein, flagellar hook-associated protein FlgL, CsgG/HfaB family protein and histidine phosphatase family protein) of the six proteins induced a specific humoral immune response and a significant reduction in the *Campylobacter* caecal load of between 2 and 4.2 log10 CFU/g [55,56]. However, the protective effect of these proteins has not been confirmed in subsequent studies [55,56].

A recent study identified new candidate antigens against pullorum disease. The *Salmonella* Pullorum strain R51 was selected for its representativeness and broader significance after initial epidemiological investigation and virulence tests. The complete genome sequence of R51 was used to predict potential vaccine candidates among the 4713 proteins of the proteome. Based on the analysis of several criteria, such as the identification of outer membrane and extracellular proteins and transmembrane domains, protein abundance, antigenicity and solubility, 22 proteins were identified and 18 recombinant proteins were produced and purified. These vaccine candidates were injected *in ovo* into 18-day-old chick embryos to evaluate immunogenicity and protective efficacy. The results showed that the vaccine candidates PstS, SinH, LpfB and SthB trigger immune responses. In particular, PstS has a significant protective effect, with a survival rate of 75% compared with 31.25% for the control group [57]. As a last study presented here, B- and T-cell epitopes were identified on three major structural protein of Avian Leukosis Virus serogroup J (ALV-J): the group antigen Gag, the envelop protein Env and the transcriptase Pol [58]. One single viral strain was included in this study. A multi-epitope based vaccine candidate composed of fourteen B-cell epitopes and twenty-one T-cell epitopes coming from these three antigens was designed and produced. It was positively recognised by chicken anti-ALV-J sera.

In the three studies above, the starting point of the reverse vaccinology studies was a single genome of a pathogenic strain, allowing the identification of proteins able to confer some protective immunity, even when an avirulent strain served as the genome reference [48]. Therefore, only the vaccine candidates present in the single genome were studied. It is possible that other potential vaccine antigens (present in other strains of the same pathogen) have been overlooked, and these may be cross-protective and/or more efficient. Thus, studying a single genome represents a potential loss of opportunity to identify more effective vaccine antigens. The pangenomic reverse vaccinology approach, which takes into account all the available genome strains of a given pathogen, has also been applied to poultry (Table 3). In one study, this method was used to identify potential vaccine candidates against *Gallibacterium anatis*, which causes salpingitis and peritonitis in commercial laying hens. To ensure maximum diversity, 10 genomes of *Gallibacterium* strains isolated from ducks, chickens and hens were selected based on their pathogenic potential, prevalence in the field and genetic diversity. Based on predicted surface exposure or secretion, no or only one transmembrane helix and presence in at least 6 out of the 10 genomes, 42 proteins were selected. After screening using ELISA to detect a specific antibody response with serum from chickens that had been infected with *G. anatis* and using VacFinder®, a bioinformatics tool that predicts the protective response based on a protein property prediction method, five vaccine candidates were ultimately identified. An *in vivo* trial of these five most promising vaccine candidates revealed three proteins (GtxA-N, FlfA and Gab_1309) that induce a lower lesion score and thus have protective potential [59]. Subsequent studies have shown that some of these proteins confer protection against *G. anatis* [59–63].

The pangenomic reverse vaccinology approach has also recently
<caption>**Table 2**</caption>

Overview of reverse vaccinology approaches in poultry starting based on a single strain of the pathogen to identify potential vaccine antigens. The bioinformatics methods (in bold) used in each study and their order of implementation (STEP 1–6) are indicated in bold.*

<table><thead><tr><th colspan="2">Bacteria or virus</th><th colspan="4">Criteria and bioinformatics tools used for protective antigen prediction and selection</th><th rowspan="2">Relevant identified antigens</th><th rowspan="2">Refs.</th></tr><tr><th>Species</th><th>Strain</th><th>Localization</th><th>Properties</th><th>Antigenicity</th><th>Conservation</th></tr></thead><tbody><tr><td><i>Pasteurella multocida</i></td><td>Pm70 non-virulent</td><td><b>STEP 1 PSORTb</b><br>Extracellular proteins<br><b>STEP 2 Proteome</b><br>Analyst Secreted proteins and lipoproteins</td><td></td><td></td><td></td><td>Lipoprotein E Protective antigen</td><td>[48,49]</td></tr><tr><td><i>Campylobacter jejuni</i></td><td>81–176<br>virulent for humans, not for chickens<br>1758<br>putative ORFs</td><td><b>STEP 1 PSORTb v2.0</b><br>Extracellular proteins</td><td><b>STEP 2 SPAAN</b><br>Adhesins HMMTOP<br>Proteins with fewer than 2 TM domains</td><td><b>STEP 3 VaxiJen v2.0</b><br>High antigenic score<br><b>STEP 4 BCPreds</b><br>Proteins with high B-cell epitopes density<br><b>STEP 5 VaxiJen v2.0</b><br>High antigenic scores</td><td><b>STEP 6 tblastn</b><br>Conserved over 93 strains <i>C. jejuni</i> and 9 strains <i>C. coli</i>.<br><b>blastp</b> Exclusion of proteins common to <i>Gallus gallus</i></td><td>-ShlB/FhaC/HecB family hemolysin secretion/ activation protein-Flagellar hook-associated protein FlgL-CsgG/HfaB family protein-Histidine phosphatase family protein All the proteins are immunogenic Conflicting results regarding their protective effects</td><td>[37,56]</td></tr><tr><td><i>Salmonella enterica</i><br>serovar Gallinarum<br>biovar Pullorum<br>(<i>Salmonella</i><br>Pullorum)</td><td>R51 Virulent<br>strain 4713<br>encoded proteins</td><td><b>STEP 1 PSORTb v3.02, BUSCA, SOSUIGramN, CELLO2GO</b><br>Surface-exposed proteins using at least one tool</td><td><b>STEP 2 SignalP v.5.0</b><br>Proteins with signal peptides<br><b>STEP 4 SPAAN</b><br>Adhesins <b>STEP 5 SoDoPE</b><br>Soluble proteins <b>TMHMM v.2.0</b><br>Proteins with fewer than 2 TM domains</td><td><b>STEP 3 VaxiJen v.3.0</b><br>Proteins with a high antigenic score</td><td><b>STEP 6 Blastp</b><br>Conserved proteins over 182 strains of <i>S. Pullorum</i></td><td>-PstS: phosphate ABC transporter, periplasmic phosphate-binding protein -SinH: an autotransporter protein that facilitates adhesion and cell invasion-LpfB: chaperone protein lpfB precursor-StB: Fimbrial operons All are immunogenic proteins<br>PstS is the most protective antigen<br>Incorporation of 14 B-cell epitopes and 21 T-cell epitopes in a multi-epitope based vaccine candidate composed of epitopes from Gag, Pol and Env<br>Construct validated by Western-blotting and Elisa</td><td>[57]</td></tr><tr><td>Avian Leukosis<br>Virus serotype J</td><td>NX0101</td><td><b>STEP 1 Basic virology knowledge</b>: Gag, Pol and Env are structural proteins of the virus</td><td></td><td><b>STEP 2 BCPreds</b><br>Epitopes prediction</td><td></td><td></td><td>[58]</td></tr></tbody></table>

been applied to *Campylobacter* [64]. The genome sequences of four *C. jejuni* strains from different poultry sources led to the identification of 7132 proteins, of which 6 highly conserved antigenic proteins were selected after *in silico* analysis of the genome of *C. jejuni* and *C. coli* strains. After eliminating candidates previously tested in the literature showing lack of cross-protection and short-lived efficacy in previous *in vivo* experiments, three conserved potential vaccine candidates were ultimately selected: phospholipase A (PldA), TonB-dependent vitamin B12 transporter (BtuB) and cytolethal distending toxin subunit B (CdtB). Macrophage HD11 cells were infected with *C. jejuni* strains and RT-qPCR was performed to determine the expression of the predicted genes, because the expression levels of candidate genes may be related to the efficacy of a future vaccine. All three predicted genes were up-regulated in four *C. jejuni* strains from four different sources (broiler cloacal swab, retail chicken meat, human clinical and bovine faecal isolates) [64]. However, to the best of our knowledge, these vaccine candidates have not yet been tested *in vivo*.

One study used 58 genomes of avian pathogenic *E. coli* (APEC), which causes colibacillosis in poultry in China. The APEC genomes were isolated from sick and dead ducks. Based on the identification of 3001 core genes, prediction of subcellular localisation, transmembrane helices and antigenicity, 53 proteins were selected as potential vaccine antigens. Four of these were shown to be recognised by antibodies of sick ducks based on Western blot analysis with three antisera of the major serotypes of APEC (O1, O2 and O78) [65]. A few months later, the same team carried out another study to screen for universal protective

antigens against the three predominant serogroups and the newly emerged O145 [66]. From 127 APEC genomes, 61 proteins were identified as likely vaccine antigens against these four serogroups. Six proteins were then selected for evaluation of immunoreactivity by Western blot; three of them (outer membrane protein A (OmpA), probable N-acetylleuraminic acid outer membrane channel protein (NanC) and murein hydrolase activator (EnvC)) were shown to be immunoreactive with four antisera from four serogroups, providing a basis for the development of APEC subunit vaccines against avian colibacillosis [66]. The proteins identified in this study are different to those identified in the previous study. However, to the best of our knowledge, *in vivo* protection has still not been studied or published.

Another recent study evaluated potential cross-protective antigens against *Riemerella anatipestifer*, the bacterial species responsible for polyserositis and septicaemia in ducklings. Based on 52 genomes, the core genome revealed 30 proteins that may act as potential cross-protective antigens. These proteins were selected based on their predicted antigenicity, subcellular localisation, transmembrane helices, signal peptide and molecular weight. Western blots performed with three of these recombinant proteins demonstrated their immunoreactivity with five antisera (anti-serotypes 1, 2, 6, 10, and 11) [67]. Again, no study on the protective potential of these proteins has yet been published.

Pangenome reverse vaccinology approach was also used to design a multi-epitope peptide vaccine candidate against all enteric avian rotavirus strains [68]. The authors first identified two structural antigens,

<footnote>* TM = transmembrane.</footnote>
<caption>**Table 3**</caption>

Overview of reverse vaccinology approaches in poultry using the pangenomic approach to identify potential vaccine antigens. The bioinformatic methods used in the study and their order of implementation (STEP 1–7) are indicated in bold.

<table><thead><tr><th colspan="2">Bacteria or virus</th><th colspan="4">Criteria and bioinformatics tools used for protective antigen prediction and selection</th><th rowspan="2">Relevant identified antigens</th><th rowspan="2">Refs.</th></tr><tr><th>Species</th><th>Strains</th><th>Localization</th><th>Properties</th><th>Antigenicity</th><th>Conservation</th></tr></thead><tbody><tr><td><i>Gallibacterium anatis</i></td><td>10 strains selected based on pathogenic potential, prevalence in the field and genetic diversity</td><td>STEP 1 PSORTb v.3.0 Extracellular and outer membrane proteins<br>Selection of proteins present in at least 6 genomes</td><td>STEP 2 SignalP v.3.0 Proteins with signal peptides<br>TMHMM v.2.0 Proteins with low number of TM domains</td><td>STEP 4 VacFinder® Proteins with B-cell epitopes Top 20 antigens with high potential protective effect (based on the whole genome)</td><td>STEP 3 BLASTp v.2.2.22 Conserved proteins among the pathogen strains</td><td>- RTXtoxinsand-related Ca2 + - binding-proteins - F17-like fimbrial subunit - Membrane proteins related to metalloendopeptidases Recognised by sera of infected chickens Reduced lesion scores upon infection - Phospholipase A - TonB-dependent vitamin B12 transporter - Cytolethal distending toxin subunit B Proteins expressed in in vitro macrophages infected by C. jejuni</td><td>[59]</td></tr><tr><td><i>Campylobacter jejuni</i></td><td>4 strains selected from various poultry sources 7132 proteins in total</td><td>STEP 1 PSORTb v.2.0 Extracellular and outer membrane proteins</td><td>STEP 2 SPAAN Adhesin HMMTOP Proteins with fewer than 2 TM domains</td><td>STEP 3 VaxiJen v.2.0 Proteins with a high antigenic score STEP 5 BCPreds Proteins with high B-epitope density<br>STEP 6 VaxiJen v.2.0 Validation of the high antigenic scores on selected antigens</td><td>STEP 4 tblstn Conserved proteins over 206C. jejuni strains (including 18 poultry strains) and 34C. coli strains blastp Exclusion of proteins common to Gallus gallus STEP 7 MAFFT Multiple alignment of the predicted protein sequences obtained from the C. jejuni strains<br>Blastp Exclusion of previously characterised vaccine candidates</td><td>- Phospholipase A - TonB-dependent vitamin B12 transporter - Cytolethal distending toxin subunit B Proteins expressed in in vitro macrophages infected by C. jejuni</td><td>[64,70]</td></tr><tr><td>Avian pathogenic Escherichia coli (APEC)</td><td>58 strains 3001 core genes in total</td><td>STEP 1 PSORTb v3.0.2 Extracellular and outer membrane proteins</td><td>STEP 2 TMHMM v2.0 Proteins with fewer than 3 TM domains and a molecular weight &lt; 110 kDa</td><td>STEP 3 VaxiJen v.2.0 Proteins with high antigenic scores</td><td></td><td>53 potential vaccine antigens against the 3 major serotypes (O1, O2 and O78)</td><td>[65]</td></tr><tr><td>Avian pathogenic Escherichia coli (APEC)</td><td>127 strains</td><td>STEP 1 PSORTb v3.0.2 Extracellular and outer membrane proteins</td><td>STEP 2 TMHMM v2.0 Proteins with fewer than 3 TM domains and a molecular weight &lt; 110KD</td><td>STEP 3 VaxiJen v.2.0 Proteins with high antigenic score</td><td></td><td>61 potential vaccine antigens against the 3 major (O1, O2 and O78) and the emerged 0145 serotypes Immuno-reactive for all serogroups: - Outer membrane protein A - Probable N-acetyl-neuraminic acid outer membrane channel protein - Murein hydrolase activator<br>30 proteins identified as potential vaccine antigens Immuno-reactive proteins for 5 serogroups: - Ompa/motb domain protein - Transporter - Penicillin-binding protein or type IX secretion system protein PorQ</td><td>[66]</td></tr><tr><td>Riemerella anatipestifer</td><td>52 strains Including 32 strains from NCBI database and 20 draft genome strains</td><td>STEP 2 PSORTb v3.0.3 Extracellular and outer membrane proteins</td><td>STEP 3 Deep TMHMM v1.0.13 Proteins with less than 3 TM domains and a molecular weight &lt; 110KD</td><td>STEP 1 VaxiJen v.2.0 Proteins with high antigenic score</td><td></td><td>Selection of VP4 and VP7 Incorporation of 2 CTL, 2 HTL and 3 B-cell epitopes in a multi-epitope based peptide vaccine candidate<br>Incorporation of 4 CTL, 7 HTL and 11 B-cell epitopes in a multi-epitope based peptide vaccine candidate</td><td>[67]</td></tr><tr><td>Enteric avian rotavirus</td><td>Strains of the 4 serotypes A, D, F et G</td><td>STEP 2 Basic virology knowledge: VP4 and VP7 are components of the viral capsid</td><td></td><td>STEP 3 IEBD server B- and T-cell epitopes STEP 4 VaxiJen and docking with TLRs Validate the immunogenicity of the multi-epitope protein</td><td>STEP 1 Blast and Clustal Omega Server Conserved antigens among the 4 serotypes</td><td></td><td>[68]</td></tr><tr><td>Infectious bursal disease virus</td><td>10 strains from various serotypes</td><td>STEP 1 Basic virology knowledge: VP2 and VP3 are components of the viral capsid</td><td></td><td>STEP 3 NetCTL and IEBD server T-cell epitopes STEP 4 ABCPRED B-cell epitopes Step 5 VaxiJen, c-Immsim and docking with TLRs Validate the immunogenicity of the multi-epitope protein</td><td>STEP 2 DNAstar Conserved regions on VP2 and VP3 among the 10 strains</td><td></td><td>[69]</td></tr></tbody></table>
viral protein (VP) 4 and VP7, that were highly conserved among the four serotypes of this virus (A, D, F and G). Two strong MHC-I cytotoxic T-cell-epitopes, two strong MHC-II helper T-cell-epitopes and three strong B-cells epitopes were identified on these two proteins and combined into a multi-epitope based peptide vaccine. As of the writing this review, the efficacy of this vaccine candidate was only predicted *in silico*. Using the same approach, the genomes of ten different strains of infectious bursal disease virus were used as the starting point to design a multi-epitope-based candidate vaccine composed of four cytotoxic T-cell-epitopes, seven helper T-cell-epitopes and eleven B-cells epitopes identified from the capsid VP2 and VP3 proteins [69]. Again, the antigenicity of this construction was only predicted *in silico*.

Pangenomic and subtractive reverse vaccinology introduces an additional step forward in the *in silico* analysis [71,72]. All the available genomes of virulent and non-virulent strains are taken into consideration; it consists in discarding in the final step of the analysis the antigens found in the virulent strains that are shared with the non-virulent ones, as these antigens are not typical of the virulent strains and are not involved in the pathogenicity. It has been used in poultry to compare 16 sequenced *C. perfringens* strains based on toxinotype, PFGE profile, type IV pilus profile and the ability to cause avian necrotic enteritis (Table 4). A total of 155,700 proteins were identified from all genomes, of which 65 % were cytoplasmic, 22 % were part of the bacterial membrane and 13 % were extracellular. Of these, six potential candidates (P264-1, P1074, P153, P209, P509 and P561) were selected based on their antigenic potential, extracellular localisation and possible role in *C. perfringens* virulence. The number of identified antigens, the role in bacterial adhesion, the antigenic score and the number of extracellular proteins were higher in virulent *C. perfringens* than in its commensal counterpart [72]. Further *in vivo* investigations showed that chickens immunised with five of these proteins produced antibodies that specifically recognise both the recombinant and native forms of the protein in pathogenic *C. perfringens* [71].

## 4. Critical considerations for antigen prediction using reverse vaccinology

The different steps and bioinformatics tools used in reverse vaccinology in poultry to predict antigens based on specific criteria are presented in Tables 1, 2, 3 and 4. The first step with the selection of the

pathogen strain(s) involved in the study will greatly influence the identification of effective vaccine candidates; the subsequent steps will contribute to the select the most promising antigens meeting more or less precise criteria, depending on the number and nature of the different steps. The more criteria there are, the more relevant the analysis will be, and the least effective antigens will be progressively eliminated to keep only a few antigens at the end, potentially the best ones. In general, fewer than ten vaccine candidates are identified, but this number can rise to 30–50 in certain cases. Different selection tools have been used in the studies over time, because they are constantly being updated and/or developed as more pathogen strain genomes, bioinformatics tools and systems biology become available.

Studies have chosen different reverse vaccinology approaches (single bacterial genome as starting point, pangenomic approach or subtractive pangenomic approach for antigen selection) and/or different protein criteria for antigen prediction. Additionally, the order in which the different steps and/or the bioinformatics tools are used varies (Table 2), but the impact on the resulting antigen prediction is limited (since, at the end the selected antigens will meet the criteria of each step), except for the selection of the pathogen strain(s) used in the analysis. The prediction of subcellular localisation is generally an important step in antigen prediction and PSORTb has been used alone or in combination with other tools in all the reported studies in poultry (Table 2). Localisation on the cell surface is required for the predicted antigen to be accessible to the host immune system. However, most proteins are cytoplasmic and are therefore not exposed to the immune system, precluding their selection as potential vaccine antigens [72]. Nevertheless, some bacterial cytosol proteins, called moonlighting proteins, are excreted or exported to the bacterial cell surface. These proteins were positively evaluated as vaccine antigens in studies unrelated to reverse vaccinology [73]. Future tools for predicting the cellular localization of proteins should incorporate the fact that these proteins are not only intracellular, in order to facilitate their selection. The advent of artificial intelligence in bioinformatics should help to achieve this. Moreover, the presence of two or more transmembrane domains on the proteins is generally, but not always, considered as an exclusion criterion so as to facilitate the production, isolation and purification of the identified candidates under laboratory conditions [13,27] and, in most of the studies in poultry, was used as a filtering step (Table 1). However, one study [74] demonstrated that removing the C-terminal region of the transmembrane domain

<caption>**Table 4**</caption>

Overview of reverse vaccinology approaches in poultry using the subtractive pangenomic approach to identify potential vaccine antigens. The bioinformatics methods used in each study and their order of implementation (STEP 1–4) are indicated in bold.

<table><thead><tr><th colspan="2">Bacteria</th><th colspan="4">Criteria and bioinformatics tools used for protective antigen prediction and selection</th><th rowspan="2">Relevant identified antigens</th><th rowspan="2">Refs.</th></tr><tr><th>Species</th><th>Strain</th><th>Location</th><th>Properties</th><th>Antigenicity</th><th>Conservation</th></tr></thead><tbody><tr><td><i>Clostridium perfringens</i></td><td>10 commensal strains<br>6 virulent strains<br>Commensal and virulent strains analysed first separately, then pooled together<br>155,700 proteins in total</td><td><b>STEP 2</b><br><b>PSORTb</b><br><b>CELLO</b><br><b>Gpos-mPLoc</b><br><b>FUEL-mLoc</b><br>Extracellular and outer membrane proteins</td><td><b>STEP 3</b><br><b>TMHMM</b><br>Proteins with fewer than 4 TM* domains<br><b>SignalP</b><br>Proteins with signal peptides<br><b>SecretomeP</b><br>Proteins with an alternative secretion pathway</td><td><b>STEP 1</b><br><b>VaxiJen</b><br>Proteins with a high antigenic score<br>Classified into 4 categories of scores</td><td><b>STEP 4</b><br><b>tblastn</b><br>Selection of conserved genes among all available <i>C. perfringens</i> genomes<br>Exclusion of antigens also present in commensal strains</td><td>Identification of putative antigens found exclusively in virulent strains<br>4 recombinant proteins were successfully produced:<br>- Hypothetical protein #1<br>Hypothetical protein #2<br>- Prepinil N-terminal cleavage methylation domain #1<br>- Prepinil N-terminal cleavage methylation domain #2<br>Induction of antibodies upon vaccination</td><td>[71,72]</td></tr></tbody></table>

<footnote>* TM = transmembrane.</footnote>

<footnote>* TM = transmembrane.</footnote>
results in high levels of recombinant protein expression. Therefore, in a subsequent study [72], candidate proteins were not discarded solely based on the presence of transmembrane regions, and two candidate proteins with transmembrane regions and a high predicted immunogenicity score were even selected for further characterization [72]. The VaxiJen server [70] was used to predict protective antigens in all the studies on poultry (Tables 2–4). B-cell epitope prediction was carried out in several studies [37,59,64], but future studies on poultry reverse vaccinology should include T-cell epitope prediction to consider the potential induction of the cellular immune response also. In fact, immune responses in poultry are often limited to the induction of antibodies. Today, cellular immunity is increasingly evaluated in poultry [75], thereby enhancing knowledge on major histocompatibility class (MHC) alleles in poultry. These new findings will surely be compiled in the databases used by the reverse vaccinology bioinformatics tools.

After *in silico* analysis, experimental studies are required to assess the actual immunogenicity of the predicted proteins and to evaluate their protective efficacy. The predicted vaccine antigens are usually expressed as a recombinant protein for these evaluations. However, sometimes the recombinant proteins are difficult to obtain, are insoluble, have low yield or contain impurities [59], rendering further immune evaluation difficult or impossible. Alternatively, potential vaccine antigens may be harboured by viral or bacterial derived vectors or can be inoculated as nucleic vaccination. DNA vaccination was assessed in the case of vaccination against *Campylobacter* in chickens using flagellin A as a vaccine antigen. Specific pathogen-free Leghorn hens were protected, but not conventional leghorn chickens, which are the real targets of vaccination [76].

In most cases, *in vitro* tests such as Western blots or ELISA using recombinant identified antigens are performed to evaluate the immunoreactivity of these proteins or the specific antibody response produced [65,66]. However, the results of these tests do not necessarily imply that the studied antigen is immunogenic or not in its native form. In fact, in certain cases, the folding of the recombinant protein and of the native protein is different and can lead to false ELISA responses [59]. Elucidating these differences in protein structures requires using for example X-ray crystallography or nuclear magnetic resonance, which demands the crystallisation of almost pure proteins. Moreover, prediction algorithms cannot yet identify conformational epitopes, which may exclude such epitopes from the prediction. One way to overcome this problem is to align each epitope that has been identified on a known template [26]. However, this approach requires knowing the antigen template, if it exists. Perhaps in the future and with the introduction of artificial intelligence (AI) in the bioinformatics process, conformational epitopes will be more easily predicted. Furthermore, one study showed that proteins with a lower predicted protectiveness can nonetheless prove to be highly immunogenic in *in vitro* tests [59], which again shows that predicted antigens have to be taken only as possible candidates. The immune and protective potential of the vaccine candidates can only be evaluated *in vivo*, if possible in the target species. Again, it is important to note that high antibody levels do not necessarily indicate protection against the pathogen infection [55,56,77].

Together, *in vitro* and *in vivo* methods complement each other in the effort to evaluate the protective immunity induced by antigens identified by a reverse vaccinology approach. Therefore, reverse vaccinology as such only indicates research leads in the development of a new and broadly protective vaccine against microorganisms. There are still some limitations in this approach, such as the identification of conformational epitopes and the identification of T-cell epitopes for certain livestock species, including poultry. New knowledges in immunology as well as new bioinformatics tools will progressively render the reverse vaccinology predictions more and more reliable. Although reverse vaccinology allows the discovery of new vaccine antigens, developing a more systematic methodology can help achieve better, more robust results. An impressive step forward is taking place due to the incorporation of artificial intelligence, machine learning and the integration of on all

available knowledge on pathogen infestation, host responses and the environment to design vaccine targets [78]. This is made possible because of big data management [79], which includes, for example, vaccinomics and quantum vaccinomics [79–81], systems biology [79], omics and interactome studies [80]. Furthermore, these advances will enable real-time analyses and the development of personalized vaccines, taking into account factors such as local pathogen strains present in an area, modifications of pathogen strains and patient profiles [80].

One particular application, using knowledges already acquired on vaccine antigens (identified using reverse vaccinology or other approaches) can also help enhance knowledge on epitopes or propose new strategies for epitope assembly. For example, reverse vaccinology and immunoinformatic tools have been used to design multi-epitope vaccines in humans [82,83]. The design and construction of multi-epitope subunit vaccines may be a solution for the development of multi-serotype cross-protective subunit vaccines, because epitopes in a single protein may offer insufficient protection or this protein is poorly soluble or difficult to purify. In poultry, a multi-epitope vaccine against the avian *Mycoplasma gallisepticum*, which causes chronic respiratory disease, combined cytotoxic T lymphocyte epitopes, helper T lymphocyte epitopes and B-cell epitopes from different antigenic proteins [84]. In another study, an effective multi-epitope vaccine combined T-cell and B-cell epitopes with the appropriate linkers and adjuvants against the haemagglutinin neuraminidase protein of 26 Newcastle disease virus (an avian orthoavulvirus) strains from Pakistan [85]. The strength of the reverse vaccinology lies in the fact that all the proteins encoded by the pathogen's genome are taken into account to identify potential vaccine antigens. Therefore, owing that this approach predicts a large number of epitopes, the design of multi-epitope poultry vaccines should be more efficient in the future. All the advances described above combined with efficient vaccine platforms will help to design highly promising vaccine candidates and to evaluate their protective potential within a short period, which is an undeniable asset in the event of the emergence of a novel pathogen [44].

## 5. Conclusions

Reverse vaccinology is now being applied to many bacterial, viral and parasitic pathogens and is therefore considered a competitive approach for discovering vaccine candidates in veterinary fields, including for poultry. This method is constantly evolving, and its use varies from study to study depending on the microorganism being tested and the bioinformatics tools available at the time of analysis. Like for human vaccinology, poultry has benefited from advances in terms of next-generation sequencing and bioinformatics tools. Several studies used a single genome of a given pathogen as a starting point, and not necessarily the most relevant one, depending mainly on the availability of genome sequences. Thereafter, the pangenomic reverse vaccinology approach integrated multiple bacterial strains belonging to the same pathogen family, leading to the more efficient design of cross-protective vaccines. Likewise, the subtractive strategy can eliminate antigens that are also present in non-pathogenic strains, making it possible to focus only on the most virulent putative antigens. We anticipate further progress in the reverse vaccinology approach, perhaps involving the identification of poultry T-cell epitopes and conformational epitopes and the standardisation of the steps of evaluating the immunogenic and protective properties of the identified antigens. AI will likely help to obtain more efficient bioinformatics tools. Other valuable progress includes developing strategies to reduce the number of animals needed to evaluate the protective immunity of the vaccine candidates, requiring new technologies such as microarrays, RNA-Seq, proteomics, organoids, etc. Furthermore, with advances in characterising the antibody repertoire of infected birds and in *in silico* antibody-antigen docking, this information could be may also offer in the future possibilities to add characterization steps of the reverse vaccinology identified antigens before animal experiments.
## Funding

N.G. was the recipient of a thesis funded by ANSES, Saint Brieuc Armor Agglomération and the Conseil départemental des Côtes d'Armor.

### CRediT authorship contribution statement

**Noémie Gloanec:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis. **Muriel Guyard-Nicodème:** Writing – review & editing, Supervision. **Marianne Chemaly:** Writing – review & editing. **Daniel Dory:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

The authors thank Saint Brieuc Armor Agglomération and the Conseil départemental des Côtes d'Armor for their financial support.

## Data availability

No data was used for the research described in the article.

## References

[1] Pasteur. On the germ theory. Science 1881;2:420-2.

[2] Ravanfar P, Satyaprakash A, Creed R, Mendoza N. Existing antiviral vaccines. Dermatol Ther 2009;22:110-28.

[3] Bandyopadhyay AS, Garon J, Seib K, Orenstein WA. Polio vaccination: past, present and future. Future Microbiol 2015;10:791-808.

[4] Hilleman MR. Vaccines in historic evolution and perspective: a narrative of vaccine discoveries. Vaccine 2000;18:1436-47.

[5] Michel ML, Tiollais P. Hepatitis B vaccines: protective efficacy and therapeutic potential. Pathol Biol (Paris) 2010;58:288-95.

[6] Scott LJ, McCormack PL. Reduced-antigen, combined diphtheria, tetanus, and acellular pertussis vaccine, adsorbed (boostrix®): a guide to its use as a single-dose booster immunization against pertussis. BioDrugs 2013;27:75-81.

[7] Cox MM. Recombinant protein vaccines produced in insect cells. Vaccine 2012;30:1759-66.

[8] Self WH, Tenforde MW, Rhoads JP, Gaglani M, Ginde AA, Douin DJ, et al. Comparative effectiveness of Moderna, Pfizer-BioNTech, and Janssen (Johnson & Johnson) vaccines in preventing COVID-19 hospitalizations among adults without immunocompromising conditions - United States, march-august 2021. Morb Mortal Wkly Rep 2021;70:1337-43.

[9] Hansson I, Pudas N,HBarbom B, Engvall EO. Within-flock variations of Campylobacter loads in caeca and on carcasses from broilers. Int J Food Microbiol 2010;141:51-5.

[10] Libon C, Corvaïa N, Haeuw JF, Nguyen TN, Ståhl S, Bonnefoy JY, et al. The serum albumin-binding region of streptococcal protein G (BB) potentiates the immunogenicity of the G130-230 RSV-A protein. Vaccine 1999;17:406-14.

[11] Makrides SC, Nygren PA, Andrews B, Ford PJ, Evans KS, Hayman EG, et al. Extended in vivo half-life of human soluble complement receptor type 1 fused to a serum albumin-binding receptor. J Pharmacol Exp Ther 1996;277:534-42.

[12] Wu F, Qin M, Wang H, Sun X. Nanovaccines to combat virus-related diseases. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2023;15:e1857.

[13] Pizza M, Scarlato V, Masignani V, Giuliani MM, Aricò B, Comanducci M, et al. Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science 2000;287:1816-20.

[14] Meeusen EN, Walker J, Peters A, Pastoret PP, Jungersen G. Current status of veterinary vaccines. Clin Microbiol Rev 2007;20:489-510 [table of contents].

[15] Mohd-Aris A, Muhamad-Sofie MHN, Zamri-Saad M, Daud HM, Ina-Salwany MY. Live vaccines against bacterial fish diseases: a review. Vet World 2019;12:1806-15.

[16] Bello MB, Yusoff K, Ideris A, Hair-Bejo M, Peeters BPH, Omar AR. Diagnostic and vaccination approaches for Newcastle disease virus in poultry: the current and emerging perspectives. Biomed Res Int 2018;2018:7278459.

[17] Niqueux E, Flodrops M, Allee C, Lebras MO, Pierre I, Louboutin K, et al. Evaluation of three hemagglutinin-based vaccines for the experimental control of a panzootic clade 2.3.4.4b (aHSN8) high pathogenicity avian influenza virus in mule ducks. Vaccine 2023;41:145-58.

[18] World Health Organization (WHO). Antigenic and genetic characteristics of zoonotic influenza a viruses and development of candidate vaccine viruses for pandemic preparedness. https://dn.who.int/media/docs/default-source/influenza/who-influenza-recommendations/vcm-southern-hemisphere-recommendation-2024/202309_zoonotic_vaccinivirusupdate.pdf?sfvrsn=e78676a0_5..WHO;.

[19] He Y, Rappuoli R, De Groot AS, Chen RT. Emerging vaccine informatics. J Biomed Biotechnol 2010;2010:218590.

[20] Horsfall AC, Hay FC, Soltys AJ, Jones MG. Epitope mapping. Immunol Today 1991;12:211-3.

[21] Steimer KS, Scandella CJ, Skiles PV, Haigwood NL. Neutralization of divergent HIV-1 isolates by conformation-dependent human antibodies to Gp120. Science 1991;254:105-8.

[22] Andrews SM, Pollard AJ. A vaccine against serogroup B *Neisseria meningitidis*: dealing with uncertainty. Lancet Infect Dis 2014;14:426-34.

[23] He Y, Xiang Z, Mobley HL. Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J Biomed Biotechnol 2010;2010:297505.

[24] Wizemann TM, Heinrichs JH, Adamou JE, Erwin AL, Kunsch C, Choi GH, et al. Use of a whole genome approach to identify vaccine molecules affording protection against *Streptococcus pneumoniae* infection. Infect Immun 2001;69:1593-8.

[25] Ross BC, Czajkowski L, Hocking D, Margetts M, Webb E, Rothel L, et al. Identification of vaccine candidate antigens from a genomic analysis of *Porphyromonas gingivalis*. Vaccine 2001;19:4135-42.

[26] Oprea M, Antohe F. Reverse-vaccinology strategy for designing T-cell epitope candidates for *Staphylococcus aureus* endocarditis vaccine. Biologicals 2013;41:148-53.

[27] Monterrubio-López GP, González YMJA, Ribas-Aparicio RM. Identification of novel potential vaccine candidates against tuberculosis based on reverse vaccinology. Biomed Res Int 2015;2015:483150.

[28] Jaiswal AK, Tiwari S, Jamal SB, Oliveira LC, Sales-Campos H, Andrade-Silva LE, et al. Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against *Mycobacterium leprae* and *Mycobacterium lepromatosis*. J Venom Anim Toxins Incl Trop Dis 2021;27:e20200027.

[29] Xiang Z, He Y. Genome-wide prediction of vaccine targets for human herpes simplex viruses using Vaxign reverse vaccinology. BMC Bioinformatics 2013;14(Suppl. 4):S2.

[30] Jahangirian E, Jamal GA, Nouroozi M, Mohammadpour A. A reverse vaccinology and immunoinformatics approach for designing a multipitope vaccine against SARS-CoV-2. Immunogenetics 2021;73:459-77.

[31] Kumar V, Kancharla S, Kolli P, Jena M. Reverse vaccinology approach towards the in-silico multipitope vaccine development against SARS-CoV-2. F1000Res 2021; 10:44.

[32] Tahir Ul Qamar M, Shahid F, Aslam S, Ashfaq UA, Aslam S, Fatima I, et al. Reverse vaccinology assisted designing of multipitope-based subunit vaccine against SARS-CoV-2. Infect Dis Poverty 2020;9:132.

[33] Manque PA, Tenjo F, Woehlbier U, Lara AM, Serrano MG, Xu P, et al. Identification and immunological characterization of three potential vaccinogens against *Cryptosporidium* species. Clin Vaccine Immunol 2011;18:1796-802.

[34] Carvalho GB, Pacifico LG, Pimenta DL, Siqueira LM, Teixeira-Carvalho A, Coelho PM, et al. Evaluation of the use of C-terminal part of the *Schistosoma mansoni* 200kDa tegumental protein in schistosomiasis diagnosis and vaccine formulation. Exp Parasitol 2014;139:24-32.

[35] Carvalho GB, Silva-Pereira RA, Pacífico LG, Fonseca CT. Identification of *Schistosoma mansoni* candidate antigens for diagnosis of schistosomiasis. Mem Inst Oswaldo Cruz 2011;106:837-43.

[36] Akhtar N, Singh A, Upadhyay AK, Mannan MA. Design of a multi-epitope vaccine against the pathogenic fungi *Candida tropicalis* using an in silico approach. J Genet Eng Biotechnol 2022;20:140.

[37] Meunier M, Guyard-Nicodème M, Hirchaud E, Parra A, Chemaly M, Dory D. Identification of novel vaccine candidates against *Campylobacter* through reverse vaccinology. J Immunol Res 2016;2016:5715790.

[38] Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of *Streptococcus agalactiae*: implications for the microbial “pan-genome”. Proc Natl Acad Sci USA 2005;102:13950-5.

[39] Tettelin H, Medini D, Donati C, Masignani V. Towards a universal group B *Streptococcus* vaccine using multistrain genome analysis. Expert Rev Vaccines 2006;5:687-94.

[40] Maione D, Margarit I, Rinaudo CD, Masignani V, Mora M, Scarselli M, et al. Identification of a universal group B *Streptococcus* vaccine by multiple genome screen. Science 2005;309:148-50.

[41] Moriel DG, Bertoldi I, Spagnuolo A, Marchi S, Rosini R, Nesta B, et al. Identification of protective and broadly conserved vaccine antigens from the genome of extraintestinal pathogenic *Escherichia coli*. Proc Natl Acad Sci USA 2010;107:9072-7.

[42] de la Fuente J, Kopacek P, Lew-Tabor A, Maritz-Olivier C. Strategies for new and improved vaccines against ticks and tick-borne diseases. Parasite Immunol 2016;38:754-69.

[43] Kasaija PD, Contreras M, Kirunda H, Nanteza A, Kabi F, Mugerwa S, et al. Inspiring anti-tick vaccine research, development and deployment in tropical Africa for the control of cattle ticks. Review and Insights Vaccines (Basel) 2021:2022:11.

[44] Rappuoli R, Alter G, Pulendran B. Transforming vaccinology. Cell 2024;187:5171-94.

[45] Siddique A, Wang Z, Zhou H, Huang L, Jia C, Wang B, et al. The evolution of vaccines development across Salmonella Serovars among animal hosts: a systematic review. Vaccines (Basel) 2024:12.
[46] Li Y, Farhan MHR, Yang X, Guo Y, Sui Y, Chu J, et al. A review on the development of bacterial multi-epitope recombinant protein vaccines via reverse vaccinology. Int J Biol Macromol 2024;282:136827.

[47] Maiden MC. High-throughput sequencing in the population analysis of bacterial pathogens of humans. Int J Med Microbiol 2000;290:183-90.

[48] Al-Hasani K, Boyce J, McCarl VP, Bottomley S, Wilkie I, Adler B. Identification of novel immunogens in *Pasteurella multocida*. Microb Cell Factories 2007;6:3.

[49] Boyce JD, Cullen PA, Nguyen V, Wilkie I, Adler B. Analysis of the *Pasteurella multocida* outer membrane sub-proteome and its response to the in vivo environment of the natural host. Proteomics 2006;6:870-80.

[50] Hatfaludi T, Al-Hasani K, Gong L, Boyce JD, Ford M, Wilkie IW, et al. Screening of 71 *P. Multocida* proteins for protective efficacy in a fowl cholera infection model and characterization of the protective antigen PlpE. PLoS One 2012;7:e39973.

[51] Mostaan S, Ghasemzadeh A, Asadi Karam MR, Ehsani P, Sardari S, Shokrgozar MA, et al. *Pasteurella multocida* PlpE protein polytope as a potential subunit vaccine candidate. Vector Borne Zoonotic Dis 2021;21:870-4.

[52] Okay S, Özcengiz E, Gürsel I, Özcengiz G. Immunogenicity and protective efficacy of the recombinant *Pasteurella* lipoprotein E and outer membrane protein H from *Pasteurella multocida* a:3 in mice. Res Vet Sci 2012;93:1261-5.

[53] Singh AP, Singh S, Ranjan R, Gupta SK, Singh VP, Sharma B. Molecular heterogeneity of plpE gene in Indian isolates of *Pasteurella multocida* and expression of recombinant PlpE in vaccine strain of *P. Multocida* serotype B: 2. J Vet Sci 2010; 11:227-33.

[54] Wu JR, Shien JH, Shieh HK, Chen CF, Chang PC. Protective immunity conferred by recombinant *Pasteurella multocida* lipoprotein E (PlpE). Vaccine 2007;25:4140-8.

[55] Gloanec N, Guyard-Nicodème M, Brunetti R, Quesne S, Keita A, Chemaly M, et al. Plasmid DNA prime/protein boost vaccination against *Campylobacter jejuni* in broilers: impact of vaccine candidates on immune responses and gut microbiota. Pharmaceutics 2023;15.

[56] Meunier M, Guyard-Nicodème M, Vigouroux E, Poezevara T, Beven V, Quesne S, et al. Promising new vaccine candidates against *Campylobacter* in broilers. PLoS One 2017;12:e0188472.

[57] Jiang Z, Kang X, Song Y, Zhou X, Yue M. Identification and evaluation of novel antigen candidates against *Salmonella Pullorum* infection using reverse vaccinology. Vaccines (Basel) 2023:11.

[58] Xu Q, Ma X, Wang F, Li H, Xiao Y, Zhao X. Design and construction of a chimeric multi-epitope gene as an epitope-vaccine strategy against ALV-J. Protein Expr Purif 2015;106:18-24.

[59] Bager RJ, Kudirkiene E, da Piedade I, Seemann T, Nielsen TK, Pors SE, et al. In silico prediction of *Gallibacterium anatis* pan-immunogens. Vet Res 2014;45:80.

[60] Ataei S, Ranjbar MM, Motamed N, Ataei Kachooei S, Amini Njafi F. Designing a polytopic complex vaccine candidate against *Gallibacterium anatis*: an in-silico study. Arch Razi Inst 2019;74:7-20.

[61] Pedersen IJ, Pors SE, Bager Skjerning RJ, Nielsen SS, Bojesen AM. Immunogenic and protective efficacy of recombinant protein GtxA-N against *Gallibacterium anatis* challenge in chickens. Avian Pathol 2015;44:386-91.

[62] Persson G, Pors SE, Thöfner ICN, Bojesen AM. Vaccination with outer membrane vesicles and the fimbrial protein FlA offers improved protection against lesions following challenge with *Gallibacterium anatis*. Vet Microbiol 2018;217:104-11.

[63] Pors SE, Skjerning RB, Flachs EM, Bojesen AM. Recombinant proteins from *Gallibacterium anatis* induces partial protection against heterologous challenge in egg-laying hens. Vet Res 2016;47:36.

[64] Poudel S, Jia L, Arick 2nd MA, Hsu CY, Thrash A, Sukumaran AT, et al. In silico prediction and expression analysis of vaccine candidate genes of *Campylobacter jejuni*. Poult Sci 2023;102:102592.

[65] Wang Z, Zheng X, Guo G, Dong Y, Xu Z, Wei X, et al. Combining pangenome analysis to identify potential cross-protective antigens against avian pathogenic *Escherichia coli*. Avian Pathol 2022;51:66-75.

[66] Wang Z, Xu S, Zheng X, Zheng X, Liu M, Guo G, et al. Identification of subunits for novel universal vaccines against three predominant serogroups and the emerging O145 among avian pathogenic *Escherichia coli* by Pan-RV pipeline. Appl Environ Microbiol 2023;89:e0106122.

[67] Zheng X, Xu S, Wang Z, Tao X, Liu Y, Dai L, et al. Sifting through the core-genome to identify putative cross-protective antigens against *Riemerella anatipestifer*. Appl Microbiol Biotechnol 2023;107:3085-98.

[68] Hasan M, Ahmed S, Imranuzzaman M, Bari R, Roy S, Hasan MM, et al. Designing and development of efficient multi-epitope-based peptide vaccine candidate against emerging avian rotavirus strains: a vaccinomic approach. J Genet Eng Biotechnol 2024;22:100398.

[69] Gul I, Hassan A, Muneeb JM, Akram T, Haq E, Shah RA, et al. A multiepitope vaccine candidate against infectious bursal disease virus using immunoinformatics-based reverse vaccinology approach. Front Vet Sci 2022;9:1116400.

[70] Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007;8:4.

[71] Heidarpanah S, Thibodeau A, Parreira VR, Quessy S, Segura M, Meniai I, et al. Immunization of broiler chickens with five newly identified surface-exposed proteins unique to *Clostridium perfringens* causing necrotic enteritis. Sci Rep 2023; 13:5254.

[72] Meniai I, Thibodeau A, Quessy S, Parreira VR, Fravalo P, Beauchamp G, et al. Putative antigenic proteins identified by comparative and subtractive reverse vaccinology in necrotic enteritis-causing *Clostridium perfringens* isolated from broiler chickens. BMC Genomics 2021;22:890.

[73] Ebner P, Gotz F. Bacterial excretion of cytoplasmic proteins (ECP): occurrence, mechanism, and function. Trends Microbiol 2019;27:176-87.

[74] Lepp D, Ojha S, Mehdizadeh Gohari I, Chakravarty B, Prescott JF, Gong J. Immunization with subunits of a novel pilus produced by virulent *Clostridium perfringens* strains confers partial protection against necrotic enteritis in chickens. Vet Microbiol 2019;230:7-13.

[75] Hao X, Zhang F, Yang Y, Shang S. The evaluation of cellular immunity to avian viral diseases: methods, applications, and challenges. Front Microbiol 2021;12: 794514.

[76] Meunier M, Guyard-Nicodeme M, Vigouroux E, Poezevara T, Beven V, Quesne S, et al. A DNA prime/protein boost vaccine protocol developed against *Campylobacter jejuni* for poultry. Vaccine 2018;36:2119-25.

[77] Gloanec N, Dory D, Quesne S, Béven V, Poezevara T, Keita A, et al. Impact of DNA prime/protein boost vaccination against *Campylobacter jejuni* on immune responses and gut microbiota in chickens. Vaccines (Basel) 2022:10.

[78] de la Fuente J, Ghosh S. Evolution of tick vaccinology. Parasitology 2024:1-8.

[79] de la Fuente J, Contreras M. Quantum vaccinomics platforms to advance in vaccinology. Front Immunol 2023;14:1172734.

[80] Al Meslamani AZ, Sobrino I, de la Fuente J. Machine learning in infectious diseases: potential applications and limitations. Ann Med 2024;56:2362869.

[81] Giacobbe DR, Zhang Y, de la Fuente J. Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges. Ann Med 2023;55:2286336.

[82] Aldakheel FM, Abrar A, Munir S, Aslam S, Allemailem KS, Khurshid M, et al. Proteome-wide mapping and reverse vaccinology approaches to design a multi-epitope vaccine against *Clostridium perfringens*. Vaccines (Basel) 2021:9.

[83] Nasiri O, Hajihassani M, Noori Goodarzi N, Fereshteh S, Bolourchi N, Firoozeh F, et al. Reverse vaccinology approach to identify novel and immunogenic targets against *Porphyromonas gingivalis*: an in silico study. PLoS One 2022;17:e0273770.

[84] Mugunthan SP, Mani Chandra H. A computational reverse vaccinology approach for the design and development of multi-epitopic vaccine against avian pathogen *mycoplasma gallisepticum*. Front Vet Sci 2021;8:721061.

[85] Raza A, Asif Rasheed M, Raza S, Tariq Navid M, Afzal A, Jamil F. Prediction and analysis of multi epitope based vaccine against Newcastle disease virus based on haemagglutinin neuraminidase protein. Saudi J Biol Sci 2022;29:3006-14.