Next Article in Journal
Alginate-Based 3D A549 Cell Culture Model to Study Paracoccidioides Infection
Next Article in Special Issue
Performance of a Real-Time PCR Assay for the Detection of Five Candida Species in Blood Samples from ICU Patients at Risk of Candidemia
Previous Article in Journal
Impact of Iron Mining Activity on the Endophytic Fungal Community of Aspilia grazielae
Previous Article in Special Issue
Phenotypic Array for Identification and Screening of Antifungals against Aspergillus Isolates from Respiratory Infections in KwaZulu Natal, South Africa
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JoF
Volume 9
Issue 6
10.3390/jof9060633
jof-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics

by
Moisés Morais Inácio
1,2,
André Luís Elias Moreira
1,
Vanessa Rafaela Milhomem Cruz-Leite
1,
Karine Mattos
3,
Lana O’Hara Souza Silva
1,
James Venturini
3,
Orville Hernandez Ruiz
4,
Fátima Ribeiro-Dias
5,
Simone Schneider Weber
6,*,
Célia Maria de Almeida Soares
1 and
Clayton Luiz Borges
1,*
1
Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia 74605-170, Brazil
2
Estácio de Goiás University Center, Goiânia 74063-010, Brazil
3
Faculty of Medicine, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
4
MICROBA Research Group—Cellular and Molecular Biology Unit—CIB, School of Microbiology, University of Antioquia, Medellín 050010, Colombia
5
Laboratório de Imunidade Natural (LIN), Instituto de Patologia Tropical e Saúde Pública, Federal University of Goiás, Goiânia 74001-970, Brazil
6
Bioscience Laboratory, Faculty of Pharmaceutical Sciences, Food and Nutrition, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
*
Authors to whom correspondence should be addressed.
J. Fungi 2023, 9(6), 633; https://0-doi-org.brum.beds.ac.uk/10.3390/jof9060633
Submission received: 3 March 2023 / Revised: 12 May 2023 / Accepted: 19 May 2023 / Published: 31 May 2023
(This article belongs to the Special Issue Diagnosis and Treatments of Invasive Fungal Diseases)
Browse Figures
Review Reports Versions Notes

Abstract

:
Fungal infections represent a serious global health problem, causing damage to health and the economy on the scale of millions. Although vaccines are the most effective therapeutic approach used to combat infectious agents, at the moment, no fungal vaccine has been approved for use in humans. However, the scientific community has been working hard to overcome this challenge. In this sense, we aim to describe here an update on the development of fungal vaccines and the progress of methodological and experimental immunotherapies against fungal infections. In addition, advances in immunoinformatic tools are described as an important aid by which to overcome the difficulty of achieving success in fungal vaccine development. In silico approaches are great options for the most important and difficult questions regarding the attainment of an efficient fungal vaccine. Here, we suggest how bioinformatic tools could contribute, considering the main challenges, to an effective fungal vaccine.

Graphical Abstract

1. Introduction

Fungal infections represent a worldwide health problem, with an annual death rate of around 1.5 million individuals, exceeding the number of deaths caused by malaria and being close to that of tuberculosis and the acquired immunodeficiency virus [1]. Furthermore, these agents are responsible for economic losses on the scale of billions of dollars. In 2017, in the United States of America, more than USD 7.2 billion was spent because of the occurrence of these infections [2].
The most worrying global scenario is triggered by systemic mycoses caused by fungi such as Paracoccidioides spp., Histoplasma capsulatum, Aspergillus spp. Coccidioides spp., Candida spp. and Cryptococcus, which are responsible for paracoccidioidomycosis (PCM), histoplasmosis, aspergillosis, coccidioidomycosis, candidiasis and cryptococcosis, respectively. These mycoses in general present as pulmonary disease, skin involvement and disseminate into tissues and systems. Furthermore, invasive fungal infectious diseases have led to concern, especially during the coronavirus disease (COVID-19) pandemic [3]. The emergent mucormycosis disease, caused by fungal members of the Mucorales order, causes severe and potentially life-threating fungal infections in immunocompromised individuals, and is a worldwide-distributed species commonly involved in the human diseases Cunninghamella, Lichtheimia, Mucor, Rhizomucor, and Rhizopus [4].
The knowledge of the fungal biology is crucial for the development of strategies against parasites, which include therapies and vaccines, as well as rapid diagnostic tests. In addition, the antifungal drug arsenal is often limited by toxicity, resistance, and a high cost. To circumvent these difficulties, alternative approaches for their prevention and treatment are being developed, including vaccines and passive immunotherapy [5].
Currently, there are no fungal vaccines approved for use in human beings, but several promising strategies have been developed. Here, we discuss the main methods applied in the development of vaccines and passive immunotherapy against pathogenic fungus. Additionally, we consider the promising application of computer tools based on the immune response in the context of vaccine development, called immunoinformatics, and the future perspectives of its application in fungal vaccine development.

2. Vaccine Approaches to Protect against Fungal Infections

2.1. Inactivated and Live-Attenuated Vaccines

The inactivated vaccine was the first approach applied in the development of vaccines and involves the inactivation or killing of etiological agents using chemicals, heat, or radiation. A good example of a fungal inactivated vaccine is the use of radiation to produce a vaccine against P. brasiliensis, which was found to be able to induce protection and reduce the clinical symptoms and fungemia of mice [6]. In this context, a new method using a heat-killed Saccharomyces cerevisiae (HKY) vaccine that can induce protection against non-specific fungal infection is considered to be a special example of a pan-fungal vaccine [7]. The HKY vaccine is effective in protecting CD-1 and BALB/c mice against systemic mycosis caused by Coccidioides [8], C. albicans [7], A. fumigatus [7] and can be used as a therapeutic vaccine [9]. Presumably, S. cerevisiae can induce protection against a variety of fungi because it shares with them common polysaccharide epitopes that are present in its cell wall (Table 1).
The first vaccine against coccidioidomycosis, the formalin-killed Coccidioides immitis spherules (FKS) vaccine, is another important example of a fungal vaccine in this category that has led to good results in experimental studies [10]. In particular, in a formulation used for intramuscular administration in CD-1 mice, the FKS vaccine demonstrated full protection against C. immitis lethal challenge [11]. However, when tested in phase 3 clinical trials, it failed to significantly reduce the incidence of disease or its severity [12]. The HKY vaccine was compared to the FKS vaccine regarding its ability to induce protection against C. immitis challenges, inducing 70% and 100% protection in CD1 mice, respectively [8]. These results are similar to those found with a live-attenuated strain of C. posadasii (Δcts2/ard1/cts3 or ΔT), which could not endosporulate due to the disruption of two chitinase genes when a triple-attenuated vaccine was employed. This vaccine was able to protect 75–100% of the animals challenged with a virulent C735 strain of C. posadasii when using two subcutaneous vaccination injections (14 days interval) [13]. The groups highlighted the fact that the live spores of the attenuated strain showed a low degree of reactogenicity compared to the results for the FKS-vaccinated mice [13]. It is important to note that the Coccidioides posadasii CPS1 deletion mutant, which is an avirulent strain, was able to protect over 95% survival, with mean residual lung fungal burdens of <1000 CFU in contrast to an otherwise lethal C. posadasii intranasal infection [14]. In the context of a vaccine against coccidioidomycosis, a recent study deserves attention regarding its pioneering work. Mendel et al. (2022) deleted a conserved transcription factor in Coccidioides, Ryp1, which plays a dual role in both hyphal and spherule development. Although the vaccination of C57BL/6 mice with live Δryp1 spores was not found to provide any protection from lethal C. posadasii intranasal infection, this work identifies the first transcription factor that drives mature spherulation and virulence in Coccidioides [15].
Table
Table 1. Vaccine-based approaches proposed to protect against fungal infections (Nd; non-determined).
Table 1. Vaccine-based approaches proposed to protect against fungal infections (Nd; non-determined).
Target PathogenAntigen/StrainAdjuvant/Carrier/VehicleVaccine TypeModelRoute of InjectionHuman Clinical TrialReference(s)
Paracoccidioidomycosis (PCM)P. brasiliensisNdInactivated/Live attenuatedMice-Nd[6]
Major 43-kDa antigenic glycoprotein (gp43), (P10)Plasmid vectorDNA VaccineMiceIntramuscular/Intradermal-[16]
Mycobacterium leprae derived HSP65Vector pVAX1/Recombinant DNAMiceIntramuscular-[17]
Major 43-kDa antigenic glycoprotein (gp43), (P10)Plasmid vector/IL-12 recombinantDNA VaccineMiceIntratracheal-[18]
Major 43-kDa antigenic glycoprotein (gp43), (P10)S. cerevisiae expressing gp43Recombinant proteinMiceIntraperitoneal-[19]
P10- FliC fusion proteinFreund adjuvant (CFA)/multiple-antigen peptide (MAP)Recombinant proteinMiceIntranasal-[20]
Recombinant rPb27Corynebacterium parvum/aluminumRecombinant proteinMiceSubcutaneous-[21]
Heat shock protein 60 (HSP60)Monophosphoryl lipid A, synthetic trehalose dicorynomycolate, and cell wall skeletonRecombinant proteinMiceSubcutaneous-[22]
Panfungalβ-glucans of S. cerevisiaeNdHeat Killed Yeast (HKY)MiceSubcutaneousNd[23]
CoccidioidomycosisFormalin Killed Spherules (FKS)NdWhole organism/InactivatedHumanIntramuscular Phase 3[12]
Antigen 2 (Ag2)NdDNA vaccineMiceIntraperitoneal-[24]
Coccidioides posadasii CPS1 Deletion MutantrAg2/PRA1–106-CSA with MPL-SE (25 μg)/CpG (10 μg) adjuvantWhole organism/Live-attenuatedMiceSubcutaneous/Intraperitoneal-[14]
Δcts2/ard1/cts3 or ΔT—triple attenuated vaccineNdWhole organism/Live-attenuatedMiceSubcutaneous-[13]
Recombinant Coccidioides polypeptide antigen (rCpa1) encapsulated into glucan-chitin particles (GCP-rCpa1)Mouse serum albumin (MSA) and incomplete Freund’s adjuvantRecombinant proteinMiceSubcutaneous-[25]
BlastomycosisAdhesin BAD1 geneNdWhole organism/Live-attenuatedMice (T CD4+ depleted)Subcutaneous-[26]
CryptococcosisC. neoformans strain H99γNdLive-attenuatedT-cell depleted miceNasal inhalation-[27]
Glucuronoxylomannan (GXM)Tetanus toxoid (GXM-TT)Conjugate/Solubleantigenic fractionsMiceSubcutaneous-[28]
C. neoformans Δsgl1NdWhole organism/Live-attenuatedMiceIntranasal-[29]
C. neoformans deletion of ZNF2NdWhole organism/Live-attenuatedMiceIntranasal-[30]
CandidiasisAgglutinin-like sequence 3 (Als3p)Aluminium hydroxide (Alum)Recombinant protein (NDV-3)Mice/HumanOropharyngeal, Vaginal and IntravenousPhase I[31,32]
Recombinant secretory aspartyl proteinase (r-SAP-2)Cholera toxin (CT)RecombinantRatIntravaginal-[33]
PEV7 (r-Sap2 virosomesCholera toxin (CT)/Virosomal carrierRecombinant proteinMice/HumanIntravaginalPhase I (delivered by intramuscula)[34]
Laminarin (Lam) β-glucanComplete Freund’s adjuvant (CFA)Lam- diphtheria toxoid CRM197 conjugateMicePriming dose: Subcutaneous Booster: Intranasal-[35,36]
Fructose bisphosphate aldolase (Fba) (cytosolic and cell wall peptides)Alum or monophosphoryl lipid A (MPL)SubunitMiceSubcutaneous-[36]
The β-mannose trisaccharide, the Fba peptide T-cell epitope, a dectin-1 ligand, β1,3 glucan hexasaccharideFreund’s incomplete adjuvant/with and without alumConjugate MiceSubcutaneous [37]
C. albicans serotypes a and b ribosomesNonencapsulated Klebsiella pneumoniae proteoglycanRecombinant/Conjugate capsuleWomen with vulvovaginal candidiasis (VVC)Oralphase II[38]
HistoplasmosisHeat Shock Protein 60 (HSP-60)Monophosphoryl lipid A, synthetic trehalose dicorynomycolate, and cell wall skeletonRecombinant proteinMiceSubcutaneous [39]
HIS-62Complete Freund’s adjuvant (CFA) or incomplete Freund’s adjuvant (IFA)Recombinant proteinMiceSubcutaneous-[40]
80-kilodalton antigenComplete Freund’s adjuvant (CFA) or incomplete Freund’s adjuvant (IFA)Recombinant proteinMiceSubcutaneous-[41]
H AntigenMonophosphoryl lipid A, synthetic trehalose dicorynomycolate, and cell wall skeletonRecombinant proteinMiceSubcutaneous-[42]
PneumocystosisKexin genesVector: CMV to express Antigen EF-1α to express CD40LKexin-CD40 L DNA vaccineCD4-deficient miceIntramuscular-[43]
AspergillosisAspergillus fumigatus ΔsglANdWhole organism/Live-attenuatedMiceIntranasal-[44]
Antigen Asp f 3 and Asp f 9 (VesiVax® Af3/9)Lipidated Tucaresol, monophosphoryl lipid A or Pam3CAGRecombinant protein and VesiVax liposomesMiceSubcutaneous and inguinal region-[45]
SporotrichosisZR8 peptide is from the GP70 proteinFreund’s incomplete adjuvantRecombinant proteinMiceIntramuscular-[46]
Indeed, the use of live-attenuated vaccine is an approach commonly applied because of their similarity to the effects of the infectious agent, producing a long-term and strong immune response. Currently, there are several studies in this field evaluating live and inactivated fungi with regard to the use of different strategies. However, although live-attenuated vaccines generally have a good safety profile in immunocompetent individuals, they may still cause an infection or a dysregulated inflammatory response in immunosuppressed individuals [47], who are the most susceptible to fungal infections [48]. As a consequence of this, some other studies have been developed in order to improve the immunization of this group. For example, the deletion of the Blastomyces adhesion 1 (BAD-1) gene is an attenuated vaccine candidate that is able to induce immunity against blastomycosis in immunocompromised patients [26]. This optimistic result can be explained by the fact that CD8+ T cells compensate in the absence of CD4+ cells [49] and, alone, mediate efficient antifungal vaccine immunity. Immunity via CD8+ T cells was restricted by MHC class I and mediated by the production of cytokines, such as tumor necrosis factor (TNF), interferon gamma (IFN-γ), and granulocyte/macrophage colony-stimulating factor (GM-CSF). This study indicates that CD8+ T cells could be a target for robust vaccine-induced immunity against experimental fungal pulmonary infections caused by Blastomyces dermatitidis and H. capsulatum [26].
Another special live-attenuated vaccine strategy that induced protection against Cryptococcosis used a murine gamma interferon-producing Cryptococcus neoformans strain. This is the first instance that a pathogenic fungus has been genetically altered to express a cytokine with biological effects in vivo and that has previously been shown to be protective towards the resolution of disease. The “immune-deficient” A/Jcr mice infected with the IFN-γ-expressing C. neoformans H99 not only recovered from the primary infection, but were also completely protected against a second challenge with a pathogenic C. neoformans strain, as a result of the protection mediated by IFN-γ-producing CD4+ Th1 cells [27]. Recently, immunization with cryptococcal cells overexpressing ZNF2 (a transcription factor related to C. neoformans filamentation), either in a live or heat-inactivated form, was found to offer significant protection to the host from a subsequent challenge by the otherwise lethal wild-type H99 strain [30]. In another study, a heat-killed Cryptococcus neoformans Δsgl1 mutant accumulating sterylglucosides protected mice in the absence of CD4+ T cells, a condition that is most often associated with cryptococcosis. In addition, this vaccine was able to decrease the lung fungal burden and had a robust therapeutic effect [29]. A similar study was conducted against A. fumigatus. Vaccination using the strain with the deletion of the sterylglucosidase-encoding gene (Aspergillus fumigatus ΔsglA) fully protected immunocompromised mice against a lethal wild-type A. fumigatus challenge [44].
It is important to highlight that this is a live vaccine and that there are, therefore, concerns about its use in human individuals. In particular, the commercialization and the safety of attenuated vaccines in immunosuppressed hosts cannot be guaranteed. However, several strategies could be considered for the immunization of CD4+ T cell-deficient subjects, particularly HIV/AIDS patients. Furthermore, in immunocompetent individuals, attenuated vaccines against viral infections have been highly successful and perhaps an attenuated vaccine against an endemic fungal pathogen may contribute to the eradication of these diseases where they are prevalent [48].

2.2. Recombinant (Subunit) Vaccines

Subunit vaccines are one of the most investigated types of fungal vaccine and consist of one or more purified recombinant protein (or epitope) or polysaccharide of fungi. This approach is very safe when compared to attenuated vaccines. They are especially important when thinking about a vaccine against fungal infection because of the high susceptibility of immunocompromised patients. Because of this, there are a large number of antigenicity studies on various strains, including P. brasiliensis [19,21,50], H. capsulatum [39,40,41,50], A. fumigatus [51,52,53], C. neoformans [54,55], C. immitis [56,57,58], C. posadasii [25,57,58,59,60] and Candida albicans [31,33,34,61,62,63]. The scientific basis of this technology comprises the identification of the immunogenic targets, which are obtained via the use of recombinant DNA technology. This involves the insertion of DNA that encodes an antigen (such as a bacterial surface protein) expressed in bacterial, fungal or mammalian cells, purifying it from them, and then applying it in order to trigger the desired immune response. In fact, in this approach, a gene that is transmitted encodes a molecule portion related to the virulence and pathogenicity of a microorganism [64]. It is important to enhance the role of immunoinformatics in this process. This new science comprises a set of computer tools and a database based on immune responses to help in the identification of immunogenic molecules; this makes the vaccine workflow more rational, and reducing the cost and time [65].
When identified, these epitopes or antigens are often combined with an appropriate adjuvant or protein carrier, mostly bacterial toxoids, to establish an efficient immune response and prolonged immunity. For example, NDV-3, an anti-Candida vaccine that includes the invasin protein agglutinin-like sequence 3 (Als3p) and alum adjuvant in its formulation [31], prevents yeast attachment and the invasion of epithelial and endothelial cells via IFN-γ and IL-17A-production. This immune response was observed as a result of the improved outcomes in mouse models of S. aureus and C. albicans infection by inducing upstream Th1, Th17, and Th1/17 lymphocytes, which enhanced the recruitment and activation of neutrophils in infected tissues, thereby reducing the tissue infectious burden [66]. These data are in consonance with the first-in-human Phase I clinical trial [31]. In addition, because of the high homology between Als3p and clumping factor A on the surface of Staphylococcus aureus, NDV-3 has also been shown to be protective against this highly virulent bacterial pathogen in animal models [67]. Furthermore, NDV-3 was protective against recurrent vulvovaginal candidiasis (RVVC) [68], and vaccination inhibited the dissemination of C. albicans to the kidneys, preventing the colonization of central venous catheters in a murine model of infection [69].
Another promising strategy with regard to a recombinant vaccine against Candida is the use of recombinant secretory aspartyl proteinase (r-SAP-2). The administration of a intravaginal vaccine was able to induce the immunoglobulins G (IgG) and A (IgA), leading to protection against an intravaginal challenge with C. albicans [33]. Pevion, a Swiss biotech company, has incorporated r-Sap2 into influenza virosomes. This novel vaccine, PEV7 (r-Sap2 virosomes), conferred protection to rats experimentally challenged with C. albicans [34]. The clearance of the fungus from the vagina was accelerated and the resolution of the infection occurred at least one week earlier when compared to the administration of empty virosomes and a challenge with the fungus. This vaccine was evaluated in a phase I clinical trial test and demonstrated favorable safety and the generation of specific and functional B cell memory in 100% of the vaccinated women, encouraging the use of this vaccine as potential therapy [70]. The commercial rights of the r-Sap2 vaccine were acquired by NovaDigm Therapeutics Inc. (Grand Forks, ND, USA), which developed the vaccine containing the Als3 antigen (NDV-3) mentioned above. In addition, NovaDigm has acquired the rights to a hyphally regulated protein 1 (Hyr1) vaccine [71] and to a β-mannan conjugate vaccine [72]. The company intends to produce a multivalent vaccine that can induce an immune response against multiple virulence molecules of Candida [73]. It is important to note the successful use of the peptide SLAQVKYTSASSI in the Sap2 vaccine via the application of phage display technology, with its innovative character inducing strong immune responses in the animal model [74]. Another example of a recombinant vaccine against Candida was developed: a double-peptide construct used to target epitopes derived from fructose bisphosphate aldolase (Fba) and methionine synthase (Met6), which are expressed on the C. albicans cell surface, resulting in a high humoral response [75].
In the context of a multivalent vaccine, we highlighted the multivalent recombinant Coccidioides polypeptide antigen (rCpa1) that consists of three previously identified antigens (i.e., Ag2/Pra, Cs-Ag, and Pmp1) and five pathogen-derived peptides. The purified rCPA1 was encapsulated into four types of yeast cell wall particles containing β-glucan, mannan, and chitin (in different proportions), or was mixed with an oligonucleotide containing two methylated dinucleotide CpG motifs, showing a high survival rate [25].
A robust in silico analysis of a global proteome of Candida using a concept of reverse vaccinology was recently published. The goal of this study was to find vaccine targets using several steps of computational tools to achieve a list of the best targets to be employed in the development of a new vaccine [76]. The study represents the first proteome-wide immunoinformatic approach used to identify the immunodominant epitopes and design a multivalent subunit vaccine against C. albicans [77]. Eight antigenic proteins with known functions in hyphal formation (Als4p, Als3p, Fav2p, Als2p, Eap1p, Hyr1p, Hwp1p, Sap2p) were identified. Immunogenicity testing led to the selection of 18 unique epitopes and conservation analysis also showed that the selected epitopes (in addition to the eight hyphal proteins) were present in other Candida proteins presenting sequence homology (Sap1p, Sap3p and Als1p). Obviously, it is necessary to confirm the immunogenicity of these antigens via experimental tests. The goal of this approach was to select the most promising targets in a short analysis time and with a low cost for subsequent experimental tests, improving the development of a vaccine with rational drawing, as has been shown for protein-based vaccines against serotype B meningococcal vaccines [78]. In Section 3, we will discuss some of the tools used for the in silico analysis that was conducted in order to identify targets in fungal pathogens for the development of a vaccine by using bioinformatics.

2.3. Conjugate Vaccines

Conjugate vaccines are another important approach employed in fungal vaccine development; they involve the association of a weak with a strong immunogenic antigen, commonly a polysaccharide and protein, respectively. The goal of this strategy is to generate a potent immune response to the weak antigen as a result of the B and T cell interaction. The epitopes on polysaccharides can be recognized by B cell receptors and peptides can be recognized by T cell receptors during the process of antigen presentation by class II MHC molecules expressed on B cells, which act as antigen-presenting cells (APCs) to T cells [79].
The first conjugate vaccine developed for fungal infections was against C. neoformans, with a capsular polysaccharide, glucuronoxylomannan (GXM), covalently linked to tetanus toxoid (TT) and monophosphoryl lipid A (MPL) as an adjuvant. This vaccine elicited high levels of IgG and IgA specific to GXM and protected 70% of mice after being intravenously challenged. Additionally, passive immunization with antisera from immunized animals also protected naive mice from a lethal inoculum of intravenously administered C. neoformans [28]. This study was a pioneer and basis for the development of various forms of a conjugate vaccine against cryptococcosis [80]. Recently, a GXM oligosaccharide structure (a serotype A decasaccharide) was identified for the first time, offering insight into the binding epitopes of a range of protective monoclonal antibodies and furthering our efforts to develop semi-synthetic conjugate vaccine candidates against C. neoformans [81].
One major advantage of the conjugate vaccine strategy is that these vaccines are based on targeting the polysaccharide epitopes, which are common in all fungi, especially β-glucans [64]. It is possible to highlight some studies evaluating these molecules, most of them targeting Candida. For example, the parenteral administered β-glucan-conjugate vaccine formulated with the human-compatible MF59 adjuvant was assessed in a murine model of vaginal candidiasis. It conferred significant protection, and this was associated with the production of serum and vaginal anti-β-glucan IgG antibodies [82]. Additionally, a glycoconjugate vaccine comprising laminarin (a β-glucan polysaccharide) conjugated with the diphtheria toxoid CRM197 showed protection against both lethal systemic infection in mice, as well as against a self-healing vaginal C. albicans infection in rats, showing additional protection against A. fumigatus [82]. Similar results were observed with another vaccine, β-glucan-CRM197, formulated with the human-acceptable adjuvant MF59, which conferred protection to mice lethally challenged with C. albicans [35]. More recently, the 1,2-linked β-mannose trisaccharide was used in a synthetic conjugate vaccine mixing the Fba peptide T-cell epitope, a dectin-1 ligand, and β1,3 glucan hexasaccharide, stimulating the immune response of mice to a fully synthetic conjugate prepared using these components [37]. Additionally, the same group developed mimotopes that structurally mimic the protective glycan epitope β-(Man)3 as an alternative solution to the complexity of oligosaccharide synthesis [83]. β-glucan, also used in a conjugate vaccine against A. fumigatus, was successful in protecting against systemic aspergillosis infection [84].
Other molecules have been used as a conjugate anti-Candida vaccine [38,82,85,86]. A good example is the synthetic glycopeptide vaccine β-(Man)3-Fba, constructed by conjugating β-1,2-mannotriose to a peptide segment from fructose-bisphosphate aldolase (Fba), which is a surface antigen of Candida spp. This formulation was modified by coupling it to tetanus toxoid (TT), β-(Man)3-Fba-TT, in order to improve immunogenicity and enable it to be used as an adjuvant that is suitable for human use. This modification was crucial for the success of the vaccine, inducing a robust antibody response without the need of an additional adjuvant [36].
In fact, the strategies used to conjugate proteins to polysaccharides enable the immune system to recognize abundant fungal cell wall glycan components, increasing the probability of antibodies recognizing pathogens. Additionally, this strategy can be used to target saccharide epitopes that are conserved in fungal species, particularly β-glucans, thereby creating one vaccine that is effective against a broad range of pathogenic fungi, as a pan-fungal vaccine [48].

2.4. Pan-Fungal Vaccine Strategy

Traditional vaccine preparations involve the specificity of adaptive immunity to target one or more antigens found in a single microorganism in order to confer protection. However, current studies support the concept of generating “universal” vaccines that target multiple heterologous pathogens via the use of conserved target antigens [87]. Thus, among the works already cited, there are three vaccines that are able to induce protection against different fungal pathogens: BAD-1 (B. dermatitidis and H. capsulatum) [26], HKY (C. posadasii [8], C. albicans [7], A. fumigatus [7]) and a conjugate vaccine comprising a β-glucan polysaccharide laminarin conjugated with the diphtheria toxoid CRM197 (C. albicans and Aspergillus [88]). Moreover, there are robust studies, such as those by Wüthrich and Klein (2011), that have generated T cell receptor transgenic mice (TCR-Tg), also called 1807 mice, using a clone of CD4+ T cells isolated from mice infected with B. dermatitidis, which can elicit an immune response against B. dermatitidis and other dimorphic fungi including H. capsulatum and C. posadasii [89]. Lastly, calnexin, typically an ER protein, localized on the surface of yeast, hyphae, and spores, has shown promising results for the development of a pan-vaccine. The peptide identified in this antigen induces CD4+ T cell responses and it is conserved among the endemic, systemic dimorphic fungi, as well as the clinically important Aspergillus species, Fonsecaea pedrosoi, and even Pseudogymnoascus destructans. Moreover, because of the ability of calnexin to induce the clonal expansion of calnexin-specific CD4+ T cells during infection, this vaccine may present an immunotherapeutic effect [90].
In the context of a pan-fungal vaccine, it is important to comment on the use of the Pneumocystis endoprotease Kexin (KEX1) from Pneumocystis jirovecii, which was previously protective against pneumocystis in a model of HIV and Pneumocystis coinfection. As a consequence of the KEX1 sequence being highly conserved among pathogenic fungi, the Aspergillus-specific KEX1 recombinant homolog was tested in murine models of combination drug-induced immunosuppression that induced decreasing rates of mortality and a lower lung organism burden. Based on the evidence concerning the protective efficacy of the KEX1, the recombinant pan-fungal protein (NXT-2) was used in murine and non-human primate models of invasive aspergillosis, systemic candidiasis, and pneumocystis, resulting in a decreased mortality and morbidity compared to unvaccinated animals. This study supports the concept of a pan-fungal vaccine and was highly optimistic regarding the induction of immune protection to immunocompromised patients [91].
Collectively, these fungal vaccine development studies suggest the use of strategies that confer protection against heterologous pathogens via conserved epitopes, either by structure or linear sequence, which induce convergent mechanisms of immunity. This potential can be increased with the use of computational strategies [79]. In light of this, there is great optimism regarding the conserved molecular structures that are exposed during fungal growth in host tissues and their application as vaccine candidates.

2.5. DNA Vaccines

DNA vaccines are based on cassettes from plasmids plus the cDNA that encodes the desired antigen, driven by efficient eukaryotic promoters and the transfer of the gene-containing plasmid to the host. Subsequently, the expressed antigen induces the desired immune response [92].
Plasmid DNA represents an attractive strategy for developing new vaccines against variable type of pathogens. In fungi, the first report of a DNA vaccination was described in 1999 by Jiang et al., in which a plasmid containing the cDNA of antigen 2 of C. immitis was used; this lead to superior efficacy regarding the protection of mice when compared to the recombinant Ag2 vaccine [24]. The next year, Pinto et al. (2000), described the first DNA vaccine against paracoccidioidomycosis, in which a mammalian expression vector carried the full gene of the gp43 of P. brasiliensis under the control of the human cytomegalovirus (CMV) promoter. The immunization of BALC/c with this vaccine induced protection against the intratracheal challenge with virulent P. brasiliensis yeast cells [16]. One decade later, the administration of the vector pVAX1 carrying the Mycobacterium heat shock protein 65 (HSP65) DNA gene was able to reduce the pulmonary fungal burden after infection with the P. brasiliensis strain 18 [17]. Additionally, in 2012, the group that evaluated the first DNA vaccine against PCM elaborated a new DNA vaccine against P. brasiliensis. They used an expression vector carrying the immunodominant peptide P10 from gp43 and administrated it with or without an IL-12-encoding plasmid. The vaccine, given prior to or after infection with the P. brasiliensis Pb18 virulent strain, was able to reduce the fungal burden in the lungs of infected mice [18]. Furthermore, similar results regarding mouse immunization with the pcDNA3-P10 depicted a significant reduction in the mouse pulmonary fungal burden after 30, 60 and 120 days of intratracheal infection challenge [93]. These data suggest that this strategy is promising for the prevention and treatment of PCM.
It is possible to mention another study described by Ivey et al. (2003) that used “expression library immunization” (ELI) to identify a Coccidioides gene named ELI-Ag1, which has a protective capacity in BALB/c mice against an intraperitoneal challenge with the arthroconidia of this fungus [94]. Additionally, Zheng et al. (2005) identified, on the surface of Pneumocystis, a protein named Kexin and have used it to validate DNA vaccination in CD4-depleted mice. Immunization with plasmid-expressing Kexin under the CMV promoter resulted in significant anti-Pneumocystis IgG1 and IgG2a titers in CD4-competent mice, whereas titers were significantly lower in CD4-depleted mice [43].
In the context of the DNA vaccine, it is impossible not to comment upon the success of this approach in the development of the vaccine in the fight against COVID-19 [95]. As a consequence of the ethical implications, particularly with regard to safety and health, until then no DNA vaccine had been approved for human use. However, the success of the platform in the context of COVID-19 allowed science to answer several questions about safety and health, especially concerns regarding the possibility of adverse effects to the use of viral vectors, whether related to the activation of oncogenes [96] or to an excessive immune response [97]. The progress of science enables the rapidity, simple development, reproducibility, thermostability and manufacture of this approach with reduced development costs and risks to be affirmed [95].

3. Immune Response against Fungal Infections and Approaches Vaccines

The fungi–host relationship depends on the balance between the characteristics of the fungus, such as its virulence and inoculum size, and the host, such as its genetic background (HLA and SNPs), hormonal and nutritional aspects, age, and comorbidities and associated infections [98]. For instance, acute pulmonary histoplasmosis, which mainly affects poultry farmers, construction workers, travelers and cave explorers, shows that the inhalation of a high fungal burden might cause pneumonia in immunocompetent individuals [96]. On the host side, the hormone estrogen is known to protect women of reproductive age from fungi of the genus Paracoccidioides [99]. The presence of cavitary lung injury, as a result of previously treated tuberculosis, is an example of tissue alterations that favor the appearance of chronic pulmonary aspergillosis, also known as aspergilloma or fungal ball [100].
The immune response of fungal systemic mycoses is complex, involving several innate and adaptive immune mechanisms that even overlap due to the chronic nature of these fungal infections [101]. However, the main immunological mechanisms of each fungal disease become more evident when the different types of immunosuppression are associated with the development of different clinical manifestations of systemic mycoses according to the type of immunosuppression. For example, HIV/AIDS patients, who are severely immunocompromised, might manifest disseminated histoplasmosis, pneumocystis pneumonia, esophageal candidiasis, cryptococcal meningitis, coccidioidomycosis and paracoccidioidomycosis [101]. These fungi in the morphology of yeast, in their pathogenic form, are mainly eliminated by macrophages activated by the secretion of IFN-y by CD4+ helper T lymphocytes. On the other hand, patients with neutropenia or with functional abnormalities in polymorphonuclear (PMN) cells, especially those with hematological malignancies, are more susceptible to the development of invasive aspergillosis, chronic disseminated candidiasis, and candidemia [102]. These filamentous fungi in their pathogenic form are mainly eliminated by undergoing receptor-mediated respiratory burst and degranulation by PMN cells, which are recruited by Th17 cells [103]. In the context of endemic mycoses, which occur mainly in immunocompetent individuals, such as paracoccidioidomycosis, the impairment of cellular immunity is antigen-specific and the intrinsic host factors that predispose an individual to this type of immunosuppression are poorly understood [104]. An overview of the adaptive immune response to pathogenic fungi is demonstrated in Figure 1.
The development of fungal vaccines has been a challenging task due to the complex nature of fungal immunity, including the varied morphological aspects. As shown, for instance, the immune response to Cryptococcus yeast differs significantly from the response to A. fumigatus spores. In the former, the development of the Th1 immune response is crucial, while the host defense against A. fumigatus is contingent upon T helper responses, followed by the action of neutrophils. To surmount these challenges, researchers have directed their efforts towards identifying specific antigens that can elicit specific and more proper protective immune responses against fungal pathogens. Vaccination with a genetically modified yeast strain of C. neoformans, known as H99γ, which produces IFN-γ, has been shown to induce protective immunity against cryptococcosis in mice that lack CD4+ T cells [105]. When immune-competent mice were inoculated with C. neoformans H99γ and subsequently depleted of both CD4+ and CD8+ T cells before and during challenge with wild-type (WT) C. neoformans, they were fully protected, as evidenced by the 100% survival rate and sterilizing immunity [27]. Various fungal components possess unique abilities to activate Th cell responses in murine vaccination models against A. fumigatus. For instance, secreted proteins induce Th2 cell activation, membrane proteins induce Th1/Treg responses, glycolipids activate Th17 responses, while polysaccharides primarily stimulate IL-10 production [53]. Therefore, understanding the type of immune response required for each fungal pathogen and identifying specific antigens are critical in the development of effective fungal vaccines.
In addition to identifying specific antigens that elicit the appropriate immune response, it is imperative to ensure that these antigens do not cross-react with other fungi, particularly those present in the human microbiota, in order to avoid any adverse outcomes [106]. There is a theoretical concern that Candida vaccines could disrupt the normal microbiota [106], but targeting specific Candida antigens in the invasive hyphal form could minimize this potential drawback [107]. Therefore, prospective studies must take care when selecting antigens with cross-reactivity to microorganisms in consideration of the microbiota. Additionally, the monitoring of the microbiota should be included in preclinical and clinical trials.

3.1. Vaccines Based on Antibody

Unfortunately, there are currently no therapeutic vaccines licensed against fungal infections for human or veterinary use. A variety of studies have shown the positive effect of antibody administration against fungal infection, alone or in combination with antifungal drugs [108]. One of the most known studies is on Efungumab (Mycograb®), a human genetically recombinant antibody that binds to Candida’s HSP90, protecting against several Candida species and synergizing with antifungal drugs when evaluated in vitro and in preclinical studies [109]. Furthermore, in a multinational phase II clinical trial, Mycograb® combined with lipid-associated amphotericin B (AMB) improved the overall clinical response from 48 to 84%; this was compared to AMB monotherapy in patients with invasive candidiasis [110]. However, due to production difficulties, as well as safety and quality issues, the authorization of marketing was refused [111].
Another antibody with potential therapeutic use against Candida is the IgM mAb C7 (mAbC7), which was produced via the immunization of BALB/c mice with a 200 kDa stress mannoprotein present in the C. albicans cell wall [112]. This molecule can react with an Als3p peptide epitope [113], C. albicans enolase and the nuclear pore complex protein Nup88 [114]. Additionally, the protection of the NDV-3A vaccine against multidrug-resistant Candida auris infection is attributed to anti-Als3p antibodies and to CD4+ T helper cells that activate the tissue macrophages. Thus, the mAbs to Als3 may be a powerful therapy by which to combat this emerging fungal pathogen [115].
Recently, the special role of antibodies against fungal cell components, such as mAbs against β-(1→3)-D-Glucan, an essential component of the fungal cell wall, was shown. The antibodies were produced and named mAb 5H5 (IgG3 class) and mAb 3G11 (IgG1 class). Both can interact with yeast and filamentous fungi, including species from Aspergillus, Candida, Penicillium and S. cerevisiae. Furthermore, the mAbs could inhibit the germination of A. fumigatus conidia and demonstrated synergy with the antifungal fluconazole in the killing of C. albicans in vitro. In addition, mAbs 3G11 and 5H5 demonstrated protective activity in in vivo experiments against A. fumigatus, suggesting that these β-glucan-specific mAbs could be useful in combinatorial antifungal therapy [116].
The majority of the study regarding antibody therapy is about Candida and Aspergillus. However, in a recent review about immunotherapy against systemic fungal infections based on mAbs, it was possible to identify several experiments, in which the use of antibodies in fungal infection was promising, such as in Sporothrix spp., P. brasiliensis, B. dermatitidis, Pneumocystis spp. and H. capsulatum [117]. Nevertheless, the high cost of the production of antibodies for therapy and the neglected nature of fungal infections [118] means that the use of this approach is very limited and there are no licensed antibodies for therapy against fungal infections [117].

3.2. Dendritic Cell Vaccination and Immunotherapy

Dendritic cells (DCs) play an important role in connecting the innate and adaptive immune system after activation by fungal pathogen recognition via their pattern recognition receptors (PRR), phagocytosis of fungal particles, antigen processing and presentation to T helper cells, as well as their secretion of cytokines/chemokines [119]. Thus, DC immunotherapy involves the incubation of DCs ex vivo with selected antigens or pathogens, then returning the cells to the host to boost their protection against an infectious agent. The DC vaccination is like DC immunotherapy, differing with regard to the moment of application. The DC vaccination is performed before infection and DC immunotherapy happens after diagnosis [48]. This immunotherapy approach is mainly used in cancer patients [120]. However, several studies have focused on the use of DC immunotherapy against fungal infections as well [111].
In A. fumigatus, DCs have a remarkable functional plasticity in response to conidia and hyphae, showing a capacity to generate antifungal immunity in vivo after activation with live fungi or fungal RNA [121]. Similar results were obtained with recombinant Aspergillus proteins and CpG oligodeoxynucleotides (ODNs) as adjuvants [122]. In another study, the vaccine formulation of the DCs transduced with an adenovirus vector encoding the cDNA of IL-12 and pulsed with heat-inactivated A. fumigatus induced a protective immune response against invasive pulmonary aspergillosis [123]. Additionally, the DC immunization approach proved to be a powerful way of overcoming the relatively weak immune response of the mouse to the defined small carbohydrates and peptide antigens of C. albicans [86]. More recently, DCs pulsed with an acapsular C. gattii were used in a vaccine against C. gattii and were able to induce cytokine-producing CD4+ T cells and multinucleated giant cells, which were associated with protection against pulmonary cryptococcosis in an experimental model [124].
Another DC vaccine approach was developed against P. brasiliensis. Bone marrow DCs were pulsed with peptide P10, derived from P. brasiliensis glycoprotein 43 (gp43), and administrated subcutaneously to naive mice that were subsequently challenged. An increased production of IFN-γ and IL-12 was observed, along with decreased pulmonary damage and significantly reduced fungal burdens, suggesting that this strategy can be therapeutic as well as prophylactic [125].

3.3. Vaccines Based on T Helper Lymphocytes

The CD4+ T cells, more specifically T helper and Th17, play major roles in eliciting protective and inflammatory responses [126]. In fungal infection, these cells generally play roles in both the resolution and worsening of superficial or invasive infections [127]. In addition, CD8+ T cells have an important role in the immune response against fungal infection [26]. Cell-based vaccines designed to prevent invasive fungal infections are currently being investigated in clinical trials and their use could play an especially important role in AIDS patients [128].
The goal of T cell vaccines or immunotherapy is to induce CD4+ and/or CD8+ T cells of sufficient magnitude and the necessary phenotype or effector functions that directly contribute to pathogen clearance via cell-mediated effector mechanisms [129]. This approach is used to treat mainly cancer and chronic infections via the intravenous injection of autologous T cells, which have been stimulated in vitro using antigens or modified using a gene encoding a specific antigen receptor, and expanded to a large quantity before being infused back into the patient [130]. For fungi, these strategies have been focused on vaccine studies based on the T cell-mediated immune response [64], predominantly against candidiasis and aspergillosis [64,131].
The treatment of immunocompetent mice with Aspergillus crude culture filtrate antigens resulted in the development of local and peripheral protective Th1 memory responses, mediated by antigen-specific IL-2- and IFN-γ-producing CD4+ T cells capable of conferring protection upon its adoptive transfer to naive mice [132,133]. The adoptive transfer of Asp f16 peptide-specific CD8+ T cells significantly extended the overall survival time of the A. fumigatus-infected immunocompromised mice [133]. In BALB/c mice, the cell glucanase Crf1 from A. fumigatus was found to induce memory CD4+ Th1 cells and cross-protection against lethal infection with C. albicans [51].
It is clear that Th cell-mediated vaccine responses or immunotherapy are a promising alternative regarding the treatment of fungal infection, mainly in patients who have undergone a hematopoietic stem cell transplant and chemotherapy. However, more studies are needed to determine the impact of this approach on the host organism and prognosis [134].

4. HLA and Its Importance in Identification of Therapeutic Epitopes

Human leukocyte antigens (HLAs) are part of the immunoglobulin gene family. These genes are extremely important in the immune response and are found on the short arm of human chromosome number 6 (region 6.21.3). The presence of these protein molecules in the cell membrane results in the control of immune responses. The presence of these protein molecules in the cell membrane results in the control of immune responses. These molecules operate via cell–cell interaction, acting in the recognition of what is proper and what is not proper for the human system, and can be considered as the hands and eyes of the immune system. HLAs are antigens classified as belonging to class I (HLA-A, -B, -C, -E, -F, -G and -H genes), II (HLA-DR, -DP and -DQ genes) and III (includes genes encoding the complement system and TNF), and are extremely polymorphic and show genetic variability from one population to another [135].
For the selection of the best candidates for vaccines, some characteristics of the immunogenic epitopes must be taken into account. These include stability and hydrophilicity, since these characteristics can influence the synthesis of these molecules. Another important characteristic is the solubility of the antigen, since this characteristic can directly influence the recognition of these vaccine candidate molecules by antigen-presenting cells (APCs) [136]. Finally, verifying the levels of interaction between the epitopes and HLA molecules (class I or II) enables the effective selection of targets for further experimental analysis. Currently, with advances and improvements in immunoinformatics tools, these characteristics mentioned above can be evaluated, as well as predicted [137,138,139,140]. In this review, these prediction tools are cited. Next, some studies involving HLA analyses related to fungal infection processes will be highlighted, as well as their use in the identification of new therapeutic targets. It is noteworthy that all HLA or MHC alleles described in this study are shown in Table 2.
Table
Table 2. HLA and MHC alleles expressed during fungal infections (+; present/−; absent).
Table 2. HLA and MHC alleles expressed during fungal infections (+; present/−; absent).
Target PathogenHLABioinformaticsExperimentalModelReference(s)
Paracoccidioides spp.A1-+Human[135]
A2-+Human
B7-+Human
B21-+Human
CW1-+Human
B15-+Human
A9-+Human[141,142,143,144]
B13-+Human
B22-+Human
B40-+Human
B40-+Human [141,144,145]
DRB1-0101-+Human[145]
DRB1-0301-+Human
DRB1-0401-+Human
DRB1-0701-+Human
DRB1-1101-+Human
DRB1-1301-+Human
DRB1-0404-+Human
DRB1-0802-+Human
DRB1-0205-+Human
DRB1-1302-+Human
DRB1-1501-+Human
Histoplasma spp.B7-+Human
B7-+Human[146]
DR-15-+Human
DQ-6-+Human
Cryptococcus spp.DR4-+Mouse
C1203+-Human[55,147]
DRB1-0101+-Human
Coccidioides spp.DRB1-0401-+Mouse[148,149]

4.1. Paracoccidioidomycosis

In studies developed by Dias et al. [135], HLA mapping was performed in patients affected by the chronic form of paracoccidioidomycosis (PCM). Twenty-one male patients were analyzed, showing an increase in the frequency of alleles at locus A (14 alleles), B (19 alleles) and C (10 alleles) of the HLAs. Among the identified HLA alleles, some showed significant increases in their frequency and were considered strongly linked to PCM, such as HLA-A1, -A2, -B7, -B21 and -CW1. HLA-B15 also appeared with increased frequency in the patients affected. However, HLA-A1 alleles in PCM are related to an immunological deficiency, which could be related to an inhibitory response or even to the success of an infection caused by Paracoccidioides spp. [141]. However, other studies have shown that HLA-A1 is involved in the phagocytosis processes of the fungus caused by neutrophils [150]. Other studies have shown the positive association between Paracoccidioides sp. and the expression of HLA-A9, B13, B22 and B40 [141,143,144].
In silico prediction analyses of peptides that bind to HLA-DR molecules (Class II) enabled the identification of the immunodominant epitopes of the 43-kDa glycoprotein (GP43) [151]. For the prediction of immunodominant T cell epitopes in GP43, the TEPITOPE algorithm was employed, using nine different HLA-DR alleles for analyses. With this, five of the most promising epitopes were predicted and selected, and then these molecules were tested in proliferation assays using peripheral blood mononuclear cells (PBMC) from patients with PCM after chemotherapy and PBMCs from control patients. In total, 14 out of 19 patients recognized at least one of the promiscuous epitopes predicted using immunoinformatics [151]. This highlights the importance of HLA-based immunogenic epitope prediction algorithms, demonstrating that bioinformatics tools can lead to promising results. In other studies, the immune response to synthetic GP43 epitopes that bind to several HLA alleles was verified. All epitopes were predicted using bioinformatics tools and synthesized. Then, the responses of peripheral blood T lymphocytes from 29 patients with PCM to the synthesized peptides were evaluated [152]. After analyses, it was found that all patients were typed for HLA class II, and a great diversity of HLA-DR molecules were associated with the recognition of the analyzed GP43 epitopes. In studies carried out by Travassos et al. [153], the immunization potential of the P10 peptide of GP43 was analyzed. In immunization assays, using the P10 peptide, it was observed that this molecule provided protection to Balb/C mice against intratracheal infections caused by virulent strains of P. brasiliensis. In addition, immunoinformatics analyses were used, making it possible to verify that 21 HLA-DR molecules could recognize and bind to the P10 peptide.
HLA frequency identification and analyses were also extended to studies of PCM in the central nervous system (NPCM), where six patients with NPCM had a HLA class I and II frequency, assessed by means of microlymphocytotoxicity. However, it was observed that the frequency of HLAs found in the studied patients (HLA-A, -B, -C, -DR and -DQ) was similar to alleles found in other populations affected by PCM [154]. However, among the HLAs analyzed, the increase in the frequency of HLA-B40 stood out. In other studies, this molecule has been reported to be involved in the development and progression of the systemic form of PCM [141,144].
In studies carried out by Mamoni et al. [145], the HLA-DR of PCM infection (PI), in its adult forms (AF) and juvenile forms (JF), was evaluated. However, a higher frequency of HLA-DR was observed in PI patients when compared to JF and AF patients. In other studies, the functional characterization of the P27 protein of Paracoccidioides sp. was carried out, where bioinformatics tools were used to trace the immunogenic profile of this molecule. For this, HLA class II molecules (DRB1-0101, -0301, -0401, -0701, -1101, -1301, -0404, -0802, -0405, -1302, -1501 and DRB5-0101) were used to probe the entire P27 protein sequence (220 amino acids). Interestingly, four peptides of P27 showed a high recognition affinity for nine of the twelve HLA-DR molecules selected for analysis [155].

4.2. Histoplasmosis

In studies carried by Braley et al. [146], eighteen patients who presented hemorrhagic macular lesions or peripapillary lesions originating from histoplasmosis had their HLA frequency identified. A total of 78% of the patients showed an increase in the frequency of HLA-B7. Furthermore, it was shown that the imbalance between the interaction of HLA-B and genes of the D locus is directly related to immune response genes. Locus B HLAs are involved in the specificity of cytotoxic T cells. This demonstrates that HLA-B may be linked to responses mediated by T lymphocytes, thus being more effective against invading organisms [146].
In studies related to presumptive ocular histoplasmosis syndrome (POHS), this disease has been found to be associated with other HLA alleles within the HLA-A, -B, -DQ, and -DR loci. A total of 34 patients diagnosed with POHS had their DNA analyzed for HLA gene typing. Significant associations were observed between sick patients and HLA-B7, HLA-DR15 and HLA-DQ6. Thus, it was concluded that HLA-DR15, -B7 and -DQ6 are strongly associated with the development of POHS, suggesting that these alleles help regarding individuals’ susceptibility to histoplasmosis [156].
In studies carried by Kischkel et al. [156], epitopes of Histoplasma capsulatum were investigated, in order to be used in the construction of diagnostic tests, or in the identification of molecules with vaccine potential. Thus, epitopes of Histoplasma sp. that would be recognized by human HLA class I and class II molecules were analyzed. After these analyses, they selected promising epitopes from molecules such as HSP60, Enolase and ATP-dependent molecular chaperone HSC82. The initial assays demonstrated the proliferation of CD4+ and T CD8+ lymphocytes, in addition to inducing the production of cytokines IFN-γ, IL-17 and IL-2, representing a Th1 and Th17 cell profile; this demonstrates the potential of immunization processes to activate a cellular response [157]. HSP70 of Histoplasma sp. also had its immune system excitation potential investigated, where, through immunoblotting and using anti-HLA-DR, the interaction between HLA-DR and HSP70 was shown [158].

4.3. Cryptococcosis

The development and construction of a vaccine for immunization against cryptococcosis has become a priority, due mainly to an increase in the number of cases registered in individuals with weakened immune systems [55,159,160]. In studies carried out by Specht et al. [55], glucan particles (GPs) were used as a delivery system for the antigens to be studied. Initially, six recombinant antigens were analyzed, but only four (GP-Cda1, GP-Cda2, GP-Cda3 and GP-Sod1) were used in the studies. Mice of the C57BL/6, BALB/c and HLA-DR4 lineage (transgenic) were challenged with virulent strains of Cryptococcus neoformans and Cryptococcus gattii, and subsequently immunized with the recombinant antigens. After immunization and analysis, it was observed that the recombinant antigens provided greater protection and chances of survival to the animals. For HLA-DR4 mice, the combination of GP-Cda1 and GP-Cda2 induced protective responses, similar to the responses observed in C57BL/6 mice, thus demonstrating the importance of human HLA-DR4 alleles in relation to the immunization process of these animals [55].
In other studies, the prediction of T cell epitopes was carried out with the aim of synthesizing peptide vaccines against C. neoformans. Among the candidate epitopes, the peptide YMAADQFCL showed interaction with nine MHC-I alleles and HLA-A*02:01 alleles. On the other hand, the peptides YARLLSLNA, ISYGTAMAV and INQTSYARL were predicted to bind with MHC-II, representing the central high-binding affinity epitopes. The predicted peptide, including YMAADQFCL and ISYGTAMAV, had an average population coverage of 69.75% and 74.39%, thus demonstrating the importance of immunoinformatics analysis. Such data may help in the construction of a polypeptide vaccine aimed at the immunization or therapy of cryptococcosis [147].
In addition, the use of immunoinformatics has also shown epitopes of HSP70 T cells from C. neoformans, with the aim of identifying new vaccine candidates [147,161]. Initially, 10 promising epitopes were predicted and verified by using computational tools, all of which triggered both the cellular and humoral immune response. Furthermore, other analyses found that the predicted epitopes had a population coverage of 90%. These data were obtained through molecular docking, using HLA-C*12:03, HLA-DRB1*01:01 and Immunoglobulin G as a model. of the strength of the association between HLA binding sites and promiscuous peptides were also performed, thus highlighting the importance of HLA-based epitope predictions [147].

4.4. Coccidioidomycosis

Studies involving epitopes from Coccidioides sp. in the process of immunizing mice and humans have already had their effectiveness evaluated as a vaccine for coccidioidomycosis [147,162]. Other studies have shown infection assays by inoculating spores of Coccidioides posadasii in HLA-DR4 transgenic mice (DRB1-0401 allele), which expresses human class II HLA, thus inducing the cellular response of T CD4+ lymphocytes. The vaccinated transgenic mice exhibited three distinct clinical manifestations: acute fatal disease, disseminated disease and pulmonary disease. In addition, immunized mice showed activation of heterogeneous immunity against pulmonary infection by Coccidioides sp., expressing Th1 and Th17 profile cytokines. The recruitment of innate immunity cells was also observed after nine days of the immunization process. The same immunization procedure was performed in C57BL/6 mice, where 100% survival was observed for mice of this strain [148].
In other studies, the use of a recombinant multivalent vaccine (rCpa1) induced protection against C. posadasii and C. immitis in HLA-DR4 transgenic mice (DRB1*0401) and in C57BL/6 mice. A significant reduction in the fungal burden was observed in animals initially immunized and subsequently challenged with fungal infection. There was also an increase in the production of IFN-γ and IL-17, represented by the Th1 and Th17 cell profiles [149].

5. Bioinformatic Tools for Vaccine Development against Fungi

There are seven challenges posed to the development of an effective vaccine against fungal human diseases: (i) the population most at risk is immunocompromised people; (ii) the diverse sites of infection in the host; (iii) intraspecies and interspecies antigenic variation among fungi; (iv) molecular similarities between fungi and animalia kingdoms; (v) translation from animal models to humans; (vi) formulation; and (vii) commercialization [107]. Among these seven points, bioinformatics can act strongly in most of them. These tools have the potential to perform analyses that are not possible when using traditional approaches. By employing specific tools, it is possible to identify the most promising antigen or epitope from different stages of the fungal infection, as well as those specific for each species without homology in humans. In addition, the immunoinformatic tools are able to predict the epitope using the MHC molecules associated with the fungal infections, specifically in humans (HLA, human leukocyte antigens) [163]. Thus, the clusters of tools and biotechnology can generate a more effective and safe vaccine, which is especially important in the context of immunocompromised individuals [164].
Bioinformatics has an important role in the rational identification of targets, especially in the context of pathogens with a complex life cycle. However, it is relevant to point out that the rational answer to the initial questions is strongly associated with the pathogen involved. Thus, in the fungal infections caused by a large number of species with different characteristics, it is extremely difficult to determine the general workflow that can obtain the specificity of all pathogenic fungal species. In addition, the majority of these infections are superficial and easy to treat, but roughly 150 million cases might be serious or life-threatening to individuals [1]. Invasive fungal infections kill over a million people around the world [165]. In this sense, a promising vaccine target needs to have some main characteristics that allow an immune response against the infection to be elicited, such as virulence, an involvement in the invasion of the host, adherence or phenotypic switch, localization at the cell membrane or an ability to be secreted, as well as the capacity to evade the host's immune system [166].
In late 2022, the World Health Organization (WHO) released a list of fungal priority pathogens with the aim of moving research efforts and investment towards fungal infections and antifungal resistance. This list was organized into three groups—critical, high, and medium priority—based on the rate of mortality, incidence over the last 10 years, geographical distribution, availability of diagnostics and treatments, transmissibility, drug resistance, and complications of the disease. On the other hand, the causative agents of Nakaseomyces glabrata, Fusarium spp., Candida parapsilosis, Histoplasma spp., Mucorales, Candida tropicalis and Eumycetoma belong to the high-priority group [167]. Some of them are listed in Table 3, which shows the most recent study in fungal vaccine development using bioinformatics.
Unfortunately, there is only one database of immunoinformatics that specifically concerns fungi FungalRV [168]; this is a server that has gathered several tools, including an adhesin predictor, cellular localization predictor, linear and conformational B cell epitope predictor, and T cell epitope predictor. One detailed protocol of FungalRV can be found in the study of Chaudhuri and Ramachandran (2017) [65].
Table
Table 3. Bioinformatics as a tool for fungal vaccine development.
Table 3. Bioinformatics as a tool for fungal vaccine development.
PathogenSubcellular Location andB Cell Epitope PredictionT Cell Epitope PredictionCytokinesImmunogenicity and AntigenicNumber of Final TargetsYearRef.
Histoplasma capsulatumPSORT II; McGeoch method; TMHMM------VaxiJen 2.0,5 targets2023[169]
Candida aurisTargetP; SignalP; Phobius; FunsecKB; PredGPI; TMHMM; EffectorP; FungalRV; FaaPred;--NetMHCII 2.3 (IEDB)--VaxiJen server39 targets2022[170]
Rhizopus delemarSignalP; PredGPI; TMHMM; GPI- anchorBCPREDS; Ellipro toolIEDB (MHC class I and II); MHC class I processingIL-4Pred; IL-10Pred; IFNepitopeVaxiJen 2.04 targets2022[171]
Sporothrix brasiliensis--Bepipred 2.0PredBALC/C server;IL-4pred; IFNepitope; 17eScan server;SsEnoEnolase2022[172]
Cryptococcus neoformans var. grubii--IEDB Bcell epitope prediction tool; BepiPred; ElliProIEDB MHC-I prediction tool; IEDB MHC-II prediction tool--Kolaskar and Tongaonkar antigenicity methodheat shock 70 kDa protein2021[147]
Candida glabrata--ElliPro; Bepipred tool from IEDB;IEDB MHC I prediction tool/IEDB MHC II prediction--Kolaskar and Tongaonkar antigenicity methodFructose Bisphosphate Aldolase2021[173]
Candida dubliniensis----IEDB B-cell epitope prediction tool; NetMHCII 2.3; NETMHCpan 4.0 web serversIL2Pred, IL4Pred, and IFNepitopeVaxiJen 2.0; AllergenFPSecreted aspartyl proteinases (SAP) proteins2023[174]
Candida glabrataSignalP-5; DeepLoc-1.0-- --VaxiJen v2.0 server33 targets2022[175]
Aspergillus fumigatus----NetMHCIIpan ver.3.2 server;--AllergenFP; VaxiJen ver.2.05,8-linoleate diol synthase; ChainB-chitinase A12022[175]
Rhizopus microsporusSignalP-5.0 server--IEDB MHC I prediction tool/IEDB MHC II prediction; Docking by AutoDock VinaINF predictionserver--Spore coat (CotH) and Serine protease (SP) proteins as2021[176]
Candida albicansCELLO2GO--NetCTL server; IEDB MHC I prediction tool/IEDB MHC II prediction--VaxiJen server, ANTIGENpro; AllerTOP; NetChop3.1; MHCII-NPAls4p, Als3p, Fav2p, Als2p, Eap1p, Hyr1p, Hwp1p, Sap2p2020[77]
Candida aurisCELLOABCPred; Ellipro serviceNetCTL 1.2; IEDB MHC II predictionIFNepitopeVaxiJen server; Algpred serverMitochondrial import receptor subunit, Putative beta-glucanase/Beta-glucan synthetases, 1,3-beta-glucanosyltransferase, Uricase, and a putative SUN family protein.2022[177]
Rhizopus delemarTMHMM v2.0 serverIEDB Bcell epitope prediction tool (BepiPred and ElliPro)NetCTL 1.2; IEDB MHC II predictionIFNepitope; IL4pred; IL10predVaxiJen server; AllerTOP v2.0; MHCII-NP (IEDB); NetChop3.1Cell membrane by the copper oxidase-iron permease (FTR1) complex2022[140]
Candida tropicalisCELLO2GO; PSORT II--NETMHC 2.3; NETMHC 4.0; Bepipred (IEDB)IFNepitopeVaxiJen 2.0; AllergenFP version 1.Secreted aspartic protease 2 (SAP2) protein2022[166]
Next, we summarized some aspects of the bioinformatic tools that could be used in the investigation and development of a promising vaccine against fungal infections. Figure 2 proposes the summarized workflow by which to predict targets for a fungal vaccine based on MHC, B and T cell epitopes and an antigenicity analysis.

5.1. T Cell Epitope Prediction

In the context of fugal pathogens, the main mechanisms involved in the immune response against infection are phagocytosis and the activation of the adaptive immune response via the development of different CD4+ T helper and regulatory T cells [64,127]. In this way, the identification of HLA class II epitopes in fungal pathogens is a great approach to the development of vaccines and immunotherapy that can be used to elicit antigen-specific immune responses [77]. Nevertheless, via the cross-presentation mechanism, APCs can present extracellular antigens through HLA class I molecules as well [178].
In general, the antigen presentation mechanism with HLA class I and II occurs via the proteolytic cleavage of pathogen proteins into small peptides of 8–14 and 15–35 amino acids, and their binding to the peptide-binding cleft of class I and class II MHC receptors, respectively. This is followed by the positioning of this peptide/MHC complex on the cell surface and the subsequent interaction with T cell receptors. In this context, it is important to point out that the HLA class II binding predictions are currently slightly less accurate than the HLA class I binding predictions because they involve conformational criteria. In addition, HLA class II epitopes are longer (around 15 to 25 mer) and several binding registers or cores may be present in the same peptide. A peptide needs to be presented by an MHC I molecule for it to be able to elicit effector T cell responses. Contrarily to MHC II molecules, which can bind to peptides that are longer and more variable, MHC I binding is restricted to peptides typically 8–14 amino acids long in sequence; in addition, some of the residues in the peptide, denoted as anchor residues, are important for peptide–MHC binding [179].
Before commenting upon the tools, it is important to explain the steps involved in the analysis of T cell epitope prediction. Briefly, the identification of epitopes depends on the interaction of them with the binding region in the HLA molecule. Thus, it is important to define the HLA allele for analysis and the methods used for prediction. Firstly, the choice of allele, which defines the vaccine coverage, can be solved using a list of those alleles that represent the reference sets that should provide >97% and >99% in the human population, for class I and II, respectively; these are available in The Immune Epitope Database (IEDB), the biggest database of immunomic and host tools, that can be referred to in order to assist in the prediction and analysis of epitopes [180]. Furthermore, the MHC-II binding predictions from IEDB are available with seven alleles (DRB1*03:01, DRB1*07:01, DRB1*15:01, DRB3*01:01, DRB3*02:02, DRB4*01:01, DRB5*01:01), which have the best results in the definition of the IEDB consensus percentile rank [181]. In the context of systemic mycosis, it is important to point out that several studies have demonstrated the influence of HLA genes on the susceptibility of infections, highlighting the importance of certain alleles in the analysis [182]. The other point is the choice of the prediction method, which is more difficult because currently there are several online tools can be used to predict the T cell epitope [183]. Therefore, here we introduce several tools for the epitope prediction of MHC class I and II molecules.
Firstly, the set of tools used for the prediction of T cells, called T Cell Epitope Prediction Tools, from IEDB, has great potential. This database provides tools for MHC class I and class II epitope prediction, MHC I processing (Proteasome, TAP) and the analysis of immunogenicity to epitope class I binding [184]. Currently, the methods recommended by IEDB for MHC epitope prediction are the consensus approach, combining NN-align, SMM-align, CombLib, and Sturniolo if any corresponding predictor is available for the molecule, otherwise, NetMHCIIpan is used. Also used are the NetMHCpan (4.0) for MHC I and the NetMHCIIpan-4.0 server [185] for MHC II, which are based on artificial neural networks (ANNs). This method can obtain accurate results for molecules using little or no experimental data and provides more than 200 MHC I molecules, employing binding affinity and eluted ligand mass spectrometry for human (HLA-A, B, C, E), mouse (H-2), cattle (BoLA), primate (Patr, Mamu, Gogo) and swine (SLA) molecules [186]. For MHC II epitope prediction, a combination of experimental and in silico data is used, covering the three human MHC class II molecules, HLA-DR, HLA-DQ and HLA-DP, as well as the H-2 mouse molecules. It can perform a prediction for any MHC II molecule of a known sequence.
In the context of fungi, it is possible to highlight a study focusing on C. albicans [77] and Aspergillus flavus [187]. The tool was used in the global analysis of the C. albicans proteome and was able to identify eight antigenic proteins with comparative functions in hyphal formation (Als4p, Als3p, Fav2p, Als2p, Eap1p, Hyr1p, Hwp1p, Sap2p) and select 18 epitopes that are conserved among 22 C. albicans strains [77]. In addition, more recently, it was used in a robust immunoinformatic analysis of the HSP70 kDa protein complex of C. neoformans var. grubii, with a promising epitope identification and a massive global population coverage (based on the allele frequency and geographic distribution) [147]. The immunoproteomic from Sporothrix brasiliensis, with subsequent epitope prediction using the IEDB MHC II tool, is a good example of the bioinformatics that can be applied to vaccine development against fungal infection. ZR8 peptide from the GP70 protein, the main antigen of the Sporothrix complex, was the best potential vaccine candidate, inducing a strong cellular immune response [46]. In addition, another important study of mucormycosis was based on the reverse vaccinology approach. Six final proteins were identified from a total of 29.447 proteins obtained by Uniprot. This set of proteins was subjected to adhesin prediction, localization prediction, immunogenicity analysis, analysis of their homology with humans, allergen analysis, and T and B cells analysis via IEDB in order to obtain the final targets [188].
Another immunoproteomic study that used a group of combined immunoproteomic and immunopeptidomic methods, based on co-immunoprecipitation, to map H. capsulatum epitopes for the first time in a natural context using murine dendritic cells and macrophages can be highlighted. Additionally, a robust in silico analysis was used to predict MHC I and II epitope binding from human and mice, as well as immunogenicity and IFN-γ induction prediction. The four most promising peptides, derived from heat shock protein 60, enolase, and the ATP-dependent molecular chaperone HSC82, were synthesized, as well as the peptides with and without incorporation into glucan particles that induced a strong immune response in the vaccination. The authors point out the fact that these proteins have a high degree of identity with the proteins expressed by other medically important pathogenic fungi, which is interesting in the context of the pan-fungal vaccine [157].
In addition, it is possible to highlight ProPred1, which is an online tool used for the prediction of the MHC I epitope. It implements matrices for 47 MHC Class I alleles, and proteasomal and immunoproteasomal models [189]. Similarly, RANKPEP identifies epitope binding to MHC I and MHC II molecules from protein sequences or sequence alignments using position-specific scoring matrices (PSSMs) [190]. Likewise, nHLAPred is an MHC class I epitope prediction tool that can identify epitopes binding to 67 alleles and also allows the prediction of proteasome cleavage at the C-terminus [191].
For MHC class II molecules, another important tool is TEPITOPEpan, which is similar to NetMHCIIpan (the current version (NetMHCIIpan-4.0) [186]) and is able to predict epitope binding with very restricted or no experimental data; it has been evaluated as the second-best method after NETMHCIIpan-2.0 (among the four pan-specific methods: NetMHCIIpan-2.0, NetMHCIIpan-1.0, MultiRTA and TEPITOPEpan) for predicting the binding specificities of an unknown allele. In the context of MHC class II, TEPITOPEpan was applied to the gp43 antigen from P. brasiliensis in a key study. In another example, the application of the recombinant C. neoformans chitin deacetylase 2 (Cda2), that has been discussed before, in a vaccine against fungal infection; it was revealed a peptide sequence predicted to have strong binding to the MHC II, H2-IAd allele was found in BALB/c mice. The protection was lost after the induction of a mutation in the sequence of Cda2, indicating that the immune response is dependent on the strong binding of the Cda2 [192].
Lastly, a recent review of the methods used to predict the epitope binding of MHC suggested that the combination of sequence and structure-based methods would be the best method, but that this is hindered by the lack of 3D structures [191]. However, it is possible to use an approach employing different tools based on a sequence and 3D structure. An example of this is the possibility of combining the sequence-based tools, as mentioned here, with an EpiDock, the first structure-based server for MHC class II binding prediction. This tool can predict binding to the 23 most frequent human alleles and in one study, was able to identify 90% of true binders and 76% of true non-binders, with an overall accuracy of 83% [183].
Therefore, based on the tools discussed here, Figure 2 proposes a protocol that can be used to analyze the prediction of MHC molecule epitopes.

5.2. B Cell Epitope Prediction

B cell epitope prediction is essential in the context of the modern analysis and development of vaccines and diagnostics. B cell epitope mapping is essential in the production of diagnostic tests, although it is only the first step in designing potent vaccines. In addition, B cells are able to recognize linear (continuous) and conformational (discontinuous) epitopes. The linear epitopes have their amino acid residue organized in the primary sequence of the protein, while the discontinuous epitopes are formed by residues organized far in the primary structure, but become nearer as a consequence of the folded protein [185]. Linear epitope prediction is more simple than conformational prediction and normally the amino acid sequence is required; however, it represents only 10% of the B cell epitopes. On the other hand, the conformational epitopes represent 90% of the total B cell epitopes but present the difficulty of prediction, especially in cases of neglected tropical diseases; this is because they frequently require the PDB format as input [185], with a few exceptions [193]. This point represents an important challenge in this analysis, which requires an available 3D prediction model. In addition, for linear B cell epitopes, topology analysis is required in order to localize those located on the protein surface, considering the higher probability of their interaction with the immune system. Lastly, there is a vast number of B cell epitope prediction tools and choosing the best is very hard. It is recommended that a combined method is used for greater precision. We recently published a review about the application of B cell epitope prediction that described a workflow that can be used to identify new targets for the development of fungal infection diagnoses. Additionally, we suggested different methods by which to predict the function and location of proteins [163].
Currently, a robust list of approaches has been proposed in linear and discontinuous B cell epitope prediction [163,194,195]. For linear epitopes, the focus is on the BCPREDS, ABCpred, BepiPred, SVMTriP and CoBepro, which are used in immunogenic and diagnostic studies against C. albicans [77], A. flavus [186], the S. schenckii complex [196], C. gattii [197], Rhizopus oryzae [198] and Paracoccidioides spp. [199,200]. In general, these tools comprise a combination of multiple physical–chemical properties. They are free online via a webserver and are easy to use, requiring the protein sequence in FASTA format.
In the context of the tools used for discontinuous epitopes prediction, there is a group of tools such as BEST, BepiPred-2.0 and CBTOPE that perform the prediction using a primary sequence of proteins. These tools are particularly important when the protein does not have a tertiary structure. On the other hand, tools such as DicoTope [201], SEPPA [202], the BEpro server (formerly known as PEPITO) [203], Ellipro [204] and EPITOPIA [205] use structure-based approaches and require 3D structure information. EPITOPIA yields a higher success rate of 89.4% compared to ElliPro and DiscoTope [205], when used in the analysis of the immunogenic properties of biopharmaceutical enzyme uricase from A. flavus and Bacillus subtilis [186]. In the other study, SEPPA gave the best performance among the six tools, followed by DiscoTope and BEpro [206].
Like tools for linear epitope prediction, it is difficult to determine the best tool for discontinuous epitope prediction, but it is recommended that tools with a different methodology for analysis are used in order to obtain a better accuracy [207]. It is important to highlight that the prediction of the B cell epitope plays a supporting role alongside the in silico methodology used for vaccine development, while the identification of epitopes for the T cell receptor is the most important goal. In this context, the prediction of the linear B cell epitope can be commonly associated with the initial analysis or associated with discontinuous B cell epitope prediction, which can support the confirmation of the final target once the tertiary structure is elucidated [208,209,210].

5.3. Antigenicity Prediction

The use of antigenicity prediction, and its capacity to be recognized by the T cell and B cell receptor, is a particularly important step during the identification of targets for diagnosis and the development of vaccines. Currently, it is possible to cite several tools for this analysis, such as VaxiJen [211], NERVE [212], Vaxign [213], ANTIGENpro [214], the Jenner-Predict server [215], iVAX [216] and VACSol [217]. However, only the VaxiJen v.2.0 [218] was trained with fungal data and because of this, it is used for these pathogens. Its prediction uses the FASTA sequence and is independent of an alignment with experimentally confirmed antigens, but it is based on the physical–chemical properties of proteins with an accuracy of approximately 70% and 89% [211]. This tool was developed in 2007, is available online and nowadays is one of the most cited tools regarding antigenicity, especially in the context of fungal vaccine development (Table 3) [219].

6. Concluding Remarks and Perspectives

Fungal infection is a global health problem that is associated with the limitations of therapeutic availability, emergent drug resistance, a large variability in pathogen species, and a difficult diagnosis, which is reflected in a high number of annual deaths.
In view of this, new vaccines and therapeutic approaches represent the best option to circumvent this. Thus, we summarized here the current methodology applied in the development of vaccines against fungal infection. Remarkable progress has been achieved, considering all the major medically important mycoses, by using a variety of vaccine designs in animal studies. A new perspective on the pan-fungal vaccine strategy and on DNA vaccines may represent a promising horizon for the future of fungal vaccine development.
Although great advances have been achieved, there are still several challenges to be overcome. In the current scenario, a safe vaccine for immunocompromised patients (populations most at risk) is required. The diverse sites of infection in the host and the diversity among fungi need to be overcome, and formulations need to be established and commercialization needs to be systematized. Part of this challenge is dependent on science. However, government politics is very important for this progress.
Although it is difficult to determine the tools and methods recommended to better predict vaccine candidates, considering the diversity of fungal infections, we were able to show here a general workflow for in silico analysis. In this sense, we reinforce that these immunoinformatic tools could be the missing piece required in order to successfully identify an effective and safe fungal vaccine candidate.

Author Contributions

Conceptualization and methodology, S.S.W., C.L.B., J.V. and M.M.I. Software, M.M.I. Data curation, M.M.I. Writing—original draft preparation, M.M.I., K.M., F.R.-D., V.R.M.C.-L., L.O.S.S. and A.L.E.M. Writing—review and editing C.M.d.A.S., O.H.R. and F.R.-D. Supervision, S.S.W. and C.L.B. Project administration and funding acquisition, S.S.W., C.L.B. and C.M.d.A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed by grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico (Edital Universal-CNPq- 408042/2021-4), Instituto Nacional de Ciência e Tecnologia (INCT-IPH), Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT/DECIT-MS/CNPq/SES No. 03/2016-PPSUS-MS, PPSUS/FUNDECT No. 08/2020) and, in part by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)—Finance Code 001. The doctoral K.M. grant number was 88882.458447/2019-01. M.M.I. is fellowship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). C.L.B. has a fellowship from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). The Universidade Federal de Mato Grosso do Sul (UFMS) supported publication fees (EDITAL Nº 19/2023 PROPP/UFMS).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bongomin, F.; Gago, S.; Oladele, R.O.; Denning, D.W. Global and Multi-National Prevalence of Fungal Diseases—Estimate Precision. J. Fungi 2017, 3, 57. [Google Scholar] [CrossRef]
  2. Benedict, K.; Jackson, B.R.; Chiller, T.; Beer, K.D. Estimation of Direct Healthcare Costs of Fungal Diseases in the United States. Clin. Infect. Dis. 2019, 68, 1791–1797. [Google Scholar] [CrossRef] [PubMed]
  3. Seyedjavadi, S.S.; Bagheri, P.; Nasiri, M.J.; Razzaghi-Abyaneh, M.; Goudarzi, M. Fungal Infection in Co-Infected Patients With COVID-19: An Overview of Case Reports/Case Series and Systematic Review. Front. Microbiol. 2022, 13, 888452. [Google Scholar] [CrossRef]
  4. Skiada, A.; Pavleas, I.; Drogari-Apiranthitou, M. Epidemiology and Diagnosis of Mucormycosis: An Update. J. Fungi 2020, 6, 265. [Google Scholar] [CrossRef]
  5. Gebrehiwet, T.; Gebremichael, G. Development of Vaccination against Fungal Disease: A Review Article. Int. J. Trop. Dis. 2018, 1, 1–8. [Google Scholar] [CrossRef]
  6. do Nascimento Martins, E.M.; Reis, B.S.; de Resende, M.A.; de Andrade, A.S.R.; Goes, A.M. Mice Immunization with Radioattenuated Yeast Cells of Paracoccidiodes Brasiliensis: Influence of the Number of Immunizations. Mycopathologia 2009, 168, 51–58. [Google Scholar] [CrossRef]
  7. Liu, M.; Clemons, K.V.; Johansen, M.E.; Martinez, M.; Chen, V.; Stevens, D.A. Saccharomyces as a Vaccine against Systemic Candidiasis. Immunol. Investig. 2012, 41, 847–855. [Google Scholar] [CrossRef]
  8. Capilla, J.; Clemons, K.V.; Liu, M.; Levine, H.B.; Stevens, D.A. Saccharomyces Cerevisiae as a Vaccine against Coccidioidomycosis. Vaccine 2009, 27, 3662–3668. [Google Scholar] [CrossRef]
  9. Ardiani, A.; Higgins, J.P.; Hodge, J.W. Vaccines Based on Whole Recombinant Saccharomyces Cerevisiae Cells. FEMS Yeast Res. 2010, 10, 1060–1069. [Google Scholar] [CrossRef]
  10. Levine, H.B.; Cobb, J.M.; Smith, C.E. Immunity to Coccidioi-Domycosis Induced in Mice by Purified Spherule, Arthrospore, and Mycelial Vaccines. Trans. N. Y. Acad. Sci. 1960, 22, 436–449. [Google Scholar] [CrossRef]
  11. Levine, H.B.; Kong, Y.-C.M.; Smith, C.E. Immunization of Mice to Coccidioides Immitis: Dose, Regimen and Spherulation Stage of Killed Spherule Vaccines. J. Immunol. 1965, 94, 132–142. [Google Scholar] [CrossRef]
  12. Pappagianis, D.; Brown, B.W.; Cunningham, R.; Einstein, H.; Ellsworth, R.; Galgfani, J.; Hampson, C.R.; Holeman, C.W.; Johnson, R.H.; Larwood, T.R.; et al. Evaluation of the Protective Efficacy of the Killed Coccidioides Immitis Spherule Vaccine in Humans. Am. Rev. Respir. Dis. 1993, 148, 656–660. [Google Scholar] [CrossRef]
  13. Xue, J.; Chen, X.; Selby, D.; Hung, C.Y.; Yu, J.J.; Cole, G.T. A Genetically Engineered Live Attenuated Vaccine of Coccidioides Posadasii Protects BALB/c Mice against Coccidioidomycosis. Infect. Immun. 2009, 77, 3196–3208. [Google Scholar] [CrossRef] [PubMed]
  14. Narra, H.P.; Shubitz, L.F.; Mandel, M.A.; Trinh, H.T.; Griffin, K.; Buntzman, A.S.; Frelinger, J.A.; Galgiani, J.N.; Orbach, M.J. A Coccidioides Posadasii CPS1 Deletion Mutant Is Avirulent and Protects Mice from Lethal Infection. Infect. Immun. 2016, 84, 3007–3016. [Google Scholar] [CrossRef] [PubMed]
  15. Mandel, M.A.; Beyhan, S.; Voorhies, M.; Shubitz, L.F.; Galgiani, J.N.; Orbach, M.J.; Sil, A. The WOPR Family Protein Ryp1 Is a Key Regulator of Gene Expression, Development, and Virulence in the Thermally Dimorphic Fungal Pathogen Coccidioides Posadasii. PLoS Pathog. 2022, 18, e1009832. [Google Scholar] [CrossRef]
  16. Pinto, A.R.; Puccia, R.; Diniz, S.N.; Franco, M.F.; Travassos, L.R. DNA-Based Vaccination against Murine Paracoccidioidomycosis Using the Gp43 Gene from Paracoccidioides Brasiliensis. Vaccine 2000, 18, 3050–3058. [Google Scholar] [CrossRef]
  17. Ribeiro, A.M.; Bocca, A.L.; Amaral, A.C.; Souza, A.C.C.O.; Faccioli, L.H.; Coelho-Castelo, A.A.M.; Figueiredo, F.; Silva, C.L.; Felipe, M.S.S. HSP65 DNA as Therapeutic Strategy to Treat Experimental Paracoccidioidomycosis. Vaccine 2010, 28, 1528–1534. [Google Scholar] [CrossRef]
  18. Rittner, G.M.G.; Muñoz, J.E.; Marques, A.F.; Nosanchuk, J.D.; Taborda, C.P.; Travassos, L.R. Therapeutic DNA Vaccine Encoding Peptide P10 against Experimental Paracoccidioidomycosis. PLoS Negl. Trop. Dis. 2012, 6, e1519. [Google Scholar] [CrossRef]
  19. Assis-Marques, M.A.; Oliveira, A.F.; Ruas, L.P.; dos Reis, T.F.; Roque-Barreira, M.C.; Coelho, P.S.R. Saccharomyces Cerevisiae Expressing Gp43 Protects Mice against Paracoccidioides Brasiliensis Infection. PLoS ONE 2015, 10, e0120201. [Google Scholar] [CrossRef]
  20. Braga, C.J.M.; Rittner, G.M.G.; Henao, J.E.M.; Teixeira, A.F.; Massis, L.M.; Sbrogio-Almeida, M.E.; Taborda, C.P.; Travassos, L.R.; Ferreira, L.C.S. Paracoccidioides Brasiliensis Vaccine Formulations Based on the Gp43-Derived P10 Sequence and the Salmonella Enterica FliC Flagellin. Infect. Immun. 2009, 77, 1700–1707. [Google Scholar] [CrossRef]
  21. Fernandes, V.C.; Martins, E.M.N.; Boeloni, J.N.; Coitinho, J.B.; Serakides, R.; Goes, A.M. Additive Effect of RPb27 Immunization and Chemotherapy in Experimental Paracoccidioidomycosis. PLoS ONE 2011, 6, e17885. [Google Scholar] [CrossRef] [PubMed]
  22. De Bastos Ascenço Soares, R.; Gomez, F.J.; De Almeida Soares, C.M.; Deepe, G.S. Vaccination with Heat Shock Protein 60 Induces a Protective Immune Response against Experimental Paracoccidioides Brasiliensis Pulmonary Infection. Infect. Immun. 2008, 76, 4214–4221. [Google Scholar] [CrossRef] [PubMed]
  23. Liu, M.; Clemons, K.V.; Bigos, M.; Medovarska, I.; Brummer, E.; Stevens, D.A. Immune Responses Induced by Heat Killed Saccharomyces Cerevisiae: A Vaccine against Fungal Infection. Vaccine 2011, 29, 1745–1753. [Google Scholar] [CrossRef]
  24. Jiang, C.; Magee, D.M.; Quitugua, T.N.; Cox, R.A. Genetic Vaccination against Coccidioides Immitis: Comparison of Vaccine Efficacy of Recombinant Antigen 2 and Antigen 2 CDNA. Infect. Immun. 1999, 67, 630–635. [Google Scholar] [CrossRef] [PubMed]
  25. Hung, C.-Y.; Zhang, H.; Castro-Lopez, N.; Ostroff, G.R.; Khoshlenar, P.; Abraham, A.; Cole, G.T.; Negron, A.; Forsthuber, T.; Peng, T.; et al. Glucan-Chitin Particles Enhance Th17 Response and Improve Protective Efficacy of a Multivalent Antigen (RCpa1) against Pulmonary Coccidioides Posadasii Infection. Infect. Immun. 2018, 86. [Google Scholar] [CrossRef]
  26. Wüthrich, M.; Filutowicz, H.I.; Warner, T.; Deepe, G.S.; Klein, B.S. Vaccine Immunity to Pathogenic Fungi Overcomes the Requirement for CD4 Help in Exogenous Antigen Presentation to CD8+ T Cells: Implications for Vaccine Development in Immune-Deficient Hosts. J. Exp. Med. 2003, 197, 1405–1416. [Google Scholar] [CrossRef]
  27. Wozniak, K.L.; Young, M.L.; Wormley, F.L. Protective Immunity against Experimental Pulmonary Cryptococcosis in T Cell-Depleted Mice. Clin. Vaccine Immunol. 2011, 18, 717–723. [Google Scholar] [CrossRef]
  28. Devi, S. Preclinical Efficacy of a Glucuronoxylomannan-Tetanus Toxoid Conjugate Vaccine of Cryptococcus Neoformans in a Murine Model. Vaccine 1996, 14, 841–844. [Google Scholar] [CrossRef]
  29. Normile, T.G.; Del Poeta, M. Three Models of Vaccination Strategies Against Cryptococcosis in Immunocompromised Hosts Using Heat-Killed Cryptococcus Neoformans Δsgl1. Front. Immunol. 2022, 13, 868523. [Google Scholar] [CrossRef]
  30. Lin, J.; Pham, T.; Hipsher, K.; Glueck, N.; Fan, Y.; Lin, X. Immunoprotection against Cryptococcosis Offered by Znf2 Depends on Capsule and the Hyphal Morphology. mBio 2022, 13, e0278521. [Google Scholar] [CrossRef]
  31. Schmidt, C.S.; White, C.J.; Ibrahim, A.S.; Filler, S.G.; Fu, Y.; Yeaman, M.R.; Edwards, J.E.; Hennessey, J.P. NDV-3, a Recombinant Alum-Adjuvanted Vaccine for Candida and Staphylococcus Aureus, Is Safe and Immunogenic in Healthy Adults. Vaccine 2012, 30, 7594–7600. [Google Scholar] [CrossRef]
  32. Spellberg, B.J.; Ibrahim, A.S.; Avanesian, V.; Fu, Y.; Myers, C.; Phan, Q.T.; Filler, S.G.; Yeaman, M.R.; Edwards, J.E. Efficacy of the Anti-Candida RAls3p-N or RAls1p-N Vaccines against Disseminated and Mucosal Candidiasis. J. Infect. Dis. 2006, 194, 256–260. [Google Scholar] [CrossRef] [PubMed]
  33. Sandini, S.; La Valle, R.; Deaglio, S.; Malavasi, F.; Cassone, A.; De Bernardis, F. A Highly Immunogenic Recombinant and Truncated Protein of the Secreted Aspartic Proteases Family (RSap2t) of Candida Albicans as a Mucosal Anticandidal Vaccine. FEMS Immunol. Med. Microbiol. 2011, 62, 215–224. [Google Scholar] [CrossRef] [PubMed]
  34. De Bernardis, F.; Amacker, M.; Arancia, S.; Sandini, S.; Gremion, C.; Zurbriggen, R.; Moser, C.; Cassone, A. A Virosomal Vaccine against Candidal Vaginitis: Immunogenicity, Efficacy and Safety Profile in Animal Models. Vaccine 2012, 30, 4490–4498. [Google Scholar] [CrossRef] [PubMed]
  35. Bromuro, C.; Romano, M.; Chiani, P.; Berti, F.; Tontini, M.; Proietti, D.; Mori, E.; Torosantucci, A.; Costantino, P.; Rappuoli, R.; et al. Beta-Glucan-CRM197 Conjugates as Candidates Antifungal Vaccines. Vaccine 2010, 28, 2615–2623. [Google Scholar] [CrossRef]
  36. Xin, H.; Cartmell, J.; Bailey, J.J.; Dziadek, S.; Bundle, D.R.; Cutler, J.E. Self-Adjuvanting Glycopeptide Conjugate Vaccine against Disseminated Candidiasis. PLoS ONE 2012, 7, e35106. [Google Scholar] [CrossRef]
  37. Bundle, D.R.; Paszkiewicz, E.; Elsaidi, H.R.H.; Mandal, S.S.; Sarkar, S. A Three Component Synthetic Vaccine Containing a β-Mannan T-Cell Peptide Epitope and a β-Glucan Dendritic Cell Ligand. Molecules 2018, 23, 1961. [Google Scholar] [CrossRef]
  38. Levy, D.A.; Bohbot, J.M.; Catalan, F.; Normier, G.; Pinel, A.M.; Dussourd d’Hinterland, L. Phase II Study of D.651, an Oral Vaccine Designed to Prevent Recurrences of Vulvovaginal Candidiasis. Vaccine 1989, 7, 337–340. [Google Scholar] [CrossRef]
  39. Deepe, G.S.; Gibbons, R.S. Cellular and Molecular Regulation of Vaccination with Heat Shock Protein 60 from Histoplasma Capsulatum. Infect. Immun. 2002, 70, 3759–3767. [Google Scholar] [CrossRef]
  40. Gomez, F.J.; Gomez, A.N.A.M.; Deepe, G.S. Protective Efficacy of a 62-Kilodalton Antigen, HIS-62, from the Cell Wall and Cell Membrane of Histoplasma Capsulatum Yeast Cells. Infect. Immun. 1991, 59, 4459–4464. [Google Scholar] [CrossRef]
  41. Gomez, F.J.; Gomez, A.M.; Deepe, G.S. An 80-Kilodalton Antigen from Histoplasma Capsulatum That Has Homology to Heat Shock Protein 70 Induces Cell-Mediated Immune Responses and Protection in Mice. Infect. Immun. 1992, 60, 2565–2571. [Google Scholar] [CrossRef]
  42. Deepe, G.S.; Gibbons, R. Protective Efficacy of H Antigen from Histoplasma Capsulatum in a Murine Model of Pulmonary Histoplasmosis. Infect. Immun. 2001, 69, 3128–3134. [Google Scholar] [CrossRef]
  43. Zheng, M.; Ramsay, A.J.; Robichaux, M.B.; Norris, K.A.; Kliment, C.; Crowe, C.; Rapaka, R.R.; Steele, C.; McAllister, F.; Shellito, J.E.; et al. CD4+ T Cell–Independent DNA Vaccination against Opportunistic Infections. J. Clin. Investig. 2005, 115, 3536. [Google Scholar] [CrossRef] [PubMed]
  44. Fernandes, C.M.; Normile, T.G.; Fabri, J.H.T.M.; Brauer, V.S.; Araújo, G.R.d.S.; Frases, S.; Nimrichter, L.; Malavazi, I.; Del Poeta, M. Vaccination with Live or Heat-Killed Aspergillus Fumigatus ΔsglA Conidia Fully Protects Immunocompromised Mice from Invasive Aspergillosis. mBio 2022, 13, e0232822. [Google Scholar] [CrossRef] [PubMed]
  45. Slarve, M.; Holznecht, N.; Reza, H.; Gilkes, A.; Slarve, I.; Olson, J.; Ernst, W.; Ho, S.O.; Adler-Moore, J.; Fujii, G. Recombinant Aspergillus Fumigatus Antigens Asp f 3 and Asp f 9 in Liposomal Vaccine Protect Mice against Invasive Pulmonary Aspergillosis. Vaccine 2022, 40, 4160–4168. [Google Scholar] [CrossRef] [PubMed]
  46. De Almeida, J.R.F.; Jannuzzi, G.P.; Kaihami, G.H.; Breda, L.C.D.; Ferreira, K.S.; De Almeida, S.R. An Immunoproteomic Approach Revealing Peptides from Sporothrix Brasiliensis That Induce a Cellular Immune Response in Subcutaneous Sporotrichosis. Sci. Rep. 2018, 8, 4912. [Google Scholar] [CrossRef]
  47. Pirofski, L.A.; Casadevall, A. Use of Licensed Vaccines for Active Immunization of the Immunocompromised Host. Clin. Microbiol. Rev. 1998, 11, 1–26. [Google Scholar] [CrossRef]
  48. Santos, E.; Levitz, S.M. Fungal Vaccines and Immunotherapeutics. Cold Spring Harb. Perspect. Med. 2014, 4, 1–14. [Google Scholar] [CrossRef]
  49. Wang, B.; Norbury, C.C.; Greenwood, R.; Bennink, J.R.; Yewdell, J.W.; Frelinger, J.A. Multiple Paths for Activation of Naive CD8+ T Cells: CD4-Independent Help. J. Immunol. 2001, 167, 1283–1289. [Google Scholar] [CrossRef]
  50. Scheckelhoff, M.; Deepe, G.S. The Protective Immune Response to Heat Shock Protein 60 of Histoplasma Capsulatum Is Mediated by a Subset of Vβ8.1/8.2 + T Cells. J. Immunol. 2002, 169, 5818–5826. [Google Scholar] [CrossRef]
  51. Stuehler, C.; Khanna, N.; Bozza, S.; Zelante, T.; Moretti, S.; Kruhm, M.; Lurati, S.; Conrad, B.; Worschech, E.; Stevanović, S.; et al. Cross-Protective TH1 Immunity against Aspergillus Fumigatus and Candida Albicans. Blood 2011, 117, 5881–5891. [Google Scholar] [CrossRef] [PubMed]
  52. Ito, J.I.; Lyons, J.M.; Hong, T.B.; Tamae, D.; Liu, Y.K.; Wilczynski, S.P.; Kalkum, M. Vaccinations with Recombinant Variants of Aspergillus Fumigatus Allergen Asp f 3 Protect Mice against Invasive Aspergillosis. Infect. Immun. 2006, 74, 5075–5084. [Google Scholar] [CrossRef] [PubMed]
  53. Bozza, S.; Clavaud, C.; Giovannini, G.; Fontaine, T.; Beauvais, A.; Sarfati, J.; D’Angelo, C.; Perruccio, K.; Bonifazi, P.; Zagarella, S.; et al. Immune Sensing of Aspergillus Fumigatus Proteins, Glycolipids, and Polysaccharides and the Impact on Th Immunity and Vaccination. J. Immunol. 2009, 183, 2407–2414. [Google Scholar] [CrossRef] [PubMed]
  54. Specht, C.A.; Nong, S.; Dan, J.M.; Lee, C.K.; Levitz, S.M. Contribution of Glycosylation to T Cell Responses Stimulated by Recombinant Cryptococcus Neoformans Mannoprotein. J. Infect. Dis. 2007, 196, 796–800. [Google Scholar] [CrossRef] [PubMed]
  55. Specht, C.A.; Lee, C.K.; Huang, H.; Hester, M.M.; Liu, J.; Luckie, B.A.; Torres Santana, M.A.; Mirza, Z.; Khoshkenar, P.; Abraham, A.; et al. Vaccination with Recombinant Cryptococcus Proteins in Glucan Particles Protects Mice against Cryptococcosis in a Manner Dependent upon Mouse Strain and Cryptococcal Species. mBio 2017, 8, 1–14. [Google Scholar] [CrossRef] [PubMed]
  56. Abuodeh, R.O.; Shubitz, L.F.; Siegel, E.; Snyder, S.; Peng, T.; Orsborn, K.I.; Brummer, E.; Stevens, D.A.; Galgiani, J.N. Resistance to Coccidioides Immitis in Mice after Immunization with Recombinant Protein or a DNA Vaccine of a Proline-Rich Antigen. Infect. Immun. 1999, 67, 2935–2940. [Google Scholar] [CrossRef]
  57. Hurtgen, B.J.; Hung, C.Y.; Ostroff, G.R.; Levitz, S.M.; Cole, G.T. Construction and Evaluation of a Novel Recombinant T Cell Epitope-Based Vaccine against Coccidioidomycosis. Infect. Immun. 2012, 80, 3960–3974. [Google Scholar] [CrossRef]
  58. Orsborn, K.I.; Shubitz, L.F.; Peng, T.; Kellner, E.M.; Orbach, M.J.; Haynes, P.A.; Galgiani, J.N. Protein Expression Profiling of Coccidioides Posadasii by Two-Dimensional Differential in-Gel Electrophoresis and Evaluation of a Newly Recognized Peroxisomal Matrix Protein as a Recombinant Vaccine Candidate. Infect. Immun. 2006, 74, 1865–1872. [Google Scholar] [CrossRef]
  59. Shubitz, L.F.; Yu, J.J.; Hung, C.Y.; Kirkland, T.N.; Peng, T.; Perrill, R.; Simons, J.; Xue, J.; Herr, R.A.; Cole, G.T.; et al. Improved Protection of Mice against Lethal Respiratory Infection with Coccidioides Posadasii Using Two Recombinant Antigens Expressed as a Single Protein. Vaccine 2006, 24, 5904–5911. [Google Scholar] [CrossRef]
  60. Tarcha, E.J.; Basrur, V.; Hung, C.Y.; Gardner, M.J.; Cole, G.T. A Recombinant Aspartyl Protease of Coccidioides Posadasii Induces Protection against Pulmonary Coccidioidomycosis in Mice. Infect. Immun. 2006, 74, 516–527. [Google Scholar] [CrossRef]
  61. Ibrahim, A.S.; Luo, G.; Gebremariam, T.; Lee, H.; Schmidt, C.S.; Hennessey, J.P.; French, S.W.; Yeaman, M.R.; Filler, S.G.; Edwards, J.E. NDV-3 Protects Mice from Vulvovaginal Candidiasis through T- and B-Cell Immune Response. Vaccine 2013, 31, 5549–5556. [Google Scholar] [CrossRef] [PubMed]
  62. Baquir, B.; Lin, L.; Ibrahim, A.S.; Fu, Y.; Avanesian, V.; Tu, A.; Edwards, J., Jr.; Spellberg, B. Immunological Reactivity of Blood from Healthy Humans to the RAls3p-N Vaccine Protein. J. Infect. Dis. 2010, 201, 473–477. [Google Scholar] [CrossRef]
  63. Li, W.Q.; Hu, X.C.; Zhang, X.; Ge, Y.; Zhao, S.; Hu, Y.; Ashman, R.B. Immunisation with the Glycolytic Enzyme Enolase Confers Effective Protection against Candida Albicans Infection in Mice. Vaccine 2011, 29, 5526–5533. [Google Scholar] [CrossRef]
  64. Nami, S.; Mohammadi, R.; Vakili, M.; Khezripour, K.; Mirzaei, H.; Morovati, H. Fungal Vaccines, Mechanism of Actions and Immunology: A Comprehensive Review. Biomed. Pharmacother. 2019, 109, 333–344. [Google Scholar] [CrossRef] [PubMed]
  65. Chaudhuri, R.; Ramachandran, S. Immunoinformatics as a Tool for New Antifungal Vaccines. Methods Mol. Biol. 2017, 1625, 31–43. [Google Scholar] [CrossRef] [PubMed]
  66. Lin, L.; Ibrahim, A.S.; Xu, X.; Farber, J.M.; Avanesian, V.; Baquir, B.; Fu, Y.; French, S.W.; Edwards, J.E.; Spellberg, B. Th1-Th17 Cells Mediate Protective Adaptive Immunity against Staphylococcus Aureus and Candida Albicans Infection in Mice. PLoS Pathog. 2009, 5. [Google Scholar] [CrossRef]
  67. Spellberg, B.; Ibrahim, A.S.; Yeaman, M.R.; Lin, L.; Fu, Y.; Avanesian, V.; Bayer, A.S.; Filler, S.G.; Lipke, P.; Otoo, H.; et al. The Antifungal Vaccine Derived from the Recombinant N Terminus of Als3p Protects Mice against the Bacterium Staphylococcus Aureus. Infect. Immun. 2008, 76, 4574–4580. [Google Scholar] [CrossRef]
  68. Edwards, J.E.; Schwartz, M.M.; Schmidt, C.S.; Sobel, J.D.; Nyirjesy, P.; Schodel, F.; Marchus, E.; Lizakowski, M.; Demontigny, E.A.; Hoeg, J.; et al. A Fungal Immunotherapeutic Vaccine (NDV-3A) for Treatment of Recurrent Vulvovaginal Candidiasis-A Phase 2 Randomized, Double-Blind, Placebo-Controlled Trial. Clin. Infect. Dis. 2018, 66, 1928–1936. [Google Scholar] [CrossRef]
  69. Alqarihi, A.; Singh, S.; Edwards, J.E.; Ibrahim, A.S.; Uppuluri, P. NDV-3A Vaccination Prevents C. Albicans Colonization of Jugular Vein Catheters in Mice. Sci. Rep. 2019, 9, 6194. [Google Scholar] [CrossRef]
  70. NCT. Safety and Immunogenicity Study of a Virosomal Vaccine Against Recurrent Vulvovaginal Candida Infection. 2010. Available online: https://clinicaltrials.gov/show/NCT01067131 (accessed on 18 May 2023).
  71. Luo, G.; Ibrahim, A.S.; Spellberg, B.; Nobile, C.J.; Mitchell, A.P.; Fu, Y. Candida Albicans Hyr1p Confers Resistance to Neutrophil Killing and Is a Potential Vaccine Target. J. Infect. Dis. 2010, 201, 1718–1728. [Google Scholar] [CrossRef]
  72. Nitz, M.; Ling, C.C.; Otter, A.; Cutler, J.E.; Bundle, D.R. The Unique Solution Structure and Immunochemistry of the Candida Albicans β-1,2-Mannopyranan Cell Wall Antigens. J. Biol. Chem. 2002, 277, 3440–3446. [Google Scholar] [CrossRef] [PubMed]
  73. De Bernardis, F.; Graziani, S.; Tirelli, F.; Antonopoulou, S. Candida Vaginitis: Virulence, Host Response and Vaccine Prospects. Med. Mycol. 2018, 56, S26–S31. [Google Scholar] [CrossRef] [PubMed]
  74. Wang, Y.; Wang, L. Vaccination with Phage-Displayed Antigenic Epitope. Methods Mol. Biol. 2017, 1625, 225–235. [Google Scholar] [CrossRef] [PubMed]
  75. Adams, A.L.; Eberle, K.; Colón, J.R.; Courville, E.; Xin, H. Synthetic Conjugate Peptide Fba-Met6 (MP12) Induces Complement-Mediated Resistance against Disseminated Candida Albicans. Vaccine 2021, 39, 4099–4107. [Google Scholar] [CrossRef]
  76. Rappuoli, R.; Bottomley, M.J.; D’Oro, U.; Finco, O.; De Gregorio, E. Reverse Vaccinology 2.0: Human Immunology Instructs Vaccine Antigen Design. J. Exp. Med. 2016, 213, 469–481. [Google Scholar] [CrossRef]
  77. Tarang, S.; Kesherwani, V.; LaTendresse, B.; Lindgren, L.; Rocha-Sanchez, S.M.; Weston, M.D. In Silico Design of a Multivalent Vaccine Against Candida Albicans. Sci. Rep. 2020, 10, 1066. [Google Scholar] [CrossRef]
  78. Pizza, M.; Scarlato, V.; Masignani, V.; Giuliani, M.M.; Arico, B.; Comanducci, M.; Jennings, G.T.; Baldi, L.; Bartolini, E.; Capecchi, B.; et al. Identification of Vaccine Candidates against Serogroup B Meningococcus by Whole-Genome Sequencing Identification of Vaccine Candidates Against Serogroup B Meningococcus by Whole-Genome Sequencing. Science (1979) 2000, 287, 1816–1820. [Google Scholar]
  79. Karch, C.P.; Burkhard, P. Vaccine Technologies: From Whole Organisms to Rationally Designed Protein Assemblies. Biochem. Pharmacol. 2016, 120, 1–14. [Google Scholar] [CrossRef]
  80. Pietrella, D.; Rachini, A.; Torosantucci, A.; Chiani, P.; Brown, A.J.P.; Bistoni, F.; Costantino, P.; Mosci, P.; D’Enfert, C.; Rappuoli, R.; et al. A β-Glucan-Conjugate Vaccine and Anti-β-Glucan Antibodies Are Effective against Murine Vaginal Candidiasis as Assessed by a Novel in Vivo Imaging Technique. Vaccine 2010, 28, 1717–1725. [Google Scholar] [CrossRef]
  81. Guazzelli, L.; Crawford, C.J.; Ulc, R.; Bowen, A.; McCabe, O.; Jedlicka, A.J.; Wear, M.P.; Casadevall, A.; Oscarson, S. A Synthetic Glycan Array Containing Cryptococcus Neoformans Glucuronoxylomannan Capsular Polysaccharide Fragments Allows the Mapping of Protective Epitopes. Chem. Sci. 2020, 11, 9209–9217. [Google Scholar] [CrossRef]
  82. Han, Y.; Ulrich, M.A.; Cutler, J.E. Candida Albicans Mannan Extract–Protein Conjugates Induce a Protective Immune Response against Experimental Candidiasis. J. Infect. Dis. 1999, 179, 1477–1484. [Google Scholar] [CrossRef] [PubMed]
  83. Xin, H.; Glee, P.; Adams, A.; Mohiuddin, F.; Eberle, K. Design of a Mimotope-Peptide Based Double Epitope Vaccine against Disseminated Candidiasis. Vaccine 2019, 37, 2430–2438. [Google Scholar] [CrossRef] [PubMed]
  84. Clemons, K.V.; Danielson, M.E.; Michel, K.S.; Liu, M.; Ottoson, N.C.; Leonardo, S.M.; Martinez, M.; Chen, V.; Antonysamy, M.A.; Stevens, D.A. Whole Glucan Particles as a Vaccine against Murine Aspergillosis. J. Med. Microbiol. 2014, 63, 1750–1759. [Google Scholar] [CrossRef] [PubMed]
  85. Paulovičová, E.; Machová, E.; Tulinská, J.; Bystrický, S. Cell and Antibody Mediated Immunity Induced by Vaccination with Novel Candida Dubliniensis Mannan Immunogenic Conjugate. Int. Immunopharmacol. 2007, 7, 1325–1333. [Google Scholar] [CrossRef] [PubMed]
  86. Xin, H.; Dziadek, S.; Bundle, D.R.; Cutler, J.E. Synthetic Glycopeptide Vaccines Combining β-Mannan and Peptide Epitopes Induce Protection against Candidiasis. Proc. Natl. Acad. Sci. USA 2008, 105, 13526–13531. [Google Scholar] [CrossRef]
  87. Rivera, A.; Hohl, T.M. Calnexin Bridges the Gap toward a Pan-Fungal Vaccine. Cell Host Microbe 2015, 17, 421–423. [Google Scholar] [CrossRef]
  88. Torosantucci, A.; Bromuro, C.; Chiani, P.; De Bernardis, F.; Berti, F.; Galli, C.; Norelli, F.; Bellucci, C.; Polonelli, L.; Costantino, P.; et al. A Novel Glyco-Conjugate Vaccine against Fungal Pathogens. J. Exp. Med. 2005, 202, 597–606. [Google Scholar] [CrossRef]
  89. Wüthrich, M.; Gern, B.; Hung, C.Y.; Ersland, K.; Rocco, N.; Pick-Jacobs, J.; Galles, K.; Filutowicz, H.; Warner, T.; Evans, M.; et al. Vaccine-Induced Protection against 3 Systemic Mycoses Endemic to North America Requires Th17 Cells in Mice. J. Clin. Investig. 2011, 121, 554–568. [Google Scholar] [CrossRef]
  90. Wüthrich, M.; Brandhorst, T.T.; Sullivan, T.D.; Filutowicz, H.; Sterkel, A.; Stewart, D.; Li, M.; Lerksuthirat, T.; Lebert, V.; Shen, Z.T.; et al. Calnexin Induces Expansion of Antigen-Specific CD4+ T Cells That Confer Immunity to Fungal Ascomycetes via Conserved Epitopes. Cell Host Microbe 2015, 17, 452–465. [Google Scholar] [CrossRef]
  91. Rayens, E.; Rabacal, W.; Willems, H.M.E.; Kirton, G.M.; Barber, J.P.; Mousa, J.J.; Celia-Sanchez, B.N.; Momany, M.; Norris, K.A. Immunogenicity and Protective Efficacy of a Pan-Fungal Vaccine in Preclinical Models of Aspergillosis, Candidiasis, and Pneumocystosis. PNAS Nexus 2022, 1, pgac248. [Google Scholar] [CrossRef]
  92. Lee, J.; Arun Kumar, S.; Jhan, Y.Y.; Bishop, C.J. Engineering DNA Vaccines against Infectious Diseases. Acta Biomater. 2018, 80, 31–47. [Google Scholar] [CrossRef] [PubMed]
  93. de Amorim, J.; Magalhães, A.; Muñoz, J.E.; Rittner, G.M.G.; Nosanchuk, J.D.; Travassos, L.R.; Taborda, C.P. DNA Vaccine Encoding Peptide P10 against Experimental Paracoccidioidomycosis Induces Long-Term Protection in Presence of Regulatory T Cells. Microbes Infect. 2013, 15, 181–191. [Google Scholar] [CrossRef]
  94. Ivey, F.D.; Magee, D.M.; Woitaske, M.D.; Johnston, S.A.; Cox, R.A. Identification of a Protective Antigen of Coccidioides Immitis by Expression Library Immunization. Vaccine 2003, 21, 4359–4367. [Google Scholar] [CrossRef] [PubMed]
  95. Shafaati, M.; Saidijam, M.; Soleimani, M.; Hazrati, F.; Mirzaei, R.; Amirheidari, B.; Tanzadehpanah, H.; Karampoor, S.; Kazemi, S.; Yavari, B.; et al. A Brief Review on DNA Vaccines in the Era of COVID-19. Future Virol. 2022, 17, 49–66. [Google Scholar] [CrossRef] [PubMed]
  96. Faiolla, R.C.L.; Coelho, M.C.; de Santana, R.C.; Martinez, R. Histoplasmosis in Immunocompetent Individuals Living in an Endemic Area in the Brazilian Southeast. Rev. Soc. Bras. Med. Trop. 2013, 46, 461–465. [Google Scholar] [CrossRef]
  97. Fox, J. Gene Therapy Safety Issues Come to Fore. Nat. Biotechnol. 1999, 17, 1153. [Google Scholar] [CrossRef]
  98. Gow, N.A.R.; Netea, M.G. Medical Mycology and Fungal Immunology: New Research Perspectives Addressing a Major World Health Challenge. Philos. Trans. R. Soc. B Biol. Sci. 2016, 371, 20150462. [Google Scholar] [CrossRef]
  99. Mendes, R.P.; de Cavalcante, R.S.; Marques, S.A.; Marques, M.E.A.; Venturini, J.; Sylvestre, T.F.; Paniago, A.M.M.; Pereira, A.C.; da de Silva, J.F.; Fabro, A.T.; et al. Paracoccidioidomycosis: Current Perspectives from Brazil. Open Microbiol. J. 2017, 11, 224–282. [Google Scholar] [CrossRef]
  100. Bongomin, F. Post-Tuberculosis Chronic Pulmonary Aspergillosis: An Emerging Public Health Concern. PLoS Pathog. 2020, 16, e1008742. [Google Scholar] [CrossRef]
  101. Romani, L. Immunity to Fungal Infections. Nat. Rev. Immunol. 2004, 4, 11–24. [Google Scholar] [CrossRef]
  102. Kurosawa, M.; Yonezumi, M.; Hashino, S.; Tanaka, J.; Nishio, M.; Kaneda, M.; Ota, S.; Koda, K.; Suzuki, N.; Yoshida, M.; et al. Epidemiology and Treatment Outcome of Invasive Fungal Infections in Patients with Hematological Malignancies. Int. J. Hematol. 2012, 96, 748–757. [Google Scholar] [CrossRef] [PubMed]
  103. Bacher, P.; Hohnstein, T.; Beerbaum, E.; Röcker, M.; Blango, M.G.; Kaufmann, S.; Röhmel, J.; Eschenhagen, P.; Grehn, C.; Seidel, K.; et al. Human Anti-Fungal Th17 Immunity and Pathology Rely on Cross-Reactivity against Candida Albicans. Cell 2019, 176, 1340–1355.e15. [Google Scholar] [CrossRef] [PubMed]
  104. Benard, G. An Overview of the Immunopathology of Human Paracoccidioidomycosis. Mycopathologia 2008, 165, 209–221. [Google Scholar] [CrossRef] [PubMed]
  105. Wormley, F.L.; Perfect, J.R.; Steele, C.; Cox, G.M. Protection against Cryptococcosis by Using a Murine Gamma Interferon-Producing Cryptococcus Neoformans Strain. Infect. Immun. 2007, 75, 1453–1462. [Google Scholar] [CrossRef] [PubMed]
  106. Mochon, A.B.; Cutler, J.E. Is a Vaccine Needed against Candida Albicans? Med. Mycol. 2005, 43, 97–115. [Google Scholar] [CrossRef]
  107. Oliveira, L.V.N.; Wang, R.; Specht, C.A.; Levitz, S.M. Vaccines for Human Fungal Diseases: Close but Still a Long Way to Go. NPJ Vaccines 2021, 6, 1–8. [Google Scholar] [CrossRef]
  108. Rabaan, A.A.; Alfaraj, A.H.; Alshengeti, A.; Alawfi, A.; Alwarthan, S.; Alhajri, M.; Al-Najjar, A.H.; Al Fares, M.A.; Najim, M.A.; Almuthree, S.A.; et al. Antibodies to Combat Fungal Infections: Development Strategies and Progress. Microorganisms 2023, 11, 671. [Google Scholar] [CrossRef]
  109. Karwa, R.; Wargo, K.A. Efungumab: A Novel Agent in the Treatment of Invasive Candidiasis. Ann. Pharmacother. 2009, 43, 1818–1823. [Google Scholar] [CrossRef]
  110. Pachl, J.; Svoboda, P.; Jacobs, F.; Vandewoude, K.; van der Hoven, B.; Spronk, P.; Masterson, G.; Malbrain, M.; Aoun, M.; Garbino, J.; et al. A Randomized, Blinded, Multicenter Trial of Lipid-Associated Amphotericin B Alone versus in Combination with an Antibody-Based Inhibitor of Heat Shock Protein 90 in Patients with Invasive Candidiasis. Clin. Infect. Dis. 2006, 42, 1404–1413. [Google Scholar] [CrossRef]
  111. Loreto, É.S.; Tondolo, J.S.M.; Alves, S.H.; Santurio, J.M. Immunotherapy for Fungal Infections. In Immunotherapy—Myths, Reality, Ideas, Future; InTech: Houston, TX, USA, 2017. [Google Scholar]
  112. Omaetxebarria, M.; Moragues, M.; Elguezabal, N.; Rodriguez-Alejandre, A.; Brena, S.; Schneider, J.; Polonelli, L.; Ponton, J. Antifungal and Antitumor Activities of a Monoclonal Antibody Directed Against a Stress Mannoprotein of Candida Albicans. Curr. Mol. Med. 2005, 5, 393–401. [Google Scholar] [CrossRef]
  113. Brena, S.; Omaetxebarría, M.J.; Elguezabal, N.; Cabezas, J.; Moragues, M.D.; Pontón, J. Fungicidal Monoclonal Antibody C7 Binds to Candida Albicans Als3. Infect. Immun. 2007, 75, 3680–3682. [Google Scholar] [CrossRef] [PubMed]
  114. Rodríguez, M.J.; Schneider, J.; Moragues, M.D.; Martínez-Conde, R.; Pontón, J.; Aguirre, J.M. Cross-Reactivity between Candida Albicans and Oral Squamous Cell Carcinoma Revealed by Monoclonal Antibody C7. Anticancer Res. 2007, 27, 3639–3643. [Google Scholar] [PubMed]
  115. Singh, S.; Uppuluri, P.; Mamouei, Z.; Alqarihi, A.; Elhassan, H.; French, S.; Lockhart, S.R.; Chiller, T.; Edwards, J.E.; Ibrahim, A.S. The NDV-3A Vaccine Protects Mice from Multidrug Resistant Candida Auris Infection. PLoS Pathog. 2019, 15. [Google Scholar] [CrossRef] [PubMed]
  116. Matveev, A.L.; Krylov, V.B.; Khlusevich, Y.A.; Baykov, I.K.; Yashunsky, D.V.; Emelyanova, L.A.; Tsvetkov, Y.E.; Karelin, A.A.; Bardashova, A.V.; Wong, S.S.W.; et al. Novel Mouse Monoclonal Antibodies Specifically Recognizing β-(1!3)-D-Glucan Antigen. PLoS ONE 2019, 14. [Google Scholar] [CrossRef]
  117. Boniche, C.; Rossi, S.A.; Kischkel, B.; Barbalho, F.V.; Moura, Á.N.D.; Nosanchuk, J.D.; Travassos, L.R.; Taborda, C.P. Immunotherapy against Systemic Fungal Infections Based on Monoclonal Antibodies. J. Fungi 2020, 6, 1–28. [Google Scholar] [CrossRef] [PubMed]
  118. Brown, G.D.; Denning, D.W.; Gow, N.A.R.R.; Levitz, S.M.; Netea, M.G.; White, T.C. Hidden Killers: Human Fungal Infections. Sci. Transl. Med. 2012, 4, 1–10. [Google Scholar] [CrossRef]
  119. Ramirez-Ortiz, Z.G.; Means, T.K. The Role of Dendritic Cells in the Innate Recognition of Pathogenic Fungi (A. fumigatus, C. neoformans and C. albicans). Virulence 2012, 3, 635–646. [Google Scholar] [CrossRef]
  120. Sabado, R.L.; Balan, S.; Bhardwaj, N. Dendritic Cell-Based Immunotherapy. Cell Res. 2017, 27, 74–95. [Google Scholar] [CrossRef]
  121. Bozza, S.; Perruccio, K.; Montagnoli, C.; Gaziano, R.; Bellocchio, S.; Burchielli, E.; Nkwanyuo, G.; Pitzurra, L.; Velardi, A.; Romani, L. A Dendritic Cell Vaccine against Invasive Aspergillosis in Allogeneic Hematopoietic Transplantation. Blood 2003, 102, 3807–3814. [Google Scholar] [CrossRef]
  122. Bozza, S.; Gaziano, R.; Lipford, G.B.; Montagnoli, C.; Bacci, A.; Di Francesco, P.; Kurup, V.P.; Wagner, H.; Romani, L. Vaccination of Mice against Invasive Aspergillosis with Recombinant Aspergillus Proteins and CpG Oligodeoxynucleotides as Adjuvants. Microbes Infect. 2002, 4, 1281–1290. [Google Scholar] [CrossRef]
  123. Shao, C.; Qu, J.; He, L.; Zhang, Y.; Wang, J.; Zhou, H.; Wang, Y.; Liu, X. Dendritic Cells Transduced with an Adenovirus Vector Encoding Interleukin-12 Are a Potent Vaccine for Invasive Pulmonary Aspergillosis. Genes Immun. 2005, 6, 103–114. [Google Scholar] [CrossRef] [PubMed]
  124. Ueno, K.; Kinjo, Y.; Okubo, Y.; Aki, K.; Urai, M.; Kaneko, Y.; Shimizu, K.; Wang, D.N.; Okawara, A.; Nara, T.; et al. Dendritic Cell-Based Immunization Ameliorates Pulmonary Infection with Highly Virulent Cryptococcus Gattii. Infect. Immun. 2015, 83, 1577–1586. [Google Scholar] [CrossRef] [PubMed]
  125. Magalhães, A.; Ferreira, K.S.; Almeida, S.R.; Nosanchuk, J.D.; Travassos, L.R.; Taborda, C.P. Prophylactic and Therapeutic Vaccination Using Dendritic Cells Primed with Peptide 10 Derived from the 43-Kilodalton Glycoprotein of Paracoccidioides Brasiliensis. Clin. Vaccine Immunol. 2012, 19, 23–29. [Google Scholar] [CrossRef] [PubMed]
  126. Borghi, M.; Renga, G.; Puccetti, M.; Oikonomou, V.; Palmieri, M.; Galosi, C.; Bartoli, A.; Romani, L. Antifungal Th Immunity: Growing up in Family. Front. Immunol. 2014, 5, 506. [Google Scholar] [CrossRef]
  127. Speakman, E.A.; Dambuza, I.M.; Salazar, F.; Brown, G.D. T Cell Antifungal Immunity and the Role of C-Type Lectin Receptors. Trends Immunol. 2020, 41, 61–76. [Google Scholar] [CrossRef] [PubMed]
  128. Kumaresan, P.R.; da Silva, T.A.; Kontoyiannis, D.P. Methods of Controlling Invasive Fungal Infections Using CD8+ T Cells. Front. Immunol. 2018, 8, 1939. [Google Scholar] [CrossRef]
  129. Gilbert, S.C. T-Cell-Inducing Vaccines—What’s the Future. Immunology 2012, 135, 19–26. [Google Scholar] [CrossRef]
  130. Li, D.; Li, X.; Zhou, W.L.; Huang, Y.; Liang, X.; Jiang, L.; Yang, X.; Sun, J.; Li, Z.; Han, W.D.; et al. Genetically Engineered t Cells for Cancer Immunotherapy. Signal Transduct. Target. Ther. 2019, 4, 1–17. [Google Scholar] [CrossRef]
  131. Lum, L.G.; Bollard, C.M. Specific Adoptive T-Cell Therapy for Viral and Fungal Infections. In Management of Infections in the Immunocompromised Host; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 395–411. ISBN 9783319776743. [Google Scholar]
  132. Cenci, E.; Mencacci, A.; Bacci, A.; Bistoni, F.; Kurup, V.P.; Romani, L. T Cell Vaccination in Mice with Invasive Pulmonary Aspergillosis. J. Immunol. 2000, 165, 381–388. [Google Scholar] [CrossRef]
  133. Sun, Z.; Zhu, P.; Li, L.; Wan, Z.; Zhao, Z.; Li, R. Adoptive Immunity Mediated by HLA-A*0201 Restricted Asp F16 Peptides-Specific CD8+ T Cells against Aspergillus Fumigatus Infection. Eur. J. Clin. Microbiol. Infect. Dis. 2012, 31, 3089–3096. [Google Scholar] [CrossRef]
  134. Posch, W.; Steger, M.; Wilflingseder, D.; Lass-Flörl, C. Promising Immunotherapy against Fungal Diseases. Expert Opin. Biol. Ther. 2017, 17, 861–870. [Google Scholar] [CrossRef] [PubMed]
  135. Dias, M.F.; Pereira, A.C.; Pereira, A.; Alves, M.S. The Role of HLA Antigens in the Development of Paracoccidioidomycosis. J. Eur. Acad. Dermatol. Venereol. 2000, 14, 166–171. [Google Scholar] [CrossRef] [PubMed]
  136. Sanchez-Trincado, J.L.; Gomez-Perosanz, M.; Reche, P.A. Fundamentals and Methods for T- and B-Cell Epitope Prediction. J. Immunol. Res. 2017, 2017, 2680160. [Google Scholar] [CrossRef] [PubMed]
  137. Osama, A.; Sati, M.; Abdelmoneim, A.H. Multi-Epitope Peptide Vaccine Prediction for Candida Albicans Targeting Pyruvate Kinase Protein; an Immunoinformatics Approach Vaccines Designing View Project Monosoduim Glutamate Toxicity View Project SEE PROFILE. bioRxiv 2019. [Google Scholar] [CrossRef]
  138. Raoufi, E.; Hemmati, M.; Eftekhari, S.; Khaksaran, K.; Mahmodi, Z.; Farajollahi, M.M.; Mohsenzadegan, M. Epitope Prediction by Novel Immunoinformatics Approach: A State-of-the-Art Review. Int. J. Pept. Res. Ther. 2020, 26, 1155–1163. [Google Scholar] [CrossRef]
  139. Repac, J.; Mandić, M.; Lunić, T.; Božić, B.; Božić Nedeljković, B. Mining the Capacity of Human-Associated Microorganisms to Trigger Rheumatoid Arthritis—A Systematic Immunoinformatics Analysis of T Cell Epitopes. PLoS ONE 2021, 16, e0253918. [Google Scholar] [CrossRef]
  140. Araf, Y.; Moin, A.T.; Timofeev, V.I.; Faruqui, N.A.; Saiara, S.A.; Ahmed, N.; Parvez, M.S.A.; Rahaman, T.I.; Sarkar, B.; Ullah, M.A.; et al. Immunoinformatic Design of a Multivalent Peptide Vaccine Against Mucormycosis: Targeting FTR1 Protein of Major Causative Fungi. Front. Immunol. 2022, 13, 863234. [Google Scholar] [CrossRef]
  141. Goldani, L.Z.; Monteiro, C.M.C.; Donadi, E.A.; Martinez, R.; Voltarelli, J.C. HLA Antigens in Brazilian Patients with Paracoccidioidomycosis. Mycopathologia 1991, 114, 89–91. [Google Scholar] [CrossRef]
  142. de Restrepo, F.M.; Restrepo, M.; Restrepo, A. Blood Groups and HLA Antigens in Paracoccidioidomycosis. Med. Mycol. 1983, 21, 35–39. [Google Scholar] [CrossRef]
  143. González, N.; Albornoz, M.; Ríos, R.; Prado, L. HLA y Paracoccidioidomycosis. Cien. Tecnol. Venez. 1985, 2, 229–234. [Google Scholar]
  144. Lacerda, G.B.; Arce-Gomez, B.; Filho, F.Q.T.; Mycology, V.; Mkdica, M.; Clinicas, H. De Increased Frequency of HLA-B40 in Patients with Paracoccidioidomycosis. Med. Mycol. 1988, 26, 253–256. [Google Scholar] [CrossRef] [PubMed]
  145. Mamoni, R.L.; Blotta, M.H.S.L. Flow-Cytometric Analysis of Cytokine Production in Human Paracoccidioidomycosis. Cytokine 2006, 35, 207–216. [Google Scholar] [CrossRef] [PubMed]
  146. Braley, R.E.; Meredith, T.A.; Aaberg, T.M.; Koethe, S.M.; Witkowski, J.A. The Prevalence of HLA-B7 in Presumed Ocular Histoplasmosis. Am. J. Ophthalmol. 1978, 85, 859–861. [Google Scholar] [CrossRef]
  147. Elhassan, R.M.; Alsony, N.M.; Othman, K.M.; Izz-Aldin, D.T.; Alhaj, T.A.; Ali, A.A.; Abashir, L.A.; Ahmed, O.H.; Hassan, M.A. Epitope-Based Immunoinformatic Approach on Heat Shock 70 KDa Protein Complex of Cryptococcus Neoformans Var. Grubii. J. Immunol. Res. 2021, 2021, 1–16. [Google Scholar] [CrossRef]
  148. Hurtgen, B.J.; Castro-Lopez, N.; Jiménez-Alzate, M.d.P.; Cole, G.T.; Hung, C.-Y. Preclinical Identification of Vaccine Induced Protective Correlates in Human Leukocyte Antigen Expressing Transgenic Mice Infected with Coccidioides Posadasii. Vaccine 2016, 34, 5336–5343. [Google Scholar] [CrossRef] [PubMed]
  149. Campuzano, A.; Devi Pentakota, K.; Liao, Y.-R.; Zhang, H.; Ostroff, G.; Hung, C.-Y. A Recombinant Multivalent Vaccine (RCpa1) Induces Protection for C57BL/6 and HLA Transgenic Mice Against Pulmonary Infection 2 with Both Species of Coccidioides. bioRxiv 2021. [Google Scholar] [CrossRef]
  150. Kurita, N.; Biswas, S.K.; Oarada, M.; Sano, A.; Nishimura, K.; Miyaji, M. Fungistatic and Fungicidal Activities of Murine Polymorphonuclear Leucocytes against Yeast Cells of Paracoccidioides Brasiliensis. Med. Mycol. 1999, 37, 19–24. [Google Scholar] [CrossRef]
  151. Iwai, L.K.; Yoshida, M.; Sidney, J.; Shikanai-Yasuda, M.A.; Goldberg, A.C.; Juliano, M.A.; Hammer, J.; Juliano, L.; Sette, A.; Kalil, J.; et al. In Silico Prediction of Peptides Binding to Multiple HLA-DR Molecules Accurately Identifies Immunodominant Epitopes from Gp43 of Paracoccidioides Brasiliensis Frequently Recognized in Primary Peripheral Blood Mononuclear Cell Responses from Sensitized Ind. Mol. Med. 2003, 9, 209–219. [Google Scholar] [CrossRef]
  152. Iwai, L.K.; Yoshida, M.; Sadahiro, A.; da Silva, W.R.; Marin, M.L.; Goldberg, A.C.; Juliano, M.A.; Juliano, L.; Shikanai-Yasuda, M.A.; Kalil, J.; et al. T-Cell Recognition of Paracoccidioides Brasiliensis Gp43-Derived Peptides in Patients with Paracoccidioidomycosis and Healthy Individuals. Clin. Vaccine Immunol. 2007, 14, 474–476. [Google Scholar] [CrossRef]
  153. Travassos, L.R.; Rodrigues, E.G.; Iwai, L.K.; Taborda, C.P. Attempts at a Peptide Vaccine against Paracoccidioidomycosis, Adjuvant to Chemotherapy. Mycopathologia 2008, 165, 341–352. [Google Scholar] [CrossRef]
  154. de Almeida, S.M.; Rebelatto, C.L.K.; Queiroz-telles, F.; Werneck, L.C.; Monteiro, S.; Almeida, D.; Lu, C.; Queiroz-telles, F.; Cesar, L. Major Histocompatibility Complex and Central Nervous System Involvement by Paracoccidioidomycosis. J. Infect. 2005, 51, 140–143. [Google Scholar] [CrossRef] [PubMed]
  155. Torres, I.; Hernandez, O.; Tamayo, D.; Muñoz, J.F.; García, A.M.; Gómez, B.L.; Restrepo, A.; McEwen, J.G. Paracoccidioides Brasiliensis PbP27 Gene: Knockdown Procedures and Functional Characterization. FEMS Yeast Res. 2014, 14, 270–280. [Google Scholar] [CrossRef] [PubMed]
  156. Dabil, H.; Kaplan, H.J.; Duffy, B.F.; Phelan, D.L.; Mohanakumar, T.; Jaramillo, A. Association of the HLA-DR15/HLA-DQ6 Haplotype with Development of Choroidal Neovascular Lesions in Presumed Ocular Histoplasmosis Syndrome. Hum. Immunol. 2003, 64, 960–964. [Google Scholar] [CrossRef] [PubMed]
  157. Kischkel, B.; Boniche-Alfaro, C.; de Menezes, I.G.; Rossi, S.A.; Angeli, C.B.; de Almeida, S.R.; Palmisano, G.; Lopes-Bezerra, L.; Nosanchuk, J.D.; Taborda, C.P. Immunoproteomic and Immunopeptidomic Analyses of Histoplasma Capsulatum Reveal Promiscuous and Conserved Epitopes Among Fungi With Vaccine Potential. Front. Immunol. 2021, 12, 764501. [Google Scholar] [CrossRef] [PubMed]
  158. Allendoerfer, R.; Maresca, B.; Deepe, G.S. Cellular Immune Responses to Recombinant Heat Shock Protein 70 from Histoplasma Capsulatum. Infect. Immun. 1996, 64, 4123–4128. [Google Scholar] [CrossRef] [PubMed]
  159. Leopold Wager, C.M.; Wormley, F.L. Is Development of a Vaccine against Cryptococcus Neoformans Feasible? PLoS Pathog. 2015, 11, e1004843. [Google Scholar] [CrossRef]
  160. Datta, K.; Pirofski, L. Towards a Vaccine for Cryptococcus Neoformans: Principles and Caveats. FEMS Yeast Res. 2006, 6, 525–536. [Google Scholar] [CrossRef]
  161. Elhassan, R.M.; Alsony, N.M.; Othman, K.M.; Izz-Aldin, D.T.; Alhaj, T.A.; Ali, A.A.; Abashir, L.A.; Ahmed, O.H.; Hassan, M.A. Computational Vaccinology Approach: Designing an Efficient Multi-Epitope Peptide Vaccine against Cryptococcus Neoformans Var. Grubii’s Heat Shock 70KDa Protein. bioRxiv 2019. [Google Scholar] [CrossRef]
  162. Williams, P.L.; Sable, D.L.; Sorgen, S.P.; Pappagianis, D.; Levine, H.B.; Brodine, S.K.; Brown, B.W.; Grumet, F.C.; Stevens, D.A. Immunologic Responsiveness and Safety Associated with the Coccidioides Immitis Spherule Vaccine in Volunteers of White, Black, and Filipino Ancestry. Am. J. Epidemiol. 1984, 119, 591–602. [Google Scholar] [CrossRef]
  163. Soleymani, S.; Tavassoli, A.; Housaindokht, M.R. An Overview of Progress from Empirical to Rational Design in Modern Vaccine Development, with an Emphasis on Computational Tools and Immunoinformatics Approaches. Comput. Biol. Med. 2021, 140, 105057. [Google Scholar] [CrossRef]
  164. Bonilla, F.A.; Boston, P. Update: Vaccines in Primary Immunodeficiency. J. Allergy Clin. Immunol. 2018, 141, 474–481. [Google Scholar] [CrossRef] [PubMed]
  165. Almeida, F.; Rodrigues, M.L.; Coelho, C. The Still Underestimated Problem of Fungal Diseases Worldwide. Front. Microbiol. 2019, 10, 214. [Google Scholar] [CrossRef] [PubMed]
  166. Akhtar, N.; Singh, A.; Upadhyay, A.K.; Mannan, M.A.-u. Design of a Multi-Epitope Vaccine against the Pathogenic Fungi Candida Tropicalis Using an in Silico Approach. J. Genet. Eng. Biotechnol. 2022, 20, 140. [Google Scholar] [CrossRef]
  167. World Health Organization. WHO Fungal Priority Pathogens List to Guide Research, Development and Public Health Action; World Health Organization: Geneva, Switzerland, 2022; Volume 1, ISBN 9789240060241. [Google Scholar]
  168. Chaudhuri, R.; Ansari, F.A.; Raghunandanan, M.V.; Ramachandran, S. FungalRV: Adhesin Prediction and Immunoinformatics Portal for Human Fungal Pathogens. BMC Genom. 2011, 12, 192. [Google Scholar] [CrossRef] [PubMed]
  169. Almeida, P.C.S.; Roque, B.S.; Felice, A.G.; Jaiswal, A.K.; Tiwari, S.; Azevedo, V.; Silva-Vergara, M.L.; de Castro Soares, S.; Ferreira-Paim, K.; Fonseca, F.M. Comparative Genomics of Histoplasma Capsulatum and Prediction of New Vaccines and Drug Targets. J. Fungi 2023, 9, 193. [Google Scholar] [CrossRef] [PubMed]
  170. Gupta, S.K.; Osmanoglu, Ö.; Minocha, R.; Bandi, S.R.; Bencurova, E.; Srivastava, M.; Dandekar, T. Genome-Wide Scan for Potential CD4+ T-Cell Vaccine Candidates in Candida Auris by Exploiting Reverse Vaccinology and Evolutionary Information. Front. Med. 2022, 9, 1008527. [Google Scholar] [CrossRef]
  171. Pritam, M.; Singh, G.; Kumar, R.; Singh, S.P. Screening of Potential Antigens from Whole Proteome and Development of Multi-Epitope Vaccine against Rhizopus Delemar Using Immunoinformatics Approaches. J. Biomol. Struct. Dyn. 2023, 41, 2118–2145. [Google Scholar] [CrossRef]
  172. Portuondo, D.L.; Batista-Duharte, A.; Cardenas, C.; de Oliveira, C.S.; Borges, J.C.; Téllez-Martínez, D.; Santana, P.A.; Gauna, A.; Mercado, L.; Mateus de Castilho, B.; et al. A Sporothrix Spp. Enolase Derived Multi-Epitope Vaccine Confers Protective Response in BALB/c Mice Challenged with Sporothrix Brasiliensis. Microb. Pathog. 2022, 166, 105539. [Google Scholar] [CrossRef]
  173. Elamin Elhasan, L.M.; Hassan, M.B.; Elhassan, R.M.; Abdelrhman, F.A.; Salih, E.A.; Ibrahim, H.A.; Mohamed, A.A.; Osman, H.S.; Khalil, M.S.M.; Alsafi, A.A.; et al. Epitope-Based Peptide Vaccine Design against Fructose Bisphosphate Aldolase of Candida Glabrata: An Immunoinformatics Approach. J. Immunol. Res. 2021, 2021, 1–19. [Google Scholar] [CrossRef]
  174. Akhtar, N.; Magdaleno, J.S.L.; Ranjan, S.; Wani, A.K.; Grewal, R.K.; Oliva, R.; Shaikh, A.R.; Cavallo, L.; Chawla, M. Secreted Aspartyl Proteinases Targeted Multi-Epitope Vaccine Design for Candida Dubliniensis Using Immunoinformatics. Vaccines 2023, 11, 364. [Google Scholar] [CrossRef]
  175. Kamli, M.R.; Sabir, J.S.M.; Malik, M.A.; Ahmad, A. Characterization of the Secretome of Pathogenic Candida Glabrata and Their Effectiveness against Systemic Candidiasis in BALB/c Mice for Vaccine Development. Pharmaceutics 2022, 14, 1989. [Google Scholar] [CrossRef] [PubMed]
  176. Soltan, M.A.; Eldeen, M.A.; Elbassiouny, N.; Kamel, H.L.; Abdelraheem, K.M.; El-Gayyed, H.A.; Gouda, A.M.; Sheha, M.F.; Fayad, E.; Ali, O.A.A.; et al. In Silico Designing of a Multitope Vaccine against Rhizopus Microsporus with Potential Activity against Other Mucormycosis Causing Fungi. Cells 2021, 10, 3014. [Google Scholar] [CrossRef] [PubMed]
  177. Khan, T.; Suleman, M.; Ali, S.S.; Sarwar, M.F.; Ali, I.; Ali, L.; Khan, A.; Rokhan, B.; Wang, Y.; Zhao, R.; et al. Subtractive Proteomics Assisted Therapeutic Targets Mining and Designing Ensemble Vaccine against Candida Auris for Immune Response Induction. Comput. Biol. Med. 2022, 145, 105462. [Google Scholar] [CrossRef] [PubMed]
  178. Duraes, F.V.; Niven, J.; Dubrot, J.; Hugues, S.; Gannagé, M. Macroautophagy in Endogenous Processing of Self- and Pathogen-Derived Antigens for MHC Class II Presentation. Front. Immunol. 2015, 6, 459. [Google Scholar] [CrossRef] [PubMed]
  179. Sette, A.; Rappuoli, R. Reverse Vaccinology: Developing Vaccines in the Era of Genomics. Immunity 2010, 33, 530–541. [Google Scholar] [CrossRef]
  180. Vita, R.; Mahajan, S.; Overton, J.A.; Dhanda, S.K.; Martini, S.; Cantrell, J.R.; Wheeler, D.K.; Sette, A.; Peters, B. The Immune Epitope Database (IEDB): 2018 Update. Nucleic Acids Res. 2019, 47, D339–D343. [Google Scholar] [CrossRef]
  181. Paul, S.; Lindestam Arlehamn, C.S.; Scriba, T.J.; Dillon, M.B.C.; Oseroff, C.; Hinz, D.; McKinney, D.M.; Carrasco Pro, S.; Sidney, J.; Peters, B.; et al. Development and Validation of a Broad Scheme for Prediction of HLA Class II Restricted T Cell Epitopes. J. Immunol. Methods 2015, 422, 28–34. [Google Scholar] [CrossRef]
  182. Cezar-dos-Santos, F.; Assolini, J.P.; Okuyama, N.C.M.; Viana, K.F.; de Oliveira, K.B.; Itano, E.N. Unraveling the Susceptibility of Paracoccidioidomycosis: Insights towards the Pathogen-Immune Interplay and Immunogenetics. Infect. Genet. Evol. 2020, 86, 104586. [Google Scholar] [CrossRef]
  183. Kar, P.; Ruiz-Perez, L.; Arooj, M.; Mancera, R.L. Current Methods for the Prediction of T-Cell Epitopes. Pept. Sci. 2018, 110, e24046. [Google Scholar] [CrossRef]
  184. Dhanda, S.K.; Mahajan, S.; Paul, S.; Yan, Z.; Kim, H.; Jespersen, M.C.; Jurtz, V.; Andreatta, M.; Greenbaum, J.A.; Marcatili, P.; et al. IEDB-AR: Immune Epitope Database - Analysis Resource in 2019. Nucleic Acids Res. 2019, 47, W502–W506. [Google Scholar] [CrossRef]
  185. Ansari, H.R.; Raghava, G.P. Identification of Conformational B-Cell Epitopes in an Antigen from Its Primary Sequence. Immunome Res. 2010, 6, 6. [Google Scholar] [CrossRef] [PubMed]
  186. Reynisson, B.; Alvarez, B.; Paul, S.; Peters, B.; Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved Predictions of MHC Antigen Presentation by Concurrent Motif Deconvolution and Integration of MS MHC Eluted Ligand Data. Nucleic Acids Res. 2020, 48, 449–454. [Google Scholar] [CrossRef] [PubMed]
  187. Tripathi, S.; Parmar, J.; Kumar, A. Structure-Based Immunogenicity Prediction of Uricase from Fungal (Aspergillus Flavus), Bacterial (Bacillus Subtillis) and Mammalian Sources Using Immunoinformatic Approach. Protein J. 2020, 39, 133–144. [Google Scholar] [CrossRef] [PubMed]
  188. Bhargav, A.; Fatima, F.; Chaurasia, P.; Seth, S.; Ramachandran, S. Computer-Aided Tools and Resources for Fungal Pathogens: An Application of Reverse Vaccinology for Mucormycosis. Monoclon. Antibodies Immunodiagn. Immunother. 2022, 41, 243–254. [Google Scholar] [CrossRef] [PubMed]
  189. Singh, H.; Raghava, G.P.S. ProPred1: Prediction of Promiscuous MHC Class-I Binding Sites. Bioinformatics 2003, 19, 1009–1014. [Google Scholar] [CrossRef]
  190. Reche, P.A.; Glutting, J.P.; Zhang, H.; Reinherz, E.L. Enhancement to the RANKPEP Resource for the Prediction of Peptide Binding to MHC Molecules Using Profiles. Immunogenetics 2004, 56, 405–419. [Google Scholar] [CrossRef]
  191. Bhasin, M.; Raghava, G.P.S. A Hybrid Approach for Predicting Promiscuous MHC Class I Restricted T Cell Epitopes. J. Biosci. 2007, 32, 31–42. [Google Scholar] [CrossRef]
  192. Specht, C.A.; Homan, E.J.; Lee, C.K.; Mou, Z.; Gomez, C.L.; Hester, M.M.; Abraham, A.; Rus, F.; Ostroff, G.R.; Levitz, S.M. Protection of Mice against Experimental Cryptococcosis by Synthesized Peptides Delivered in Glucan Particles. mBio 2022, 13. [Google Scholar] [CrossRef]
  193. Zhang, J.; Zhao, X.; Sun, P.; Gao, B.; Ma, Z. Conformational B-Cell Epitopes Prediction from Sequences Using Cost-Sensitive Ensemble Classifiers and Spatial Clustering. BioMed Res. Int. 2014, 2014, 689219. [Google Scholar] [CrossRef]
  194. Kalita, P.; Tripathi, T. Methodological Advances in the Design of Peptide-Based Vaccines. Drug Discov. Today 2022, 27, 1367–1380. [Google Scholar] [CrossRef]
  195. Sharma, V.; Singh, S.; Ratnakar, T.S.; Prajapati, V.K. Immunoinformatics and Reverse Vaccinology Methods to Design Peptide-Based Vaccines. In Advances in Protein Molecular and Structural Biology Methods; Academic Press: Cambridge, MA, USA, 2022; pp. 477–487. ISBN 9780323902649. [Google Scholar]
  196. Rodrigues, A.M.; Kubitschek-Barreira, P.H.; Fernandes, G.F.; de Almeida, S.R.; Lopes-Bezerra, L.M.; de Camargo, Z.P. Immunoproteomic Analysis Reveals a Convergent Humoral Response Signature in the Sporothrix Schenckii Complex. J. Proteom. 2015, 115, 8–22. [Google Scholar] [CrossRef] [PubMed]
  197. Martins, L.M.S.; De Andrade, H.M.; Vainstein, M.H.; Wanke, B.; Schrank, A.; Balaguez, C.B.; Dos Santos, P.R.; Santi, L.; Pires, S.D.F.; Da Silva, A.S.; et al. Immunoproteomics and Immunoinformatics Analysis of Cryptococcus Gattii: Novel Candidate Antigens for Diagnosis. Future Microbiol. 2013, 8, 549–563. [Google Scholar] [CrossRef] [PubMed]
  198. Sircar, G.; Jana, K.; Dasgupta, A.; Saha, S.; Bhattacharya, S.G. Epitope Mapping of Rhi o 1 and Generation of a Hypoallergenic Variant. J. Biol. Chem. 2016, 291, 18016–18029. [Google Scholar] [CrossRef]
  199. Moreira, A.L.E.; Oliveira, M.A.P.; Silva, L.O.S.; Inácio, M.M.; Bailão, A.M.; Parente-Rocha, J.A.; Cruz-Leite, V.R.M.; Paccez, J.D.; de Almeida Soares, C.M.; Weber, S.S.; et al. Immunoproteomic Approach of Extracellular Antigens From Paracoccidioides Species Reveals Exclusive B-Cell Epitopes. Front. Microbiol. 2020, 10, 2968. [Google Scholar] [CrossRef] [PubMed]
  200. Rodrigues, A.M.; Kubitschek-Barreira, P.H.; Pinheiro, B.G.; Teixeira-Ferreira, A.; Hahn, R.C.; de Camargo, Z.P. Immunoproteomic Analysis Reveals Novel Candidate Antigens for the Diagnosis of Paracoccidioidomycosis Due to Paracoccidioides Lutzii. J. Fungi 2020, 6, 357. [Google Scholar] [CrossRef]
  201. Kringelum, J.V.; Lundegaard, C.; Lund, O.; Nielsen, M. Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking. PLoS Comput. Biol. 2012, 8, e1002829. [Google Scholar] [CrossRef] [PubMed]
  202. Zhou, C.; Chen, Z.; Zhang, L.; Yan, D.; Mao, T.; Tang, K.; Qiu, T.; Cao, Z. SEPPA 3.0—Enhanced Spatial Epitope Prediction Enabling Glycoprotein Antigens. Nucleic Acids Res. 2019, 47, W388–W394. [Google Scholar] [CrossRef]
  203. Sweredoski, M.J.; Baldi, P. PEPITO: Improved Discontinuous B-Cell Epitope Prediction Using Multiple Distance Thresholds and Half Sphere Exposure. Bioinformatics 2008, 24, 1459–1460. [Google Scholar] [CrossRef]
  204. Ponomarenko, J.; Bui, H.-H.H.; Li, W.; Fusseder, N.; Bourne, P.E.; Sette, A.; Peters, B. ElliPro: A New Structure-Based Tool for the Prediction of Antibody Epitopes. BMC Bioinform. 2008, 9, 514. [Google Scholar] [CrossRef]
  205. Rubinstein, N.D.; Mayrose, I.; Martz, E.; Pupko, T. Epitopia: A Web-Server for Predicting B-Cell Epitopes. BMC Bioinform. 2009, 10, 287. [Google Scholar] [CrossRef]
  206. Xu, X.L.; Sun, J.; Liu, Q.; Wang, X.J.; Xu, T.L.; Zhu, R.X.; Wu, D.; Cao, Z.W. Evaluation of Spatial Epitope Computational Tools Based on Experimentally-Confirmed Dataset for Protein Antigens. Chin. Sci. Bull. 2010, 55, 2169–2174. [Google Scholar] [CrossRef]
  207. El-Manzalawy, Y.; Dobbs, D.; Honavar, V.G. In Silico Prediction of Linear B-Cell Epitopes on Proteins. Methods Mol. Biol. 2017, 1484, 255–264. [Google Scholar] [CrossRef] [PubMed]
  208. Khan, M.A.A.; Ami, J.Q.; Faisal, K.; Chowdhury, R.; Ghosh, P.; Hossain, F.; Abd El Wahed, A.; Mondal, D. An Immunoinformatic Approach Driven by Experimental Proteomics: In Silico Design of a Subunit Candidate Vaccine Targeting Secretory Proteins of Leishmania Donovani Amastigotes. Parasites Vectors 2020, 13, 1–21. [Google Scholar] [CrossRef]
  209. Sanches, R.C.O.; Tiwari, S.; Ferreira, L.C.G.; Oliveira, F.M.; Lopes, M.D.; Passos, M.J.F.; Maia, E.H.B.; Taranto, A.G.; Kato, R.; Azevedo, V.A.C.; et al. Immunoinformatics Design of Multi-Epitope Peptide-Based Vaccine Against Schistosoma Mansoni Using Transmembrane Proteins as a Target. Front. Immunol. 2021, 12, 490. [Google Scholar] [CrossRef] [PubMed]
  210. Vilela Rodrigues, T.C.; Jaiswal, A.K.; Lemes, M.R.; da Silva, M.V.; Sales-Campos, H.; Alcântara, L.C.J.; de Tosta, S.F.O.; Kato, R.B.; Alzahrani, K.J.; Barh, D.; et al. An Immunoinformatics-Based Designed Multi-Epitope Candidate Vaccine (Mpme-VAC/STV-1) against Mycoplasma Pneumoniae. Comput. Biol. Med. 2022, 142, 105194. [Google Scholar] [CrossRef]
  211. Doytchinova, I.A.; Flower, D.R. VaxiJen: A Server for Prediction of Protective Antigens, Tumour Antigens and Subunit Vaccines. BMC Bioinform. 2007, 8, 4. [Google Scholar] [CrossRef]
  212. Vivona, S.; Bernante, F.; Filippini, F. NERVE: New Enhanced Reverse Vaccinology Environment. BMC Biotechnol. 2006, 6, 35. [Google Scholar] [CrossRef]
  213. Xiang, Z.; He, Y. Vaxign: A Web-Based Vaccine Target Design Program for Reverse Vaccinology. Procedia Vaccinol. 2009, 1, 23–29. [Google Scholar] [CrossRef]
  214. Magnan, C.N.; Zeller, M.; Kayala, M.A.; Vigil, A.; Randall, A.; Felgner, P.L.; Baldi, P. High-Throughput Prediction of Protein Antigenicity Using Protein Microarray Data. Bioinformatics 2010, 26, 2936–2943. [Google Scholar] [CrossRef]
  215. Jaiswal, V.; Chanumolu, S.K.; Gupta, A.; Chauhan, R.S.; Rout, C. Jenner-Predict Server: Prediction of Protein Vaccine Candidates (PVCs) in Bacteria Based on Host-Pathogen Interactions. BMC Bioinform. 2013, 14, 211. [Google Scholar] [CrossRef]
  216. Moise, L.; Gutierrez, A.; Kibria, F.; Martin, R.; Tassone, R.; Liu, R.; Terry, F.; Martin, B.; De Groot, A.S. Ivax: An Integrated Toolkit for the Selection and Optimization of Antigens and the Design of Epitope-Driven Vaccines. Hum. Vaccines Immunother. 2015, 11, 2312–2321. [Google Scholar] [CrossRef] [PubMed]
  217. Rizwan, M.; Naz, A.; Ahmad, J.; Naz, K.; Obaid, A.; Parveen, T.; Ahsan, M.; Ali, A. VacSol: A High Throughput in Silico Pipeline to Predict Potential Therapeutic Targets in Prokaryotic Pathogens Using Subtractive Reverse Vaccinology. BMC Bioinform. 2017, 18, 106. [Google Scholar] [CrossRef] [PubMed]
  218. Doytchinova, I. Flower DR Bioinformatic Approach for Identifying Parasite and Fungal Candidate Subunit Vaccines. Open Vaccine J. 2008, 1, 4. [Google Scholar] [CrossRef]
  219. Flower, D.R.; Doytchinova, I.; Zaharieva, N.; Dimitrov, I. Immunogenicity Prediction by VaxiJen: A Ten Year Overview. J. Proteomics Bioinform. 2017, 10, 298–310. [Google Scholar] [CrossRef]
Jof 09 00633 g001 550
Figure 1. Simplified overview of proposed adaptive immune response to pathogenic fungi. Panel (A) illustrates the adaptive immune response to yeast, which necessitates a substantial quantity of the Th1 cell subtype. These cells secrete cytokines, such as IFN-γ, to activate macrophages for phagocytosis, and TNF-α to facilitate granuloma formation, as well as local and systemic inflammatory responses. A regulated response is considered the most effective approach to eliminating pathogenic yeasts. However, the response triggered by the Th17 subtype produces cytokines, such as IL-17, responsible for neutrophil recruitment, and IL-22, which stimulates the recruitment of antigen-presenting cells. During inflammation, the recruitment of neutrophils by Th17 subtypes may cause tissue destruction and aggravate the inflammatory process. Conversely, the response caused by the Th2 subtype results in increased antibody production, which contributes to the opsonization/neutralization of the pathogen. Nevertheless, the efficacy of these functions during pathogenic yeast infections remains undefined. For instance, in individuals with HIV infection, the suppression of CD4+ T lymphocytes leads to the host’s inability to eliminate yeast pathogens. Panel (B) offers a proposed overview of the adaptive immune response to hyphae, spores, and conidia. In this scenario, the Th17 cell subtype is the most indispensable. As previously mentioned, these cells produce IL-17 and IL-22, which prompt neutrophil recruitment to the inflammation site. Consequently, polymorphonuclear cells secrete various fungicide and fungistatic molecules, including neutrophil extracellular traps (NETs), to eradicate the hyphae. In addition, it triggers inflammatory responses and tissue damage. The Th1 subtype response proves less effective due to the hyphae’s considerable size, rendering phagocytosis by activated macrophages an ineffectual process. Instead, a strong local and systemic inflammatory response ensues. The Th2 subtype response is the least effective, leading to a high production of antibodies. For instance, patients with neutropenia exhibit increased susceptibility to infections caused by fungi in the mycelial form. The arrow depicted in the upper part of the figure represents the frequency of the immune response, with larger arrows signifying a higher occurrence.
Figure 1. Simplified overview of proposed adaptive immune response to pathogenic fungi. Panel (A) illustrates the adaptive immune response to yeast, which necessitates a substantial quantity of the Th1 cell subtype. These cells secrete cytokines, such as IFN-γ, to activate macrophages for phagocytosis, and TNF-α to facilitate granuloma formation, as well as local and systemic inflammatory responses. A regulated response is considered the most effective approach to eliminating pathogenic yeasts. However, the response triggered by the Th17 subtype produces cytokines, such as IL-17, responsible for neutrophil recruitment, and IL-22, which stimulates the recruitment of antigen-presenting cells. During inflammation, the recruitment of neutrophils by Th17 subtypes may cause tissue destruction and aggravate the inflammatory process. Conversely, the response caused by the Th2 subtype results in increased antibody production, which contributes to the opsonization/neutralization of the pathogen. Nevertheless, the efficacy of these functions during pathogenic yeast infections remains undefined. For instance, in individuals with HIV infection, the suppression of CD4+ T lymphocytes leads to the host’s inability to eliminate yeast pathogens. Panel (B) offers a proposed overview of the adaptive immune response to hyphae, spores, and conidia. In this scenario, the Th17 cell subtype is the most indispensable. As previously mentioned, these cells produce IL-17 and IL-22, which prompt neutrophil recruitment to the inflammation site. Consequently, polymorphonuclear cells secrete various fungicide and fungistatic molecules, including neutrophil extracellular traps (NETs), to eradicate the hyphae. In addition, it triggers inflammatory responses and tissue damage. The Th1 subtype response proves less effective due to the hyphae’s considerable size, rendering phagocytosis by activated macrophages an ineffectual process. Instead, a strong local and systemic inflammatory response ensues. The Th2 subtype response is the least effective, leading to a high production of antibodies. For instance, patients with neutropenia exhibit increased susceptibility to infections caused by fungi in the mycelial form. The arrow depicted in the upper part of the figure represents the frequency of the immune response, with larger arrows signifying a higher occurrence.
Jof 09 00633 g001
Jof 09 00633 g002 550
Figure 2. Workflow for prediction of targets for vaccine and diagnosis. Obtention of proteomes by FungiDB or Uniprot; location prediction to find secreted protein; B cell epitope prediction—linear and conformational; antigenicity prediction by VaxiJen; T cell epitope prediction—MHC I (Proteasome, TAP and immunogenicity) and MHCI. Literature investigation: epitope refinement (evaluation)—analyses of solubility, position in the 3D structure and epitope conservancy.
Figure 2. Workflow for prediction of targets for vaccine and diagnosis. Obtention of proteomes by FungiDB or Uniprot; location prediction to find secreted protein; B cell epitope prediction—linear and conformational; antigenicity prediction by VaxiJen; T cell epitope prediction—MHC I (Proteasome, TAP and immunogenicity) and MHCI. Literature investigation: epitope refinement (evaluation)—analyses of solubility, position in the 3D structure and epitope conservancy.
Jof 09 00633 g002
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Inácio, M.M.; Moreira, A.L.E.; Cruz-Leite, V.R.M.; Mattos, K.; Silva, L.O.S.; Venturini, J.; Ruiz, O.H.; Ribeiro-Dias, F.; Weber, S.S.; Soares, C.M.d.A.; et al. Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics. J. Fungi 2023, 9, 633. https://0-doi-org.brum.beds.ac.uk/10.3390/jof9060633

AMA Style

Inácio MM, Moreira ALE, Cruz-Leite VRM, Mattos K, Silva LOS, Venturini J, Ruiz OH, Ribeiro-Dias F, Weber SS, Soares CMdA, et al. Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics. Journal of Fungi. 2023; 9(6):633. https://0-doi-org.brum.beds.ac.uk/10.3390/jof9060633

Chicago/Turabian Style

Inácio, Moisés Morais, André Luís Elias Moreira, Vanessa Rafaela Milhomem Cruz-Leite, Karine Mattos, Lana O’Hara Souza Silva, James Venturini, Orville Hernandez Ruiz, Fátima Ribeiro-Dias, Simone Schneider Weber, Célia Maria de Almeida Soares, and et al. 2023. "Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics" Journal of Fungi 9, no. 6: 633. https://0-doi-org.brum.beds.ac.uk/10.3390/jof9060633

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop