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Van Poelvoorde LAE, Delcourt T, Vuylsteke M, De Keersmaecker SCJ, Thomas I, Van Gucht S, Saelens X, Roosens N, Vanneste K. A general approach to identify low-frequency variants within influenza samples collected during routine surveillance. Microb Genom 2022; 8. [PMID: 36169645 DOI: 10.1099/mgen.0.000867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Influenza viruses exhibit considerable diversity between hosts. Additionally, different quasispecies can be found within the same host. High-throughput sequencing technologies can be used to sequence a patient-derived virus population at sufficient depths to identify low-frequency variants (LFV) present in a quasispecies, but many challenges remain for reliable LFV detection because of experimental errors introduced during sample preparation and sequencing. High genomic copy numbers and extensive sequencing depths are required to differentiate false positive from real LFV, especially at low allelic frequencies (AFs). This study proposes a general approach for identifying LFV in patient-derived samples obtained during routine surveillance. Firstly, validated thresholds were determined for LFV detection, whilst balancing both the cost and feasibility of reliable LFV detection in clinical samples. Using a genetically well-defined population of influenza A viruses, thresholds of at least 104 genomes per microlitre and AF of ≥5 % were established as detection limits. Secondly, a subset of 59 retained influenza A (H3N2) samples from the 2016-2017 Belgian influenza season was composed. Thirdly, as a proof of concept for the added value of LFV for routine influenza monitoring, potential associations between patient data and whole genome sequencing data were investigated. A significant association was found between a high prevalence of LFV and disease severity. This study provides a general methodology for influenza LFV detection, which can also be adopted by other national influenza reference centres and for other viruses such as SARS-CoV-2. Additionally, this study suggests that the current relevance of LFV for routine influenza surveillance programmes might be undervalued.
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Affiliation(s)
- Laura A E Van Poelvoorde
- Transversal activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium.,National Influenza Centre, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium.,Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Thomas Delcourt
- Transversal activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | | | | | - Isabelle Thomas
- National Influenza Centre, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | - Steven Van Gucht
- National Influenza Centre, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | - Xavier Saelens
- Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Nancy Roosens
- Transversal activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
| | - Kevin Vanneste
- Transversal activities in Applied Genomics, Sciensano, Juliette Wytsmanstraat 14, Brussels, Belgium
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Van Poelvoorde L, Vanneste K, De Keersmaecker SCJ, Thomas I, Van Goethem N, Van Gucht S, Saelens X, Roosens NHC. Whole-Genome Sequence Approach and Phylogenomic Stratification Improve the Association Analysis of Mutations With Patient Data in Influenza Surveillance. Front Microbiol 2022; 13:809887. [PMID: 35516436 PMCID: PMC9063638 DOI: 10.3389/fmicb.2022.809887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/28/2022] [Indexed: 12/02/2022] Open
Abstract
Each year, seasonal influenza results in high mortality and morbidity. The current classification of circulating influenza viruses is mainly focused on the hemagglutinin gene. Whole-genome sequencing (WGS) enables tracking mutations across all influenza segments allowing a better understanding of the epidemiological effects of intra- and inter-seasonal evolutionary dynamics, and exploring potential associations between mutations across the viral genome and patient’s clinical data. In this study, mutations were identified in 253 Influenza A (H3N2) clinical isolates from the 2016-2017 influenza season in Belgium. As a proof of concept, available patient data were integrated with this genomic data, resulting in statistically significant associations that could be relevant to improve the vaccine and clinical management of infected patients. Several mutations were significantly associated with the sampling period. A new approach was proposed for exploring mutational effects in highly diverse Influenza A (H3N2) strains through considering the viral genetic background by using phylogenetic classification to stratify the samples. This resulted in several mutations that were significantly associated with patients suffering from renal insufficiency. This study demonstrates the usefulness of using WGS data for tracking mutations across the complete genome and linking these to patient data, and illustrates the importance of accounting for the viral genetic background in association studies. A limitation of this association study, especially when analyzing stratified groups, relates to the number of samples, especially in the context of national surveillance of small countries. Therefore, we investigated if international databases like GISAID may help to verify whether observed associations in the Belgium A (H3N2) samples, could be extrapolated to a global level. This work highlights the need to construct international databases with both information of viral genome sequences and patient data.
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Affiliation(s)
- Laura Van Poelvoorde
- Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium
- National Influenza Centre, Sciensano, Brussels, Belgium
- Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Kevin Vanneste
- Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium
| | | | | | - Nina Van Goethem
- Public Health and Genome, Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | | | - Xavier Saelens
- Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Nancy H. C. Roosens
- Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium
- *Correspondence: Nancy H. C. Roosens,
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Van Goethem N, Robert A, Bossuyt N, Van Poelvoorde LAE, Quoilin S, De Keersmaecker SCJ, Devleesschauwer B, Thomas I, Vanneste K, Roosens NHC, Van Oyen H. Evaluation of the added value of viral genomic information for predicting severity of influenza infection. BMC Infect Dis 2021; 21:785. [PMID: 34376182 PMCID: PMC8353062 DOI: 10.1186/s12879-021-06510-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 07/18/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND The severity of an influenza infection is influenced by both host and viral characteristics. This study aims to assess the relevance of viral genomic data for the prediction of severe influenza A(H3N2) infections among patients hospitalized for severe acute respiratory infection (SARI), in view of risk assessment and patient management. METHODS 160 A(H3N2) influenza positive samples from the 2016-2017 season originating from the Belgian SARI surveillance were selected for whole genome sequencing. Predictor variables for severity were selected using a penalized elastic net logistic regression model from a combined host and genomic dataset, including patient information and nucleotide mutations identified in the viral genome. The goodness-of-fit of the model combining host and genomic data was compared using a likelihood-ratio test with the model including host data only. Internal validation of model discrimination was conducted by calculating the optimism-adjusted area under the Receiver Operating Characteristic curve (AUC) for both models. RESULTS The model including viral mutations in addition to the host characteristics had an improved fit ([Formula: see text]=12.03, df = 3, p = 0.007). The optimism-adjusted AUC increased from 0.671 to 0.732. CONCLUSIONS Adding genomic data (selected season-specific mutations in the viral genome) to the model containing host characteristics improved the prediction of severe influenza infection among hospitalized SARI patients, thereby offering the potential for translation into a prospective strategy to perform early season risk assessment or to guide individual patient management.
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Affiliation(s)
- Nina Van Goethem
- Scientific Directorate of Epidemiology and Public Health, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium.
- Department of Epidemiology and Biostatistics, Institut de Recherche Expérimentale et Clinique, Faculty of Public Health, Université Catholique de Louvain, Clos Chapelle-aux-champs 30, 1200, Woluwe-Saint-Lambert, Belgium.
| | - Annie Robert
- Department of Epidemiology and Biostatistics, Institut de Recherche Expérimentale et Clinique, Faculty of Public Health, Université Catholique de Louvain, Clos Chapelle-aux-champs 30, 1200, Woluwe-Saint-Lambert, Belgium
| | - Nathalie Bossuyt
- Scientific Directorate of Epidemiology and Public Health, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium
| | - Laura A E Van Poelvoorde
- Transversal Activities in Applied Genomics, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium
| | - Sophie Quoilin
- Scientific Directorate of Epidemiology and Public Health, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium
| | | | - Brecht Devleesschauwer
- Scientific Directorate of Epidemiology and Public Health, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium
- Department of Veterinary Public Health and Food Safety, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium
| | - Isabelle Thomas
- National Reference Center Influenza, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium
| | - Kevin Vanneste
- Transversal Activities in Applied Genomics, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium
| | - Nancy H C Roosens
- Transversal Activities in Applied Genomics, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium
| | - Herman Van Oyen
- Scientific Directorate of Epidemiology and Public Health, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium
- Department of Public Health and Primary Care, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium
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