Title |
SARS-CoV-2 RNA reverse-transcribed and integrated into the human genome
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Published in |
bioRxiv, December 2020
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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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France | 382 | 5% |
Japan | 287 | 4% |
United Kingdom | 234 | 3% |
Netherlands | 197 | 3% |
Canada | 108 | 1% |
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Italy | 88 | 1% |
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Demographic breakdown
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Members of the public | 6887 | 90% |
Scientists | 533 | 7% |
Practitioners (doctors, other healthcare professionals) | 128 | 2% |
Science communicators (journalists, bloggers, editors) | 58 | <1% |
Unknown | 21 | <1% |
Mendeley readers
Geographical breakdown
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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% |
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Medicine and Dentistry | 22 | 12% |
Immunology and Microbiology | 16 | 9% |
Nursing and Health Professions | 5 | 3% |
Other | 24 | 13% |
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