bioRxivMicrobiology
Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform
T. T. N. Thao, F. Labroussaa, N. Ebert, P. V'kovski, H. Stalder, J. Portmann, J. Kelly, S. Steiner, M. Holwerda, A. Kratzel, M. Gultom, L. Laloli, L. Huesser, M. Wider, S. Pfaender, D. Hirt, V. Cippa, S. Crespo-Pomar, S. Schroeder, D. Muth, D. Niemeyer, M. A. Mueller, C. Drosten, R. Dijkman, J. Jores, V. Thiel
Abstract
Reverse genetics has been an indispensable tool revolutionising our insights into viral pathogenesis and vaccine development. Large RNA virus genomes, such as from Coronaviruses, are cumbersome to clone and to manipulate in E. coli hosts due to size and occasional instability1-3. Therefore, an alternative rapid and robust reverse genetics platform for RNA viruses would benefit the research community. Here we show the full functionality of a yeast-based synthetic genomics platform for the genetic reconstruction of diverse RNA viruses, including members of the Coronaviridae, Flaviviridae and Paramyxoviridae families. Viral subgenomic fragments were generated using viral isolates, cloned viral DNA, clinical samples, or synthetic DNA, and reassembled in one step in Saccharomyces cerevisiae using transformation associated recombination (TAR) cloning to maintain the genome as a yeast artificial chromosome (YAC). T7-RNA polymerase has been used to generate infectious RNA, which was then used to rescue viable virus. Based on this platform we have been able to engineer and resurrect chemically-synthetized clones of the recent epidemic SARS-CoV-24 in only a week after receipt of the synthetic DNA fragments. The technical advance we describe here allows to rapidly responding to emerging viruses as it enables the generation and functional characterization of evolving RNA virus variants - in real-time - during an outbreak.
- organism: yeast
- assay: whole-genome sequencing
- research focus: genome assembly
- research focus: variant calling
- research focus: drug response / drug discovery
- research focus: protein structure prediction
- approach: experimental assay
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