Geodesic
Predictive modeling of respiratory virus evolution: Current capabilities and limitations
Matematičeskaâ biologiâ i bioinformatika, Tome 19 (2024) no. 2, pp. 579-592

Voir la notice de l'article provenant de la source Math-Net.Ru

Modeling of viral evolution trajectories is of primary practical importance when selecting strains to be included in influenza and SARS-CoV-2 vaccines. Regular updating of the vaccine composition has a pronounced effect on their efficacy, which depends, first of all, on the correspondence of the strains selected for inclusion in the vaccine to those that will circulate during the epidemic season. Given the complexity of the choice, which is based on a large amount of experimental data, and the need to make it in advance for early deployment of vaccine production, the development of predictive methods can significantly improve the correspondence of the selected vaccine strains to those circulating in the upcoming epidemic season. The article considers the main approaches to modeling viral evolution, describes their strengths and limitations. The optimal approach is selected for predicting the evolution of influenza viruses circulating in Russia. The results of using this approach with an assessment of its predictive power on retrospective genomic data of the A(H1N1)pdm09 subtype from the EpiFlu GISAID database are presented. A method for assessing the correspondence of the set of the most adapted strains obtained as a result of the predictive model to the strains selected by experts for inclusion in influenza vaccines in the corresponding epidemic seasons is proposed. This approach can be expanded to predict the evolution of other respiratory viruses, including SARS-CoV-2. 
@article{MBB_2024_19_2_a22,
     author = {V. I. Tychkova and V. Leonenko and D. M. Danilenko},
     title = {Predictive modeling of respiratory virus evolution: {Current} capabilities and limitations},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {579--592},
     publisher = {mathdoc},
     volume = {19},
     number = {2},
     year = {2024},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MBB_2024_19_2_a22/}
}
TY  - JOUR
AU  - V. I. Tychkova
AU  - V. Leonenko
AU  - D. M. Danilenko
TI  - Predictive modeling of respiratory virus evolution: Current capabilities and limitations
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2024
SP  - 579
EP  - 592
VL  - 19
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2024_19_2_a22/
LA  - ru
ID  - MBB_2024_19_2_a22
ER  - 
%0 Journal Article
%A V. I. Tychkova
%A V. Leonenko
%A D. M. Danilenko
%T Predictive modeling of respiratory virus evolution: Current capabilities and limitations
%J Matematičeskaâ biologiâ i bioinformatika
%D 2024
%P 579-592
%V 19
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2024_19_2_a22/
%G ru
%F MBB_2024_19_2_a22
V. I. Tychkova; V. Leonenko; D. M. Danilenko. Predictive modeling of respiratory virus evolution: Current capabilities and limitations. Matematičeskaâ biologiâ i bioinformatika, Tome 19 (2024) no. 2, pp. 579-592. http://geodesic.mathdoc.fr/item/MBB_2024_19_2_a22/

[1] Global Burden of Disease Study 2013 Collaborators, “Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013”, Lancet, 386:9995 (2015), 743–800 | DOI | DOI

[2] Waterer G., “The global burden of respiratory infectious diseases before and beyond COVID”, Respirology, 28:2 (2023), 95–96 | DOI | DOI

[3] AdaborE. S., “Astatistical analysis of antigenic similarity among influenzaA(H3N2) viruses”, Heliyon, 7:11 (2021) | DOI | DOI

[4] Russell C.A, T. C. Jones, I. G. Barr, N. J. Cox, R. J. Garten, V. Gregory, I. D. Gust, A. W. Hampson, Hay A.J, Hurt A.C et al, “Influenza vaccine strain selection and recent studies on the global migration of seasonal influenza viruses”, Vaccine, 26 (2008), D31-D34 | DOI | DOI

[5] W. K. WHO Writing Group; Ampofo, N. Baylor, S. Cobey, N. J. Cox, N. J. Cox, S. Daves, S. Edwards, N. Ferguson, G. Grohmann, A. Hay, J. Katz et al, “Improving influenza vaccine virus selection: report of a WHO informal consultation held at WHO headquarters, Geneva, Switzerland, 14-16 June 2010”, Influenza Other Respir. Viruses, 6:2 (2012), 142–152 | DOI | DOI

[6] W. Zhang, S. Hirve, M. P. Kieny, “Seasonal vaccines- Critical path to pandemic influenza response”, Vaccine, 35 (2017), 851–852

[7] Technical Advisory Group on COVID-19 Vaccine Composition Home Page, (data obrascheniya: 20.11.2024) https://www.who.int/groups/technical-advisory-group-on-covid-19-vaccine-composition-(tag-co-vac)

[8] V. Petrova, C. Russell, “The evolution of seasonal influenza viruses”, Nat. Rev. Microbiol, 16 (2018), 47–60 | DOI | DOI

[9] Morris D.H, K. M. Gostic, S. Pompei, T. Bedford, M. Luksza, R. A. Neher, B. T. Grenfell, M. Lassig, J. W. McCauley, “Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology”, Trends Microbiol, 26:2 (2018), 102–118 | DOI | DOI

[10] S. Gouma, E. M. Anderson, S. E. Hensley, “Challenges of Making Effective Influenza Vaccines”, Annu. Rev. Virol, 7:1 (2020), 495–512 | DOI | DOI

[11] B. Dadonaite, J. J. Ahn, J. T. Ort, J. Yu, C. Furey, A. Dosey, W. W. Hannon, A. L. Vincent Baker, R. J. Webby et al, “Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance”, PLoS Biology, 22:11 (2024), e3002916 | DOI | DOI

[12] T. N. Starr, A. J. Greaney, S. K. Hilton, D. Ellis, K. H.D. Crawford, A. S. Dingens, M. J. Navarro, J. E. Bowen, M. A. Tortorici, A. C. Walls et al, “Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding”, Cell, 182:5 (2020), 1295–1310.E20 | DOI | DOI

[13] T. A. Timofeeva, I. A. Rudneva, N. F. Lomakina, E. B. Timofeeva, I. M. Kupriyanova, A. V. Lyashko, D. N. Shcherbinin, A. A. Shilov, M. M. Shmarov, E. L. Ryazanova, L. V. Mochalova, B. I. Timofeev, “Mutations in the genome of avian influenza viruses of the H1 and H5 subtypes responsible for adaptation to mammals”, MIR J, 8:1 (2021), 50–61 | DOI | DOI

[14] J. Lan, J. Ge, J. Yu, S. Shan, H. Zhou, S. Fan, Q. Zhang, X. Shi, Q. Wang, L. Zhang, X. Wang, “Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor”, Nature, 581 (2020), 215–220 | DOI | DOI

[15] S. Di Franco, P. Bianca, D. S. Sardina, A. Turdo, M. Gaggianesi, V. Veschi, A. Nicotra, L. R. Mangiapane, M. L. Iacono, I. Pillitteri et al, “Adipose stem cell niche reprograms the colorectal cancer stem cell metastatic machinery”, Nat. Commun, 12 (2021) | DOI | DOI

[16] A. Rambaut, O. Pybus, M. Nelson, C. Viboud, J. K. Taubenberger, E. C. Holmes, “The genomic and epidemiological dynamics of human influenza A virus”, Nature, 453 (2008), 615–619 | DOI | DOI

[17] Z. Ning, Z. Nan, F. Huafeng, D. Jie, X. Xingyu, D. Xiaoqing, D. Xiaoxiao, X. Dandan, M. Xiaoyu, YanY, G. Hongjin, M. Lingfeng, H. Min, “Mutations and PhylogeneticAnalyses of SARS-CoV-2 Among Imported COVID-19 From Abroad in Nanjing, China”, Frontiers in Microbiology, 13 (2022) | DOI | DOI

[18] N. Lefrancq, L. Duret, V. Bouchez, S. Brisse, J. Parkhill, H. Salje, Learning the fitness dynamics of pathogens from phylogenies, medRxiv, 2023 | DOI | DOI

[19] A. Waterhouse, M. Bertoni, S. Bienert, G. Studer, G. Tauriello, R. Gumienny, F. T. Heer, T. A.P. de Beer, C. Rempfer, L. Bordoli, R. Lepore, T. Schwede, “SWISS-MODEL: homology modelling of protein structures and complexes”, Nucleic Acids Research, 46:W1 (2018), W296-W303 | DOI | DOI

[20] V. P. Waman, P. Ashford, S. D. Lam, N. Sen, M. Abbasian, L. Woodridge, Y. Goldtzvik, N. Bordin, J. Wu, I. Sillitoe, C. A. Orengo, “Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics”, Scientific Reports, 14:1 (2024), 14208

[21] A. K. Padhi, P. Kalita, S. Maurya, K. M. Poluri, T. Tripathi, “From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics”, J. Phys. Chem. B, 127:41 (2023), 8717–8735 | DOI | DOI

[22] J. Li, S. Zhang, B. Li, Y. Hu, X. P. Kang, X. Y. Wu, M. T. Huang, Y. C. Li, Z. P. Zhao, C. F. Qin, T. Jiang, “Machine learning methods for predicting human-adaptive influenza a viruses based on viral nucleotide compositions”, Molecular biology and evolution, 37:4 (2020), 1224–1236

[23] B. Hie, E. D. Zhong, B. Berger, B. Bryson, “Learning the language of viral evolution and escape”, Science, 371:6526 (2021), 284–288 | Zbl | Zbl

[24] B. P. Holder, P. Simon, L. E. Liao, Y. Abed, X. Bouhy, C. A.A. Beauchemin, G. Boivin, “Assessing the in vitro fitness of an oseltamivir-resistant seasonal A/H1N1 influenza strain using a mathematical model”, PloS One, 6:3 (2011) | DOI | Zbl | DOI | Zbl

[25] L. Kepler, M. Hamins-Puertolas, D. A. Rasmussen, “Decomposing the sources of SARS-CoV-2 fitness variation in the United States”, Virus Evolution, 7:2 (2021) | DOI | DOI

[26] X. Gu, ANew Criteria to Distinguish among Different Selection Modes in Gene Evolution, bioRxiv, 2020 | DOI | DOI

[27] G. Tonkin-Hill, I. Martincorena, R. Amato, A. R.J. Lawson, M. Gerstung, I. Johnston, D. K. Jackson, N. Park, S. V. Lensing, M. A. Quailet, “al Patterns of within-host genetic diversity in SARS-CoV-2”, eLife, 10 (2021), e66857 | DOI | DOI

[28] R. Lei, A. Hernandez Garcia, T. J.C. Tan, Q. W. Teo, Y. Wang, X. Zhang, S. Luo, S. K. Nair, J. Peng, N. C. Wu, “Mutational fitness landscape of human influenza H3N2 neuraminidase”, Cell reports, 42:1 (2023), 111951

[29] J. M. Flynn, N. Samant, G. Schneider-Nachum, D. T. Barkan, N. K. Yilmaz, C. A. Schiffer, S. A. Moquin, D. Dovala, D. N.A. Bolon, “Comprehensive fitness landscape of SARS-CoV-2 Mpro reveals insights into viral resistance mechanisms”, Elife, 11 (2022), e77433

[30] R. A. Neher, C. A. Russell, B. I. Shraiman, “Predicting evolution from the shape of genealogical trees”, eLife, 3 (2014), e03568 | DOI | DOI

[31] M. N. Price, P. S. Dehal, A. P. Arkin, “FastTree: computing large minimum evolution trees with profiles instead of a distance matrix”, Molecular biology and evolution, 26:7 (2009), 1641–1650

[32] M. Luksza, M. Lassig, “A predictive fitness model for influenza”, Nature, 507 (2014), 57–61 | DOI | DOI

[33] T. Bedford, S. Riley, I. G. Barr, S. Broor, M. Chadha, “Global circulation patterns of seasonal influenza viruses vary with antigenic drift”, Nature, 523:7559 (2015), 217–220 | DOI | DOI

[34] Y. Shu, J. McCauley, “GISAID: Global initiative on sharing all influenza data from vision to reality”, Euro Surveill, 22:13 (2017), 30494 | DOI | DOI

[35] K. Katoh, D. M. Standley, “MAFFT multiple sequence alignment software version 7: improvements in performance and usability”, Molecular biology and evolution, 30:4 (2013), 772–780 | DOI | DOI

[36] A. Larsson, “AliView: a fast and lightweight alignment viewer and editor for large datasets”, Bioinformatics, 30:22 (2014), 3276–3278 | DOI | DOI

[37] E. Aparicio-Puerta, R. Lebron, A. Rueda, C. Gomez-Martin, S. Giannoukakos, D. Jaspez, J. M. Medina, A. Zubkovic, I. Jurak, B. Fromm, J. A. Marchal, J. Oliver, M. Hackenberg, “sRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression”, Nucleic Acids Research, 47:W1 (2019), W530-W535 | DOI | DOI

[38] D. J. Smith, A. S. Lapedes, J. C. de Jong, T. M. Bestebroer, G. F. Rimmelzwaan, “Mapping the antigenic and genetic evolution of influenza virus”, Science, 305:5682 (2004), 371–376 | DOI | DOI