Abstract
Background: Molecular mimicry contributes to the development of unwanted responses to self-antigens. Autoimmune phenomena have been observed in diseases caused by Aedes aegypti-transmitted arboviruses, but the occurrence of mimicry between salivary and human proteins has been unexplored. Methods: We used bioinformatic tools to determine if peptides from Aedes aegypti salivary proteins were present in the human proteome. We further characterized the potential of shared sequences to induce immunity by analyzing their predicted binding to MHC molecules and their occurrence in peptides from the Immune Epitope Database (IEDB). Results: We analyzed 9513 octapeptides from 29 Aedes aegypti salivary proteins against the human proteome and found 47 peptides identical to sequences from 52 human proteins, ranging in length from 8 to 18 amino acids. We found 302 matches of peptides predicted to bind with high affinity to MHC-I and MHC-II alleles associated with autoimmune diseases, and 14 human peptides containing shared sequences with Aedes aegypti salivary proteins validated as immunogenic in the IEDB. Conclusions: These results support the existence of molecular mimicry between Aedes aegypti salivary proteins and human antigens and provide a framework for studies to determine its contribution to responses directed to self-antigens in the context of arboviral infections.
    1. Introduction
Aedes aegypti is a mosquito vector responsible for transmitting several arboviruses that cause significant morbidity globally, including dengue virus (DENV), Zika virus (ZIKV), yellow fever virus, and Chikungunya virus (CHIKV) [,]. The diseases caused by these viruses are complex and include severe presentations, the pathophysiology of which is poorly understood [,,,]. In dengue, severe disease is characterized by plasma leakage and bleeding caused by platelet, coagulation, and endothelial cell dysfunction, which are a consequence of an excessive inflammatory response [,,]. Autoimmune phenomena have been proposed as a contributing factor due to the finding in affected patients of DENV-specific antibodies that cross react with endothelial cells, platelets, coagulation factors, and plasminogen that can alter their normal function [,,,,,,,]. DENV infection has also been reported as the trigger for the development of autoimmune pathology, including lupus and neurological disorders [,,,]. Autoimmunity has also been implicated in neurological complications of ZIKV infection, including Guillain-Barré syndrome, transverse myelitis, and dysautonomia [,,]. These observations are strengthened by several case reports that link Guillain-Barré syndrome to DENV [,], CHIKV [], and ZIKV [,,,]. Furthermore, West Nile virus, another vector-borne virus, has also been linked to Guillain-Barré syndrome [,] and autoimmune encephalitis [].
The genesis of autoimmunity remains obscure, but it is accepted that in most cases several genetic and environmental factors must concur to cause the breakdown of tolerance to self-antigens [,,]. Molecular mimicry, the existence of very similar structures in two different organisms capable of igniting a cross-reactive immune response, has been recognized as an important factor in several autoimmune diseases [,,]. Abundant evidence exists for viral and bacterial antigens cross-reacting with autoantigens recognized in human diseases, but only in a few entities, such as rheumatic fever [] and Guillain-Barré syndrome [], is molecular mimicry believed to be the main pathological mechanism. In most cases, its contribution has not been clearly established, but it is believed to be an important trigger. The occurrence of identical amino acid sequences that lead to epitope sharing between microorganisms and humans has been extensively documented for numerous viruses [,,,], bacteria [,,], and parasites [,], as well as their capacity to ignite both humoral and cellular immune responses [,,,]. In severe dengue, mimicry between DENV proteins such as NS1, E, and prM and human antigens expressed in endothelial cells and platelets, as well as coagulation factors, has been documented and proposed as the origin of autoimmune responses that contribute to pathogenesis [,,,,,,,]. Likewise, significant molecular mimicry exists between the ZIKV proteome and human proteins related to neurological structures affected in Zika infection [,].
During arbovirus infection, salivary proteins from the mosquito vector enter the human body and immediately are found in an immunogenic environment promoted by activation of innate immune receptors by viral pathogen-associated molecular patterns (PAMPs) [,,] and effects derived from the mosquito bite, which include skin trauma, salivary factors, and microbiota [,,]. Thus, the potential for them to include shared epitopes with human proteins is relevant, as molecular mimicry may contribute to the autoimmune phenomena associated with Aedes aegypti-transmitted diseases. Although many roles for Aedes aegypti salivary proteins have been described, including modulation of the immune response [,,] and altering of the coagulation and vascular systems [,,,], no studies have addressed the issue of mimicry with human proteins. However, antibodies specific for Aedes salivary proteins have been detected in humans [,,,], thus confirming their immunogenic potential. Interestingly, molecular mimicry between human desmoglein 1 and Lutzomyia longipalpis and Phlebotomus papatasi salivary proteins has been proposed as a mechanism for pemphigus foliaceus [].
Here, we used bioinformatic methods to determine if protein sequences of eight amino acids or more are shared between Aedes aegypti salivary proteins and human proteins. We found 47 shared sequences between 21 Aedes aegypti proteins and 60 human proteins, representing linear epitopes with the potential of activating autoreactive lymphocytes. We further determined the existence of peptides containing these sequences that bind MHC molecules with high affinity and to have been tested for immune reactivity.
2. Materials and Methods
2.1. Determination of Occurrence of Identical Peptide Sequences in Proteins from Aedes aegypti Saliva and Humans
Sequences from 29 proteins expressed in Aedes aegypti saliva [,,,] were retrieved from the National Center for Biotechnology Information (NCBI) protein database (Table 1). The Peptide Library Design Tool from GenScript (https://www.genscript.com/peptide_screening_tools.html (accessed on 3 September 2025)) was used to generate an octapeptide library with an overlap of seven amino acids. The length of eight amino acids was chosen because this is the shortest length that can represent a linear peptide capable of activating T cells, as MHC-I molecules bind peptides from 8 to 15 amino acids in length [], while MHC-II molecules bind peptides from 11 to 30 amino acids in length []. Identical sequences of at least eight amino acids were searched in each of the peptides generated using the NCBI Protein Basic Local Alignment Search Tool (BLAST) (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome (accessed on 3 September 2025)) by entering the sequence for each peptide and running the search for non-redundant protein sequences against the human proteome (Homo sapiens (PARIS) (taxid: 9606)) using the blastp algorithm. Search parameters were adjusted automatically by the search tool to search for a short input sequence. Results were filtered to include only sequences with 100% identity. The octapeptide library with a shift of one amino acid (overlap of seven amino acids) covered the whole sequence of each protein with the maximum amount of octapeptides, ensuring that no identical sequence of at least eight amino acids could be absent from the results. To determine if the identical sequences found were longer than eight amino acids, peptides including the flanking amino acids of each of these sequences were aligned with the corresponding matched human protein using blastp. The E value (number of alignment scores that would be expected to be found by chance with an alignment score equal or better than the observed alignment) was determined for each identified identical sequence within the matched human proteins. When the results showed multiple entries for the same protein (including isoforms) in the human proteome only one was chosen for analysis and the others were registered in an Excel file (Supplementary File S1). Sequences that matched hypothetical or uncharacterized human proteins, as well as variable regions of antigen receptors, were not considered in the results. Post-translational modifications affecting amino acids from each shared sequence were searched for in the UniProt (https://www.uniprot.org/ (accessed on 3 September 2025)) entry of each Aedes aegypti salivary protein and human protein. Each bioinformatic analysis described was run one time and the relevant results were registered in an Excel file (Supplementary File S1).
       
    
    Table 1.
    Aedes aegypti salivary proteins analyzed for matching identical sequences in the human proteome.
  
2.2. Prediction of Peptide Binding Affinity for MHC Molecules
Binding affinity of peptides containing sequences shared between Aedes aegypti salivary proteins and human proteins was predicted using T Cell Prediction—Class I and T Cell Prediction—Class II tools available in the Immune Epitope Database (IEDB) website (https://nextgen-tools.iedb.org/ (accessed on 10 September 2025)). For MHC-II predictions, the identified shared sequences from the salivary proteins were extended on both sides until a peptide of 18 to 19 amino acids was reached. These sequences were entered into the Class II tool with the following parameters: peptide length: 11–16 amino acids; peptide shift length: 1; MHC alleles: a panel of 30 MHC-II alleles associated with autoimmune diseases was selected (Table 2). For DQ genes, alleles are available in the prediction tool only as DQA1/DQB1 combinations. For this reason, the 10 combinations available for each DQA1 allele in Table 2 were selected and a total of 50 HLA-DQA1/HLA-DQB1 alleles were analyzed; prediction model: NetMHCIIpan 4.1 EL. Peptide–MHC predicted interactions with a median binding percentile < 1 were considered of high affinity. For each sequence that had at least one peptide with high binding affinity in the Aedes protein, the same process was carried out for the matching sequence of the human protein. For an Aedes peptide and a human peptide to be considered a match for high-affinity MHC binding the following conditions were required: both had a median binding percentile < 1; their length was equal, they were aligned for the shared sequence, and they had at least seven continuous amino acids from the shared sequence. Each bioinformatic analysis described was run one time and the relevant results were registered in an Excel file (Supplementary File S2).
       
    
    Table 2.
    MHC-II alleles associated with autoimmune diseases analyzed for binding to Aedes aegypti and human peptides with matching sequences.
  
For MHC-I predictions, the identified shared sequences from the salivary proteins were extended on both sides until a peptide of 10 to 11 amino acids was reached. These sequences were entered into the Class I tool with the following parameters: peptide length: 8–9 amino acids; MHC alleles: a panel of 22 MHC-I alleles associated with autoimmune diseases was selected (Table 3); prediction model: NetMHCpan 4.1 EL. Peptide–MHC predicted interactions with a median binding percentile < 1 were considered of high affinity. For each sequence that had at least one peptide with high binding affinity in the Aedes protein, the same process was carried out for the matching sequence of the human protein. For an Aedes peptide and a human peptide to be considered a match for high-affinity MHC binding the following conditions were required: both had a median binding percentile < 1 (meaning that any random peptide would have a 99% probability of having a lower binding affinity to that particular MHC allele); their length was equal, they were aligned for the shared sequence, and they had at least seven continuous amino acids from the shared sequence. Each bioinformatic analysis described was run one time and the relevant results were registered in an Excel file (Supplementary File S3).
       
    
    Table 3.
    MHC-I alleles associated with autoimmune diseases analyzed for binding to Aedes aegypti and human peptides with matching sequences.
  
2.3. Determination of the Presence of Sequences Shared Between Aedes aegypti Salivary Proteins and Human Proteins in Validated Peptides from the Immune Epitope Database
Sequences shared between Aedes Aegypti salivary proteins and human proteins were entered into the Immune Epitope Database (IEDB) search tool (https://www.iedb.org/ (accessed on 15 September 2025)) set to BLAST—70% and restricted to positive assays. Peptides resulting from this search that originated from the same human protein that shared a sequence with the corresponding Aedes aegypti salivary protein were chosen. To identify matched peptides between Aedes aegypti and human proteins, sequences were aligned at the start of the shared sequence present in the IEDB human peptide, and the sequence of the Aedes aegypti protein was extended until a peptide of the same size was obtained. A match was defined by the following conditions: at least 85% identity between the peptides and at least seven continuous amino acids from the identical shared sequence in both peptides. Each bioinformatic analysis described was run one time.
3. Results
3.1. Occurrence of Identical Peptide Sequences in Aedes aegypti Salivary Proteins and Human Proteins
A total of 9513 octapeptides obtained from 29 Aedes aegypti salivary proteins were probed to find identical sequences of at least eight amino acids in the human proteome. The complete results can be found in Supplementary File S1 and are summarized in Table 4 and Table 5. As shown in Table 4, a total of 47 peptides from 21 salivary proteins had 60 identical matches in 52 different human proteins. The length of the matching peptides ranged from 8 to 18 amino acids, with 32 out of the 47 (68.1%) being octamers. Lymphotoxin β receptor inhibitor (LTRIN), aldehyde dehydrogenase, ficolin, angiopoietin, and α-glucosidase were the salivary proteins with most matches, accounting for 28 (59.6%) of the peptides and 40 (66.7%) of the 60 matches found in human proteins.
       
    
    Table 4.
    Summary of identical sequences matches found in Aedes aegypti salivary proteins and human proteins.
  
       
    
    Table 5.
    Peptides that were identical matches in Aedes aegypti salivary proteins and human proteins.
  
The 47 shared peptides with the 60 matching human proteins are shown in Table 5. For the Aedes aegypti salivary protein LTRIN, which is the one with the most matching sequences, eight of the nine peptides are found in two regions of the protein that contain a repetition of glutamine residues (YQQQQQQQPQ, amino acids 32–41; and PQQQQQQHQQP, amino acids 59–69). These peptides include one decamer, two nonamers, and five octamers that match sequences in 17 distinct human proteins. Seven of the identified peptides were matched between the Aedes aegypti protein and its human homolog, including three enzymes (amylase, adenosine deaminase, and aldehyde dehydrogenase) and the innate immune receptor ficolin. Only 2 of the 60 matches (3.3%) contained amino acids that were the site of post-translational modifications in human proteins: sites for lysine ubiquitination in aldehyde dehydrogenase 3 and for threonine phosphorylation in phosphatidylinositol (PI)-binding clathrin assembly protein (Supplementary File S1). No post-translational modifications were found in the shared sequences in Aedes aegypti salivary proteins. In seven instances (five Aedes aegypti salivary proteins and two human proteins) the shared sequences were totally or partially part of the signal sequence of a secreted protein. This does not invalidate their potential for immunogenicity, as signal peptides have been found to be presented in the context of MHC molecules [].
3.2. High-Affinity Binding of Peptides Containing Shared Sequences Between Aedes aegypti Salivary Proteins and Human Proteins to MHC-II Molecules
T cell activation requires the ligation of the T cell receptor (TCR) by a complex formed between a peptide from the immune response-inducing protein and an MHC molecule []. The very high polymorphism of the genes that encode human MHC molecules ensures that virtually any peptide can be presented in this fashion at the population level []. However, the high-affinity binding of peptides to MHC molecules is correlated to higher efficiency for T cell activation [,]. For this reason, we sought to determine if the prediction of peptide binding to MHC-II molecules indicated that the identified shared sequences between Aedes aegypti salivary proteins and human proteins were included in peptides that bind with very high affinity to a panel of 72 MHC-II alleles that have been associated with autoimmunity (Table 2) following the strategy described in the Materials and Methods Section. The results of this analysis are found in Supplementary File S2 and are summarized in Table 6 and Table 7, while Figure 1 shows the representative examples of high affinity binding matching peptides.
       
    
    Table 6.
    Matches of peptides shared between Aedes aegypti and human proteins that bind with high affinity to MHC-II alleles associated with autoimmune diseases.
  
       
    
    Table 7.
    Frequency of matches for MHC-II alleles that bind peptides shared between Aedes aegypti and human proteins with high affinity.
  
      
    
    Figure 1.
      Representative examples of matching peptides from Aedes aegypti salivary proteins and human proteins that contain shared sequences and bind with high affinity to MHC-II alleles associated with autoimmune diseases. Peptides are aligned to the shared sequence. Identical amino acids are shown in red and similar amino acids are shown in blue. Amino acids shown in gray are neither identical nor similar. MBP: mean binding percentile.
  
We found 69 peptides from Aedes aegypti salivary proteins that bound with high affinity (<1 median binding percentile) to at least one of the MHC-II alleles associated with autoimmunity and had a matching peptide in at least one human protein. As shown in Table 6, the matched peptides were from six Aedes aegypti proteins and nine human proteins. A total of 259 matches were found that had high-affinity binding to 46 different MHC-II alleles. Angiopoietin is the Aedes aegypti salivary protein with most matches, which includes 23 different Aedes aegypti peptides matched to peptides from three different human proteins; these matching peptides bound to three MHC-II alleles with high affinity. Likewise, 14 peptides from Aedes aegypti ficolin matched peptides from three human proteins that bind 25 MHC-II alleles for a total of 67 matches. LTRIN, aldehyde dehydrogenase, amylase and venom allergen-5 were the other Aedes aegypti proteins for which matches were found, all for peptides from one human protein each. Since the length of peptides that bind MHC-II is greater than most of the shared peptides identified in our analysis, our strategy included extending these peptides and thus the resulting matches have different sequences. Thus, we only found 19 out of 259 (7.3%) identical matches, 12 of them for the longest sequence (ALVFVDNHDNQRGHGAGG from amylase). However, as shown in Table 5 and illustrated in eight representative examples in Figure 1, 144 of 259 (55.6%) matching peptides had at least 80% identity.
The frequency of matches for the 46 alleles had a wide distribution (range 1 to 58 matches), with the highest frequency observed for the three alleles that mainly bound peptides from angiopoietin (Table 7). In fact, the 11 alleles with the highest frequencies (5 or more matches) accounted for 67.2% of the matches (174 of 259), while the other 35 alleles had four or less matches each and together accounted for 32.8% of the matches (85 of 259). This analysis allows us to conclude that the shared sequences we identified can be presented as peptide–MHC-II complexes in a potentially efficient manner by binding with high affinity, although most of the instances are concentrated in a relatively small proportion of the elements probed (4 mosquito proteins and 11 MHC-II alleles).
3.3. High-Affinity Binding of Peptides Containing Shared Sequences Between Aedes aegypti Salivary Proteins and Human Proteins to MHC-I Molecules
CD8+ T cell responses are also capable of contributing to autoimmune pathology through molecular mimicry [,]. For this reason, we predicted high-affinity binding of the peptides shared by Aedes aegypti salivary proteins and human proteins for 22 MHC-I alleles associated with autoimmunity (Table 3). The results of this analysis are found in Supplementary File S3 and are summarized in Table 8 and Table 9, while Figure 2 shows representative examples of high-affinity-binding matching peptides.
       
    
    Table 8.
    Matches of peptides shared between Aedes aegypti and human proteins that bind with high affinity to MHC-I alleles associated with autoimmune diseases.
  
       
    
    Table 9.
    Frequency of matches for MHC-I alleles that bind peptides shared between Aedes aegypti and human proteins with high affinity.
  
      
    
    Figure 2.
      Representative examples of matching peptides from Aedes aegypti salivary proteins and human proteins that contain shared sequences and bind with high affinity to MHC-I alleles associated with autoimmune diseases. Peptides are aligned to the shared sequence. Identical amino acids are shown in red and similar amino acids are shown in blue. MBP: mean binding percentile.
  
A total of 19 peptides from 10 Aedes aegypti salivary proteins bound with high affinity to MHC-I alleles associated with autoimmunity, matching peptides from 14 human proteins. The highest frequency of matches was found for aldehyde dehydrogenase, which had ten matches involving nine different alleles, while ficolin, angiopoietin, and α-glucosidase had six matches each involving six alleles each (Table 8). Since the length of peptides that bind MHC-I molecules is short, we limited our analysis for octamers and nonamers and extended the octamers by one amino acid. Thus, all matches were either identical or had only one non-identical amino acid, as illustrated in Figure 2 and shown in the total identical matches in Table 8 (22 of 43, 51.2%).
Matches with high-affinity binding were found for 19 of 22 (86.4%) investigated alleles, albeit none of them had more than four matches. As shown in Table 9, HLA-A*02:07, HLA-C*06:02, HLA-C*15:02, and HLA-C*16:01 were the alleles with the most matches. In conclusion, this analysis indicates that peptides from Ae. aegypti salivary proteins that have shared sequences with human proteins can also be presented in the context of MHC-I molecules in a highly stable and immunogenic form, albeit with a lower frequency than that found for MHC-II peptide binding.
3.4. Peptides in the Immune Epitope Database Containing Sequences Shared Between Aedes aegypti Salivary Proteins and Human Proteins
Next, we searched for peptides in the IEDB that originated from the previously identified human proteins that contain sequences shared with Aedes aegypti salivary proteins. We found that 10 of the sequences from 10 different human proteins were included in 14 IEDB-validated peptides that matched an Aedes aegypti salivary protein peptide (matching peptides had ≥85% identity and at least seven continuous amino acids from the shared sequence). Figure 3 shows ten of these peptides aligned with each Aedes aegypti matching peptide. Two of the matching peptides were identical and two others had 100% similarity. Table 10 shows the validated characteristics for each peptide. Interestingly, four peptides that matched with sequences from Aedes aegypti ficolin, D7L1, and LTRIN were validated in the context of autoimmune pathology, highlighting the potential for cross-reactive immune responses.
      
    
    Figure 3.
      Matching peptides from IEDB and Aedes aegypti salivary proteins. Identical amino acid residues are shown in red and similar amino acid residues in blue. Amino acids shown in gray are neither identical nor similar. The sequences RLDGSVDF and GGWTVIQNR had more than one matching peptide from the corresponding human protein; only the one with highest identity is shown.
  
       
    
    Table 10.
    IEDB peptides that contain sequences shared between Aedes aegypti salivary proteins and human proteins.
  
4. Discussion
The fact that the etiology of most autoimmune diseases remains obscure, despite all the accumulated knowledge regarding the immune response, points to multiple significant factors concurring and interacting in a complex fashion [,,]. In this context, the description of new mechanisms for self-antigens to be targeted is a relevant contribution to the advancement of understanding of autoimmunity. The possibility of receiving protein sequences encountered on human proteins in an immunogenic form after a mosquito bite has barely been explored, and thus the bioinformatic approach taken in this present study to prove the existence of shared sequences between Aedes aegypti salivary proteins and human antigens represents the first step toward establishing a novel manner for molecular mimicry to inform autoimmune pathology. By searching for the occurrence of octapeptides derived from the Aedes aegypti salivary proteins in the human proteome, we found 47 shared peptides of 8 to 18 amino acids in length. Our analysis of MHC-binding and validation in the IEDB indicates that several of these linear sequences have the potential of generating ligands for both CD4+ and CD8+ T cells leading to effector mechanisms that would target the human tissues that express the human antigen due to cross-reactivity.
The shared peptides described in this analysis were found in 52 human proteins that encompass a variety of locations and functions, as summarized in Table 11. Self-antigens targeted in autoimmunity are not restricted to any category [,], and therefore it is possible for each of these proteins to contribute to an autoimmune response. For instance, intracellular antigens that are not normally exposed to the immune system can trigger an immune response if liberated because of inadequate clearance of apoptotic cells or tissue damage due to inflammation [,]. In this case, the presence of cross-reactive T cells previously primed by the contact with saliva could thus induce damaging inflammation. As shown in Table 11, we found 30 intracellular proteins, 20 of which had ubiquitous distribution.
       
    
    Table 11.
    Localization and function of human proteins that share peptides with Aedes aegypti salivary proteins.
  
Another important aspect of autoimmune phenomena in the context of infectious diseases is their contribution to exacerbating or inhibiting the host response. Both directions can support the pathogenesis of severe disease in Aedes aegypti-transmitted arboviral infections, as inhibition can result in increased viral loads and excessive inflammation can damage tissues [,]. Any autoimmune response that exacerbates inflammation is a potential contributor to the hyperinflammatory state that characterizes severe presentations of Aedes aegypti-transmitted infections, leading to cytokine storm, vascular leakage, and organ dysfunction [,,], and in some cases neural invasion due to increased permeability of the blood–brain barrier [,]. Thus, an immune response to several membrane and secreted proteins that have a shared sequence with Aedes aegypti salivary proteins and participate in inflammation, immune recognition, wound healing, hemostasis, and cell adhesion (Table 11) could affect the host response.
High-affinity binding of peptides to MHC molecules has been associated with the effectiveness of the immune response [] and is a requisite for MHC–peptide complex stability, which has been shown to be a determinant for immunogenicity [,,]. Our analysis found 259 matching peptides in Aedes aegypti salivary proteins and human proteins that bound to MHC-II alleles associated with autoimmune diseases with very high affinity (mean binding percentile lower than 1) and 43 such matches for MHC-I. Only three Aedes aegypti salivary proteins included peptides with matches for both types of MHC molecules: angiopoietin, ficolin, and aldehyde dehydrogenase. Together, they represented 75.2% (227 of 302) of the matches, indicating that the shared peptides found in these proteins can be stably presented in alleles associated with a variety of autoimmune conditions. Since MHC alleles are considered an important genetic factor in the development of autoimmunity [,,,,], there is a potential for these proteins to contribute to the generation of a pathogenic autoimmune response in susceptible individuals.
One of the limitations of this study is that the analysis used peptides from the reference sequence for each Aedes aegypti salivary protein against the reference human proteome and thus does not account for all possible proteoforms. The existence of proteoforms can undoubtedly affect immunogenicity as fine structural details can alter T cell activation []; accordingly, post-translational modifications have been proven to be a critical factor in the genesis of autoimmunity []. We determined that most of the shared sequences we found are not affected by post-translational modifications, but due to their high complexity, assessing the impact of the existence of proteoforms in the potential of shared sequences to ignite autoimmune responses is not possible with this analysis.
The future directions required to validate molecular mimicry between Aedes aegypti salivary proteins and human proteins as triggers of autoimmune pathology encompass several steps: (1) demonstration of cross-reactive T cells and antibodies; (2) generation of an autoimmune response in an animal model of the infection after administering the antigens containing the shared sequences; and 3) establishing an epidemiological relationship between mosquito bites and autoimmune pathologies [,]. Thus, immunological and epidemiological studies are needed, along with the development of animal models that reflect severe presentations and chronic complications of Aedes aegypti-transmitted infections.
5. Conclusions
Although Aedes aegypti salivary proteins have been characterized as antibody-inducing antigens [,,,] and allergens [,,], their immunogenic potential in the context of autoimmunity has not been explored. Our bioinformatic analysis shows the sharing of identical peptides between Aedes aegypti salivary proteins and human proteins, as well as their potential to be immunogenic by binding with high affinity to MHC molecules. This information can direct further studies to evaluate the impact of autoimmune responses to vector saliva in Aedes aegypti-transmitted infections and can also aid in enhancing the security of salivary protein-based vaccines [,,].
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/proteomes13040056/s1, Supplementary File S1. Identical peptide sequences shared between Aedes aegypti salivary proteins and human proteins. Supplementary File S2. Matching peptides in Aedes aegypti salivary proteins and human proteins binding with high affinity to MHC-II alleles associated to autoimmunity. Supplementary File S3. Matching peptides in Aedes aegypti salivary proteins and human proteins binding with high affinity to MHC-I alleles associated to autoimmunity.
Author Contributions
Conceptualization, A.A.-C. and D.R.-P.; Data curation, A.A.-C. and D.R.-P.; Formal analysis, A.A.-C. and D.R.-P.; Methodology, A.A.-C. and D.R.-P.; Supervision, D.R.-P.; Validation, D.R.-P.; Visualization, D.R.-P.; Writing—original draft, A.A.-C. and D.R.-P.; Writing—review and editing, D.R.-P. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
      
| AaVA-1 | Aedes aegypti venom allergen-1 | 
| AeMOPE | Aedes-specific modulatory peptide | 
| AgBR | Aedes aegypti bacteria-responsive protein | 
| AIH | Autoimmune hepatitis | 
| CD | Crohn’s disease | 
| CHD-1 | Chromodomain-helicase-DNA-binding protein-1 | 
| CHIKV | Chikungunya virus | 
| CLIP | Cytoplasmic linker protein | 
| CNS | Central nervous system | 
| DENV | Dengue virus | 
| ECM | Extracellular matrix | 
| EGFR | Epidermal growth factor receptor | 
| ER | Endoplasmic reticulum | 
| GD | Graves’ disease | 
| GEF | Guanine nucleotide exchange factor | 
| ITP | Immune thrombocytopenic purpura | 
| LTRIN | Lymphotoxin β receptor inhibitor | 
| MG | Myasthenia gravis | 
| MIDEAS | Mitotic deacetylase-associated SANT domain protein | 
| MS | Multiple sclerosis | 
| NeSt | Neutrophil-stimulating factor | 
| PAMP | Pathogen-associated molecular pattern | 
| PE | Phosphatidylethanolamine | 
| PI | Phosphatidylinositol | 
| PR | Polymyalgia rheumatica | 
| RA | Rheumatoid arthritis | 
| RPTP | Receptor-type tyrosine-protein phosphatase | 
| SCAPER | S phase cyclin A-associated protein in the endoplasmic reticulum | 
| SLC3A1 | Solute carrier family 3 member 1 | 
| SLE | Systemic lupus erythematosus | 
| SS | Sjögren’s syndrome | 
| T1D | Type 1 diabetes | 
| TNRC6A | Trinucleotide repeat containing adaptor 6A | 
| UC | Ulcerative colitis | 
| ZIKV | Zika virus | 
References
- Souza-Neto, J.A.; Powell, J.R.; Bonizzoni, M. Aedes aegypti vector competence studies: A review. Infect. Genet. Evol. 2019, 67, 191–209. [Google Scholar] [CrossRef]
 - Alonso-Palomares, L.A.; Moreno-García, M.; Lanz-Mendoza, H.; Salazar, M.I. Molecular Basis for Arbovirus Transmission by Aedes aegypti Mosquitoes. Intervirology 2018, 61, 255–264. [Google Scholar] [CrossRef] [PubMed]
 - Khan, Z.A.; Yadav, M.K.; Lim, D.-W.; Kim, H.; Wang, J.-H.; Ansari, A. Viral-host molecular interactions and metabolic modulation: Strategies to inhibit flaviviruses pathogenesis. World J. Virol. 2024, 13, 99110. [Google Scholar] [CrossRef]
 - Aguilar-Briseño, J.A.; Moser, J.; Rodenhuis-Zybert, I.A. Understanding immunopathology of severe dengue: Lessons learnt from sepsis. Curr. Opin. Virol. 2020, 43, 41–49. [Google Scholar] [CrossRef]
 - Christian, K.M.; Song, H.; Ming, G.-L. Pathophysiology and Mechanisms of Zika Virus Infection in the Nervous System. Annu. Rev. Neurosci. 2019, 42, 249–269. [Google Scholar] [CrossRef] [PubMed]
 - Cherie, T.J.J.; Choong, C.S.H.; Abid, M.B.; Weber, M.W.; Yap, E.S.; Seneviratne, S.L.; Abeysuriya, V.; de Mel, S. Immuno-Haematologic Aspects of Dengue Infection: Biologic Insights and Clinical Implications. Viruses 2024, 16, 1090. [Google Scholar] [CrossRef] [PubMed]
 - Lardo, S.; Soesatyo, M.H.; Juffrie, J.; Umniyati, S.R. The Autoimmune Mechanism in Dengue Hemorrhagic Fever. Acta Med. Indones. 2018, 50, 70–79. Available online: https://pubmed.ncbi.nlm.nih.gov/29686179/ (accessed on 22 July 2025).
 - Martina, B.E.E.; Koraka, P.; Osterhaus, A.D.M.E. Dengue virus pathogenesis: An integrated view. Clin. Microbiol. Rev. 2009, 22, 564–581. [Google Scholar] [CrossRef]
 - Srikiatkhachorn, A.; Mathew, A.; Rothman, A.L. Immune-mediated cytokine storm and its role in severe dengue. Semin. Immunopathol. 2017, 39, 563–574. [Google Scholar] [CrossRef]
 - Lin, C.-F.; Lei, H.-Y.; Shiau, A.-L.; Liu, C.-C.; Liu, H.-S.; Yeh, T.-M.; Chen, S.-H.; Lin, Y.-S. Antibodies from dengue patient sera cross-react with endothelial cells and induce damage. J. Med. Virol. 2003, 69, 82–90. [Google Scholar] [CrossRef]
 - Lin, C.-F.; Lei, H.-Y.; Shiau, A.-L.; Liu, H.-S.; Yeh, T.-M.; Chen, S.-H.; Liu, C.-C.; Chiu, S.-C.; Lin, Y.-S. Endothelial cell apoptosis induced by antibodies against dengue virus nonstructural protein 1 via production of nitric oxide. J. Immunol. 2002, 169, 657–664. [Google Scholar] [CrossRef]
 - Chungue, E.; Poli, L.; Roche, C.; Gestas, P.; Glaziou, P.; Markoff, L.J. Correlation between detection of plasminogen cross-reactive antibodies and hemorrhage in dengue virus infection. J. Infect. Dis. 1994, 170, 1304–1307. [Google Scholar] [CrossRef] [PubMed]
 - Wan, S.-W.; Lin, C.-F.; Yeh, T.-M.; Liu, C.-C.; Liu, H.-S.; Wang, S.; Ling, P.; Anderson, R.; Lei, H.-Y.; Lin, Y.-S. Autoimmunity in dengue pathogenesis. J. Formos. Med. Assoc. 2013, 112, 3–11. [Google Scholar] [CrossRef]
 - Falconar, A.K. The dengue virus nonstructural-1 protein (NS1) generates antibodies to common epitopes on human blood clotting, integrin/adhesin proteins and binds to human endothelial cells: Potential implications in haemorrhagic fever pathogenesis. Arch. Virol. 1997, 142, 897–916. [Google Scholar] [CrossRef]
 - Cheng, H.-J.; Luo, Y.-H.; Wan, S.-W.; Lin, C.-F.; Wang, S.-T.; Hung, N.T.; Liu, C.-C.; Ho, T.-S.; Liu, H.-S.; Yeh, T.-M.; et al. Correlation between serum levels of anti-endothelial cell autoantigen and anti-dengue virus nonstructural protein 1 antibodies in dengue patients. Am. J. Trop. Med. Hyg. 2015, 92, 989–995. [Google Scholar] [CrossRef]
 - Ghorai, T.; Sarkar, A.; Roy, A.; Bhowmick, B.; Nayak, D.; Das, S. Role of auto-antibodies in the mechanisms of dengue pathogenesis and its progression: A comprehensive review. Arch. Microbiol. 2024, 206, 214. [Google Scholar] [CrossRef]
 - Rajadhyaksha, A.; Mehra, S. Dengue fever evolving into systemic lupus erythematosus and lupus nephritis: A case report. Lupus 2012, 21, 999–1002. [Google Scholar] [CrossRef]
 - Jardim, D.L.F.; Tsukumo, D.M.L.; Angerami, R.N.; Carvalho Filho, M.A.; de Saad, M.J.A. Autoimmune features caused by dengue fever: A case report. Braz. J. Infect. Dis. 2012, 16, 92–95. [Google Scholar] [CrossRef]
 - Harris, V.K.; Danda, D.; Murali, N.S.; Das, P.K.; Abraham, M.; Cherian, A.M.; Chandy, M. Unusual association of Kikuchi’s disease and dengue virus infection evolving into systemic lupus erythematosus. J. Indian Med. Assoc. 2000, 98, 391–393. [Google Scholar] [PubMed]
 - Puccioni-Sohler, M.; Ornelas, A.M.M.; de Souza, A.S.; Cabral-Castro, M.J.; Ramos, J.T.M.A.; Rosadas, C.; Salgado, M.C.F.; Castiglione, A.A.; Ferry, F.; Peralta, J.M.; et al. First report of persistent dengue-1-associated autoimmune neurological disturbance: Neuromyelitis optica spectrum disorder. J. Neurovirol. 2017, 23, 768–771. [Google Scholar] [CrossRef] [PubMed]
 - Rivera-Correa, J.; de Siqueira, I.C.; Mota, S.; do Rosário, M.S.; Pereira de Jesus, P.A.; Alcantara, L.C.J.; Ernst, J.D.; Rodriguez, A. Anti-ganglioside antibodies in patients with Zika virus infection-associated Guillain-Barré Syndrome in Brazil. PLoS Negl. Trop. Dis. 2019, 13, e0007695. [Google Scholar] [CrossRef]
 - Acosta-Ampudia, Y.; Monsalve, D.M.; Castillo-Medina, L.F.; Rodríguez, Y.; Pacheco, Y.; Halstead, S.; Willison, H.J.; Anaya, J.-M.; Ramírez-Santana, C. Autoimmune Neurological Conditions Associated with Zika Virus Infection. Front. Mol. Neurosci. 2018, 11, 116. [Google Scholar] [CrossRef]
 - Anaya, J.-M.; Rodríguez, Y.; Monsalve, D.M.; Vega, D.; Ojeda, E.; González-Bravo, D.; Rodríguez-Jiménez, M.; Pinto-Díaz, C.A.; Chaparro, P.; Gunturiz, M.L.; et al. A comprehensive analysis and immunobiology of autoimmune neurological syndromes during the Zika virus outbreak in Cúcuta, Colombia. J. Autoimmun. 2017, 77, 123–138. [Google Scholar] [CrossRef]
 - Payus, A.O.; Ibrahim, A.; Lin, C.L.S.; Jan, T.H. Sensory Predominant Guillain-Barré Syndrome Concomitant with Dengue Infection: A Case Report. Case Rep. Neurol. 2022, 14, 281–285. [Google Scholar] [CrossRef]
 - Dalugama, C.; Shelton, J.; Ekanayake, M.; Gawarammana, I.B. Dengue fever complicated with Guillain-Barré syndrome: A case report and review of the literature. J. Med. Case Rep. 2018, 12, 137. [Google Scholar] [CrossRef] [PubMed]
 - Sreelakshmi, V.; Pattanaik, A.; Marate, S.; Mani, R.S.; Pai, A.R.; Mukhopadhyay, C. Guillain-barré syndrome (GBS) with antecedent chikungunya infection: A case report and literature review. Neurol. Res. Pract. 2024, 6, 21. [Google Scholar] [CrossRef]
 - Oehler, E.; Watrin, L.; Larre, P.; Leparc-Goffart, I.; Lastere, S.; Valour, F.; Baudouin, L.; Mallet, H.; Musso, D.; Ghawche, F. Zika virus infection complicated by Guillain-Barre syndrome—Case report, French Polynesia, December 2013. Euro. Surveill. 2014, 19, 20720. [Google Scholar] [CrossRef] [PubMed]
 - Rivera-Concepción, J.R.; Betancourt, J.P.; Cerra, J.; Reyes, E. The Zika Virus: An Association to Guillain-Barré Syndrome in the United States—A Case Report. P. R. Health Sci. J. 2018, 37, S93–S95. [Google Scholar]
 - Mancera-Páez, O.; Román, G.C.; Pardo-Turriago, R.; Rodríguez, Y.; Anaya, J.-M. Concurrent Guillain-Barré syndrome, transverse myelitis and encephalitis post-Zika: A case report and review of the pathogenic role of multiple arboviral immunity. J. Neurol. Sci. 2018, 395, 47–53. [Google Scholar] [CrossRef]
 - Bautista, L.E. Zika virus infection and risk of Guillain-Barré syndrome: A meta-analysis. J. Neurol. Sci. 2019, 403, 99–105. [Google Scholar] [CrossRef]
 - Simmer, P.E.; Powell, V.; Hoch, V.; Noblett, C.; Eckert, P.; Abdelmaseeh, P. Ascending Trouble: Guillain-Barré-Like Syndrome Due to West Nile Virus. Cureus 2025, 17, e85240. [Google Scholar] [CrossRef] [PubMed]
 - Beshai, R.; Bibawy, D.; Bibawy, J. Guillain-Barré Syndrome Secondary to West Nile Virus in New York City. Case Rep. Infect. Dis. 2020, 2020, 6501658. [Google Scholar] [CrossRef]
 - Karagianni, P.; Alexopoulos, H.; Sourdi, A.; Papadimitriou, D.; Dimitrakopoulos, A.N.; Moutsopoulos, H.M. West Nile Virus infection triggering autoimmune encephalitis: Pathophysiological and therapeutic implications. Clin. Immunol. 2019, 207, 97–99. [Google Scholar] [CrossRef]
 - Theofilopoulos, A.N.; Kono, D.H.; Baccala, R. The multiple pathways to autoimmunity. Nat. Immunol. 2017, 18, 716–724. [Google Scholar] [CrossRef] [PubMed]
 - Marson, A.; Housley, W.J.; Hafler, D.A. Genetic basis of autoimmunity. J. Clin. Investig. 2015, 125, 2234–2241. [Google Scholar] [CrossRef]
 - Rosenblum, M.D.; Remedios, K.A.; Abbas, A.K. Mechanisms of human autoimmunity. J. Clin. Investig. 2015, 125, 2228–2233. [Google Scholar] [CrossRef] [PubMed]
 - Cusick, M.F.; Libbey, J.E.; Fujinami, R.S. Molecular mimicry as a mechanism of autoimmune disease. Clin. Rev. Allergy Immunol. 2012, 42, 102–111. [Google Scholar] [CrossRef]
 - Rojas, M.; Restrepo-Jiménez, P.; Monsalve, D.M.; Pacheco, Y.; Acosta-Ampudia, Y.; Ramírez-Santana, C.; Leung, P.S.C.; Ansari, A.A.; Gershwin, M.E.; Anaya, J.-M. Molecular mimicry and autoimmunity. J. Autoimmun. 2018, 95, 100–123. [Google Scholar] [CrossRef]
 - Suliman, B.A. Potential clinical implications of molecular mimicry-induced autoimmunity. Immun. Inflamm. Dis. 2024, 12, e1178. [Google Scholar] [CrossRef]
 - Kalil, J.; Guilherme, L. Rheumatic Fever: A Model of Autoimmune Disease due to Molecular Mimicry between Human and Pathogen Proteins. Crit. Rev. Immunol. 2020, 40, 419–422. [Google Scholar] [CrossRef]
 - Shahrizaila, N.; Yuki, N. Guillain-barré syndrome animal model: The first proof of molecular mimicry in human autoimmune disorder. J. Biomed Biotechnol. 2011, 2011, 829129. [Google Scholar] [CrossRef]
 - Poole, B.D.; Scofield, R.H.; Harley, J.B.; James, J.A. Epstein-Barr virus and molecular mimicry in systemic lupus erythematosus. Autoimmunity 2006, 39, 63–70. [Google Scholar] [CrossRef]
 - Goh, L.; Kerkar, N. Hepatitis C Virus and Molecular Mimicry. Pathogens 2024, 13, 527. [Google Scholar] [CrossRef]
 - Arévalo-Cortés, A.; Rodriguez-Pinto, D.; Aguilar-Ayala, L. Evidence for Molecular Mimicry between SARS-CoV-2 and Human Antigens: Implications for Autoimmunity in COVID-19. Autoimmune Dis. 2024, 2024, 8359683. [Google Scholar] [CrossRef]
 - Hussein, H.M.; Rahal, E.A. The role of viral infections in the development of autoimmune diseases. Crit. Rev. Microbiol. 2019, 45, 394–412. [Google Scholar] [CrossRef]
 - Cunningham, M.W. Molecular Mimicry, Autoimmunity, and Infection: The Cross-Reactive Antigens of Group A Streptococci and their Sequelae. Microbiol. Spectr. 2019, 7, GPP3-0045-2018. [Google Scholar] [CrossRef]
 - Szymula, A.; Rosenthal, J.; Szczerba, B.M.; Bagavant, H.; Fu, S.M.; Deshmukh, U.S. T cell epitope mimicry between Sjögren’s syndrome Antigen A (SSA)/Ro60 and oral, gut, skin and vaginal bacteria. Clin. Immunol. 2014, 152, 1–9. [Google Scholar] [CrossRef] [PubMed]
 - Zhang, W.; Reichlin, M. A possible link between infection with burkholderia bacteria and systemic lupus erythematosus based on epitope mimicry. Clin. Dev. Immunol. 2008, 2008, 683489. [Google Scholar] [CrossRef] [PubMed]
 - Muthye, V.; Wasmuth, J.D. Proteome-wide comparison of tertiary protein structures reveals molecular mimicry in Plasmodium-human interactions. Front. Parasitol. 2023, 2, 1162697. [Google Scholar] [CrossRef]
 - Emiliani, Y.; Muzi, G.; Sánchez, A.; Sánchez, J.; Munera, M. Prediction of molecular mimicry between proteins from Trypanosoma sp. and human antigens associated with systemic lupus erythematosus. Microb. Pathog. 2022, 172, 105760. [Google Scholar] [CrossRef]
 - Trier, N.H.; Houen, G. Antibody Cross-Reactivity in Auto-Immune Diseases. Int. J. Mol. Sci. 2023, 24, 13609. [Google Scholar] [CrossRef]
 - Kammer, A.R.; van der Burg, S.H.; Grabscheid, B.; Hunziker, I.P.; Kwappenberg, K.M.; Reichen, J.; Melief, C.J.; Cerny, A. Molecular mimicry of human cytochrome P450 by hepatitis C virus at the level of cytotoxic T cell recognition. J. Exp. Med. 1999, 190, 169–176. [Google Scholar] [CrossRef]
 - Johnston, A.; Gudjonsson, J.E.; Sigmundsdottir, H.; Love, T.J.; Valdimarsson, H. Peripheral blood T cell responses to keratin peptides that share sequences with streptococcal M proteins are largely restricted to skin-homing CD8+ T cells. Clin. Exp. Immunol. 2004, 138, 83–93. [Google Scholar] [CrossRef] [PubMed]
 - Liu, I.-J.; Chiu, C.-Y.; Chen, Y.-C.; Wu, H.-C. Molecular mimicry of human endothelial cell antigen by autoantibodies to nonstructural protein 1 of dengue virus. J. Biol. Chem. 2011, 286, 9726–9736. [Google Scholar] [CrossRef]
 - Cheng, H.-J.; Lin, C.-F.; Lei, H.-Y.; Liu, H.-S.; Yeh, T.-M.; Luo, Y.-H.; Lin, Y.-S. Proteomic analysis of endothelial cell autoantigens recognized by anti-dengue virus nonstructural protein 1 antibodies. Exp. Biol. Med. 2009, 234, 63–73. [Google Scholar] [CrossRef] [PubMed]
 - Cheng, H.-J.; Lei, H.-Y.; Lin, C.-F.; Luo, Y.-H.; Wan, S.-W.; Liu, H.-S.; Yeh, T.-M.; Lin, Y.-S. Anti-dengue virus nonstructural protein 1 antibodies recognize protein disulfide isomerase on platelets and inhibit platelet aggregation. Mol. Immunol. 2009, 47, 398–406. [Google Scholar] [CrossRef]
 - Chang, H.-H.; Shyu, H.-F.; Wang, Y.-M.; Sun, D.-S.; Shyu, R.-H.; Tang, S.-S.; Huang, Y.-S. Facilitation of cell adhesion by immobilized dengue viral nonstructural protein 1 (NS1): Arginine-glycine-aspartic acid structural mimicry within the dengue viral NS1 antigen. J. Infect. Dis. 2002, 186, 743–751. [Google Scholar] [CrossRef] [PubMed]
 - Huang, Y.H.; Chang, B.I.; Lei, H.Y.; Liu, H.S.; Liu, C.C.; Wu, H.L.; Yeh, T.M. Antibodies against dengue virus E protein peptide bind to human plasminogen and inhibit plasmin activity. Clin. Exp. Immunol. 1997, 110, 35–40. [Google Scholar] [CrossRef]
 - Lin, Y.-S.; Yeh, T.-M.; Lin, C.-F.; Wan, S.-W.; Chuang, Y.-C.; Hsu, T.-K.; Liu, H.-S.; Liu, C.-C.; Anderson, R.; Lei, H.-Y. Molecular mimicry between virus and host and its implications for dengue disease pathogenesis. Exp. Biol. Med. 2011, 236, 515–523. [Google Scholar] [CrossRef]
 - Lucchese, G.; Kanduc, D. Zika virus and autoimmunity: From microcephaly to Guillain-Barré syndrome, and beyond. Autoimmun. Rev. 2016, 15, 801–808. [Google Scholar] [CrossRef]
 - França, L.C.; Fontes-Dantas, F.L.; Garcia, D.G.; de Araújo, A.D.; da Costa Gonçalves, J.P.; da Silav Rêgo, C.C.; da Silva, E.V.; do Nascimento, O.J.M.; Lopes, F.C.R.; Herlinger, A.L.; et al. Molecular mimicry between Zika virus and central nervous system inflammatory demyelinating disorders: The role of NS5 Zika virus epitope and PLP autoantigens. Arq. Neuropsiquiatr. 2023, 81, 357–368. [Google Scholar] [CrossRef]
 - Nasirudeen, A.M.A.; Wong, H.H.; Thien, P.; Xu, S.; Lam, K.-P.; Liu, D.X. RIG-I, MDA5 and TLR3 synergistically play an important role in restriction of dengue virus infection. PLoS Negl. Trop. Dis. 2011, 5, e926. [Google Scholar] [CrossRef]
 - Ye, S.; Liang, Y.; Chang, Y.; Lai, B.; Zhong, J. Dengue Virus Replicative-Form dsRNA Is Recognized by Both RIG-I and MDA5 to Activate Innate Immunity. J. Med. Virol. 2025, 97, e70194. [Google Scholar] [CrossRef] [PubMed]
 - Suthar, M.S.; Aguirre, S.; Fernandez-Sesma, A. Innate Immune Sensing of Flaviviruses. PLoS Pathog. 2013, 9, e1003541. [Google Scholar] [CrossRef] [PubMed]
 - Pingen, M.; Schmid, M.A.; Harris, E.; McKimmie, C.S. Mosquito Biting Modulates Skin Response to Virus Infection. Trends Parasitol. 2017, 33, 645–657. [Google Scholar] [CrossRef] [PubMed]
 - Demeure, C.E.; Brahimi, K.; Hacini, F.; Marchand, F.; Péronet, R.; Huerre, M.; St.-Mezard, P.; Nicolas, J.-F.; Brey, P.; Delespesse, G.; et al. Anopheles Mosquito Bites Activate Cutaneous Mast Cells Leading to a Local Inflammatory Response and Lymph Node Hyperplasia1. J. Immunol. 2005, 174, 3932–3940. [Google Scholar] [CrossRef]
 - Pingen, M.; Bryden, S.R.; Pondeville, E.; Schnettler, E.; Kohl, A.; Merits, A.; Fazakerley, J.K.; Graham, G.J.; McKimmie, C.S. Host Inflammatory Response to Mosquito Bites Enhances the Severity of Arbovirus Infection. Immunity 2016, 44, 1455–1469. [Google Scholar] [CrossRef]
 - Guerrero, D.; Cantaert, T.; Missé, D. Aedes Mosquito Salivary Components and Their Effect on the Immune Response to Arboviruses. Front. Cell. Infect. Microbiol. 2020, 10, 407. Available online: https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2020.00407/full (accessed on 23 July 2025). [CrossRef]
 - Ader, D.B.; Celluzzi, C.; Bisbing, J.; Gilmore, L.; Gunther, V.; Peachman, K.K.; Rao, M.; Barvir, D.; Sun, W.; Palmer, D.R. Modulation of Dengue Virus Infection of Dendritic Cells by Aedes aegypti Saliva. Viral Immunol. 2004, 17, 252–265. [Google Scholar] [CrossRef]
 - Schneider, B.S.; Soong, L.; Zeidner, N.S.; Higgs, S. Aedes aegypti Salivary Gland Extracts Modulate Anti-Viral and TH1/TH2 Cytokine Responses to Sindbis Virus Infection. Viral Immunol. 2004, 17, 565–573. [Google Scholar] [CrossRef]
 - Schmid, M.A.; Glasner, D.R.; Shah, S.; Michlmayr, D.; Kramer, L.D.; Harris, E. Mosquito Saliva Increases Endothelial Permeability in the Skin, Immune Cell Migration, and Dengue Pathogenesis during Antibody-Dependent Enhancement. PLoS Pathog. 2016, 12, e1005676. [Google Scholar] [CrossRef]
 - Martin-Martin, I.; Kern, O.; Brooks, S.; Smith, L.B.; Valenzuela-Leon, P.C.; Bonilla, B.; Ackerman, H.; Calvo, E. Biochemical characterization of AeD7L2 and its physiological relevance in blood feeding in the dengue mosquito vector, Aedes aegypti. FEBS J. 2021, 288, 2014–2029. [Google Scholar] [CrossRef]
 - Martin-Martin, I.; Valenzuela Leon, P.C.; Amo, L.; Shrivastava, G.; Iniguez, E.; Aryan, A.; Brooks, S.; Kojin, B.B.; Williams, A.E.; Bolland, S.; et al. Aedes aegypti sialokinin facilitates mosquito blood feeding and modulates host immunity and vascular biology. Cell Rep. 2022, 39, 110648. [Google Scholar] [CrossRef]
 - Ribeiro, J.M. Characterization of a vasodilator from the salivary glands of the yellow fever mosquito Aedes aegypti. J. Exp. Biol. 1992, 165, 61–71. [Google Scholar] [CrossRef] [PubMed]
 - Peng, Z.; Rasic, N.; Liu, Y.; Simons, F.E.R. Mosquito saliva–specific IgE and IgG antibodies in 1059 blood donors. J. Allergy Clin. Immunol. 2002, 110, 816–817. [Google Scholar] [CrossRef] [PubMed]
 - Londoño-Rentería, B.; Cárdenas, J.C.; Giovanni, J.E.; Cárdenas, L.; Villamizar, P.; Rolón, J.; Chisenhall, D.M.; Christofferson, R.C.; Carvajal, D.J.; Pérez, O.G.; et al. Aedes aegypti anti-salivary gland antibody concentration and dengue virus exposure history in healthy individuals living in an endemic area in Colombia. Biomedica 2015, 35, 572–581. [Google Scholar] [CrossRef]
 - Chea, S.; Willen, L.; Nhek, S.; Ly, P.; Tang, K.; Oristian, J.; Salas-Carrillo, R.; Ponce, A.; Leon, P.C.V.; Kong, D.; et al. Antibodies to Aedes aegypti D7L salivary proteins as a new serological tool to estimate human exposure to Aedes mosquitoes. Front. Immunol. 2024, 15, 1368066. [Google Scholar] [CrossRef]
 - Mathieu-Daudé, F.; Claverie, A.; Plichart, C.; Boulanger, D.; Mphande, F.A.; Bossin, H.C. Specific human antibody responses to Aedes aegypti and Aedes polynesiensis saliva: A new epidemiological tool to assess human exposure to disease vectors in the Pacific. PLoS Negl. Trop. Dis. 2018, 12, e0006660. [Google Scholar] [CrossRef] [PubMed]
 - Li, N.; Aoki, V.; Liu, Z.; Prisayanh, P.; Valenzuela, J.G.; Diaz, L.A. From Insect Bites to a Skin Autoimmune Disease: A Conceivable Pathway to Endemic Pemphigus Foliaceus. Front. Immunol. 2022, 13, 907424. Available online: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.907424/full (accessed on 11 October 2025). [CrossRef]
 - Ribeiro, J.M.C.; Martin-Martin, I.; Arcà, B.; Calvo, E. A Deep Insight into the Sialome of Male and Female Aedes aegypti Mosquitoes. PLoS ONE 2016, 11, e0151400. [Google Scholar] [CrossRef]
 - Ribeiro, J.M.C.; Arcà, B.; Lombardo, F.; Calvo, E.; Phan, V.M.; Chandra, P.K.; Wikel, S.K. An annotated catalogue of salivary gland transcripts in the adult female mosquito, Aedes aegypti. BMC Genom. 2007, 8, 6. [Google Scholar] [CrossRef]
 - Wasinpiyamongkol, L.; Patramool, S.; Luplertlop, N.; Surasombatpattana, P.; Doucoure, S.; Mouchet, F.; Séveno, M.; Remoue, F.; Demettre, E.; Brizard, J.-P.; et al. Blood-feeding and immunogenic Aedes aegypti saliva proteins. Proteomics 2010, 10, 1906–1916. [Google Scholar] [CrossRef]
 - Gavor, E.; Choong, Y.K.; Liu, Y.; Pompon, J.; Ooi, E.E.; Mok, Y.K.; Liu, H.; Kini, R.M.; Sivaraman, J. Identification of Aedes aegypti salivary gland proteins interacting with human immune receptor proteins. PLoS Negl. Trop. Dis. 2022, 16, e0010743. [Google Scholar] [CrossRef] [PubMed]
 - Schumacher, T.N.; De Bruijn, M.L.; Vernie, L.N.; Kast, W.M.; Melief, C.J.; Neefjes, J.J.; Ploegh, H.L. Peptide selection by MHC class I molecules. Nature 1991, 350, 703–706. [Google Scholar] [CrossRef]
 - Rammensee, H.G.; Friede, T.; Stevanoviíc, S. MHC ligands and peptide motifs: First listing. Immunogenetics 1995, 41, 178–228. [Google Scholar] [CrossRef]
 - Renaudineau, Y.; Charras, A.; Natoli, V.; Congy-Jolivet, N.; Haldenby, S.; Liu, X.; Fang, Y.; Smith, E.M.; Beresford, M.W.; Hedrich, C.M.; et al. Across ancestries, HLA-B∗08:01∼DRB1∗03:01 (DR3) and HLA-DQA∗01:02 (DR2) increase the risk to develop juvenile-onset systemic lupus erythematosus through low complement C4 levels. J. Transl. Autoimmun. 2025, 10, 100268. [Google Scholar] [CrossRef]
 - Butler-Laporte, G.; Farjoun, J.; Nakanishi, T.; Lu, T.; Abner, E.; Chen, Y.; Hultström, M.; Metspalu, A.; Milani, L.; Mägi, R.; et al. HLA allele-calling using multi-ancestry whole-exome sequencing from the UK Biobank identifies 129 novel associations in 11 autoimmune diseases. Commun. Biol. 2023, 6, 1113. [Google Scholar] [CrossRef] [PubMed]
 - Al Naqbi, H.; Mawart, A.; Alshamsi, J.; Al Safar, H.; Tay, G.K. Major histocompatibility complex (MHC) associations with diseases in ethnic groups of the Arabian Peninsula. Immunogenetics 2021, 73, 131–152. [Google Scholar] [CrossRef]
 - Fernando, M.M.A.; Stevens, C.R.; Walsh, E.C.; De Jager, P.L.; Goyette, P.; Plenge, R.M.; Vyse, T.J.; Rioux, J.D. Defining the role of the MHC in autoimmunity: A review and pooled analysis. PLoS Genet. 2008, 4, e1000024. [Google Scholar] [CrossRef]
 - Erlich, H.; Valdes, A.M.; Noble, J.; Carlson, J.A.; Varney, M.; Concannon, P.; Mychaleckyj, J.C.; Todd, J.A.; Bonella, P.; Fear, A.L.; et al. HLA DR-DQ haplotypes and genotypes and type 1 diabetes risk: Analysis of the type 1 diabetes genetics consortium families. Diabetes 2008, 57, 1084–1092. [Google Scholar] [CrossRef] [PubMed]
 - Pirie, F.J.; Hammond, M.G.; Motala, A.A.; Omar, M.A. HLA class II antigens in South African Blacks with type I diabetes. Tissue Antigens 2001, 57, 348–352. [Google Scholar] [CrossRef] [PubMed]
 - Heward, J.M.; Allahabadia, A.; Daykin, J.; Carr-Smith, J.; Daly, A.; Armitage, M.; Dodson, P.M.; Sheppard, M.C.; Barnett, A.H.; Franklyn, J.A.; et al. Linkage disequilibrium between the human leukocyte antigen class II region of the major histocompatibility complex and Graves’ disease: Replication using a population case control and family-based study. J. Clin. Endocrinol. Metab. 1998, 83, 3394–3397. [Google Scholar] [CrossRef] [PubMed]
 - Hernández-Doño, S.; Jakez-Ocampo, J.; Márquez-García, J.E.; Ruiz, D.; Acuña-Alonzo, V.; Lima, G.; Llorente, L.; Tovar-Méndez, V.H.; García-Silva, R.; Granados, J.; et al. Heterogeneity of Genetic Admixture Determines SLE Susceptibility in Mexican. Front. Genet. 2021, 12, 701373. [Google Scholar] [CrossRef]
 - Al-Harbi, E.M.; Abbassi, A.-J.; Tamim, H.; al-Jenaidi, F.; Kooheji, M.; Kamal, M.; al-Mahroos, S.; al-Nasir, F.; Motala, A.A.; Almawi, W.Y. Specific HLA-DRB and -DQB alleles and haplotypes confer disease susceptibility or resistance in Bahraini type 1 diabetes patients. Clin. Diagn. Lab. Immunol. 2004, 11, 292–296. [Google Scholar] [CrossRef]
 - Varade, J.; Wang, N.; Lim, C.K.; Zhang, T.; Zhang, Y.; Liu, X.; Piehl, F.; Matell, R.; Cao, H.; Xu, X.; et al. Novel genetic loci associated HLA-B*08:01 positive myasthenia gravis. J. Autoimmun. 2018, 88, 43–49. [Google Scholar] [CrossRef]
 - Le, T.T.V.; Vuong, T.T.B.; Ong, T.P.; Do, M.D. Allele frequency and the associations of HLA-DRB1 and HLA-DQB1 polymorphisms with pemphigus subtypes and disease severity. Medicine 2022, 101, e28855. [Google Scholar] [CrossRef] [PubMed]
 - Feng, X.; Li, W.; Song, J.; Liu, X.; Gu, Y.; Yan, C.; Wu, H.; Xi, J.; Zhou, S.; Zhao, C. HLA typing using next-generation sequencing for Chinese juvenile- and adult-onset myasthenia gravis patients. J. Clin. Neurosci. 2019, 59, 179–184. [Google Scholar] [CrossRef]
 - de Almeida, D.E.; Ling, S.; Holoshitz, J. New insights into the functional role of the rheumatoid arthritis shared epitope. FEBS Lett. 2011, 585, 3619–3626. [Google Scholar] [CrossRef]
 - van Drongelen, V.; Holoshitz, J. Human Leukocyte Antigen-Disease Associations in Rheumatoid Arthritis. Rheum. Dis. Clin. N. Am. 2017, 43, 363–376. [Google Scholar] [CrossRef]
 - Oliveira, L.C.; Porta, G.; Marin, M.L.C.; Bittencourt, P.L.; Kalil, J.; Goldberg, A.C. Autoimmune hepatitis, HLA and extended haplotypes. Autoimmun. Rev. 2011, 10, 189–193. [Google Scholar] [CrossRef]
 - Jun, K.R.; Choi, S.E.; Cha, C.H.; Oh, H.B.; Heo, Y.S.; Ahn, H.Y.; Lee, K.J. Meta-analysis of the association between HLA-DRB1 allele and rheumatoid arthritis susceptibility in Asian populations. J. Korean Med. Sci. 2007, 22, 973–980. [Google Scholar] [CrossRef]
 - Gough, S.C.L.; Simmonds, M.J. The HLA Region and Autoimmune Disease: Associations and Mechanisms of Action. Curr. Genom. 2007, 8, 453–465. [Google Scholar] [CrossRef]
 - Patsopoulos, N.A.; Barcellos, L.F.; Hintzen, R.Q.; Schaefer, C.; van Duijn, C.M.; Noble, J.A.; Raj, T.; IMSGC; ANZgene; Gourraud, P.-A.; et al. Fine-mapping the genetic association of the major histocompatibility complex in multiple sclerosis: HLA and non-HLA effects. PLoS Genet. 2013, 9, e1003926. [Google Scholar] [CrossRef] [PubMed]
 - Hachicha, H.; Kammoun, A.; Mahfoudh, N.; Marzouk, S.; Feki, S.; Fakhfakh, R.; Fourati, H.; Haddouk, S.; Frikha, F.; Gaddour, L.; et al. Human leukocyte antigens-DRB1*03 is associated with systemic lupus erythematosus and anti-SSB production in South Tunisia. Int. J. Health Sci. 2018, 12, 21–27. [Google Scholar]
 - Jean, S.; Quelvennec, E.; Alizadeh, M.; Guggenbuhl, P.; Birebent, B.; Perdriger, A.; Grosbois, B.; Pawlotsky, P.Y.; Semana, G. DRB1*15 and DRB1*03 extended haplotype interaction in primary Sjögren’s syndrome genetic susceptibility. Clin. Exp. Rheumatol. 1998, 16, 725–728. [Google Scholar] [PubMed]
 - Yeo, T.W.; De Jager, P.L.; Gregory, S.G.; Barcellos, L.F.; Walton, A.; Goris, A.; Fenoglio, C.; Ban, M.; Taylor, C.J.; Goodman, R.S.; et al. A second major histocompatibility complex susceptibility locus for multiple sclerosis. Ann. Neurol. 2007, 61, 228–236. [Google Scholar] [CrossRef]
 - Kikili, C.İ.; Kivanç, D.; Ortaboz, D.; Şentürk Çiftçi, H.; Özbalak, M.M.; Yenerel, M.N.; Nalçaci, M.; Ar, M.C.; Oğuz, F.S.; Beşişik, S.K. Identification of HLA alleles involved in immune thrombotic thrombocytopenic purpura patients from Turkey. Blood Coagul. Fibrinolysis. 2024, 35, 307–315. [Google Scholar] [CrossRef]
 - Stasiak, M.; Zawadzka-Starczewska, K.; Tymoniuk, B.; Stasiak, B.; Lewiński, A. Significance of HLA in the development of Graves’ orbitopathy. Genes Immun. 2023, 24, 32–38. [Google Scholar] [CrossRef]
 - Sirikong, M.; Tsuchiya, N.; Chandanayingyong, D.; Bejrachandra, S.; Suthipinittharm, P.; Luangtrakool, K.; Srinak, D.; Thongpradit, R.; Siriboonrit, U.; Tokunaga, K. Association of HLA-DRB1*1502-DQB1*0501 haplotype with susceptibility to systemic lupus erythematosus in Thais. Tissue Antigens 2002, 59, 113–117. [Google Scholar] [CrossRef]
 - Prinz, J.C. Human Leukocyte Antigen-Class I Alleles and the Autoreactive T Cell Response in Psoriasis Pathogenesis. Front. Immunol. 2018, 9, 954. [Google Scholar] [CrossRef] [PubMed]
 - Noble, J.A.; Valdes, A.M. Genetics of the HLA region in the prediction of type 1 diabetes. Curr. Diab. Rep. 2011, 11, 533–542. [Google Scholar] [CrossRef]
 - Bugawan, T.L.; Klitz, W.; Alejandrino, M.; Ching, J.; Panelo, A.; Solfelix, C.M.; Petrone, A.; Buzzetti, R.; Pozzilli, P.; Erlich, H.A. The association of specific HLA class I and II alleles with type 1 diabetes among Filipinos. Tissue Antigens 2002, 59, 452–469. [Google Scholar] [CrossRef] [PubMed]
 - Prinz, J.C. Melanocytes: Target Cells of an HLA-C*06:02-Restricted Autoimmune Response in Psoriasis. J. Investig. Dermatol. 2017, 137, 2053–2058. [Google Scholar] [CrossRef] [PubMed]
 - Siegel, R.J.; Bridges, S.L., Jr.; Ahmed, S. HLA-C: An Accomplice in Rheumatic Diseases. ACR Open Rheumatol. 2019, 1, 571–579. [Google Scholar] [CrossRef]
 - Kovjazin, R.; Carmon, L. The use of signal peptide domains as vaccine candidates. Hum. Vaccin Immunother. 2014, 10, 2733–2740. [Google Scholar] [CrossRef] [PubMed]
 - Smith-Garvin, J.E.; Koretzky, G.A.; Jordan, M.S. T cell activation. Annu. Rev. Immunol. 2009, 27, 591–619. [Google Scholar] [CrossRef]
 - Messaoudi, I.; Guevara Patiño, J.A.; Dyall, R.; LeMaoult, J.; Nikolich-Zugich, J. Direct link between mhc polymorphism, T cell avidity, and diversity in immune defense. Science 2002, 298, 1797–1800. [Google Scholar] [CrossRef]
 - Engels, B.; Engelhard, V.H.; Sidney, J.; Sette, A.; Binder, D.C.; Liu, R.B.; Kranz, D.M.; Meredith, S.C.; Rowley, D.A.; Schreiber, H. Relapse or eradication of cancer is predicted by peptide-MHC affinity. Cancer Cell 2013, 23, 516–526. [Google Scholar] [CrossRef]
 - Harndahl, M.; Rasmussen, M.; Roder, G.; Dalgaard Pedersen, I.; Sørensen, M.; Nielsen, M.; Buus, S. Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. Eur. J. Immunol. 2012, 42, 1405–1416. [Google Scholar] [CrossRef]
 - Vecchio, F.; Carré, A.; Korenkov, D.; Zhou, Z.; Apaolaza, P.; Tuomela, S.; Burgos-Morales, O.; Snowhite, I.; Perez-Hernandez, J.; Brandao, B.; et al. Coxsackievirus infection induces direct pancreatic β cell killing but poor antiviral CD8+ T cell responses. Sci. Adv. 2024, 10, eadl1122. [Google Scholar] [CrossRef]
 - Zheng, Z.; Mergaert, A.M.; Fahmy, L.M.; Bawadekar, M.; Holmes, C.L.; Ong, I.M.; Bridges, A.J.; Newton, M.A.; Shelef, M.A. Disordered Antigens and Epitope Overlap Between Anti-Citrullinated Protein Antibodies and Rheumatoid Factor in Rheumatoid Arthritis. Arthritis Rheumatol. 2020, 72, 262–272. [Google Scholar] [CrossRef]
 - Mayer, R.L.; Verbeke, R.; Asselman, C.; Aernout, I.; Gul, A.; Eggermont, D.; Boucher, K.; Thery, F.; Maia, T.M.; Demol, H.; et al. Immunopeptidomics-based design of mRNA vaccine formulations against Listeria monocytogenes. Nat. Commun. 2022, 13, 6075. [Google Scholar] [CrossRef]
 - Marcu, A.; Bichmann, L.; Kuchenbecker, L.; Kowalewski, D.J.; Freudenmann, L.K.; Backert, L.; Mühlenbruch, L.; Szolek, A.; Lübke, M.; Wagner, P.; et al. HLA Ligand Atlas: A benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J. Immunother. Cancer 2021, 9, e002071. [Google Scholar] [CrossRef]
 - Ritz, D.; Gloger, A.; Weide, B.; Garbe, C.; Neri, D.; Fugmann, T. High-sensitivity HLA class I peptidome analysis enables a precise definition of peptide motifs and the identification of peptides from cell lines and patients’ sera. Proteomics 2016, 16, 1570–1580. [Google Scholar] [CrossRef] [PubMed]
 - Sarkizova, S.; Klaeger, S.; Le, P.M.; Li, L.W.; Oliveira, G.; Keshishian, H.; Hartigan, C.R.; Zhang, W.; Braun, D.A.; Ligon, K.L.; et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat. Biotechnol. 2020, 38, 199–209. [Google Scholar] [CrossRef]
 - Fujiwara, K.; Shao, Y.; Niu, N.; Zhang, T.; Herbst, B.; Henderson, M.; Muth, S.; Zhang, P.; Zheng, L. Direct identification of HLA class I and class II-restricted T cell epitopes in pancreatic cancer tissues by mass spectrometry. J. Hematol. Oncol. 2022, 15, 154. [Google Scholar] [CrossRef]
 - Solleder, M.; Guillaume, P.; Racle, J.; Michaux, J.; Pak, H.-S.; Müller, M.; Coukos, G.; Bassani-Sternberg, M.; Gfeller, D. Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands. Mol. Cell. Proteom. 2020, 19, 390–404. [Google Scholar] [CrossRef]
 - Sudhir, P.-R.; Lin, T.-D.; Zhang, Q. HLA Allele-Specific Quantitative Profiling of Type 1 Diabetic B Lymphocyte Immunopeptidome. J. Proteome Res. 2022, 21, 250–264. [Google Scholar] [CrossRef] [PubMed]
 - Caron, E.; Espona, L.; Kowalewski, D.J.; Schuster, H.; Ternette, N.; Alpízar, A.; Schittenhelm, R.B.; Ramarathinam, S.H.; Lindestam Arlehamn, C.S.; Chiek Koh, C.; et al. An open-source computational and data resource to analyze digital maps of immunopeptidomes. eLife 2015, 4, e07661. [Google Scholar] [CrossRef] [PubMed]
 - Klatt, M.G.; Mack, K.N.; Bai, Y.; Aretz, Z.E.H.; Nathan, L.I.; Mun, S.S.; Dao, T.; Scheinberg, D.A. Solving an MHC allele-specific bias in the reported immunopeptidome. JCI Insight 2020, 5, 141264. [Google Scholar] [CrossRef]
 - Bassani-Sternberg, M.; Chong, C.; Guillaume, P.; Solleder, M.; Pak, H.; Gannon, P.O.; Kandalaft, L.E.; Coukos, G.; Gfeller, D. Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLoS Comput. Biol. 2017, 13, e1005725. [Google Scholar] [CrossRef] [PubMed]
 - Shraibman, B.; Barnea, E.; Kadosh, D.M.; Haimovich, Y.; Slobodin, G.; Rosner, I.; López-Larrea, C.; Hilf, N.; Kuttruff, S.; Song, C.; et al. Identification of Tumor Antigens Among the HLA Peptidomes of Glioblastoma Tumors and Plasma. Mol. Cell. Proteom. 2019, 18, 1255–1268. [Google Scholar] [CrossRef] [PubMed]
 - Yarmarkovich, M.; Marshall, Q.F.; Warrington, J.M.; Premaratne, R.; Farrel, A.; Groff, D.; Li, W.; di Marco, M.; Runbeck, E.; Truong, H.; et al. Targeting of intracellular oncoproteins with peptide-centric CARs. Nature 2023, 623, 820–827. [Google Scholar] [CrossRef]
 - Nicholas, B.; Bailey, A.; Staples, K.J.; Wilkinson, T.; Elliott, T.; Skipp, P. Immunopeptidomic analysis of influenza A virus infected human tissues identifies internal proteins as a rich source of HLA ligands. PLoS Pathog. 2022, 18, e1009894. [Google Scholar] [CrossRef]
 - Parkhurst, M.R.; Fitzgerald, E.B.; Southwood, S.; Sette, A.; Rosenberg, S.A.; Kawakami, Y. Identification of a shared HLA-A*0201-restricted T-cell epitope from the melanoma antigen tyrosinase-related protein 2 (TRP2). Cancer Res. 1998, 58, 4895–4901. [Google Scholar] [PubMed]
 - Wang, J.; Jelcic, I.; Mühlenbruch, L.; Haunerdinger, V.; Toussaint, N.C.; Zhao, Y.; Cruciani, C.; Faigle, W.; Naghavian, R.; Foege, M.; et al. HLA-DR15 Molecules Jointly Shape an Autoreactive T Cell Repertoire in Multiple Sclerosis. Cell 2020, 183, 1264–1281.e20. [Google Scholar] [CrossRef]
 - Moser, J.J.; Chan, E.K.L.; Fritzler, M.J. An SNP in the trinucleotide repeat region of the TNRC6A gene maps to a major TNGW1 autoepitope in patients with autoantibodies to GW182. Adv. Exp. Med. Biol. 2013, 768, 243–259. [Google Scholar] [CrossRef]
 - Rosen, A.; Casciola-Rosen, L. Autoantigens in systemic autoimmunity: Critical partner in pathogenesis. J. Intern. Med. 2009, 265, 625–631. [Google Scholar] [CrossRef]
 - Pedersen, A.E. The potential for induction of autoimmune disease by a randomly-mutated self-antigen. Med. Hypotheses 2007, 68, 1240–1246. [Google Scholar] [CrossRef]
 - Xiang, Y.; Zhang, M.; Jiang, D.; Su, Q.; Shi, J. The role of inflammation in autoimmune disease: A therapeutic target. Front. Immunol. 2023, 14, 1267091. Available online: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1267091/full (accessed on 10 September 2025). [CrossRef]
 - Muñoz, L.E.; Lauber, K.; Schiller, M.; Manfredi, A.A.; Herrmann, M. The role of defective clearance of apoptotic cells in systemic autoimmunity. Nat. Rev. Rheumatol. 2010, 6, 280–289. [Google Scholar] [CrossRef] [PubMed]
 - UniProt. UniProt-P54253·ATX1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P54253/entry (accessed on 3 September 2025).
 - UniProt. UniProt-O14646·CHD1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/O14646/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9UJU5·FOXD3_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9UJU5/entry (accessed on 3 September 2025).
 - UniProt. UniProt-O14686·KMT2D_HUMAN. Available online: https://www.uniprot.org/uniprotkb/O14686/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q8NBZ0·IN80E_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q8NBZ0/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q7Z3Z5·Q7Z3Z5_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q7Z3Z5/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q6PJG2·MDEAS_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q6PJG2/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q8TBK2·SETD6_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q8TBK2/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9Y618·NCOR2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9Y618/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P06748·NPM_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P06748/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q10571·MN1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q10571/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9H3D4·P63_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9H3D4/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q15911·ZFHX3_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q15911/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q5VZL5·ZMYM4_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q5VZL5/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q96KR1·ZFR_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q96KR1/entry (accessed on 3 September 2025).
 - UniProt. UniProt-A0A2R8Y5A6·A0A2R8Y5A6_HUMAN. Available online: https://www.uniprot.org/uniprotkb/A0A2R8Y5A6/entry (accessed on 3 September 2025).
 - UniProt. UniProt-E7ENM8·E7ENM8_HUMAN. Available online: https://www.uniprot.org/uniprotkb/E7ENM8/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q6Y7W6·GGYF2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q6Y7W6/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P42858·HD_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P42858/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P40126·TYRP2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P40126/entry (accessed on 3 September 2025).
 - UniProt. UniProt-C9JE40·PATL2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/C9JE40/entry (accessed on 3 September 2025).
 - UniProt. UniProt-A1IGU5·ARH37_HUMAN. Available online: https://www.uniprot.org/uniprotkb/A1IGU5/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q7L8J4·3BP5L_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q7L8J4/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q8NDV7·TNR6A_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q8NDV7/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P51648·AL3A2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P51648/entry (accessed on 3 September 2025).
 - UniProt. UniProt-O75460·ERN1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/O75460/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9BY12·SCAPE_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9BY12/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P54098·DPOG1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P54098/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P03891·NU2M_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P03891/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q2M2I8·AAK1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q2M2I8/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q5T1A1·DCST2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q5T1A1/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P21917·DRD4_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P21917/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q13492·PICAL_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q13492/entry (accessed on 3 September 2025).
 - UniProt. UniProt-B7Z2A4·B7Z2A4_HUMAN. Available online: https://www.uniprot.org/uniprotkb/B7Z2A4/entry (accessed on 3 September 2025).
 - UniProt. UniProt-A0A087X0R9·A0A087X0R9_HUMAN. Available online: https://www.uniprot.org/uniprotkb/A0A087X0R9/entry (accessed on 3 September 2025).
 - UniProt. UniProt-A0A2R8Y4T1·A0A2R8Y4T1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/A0A2R8Y4T1/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9NZK5·ADA2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9NZK5/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P0DUB6·AMY1A_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P0DUB6/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9Y264·ANGP4_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9Y264/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9UKU9·ANGL2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9UKU9/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9Y5C1·ANGL3_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9Y5C1/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q9BY76·ANGL4_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q9BY76/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q8NI99·ANGL6_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q8NI99/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P49747·COMP_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P49747/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P36222·CH3L1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P36222/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P02675·FIBB_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P02675/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P02679·FIBG_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P02679/entry (accessed on 3 September 2025).
 - UniProt. UniProt-Q08830·FGL1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/Q08830/entry (accessed on 3 September 2025).
 - UniProt. UniProt-O00602·FCN1_HUMAN. Available online: https://www.uniprot.org/uniprotkb/O00602/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P13727·PRG2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P13727/entry (accessed on 3 September 2025).
 - UniProt. UniProt-P13611·CSPG2_HUMAN. Available online: https://www.uniprot.org/uniprotkb/P13611/entry (accessed on 3 September 2025).
 - Malavige, G.N.; Jeewandara, C.; Ogg, G.S. Dysfunctional Innate Immune Responses and Severe Dengue. Front. Cell. Infect. Microbiol. 2020, 10, 590004. Available online: https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2020.590004/full (accessed on 10 September 2025). [CrossRef]
 - Khandia, R.; Munjal, A.; Dhama, K.; Karthik, K.; Tiwari, R.; Malik, Y.S.; Singh, R.K.; Chaicumpa, W. Modulation of Dengue/Zika Virus Pathogenicity by Antibody-Dependent Enhancement and Strategies to Protect Against Enhancement in Zika Virus Infection. Front. Immunol. 2018, 9, 597. [Google Scholar] [CrossRef]
 - Maucourant, C.; Queiroz, G.A.N.; Samri, A.; Grassi, M.F.R.; Yssel, H.; Vieillard, V. Zika virus in the eye of the cytokine storm. Eur. Cytokine Netw. 2019, 30, 74–81. [Google Scholar] [CrossRef]
 - Nanaware, N.; Banerjee, A.; Mullick Bagchi, S.; Bagchi, P.; Mukherjee, A. Dengue Virus Infection: A Tale of Viral Exploitations and Host Responses. Viruses 2021, 13, 1967. [Google Scholar] [CrossRef] [PubMed]
 - Clé, M.; Desmetz, C.; Barthelemy, J.; Martin, M.-F.; Constant, O.; Maarifi, G.; Foulongne, V.; Bolloré, K.; Glasson, Y.; De Bock, F.; et al. Zika Virus Infection Promotes Local Inflammation, Cell Adhesion Molecule Upregulation, and Leukocyte Recruitment at the Blood-Brain Barrier. mBio 2020, 11, e01183-20. [Google Scholar] [CrossRef] [PubMed]
 - Pavesi, A.; Tiecco, G.; Rossi, L.; Sforza, A.; Ciccarone, A.; Compostella, F.; Lovatti, S.; Tomasoni, L.R.; Castelli, F.; Quiros-Roldan, E. Inflammatory Response Associated with West Nile Neuroinvasive Disease: A Systematic Review. Viruses 2024, 16, 383. [Google Scholar] [CrossRef]
 - Lazarski, C.A.; Chaves, F.A.; Jenks, S.A.; Wu, S.; Richards, K.A.; Weaver, J.M.; Sant, A.J. The kinetic stability of MHC class II:peptide complexes is a key parameter that dictates immunodominance. Immunity 2005, 23, 29–40. [Google Scholar] [CrossRef]
 - Rasmussen, M.; Fenoy, E.; Harndahl, M.; Kristensen, A.B.; Nielsen, I.K.; Nielsen, M.; Buus, S. Pan-specific prediction of peptide-MHC-I complex stability; a correlate of T cell immunogenicity. J. Immunol. 2016, 197, 1517–1524. [Google Scholar] [CrossRef]
 - George, A.J.T.; Stark, J.; Chan, C. Understanding specificity and sensitivity of T-cell recognition. Trends Immunol. 2005, 26, 653–659. [Google Scholar] [CrossRef]
 - Fox, D.A. Citrullination: A Specific Target for the Autoimmune Response in Rheumatoid Arthritis. J. Immunol. 2015, 195, 5–7. [Google Scholar] [CrossRef] [PubMed]
 - Peng, Z.; Xu, W.W.; Sham, Y.; Lam, H.; Sun, D.; Li, C.; Rasic, N.F.; Guan, Q.; James, A.A.; Simons, F.E.R. Mosquito salivary allergen Aed a 3: Cloning, comprehensive molecular analysis, and clinical evaluation. Allergy 2016, 71, 621–628. [Google Scholar] [CrossRef]
 - Conway, M.J. Type I hypersensitivity promotes Aedes aegypti blood feeding. Sci. Rep. 2021, 11, 14891. [Google Scholar] [CrossRef]
 - Wang, Z.; Liang, Y.; Zeng, F.; Li, T.; Cheng, G. A capture enzyme-linked immunosorbent assay for detection of mosquito salivary protein-specific immunoglobulin E. PLoS Neglected Trop. Dis. 2025, 19, e0013468. [Google Scholar] [CrossRef]
 - Pandey, R.K.; Dahiya, S.; Mahita, J.; Sowdhamini, R.; Prajapati, V.K. Vaccination and immunization strategies to design Aedes aegypti salivary protein based subunit vaccine tackling Flavivirus infection. Int. J. Biol. Macromol. 2019, 122, 1203–1211. [Google Scholar] [CrossRef] [PubMed]
 - Manning, J.E.; Morens, D.M.; Kamhawi, S.; Valenzuela, J.G.; Memoli, M. Mosquito Saliva: The Hope for a Universal Arbovirus Vaccine? J. Infect. Dis. 2018, 218, 7–15. [Google Scholar] [CrossRef] [PubMed]
 - Wang, Y.; Ling, L.; Jiang, L.; Marin-Lopez, A. Research progress toward arthropod salivary protein vaccine development for vector-borne infectious diseases. PLoS Neglected Trop. Dis. 2024, 18, e0012618. [Google Scholar] [CrossRef]
 
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