A method enabling high-throughput sequencing of human cytomegalovirus complete genomes from clinical isolates

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Abstract

Human cytomegalovirus (HCMV) is a ubiquitous virus that can cause serious sequelae in immunocompromised patients and in the developing fetus. The coding capacity of the 235 kbp genome is still incompletely understood, and there is a pressing need to characterize genomic contents in clinical isolates. In this study, a procedure for the high-throughput generation of full genome consensus sequences from clinical HCMV isolates is presented. This method relies on low number passaging of clinical isolates on human fibroblasts, followed by digestion of cellular DNA and purification of viral DNA. After multiple displacement amplification, highly pure viral DNA is generated. These extracts are suitable for high-throughput next-generation sequencing and assembly of consensus sequences. Throughout a series of validation experiments, we showed that the workflow reproducibly generated consensus sequences representative for the virus population present in the original clinical material. Additionally, the performance of 454 GS FLX and/or Illumina Genome Analyzer datasets in consensus sequence deduction was evaluated. Based on assembly performance data, the Illumina Genome Analyzer was the platform of choice in the presented workflow. Analysis of the consensus sequences derived in this study confirmed the presence of gene-disrupting mutations in clinical HCMV isolates independent from in vitro passaging. These mutations were identified in genes RL5A, UL1, UL9, UL111A and UL150. In conclusion, the presented workflow provides opportunities for high-throughput characterization of complete HCMV genomes that could deliver new insights into HCMV coding capacity and genetic determinants of viral tropism and pathogenicity. © 2014 Sijmons et al.

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Sijmons, S., Thys, K., Corthout, M., Van Damme, E., Van Loock, M., Bollen, S., … Maes, P. (2014). A method enabling high-throughput sequencing of human cytomegalovirus complete genomes from clinical isolates. PLoS ONE, 9(4). https://doi.org/10.1371/journal.pone.0095501

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