The question of how Zika virus (ZIKV) changed from a seemingly mild virus to a human pathogen capable of microcephaly and sexual transmission remains unanswered. The unexpected emergence of ZIKV's pathogenicity and capacity for sexual transmission may be due to genetic changes. Future changes in phenotype may continue to occur as the virus expands its geographic range. Thus, it is important to identify patterns of genetic change that may yield a better understanding of ZIKV emergence and evolution.
We analyzed the ZIKV outbreak using bioinformatics to mine next generation sequencing data for detection of emerging mutations and for organization of large datasets to detect trends in mutation spread. Using computational analysis, we prioritized 544 mutations associated with the epidemic strain and identified mutations most likely to impact the ZIKV phenotype, according to transmission dynamics during the epidemic, prevalence and persistence in intra-host mutant spectra, and the position of the mutation on the protein structure. Ten mutations were selected for phenotypic analysis based on these criteria. An infectious clone was generated from the 2015 PRVABC-059 epidemic strain, and five mutants were generated. The phenotypes of these mutants were compared to the parental type using in vitro and in vivo assays. Preliminary data indicate that all five mutants have distinct phenotypes, with a mutant detected primarily as a variant genotype being the most virulent in the mouse model. These data indicate that even a single mutation may impact the outcome of an infection, and that analysis of both consensus and variant genotypes is necessary to estimate the propensity of a virus to cause disease.
Our research supported and leveraged Lawrence Livermore National Laboratory's core competencies in bioscience and bioengineering, including the Laboratory's expertise in viral genome evolution analysis. This study demonstrated the potential of viral quasispecies analysis for early identification of variants with the potential to impact the course of an epidemic, which is important to our biosecurity mission. We also leveraged Livermore's high-performance computing, simulation, and data science core competencies and advanced the Laboratory's synthetic biology capabilities to build predictive biology capabilities at Livermore.
Borucki, M. et al., 2017. "Multiscale Analysis for Patterns of Zika Virus Emergence, Spread, and Consequence." Pacific Northwest National Laboratory, Richland, WA, August 2017. LLNL-PRES-888210.
——— 2018. "Multiscale Analysis for Patterns of Zika Virus Emergence, Spread, and Consequence." LLNL-POST-756087.
——— 2019. "Borucki, M. et al., 2018. "Multiscale Analysis for Patterns of Zika Virus Emergence, Spread, and Consequence." SRI International, Palo Alto, CA, October 2019. LLNL-PRES-996314.
——— 2019. "Multiscale Analysis for Patterns of Zika Virus Emergence, Spread, and Consequence." ASM Biothreats, Washington D.C. LLNL-PRES-956611, LLNL-POST-956510.
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