ABSTRACT
Sporadic clusters of healthcare-associated coronavirus disease 2019 (COVID-19) occurred despite intense rostered routine surveillance and a highly vaccinated healthcare worker (HCW) population, during a community surge of the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) B.1.617.2 δ (delta) variant. Genomic analysis facilitated timely cluster detection and uncovered additional linkages via HCWs moving between clinical areas and among HCWs sharing a common lunch area, enabling early intervention.
ABSTRACT
Rapid onsite whole-genome sequencing of two suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) N gene diagnostic escape samples revealed a previously unreported N gene point mutation at genome position 29195. Because the G29195T mutation occurs within a region probed by a commonly referenced U.S. CDC N gene reverse transcription (RT)-PCR assay, we hypothesize that the G29195T mutation rendered the N gene target of a proprietary commercial assay undetectable. The putative diagnostic escape G29195T mutation demonstrates the need for nearly real-time surveillance, as emergence of a novel SARS-CoV-2 variant with the potential to escape diagnostic tests continues to be a threat. IMPORTANCE Accurate diagnostic detection of SARS-CoV-2 currently depends on the large-scale deployment of RT-PCR assays. SARS-CoV-2 RT-PCR assays target predetermined regions in the viral genomes by complementary binding of primers and probes to nucleic acid sequences in the clinical samples. Potential diagnostic escapes, such as those of clinical samples harboring the G29195T mutation, may result in false-negative SARS-CoV-2 RT-PCR results. The rapid detection and sharing of potential diagnostic escapes are essential for diagnostic laboratories and manufacturers around the world, to optimize their assays as SARS-CoV-2 continues to evolve.
Subject(s)
COVID-19/diagnosis , Point Mutation , SARS-CoV-2/genetics , Reverse Transcriptase Polymerase Chain ReactionABSTRACT
Background: The ongoing COVID-19 pandemic is a global health crisis caused by the spread of SARS-CoV-2. Establishing links between known cases is crucial for the containment of COVID-19. In the healthcare setting, the ability to rapidly identify potential healthcare-associated COVID-19 clusters is critical for healthcare worker and patient safety. Increasing sequencing technology accessibility has allowed routine clinical diagnostic laboratories to sequence SARS-CoV-2 in clinical samples. However, these laboratories often lack specialized informatics skills required for sequence analysis. Therefore, an on-site, intuitive sequence analysis tool that enables clinical laboratory users to analyze multiple genomes and derive clinically relevant information within an actionable timeframe is needed. Results: We propose CalmBelt, an integrated framework for on-site whole genome characterization and outbreak tracking. Nanopore sequencing technology enables on-site sequencing and construction of draft genomes for multiple SARS-CoV-2 samples within 12 h. CalmBelt's interactive interface allows users to analyse multiple SARS-CoV-2 genomes by utilizing whole genome information, collection date, and additional information such as predefined potential clusters from epidemiological investigations. CalmBelt also integrates established SARS-CoV-2 nomenclature assignments, GISAID clades and PANGO lineages, allowing users to visualize relatedness between samples together with the nomenclatures. We demonstrated multiple use cases including investigation of potential hospital transmission, mining transmission patterns in a large outbreak, and monitoring possible diagnostic-escape. Conclusions: This paper presents an on-site rapid framework for SARS-CoV-2 whole genome characterization. CalmBelt interactive web application allows non-technical users, such as routine clinical laboratory users in hospitals to determine SARS-CoV-2 variants of concern, as well as investigate the presence of potential transmission clusters. The framework is designed to be compatible with routine usage in clinical laboratories as it only requires readily available sample data, and generates information that impacts immediate infection control mitigations.