Multiscale analysis for patterns of Zika virus genotype emergence, spread, and consequence

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, and future changes in phenotype may continue to occur as the virus expands its geographic range. Alternatively, the sheer size of the 2015–16 epidemic may have brought attention to a pre-existing virulent ZIKV phenotype in a highly susceptible population. Thus, it is important to identify patterns of genetic change that may yield a better understanding of ZIKV emergence and evolution. However, because ZIKV has an RNA genome and a polymerase incapable of proofreading, it undergoes rapid mutation which makes it difficult to identify combinations of mutations associated with viral emergence. As next generation sequencing technology has allowed whole genome consensus and variant sequence data to be generated for numerous virus samples, the task of analyzing these genomes for patterns of mutation has become more complex. However, understanding which combinations of mutations spread widely and become established in new geographic regions versus those that disappear relatively quickly is essential for defining the trajectory of an ongoing epidemic. In this study, multiscale analysis of the wealth of genomic data generated over the course of the epidemic combined with in vivo laboratory data allowed trends in mutations and outbreak trajectory to be assessed. Mutations were detected throughout the genome via deep sequencing, and many variants appeared in multiple samples and in some cases become consensus. Similarly, amino acids that were previously consensus in pre-outbreak samples were detected as low frequency variants in epidemic strains. Protein structural models indicate that most of the mutations associated with the epidemic transmission occur on the exposed surface of viral proteins. At the macroscale level, consensus data was organized into large and interactive databases to allow the spread of individual mutations and combinations of mutations to be visualized and assessed for temporal and geographical patterns. Thus, the use of multiscale modeling for identifying mutations or combinations of mutations that impact epidemic transmission and phenotypic impact can aid the formation of hypotheses which can then be tested using reverse genetics.

寨卡病毒（Zika virus, ZIKV）如何从看似温和的病原体，演变为可引发小头症并具备性传播能力的人类致病病毒，这一问题至今仍未得到解答。寨卡病毒致病性与性传播能力的意外出现，可能源于基因变异；随着病毒地理分布范围不断扩大，其表型未来或持续发生改变。另一种可能是，2015至2016年疫情的庞大规模，使得高易感人群中原本就存在的强毒力寨卡病毒表型受到广泛关注。因此，识别基因变异模式，对于更深入理解寨卡病毒的出现与演化过程具有重要意义。然而，寨卡病毒为RNA基因组，且其聚合酶缺乏校对功能，因此会快速发生突变，这使得识别与病毒出现相关的突变组合变得极具挑战。随着下一代测序技术（next generation sequencing technology）实现对大量病毒样本的全基因组共识序列与变异序列数据的获取，分析这些基因组以挖掘突变模式的任务也愈发复杂。然而，明确哪些突变组合能够广泛传播并在新地理区域定植，而哪些又会快速消失，对于界定当前疫情的发展轨迹至关重要。本研究通过对疫情期间产生的海量基因组数据进行多尺度分析，并结合体内实验数据，得以评估突变趋势与疫情传播轨迹。研究人员通过深度测序（deep sequencing）在整个基因组中检测到了突变，众多变异在多个样本中被检出，部分变异甚至成为共识序列。类似地，在疫情暴发前样本中原本为共识序列的氨基酸位点，在疫情毒株中被检测为低频变异。蛋白质结构模型分析显示，与疫情传播相关的大多数突变均位于病毒蛋白的暴露表面。在宏观尺度层面，共识序列数据被整合至大型交互数据库中，以便可视化并评估单个突变及突变组合的传播情况，分析其时间与地理分布模式。因此，借助多尺度建模识别影响疫情传播与表型改变的突变或突变组合，有助于形成可通过反向遗传学（reverse genetics）进行验证的科学假说。