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Esser E, Schulte EC, Graf A, Karollus A, Smith NH, Michler T, Dvoretskii S, Angelov A, Sonnabend M, Peter S, Engesser C, Radonic A, Thürmer A, von Kleist M, Gebhardt F, da Costa CP, Busch DH, Muenchhoff M, Blum H, Keppler OT, Gagneur J, Protzer U. Viral genome sequencing to decipher in-hospital SARS-CoV-2 transmission events. Sci Rep 2024; 14:5768. [PMID: 38459123 PMCID: PMC10923895 DOI: 10.1038/s41598-024-56162-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/02/2024] [Indexed: 03/10/2024] Open
Abstract
The SARS-CoV-2 pandemic has highlighted the need to better define in-hospital transmissions, a need that extends to all other common infectious diseases encountered in clinical settings. To evaluate how whole viral genome sequencing can contribute to deciphering nosocomial SARS-CoV-2 transmission 926 SARS-CoV-2 viral genomes from 622 staff members and patients were collected between February 2020 and January 2021 at a university hospital in Munich, Germany, and analysed along with the place of work, duration of hospital stay, and ward transfers. Bioinformatically defined transmission clusters inferred from viral genome sequencing were compared to those inferred from interview-based contact tracing. An additional dataset collected at the same time at another university hospital in the same city was used to account for multiple independent introductions. Clustering analysis of 619 viral genomes generated 19 clusters ranging from 3 to 31 individuals. Sequencing-based transmission clusters showed little overlap with those based on contact tracing data. The viral genomes were significantly more closely related to each other than comparable genomes collected simultaneously at other hospitals in the same city (n = 829), suggesting nosocomial transmission. Longitudinal sampling from individual patients suggested possible cross-infection events during the hospital stay in 19.2% of individuals (14 of 73 individuals). Clustering analysis of SARS-CoV-2 whole genome sequences can reveal cryptic transmission events missed by classical, interview-based contact tracing, helping to decipher in-hospital transmissions. These results, in line with other studies, advocate for viral genome sequencing as a pathogen transmission surveillance tool in hospitals.
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Affiliation(s)
- Elisabeth Esser
- Institute of Virology, School of Medicine & Health, Technical University of Munich/Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Eva C Schulte
- Institute of Virology, School of Medicine & Health, Technical University of Munich/Helmholtz Munich, Munich, Germany
- Department of Psychiatry, University Hospital, LMU Munich, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry, University Hospital, Medical Faculty, University of Bonn, Bonn, Germany
- Institute of Human Genetics, University Hospital, Medical Faculty, University of Bonn, Bonn, Germany
| | - Alexander Graf
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, Munich, Germany
| | - Alexander Karollus
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Nicholas H Smith
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Thomas Michler
- Institute of Virology, School of Medicine & Health, Technical University of Munich/Helmholtz Munich, Munich, Germany
- Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Stefan Dvoretskii
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Angel Angelov
- NGS Competence Center, University of Tübingen, Tübingen, Germany
| | | | - Silke Peter
- NGS Competence Center, University of Tübingen, Tübingen, Germany
| | | | - Aleksandar Radonic
- Method development, Research Infrastructure & IT (MFI), Robert-Koch Institute (RKI), Berlin, Germany
| | - Andrea Thürmer
- Method development, Research Infrastructure & IT (MFI), Robert-Koch Institute (RKI), Berlin, Germany
| | - Max von Kleist
- Department of Mathematics and Computer Science, Freie Universität (FU) Berlin, Berlin, Germany
- Project Groups, Robert-Koch Institute (RKI), Berlin, Germany
| | - Friedemann Gebhardt
- Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany
| | - Clarissa Prazeres da Costa
- Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany
| | - Dirk H Busch
- Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany
| | - Maximilian Muenchhoff
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany
- Max Von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Helmut Blum
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, Munich, Germany
| | - Oliver T Keppler
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany
- Max Von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Institute of Human Genetics, School of Medicine & Health, Technical University of Munich, Munich, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
| | - Ulrike Protzer
- Institute of Virology, School of Medicine & Health, Technical University of Munich/Helmholtz Munich, Munich, Germany.
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany.
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Krenn F, Dächert C, Badell I, Lupoli G, Öztan GN, Feng T, Schneider N, Huber M, Both H, Späth PM, Muenchhoff M, Graf A, Krebs S, Blum H, Durner J, Czibere L, Kaderali L, Keppler OT, Baldauf HM, Osterman A. Ten rapid antigen tests for SARS-CoV-2 widely differ in their ability to detect Omicron-BA.4 and -BA.5. Med Microbiol Immunol 2023; 212:323-337. [PMID: 37561225 PMCID: PMC10501931 DOI: 10.1007/s00430-023-00775-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/11/2023] [Indexed: 08/11/2023]
Abstract
Since late 2021, the variant landscape of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been dominated by the variant of concern (VoC) Omicron and its sublineages. We and others have shown that the detection of Omicron-BA.1 and -BA.2-positive respiratory specimens by rapid antigen tests (RATs) is impaired compared to Delta VoC-containing samples. Here, in a single-center retrospective laboratory study, we evaluated the performance of ten most commonly used RATs for the detection of Omicron-BA.4 and -BA.5 infections. We used 171 respiratory swab specimens from SARS-CoV-2 RNA-positive patients, of which 71 were classified as BA.4 and 100 as BA.5. All swabs were collected between July and September 2022. 50 SARS-CoV-2 PCR-negative samples from healthy individuals, collected in October 2022, showed high specificity in 9 out of 10 RATs. When assessing analytical sensitivity using clinical specimens, the 50% limit of detection (LoD50) ranged from 7.6 × 104 to 3.3 × 106 RNA copies subjected to the RATs for BA.4 compared to 6.8 × 104 to 3.0 × 106 for BA.5. Overall, intra-assay differences for the detection of these two Omicron subvariants were not significant for both respiratory swabs and tissue culture-expanded virus isolates. In contrast, marked heterogeneity was observed among the ten RATs: to be positive in these point-of-care tests, up to 443-fold (BA.4) and up to 56-fold (BA.5) higher viral loads were required for the worst performing RAT compared to the best performing RAT. True-positive rates for Omicron-BA.4- or -BA.5-containing specimens in the highest viral load category (Ct values < 25) ranged from 94.3 to 34.3%, dropping to 25.6 to 0% for samples with intermediate Ct values (25-30). We conclude that the high heterogeneity in the performance of commonly used RATs remains a challenge for the general public to obtain reliable results in the evolving Omicron subvariant-driven pandemic.
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Affiliation(s)
- Franziska Krenn
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Christopher Dächert
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site, Munich, Germany
| | - Irina Badell
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Gaia Lupoli
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Gamze Naz Öztan
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Tianle Feng
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Nikolas Schneider
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Melanie Huber
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Hanna Both
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Patricia M. Späth
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Maximilian Muenchhoff
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site, Munich, Germany
- COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU München, Munich, Germany
| | - Alexander Graf
- Laboratory for Functional Genome Analysis, Gene Center, LMU München, Munich, Germany
| | - Stefan Krebs
- Laboratory for Functional Genome Analysis, Gene Center, LMU München, Munich, Germany
| | - Helmut Blum
- Laboratory for Functional Genome Analysis, Gene Center, LMU München, Munich, Germany
| | | | | | - Lars Kaderali
- Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany
| | - Oliver T. Keppler
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site, Munich, Germany
- COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU München, Munich, Germany
| | - Hanna-Mari Baldauf
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Andreas Osterman
- Max von Pettenkofer Institute & Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
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