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SARS-CoV-2 RNA reverse-transcribed and integrated into the human genome

Overview of attention for article published in bioRxiv, December 2020
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Title
SARS-CoV-2 RNA reverse-transcribed and integrated into the human genome
Published in
bioRxiv, December 2020
DOI 10.1101/2020.12.12.422516
Pubmed ID
Authors

Liguo Zhang, Alexsia Richards, Andrew Khalil, Emile Wogram, Haiting Ma, Richard A. Young, Rudolf Jaenisch

Abstract

Prolonged SARS-CoV-2 RNA shedding and recurrence of PCR-positive tests have been widely reported in patients after recovery, yet these patients most commonly are non-infectious. Here we investigated the possibility that SARS-CoV-2 RNAs can be reverse-transcribed and integrated into the human genome and that transcription of the integrated sequences might account for PCR-positive tests. In support of this hypothesis, we found chimeric transcripts consisting of viral fused to cellular sequences in published data sets of SARS-CoV-2 infected cultured cells and primary cells of patients, consistent with the transcription of viral sequences integrated into the genome. To experimentally corroborate the possibility of viral retro-integration, we describe evidence that SARS-CoV-2 RNAs can be reverse transcribed in human cells by reverse transcriptase (RT) from LINE-1 elements or by HIV-1 RT, and that these DNA sequences can be integrated into the cell genome and subsequently be transcribed. Human endogenous LINE-1 expression was induced upon SARS-CoV-2 infection or by cytokine exposure in cultured cells, suggesting a molecular mechanism for SARS-CoV-2 retro-integration in patients. This novel feature of SARS-CoV-2 infection may explain why patients can continue to produce viral RNA after recovery and suggests a new aspect of RNA virus replication.

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Geographical breakdown

Country Count As %
Unknown 184 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 22%
Other 18 10%
Student > Ph. D. Student 17 9%
Student > Bachelor 13 7%
Student > Master 13 7%
Other 37 20%
Unknown 46 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 37 20%
Agricultural and Biological Sciences 24 13%
Medicine and Dentistry 22 12%
Immunology and Microbiology 16 9%
Nursing and Health Professions 5 3%
Other 24 13%
Unknown 56 30%