Dataset Information

De Novo Generation and Characterization of New Zika Virus Isolate Using Sequence Data from a Microcephaly Case.

ABSTRACT: Zika virus (ZIKV) has recently emerged and is the etiological agent of congenital Zika syndrome (CZS), a spectrum of congenital abnormalities arising from neural tissue infections in utero. Herein, we describe the de novo generation of a new ZIKV isolate, ZIKVNatal, using a modified circular polymerase extension reaction protocol and sequence data obtained from a ZIKV-infected fetus with microcephaly. ZIKVNatal thus has no laboratory passage history and is unequivocally associated with CZS. ZIKVNatal could be used to establish a fetal brain infection model in IFNAR-/- mice (including intrauterine growth restriction) without causing symptomatic infections in dams. ZIKVNatal was also able to be transmitted by Aedes aegypti mosquitoes. ZIKVNatal thus retains key aspects of circulating pathogenic ZIKVs and illustrates a novel methodology for obtaining an authentic functional viral isolate by using data from deep sequencing of infected tissues. IMPORTANCE The major complications of an ongoing Zika virus outbreak in the Americas and Asia are congenital defects caused by the virus's ability to cross the placenta and infect the fetal brain. The ability to generate molecular tools to analyze viral isolates from the current outbreak is essential for furthering our understanding of how these viruses cause congenital defects. The majority of existing viral isolates and infectious cDNA clones generated from them have undergone various numbers of passages in cell culture and/or suckling mice, which is likely to result in the accumulation of adaptive mutations that may affect viral properties. The approach described herein allows rapid generation of new, fully functional Zika virus isolates directly from deep sequencing data from virus-infected tissues without the need for prior virus passaging and for the generation and propagation of full-length cDNA clones. The approach should be applicable to other medically important flaviviruses and perhaps other positive-strand RNA viruses.

SUBMITTER: Setoh YX

PROVIDER: S-EPMC5437134 | biostudies-literature | 2017 May-Jun

REPOSITORIES: biostudies-literature

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Publications

De Novo Generation and Characterization of New Zika Virus Isolate Using Sequence Data from a Microcephaly Case.

Setoh Yin Xiang YX Prow Natalie A NA Peng Nias N Hugo Leon E LE Devine Gregor G Hazlewood Jessamine E JE Suhrbier Andreas A Khromykh Alexander A AA

mSphere 20170517 3

Zika virus (ZIKV) has recently emerged and is the etiological agent of congenital Zika syndrome (CZS), a spectrum of congenital abnormalities arising from neural tissue infections in utero. Herein, we describe the de novo generation of a new ZIKV isolate, ZIKVNatal, using a modified circular polymerase extension reaction protocol and sequence data obtained from a ZIKV-infected fetus with microcephaly. ZIKVNatal thus has no laboratory passage history and is une ...[more]

PMID: 28529976

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Project description:Deep shotgun sequencing on next generation sequencing (NGS) platforms has contributed significant amounts of data to enrich our understanding of genomes, transcriptomes, amplified single-cell genomes, and metagenomes. However, deep coverage variations in short-read data sets and high sequencing error rates of modern sequencers present new computational challenges in data interpretation, including mapping and de novo assembly. New lab techniques such as multiple displacement amplification (MDA) of single cells and sequence independent single primer amplification (SISPA) allow for sequencing of organisms that cannot be cultured, but generate highly variable coverage due to amplification biases.Here we introduce NeatFreq, a software tool that reduces a data set to more uniform coverage by clustering and selecting from reads binned by their median kmer frequency (RMKF) and uniqueness. Previous algorithms normalize read coverage based on RMKF, but do not include methods for the preferred selection of (1) extremely low coverage regions produced by extremely variable sequencing of random-primed products and (2) 2-sided paired-end sequences. The algorithm increases the incorporation of the most unique, lowest coverage, segments of a genome using an error-corrected data set. NeatFreq was applied to bacterial, viral plaque, and single-cell sequencing data. The algorithm showed an increase in the rate at which the most unique reads in a genome were included in the assembled consensus while also reducing the count of duplicative and erroneous contigs (strings of high confidence overlaps) in the deliverable consensus. The results obtained from conventional Overlap-Layout-Consensus (OLC) were compared to simulated multi-de Bruijn graph assembly alternatives trained for variable coverage input using sequence before and after normalization of coverage. Coverage reduction was shown to increase processing speed and reduce memory requirements when using conventional bacterial assembly algorithms.The normalization of deep coverage spikes, which would otherwise inhibit consensus resolution, enables High Throughput Sequencing (HTS) assembly projects to consistently run to completion with existing assembly software. The NeatFreq software package is free, open source and available at https://github.com/bioh4x/NeatFreq .

Dataset Information

De Novo Generation and Characterization of New Zika Virus Isolate Using Sequence Data from a Microcephaly Case.

Publications

<i>De Novo</i> Generation and Characterization of New Zika Virus Isolate Using Sequence Data from a Microcephaly Case.

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