Multi-epitope mRNA Vaccine Design that Exploits Variola Virus and Monkeypox Virus Proteins for Elicitation of Long-lasting Humoral and Cellular Protection Against Severe Disease

Dženan Kovačić; Adna Salihović

doi:10.20883/medical.e750

Authors

Dženan Kovačić Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, International Burch University, Ilidža, Bosnia and Herzegovina https://orcid.org/0000-0003-3218-5073
Adna Salihović Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, International Burch University, Ilidža, Bosnia and Herzegovina https://orcid.org/0000-0003-0482-1861

DOI:

https://doi.org/10.20883/medical.e750

Keywords:

smallpox, monkeypox, monkeypox outbreak, monkeypox vaccine, mRNA vaccines, poxviruses

Abstract

Human monkeypox represents a relatively underexplored infection that has received increased attention since the reported outbreak in May 2022. Due to its clinical similarities with human smallpox, this virus represents a potentially tremendous health problem demanding further research in the context of host-pathogen interactions and vaccine development. Furthermore, the cross-continental spread of monkeypox has reaffirmed the need for devoting attention to human poxviruses in general, as they represent potential bioterrorism agents. Currently, smallpox vaccines are utilized in immunization efforts against monkeypox, an unsurprising fact considering their genomic and phenotypic similarities. Though it offers long-lasting protection against smallpox, its protective effects against human monkeypox continue to be explored, with encouraging results. Taking this into account, this works aims at utilizing in silico tools to identify potent peptide-based epitopes stemming from the variola virus and monkeypox virus proteomes, to devise a vaccine that would offer significant protection against smallpox and monkeypox. In theory, a vaccine that offers cross-protection against variola and monkeypox would also protect against related viruses, at least in severe clinical manifestation. Herein, we introduce a novel multi-epitope mRNA vaccine design that exploits these two viral proteomes to elicit long-lasting humoral and cellular immunity. Special consideration was taken in ensuring that the vaccine candidate elicits a Th1 immune response, correlated with protection against clinically severe disease for both viruses. Immune system simulations and physicochemical and safety analyses characterize our vaccine candidate as antigenically potent, safe, and overall stable. The protein product displays high binding affinity towards relevant immune receptors. Furthermore, the vaccine candidate is to elicit a protective, humoral and Th1-dominated cellular immune response that lasts over five years. Lastly, we build a case about the rapidity and convenience of circumventing the live attenuated vaccine platform using mRNA vaccine technology.

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References

G. McFadden, “Poxvirus tropism,” Nature Reviews Microbiology 2005 3:3, vol. 3, no. 3, pp. 201–213, Mar. 2005, doi: 10.1038/nrmicro1099. DOI: https://doi.org/10.1038/nrmicro1099

V. Panchanathan, G. Chaudhri, and G. Karupiah, “Protective Immunity against Secondary Poxvirus Infection Is Dependent on Antibody but Not on CD4 or CD8 T-Cell Function,” J Virol, vol. 80, no. 13, pp. 6333–6338, Jul. 2006, DOI: 10.1128/JVI.00115-06/ASSET/6028F5A2-E462-4916-9DFD-D56048FD574A/ASSETS/GRAPHIC/ZJV0130679310006.JPEG. DOI: https://doi.org/10.1128/JVI.00115-06

E. Hammarlund, A. Dasgupta, C. Pinilla, P. Norori, K. Früh, and M. K. Slifka, “Monkeypox virus evades antiviral CD4+ and CD8+ T cell responses by suppressing cognate T cell activation,” Proc Natl Acad Sci U S A, vol. 105, no. 38, pp. 14567–14572, Sep. 2008, DOI: 10.1073/PNAS.0800589105/SUPPL_FILE/0800589105SI.PDF. DOI: https://doi.org/10.1073/pnas.0800589105

R. B. Kennedy, I. G. Ovsyannikova, R. M. Jacobson, and G. A. Poland, “The immunology of smallpox vaccines,” Curr Opin Immunol, vol. 21, no. 3, p. 314, Jun. 2009, DOI: 10.1016/J.COI.2009.04.004. DOI: https://doi.org/10.1016/j.coi.2009.04.004

I. J. Amanna, M. K. Slifka, and S. Crotty, “Immunity and immunological memory following smallpox vaccination,” Immunol Rev, vol. 211, no. 1, pp. 320–337, Jun. 2006, DOI: 10.1111/J.0105-2896.2006.00392.X. DOI: https://doi.org/10.1111/j.0105-2896.2006.00392.x

E. Hammarlund et al., “Duration of antiviral immunity after smallpox vaccination,” Nat Med, vol. 9, no. 9, pp. 1131–1137, Sep. 2003, DOI: 10.1038/NM917. DOI: https://doi.org/10.1038/nm917

J. Stabenow, R. M. Buller, J. Schriewer, C. West, J. E. Sagartz, and S. Parker, “A Mouse Model of Lethal Infection for Evaluating Prophylactics and Therapeutics against Monkeypox Virus,” J Virol, vol. 84, no. 8, p. 3909, Apr. 2010, DOI: 10.1128/JVI.02012-09. DOI: https://doi.org/10.1128/JVI.02012-09

W. D. Arndt et al., “Evasion of the Innate Immune Type I Interferon System by Monkeypox Virus,” J Virol, vol. 89, no. 20, pp. 10489–10499, Oct. 2015, DOI: 10.1128/JVI.00304-15/ASSET/A137DDCD-D53E-4875-8349-F21F04AB867D/ASSETS/GRAPHIC/ZJV9990908700008.JPEG. DOI: https://doi.org/10.1128/JVI.00304-15

H. Yu, R. C. Bruneau, G. Brennan, and S. Rothenburg, “Battle Royale: Innate Recognition of Poxviruses and Viral Immune Evasion,” Biomedicines 2021, Vol. 9, Page 765, vol. 9, no. 7, p. 765, Jul. 2021, DOI: 10.3390/BIOMEDICINES9070765. DOI: https://doi.org/10.3390/biomedicines9070765

S. Kreiter et al., “Increased Antigen Presentation Efficiency by Coupling Antigens to MHC Class I Trafficking Signals,” The Journal of Immunology, vol. 180, no. 1, pp. 309–318, Jan. 2008, DOI: 10.4049/JIMMUNOL.180.1.309. DOI: https://doi.org/10.4049/jimmunol.180.1.309

G. Shende et al., “PBIT: Pipeline Builder for Identification of drug Targets for infectious diseases,” Bioinformatics, vol. 33, no. 6, pp. 929–931, Mar. 2017, DOI: 10.1093/BIOINFORMATICS/BTW760. DOI: https://doi.org/10.1093/bioinformatics/btw760

P. Wang et al., “Peptide binding predictions for HLA DR, DP and DQ molecules,” BMC Bioinformatics, vol. 11, p. 568, Nov. 2010, DOI: 10.1186/1471-2105-11-568. DOI: https://doi.org/10.1186/1471-2105-11-568

M. v. Larsen, C. Lundegaard, K. Lamberth, S. Buus, O. Lund, and M. Nielsen, “Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction,” BMC Bioinformatics, vol. 8, no. 1, pp. 1–12, Oct. 2007, DOI: 10.1186/1471-2105-8-424/TABLES/3. DOI: https://doi.org/10.1186/1471-2105-8-424

J. J. A. Calis et al., “Properties of MHC Class I Presented Peptides That Enhance Immunogenicity,” PLoS Comput Biol, vol. 9, no. 10, p. 1003266, 2013, DOI: 10.1371/JOURNAL.PCBI.1003266. DOI: https://doi.org/10.1371/journal.pcbi.1003266

M. C. Jespersen, B. Peters, M. Nielsen, and P. Marcatili, “BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes,” Nucleic Acids Res, vol. 45, no. Web Server issue, p. W24, Jul. 2017, DOI: 10.1093/NAR/GKX346. DOI: https://doi.org/10.1093/nar/gkx346

S. K. Dhanda, S. Gupta, P. Vir, and G. P. Raghava, “Prediction of IL4 inducing peptides.,” Clin Dev Immunol, vol. 2013, p. 263952, 2013, DOI: 10.1155/2013/263952. DOI: https://doi.org/10.1155/2013/263952

G. Nagpal et al., “Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential,” Sci Rep, vol. 7, Feb. 2017, DOI: 10.1038/SREP42851. DOI: https://doi.org/10.1038/srep42851

S. K. Dhanda, P. Vir, and G. P. S. Raghava, “Designing of interferon-gamma inducing MHC class-II binders,” Biol Direct, vol. 8, no. 1, p. 30, Dec. 2013, DOI: 10.1186/1745-6150-8-30. DOI: https://doi.org/10.1186/1745-6150-8-30

M. N. Nguyen, N. L. Krutz, V. Limviphuvadh, A. L. Lopata, G. F. Gerberick, and S. Maurer-Stroh, “AllerCatPro 2.0: a web server for predicting protein allergenicity potential,” Nucleic Acids Res, no. 1, May 2022, DOI: 10.1093/NAR/GKAC446. DOI: https://doi.org/10.1093/nar/gkac446

N. Sharma, L. D. Naorem, S. Jain, and G. P. S. Raghava, “ToxinPred2: an improved method for predicting toxicity of proteins,” Brief Bioinform, May 2022, DOI: 10.1093/BIB/BBAC174. DOI: https://doi.org/10.1093/bib/bbac174

I. A. Doytchinova and D. R. Flower, “VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines,” BMC Bioinformatics, vol. 8, no. 1, pp. 1–7, Jan. 2007, DOI: 10.1186/1471-2105-8-4/TABLES/2. DOI: https://doi.org/10.1186/1471-2105-8-4

C. N. Magnan et al., “High-throughput prediction of protein antigenicity using protein microarray data,” Bioinformatics, vol. 26, no. 23, p. 2936, Dec. 2010, DOI: 10.1093/BIOINFORMATICS/BTQ551. DOI: https://doi.org/10.1093/bioinformatics/btq551

E. Gasteiger et al., “Protein Identification and Analysis Tools on the ExPASy Server,” The Proteomics Protocols Handbook, pp. 571–607, 2005, DOI: 10.1385/1-59259-890-0:571. DOI: https://doi.org/10.1385/1-59259-890-0:571

H. Lee, L. Heo, M. S. Lee, and C. Seok, “GalaxyPepDock: A protein-peptide docking tool based on interaction similarity and energy optimization,” Nucleic Acids Res, vol. 43, no. W1, pp. W431–W435, 2015, DOI: 10.1093/nar/gkv495. DOI: https://doi.org/10.1093/nar/gkv495

L. A. Kelley, S. Mezulis, C. M. Yates, M. N. Wass, and M. J. E. Sternberg, “The Phyre2 web portal for protein modelling, prediction and analysis,” Nat Protoc, vol. 10, no. 6, p. 845, Jun. 2015, DOI: 10.1038/NPROT.2015.053. DOI: https://doi.org/10.1038/nprot.2015.053

S. Sadegh-Nasseri and A. R. Kim, “Exogenous Antigens Bind MHC Class II first, and Are Processed by Cathepsins Later,” Mol Immunol, vol. 68, no. 2 0 0, p. 81, Jul. 2015, DOI: 10.1016/J.MOLIMM.2015.07.018. DOI: https://doi.org/10.1016/j.molimm.2015.07.018

J. Ko, H. Park, L. Heo, and C. Seok, “GalaxyWEB server for protein structure prediction and refinement,” Nucleic Acids Res, vol. 40, no. Web Server issue, p. W294, Jul. 2012, DOI: 10.1093/NAR/GKS493. DOI: https://doi.org/10.1093/nar/gks493

R. Lorenz et al., “ViennaRNA Package 2.0,” Algorithms for Molecular Biology, vol. 6, no. 1, pp. 1–14, Nov. 2011, DOI: 10.1186/1748-7188-6-26/TABLES/2. DOI: https://doi.org/10.1186/1748-7188-6-26

I. L. Hofacker and P. F. Stadler, “Memory efficient folding algorithms for circular RNA secondary structures,” Bioinformatics, vol. 22, no. 10, pp. 1172–1176, May 2006, DOI: 10.1093/BIOINFORMATICS/BTL023. DOI: https://doi.org/10.1093/bioinformatics/btl023

J. R. Delanghe, M. M. Speeckaert, and M. L. de Buyzere, “COVID-19 infections are also affected by human ACE1 D/I polymorphism.,” Clin Chem Lab Med, Apr. 2020, DOI: 10.1515/cclm-2020-0425. DOI: https://doi.org/10.1515/cclm-2020-0425

S. Itoyama et al., “ACE1 polymorphism and progression of SARS,” Biochem Biophys Res Commun, vol. 323, no. 3, pp. 1124–1129, Oct. 2004, DOI: 10.1016/j.bbrc.2004.08.208. DOI: https://doi.org/10.1016/j.bbrc.2004.08.208

M. Harishankar, P. Selvaraj, and R. Bethunaickan, “Influence of genetic polymorphism towards pulmonary tuberculosis susceptibility,” Front Med (Lausanne), vol. 5, no. AUG, pp. 1–1, 2018, DOI: 10.3389/fmed.2018.00213. DOI: https://doi.org/10.3389/fmed.2018.00213

S. Anoosheh, P. Farnia, and M. Kargar, “Association between TNF-Alpha (-857) Gene Polymorphism and Susceptibility to Tuberculosis,” 2011.

M. W. Ng et al., “The association of RANTES polymorphism with severe acute respiratory syndrome in Hong Kong and Beijing Chinese,” BMC Infect Dis, vol. 7, no. 1, pp. 1–8, Jun. 2007, DOI 10.1186/1471-2334-7-50. DOI: https://doi.org/10.1186/1471-2334-7-50

H. B. Oral et al., “Interleukin-10 (IL-10) gene polymorphism as a potential host susceptibility factor in tuberculosis,” Cytokine, vol. 35, no. 3–4, pp. 143–147, Aug. 2006, DOI: 10.1016/j.cyto.2006.07.015. DOI: https://doi.org/10.1016/j.cyto.2006.07.015

P. Selvaraj, K. Alagarasu, M. Harishankar, M. Vidyarani, D. Nisha Rajeswari, and P. R. Narayanan, “Cytokine gene polymorphisms and cytokine levels in pulmonary tuberculosis,” Cytokine, vol. 43, no. 1, pp. 26–33, Jul. 2008, DOI: 10.1016/j.cyto.2008.04.011. DOI: https://doi.org/10.1016/j.cyto.2008.04.011

K. Y. K. Chan et al., “CD209 (DC-SIGN) -336A>G promoter polymorphism and severe acute respiratory syndrome in Hong Kong Chinese,” Hum Immunol, vol. 71, no. 7, pp. 702–707, Jul. 2010, DOI: 10.1016/j.humimm.2010.03.006. DOI: https://doi.org/10.1016/j.humimm.2010.03.006

Y. X. Yi, J. B. Han, L. Zhao, Y. Fang, Y. F. Zhang, and G. Y. Zhou, “Tumor necrosis factor alpha gene polymorphism contributes to pulmonary tuberculosis susceptibility: Evidence from a meta-analysis,” Int J Clin Exp Med, vol. 8, no. 11, pp. 20690–20700, Nov. 2015.

M. Akahoshi et al., “Influence of interleukin-12 receptor β1 polymorphisms on tuberculosis,” Hum Genet, vol. 112, no. 3, pp. 237–243, Mar. 2003, DOI: 10.1007/s00439-002-0873-5. DOI: https://doi.org/10.1007/s00439-002-0873-5

L. Ka, M. Hospital, S. T. Lai, K. M. So, and U.-S. Khoo, “Association of a single nucleotide polymorphism in the CD209 (DC-SIGN) promoter with SARS severity Key Messages,” Hong Kong Med J, vol. 16, no. 5, p. 37, 2010.

F. F. Yuan et al., “Influence of FcgammaRIIA and MBL polymorphisms on severe acute respiratory syndrome,” Tissue Antigens, vol. 66, no. 4, pp. 291–296, Oct. 2005, DOI: 10.1111/j.1399-0039.2005.00476.x. DOI: https://doi.org/10.1111/j.1399-0039.2005.00476.x

N. Remus et al., “Association of IL12RB1 Polymorphisms with Pulmonary Tuberculosis in Adults in Morocco,” J Infect Dis, vol. 190, no. 3, pp. 580–587, Aug. 2004, DOI: 10.1086/422534. DOI: https://doi.org/10.1086/422534

M. Bukhari et al., “TLR8 gene polymorphism and association in bacterial load in southern Punjab of Pakistan: An association study with pulmonary tuberculosis,” Int J Immunogenet, vol. 42, no. 1, pp. 46–51, Feb. 2015, DOI: 10.1111/iji.12170. DOI: https://doi.org/10.1111/iji.12170

S. P. Anand, P. Selvaraj, M. S. Jawahar, A. R. Adhilakshmi, and P. R. Narayanan, “Interleukin-12B & interleukin-10 gene polymorphisms in pulmonary tuberculosis,” Indian Journal of Medical Research, vol. 126, no. 2, pp. 135–138, Aug. 2007.

K. M. Adam, “Immunoinformatics approach for multi-epitope vaccine design against structural proteins and ORF1a polyprotein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2),” Trop Dis Travel Med Vaccines, vol. 7, no. 1, pp. 1–13, Dec. 2021, DOI: 10.1186/S40794-021-00147-1/FIGURES/7. DOI: https://doi.org/10.1186/s40794-021-00147-1

R. Monteiro-Maia, M. B. Ortigão-de-Sampaio, R. T. Pinho, and L. R. R. Castello-Branco, “Modulation of humoral immune response to oral BCG vaccination by Mycobacterium bovis BCG Moreau Rio de Janeiro (RDJ) in healthy adults,” J Immune Based Ther Vaccines, vol. 4, no. 1, pp. 1–6, Sep. 2006, DOI: 10.1186/1476-8518-4-4/FIGURES/4. DOI: https://doi.org/10.1186/1476-8518-4-4

H. H. Bui, J. Sidney, K. Dinh, S. Southwood, M. J. Newman, and A. Sette, “Predicting population coverage of T-cell epitope-based diagnostics and vaccines,” BMC Bioinformatics, vol. 7, p. 153, Mar. 2006, doi: 10.1186/1471-2105-7-153. DOI: https://doi.org/10.1186/1471-2105-7-153

T. Schlake, A. Thess, M. Fotin-Mleczek, and K. J. Kallen, “Developing mRNA-vaccine technologies,” RNA Biol, vol. 9, no. 11, p. 1319, 2012, DOI: 10.4161/RNA.22269. DOI: https://doi.org/10.4161/rna.22269

N. Pardi, M. J. Hogan, F. W. Porter, and D. Weissman, “mRNA vaccines — a new era in vaccinology,” Nature Reviews Drug Discovery 2018 17:4, vol. 17, no. 4, pp. 261–279, Jan. 2018, DOI: 10.1038/nrd.2017.243. DOI: https://doi.org/10.1038/nrd.2017.243

I. Ahammad and S. S. Lira, “Designing a novel mRNA vaccine against SARS-CoV-2: An immunoinformatics approach,” Int J Biol Macromol, vol. 162, pp. 820–837, Nov. 2020, DOI: 10.1016/j.ijbiomac.2020.06.213. DOI: https://doi.org/10.1016/j.ijbiomac.2020.06.213

M. Kozak, “Possible role of flanking nucleotides in recognition of the AUG initiator codon by eukaryotic ribosomes,” Nucleic Acids Res, vol. 9, no. 20, pp. 5233–5252, Oct. 1981, DOI: 10.1093/NAR/9.20.5233. DOI: https://doi.org/10.1093/nar/9.20.5233

J. Y. Wang et al., “Improved expression of secretory and trimeric proteins in mammalian cells via the introduction of a new trimer motif and a mutant of the tPA signal sequence,” Appl Microbiol Biotechnol, vol. 91, no. 3, pp. 731–740, Aug. 2011, DOI: 10.1007/S00253-011-3297-0/FIGURES/6. DOI: https://doi.org/10.1007/s00253-011-3297-0

C. Seillier et al., “Roles of the tissue-type plasminogen activator in immune response,” Cell Immunol, vol. 371, p. 104451, Jan. 2022, DOI: 10.1016/J.CELLIMM.2021.104451. DOI: https://doi.org/10.1016/j.cellimm.2021.104451

Y. Kou et al., “Tissue plasminogen activator (tPA) signal sequence enhances immunogenicity of MVA-based vaccine against tuberculosis,” Immunol Lett, vol. 190, pp. 51–57, Oct. 2017, DOI: 10.1016/J.IMLET.2017.07.007. DOI: https://doi.org/10.1016/j.imlet.2017.07.007

F. Teufel et al., “SignalP 6.0 predicts all five types of signal peptides using protein language models,” Nature Biotechnology 2022 40:7, vol. 40, no. 7, pp. 1023–1025, Jan. 2022, DOI: 10.1038/s41587-021-01156-3. DOI: https://doi.org/10.1038/s41587-021-01156-3

E. D. Franke et al., “Pan DR binding sequence provides T-cell help for induction of protective antibodies against Plasmodium yoelii sporozoites,” Vaccine, vol. 17, no. 9–10, pp. 1201–1205, Mar. 1999, DOI: 10.1016/S0264-410X(98)00341-7. DOI: https://doi.org/10.1016/S0264-410X(98)00341-7

J. Alexander et al., “Linear PADRE T Helper Epitope and Carbohydrate B Cell Epitope Conjugates Induce Specific High Titer IgG Antibody Responses,” The Journal of Immunology, vol. 164, no. 3, pp. 1625–1633, Feb. 2000, DOI: 10.4049/JIMMUNOL.164.3.1625. DOI: https://doi.org/10.4049/jimmunol.164.3.1625

H. Ghaffari-Nazari, J. Tavakkol-Afshari, M. R. Jaafari, S. Tahaghoghi-Hajghorbani, E. Masoumi, and S. A. Jalali, “Improving Multi-Epitope Long Peptide Vaccine Potency by Using a Strategy that Enhances CD4+ T Help in BALB/c Mice,” PLoS One, vol. 10, no. 11, Nov. 2015, DOI: 10.1371/JOURNAL.PONE.0142563. DOI: https://doi.org/10.1371/journal.pone.0142563

D. van der Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark, and H. J. C. Berendsen, “GROMACS: Fast, flexible, and free,” J Comput Chem, vol. 26, no. 16, pp. 1701–1718, Dec. 2005, DOI: 10.1002/JCC.20291. DOI: https://doi.org/10.1002/jcc.20291

F. Celada and P. E. Seiden, “A computer model of cellular interactions in the immune system,” Immunol Today, vol. 13, no. 2, pp. 56–62, 1992, DOI: 10.1016/0167-5699(92)90135-T. DOI: https://doi.org/10.1016/0167-5699(92)90135-T

R. Puzone, B. Kohler, P. Seiden, and F. Celada, “IMMSIM, a flexible model for in machina experiments on immune system responses,” Future Generation Computer Systems, vol. 18, no. 7, pp. 961–972, Aug. 2002, DOI: 10.1016/S0167-739X(02)00075-4. DOI: https://doi.org/10.1016/S0167-739X(02)00075-4

S. Gong and R. M. Ruprecht, “Immunoglobulin M: An Ancient Antiviral Weapon – Rediscovered,” Front Immunol, vol. 11, p. 1943, Aug. 2020, DOI: 10.3389/FIMMU.2020.01943/BIBTEX. DOI: https://doi.org/10.3389/fimmu.2020.01943

B. P. O’Connor, M. W. Gleeson, R. J. Noelle, and L. D. Erickson, “The rise and fall of long-lived humoral immunity: terminal differentiation of plasma cells in health and disease,” Immunol Rev, vol. 194, p. 61, Aug. 2003, DOI: 10.1034/J.1600-065X.2003.00055.X. DOI: https://doi.org/10.1034/j.1600-065X.2003.00055.x

F.-M. Lum et al., “Monkeypox: disease epidemiology, host immunity and clinical interventions,” Nature Reviews Immunology 2022, pp. 1–17, Sep. 2022, DOI: 10.1038/s41577-022-00775-4.

S. C. Johnston et al., “Cytokine modulation correlates with severity of monkeypox disease in humans,” Journal of Clinical Virology, vol. 63, pp. 42–45, Feb. 2015, DOI: 10.1016/J.JCV.2014.12.001. DOI: https://doi.org/10.1016/j.jcv.2014.12.001

F.-M. Lum et al., “Monkeypox: disease epidemiology, host immunity and clinical interventions,” Nature Reviews Immunology 2022, pp. 1–17, Sep. 2022, DOI: 10.1038/s41577-022-00775-4. DOI: https://doi.org/10.1038/s41577-022-00775-4

P. R. Pittman et al., “Clinical characterization of human monkeypox infections in the Democratic Republic of the Congo,” medRxiv, p. 2022.05.26.22273379, May 2022, DOI: 10.1101/2022.05.26.22273379. DOI: https://doi.org/10.1101/2022.05.26.22273379

J. Goulding, G. Abboud, V. Tahiliani, P. Desai, T. E. Hutchinson, and S. Salek-Ardakani, “CD8 T Cells Use IFN-γ To Protect against the Lethal Effects of a Respiratory Poxvirus Infection,” The Journal of Immunology, vol. 192, no. 11, pp. 5415–5425, Jun. 2014, DOI: 10.4049/JIMMUNOL.1400256/-/DCSUPPLEMENTAL. DOI: https://doi.org/10.4049/jimmunol.1400256

F. J. A. de Miranda, I. Alonso-Sánchez, A. Alcamí, and B. Hernaez, “TNF Decoy Receptors Encoded by Poxviruses,” Pathogens 2021, Vol. 10, Page 1065, vol. 10, no. 8, p. 1065, Aug. 2021, DOI: 10.3390/PATHOGENS10081065. DOI: https://doi.org/10.3390/pathogens10081065

P. H. Verardi, L. A. Jones, F. H. Aziz, S. Ahmad, and T. D. Yilma, “Vaccinia virus vectors with an inactivated gamma interferon receptor homolog gene (B8R) are attenuated In vivo without a concomitant reduction in immunogenicity,” J Virol, vol. 75, no. 1, pp. 11–18, Jan. 2001, DOI: 10.1128/JVI.75.1.11-18.2001. DOI: https://doi.org/10.1128/JVI.75.1.11-18.2001

B. Marshall et al., “Variola virus F1L is a Bcl-2-like protein that unlike its vaccinia virus counterpart inhibits apoptosis independent of Bim,” Cell Death & Disease 2015 6:3, vol. 6, no. 3, pp. e1680–e1680, Mar. 2015, DOI: 10.1038/cddis.2015.52. DOI: https://doi.org/10.1038/cddis.2015.52

W. L. Simon, H. M. Salk, I. G. Ovsyannikova, R. B. Kennedy, and G. A. Poland, “Cytokine production associated with smallpox vaccine responses,” Immunotherapy, vol. 6, no. 10, p. 1097, Oct. 2014, DOI: 10.2217/IMT.14.72. DOI: https://doi.org/10.2217/imt.14.72