{"paper_id":"367a779a-910e-40d0-a03d-0133f852879e","body_text":"Molecular mimicry between Trypanosoma cruzi and human β-tubulin as a potential autoimmune mechanism in Chagas disease–associated megaviscera | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Molecular mimicry between Trypanosoma cruzi and human β-tubulin as a potential autoimmune mechanism in Chagas disease–associated megaviscera Ana Valentina Centeno-Iglesias, Celeste Abigail Quille-Juarez, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7586194/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chagas disease, caused by Trypanosoma cruzi , affects a significant proportion of patients who develop digestive and cardiac complications, including megaviscera. This pathogenesis has been associated with autoimmune mechanisms mediated by molecular mimicry. In this study, an in silico evaluation of cross reactivity of β-tubulin 1.9 of T. cruzi and the human tubulin β-4A isoform 3 was conducted. Using bioinformatics tools, homologous regions were identified and potential immunogenic epitopes were predicted, considering their modeling and docking. The epitope GQSGAGNNWAKGHYTEGAELIDS was found to have high affinity and favorable antigenic properties, as well as binding capacity with TLR2. These findings suggest that this epitope could participate in molecular mimicry processes implicated in the development of chagasic megaviscera. Clinical trial number: not applicable. molecular mimicry Chagas disease tubulin autoimmune disease antigen Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Chagas disease is a parasitic infection caused by the protozoan Trypanosoma cruzi , transmitted through the infected feces of triatomine vectors such as Triatoma infestans and Rhodnius prolixus. Transmission occurs when the feces come into contact with a bite wound or the mucous membranes of the host (Bern, 2015 ). Chagas disease is endemic in Central and South America; however, due to migration, it has expanded to Europe, the United States, Canada, Japan, and Australia. It has been reported that 70% of infected individuals are asymptomatic, while the remaining 30% develop cardiac, digestive, neurological, or mixed disorders (Niborski et al., 2021 ). The immune response developed by the host against the parasite is critical for the outcome of the infection. Antibodies are central components of the host immune response, participating in the early control of the parasite, and in the pathophysiology of the disease (Acevedo, Girard and Gómez, 2018 ; Niborski et al., 2021 ), with special emphasis on the presence of anti-α-galactoside (anti-Gal) antibodies, which recognize glycans on the surface of T. cruzi , and mediate the lysis of the parasite in its trypomastigote form (Almeida et al., 1991 ). Studies have also identified another antibody in mice, the monoclonal antibody TcβTUB which recognizes β-tubulin present in the parasite’s flagellum (Montalvão et al., 2018 ). Disorders such as Chagas disease are commonly associated with the development of autoimmune diseases caused by molecular mimicry, a mechanism that occurs when similarities between the protein sequences of parasite and human epitopes trigger the activation of autoantibodies (Rojas et al., 2018 ). One example is the monoclonal autoantibody A2R1, which exhibits cross-reactivity between human β-tubulin and the β-tubulin of T. cruzi (Niborski et al., 2021 ). Autoantibodies identified as reactive with tubulin, myosin, and actin have also been associated with complications such as megaviscera (megacolon, megaesophagus, and splenomegaly), arising from neurosensory system alterations in the muscle cells of affected organs (Hugo García Orozco et al., 2022 ), induced by infection of this protozoan (Leon et al., 2001 ). Moreover, CD4 + T cells that recognize human myosin and the peptide of the T. cruzi B13 protein may drive Chagas myocardiopathy, characterized by T cell/macrophage infiltration, fibrosis and damage to cardiac fibers (Leon et al., 2001 ; Iwai et al., 2005 ). Currently, the role and origin of β-tubulin autoantibodies in the development of megaviscera after T. cruzi infection remain unclear. The aim of this study is to identify potential molecular mimicry between T. cruzi β-tubulin and human proteome through immunopeptidomics and immunoinformatics, as a possible cause of a pathological autoimmune reaction. METHODOLOGY Antigen selection - BLAST An in silico immunoinformatic study was conducted to identify T. cruzi proteins with immunological relevance. A general sequence homology analysis was performed focusing on T. cruzi proteins of interest. Using the NCBI database, the antigen sequence was retrieved, and the BLAST tool (available at: https://blast.ncbi.nlm.nih.gov/BlastAlign.cgi , accessed on May 21, 2025) (Altschul et al., 1990 ) was applied for the comparative alignment between immunogenic protein of interest from T. cruzi (Taxid: 5393) β-tubulin 1.9 (NCBI Code: >NP_001276058.1) and the Homo Sapiens proteome (Taxid: 9606), revealing proteins with high sequence similarity. Sequence alignment in Clustal Omega (dup: abstract ?) Through the Clustal Omega free server (available at: https://www.ebi.ac.uk/jdispatcher/msa/clustalo , accessed on May 23, 2025) (Madeira et al., 2024 ), a comparative alignment was performed between antigen sequences and the selected human protein tubulin β-4A isoform 3 (NCBI Code: >NP_001276058.1), focusing on regions that exhibited significant similarity to predict potential epitopes. Epitope Prediction - IEDB Using the IEDB database (available at: https://nextgen-tools.iedb.org/pipeline/56a665e4-d5e7-418f-ba12-03b7fa253065 , accessed on May 26, 2025) (Yan et al., 2024 ), the antigen sequence of T. cruzi was analyzed for T-cell epitope prediction (major histocompatibility complex class I and II) via NetMHCpan 4.1 EL algorithm, which predicts epitopes based on a large dataset of eluted ligands. For B-cell epitope prediction, the BepiPred Linear B-Cell Epitope Prediction 2.0 and Discotopoe 2.0 servers were employed, both based on Random Forest machine-learning models. Epitopes that showed sequence similarity with Homo Sapiens in the Clustal Omega analysis (accessed on May 23, 2025) and were predicted to be accessible for autoantibody interaction were selected. Properties of analyzed epitope To analyze the properties of the selected epitopes, VaxiJen v1.0 (available at: https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html , accessed on May 30, 2025) (Doytchinova and Flower, 2007 ) was used to assess antigenicity; Bepipred v2.0 (available at: https://services.healthtech.dtu.dk/services/BepiPred-2.0/ , accessed on May 30, 2025) (Jespersen et al., 2017 ) to evaluate antigenicity and epitope localization; Toxinpred v 1.0 (available at: https://webs.iiitd.edu.in/raghava/toxinpred/design.php , accessed on May 30, 2025) (Gupta et al., 2013 ) to exclude potential toxicity, and DNASTAR to calculate Jameson-Wolf index for each residue antigenicity, the hydrophobicity index for predicting exposed regions, and therefore its accessibility; and the Chou-Fasman and Garnier-Robson algorithms for secondary structure prediction of the epitope (Wisconsin, USA) (accessed on June 4, 2025). 3D protein modeling - Alpha Fold For 3D modeling of both proteins (> NP_001276058.1 and > AAL75956.1), the Alpha Fold 3 server (available at: https://alphafoldserver.com/about , accessed on June 14, 2025) (Abramson et al., 2024 )​ was used, along with the Pymol software, which employs its own rendering engine based on OpenGL (version 4.6). 3D prediction of the epitope docking with MHC I and II; TLR 1, 2, 7 and 9. Epitope docking was carried out using ClusPro (available at: https://cluspro.org/home.php , accessed on June 5, 2025) (Jones et al., 2022 ), selecting models with the lowest energy scores (more negative values indicate more favorable interactions). Subsequently, molecular dynamics were analyzed with the iMODS server (available at: https://imods.iqf.csic.es/ , accessed on June 5, 2025) (López-Blanco et al., 2014 ) through Normal Mode Analysis, calculating complex deformation under force field variations over time intervals, and estimating protein deformability based on eigenvalues. The 3D epitope models were retrieved from the RPBS database (available at: https://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#welcome , accessed on June 5, 2025) (Alland et al., 2005 ). The models of the Major Histocompatibility Complexes (MHC) and the Toll-like receptors (TLR) —namely TLR 7 (Code: 8S86), TLR 2 (Code: 1O77), and TLR 1 (Code: 7NT7)— were downloaded from the RCSB PDB database (available at: https://www.rcsb.org/ , accessed on June 5, 2025) (Berman, 2000 ). RESULTS Epitope Prediction - IEDB A similarity between the two selected sequences (> NP_001276058.1 and > AAL75956.1) was identified and is shown in Table 1 . Table 1 Proteins selected for alignment. NCBI code Protein Organism >NP_001276058.1 tubulin β-4A isoform 3 Homo sapiens >AAL75956.1 β-tubulin 1.9 Trypanosoma cruzi Sequence alignment in Clustal Omega Clustal Omega identified a high similarity (86.65%) between human tubulin β-4A isoform 3 (NP_001276058.1) and β-tubulin 1.9 protein (AAL75956.1), as shown in Fig. 1 . Epitopes prediction - IEDB Epitope prediction was performed using IEDB for T cells (MHC class I and II) and B cells. These results are shown in Table 2 . Table 2 Immunogenic epitopes with high affinity for HLA-I, HLA-II alleles, and B cells predicted in homologous regions between human tubulin β-4A isoform 3 and β-tubulin 1.9 protein of T. cruzi . Epitope Start End Affinity Length NetMHCIIpan _EL score 2 Alelle VPFPRLHFF 258 266 MHC I 9 0.9470 HLA B*15:01 NDLVSEYQQYQDATI 416 430 MHC II 15 2.5 HLA-DQA1*01:01/DQB1*05:01 GQSGAGNNWAKGHYTEGAELIDS 93 115 B cells 23 - - In Table 2 , the epitopes with the best scores and the highest percentage of similarity among amino acids from both sequences were selected. Properties of the analyzed epitope Before verifying the epitope properties, those without full sequence similarity were discarded using Clustal Omega. VaxiJen v2.0 was used to analyze antigenicity; ToxinPred v1.0 to assess toxicity; BepiPred v2.0, specifically for B cells, and Netsurfp v 3.0 to evaluate surface exposure; and DNASTAR. This data for the three selected epitopes are summarized in Table 3 . Table 3 Summary of the properties from selected epitopes. Epítopo Afinidad Vaxijen v2.0 Toxinpred v 1.0 Bepipred v 2.0/ Netsurfp v 3.0 DNASTAR VPFPRLHFF MHC I 0,8003 Non-toxic Partially exposed antigenic epitope Non-exposed epitope (hydrophobicity profile) NDLVSEYQQYQDATI MHC II 0,6380 Non-toxic Partially exposed antigenic epitope Non-exposed epitope (hydrophobicity profile) GQSGAGNNWAKGHYTEGAELIDS B cells 0,7377 Non-toxic Exposed antigenic epitope Exposed antigenic epitope (Jameson-Wolf) In Fig. 2 , the GQSGAGNNWAKGHYTEGAELIDS epitope exhibits abrupt changes in the chain, as well as disordered coils, and an alpha helix at the end of the epitope. The VPFPRLHFF epitope shows a higher presence of alpha-helical regions along with abrupt changes in the chain; whereas the NDLVSEYQQYQDATI epitope displays only alpha-helical regions in the structure and an abrupt change in the chain. Furthermore, antigenicity was assessed using the hydrophobicity profile (for T cells) and the Jameson-Wolf index (for B cells), with the results shown in Table 3 . 3D protein modeling The human tubulin β-4A isoform 3 and the β-tubulin 1.9 protein from T. cruzi were modeled, with the epitopes highlighted (shown in Fig. 3 ). High structural similarity was observed through alignment (Fig. 3 C), and the selected epitopes were found to be sequentially identical in both proteins, as shown in Fig. 3 A and 3 B. 3D alignment prediction Using the ClusPro server, the binding affinity was determined between the selected epitope and pattern recognition receptors, as well as MHC class I and II molecules. The most consistent docking was observed with TLR 2, as shown in Figs. 4 , 5 and 6 for the three selected epitopes, since it displayed negative binding energy values, indicating a favorable interaction between the epitope and the recognition receptor. After analyzing the properties of the three epitopes, the GQSGAGNNWAKGHYTEGAELIDS epitope was selected as the most suitable due to its exposure and antigenicity for interaction with TLR 2. Once the epitope was selected, the analysis of its properties continued using iMODS, which was employed to evaluate the deformability of the proteins and the complex. In Fig. 7 A, Normal Mode 1 shows an eigenvalue of 0.470684 E-05, indicating a high deformability, which may be favorable for its binding to a receptor. This suggests that the complex is flexible; however, higher-order modes require greater energy for deformation, reflecting relative stability in local configurations. In Fig. 7 B, the VPFPRLHFF (258–266) epitope exhibits an abundance of red regions, which indicates rigidity and suggests that it may be part of a protein domain. The GQSGAGNNWAKGHYTEGAELIDS (93–115) epitope displays a combination of red, blue and white regions, denoting partial flexibility, which favors immune recognition. DISCUSSION Chagas disease often presents digestive pathological manifestations such as megaviscera, associated with enteric neuronal cell death, reaching up to 85% in megaesophagus and 50% in megacolon (Dutra et al., 2009 ). Furthermore, a strong association has been reported between neuronal destruction and the presence of potentially cytotoxic cells such as eosinophils and mast cells (Adad et al., 1991 ). These cells participate in inflammatory processes that may cause tissue damage by promoting cytokines secretion including IL-1, TNF-α, and IL-6 (Cardoso, 2006 ), as well as the production of nitric oxide and free radicals, which can lead to fibrosis (Adad et al., 1991 ). Enteric neuronal cell death plays an important role in the development of megaviscera (Kierszenbaum, 2003 ). In this context, molecular mimicry between T. cruzi proteins and human proteins has been proposed as a potential mechanism underlying this process (Gironès, Cuervo and Fresno, 2005 ). For instance, the study by Niborski et al. identified cross-reactivity between T. cruzi tubulin and human β-tubulin (Code: P08562), both of which were recognized by the A2R1 monoclonal antibody (Niborski et al., 2021 ). In that study, the isoform or isotype of the T. cruzi tubulin evaluated was not specified; therefore, we performed the alignment of this protein and T. cruzi β-tubulin 1.9, which revealed high similarity, suggesting that it could also be recognized by the antibody. Nevertheless, to date, no studies have definitely identified the specific proteins implicated in the enteric neuronal damage observed in chagasic megaviscera cases. In this study, the T. cruzi β-tubulin 1.9 isoform was found to share structural and immunological similarities with the human β-tubulin 4A isoform 3. Through an in silico analysis, the interaction of three candidate epitopes with the MHC-I and MHC-II alleles, as well as TLR1, TLR2 and TLR7 receptors was evaluated. The GQSGAGNNWAKGHYTEGAELID epitope, recognized by B cells, was identified as the most suitable based on its antigenic and docking properties. Among the evaluated receptors, TLR2 showed the best docking. This receptor participates in the immune response by defining the cytokine profile to be released, including IFN- γ, TNF- α and IL-17, in the pathogenic form of Chagas disease (Mendes da Silva et al., 2017 ; Macaluso et al., 2023 ). Furthermore, endogenous ligands for TLR 2, known as alarmins, have been described as mediators of tissue damage-associated inflammatory response (Oliveira-Nascimento, Massari and Wetzler, 2012 ). However, no evidence has been found that these ligands regulate or direct the course of immune defense. Based on the results obtained, it can be suggested that the epitope identified in T. cruzi β-tubulin may participate in a molecular mimicry mechanism with the human β-tubulin 4A isoform 3, which potentially contributes to the enteric nervous system damage observed in Chagas disease. Nevertheless, it is important to note that neuronal damage could also result from an immune response directed against the parasite itself (Ricci et al., 2020 ). Thus, both direct immunopathology and autoimmunity may be involved in the pathogenesis. Therefore, although molecular mimicry might be a component of the pathological process of Chagas disease, further studies are required to investigate its role. This study has limitations, particularly the absence of experimental validation, since the approach employed was an in silico analysis. Consequently, the results obtained may not exactly represent the complexity of the immune system. CONCLUSION Within the scope of our limitations, the GQSGAGNNWAKGHYTEGAELIDS epitope was identified in human β-tubulin 4A isoform 3 and T. cruzi β-tubulin 1.9, suggesting the possibility of a molecular mimicry reaction. This phenomenon may elucidate one of the causes of megaviscera in patients with Chagas disease, as it could induce inflammatory processes, as well as enteric neuronal cell death. Although this study did not experimentally demonstrate the pathogenicity of this autoantibody, it provides an in silico basis for further experimental research. Declarations Data Availability Fig. 1 was generated using the free server Clustal Omega (available at: https://www.ebi.ac.uk/jdispatcher/msa/clustalo, accessed on May 23, 2025) (Madeira et al., 2024), where a comparative alignment was performed between antigen sequences and the selected human tubulin β-4A isoform 3 protein (NCBI code: >NP_001276058.1), focusing on regions showing significant similarity to predict potential epitopes. Table 2 was generated from the epitope prediction results using the IEDB database (available at: https://nextgen-tools.iedb.org/pipeline/56a665e4-d5e7-418f-ba12-7403b7fa253065, accessed on May 26, 2025) (Yan et al., 2024). This table summarizes the immunogenic epitopes with high affinity for HLA-I, HLA-II alleles, and B cells, which were predicted in homologous regions between human tubulin β-4A isoform 3 and T. cruzi β-tubulin 1.9 protein. For T cells, MHC class I and II epitope prediction was performed using the NetMHCpan 4.1 EL algorithm. For B-cell epitope prediction, the BepiPred Linear B-Cell Epitope Prediction 2.0 and Discotopoe 2.0 servers were employed.Table 3 presents a summary of the properties of the selected epitopes. Antigenicity was analyzed with VaxiJen v2.0 (available at: https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html, accessed on May 30, 2025) (Doytchinova and Flower, 2007). Toxicity evaluation was performed with ToxinPred v1.0 (available at: https://webs.iiitd.edu.in/raghava/toxinpred/design.php , accessed on May 30, 2025) (Gupta et al., 2013), indicating that the three selected epitopes are non-toxic. Epitope antigenicity and localization (specifically for B cells) were evaluated with BepiPred v2.0 (available at: https://services.healthtech.dtu.dk/services/BepiPred-2.0/, accessed on May 30, 2025) (Jespersen et al., 2017) and surface exposure with Netsurfp v3.0. Finally, DNASTAR (Wisconsin, USA; accessed on June 4, 2025) was used to calculate the Jameson-Wolf index for residue antigenicity, the hydrophobicity index to predict exposed and, therefore, accessible regions, and the Chou-Fasman and Garnier-Robson algorithms for epitope secondary structure prediction. Figure 2 was generated using DNASTAR (Wisconsin, USA; accessed on June 4, 2025) to calculate the Jameson-Wolf index for residue antigenicity, the hydrophobicity index to predict exposed and, therefore, accessible regions, and the Chou-Fasman and Garnier-Robson algorithms for epitope secondary structure prediction. The Fig. 3 was generated with PyMOL software, which employs its own OpenGL-based rendering engine (version 4.6). Fig. 4-6 were generated using ClusPro (available at: https://cluspro.org/home.php, accessed on June 5, 2025) (Jones et al., 2022), selecting models with the lowest energy scores, as more negative values indicate more favorable interactions. Figure 7 was generated with the iMODS server (available at: https://imods.iqf.csic.es/, accessed on June 5, 2025) (López-Blanco et al., 2014) through normal mode analysis, calculating complex deformation under force field variations over time intervals and estimating protein deformability based on eigenvalues. The results presented in the figures and tables mentioned above are published in the Figshare repository, as part of this manuscript: https://doi.org/10.6084/m9.figshare.30121390.v2 AUTHOR CONTRIBUTIONS AVCI, CAQJ, PGM and GAOP designed, implemented the research, analyzed the results and wrote the manuscript, PGM and ASRZ reviewed the manuscript, GAOP and LAPS supervised the project. FUNDING No funding was received for conducting this study ACKNOWLEDGMENTS This works is supported by Vicerrectorado de investigación of the Universidad Católica de Santa María Consent for publication All authors approved the publication of the manuscript. Conflicts of interest/competing interests The authors declare they have no financial interests. Ethics approval Approval from research ethics committees was not required for this study, because it was based entirely on in silico analyses. References Abramson, J. et al. 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(2018) ‘Molecular mimicry and autoimmunity’, Journal of Autoimmunity , 95, pp. 100–123. https://doi.org/10.1016/j.jaut.2018.10.012 Yan, Z. et al. (2024) ‘Next-generation IEDB tools: a platform for epitope prediction and analysis’, Nucleic Acids Research , 52(W1), pp. W526–W532. https://doi.org/10.1093/nar/gkae407 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7586194\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":527600034,\"identity\":\"81aafc82-86a4-47ac-847b-1e69cb494828\",\"order_by\":0,\"name\":\"Ana Valentina 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02:09:37\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":399690,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSequence alignment between human tubulin β-4A isoform 3 and β-tubulin 1.9 protein.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7586194/v1/6e8de5abb10a3cfb0475fd3e.png\"},{\"id\":93538524,\"identity\":\"614a2896-6251-475a-a175-3f5f9cef599c\",\"added_by\":\"auto\",\"created_at\":\"2025-10-15 02:09:38\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":198397,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDiagram of antigenic site prediction and three-dimensional structure analysis. The structural evaluation of the protein was performed and compared the results obtained from the Chou-Fasman and Garnier-Robson prediction methods.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7586194/v1/3361ea5c44767858ab45357e.png\"},{\"id\":93538518,\"identity\":\"2e4bd6c7-78b3-43a8-8e8f-0ba84335dce9\",\"added_by\":\"auto\",\"created_at\":\"2025-10-15 02:09:38\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":322929,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e3D model prediction of the evaluated proteins. Predicted structures with highlighted epitopes: GQSGAGNNWAKGHYTEGAEL (red), NDLVSEYQQYQDATI (cyan), and VPFPRLHFF (orange). A) Human tubulin β-4A isoform 3 prediction. B) \\u003cem\\u003eT. cruzi\\u003c/em\\u003eβ-tubulin 1.9 protein\\u003cem\\u003e \\u003c/em\\u003eprediction. C) Structural alignment of both proteins: human tubulin β-4A isoform 3 (yellow) and β-tubulin 1.9 protein from \\u003cem\\u003eT. cruzi \\u003c/em\\u003e(pink).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7586194/v1/a59e5eecf4c6a55615af3ca7.png\"},{\"id\":93539381,\"identity\":\"67cb28ac-78bb-49b6-91b7-f7d40cdd784b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-15 02:17:39\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":229216,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e3D docking of the NDLVSEYQQYQDATI epitope. A) With TLR 1, where the minimum binding energy is -733.6 and the central energy is -720.6. B) With TLR 2, showing a minimum binding energy of -685.3 and a central energy of -592.5. C) With TLR 7, showing a minimum binding energy of -691.6 and a central energy of -581.7. D) With MHC class II (HLA-DQA1*01:01/DQB1*05:01), showing a minimum binding energy of -656.0 and a central energy of -567.6. E) With MHC I (HLA-B*53:01), showing a minimum binding energy of -681.7 and a central energy of -542.2.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7586194/v1/69fc91b9f2f24b23162267a9.png\"},{\"id\":93538569,\"identity\":\"f285bce0-dc8d-4729-b0b6-5298f26c4043\",\"added_by\":\"auto\",\"created_at\":\"2025-10-15 02:09:40\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":205334,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e3D docking of the VPFPRLHFF epitope. A) With TLR 1, where the minimum binding energy is -1055.1 and the central energy is -840.9. B) With TLR 2, showing a minimum binding energy of -885.4 and a central energy of -786.1. C) With TLR 7, showing a minimum binding energy of -866.4 and a central energy of -760.1. D) With MHC class II (HLA-DQA1*01:01/DQB1*05:01), showing a minimum binding energy of -802.3 and a central energy of -746.8. E) With MHC I (HLA-B*53:01), showing a minimum binding energy of -831.1 and a central energy of -714.9.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7586194/v1/b0daae9e670628655624168b.png\"},{\"id\":93538585,\"identity\":\"b18e2503-9820-4f68-8d66-cec89872a90b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-15 02:09:40\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":244230,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e3D docking of the GQSGAGNNWAKGHYTEGAELIDSepitope. A) With TLR 1, where the minimum binding energy is -1057.6 and the central energy is -882.7. B) With TLR 2, showing a minimum binding energy of -1057.3 and a central energy of -952.7. C) With TLR 7, showing a minimum binding energy of -978.9. D) With MHC class II (HLA-DQA1*01:01/DQB1*05:01), showing a minimum binding energy of -892.5 and a central energy of -749.8. E) With MHC I (HLA-B*53:01), showing a minimum binding energy of -977.9 and a central energy of -894.2.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7586194/v1/05afc0242b8e32cf5490e332.png\"},{\"id\":93538533,\"identity\":\"722ca5f7-0928-4601-98f9-8e797bd7d8f2\",\"added_by\":\"auto\",\"created_at\":\"2025-10-15 02:09:39\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":537881,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDeformability analysis with iMODS. A) X-Y plot evaluating the eigenvalues associated with each Normal Mode, determining the rigidity of motion. B) Dynamic correlation map of each protein residue.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7586194/v1/99813ec08b16d29419f29347.png\"},{\"id\":93905433,\"identity\":\"daed8580-ddbf-4ffa-846d-9b0312deb55d\",\"added_by\":\"auto\",\"created_at\":\"2025-10-20 07:01:57\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2740417,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7586194/v1/e5ef7215-901a-4bd2-8f39-edffb02b50ea.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Molecular mimicry between Trypanosoma cruzi and human β-tubulin as a potential autoimmune mechanism in Chagas disease–associated megaviscera\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eChagas disease is a parasitic infection caused by the protozoan \\u003cem\\u003eTrypanosoma cruzi\\u003c/em\\u003e, transmitted through the infected feces of triatomine vectors such as \\u003cem\\u003eTriatoma infestans\\u003c/em\\u003e and \\u003cem\\u003eRhodnius prolixus.\\u003c/em\\u003e Transmission occurs when the feces come into contact with a bite wound or the mucous membranes of the host (Bern, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Chagas disease is endemic in Central and South America; however, due to migration, it has expanded to Europe, the United States, Canada, Japan, and Australia. It has been reported that 70% of infected individuals are asymptomatic, while the remaining 30% develop cardiac, digestive, neurological, or mixed disorders (Niborski et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe immune response developed by the host against the parasite is critical for the outcome of the infection. Antibodies are central components of the host immune response, participating in the early control of the parasite, and in the pathophysiology of the disease (Acevedo, Girard and G\\u0026oacute;mez, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Niborski et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), with special emphasis on the presence of anti-α-galactoside (anti-Gal) antibodies, which recognize glycans on the surface of \\u003cem\\u003eT. cruzi\\u003c/em\\u003e, and mediate the lysis of the parasite in its trypomastigote form (Almeida et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e). Studies have also identified another antibody in mice, the monoclonal antibody TcβTUB which recognizes β-tubulin present in the parasite\\u0026rsquo;s flagellum (Montalv\\u0026atilde;o et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eDisorders such as Chagas disease are commonly associated with the development of autoimmune diseases caused by molecular mimicry, a mechanism that occurs when similarities between the protein sequences of parasite and human epitopes trigger the activation of autoantibodies (Rojas et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). One example is the monoclonal autoantibody A2R1, which exhibits cross-reactivity between human β-tubulin and the β-tubulin of \\u003cem\\u003eT. cruzi\\u003c/em\\u003e (Niborski et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAutoantibodies identified as reactive with tubulin, myosin, and actin have also been associated with complications such as megaviscera (megacolon, megaesophagus, and splenomegaly), arising from neurosensory system alterations in the muscle cells of affected organs (Hugo Garc\\u0026iacute;a Orozco et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), induced by infection of this protozoan (Leon et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e). Moreover, CD4\\u0026thinsp;+\\u0026thinsp;T cells that recognize human myosin and the peptide of the \\u003cem\\u003eT. cruzi\\u003c/em\\u003e B13 protein may drive Chagas myocardiopathy, characterized by T cell/macrophage infiltration, fibrosis and damage to cardiac fibers (Leon et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e; Iwai et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eCurrently, the role and origin of β-tubulin autoantibodies in the development of megaviscera after \\u003cem\\u003eT. cruzi\\u003c/em\\u003e infection remain unclear. The aim of this study is to identify potential molecular mimicry between \\u003cem\\u003eT. cruzi\\u003c/em\\u003e β-tubulin and human proteome through immunopeptidomics and immunoinformatics, as a possible cause of a pathological autoimmune reaction.\\u003c/p\\u003e\"},{\"header\":\"METHODOLOGY\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eAntigen selection - BLAST\\u003c/h2\\u003e\\u003cp\\u003eAn in silico immunoinformatic study was conducted to identify \\u003cem\\u003eT. cruzi\\u003c/em\\u003e proteins with immunological relevance. A general sequence homology analysis was performed focusing on \\u003cem\\u003eT. cruzi\\u003c/em\\u003e proteins of interest. Using the NCBI database, the antigen sequence was retrieved, and the BLAST tool (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://blast.ncbi.nlm.nih.gov/BlastAlign.cgi\\u003c/span\\u003e\\u003cspan address=\\\"https://blast.ncbi.nlm.nih.gov/BlastAlign.cgi\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on May 21, 2025) (Altschul et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e1990\\u003c/span\\u003e) was applied for the comparative alignment between immunogenic protein of interest from \\u003cem\\u003eT. cruzi\\u003c/em\\u003e (Taxid: 5393) β-tubulin 1.9 (NCBI Code: \\u0026gt;NP_001276058.1) and the \\u003cem\\u003eHomo Sapiens\\u003c/em\\u003e proteome (Taxid: 9606), revealing proteins with high sequence similarity.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eSequence alignment in Clustal Omega (dup: abstract ?)\\u003c/h3\\u003e\\n\\u003cp\\u003eThrough the Clustal Omega free server (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ebi.ac.uk/jdispatcher/msa/clustalo\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ebi.ac.uk/jdispatcher/msa/clustalo\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on May 23, 2025) (Madeira et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), a comparative alignment was performed between antigen sequences and the selected human protein tubulin β-4A isoform 3 (NCBI Code: \\u0026gt;NP_001276058.1), focusing on regions that exhibited significant similarity to predict potential epitopes.\\u003c/p\\u003e\\n\\u003ch3\\u003eEpitope Prediction - IEDB\\u003c/h3\\u003e\\n\\u003cp\\u003eUsing the IEDB database (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://nextgen-tools.iedb.org/pipeline/56a665e4-d5e7-418f-ba12-03b7fa253065\\u003c/span\\u003e\\u003cspan address=\\\"https://nextgen-tools.iedb.org/pipeline/56a665e4-d5e7-418f-ba12-03b7fa253065\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on May 26, 2025) (Yan et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), the antigen sequence of \\u003cem\\u003eT. cruzi\\u003c/em\\u003e was analyzed for T-cell epitope prediction (major histocompatibility complex class I and II) via NetMHCpan 4.1 EL algorithm, which predicts epitopes based on a large dataset of eluted ligands. For B-cell epitope prediction, the BepiPred Linear B-Cell Epitope Prediction 2.0 and Discotopoe 2.0 servers were employed, both based on Random Forest machine-learning models. Epitopes that showed sequence similarity with \\u003cem\\u003eHomo Sapiens\\u003c/em\\u003e in the Clustal Omega analysis (accessed on May 23, 2025) and were predicted to be accessible for autoantibody interaction were selected.\\u003c/p\\u003e\\n\\u003ch3\\u003eProperties of analyzed epitope\\u003c/h3\\u003e\\n\\u003cp\\u003eTo analyze the properties of the selected epitopes, VaxiJen v1.0 (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on May 30, 2025) (Doytchinova and Flower, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e) was used to assess antigenicity; Bepipred v2.0 (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://services.healthtech.dtu.dk/services/BepiPred-2.0/\\u003c/span\\u003e\\u003cspan address=\\\"https://services.healthtech.dtu.dk/services/BepiPred-2.0/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on May 30, 2025) (Jespersen et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) to evaluate antigenicity and epitope localization; Toxinpred v 1.0 (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://webs.iiitd.edu.in/raghava/toxinpred/design.php\\u003c/span\\u003e\\u003cspan address=\\\"https://webs.iiitd.edu.in/raghava/toxinpred/design.php\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on May 30, 2025) (Gupta et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) to exclude potential toxicity, and DNASTAR to calculate Jameson-Wolf index for each residue antigenicity, the hydrophobicity index for predicting exposed regions, and therefore its accessibility; and the Chou-Fasman and Garnier-Robson algorithms for secondary structure prediction of the epitope (Wisconsin, USA) (accessed on June 4, 2025).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3D protein modeling - Alpha Fold\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eFor 3D modeling of both proteins (\\u0026gt;\\u0026thinsp;NP_001276058.1 and \\u0026gt;\\u0026thinsp;AAL75956.1), the Alpha Fold 3 server (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://alphafoldserver.com/about\\u003c/span\\u003e\\u003cspan address=\\\"https://alphafoldserver.com/about\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on June 14, 2025) (Abramson et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)​ was used, along with the Pymol software, which employs its own rendering engine based on OpenGL (version 4.6).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3D prediction of the epitope docking with MHC I and II; TLR 1, 2, 7 and 9.\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eEpitope docking was carried out using ClusPro (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://cluspro.org/home.php\\u003c/span\\u003e\\u003cspan address=\\\"https://cluspro.org/home.php\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on June 5, 2025) (Jones et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), selecting models with the lowest energy scores (more negative values indicate more favorable interactions). Subsequently, molecular dynamics were analyzed with the iMODS server (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://imods.iqf.csic.es/\\u003c/span\\u003e\\u003cspan address=\\\"https://imods.iqf.csic.es/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on June 5, 2025) (L\\u0026oacute;pez-Blanco et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) through Normal Mode Analysis, calculating complex deformation under force field variations over time intervals, and estimating protein deformability based on eigenvalues. The 3D epitope models were retrieved from the RPBS database (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#welcome\\u003c/span\\u003e\\u003cspan address=\\\"https://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#welcome\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on June 5, 2025) (Alland et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). The models of the Major Histocompatibility Complexes (MHC) and the Toll-like receptors (TLR) \\u0026mdash;namely TLR 7 (Code: 8S86), TLR 2 (Code: 1O77), and TLR 1 (Code: 7NT7)\\u0026mdash; were downloaded from the RCSB PDB database (available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.rcsb.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.rcsb.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, accessed on June 5, 2025) (Berman, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e).\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eEpitope Prediction - IEDB\\u003c/h2\\u003e\\u003cp\\u003eA similarity between the two selected sequences (\\u0026gt;\\u0026thinsp;NP_001276058.1 and \\u0026gt;\\u0026thinsp;AAL75956.1) was identified and is shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eProteins selected for alignment.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNCBI code\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eProtein\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eOrganism\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026gt;NP_001276058.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003etubulin β-4A isoform 3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eHomo sapiens\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026gt;AAL75956.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eβ-tubulin 1.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eTrypanosoma cruzi\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eSequence alignment in Clustal Omega\\u003c/h3\\u003e\\n\\u003cp\\u003eClustal Omega identified a high similarity (86.65%) between human tubulin β-4A isoform 3 (NP_001276058.1) and β-tubulin 1.9 protein (AAL75956.1), as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eEpitopes prediction - IEDB\\u003c/h3\\u003e\\n\\u003cp\\u003eEpitope prediction was performed using IEDB for T cells (MHC class I and II) and B cells. These results are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eImmunogenic epitopes with high affinity for HLA-I, HLA-II alleles, and B cells predicted in homologous regions between human tubulin β-4A isoform 3 and β-tubulin 1.9 protein of \\u003cem\\u003eT. cruzi\\u003c/em\\u003e.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEpitope\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eStart\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eEnd\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eAffinity\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eLength\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNetMHCIIpan _EL score 2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eAlelle\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVPFPRLHFF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e258\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e266\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMHC I\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.9470\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eHLA B*15:01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNDLVSEYQQYQDATI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e416\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e430\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMHC II\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eHLA-DQA1*01:01/DQB1*05:01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGQSGAGNNWAKGHYTEGAELIDS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e115\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eB cells\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, the epitopes with the best scores and the highest percentage of similarity among amino acids from both sequences were selected.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eProperties of the analyzed epitope\\u003c/h2\\u003e\\u003cp\\u003eBefore verifying the epitope properties, those without full sequence similarity were discarded using Clustal Omega. VaxiJen v2.0 was used to analyze antigenicity; ToxinPred v1.0 to assess toxicity; BepiPred v2.0, specifically for B cells, and Netsurfp v 3.0 to evaluate surface exposure; and DNASTAR. This data for the three selected epitopes are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eSummary of the properties from selected epitopes.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEp\\u0026iacute;topo\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAfinidad\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eVaxijen v2.0\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eToxinpred v 1.0\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eBepipred v 2.0/ Netsurfp v 3.0\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eDNASTAR\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVPFPRLHFF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMHC I\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,8003\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eNon-toxic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ePartially exposed antigenic epitope\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNon-exposed epitope\\u003c/p\\u003e\\u003cp\\u003e(hydrophobicity profile)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNDLVSEYQQYQDATI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMHC II\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,6380\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eNon-toxic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ePartially exposed antigenic epitope\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNon-exposed epitope\\u003c/p\\u003e\\u003cp\\u003e(hydrophobicity profile)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGQSGAGNNWAKGHYTEGAELIDS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eB cells\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,7377\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eNon-toxic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eExposed antigenic epitope\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eExposed antigenic epitope\\u003c/p\\u003e\\u003cp\\u003e(Jameson-Wolf)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, the GQSGAGNNWAKGHYTEGAELIDS epitope exhibits abrupt changes in the chain, as well as disordered coils, and an alpha helix at the end of the epitope. The VPFPRLHFF epitope shows a higher presence of alpha-helical regions along with abrupt changes in the chain; whereas the NDLVSEYQQYQDATI epitope displays only alpha-helical regions in the structure and an abrupt change in the chain.\\u003c/p\\u003e\\u003cp\\u003eFurthermore, antigenicity was assessed using the hydrophobicity profile (for T cells) and the Jameson-Wolf index (for B cells), with the results shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3D protein modeling\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe human tubulin β-4A isoform 3 and the β-tubulin 1.9 protein from \\u003cem\\u003eT. cruzi\\u003c/em\\u003e were modeled, with the epitopes highlighted (shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eHigh structural similarity was observed through alignment (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC), and the selected epitopes were found to be sequentially identical in both proteins, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA and \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3D alignment prediction\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eUsing the ClusPro server, the binding affinity was determined between the selected epitope and pattern recognition receptors, as well as MHC class I and II molecules.\\u003c/p\\u003e\\u003cp\\u003eThe most consistent docking was observed with TLR 2, as shown in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e for the three selected epitopes, since it displayed negative binding energy values, indicating a favorable interaction between the epitope and the recognition receptor.\\u003c/p\\u003e\\u003cp\\u003eAfter analyzing the properties of the three epitopes, the GQSGAGNNWAKGHYTEGAELIDS epitope was selected as the most suitable due to its exposure and antigenicity for interaction with TLR 2.\\u003c/p\\u003e\\u003cp\\u003eOnce the epitope was selected, the analysis of its properties continued using iMODS, which was employed to evaluate the deformability of the proteins and the complex.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA, Normal Mode 1 shows an eigenvalue of 0.470684 E-05, indicating a high deformability, which may be favorable for its binding to a receptor. This suggests that the complex is flexible; however, higher-order modes require greater energy for deformation, reflecting relative stability in local configurations. In Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB, the VPFPRLHFF (258\\u0026ndash;266) epitope exhibits an abundance of red regions, which indicates rigidity and suggests that it may be part of a protein domain. The GQSGAGNNWAKGHYTEGAELIDS (93\\u0026ndash;115) epitope displays a combination of red, blue and white regions, denoting partial flexibility, which favors immune recognition.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eChagas disease often presents digestive pathological manifestations such as megaviscera, associated with enteric neuronal cell death, reaching up to 85% in megaesophagus and 50% in megacolon (Dutra et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Furthermore, a strong association has been reported between neuronal destruction and the presence of potentially cytotoxic cells such as eosinophils and mast cells (Adad et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e). These cells participate in inflammatory processes that may cause tissue damage by promoting cytokines secretion including IL-1, TNF-α, and IL-6 (Cardoso, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e), as well as the production of nitric oxide and free radicals, which can lead to fibrosis (Adad et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eEnteric neuronal cell death plays an important role in the development of megaviscera (Kierszenbaum, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). In this context, molecular mimicry between \\u003cem\\u003eT. cruzi\\u003c/em\\u003e proteins and human proteins has been proposed as a potential mechanism underlying this process (Giron\\u0026egrave;s, Cuervo and Fresno, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). For instance, the study by Niborski et al. identified cross-reactivity between \\u003cem\\u003eT. cruzi\\u003c/em\\u003e tubulin and human β-tubulin (Code: P08562), both of which were recognized by the A2R1 monoclonal antibody (Niborski et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In that study, the isoform or isotype of the \\u003cem\\u003eT. cruzi\\u003c/em\\u003e tubulin evaluated was not specified; therefore, we performed the alignment of this protein and \\u003cem\\u003eT. cruzi\\u003c/em\\u003e β-tubulin 1.9, which revealed high similarity, suggesting that it could also be recognized by the antibody. Nevertheless, to date, no studies have definitely identified the specific proteins implicated in the enteric neuronal damage observed in chagasic megaviscera cases.\\u003c/p\\u003e\\u003cp\\u003eIn this study, the \\u003cem\\u003eT. cruzi\\u003c/em\\u003e β-tubulin 1.9 isoform was found to share structural and immunological similarities with the human β-tubulin 4A isoform 3. Through an in silico analysis, the interaction of three candidate epitopes with the MHC-I and MHC-II alleles, as well as TLR1, TLR2 and TLR7 receptors was evaluated. The GQSGAGNNWAKGHYTEGAELID epitope, recognized by B cells, was identified as the most suitable based on its antigenic and docking properties.\\u003c/p\\u003e\\u003cp\\u003eAmong the evaluated receptors, TLR2 showed the best docking. This receptor participates in the immune response by defining the cytokine profile to be released, including IFN- γ, TNF- α and IL-17, in the pathogenic form of Chagas disease (Mendes da Silva et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Macaluso et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Furthermore, endogenous ligands for TLR 2, known as alarmins, have been described as mediators of tissue damage-associated inflammatory response (Oliveira-Nascimento, Massari and Wetzler, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). However, no evidence has been found that these ligands regulate or direct the course of immune defense.\\u003c/p\\u003e\\u003cp\\u003eBased on the results obtained, it can be suggested that the epitope identified in \\u003cem\\u003eT. cruzi\\u003c/em\\u003e β-tubulin may participate in a molecular mimicry mechanism with the human β-tubulin 4A isoform 3, which potentially contributes to the enteric nervous system damage observed in Chagas disease. Nevertheless, it is important to note that neuronal damage could also result from an immune response directed against the parasite itself (Ricci et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Thus, both direct immunopathology and autoimmunity may be involved in the pathogenesis. Therefore, although molecular mimicry might be a component of the pathological process of Chagas disease, further studies are required to investigate its role.\\u003c/p\\u003e\\u003cp\\u003eThis study has limitations, particularly the absence of experimental validation, since the approach employed was an in silico analysis. Consequently, the results obtained may not exactly represent the complexity of the immune system.\\u003c/p\\u003e\"},{\"header\":\"CONCLUSION\",\"content\":\"\\u003cp\\u003eWithin the scope of our limitations, the GQSGAGNNWAKGHYTEGAELIDS epitope was identified in human β-tubulin 4A isoform 3 and \\u003cem\\u003eT. cruzi\\u003c/em\\u003e β-tubulin 1.9, suggesting the possibility of a molecular mimicry reaction. This phenomenon may elucidate one of the causes of megaviscera in patients with Chagas disease, as it could induce inflammatory processes, as well as enteric neuronal cell death. Although this study did not experimentally demonstrate the pathogenicity of this autoantibody, it provides an in silico basis for further experimental research.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFig. 1 was generated using the free server Clustal Omega (available at: https://www.ebi.ac.uk/jdispatcher/msa/clustalo, accessed on May 23, 2025) (Madeira et al., 2024), where a comparative alignment was performed between antigen sequences and the selected human tubulin \\u0026beta;-4A isoform 3 protein (NCBI code: \\u0026gt;NP_001276058.1), focusing on regions showing significant similarity to predict potential epitopes.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2 was generated from the epitope prediction results using the IEDB database (available at: https://nextgen-tools.iedb.org/pipeline/56a665e4-d5e7-418f-ba12-7403b7fa253065, accessed on May 26, 2025) (Yan et al., 2024). This table summarizes the immunogenic epitopes with high affinity for HLA-I, HLA-II alleles, and B cells, which were predicted in homologous regions between human tubulin \\u0026beta;-4A isoform 3 and T. cruzi \\u0026beta;-tubulin 1.9 protein. For T cells, MHC class I and II epitope prediction was performed using the NetMHCpan 4.1 EL algorithm. For B-cell epitope prediction, the BepiPred Linear B-Cell Epitope Prediction 2.0 and Discotopoe 2.0 servers were employed.Table 3 presents a summary of the properties of the selected epitopes. Antigenicity was analyzed with VaxiJen v2.0 (available at: https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html, accessed on May 30, 2025) (Doytchinova and Flower, 2007). Toxicity evaluation was performed with ToxinPred v1.0 (available at: https://webs.iiitd.edu.in/raghava/toxinpred/design.php , accessed on May 30, 2025) (Gupta et al., 2013), indicating that the three selected epitopes are non-toxic. Epitope antigenicity and localization (specifically for B cells) were evaluated with BepiPred v2.0 (available at: https://services.healthtech.dtu.dk/services/BepiPred-2.0/, accessed on May 30, 2025) (Jespersen et al., 2017) and surface exposure with Netsurfp v3.0. Finally, DNASTAR (Wisconsin, USA; accessed on June 4, 2025) was used to calculate the Jameson-Wolf index for residue antigenicity, the hydrophobicity index to predict exposed and, therefore, accessible regions, and the Chou-Fasman and Garnier-Robson algorithms for epitope secondary structure prediction.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 2 was generated using DNASTAR (Wisconsin, USA; accessed on June 4, 2025) to calculate the Jameson-Wolf index for residue antigenicity, the hydrophobicity index to predict exposed and, therefore, accessible regions, and the Chou-Fasman and Garnier-Robson algorithms for epitope secondary structure prediction.\\u003c/p\\u003e\\n\\u003cp\\u003eThe Fig. 3 was generated with PyMOL software, which employs its own OpenGL-based rendering engine (version 4.6).\\u003c/p\\u003e\\n\\u003cp\\u003eFig. 4-6 were generated using ClusPro (available at: https://cluspro.org/home.php, accessed on June 5, 2025) (Jones et al., 2022), selecting models with the lowest energy scores, as more negative values indicate more favorable interactions.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 7 was generated with the iMODS server (available at: https://imods.iqf.csic.es/, accessed on June 5, 2025) (L\\u0026oacute;pez-Blanco et al., 2014) through normal mode analysis, calculating complex deformation under force field variations over time intervals and estimating protein deformability based on eigenvalues.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe results presented in the figures and tables mentioned above are published in the Figshare repository, as part of this manuscript: https://doi.org/10.6084/m9.figshare.30121390.v2\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAUTHOR CONTRIBUTIONS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAVCI, CAQJ, PGM and GAOP designed, implemented the research, analyzed the results and wrote the manuscript, PGM and ASRZ reviewed the manuscript, GAOP and LAPS supervised the project.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFUNDING\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo funding was received for conducting this study\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eACKNOWLEDGMENTS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis works is supported by Vicerrectorado de investigaci\\u0026oacute;n of the Universidad Cat\\u0026oacute;lica de Santa Mar\\u0026iacute;a\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e All authors approved the publication of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of interest/competing interests\\u0026nbsp;\\u003c/strong\\u003eThe authors declare they have no financial interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval\\u0026nbsp;\\u003c/strong\\u003eApproval from research ethics committees was not required for this study, because it was based entirely on in silico analyses.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAbramson, J. \\u003cem\\u003eet al.\\u003c/em\\u003e (2024) \\u0026lsquo;Accurate structure prediction of biomolecular interactions with AlphaFold 3\\u0026rsquo;, \\u003cem\\u003eNature\\u003c/em\\u003e, 630(8016), pp. 493\\u0026ndash;500. https://doi.org/10.1038/s41586-024-07487-w\\u003c/li\\u003e\\n\\u003cli\\u003eAcevedo, G.R., Girard, M.C. and G\\u0026oacute;mez, K.A. 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(2007) \\u0026lsquo;VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines\\u0026rsquo;, \\u003cem\\u003eBMC Bioinformatics\\u003c/em\\u003e, 8(1), p. 4. https://doi.org/10.1186/1471-2105-8-4\\u003c/li\\u003e\\n\\u003cli\\u003eDutra, W.O. \\u003cem\\u003eet al.\\u003c/em\\u003e (2009) \\u0026lsquo;Cellular and genetic mechanisms involved in the generation of protective and pathogenic immune responses in human Chagas disease\\u0026rsquo;, \\u003cem\\u003eMem\\u0026oacute;rias do Instituto Oswaldo Cruz\\u003c/em\\u003e, 104(suppl 1), pp. 208\\u0026ndash;218. https://doi.org/10.1590/S0074-02762009000900027\\u003c/li\\u003e\\n\\u003cli\\u003eGiron\\u0026egrave;s, N., Cuervo, H. and Fresno, M. (2005) \\u0026lsquo;Trypanosoma cruzi-Induced Molecular Mimicry and Chagas\\u0026rsquo; Disease\\u0026rsquo;, in \\u003cem\\u003eMolecular Mimicry: Infection-Inducing Autoimmune Disease\\u003c/em\\u003e. Berlin/Heidelberg: Springer-Verlag, pp. 89\\u0026ndash;123. https://doi.org/10.1007/3-540-30791-5_6\\u003c/li\\u003e\\n\\u003cli\\u003eGupta, S. \\u003cem\\u003eet al.\\u003c/em\\u003e (2013) \\u0026lsquo;In Silico Approach for Predicting Toxicity of Peptides and Proteins\\u0026rsquo;, \\u003cem\\u003ePLoS ONE\\u003c/em\\u003e, 8(9), p. e73957. https://doi.org/10.1371/journal.pone.0073957\\u003c/li\\u003e\\n\\u003cli\\u003eHugo Garc\\u0026iacute;a Orozco, V. \\u003cem\\u003eet al.\\u003c/em\\u003e (2022) \\u0026lsquo;Digestive Disorders in Chagas Disease: Megaesophagus and Chagasic Megacolon\\u0026rsquo;, in \\u003cem\\u003eChagas Disease - From Cellular and Molecular Aspects of Trypanosoma cruzi-Host Interactions to the Clinical Intervention\\u003c/em\\u003e. IntechOpen. https://doi.org/10.5772/intechopen.102871\\u003c/li\\u003e\\n\\u003cli\\u003eIwai, L.K. \\u003cem\\u003eet al.\\u003c/em\\u003e (2005) \\u0026lsquo;T-cell molecular mimicry in Chagas disease: identification and partial structural analysis of multiple cross-reactive epitopes between Trypanosoma cruzi B13 and cardiac myosin heavy chain\\u0026rsquo;, \\u003cem\\u003eJournal of Autoimmunity\\u003c/em\\u003e, 24(2), pp. 111\\u0026ndash;117. https://doi.org/10.1016/j.jaut.2005.01.006\\u003c/li\\u003e\\n\\u003cli\\u003eJespersen, M.C. \\u003cem\\u003eet al.\\u003c/em\\u003e (2017) \\u0026lsquo;BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes\\u0026rsquo;, \\u003cem\\u003eNucleic Acids Research\\u003c/em\\u003e, 45(W1), pp. W24\\u0026ndash;W29. https://doi.org/10.1093/nar/gkx346\\u003c/li\\u003e\\n\\u003cli\\u003eJones, G. \\u003cem\\u003eet al.\\u003c/em\\u003e (2022) \\u0026lsquo;Elucidation of protein function using computational docking and hotspot analysis by \\u003cem\\u003eClusPro\\u003c/em\\u003e and \\u003cem\\u003eFTMap\\u003c/em\\u003e\\u0026rsquo;, \\u003cem\\u003eActa Crystallographica Section D Structural Biology\\u003c/em\\u003e, 78(6), pp. 690\\u0026ndash;697. https://doi.org/10.1107/S2059798322002741\\u003c/li\\u003e\\n\\u003cli\\u003eKierszenbaum, F. (2003) \\u0026lsquo;Views on the autoimmunity hypothesis for Chagas disease pathogenesis\\u0026rsquo;, \\u003cem\\u003eFEMS Immunology \\u0026amp; Medical Microbiology\\u003c/em\\u003e, 37(1), pp. 1\\u0026ndash;11. https://doi.org/10.1016/S0928-8244(03)00097-X\\u003c/li\\u003e\\n\\u003cli\\u003eLeon, J.S. \\u003cem\\u003eet al.\\u003c/em\\u003e (2001) \\u0026lsquo;Cardiac Myosin Autoimmunity in Acute Chagas\\u0026rsquo; Heart Disease\\u0026rsquo;, \\u003cem\\u003eInfection and Immunity\\u003c/em\\u003e, 69(9), pp. 5643\\u0026ndash;5649. https://doi.org/10.1128/IAI.69.9.5643-5649.2001\\u003c/li\\u003e\\n\\u003cli\\u003eL\\u0026oacute;pez-Blanco, J.R. \\u003cem\\u003eet al.\\u003c/em\\u003e (2014) \\u0026lsquo;iMODS: internal coordinates normal mode analysis server\\u0026rsquo;, \\u003cem\\u003eNucleic Acids Research\\u003c/em\\u003e, 42(W1), pp. W271\\u0026ndash;W276. https://doi.org/10.1093/nar/gku339\\u003c/li\\u003e\\n\\u003cli\\u003eMacaluso, G. \\u003cem\\u003eet al.\\u003c/em\\u003e (2023) \\u0026lsquo;A Review on the Immunological Response against Trypanosoma cruzi.\\u0026rsquo;, \\u003cem\\u003ePathogens (Basel, Switzerland)\\u003c/em\\u003e, 12(2). https://doi.org/10.3390/pathogens12020282\\u003c/li\\u003e\\n\\u003cli\\u003eMadeira, F. \\u003cem\\u003eet al.\\u003c/em\\u003e (2024) \\u0026lsquo;The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024\\u0026rsquo;, \\u003cem\\u003eNucleic Acids Research\\u003c/em\\u003e, 52(W1), pp. W521\\u0026ndash;W525. https://doi.org/10.1093/nar/gkae241\\u003c/li\\u003e\\n\\u003cli\\u003eMendes da Silva, L.D. \\u003cem\\u003eet al.\\u003c/em\\u003e (2017) \\u0026lsquo;Participation of TLR2 and TLR4 in Cytokines Production by Patients with Symptomatic and Asymptomatic Chronic Chagas Disease\\u0026rsquo;, \\u003cem\\u003eScandinavian Journal of Immunology\\u003c/em\\u003e, 85(1), pp. 58\\u0026ndash;65. https://doi.org/10.1111/sji.12501\\u003c/li\\u003e\\n\\u003cli\\u003eMontalv\\u0026atilde;o, F. \\u003cem\\u003eet al.\\u003c/em\\u003e (2018) \\u0026lsquo;Antibody Repertoires Identify \\u0026beta;-Tubulin as a Host Protective Parasite Antigen in Mice Infected With Trypanosoma cruzi\\u0026rsquo;, \\u003cem\\u003eFrontiers in Immunology\\u003c/em\\u003e, 9. https://doi.org/10.3389/fimmu.2018.00671\\u003c/li\\u003e\\n\\u003cli\\u003eNiborski, L.L. \\u003cem\\u003eet al.\\u003c/em\\u003e (2021) \\u0026lsquo;Recombinant antibody against Trypanosoma cruzi from patients with chronic Chagas heart disease recognizes mammalian nervous system.\\u0026rsquo;, \\u003cem\\u003eEBioMedicine\\u003c/em\\u003e, 63, p. 103206. https://doi.org/10.1016/j.ebiom.2020.103206\\u003c/li\\u003e\\n\\u003cli\\u003eOliveira-Nascimento, L., Massari, P. and Wetzler, L.M. (2012) \\u0026lsquo;The Role of TLR2 in Infection and Immunity\\u0026rsquo;, \\u003cem\\u003eFrontiers in Immunology\\u003c/em\\u003e, 3. https://doi.org/10.3389/fimmu.2012.00079\\u003c/li\\u003e\\n\\u003cli\\u003eRicci, M.F. \\u003cem\\u003eet al.\\u003c/em\\u003e (2020) \\u0026lsquo;Neuronal Parasitism, Early Myenteric Neurons Depopulation and Continuous Axonal Networking Damage as Underlying Mechanisms of the Experimental Intestinal Chagas\\u0026rsquo; Disease\\u0026rsquo;, \\u003cem\\u003eFrontiers in Cellular and Infection Microbiology\\u003c/em\\u003e, 10. https://doi.org/10.3389/fcimb.2020.583899\\u003c/li\\u003e\\n\\u003cli\\u003eRojas, M. \\u003cem\\u003eet al.\\u003c/em\\u003e (2018) \\u0026lsquo;Molecular mimicry and autoimmunity\\u0026rsquo;, \\u003cem\\u003eJournal of Autoimmunity\\u003c/em\\u003e, 95, pp. 100\\u0026ndash;123. https://doi.org/10.1016/j.jaut.2018.10.012\\u003c/li\\u003e\\n\\u003cli\\u003eYan, Z. \\u003cem\\u003eet al.\\u003c/em\\u003e (2024) \\u0026lsquo;Next-generation IEDB tools: a platform for epitope prediction and analysis\\u0026rsquo;, \\u003cem\\u003eNucleic Acids Research\\u003c/em\\u003e, 52(W1), pp. W526\\u0026ndash;W532. https://doi.org/10.1093/nar/gkae407\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"molecular mimicry, Chagas disease, tubulin, autoimmune disease, antigen\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7586194/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7586194/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eChagas disease, caused by \\u003cem\\u003eTrypanosoma cruzi\\u003c/em\\u003e, affects a significant proportion of patients who develop digestive and cardiac complications, including megaviscera. This pathogenesis has been associated with autoimmune mechanisms mediated by molecular mimicry. In this study, an in silico evaluation of cross reactivity of β-tubulin 1.9 of \\u003cem\\u003eT. cruzi\\u003c/em\\u003e and the human tubulin β-4A isoform 3 was conducted. Using bioinformatics tools, homologous regions were identified and potential immunogenic epitopes were predicted, considering their modeling and docking. The epitope GQSGAGNNWAKGHYTEGAELIDS was found to have high affinity and favorable antigenic properties, as well as binding capacity with TLR2. These findings suggest that this epitope could participate in molecular mimicry processes implicated in the development of chagasic megaviscera.\\u003c/p\\u003e\\u003cp\\u003eClinical trial number: not applicable.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Molecular mimicry between Trypanosoma cruzi and human β-tubulin as a potential autoimmune mechanism in Chagas disease–associated megaviscera\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-15 02:09:26\",\"doi\":\"10.21203/rs.3.rs-7586194/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"b345799b-ba14-4939-80f8-8b6d50c5636f\",\"owner\":[],\"postedDate\":\"October 15th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-10-20T06:53:41+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-15 02:09:26\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7586194\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7586194\",\"identity\":\"rs-7586194\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}