Multiepitope mRNA vaccine mRNA-mEp21-FL-idt provides effective protection against M. tuberculosis

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Abstract

Tuberculosis is a leading bacterial infection in terms of lethality. The development of new tuberculosis vaccines may reduce the number of new cases and deaths from it. One of the most promising areas of vaccination is represented by mRNA vaccines, which have already proven their high effectiveness against COVID-19 and other viral infections. In our study, we developed four new multiepitope mRNA antituberculosis vaccines by modern immunoinformatic methods; the vaccines differ from each other in sequences of encoded adjuvants and codon composition. Their immunogenicity and protectivity were tested in experiments on mice. Most of the developed mRNA vaccines have led to the formation of both cellular and humoral immunity. Meanwhile, the adaptive response was stronger in the case of vaccines with adjuvant RpfE. Nonetheless, the best protective response was elicited by the mRNA-mEp21-FL-IDT vaccine (with adjuvant FL), which decreased mycobacterial load in the lungs after infection of mice with M. tuberculosis and increased animal survival. Altogether, our results indicate that the mRNA-mEp21-FL-IDT vaccine, developed by mRNA immunoinformatic methods, provides effective protection comparable to that seen after BCG vaccination.

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About the authors

A. A. Kazakova

Sirius University of Science and Technology

Email: reshetnikov.vv@talantiuspeh.ru
Russian Federation, 354340 Federal Territory “Sirius”

G. S. Shepelkova

Central Scientific Research Institute of Tuberculosis

Email: reshetnikov.vv@talantiuspeh.ru
Russian Federation, 107564 Moscow

I. S. Kukushkin

Sirius University of Science and Technology

Email: reshetnikov.vv@talantiuspeh.ru
Russian Federation, 354340 Federal Territory “Sirius”

V. V. Yeremeev

Central Scientific Research Institute of Tuberculosis

Email: reshetnikov.vv@talantiuspeh.ru
Russian Federation, 107564 Moscow

R. A. Ivanov

Sirius University of Science and Technology

Email: reshetnikov.vv@talantiuspeh.ru
Russian Federation, 354340 Federal Territory “Sirius”

V. V. Reshetnikov

Sirius University of Science and Technology

Author for correspondence.
Email: reshetnikov.vv@talantiuspeh.ru
Russian Federation, 354340 Federal Territory “Sirius”

References

  1. Global tuberculosis report 2024 (2024) URL: https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2024.
  2. Colditz, G. A., Brewer, T. F., Berkey, C. S., Wilson, M. E., Burdick, E., Fineberg, H. V., and Mosteller, F. (1994) Efficacy of BCG vaccine in the prevention of tuberculosis. Meta-analysis of the published literature, JAMA, 271, 698-702, https://doi.org/10.1001/jama.1994.03510330076038.
  3. Gandhi, R. T., Kwon, D. S., Macklin, E. A., Shopis, J. R., McLean, A. P., McBrine, N., Flynn, T., Peter, L., Sbrolla, A., Kaufmann, D. E., Porichis, F., Walker, B. D., Bhardwaj, N., Barouch, D. H., and Kavanagh, D. G. (2016) Immunization of HIV-1-infected persons with autologous dendritic cells transfected with mRNA encoding HIV-1 Gag and Nef: results of a randomized, placebo-controlled clinical trial, J. Acquir Immune Defic., 71, 246-253, https://doi.org/10.1097/QAI.0000000000000852.
  4. Richner, J. M., Himansu, S., Dowd, K. A., Butler, S. L., Salazar, V., Fox, J. M., Julander, J. G., Tang, W. W., Shresta, S., Pierson, T. C., Ciaramella, G., and Diamond, M. S. (2017) Modified mRNA vaccines protect against Zika virus infection, Cell, 6, 1114-1125, https://doi.org/10.1016/j.cell.2017.02.017.
  5. Alberer, M., Gnad-Vogt, U., Hong, H. S., Mehr, K. T., Backert, L., Finak, G., Gottardo, R., Bica, M. A., Garofano, A., Koch, S. D., Fotin-Mleczek, M., Hoerr, I., Clemens, R., and von Sonnenburg, F. (2017) Safety and immunogenicity of a mRNA rabies vaccine in healthy adults: an open-label, non-randomised, prospective, first-in-human phase 1 clinical trial, Lancet, 390, 1511-1520, https://doi.org/10.1016/S0140-6736 (17)31665-3.
  6. Bahl, K., Senn, J. J., Yuzhakov, O., Bulychev, A., Brito, L. A., Hassett, K. J., Laska, M. E., Smith, M., Almarsson, Ö., Thompson, J., Ribeiro, A. M., Watson, M., Zaks, T., and Ciaramella, G. (2017) Preclinical and clinical demonstration of immunogenicity by mRNA vaccines against H10N8 and H7N9 influenza viruses, Mol. Ther., 6, 1316-1327, https://doi.org/10.1016/j.ymthe.2017.03.035.
  7. Pateev, I., Seregina, K., Ivanov, R., and Reshetnikov, V. (2023) Biodistribution of RNA vaccines and of their products: evidence from human and animal studies, Biomedicines, 12, 59, https://doi.org/10.3390/ biomedicines12010059.
  8. Hu, C., Bai, Y., Liu, J., Wang, Y., He, Q., Zhang, X., Cheng, F., Xu, M., Mao, Q., and Liang, Z. (2024) Research progress on the quality control of mRNA vaccines, Expert Rev. Vaccines, 23, 570-583, https://doi.org/10.1080/ 14760584.2024.2354251.
  9. Fan, X. Y., and Lowrie, D. B. (2021) Where are the RNA vaccines for TB? Emerg. Microbes Infect., 10, 1217-1218, https://doi.org/10.1080/22221751.2021.1935328.
  10. Khlebnikova, A., Kirshina, A., Zakharova, N., Ivanov, R., and Reshetnikov, V. (2024) Current progress in the development of mRNA Vaccines against bacterial infections, Int. J. Mol. Sci., 25, 13139, https://doi.org/10.3390/ijms252313139.
  11. Cianci, R., and Franza, L. (2022) Recent advances in vaccine technology and design, Vaccines (Basel), 10, 624, https://doi.org/10.3390/vaccines10040624.
  12. Zhang L. (2018) Multi-epitope vaccines: a promising strategy against tumors and viral infections, Cell. Mol. Immunol., 15, 182-184, https://doi.org/10.1038/cmi.2017.92.
  13. Mahapatra, S. R., Dey, J., Kaur, T., Sarangi, R., Bajoria, A. A., Kushwaha, G. S., Misra, N., and Suar, M. (2021) Immunoinformatics and molecular docking studies reveal a novel multi-epitope peptide vaccine against pneumonia infection, Vaccine, 39, 6221-6237, https://doi.org/10.1016/j.vaccine.2021.09.025.
  14. Dey, J., Mahapatra, S. R., Lata, S., Patro, S., Misra, N., and Suar, M. (2022) Exploring Klebsiella pneumoniae capsule polysaccharide proteins to design multiepitope subunit vaccine to fight against pneumonia, Expert Rev. Vaccines, 21, 569-587, https://doi.org/10.1080/14760584.2022.2021882.
  15. Dey, J., Mahapatra, S. R., Patnaik, S., Lata, S., Kushwaha, G. S., Panda, R. K., Misra, N., and Suar, M. (2022) Molecular characterization and designing of a novel multiepitope vaccine construct against Pseudomonas aeruginosa, Int. J. Pept. Res. Ther., 28, https://doi.org/10.1007/s10989-021-10356-z.
  16. Alharbi, M., Alshammari, A., Alasmari, A. F., Alharbi, S. M., Tahir Ul Qamar, M., Ullah, A., Ahmad, S., Irfan, M., and Khalil, A. A. K. (2022) Designing of a recombinant multi-epitopes based vaccine against Enterococcus mundtii using bioinformatics and immunoinformatics approaches, Int. J. Environ. Res. Public Health, 19, https://doi.org/10.3390/ijerph19063729.
  17. Jiang, Q., Zhang, J., Chen, X., Xia, M., Lu, Y., Qiu, W., Feng, G., Zhao, D., Li, Y., He, F., Peng, G., and Wang, Y. (2013) A novel recombinant DNA vaccine encoding Mycobacterium tuberculosis ESAT-6 and FL protects against Mycobacterium tuberculosis challenge in mice, J. Biomed. Res., 5, 406-420, https://doi.org/10.7555/JBR. 27.20120114.
  18. Choi, H. G., Kim, W. S., Back, Y. W., Kim, H., Kwon, K. W., Kim, J. S., Shin, S. J., and Kim, H. J. (2015) Mycobacterium tuberculosis RpfE promotes simultaneous Th1- and Th17-type T-cell immunity via TLR4-dependent maturation of dendritic cells, Eur. J. Immunol., 45, 1957-1971, https://doi.org/10.1002/eji.201445329.
  19. Jung, I. D., Jeong, S. K., Lee, C. M., Noh, K. T., Heo, D. R., Shin, Y. K., Yun, C. H., Koh, W. J., Akira, S., Whang, J., Kim, H. J., Park, W. S., Shin, S. J., and Park, Y. M. (2011) Enhanced efficacy of therapeutic cancer vaccines produced by co-treatment with Mycobacterium tuberculosis heparin-binding hemagglutinin, a novel TLR4 agonist, Cancer Res., 71, 2858-2870, https://doi.org/10.1158/0008-5472.CAN-10-3487.
  20. Lee, S. J., Shin, S. J., Lee, M. H., Lee, M. G., Kang, T. H., Park, W. S., Soh, B. Y., Park, J. H., Shin, Y. K., Kim, H. W., Yun, C. H., Jung, I. D., and Park, Y. M. (2014) A potential protein adjuvant derived from Mycobacterium tuberculosis Rv0652 enhances dendritic cells-based tumor immunotherapy, PLoS One, 9, e104351, https://doi.org/10.1371/journal.pone.0104351.
  21. Gote, V., Bolla, P. K., Kommineni, N., Butreddy, A., Nukala, P. K., Palakurthi, S. S., and Khan, W. (2023) A comprehensive review of mRNA vaccines, Int. J. Mol. Sci., 24, https://doi.org/10.3390/ijms24032700.
  22. Muslimov, A., Tereshchenko, V., Shevyrev, D., Rogova, A., Lepik, K., Reshetnikov, V., and Ivanov, R. (2023) The dual role of the innate immune system in the effectiveness of mRNA therapeutics, Int. J. Mol. Sci., 24, https://doi.org/10.3390/ijms241914820.
  23. Mauger, D. M., Cabral, B. J., Presnyak, V., Su, S. V., Reid, D. W., Goodman, B., Link, K., Khatwani, N., Reynders, J., Moore, M. J., and McFadyen, I. J. (2019) mRNA structure regulates protein expression through changes in functional half-life, Proc. Natl. Acad. Sci. USA, 116, 24075-24083, https://doi.org/10.1073/pnas. 1908052116.
  24. Wayment-Steele, H. K., Kim, D. S., Choe, C. A., Nicol, J. J., Wellington-Oguri, R., Watkins, A. M., Parra Sperberg, R. A., Huang, P. S., Participants, E., and Das, R. (2021) Theoretical basis for stabilizing messenger RNA through secondary structure design, Nucleic Acids Res., 49, 10604-10617, https://doi.org/10.1093/nar/gkab764.
  25. María, R. R., Arturo, C. J., Alicia, J. A., Paulina, M. G., and Gerardo, A. O. (2017) The impact of bioinformatics on vaccine design and development, InTech, https://doi.org/10.5772/intechopen.69273.
  26. Kazakova, A., Zhelnov, P., Sidorov, R., Rogova, A., Vasileva, O., Ivanov, R., Reshetnikov, V., and Muslimov, A. (2024) DNA and RNA vaccines against tuberculosis: a scoping review of human and animal studies, Front. Immunol., 15, 1457327, https://doi.org/10.3389/fimmu.2024.1457327.
  27. Yang, Y., Chen, Y. Z., and Xia, T. (2024) Optimizing antigen selection for the development of tuberculosis vaccines, Cell Insight, 3, 100163, https://doi.org/10.1016/j.cellin.2024.100163.
  28. Saha, S., and Raghava, G. P. (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network, Proteins, 1, 40-48, https://doi.org/10.1002/prot.21078.
  29. Saha, S., and Raghava, G. P. S. (2004) BcePred: prediction of continuous B-Cell epitopes in antigenic sequences using physico-chemical properties, Lecture Notes Comput. Sci., 3239, 197-204, https://doi.org/10.1007/ 978-3-540-30220-9_16.
  30. Fleri, W., Paul, S., Dhanda, S. K., Mahajan, S., Xu, X., Peters, B., and Sette, A. (2017) The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design, Front. Immunol., 8, 278, https://doi.org/10.3389/fimmu.2017.00278.
  31. Johnson, M., Zaretskaya, I., Raytselis, Y., Merezhuk, Y., McGinnis, S., and Madden, T. L. (2008) NCBI BLAST: a better web interface, Nucleic Acids Res., 36, w5-9, https://doi.org/10.1093/nar/gkn201.
  32. Doytchinova, I. A., and Flower, D. R. (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines, BMC Bioinform., 8, 4, https://doi.org/10.1186/1471-2105-8-4.
  33. Dimitrov, I., Bangov, I., Flower, D. R., and Doytchinova, I. (2014) AllerTOP v. 2 – a server for in silico prediction of allergens, J. Mol. Model, 20, 2278, https://doi.org/10.1007/s00894-014-2278-5.
  34. Sharma, N., Naorem, L. D., Jain, S., and Raghava, G. P. S. (2022) ToxinPred2: an improved method for predicting toxicity of proteins, Brief. Bioinform., 23, bbac174, https://doi.org/10.1093/bib/bbac174.
  35. Gupta, S., Ansari, H. R., Gautam, A., Open Source Drug Discovery Consortium, and Raghava, G. P. (2013) Identification of B-cell epitopes in an antigen for inducing specific class of antibodies, Biol. Direct., 8, 27, https://doi.org/10.1186/1745-6150-8-27.
  36. Dhanda, S. K., Vir, P., and Raghava, G. P. (2013) Designing of interferon-gamma inducing MHC class-II binders, Biol. Direct., 8, 30, https://doi.org/10.1186/1745-6150-8-30.
  37. Dhanda, S. K., Gupta, S., Vir, P., and Raghava, G. P. (2013) Prediction of IL4 inducing peptides, Clin. Dev. Immunol., 2013, 263952, https://doi.org/10.1155/2013/263952.
  38. Nagpal, G., Usmani, S. S., Dhanda, S. K., Kaur, H., Singh, S., Sharma, M., and Raghava, G. P. (2017) Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential, Sci. Rep., 7, 42851, https://doi.org/10.1038/srep42851.
  39. Bui, H. H., Sidney, J., Dinh, K., Southwood, S., Newman, M. J., and Sette, A. (2006) Predicting population coverage of T-cell epitope-based diagnostics and vaccines, BMC Bioinformatics, 7, 153, https://doi.org/10.1186/ 1471-2105-7-153.
  40. Kurcinski, M., Jamroz, M., Blaszczyk, M., Kolinski, A., and Kmiecik, S. (2015) CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site, Nucleic Acids Res., 43, 419-424, https://doi.org/10.1093/nar/gkv456.
  41. Weng, G., Wang, E., Wang, Z., Liu, H., Zhu, F., Li, D., and Hou, T. (2019) HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA, Nucleic Acids Res., 47, 322-330, https://doi.org/10.1093/nar/gkz397.
  42. Gonzalez-Galarza, F. F., McCabe, A., Santos, E. J. M. D., Jones, J., Takeshita, L., Ortega-Rivera, N. D., Cid-Pavon, G. M. D., Ramsbottom, K., Ghattaoraya, G., Alfirevic, A., Middleton, D., and Jones, A. R. (2020) Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools, Nucleic Acids Res., 48, 783-788, https://doi.org/10.1093/nar/gkz1029.
  43. Arai, R., Ueda, H., Kitayama, A., Kamiya, N., and Nagamune, T. (2001) Design of the linkers which effectively separate domains of a bifunctional fusion protein, Protein Eng., 8, 529-532, https://doi.org/10.1093/protein/14.8.529.
  44. Meza, B., Ascencio, F., Sierra-Beltrán, A. P., Torres, J., and Angulo, C. (2017) A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: an in silico approach, Infect. Genet. Evol., 49, 309-317, https://doi.org/10.1016/j.meegid.2017.02.007.
  45. Noderer, W. L., Flockhart, R. J., Bhaduri, A., Diaz de Arce, A. J., Zhang, J., Khavari, P. A., and Wang, C. L. (2014) Quantitative analysis of mammalian translation initiation sites by FACS-seq, Mol. Syst. Biol., 10, 748, https://doi.org/10.15252/msb.20145136.
  46. Sample, P. J., Wang, B., Reid, D. W., Presnyak, V., McFadyen, I. J., Morris, D. R., and Seelig, G. (2019) Human 5′ UTR design and variant effect prediction from a massively parallel translation assay, Nat. Biotechnol., 7, 803-809, https://doi.org/10.1038/s41587-019-0164-5.
  47. Reshetnikov, V., Terenin, I., Shepelkova, G., Yeremeev, V., Kolmykov, S., Nagornykh, M., Kolosova, E., Sokolova, T., Zaborova, O., Kukushkin, I., Kazakova, A., Kunyk, D., Kirshina, A., Vasileva, O., Seregina, K., Pateev, I., Kolpakov, F., and Ivanov, R. (2024) Untranslated region sequences and the efficacy of mRNA vaccines against tuberculosis, Int. J. Mol. Sci., 25, 888, https://doi.org/10.3390/ijms25020888.
  48. Magnan, C. N., Zeller, M., Kayala, M. A., Vigil, A., Randall, A., Felgner, P. L., and Baldi, P. (2010) High-throughput prediction of protein antigenicity using protein microarray data, Bioinformatics, 23, 2936-2943, https://doi.org/10.1093/bioinformatics/btq551.
  49. Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R. D., and Bairoch, A. (2003) ExPASy: The proteomics server for in-depth protein knowledge and analysis, Nucleic Acids Res., 13, 3784-3788, https://doi.org/10.1093/nar/gkg563.
  50. Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., and Baker, D. (2020) Improved protein structure prediction using predicted interresidue orientations, Proc. Natl. Acad. Sci. USA, 3, 1496-1503, https://doi.org/ 10.1073/pnas.1914677117.
  51. Kringelum, J. V., Nielsen, M., Padkjær, S. B., and Lund, O. (2013) Structural analysis of B-cell epitopes in antibody:protein complexes, Mol. Immunol., 53, 24-34, https://doi.org/10.1016/j.molimm.2012.06.001.
  52. Ponomarenko, J., Bui, H. H., Li, W., Fusseder, N., Bourne, P. E., Sette, A., and Peters, B. (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes, BMC Bioinform., 9, 514, https://doi.org/10.1186/ 1471-2105-9-514.
  53. Solanki, V., and Tiwari, V. (2018) Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii, Sci. Rep., 8, https://doi.org/10.1038/s41598-018-26689-7.
  54. Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., and Vajda, S. (2017) The ClusPro web server for protein-protein docking, Nat. Protoc., 2, 255-278, https://doi.org/10.1038/nprot.2016.169.
  55. Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M., and Vangone, A. (2016) PRODIGY: a web server for predicting the binding affinity of protein-protein complexes, Bioinformatics, 23, 3676-3678, https://doi.org/10.1093/bioinformatics/btw514.
  56. Laskowski, R. A., Jabłońska, J., Pravda, L., Vařeková, R. S., and Thornton, J. M. (2018) PDBsum: Structural summaries of PDB entries, Protein Sci., 1, 129-134, https://doi.org/10.1002/pro.3289.
  57. Rapin, N., Lund, O., Bernaschi, M., and Castiglione, F. (2010) Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system, PLoS One, 5, e9862, https://doi.org/10.1371/journal.pone.0009862.
  58. Zhang, H., Zhang, L., Lin, A., Xu, C., Li, Z., Liu, K., Liu, B., Ma, X., Zhao, F., Jiang, H., Chen, C., Shen, H., Li, H., Mathews, D. H., Zhang, Y., and Huang, L. (2023) Algorithm for optimized mRNA design improves stability and immunogenicity, Nature, 621, 396-403, https://doi.org/10.1038/s41586-023-06127-z.
  59. Diez, M., Medina-Muñoz, S. G., Castellano, L. A., da Silva Pescador, G., Wu, Q., and Bazzini, A. A. (2022) iCodon customizes gene expression based on the codon composition, Sci. Rep., 12, 12126, https://doi.org/10.1038/s41598-022-15526-7.
  60. Meade, R. K., and Smith, C. M. (2024) Immunological roads diverged: mapping tuberculosis outcomes in mice, Trends Microbiol., 33, 15-33, https://doi.org/10.1016/j.tim.2024.06.007.
  61. Еремеев В. В., Духовлинов И. В., Орлов А. И., Шепелькова Г. С., Федорова Е. А., Балазовский М. Б., Гергерт В. Я. (2018) Изучение продолжительности иммунного ответа, индуцированного вакциной на основе рекомбинантных белков Ag85, TB10 И FliC, Мед. Иммунол., 20, 271-276, https://doi.org/10.15789/1563-0625-2018-2-271-276.
  62. Abou-Zeid, C., Gares, M. P., Inwald, J., Janssen, R., Zhang, Y., Young, D. B., Hetzel, C., Lamb, J. R., Baldwin, S. L., Orme, I. M., Yeremeev, V., Nikonenko, B. V., and Apt, A. S. (1997) Induction of a type 1 immune response to a recombinant antigen from Mycobacterium tuberculosis expressed in Mycobacterium vaccae, Infect. Immun., 65, 1856-1862, https://doi.org/10.1128/iai.65.5.1856-1862.1997.
  63. Авдиенко В. Г., Бабаян С. С., Бочарова И. В. (2024) Характеристики и применение моноклональных антител против Mycobacterium kansasii, Вестник ЦНИИТ, 8, 51-63, https://doi.org/10.57014/2587-6678-2024-8-4-51-63.
  64. Kou, Y., Xu, Y., Zhao, Z., Liu, J., Wu, Y., You, Q., Wang, L., Gao, F., Cai, L., and Jiang, C. (2017) Tissue plasminogen activator (tPA) signal sequence enhances immunogenicity of MVA-based vaccine against tuberculosis, Immunol. Lett., 190, 51-57, https://doi.org/10.1016/j.imlet.2017.07.007.
  65. Kreiter, S., Selmi, A., Diken, M., Sebastian, M., Osterloh, P., Schild, H., Huber, C., Türeci, O., and Sahin, U. (2008) Increased antigen presentation efficiency by coupling antigens to MHC class I trafficking signals, J. Immunol., 1, 309-318, https://doi.org/10.4049/jimmunol.180.1.309.
  66. Yang, H., Chen, H., Liu, Z., Ma, H., Qin, L., Jin, R., Zheng, R., Feng, Y., Cui, Z., Wang, J., Liu, J., and Hu, Z. (2013) A novel B-cell epitope identified within Mycobacterium tuberculosis CFP10/ESAT-6 protein, PLoS One, 8, e52848, https://doi.org/10.1371/journal.pone.0052848.
  67. Ganguly, N., Siddiqui, I., and Sharma, P. (2008) Role of M. tuberculosis RD-1 region encoded secretory proteins in protective response and virulence, Tuberculosis, 88, 510-517, https://doi.org/10.1016/j.tube.2008.05.002.
  68. Yuan, Y., Crane, D. D., and Barry, C. E. (1996) Stationary phase-associated protein expression in Mycobacterium tuberculosis: function of the mycobacterial alpha-crystallin homolog, J. Bacteriol., 178, 4484-4492, https://doi.org/10.1128/jb.178.15.4484-4492.1996.
  69. Cunningham, A. F., and Spreadbury, C. L. (1998) Mycobacterial stationary phase induced by low oxygen tension: cell wall thickening and localization of the 16-kilodalton alpha-crystallin homolog, J. Bacteriol., 180, 801-808, https://doi.org/10.1128/JB.180.4.801-808.1998.
  70. Becker, K., and Sander, P. (2016) Mycobacterium tuberculosis lipoproteins in virulence and immunity – fighting with a double-edged sword, FEBS Lett., 21, 3800-3819, https://doi.org/10.1002/1873-3468.12273.
  71. Ahmed, A., Das, A., and Mukhopadhyay, S. (2015) Immunoregulatory functions and expression patterns of PE/PPE family members: roles in pathogenicity and impact on anti-tuberculosis vaccine and drug design, IUBMB Life, 67, 414-427, https://doi.org/10.1002/iub.1387.
  72. Coler, R. N., Campos-Neto, A., Ovendale, P., Day, F. H., Fling, S. P., Zhu, L., Serbina, N., Flynn, J. L., Reed, S. G., and Alderson, M. R. (2001) Vaccination with the T cell antigen Mtb 8.4 protects against challenge with Mycobacterium tuberculosis, J. Immunol., 166, 6227-6235, https://doi.org/10.4049/jimmunol.166.10.6227.
  73. Coppola, M., van den Eeden, S. J., Wilson, L., Franken, K. L., Ottenhoff, T. H., and Geluk, A. (2015) Synthetic long peptide derived from Mycobacterium tuberculosis latency antigen Rv1733c protects against tuberculosis, Clin. Vaccine Immunol., 22, 1060-1069, https://doi.org/10.1128/CVI.00271-15.
  74. Fan Q., Lu M., Xia Z. Y., and Bao L. (2013) Mycobacterium tuberculosis MPT64 stimulates the activation of murine macrophage modulated by IFN-γ, Eur. Rev. Med. Pharmacol. Sci., 24, 3296-3305.
  75. Lin, M. Y., Geluk, A., Smith, S. G., Stewart, A. L., Friggen, A. H., Franken, K. L., Verduyn, M. J., van Meijgaarden, K. E., Voskuil, M. I., Dockrell, H. M., Huygen, K., Ottenhoff, T. H., and Klein, M. R. (2007) Lack of immune responses to Mycobacterium tuberculosis DosR regulon proteins following Mycobacterium bovis BCG vaccination, Infect. Immun., 75, 3523-3530, https://doi.org/10.1128/IAI.01999-06.
  76. Sharpe, M. L., Gao, C., Kendall, S. L., Baker, E. N., and Lott, J. S. (2008) The structure and unusual protein chemistry of hypoxic response protein 1, a latency antigen and highly expressed member of the DosR regulon in Mycobacterium tuberculosis, J. Mol. Biol., 383, 822-836, https://doi.org/10.1016/j.jmb.2008.07.001.
  77. Yao, L., Hu, Q., Chen, S., Zhou, T., Yu, X., Ma, H., Ghonaim, A., Wu, H., Sun, Q., Fan, S., and He, Q. (2021) Recombinant pseudorabies virus with TK/gE Gene deletion and Flt3L co-expression enhances the innate and adaptive immune response via activating dendritic cells, Viruses, 13, 691, https://doi.org/10.3390/v13040691.
  78. Boopathy, A. V., Nekkalapudi, A., Sung, J., Schulha, S., Jin, D., Sharma, B., Ng, S., Lu, S., Wimmer, R., Suthram, S., Ahmadi-Erber, S., Lauterbach, H., Orlinger, K. K., Hung, M., Carr, B., Callebaut, C., Geleziunas, R., Kuhne, M., Schmidt, S., and Falkard, B. (2024) Flt3 agonist enhances immunogenicity of arenavirus vector-based simian immunodeficiency virus vaccine in macaques, J. Virol., 7, e0029424, https://doi.org/10.1128/ jvi.00294-24.
  79. Zhu, J., Ke, Y., Liu, Q., Yang, J., Liu, F., Xu, R., Zhou, H., Chen, A., Xiao, J., Meng, F., Yu, L., Li, R., Wei, J., and Liu, B. (2022) Engineered Lactococcus lactis secreting Flt3L and OX40 ligand for in situ vaccination-based cancer immunotherapy, Nat. Commun., 13, 7466, https://doi.org/10.1038/s41467-022-35130-7.
  80. Maraskovsky, E., Brasel, K., Teepe, M., Roux, E. R., Lyman, S. D., Shortman, K., McKenna, H. J. (1996) Dramatic increase in the numbers of functionally mature dendritic cells in Flt3 ligand-treated mice: multiple dendritic cell subpopulations identified, J. Exp. Med., 184, 1953-1962, https://doi.org/10.1084/jem.184.5.1953.
  81. Brasel, K., De Smedt, T., Smith, J. L., and Maliszewski, C. R. (2000) Generation of murine dendritic cells from flt3-ligand-supplemented bone marrow cultures, Blood, 96, 3029-3039.
  82. Pulendran, B., Banchereau, J., Burkeholder, S., Kraus, E., Guinet, E., Chalouni, C., Caron, D., Maliszewski, C., Davoust, J., Fay, J., and Palucka, K. (2000) Flt3-ligand and granulocyte colony-stimulating factor mobilize distinct human dendritic cell subsets in vivo, J. Immunol., 165, 566-572, https://doi.org/10.4049/jimmunol. 165.1.566.
  83. Triccas, J. A., Shklovskaya, E., Spratt, J., Ryan, A. A., Palendira, U., Fazekas de St Groth, B., and Britton W. J. (2007) Effects of DNA- and Mycobacterium bovis BCG-based delivery of the Flt3 ligand on protective immunity to Mycobacterium tuberculosis, Infect. Immun., 75, 5368-5375, https://doi.org/10.1128/IAI.00322-07.
  84. Ahn, S. S., Jeon, B. Y., Kim, K. S., Kwack, J. Y., Lee, E. G., Park, K. S., Sung, Y. C., and Cho, S. N. (2012) Mtb32 is a promising tuberculosis antigen for DNA vaccination in pre- and post-exposure mouse models, Gene Ther., 19, 570-575, https://doi.org/10.1038/gt.2011.140.
  85. Xu, J., Xu, W., Chen, X., Zhao, D., and Wang, Y. (2008) Recombinant DNA vaccine of the early secreted antigen ESAT-6 by Mycobacterium tuberculosis and Flt3 ligand enhanced the cell-mediated immunity in mice, Vaccine, 26, 4519-4525, https://doi.org/10.1016/j.vaccine.2008.06.044.
  86. Branger, J., Leemans, J. C., Florquin, S., Weijer, S., Speelman, P., and Van Der Poll, T. (2004) Toll-like receptor 4 plays a protective role in pulmonary tuberculosis in mice, Int. Immunol., 3, 509-516, https://doi.org/10.1093/intimm/dxh052.
  87. Xin, Q., Niu, H., Li, Z., Zhang, G., Hu, L., Wang, B., Li, J., Yu, H., Liu, W., Wang, Y., Da, Z., Li, R., Xian, Q., Wang, Y., Zhang, Y., Jing, T., Ma, X., and Zhu, B. (2013) Subunit vaccine consisting of multi-stage antigens has high protective efficacy against Mycobacterium tuberculosis infection in mice, PLoS One, 8, e72745, https://doi.org/ 10.1371/journal.pone.0072745.
  88. Oksanen, K. E., Myllymäki, H., Ahava, M. J., Mäkinen, L., Parikka, M., and Rämet, M. (2016) DNA vaccination boosts Bacillus Calmette-Guérin protection against mycobacterial infection in zebrafish, Dev. Comp. Immunol., 54, 89-96, https://doi.org/10.1016/j.dci.2015.09.001.
  89. Vakili, B., Nezafat, N., Zare, B., Erfani, N., Akbari, M., Ghasemi, Y., Rahbar, M. R., and Hatam, G. R. (2020) A new multi-epitope peptide vaccine induces immune responses and protection against Leishmania infantum in BALB/c mice, Med. Microbiol. Immunol., 209, 69-79, https://doi.org/10.1007/s00430-019-00640-7.
  90. Kozlova, A., Pateev, I., Shepelkova, G., Vasileva, O, Zakharova, N, Yeremeev, V, Ivanov, R, and Reshetnikov, V. (2024) A cap-optimized mRNA encoding multiepitope antigen ESAT6 induces robust cellular and humoral immune responses against Mycobacterium tuberculosis, Vaccines (Basel), 12, 1267, https://doi.org/10.3390/ vaccines12111267.
  91. Shepelkova, G. S., Reshetnikov, V. V., Avdienko, V. G., Sheverev, D. V., Yeremeev, V. V., and Ivanov, R. A. (2023) Impact of untranslated mRNA sequences on immunogenicity of mRNA vaccines against M. tuberculosis in mice, Bull. RSMU, 6, 27-33, https://doi.org/10.24075/brsmu.2023.054.
  92. Wu, M., Li, M., Yue, Y., and Xu, W. (2016) DNA vaccine with discontinuous T-cell epitope insertions into HSP65 scaffold as a potential means to improve immunogenicity of multi-epitope Mycobacterium tuberculosis vaccine, Microbiol. Immunol., 60, 634-645, https://doi.org/10.1111/1348-0421.12410.
  93. Cai, H., Tian, X., Hu, X., Pan, Y., Li, G., Zhuang, Y., and Zhu, Y. (2003) Immunogenicity and protective efficacy study using combination of four tuberculosis DNA vaccines, Sci. China C. Life Sci., 46, 495-502, https://doi.org/10.1360/02yc0121.

Supplementary files

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2. Fig. 1. General scheme of the developed mRNA vaccines. Four mRNA sequences were used in the work: mRNA-mEp21-RpfE-LD, mRNA-mEp21-FL-LD, mRNA-mEp21-RpfE-IDT and mRNA-mEp21-FL-IDT, differing in the sequence of adjuvants (RpfE or FL) and secondary structure optimization options (LD or IDT). Each vaccine variant included 7 HTL epitopes, 7 LBL epitopes and 7 CTL epitopes.

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3. Fig. 2. Stages of development of a multi-epitope mRNA vaccine against tuberculosis

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4. Fig. 3. Design of in vivo experiment. a – Scheme of the experiment to evaluate the immunogenicity of candidate vaccines on B6 mice; b – scheme of the experiment to evaluate the protective properties of candidate vaccines on I/St mice.

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5. Fig. 4. Secondary MFE (minimum free energy) structures of vaccine mRNAs. a – mRNA-mEp21-RpfE (IDT); b – mRNA-mEp21-RpfE (Linear Design); c – mRNA-mEp21-FL (Linear Design); d – mRNA-mEp21-FL (IDT). The color map indicates the probability of base pairing, where 0 is the minimum probability and 1 is the maximum.

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6. Fig. 5. Results of in vivo experiments. Estimations of IFN-γ-secreting splenocytes, IgG titers, and CFU in the lungs and spleen were performed in groups of 5 mice. Evaluation of survival after infection was performed in groups of 10 mice. a – Results of ELISpot analysis – number of cells secreting IFN-γ in response to stimulation with M. tuberculosis antigens; b – bacterial load in the lungs – number of M. tuberculosis CFU in lung homogenate 50 days after infection; c – bacterial load in the spleen – number of M. tuberculosis CFU in spleen homogenate 50 days after infection; d – IgG titers in response to stimulation with M. tuberculosis antigens; d – survival of mice after infection. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001

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7. Appendix 2
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