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Unravelling the epidemiological diversity of Zika virus by analyzing key protein variations

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Abstract

The consequences of Zika virus (ZIKV) infections were limited to sporadic mild diseases until almost a decade ago, when epidemic outbreaks took place, with quick spread into the Americas. Simultaneously, novel severe neurological manifestations of ZIKV infections were identified, including congenital microcephaly. However, why the epidemic strains behave differently is not yet completely understood, and many questions remain about the actual significance of genetic variations in the epidemiology and biology of ZIKV. In this study, we analysed a large number of viral sequences to identify genes with different levels of variability and patterns of genomic variations that could be associated with ZIKV diversity. We compared numerous epidemic strains with pre-epidemic strains, using the BWA-mem algorithm, and we also examined specific variations among the epidemic ZIKV strains derived from microcephaly cases. We identified several viral genes with dissimilar mutation rates among the ZIKV strain groups and novel protein variation profiles that might be associated with epidemiological particularities. Finally, we assessed the impact of the detected changes on the structure and stability of the NS1, NS5, and E proteins using the I-TASSER, trRosetta, and RaptorX modelling algorithms, and we found some interesting variations that might help to explain the heterogeneous features of the diverse ZIKA strains. This work contributes to the identification of genetic differences in the ZIKV genome that might have a phenotypic impact, providing a basis for future experimental analysis to elucidate the genetic causes of the recent ZIKV emergency.

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Data availability

All data generated or analysed during this study are included in this article and its supplementary information files, and the code used for the analysis is publically available in the GitHub repository at https://github.com/Santi8Leiva/Zika.

Abbreviations

BWA-mem:

Burrows-Wheeler Aligner maximal exact matches

C:

Capsid

E:

Envelope

FV:

Flavivirus

IGV:

Integrative Genomics Viewer

NCBI:

National Center for Biotechnology Information

prM/M:

Premembrane/membrane

SNP:

Single-nucleotide polymorphism

vcf:

Variant call format

ZIKV:

Zika virus

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Funding

Marina Bugnon Valdano and Santiago Leiva were supported by fellowships from CONICET and Agencia de Promoción Científica y Tecnológica, respectively. This work was supported by a research grant from the Agencia de Promoción Científica y Tecnológica (Argentina, PICT 2016-1725, PICT 2019-03050).

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Authors and Affiliations

  1. Facultad de Ciencias Bioquímicas y Farmacéuticas, Instituto de Biología Molecular y Celular de Rosario-CONICET, Universidad Nacional de Rosario, Suipacha 531, 2000, Rosario, Argentina

    Santiago Leiva, Marina Bugnon Valdano & Daniela Gardiol

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  1. Santiago Leiva
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  2. Marina Bugnon Valdano
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  3. Daniela Gardiol
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Contributions

SL planned and performed bioinformatics analysis, analysed data, and wrote the paper. MBV planned and analysed the data and wrote the paper. DG supervised the project, analysed data, and wrote the paper.

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Correspondence to Marina Bugnon Valdano or Daniela Gardiol.

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Leiva, S., Bugnon Valdano, M. & Gardiol, D. Unravelling the epidemiological diversity of Zika virus by analyzing key protein variations. Arch Virol 168, 115 (2023). https://doi.org/10.1007/s00705-023-05726-5

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