ISSN: 0443-511
e-ISSN: 2448-5667
Usuario/a
Idioma
Herramientas del artículo
Envíe este artículo por correo electrónico (Inicie sesión)
Enviar un correo electrónico al autor/a (Inicie sesión)
Tamaño de fuente

Open Journal Systems

Vacunología reversa: estrategia contra patógenos emergentes / Reverse vaccinology: strategy against emerging pathogens

Gloria Paulina Monterrubio-López, Karen Delgadillo-Gutiérrez

Resumen


Resumen

Las nuevas tecnologías en vacunología son capaces de lograr un desarrollo rápido, así como una producción a gran escala de vacunas seguras y eficaces. La vacunología reversa es una metodología in silico que estudia diferentes características de los agentes infecciosos, con el objetivo de identificar antígenos que sean buenos candidatos vacunales, sin la necesidad del cultivo tradicional. Esta estrategia se basa en el uso de herramientas bioinformáticas, por lo que es una metodología sencilla, segura, económica y que reduce de forma significativa el tiempo de diseño de una vacuna, en comparación con la vacunología tradicional. En los últimos años, la rápida diseminación de infecciones por patógenos emergentes ha requerido del desarrollo oportuno de nuevas vacunas. Las estrategias bioinformáticas aunadas a los más recientes diseños de vacunas de nueva generación permiten la selección de candidatos vacunales en corto tiempo, lo cual es muy importante en el desarrollo de nuevas vacunas contra patógenos con potencial pandémico.

 

Abstract

New technologies in vaccinology are capable of achieving fast development, as well as large-scale production of effective and safe vaccines. Reverse vaccinology is an in silico methodology, which studies different characteristics of infectious agents, in order to identify antigens that are good vaccine candidates, without the need of traditional culture. This strategy is based on bioinformatics tools, that in a simple, safety and inexpensive way, reduces time and effort significantly in the new vaccine design, against traditional vaccinology. In recent years, the rapid spread of infections by emerging pathogens requires prompt development of new vaccines. Bioinformatic strategies joined with the latest next-generation vaccines allow the selection of vaccine candidates in a short time, which is relevant in the development of new vaccines against pathogens with pandemic potential.

 


Palabras clave


Microbiología; Biología Computacional; Vacunología; Vacunas; Pandemias / Microbiology; Computational Biology; Vaccinology; Vaccines; Pandemics

Texto completo:

PDF PubMed

Referencias


Morens DM, Folkers GK, Fauci AS. What is a pandemic? J Infect Dis. 2009;200(7):1018‑21. doi: 10.1086/644537

 

Rauch S, Jasny E, Schmidt KE, Petsch B. New vaccine technologies to combat outbreak situations. Front Immunol. 2018;9:1-24. doi: 10.3389/fimmu.2018.01963

 

Canouï E, Launay O. History and principles of vaccination. Rev Mal Respir. 2019;36(1):74‑81. doi: 10.1016/j.rmr.2018.02.015

 

Rusnock AA. Historical context and the roots of Jenner’s discovery. Hum Vaccines Immunother. 2016;12(8):2025-8. doi: 10.1080/21645515.2016.1158369

 

Berche P. Louis Pasteur, from crystals of life to vaccination. Clin Microbiol Infect. 2012;18(5):1‑6. doi: 10.1111/j.1469-0691.2012.03945.x

 

Plotkin S. History of vaccination. Proc Natl Acad Sci U S A. 2014;111(34):12283‑7. doi: 10.1073/pnas.1400472111

 

De Gregorio E, Rappuoli R. From empiricism to rational design: A personal perspective of the evolution of vaccine development. Nat Rev Immunol. 2014;14(7):505‑14. doi: 10.1038/nri3694

 

Loomis RJ, Johnson PR. Emerging vaccine technologies. Vaccines. 2015;3(2):429‑47. doi: 10.3390/vaccines3020429

 

McAleer WJ, Buynak EB, Maigetter RZ, Wampler DE, Miller WJ, Hilleman MR. Human hepatitis B vaccine from recombinant yeast. Nature. 1984;307:178‑80. doi: 10.1038/307178a0

 

Dhiman N, Bonilla R, O’Kane JD, Poland GA. Gene expression microarrays: a 21st century tool for directed vaccine design. Vaccine. 2001;20(1‑2):22‑30. doi: 10.1016/s0264-410x(01)00319-x

 

Guirakhoo F, Kitckener S, Morrison D, Forrat R, McCarthy K, Nichols R, et al. Live attenuated chimeric yellow fever dengue type 2 (ChimeriVaxTM- DEN2) vaccine: Phase I clinical trial for safety and immunogenicity - Effect of yellow fever pre-immunity in induction of cross neutralizing antibody responses to all 4 dengue serotypes. Hum Vaccin. 2006;2(2):60‑7. doi: doi.org/10.4161/hv.2.2.2555

 

Castellsagué X, Bosch FX. Avances en la prevención del cáncer de cuello de útero: Vacunas VPH. Farm Hosp. 2007;31(5):261‑3. doi: 10.1016/S1130-6343(07)75388-6

 

Wecker M, Gilbert P, Russell N, Hural J, Allen M, Pensiero M, et al. Phase I safety and immunogenicity evaluations of an alphavirus replicon HIV-1 subtype C gag vaccine in healthy HIV-1-uninfected adults. Clin Vaccine Immunol. 2012;19(10):1651‑60. doi: 10.1128/CVI.00258-12

 

Finco O, Rappuoli R. Designing vaccines for the twenty-first century society. Front Immunol. 2014;5(12):1‑6. doi: 10.3389/fimmu.2014.00012

 

Excler JL, Kim JH. Novel prime-boost vaccine strategies against HIV-1. Expert Rev Vaccines. 2019;18(8):765‑79. doi: 10.1080/14760584.2019.1640117

 

Polack FP, Thomas SJ, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. N Engl J Med. 2020;383(27):2603 15. doi: 10.1056/nejmoa2034577

 

Jones I, Roy P. Sputnik V COVID 19 vaccine candidate appears safe and effective. Lancet. 2021;397(10275):642-643. doi: https://doi.org/10.1016/S0140-6736(21)00191-4

 

Seib KL, Zhao X, Rappuoli R. Developing vaccines in the era of genomics: A decade of reverse vaccinology. Clin Microbiol Infect. 2012;18(5):109‑16. doi: 10.1111/j.1469-0691.2012.03939.x

 

Mora M, Telford JL. Genome-based approaches to vaccine development. J Mol Med. 2010;88(2):143‑7. doi: 10.1007/s00109-009-0574-9

 

Michalik M, Djahanshiri B, Leo JC, DL Linke. Vaccine Desing. Thomas S, editor. Vol. 1. Philadelphia, PA, USA: Springer; 2016. pp. 87-106.

 

Ferreira J, Porco A. Vacunas derivadas del análisis de los genomas: vacunología inversa. Interciencia. 2008;33(5):353‑8. Disponible en: http://www.redalyc.org/articulo.oa?id=33933506

 

Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, et al. WoLF PSORT: Protein localization predictor. Nucleic Acids Res. 2007;35(2):585‑7. doi: 10.1093/nar/gkm259

 

De Alvarenga Mudadu M, Carvalho V, Leclercq SY. Nonclassically Secreted Proteins as Possible Antigens for Vaccine Development: A Reverse Vaccinology Approach. Appl Biochem Biotechnol. 2015;175(7):3360‑70. doi: 10.1007/s12010-015-1507-4

 

Sachdeva G, Kumar K, Jain P, Ramachandran S. SPAAN: A software program for prediction of adhesins and adhesin-like proteins using neural networks. Bioinformatics. 2005;21(4):483‑91. doi: 10.1093/bioinformatics/bti028

 

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403‑10. doi: 10.1016/S00222836(05)80360-2

 

Tusnády GE, Simon I. The HMMTOP transmembrane topology prediction server. Bioinformatics. 2001;17(9):849‑50. doi: 10.1093/bioinformatics/17.9.849

 

Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: Implications on vaccine development. J Biomed Inform. 2015;53:405‑14. doi: 10.1016/j.jbi.2014.11.003

 

Vivona S, Bernante F, Filippini F. NERVE: New Enhanced Reverse Vaccinology Environment. BMC Biotechnol. 2006;6:1‑8. doi: 10.1186/1472-6750-6-35

 

Rizwan M, Naz A, Ahmad J, Naz K, Obaid A, Parveen T, et al. VacSol: A high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology. BMC Bioinformatics. 2017;18(1):1‑7. doi: 10.1186/s12859-017-1540-0

 

D’Mello A, Ahearn CP, Murphy TF, Tettelin H. ReVac: A reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidates. BMC Genomics. 2019;20(1):1-21. doi: 10.1186/s12864-019-6195-y

 

Goodswen SJ, Kennedy PJ, Ellis JT. Vacceed: A high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology. Bioinformatics. 2014;30(16):2381‑3. doi: 10.1093/bioinformatics/btu300

 

Xiang Z, He Y. Vaxign: a web-based vaccine target design program for reverse vaccinology. Procedia Vaccinol. 2009;1(1):23‑9. doi: 10.1016/j.provac.2009.07.005

 

Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8(4):1-7. doi: 10.1186/1471-2105-8-4

 

Dalsass M, Brozzi A, Medini D, Rappuoli R. Comparison of open-source reverse vaccinology programs for bacterial vaccine antigen discovery. Front Immunol. 2019;10(113):1‑12. doi: 10.3389/fimmu.2019.00113

 

Ong E, Wang H, Wong MU, Seetharaman M, Valdez N, He Y. Vaxign-ML: Supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics. 2020;36(10):3185‑91. doi: 10.1093/bioinformatics/btaa119

 

Naz K, Naz A, Ashraf ST, Rizwan M, Ahmad J, Baumbach J, et al. PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome. BMC Bioinformatics. 2019;20(1):1‑10. doi: 10.1186/s12859-019-2713-9

 

Donati C, Rappuoli R. Reverse vaccinology in the 21st century: Improvements over the original design. Ann N Y Acad Sci. 2013;1285(1):115‑32. doi: 10.1111/nyas.12046

 

Dormitzer PR, Grandi G, Rappuoli R. Structural vaccinology starts to deliver. Nat Rev Microbiol. 2012;10(12):807‑13. doi: 10.1038/nrmicro2893

 

Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: The use of prediction tools for molecular binding in the simulation of the immune system. PLoS One. 2010;5(4):1‑14. doi: 10.1371/journal.pone.0009862

 

Abad R, Martinón-Torres F, Santolaya de Pablo M, Banzhoff A, González-Inchausti C, Graña M, et al. Del genoma de un patógeno a una vacuna efectiva: La vacuna de cuatro componentes frente a los meningococos del serogrupo B. Rev Esp Quimioter. 2019;32(3):208‑16.

 

Laurens MB. RTS,S/AS01 vaccine (MosquirixTM): an overview. Hum Vaccines Immunother. 2019;16(3):480‑9. doi: 10.1080/21645515.2019.1669415

 

Kaliamurthi S, Selvaraj G, Junaid M, Khan A, Gu K, Wei D-Q. Cancer Immunoinformatics: A Promising Era in the Development of Peptide Vaccines for Human Papillomavirus-induced Cervical Cancer. Curr Pharm Des. 2019;24(32):3791‑817. doi: 10.2174/1381612824666181106094133

 

Klade CS, Wedemeyer H, Berg T, Hinrichsen H, Cholewinska G, Zeuzem S, et al. Therapeutic Vaccination of Chronic Hepatitis C Nonresponder Patients With the Peptide Vaccine IC41. Gastroenterology. 2008;134(5):1385‑95. doi: 10.1053/j.gastro.2008.02.058

 

Bruno L, Cortese M, Rappuoli R, Merola M. Lessons from Reverse Vaccinology for viral vaccine design. Curr Opin Virol. 2015;11:89‑97. doi: 10.1016/j.coviro.2015.03.001

 

Deng L, Cho KJ, Fiers W, Saelens X. M2e-based universal influenza a vaccines. Vaccine. 2015;3(1):105‑36. doi: 10.3390/vaccines3010105

 

Nuccitelli A, Cozzi R, Gourlay LJ, Donnarumma D, Necchi F, Norais N, et al. Structure-based approach to rationally design a chimeric protein for an effective vaccine against Group B Streptococcus infections. Proc Natl Acad Sci U S A. 2011;108(25):10278‑83. doi: 10.1073/pnas.1106590108

 

Mamede LD, de Paula KG, de Oliveira B, dos Santos JSC, Cunha LM, Junior MC, et al. Reverse and structural vaccinology approach to design a highly immunogenic multi-epitope subunit vaccine against Streptococcus pneumoniae infection. Infect Genet Evol. 2020;85:1‑28. doi: 10.1016/j.meegid.2020.104473

 

Moriel DG, Bertoldi I, Spagnuolo A, Marchi S, Rosini R, Nesta B, et al. Identification of protective and broadly conserved vaccine antigens from the genome of extraintestinal pathogenic Escherichia coli. Proc Natl Acad Sci U S A. 2010;107(20):9072‑7. doi: 10.1073/pnas.0915077107

 

Bianconi I, Alcalá-Franco B, Scarselli M, Dalsass M, Buccato S, Colaprico A, et al. Genome-based approach delivers vaccine candidates against Pseudomonas aeruginosa. Front Immunol. 2019;9(3021):1‑9. doi: 10.3389/fimmu.2018.03021

 

Gat O, Grosfeld H, Ariel N, Inbar I, Zaide G, Broder Y, et al. Search for Bacillus anthracis potential vaccine candidates by a functional genomic-serologic screen. Infect Immun. 2006;74(7):3987‑4001. doi: 10.1128/IAI.00174-06

 

Thorpe C, Edwards L, Snelgrove R, Finco O, Rae A, Grandi G, et al. Discovery of a vaccine antigen that protects mice from Chlamydia pneumoniae infection. Vaccine. 2007;25(12):2252‑60. doi: 10.1016/j.vaccine.2006.12.003

 

Ross BC, Czajkowski L, Hocking D, Margetts M, Webb E, Rothel L, et al. Identification of vaccine candidate antigens from a genomic analysis of Porphyromonas gingivalis. Vaccine. 2001;19(30):4135‑42. doi: 10.1016/S0264-410X(01)00173-6

 

Oliveira TL, Bacelo KL, Forster KM, Ilha V, Rodrigues OE, Hartwig DD. DNA nanovaccines prepared using LemA antigen protect Golden Syrian hamsters against Leptospira lethal infection. Mem Inst Oswaldo Cruz. 2020;115(2):1‑6. doi: 10.1590/0074-02760190396

 

Soltan MA, Magdy D, Solyman SM, Hanora A. Design of Staphylococcus aureus New Vaccine Candidates with B and T Cell Epitope Mapping, Reverse Vaccinology, and Immunoinformatics. Omi A J Integr Biol. 2020;24(4):195‑204. doi: 10.1089/omi.2019.0183

 

Moise L, McMurry JA, Buus S, Frey S, Martin WD, De Groot AS. In Silico-Accelerated Identification of Conserved and Immunogenic Variola/Vaccinia T-Cell Epitopes Leonard. Vaccine. 2009;23(1):1‑7. doi: 10.1016/j.vaccine.2009.06.018

 

McMurry JA, Gregory SH, Moise L, Rivera D, Buus S, De Groot AS. Diversity of Francisella tularensis Schu4 antigens recognized by T lymphocytes after natural infections in humans: Identification of candidate epitopes for inclusion in a rationally designed tularemia vaccine. Vaccine. 2007;25(16 SPEC. ISS.):3179‑91. doi: 10.1016/j.vaccine.2007.01.039

 

Muruato LA, Tapia D, Hatcher CL, Kalita M, Brett PJ, Gregory AE, et al. Use of reverse vaccinology in the design and construction of nanoglycoconjugate vaccines against burkholderia pseudomallei. Clin Vaccine Immunol. 2017;24(11):1‑13. doi: 10.1128/CVI.00206-17

 

Ranjbar MH, Ebrahimi MM, Shahsavandi MM, Farhadi S, Mirjalili T, Tebianian A, et al. Novel applications of immuno-bioinformatics in vaccine and bio-product developments at research institutes. Arch Razi Inst. 2019;74(3):219‑33. doi: 10.22092/ari.2018.122523.1224

 

Heinson AI, Woelk CH, Newell ML. The promise of reverse vaccinology. Int Health. 2015;7(2):85‑9. doi: 10.1093/inthealth/ihv002

 

Masignani V, Pizza M, Moxon ER. The development of a vaccine against Meningococcus B using reverse vaccinology. Front Immunol. 2019;10:1‑14. doi: 10.3389/fimmu.2019.00751

 

Taylor DR. Obstacles and advances in SARS vaccine development. Vaccine. 2006;24(7):863‑71. doi: 10.1016/j.vaccine.2005.08.102

 

Ong E, Wong MU, Huffman A, He Y. COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning. Front Immunol. 2020;11(1581):1‑13. doi: 10.3389/fimmu.2020.01581

 

Wallis J, Shenton DP, Carlisle RC. Novel approaches for the design, delivery and administration of vaccine technologies. Clin Exp Immunol. 2019;196(2):189‑204. doi: 10.1111/cei.13287


Enlaces refback

  • No hay ningún enlace refback.