Feasibility and clinical utility of local rapid Nanopore influenza A virus whole genome sequencing for integrated outbreak management, genotypic resistance detection and timely surveillance

Abstract

Rapid respiratory viral whole genome sequencing (WGS) in a clinical setting can inform real-time outbreak and patient treatment decisions, but the feasibility and clinical utility of influenza A virus (IAV) WGS using Nanopore technology has not been demonstrated. A 24 h turnaround Nanopore IAV WGS protocol was performed on 128 reverse transcriptase PCR IAV-positive nasopharyngeal samples taken over seven weeks of the 2022-2023 winter influenza season, including 25 from patients with nosocomial IAV infections and 102 from patients attending the Emergency Department. WGS results were reviewed collectively alongside clinical details for interpretation and reported to clinical teams. All eight segments of the IAV genome were recovered for 97/128 samples (75.8 %) and the haemagglutinin gene for 117/128 samples (91.4 %). Infection prevention and control identified nosocomial IAV infections in 19 patients across five wards. IAV WGS revealed two separate clusters on one ward and excluded transmission across different wards with contemporaneous outbreaks. IAV WGS also identified neuraminidase inhibitor resistance in a persistently infected patient and excluded avian influenza in a sample taken from an immunosuppressed patient with a history of travel to Singapore which had failed PCR subtyping. Accurate IAV genomes can be generated in 24 h using a Nanopore protocol accessible to any laboratory with SARS-CoV-2 Nanopore sequencing capacity. In addition to replicating reference laboratory surveillance results, IAV WGS can identify antiviral resistance and exclude avian influenza. IAV WGS also informs management of nosocomial outbreaks, though molecular and clinical epidemiology were concordant in this study, limiting the impact on decision-making.

Keywords: Nanopore; antiviral drug resistance; avian influenza; influenza A; nosocomial outbreaks; whole genome sequencing.

Fig. 1.

(a) IAV sample identification and categorization for molecular epidemiological analysis, and (b) workflow from RNA extraction to IPC communication of results – total time in black, hands-on time in red.

Fig. 2.

IAV incidence (a) and WGS maximum-likelihood phylogeny with sample dates of pdmH1N1 (b) and H3N2 (c) samples – alphanumeric names refer to patient location (e.g. A for ward A, ED for Emergency Department) followed by a patient number. Patients with multiple samples are labelled using a numerical suffix, for example A1.1 for the first sample and A1.2 for the second sample. *Samples with no SNP differences to a reference laboratory complete genome sequence; **samples with no SNP differences to a reference laboratory partial genome sequence. Samples from outbreak patients and clinical requests are highlighted in red. Reference laboratory sequences differing from local sequences are shown with the suffix ‘REF’. Node colour represents bootstrap support, with black nodes indicating 100 % support, grey 95–99 % support and white <95 % support. Outgroups are the Northern Hemisphere Winter Season 2022/2023 vaccine strains, A/Victoria/2570/2019 (EPI_ISL_417210) and A/Wisconsin/588/2019 (EPI_ISL_404460) for pdmH1N1, and A/Darwin/9/2021 (EPI_ISL_2233240) and A/Darwin/6/2021 (EPI_ISL_2233238) for H3N2. Maximum-likelihood phylogeny scale bars represent nucleotide substitutions per site. POC, point-of-care.

Fig. 3.

Timeline of persistent IAV infection. (a) AusDiagnostics nested PCR corrected Ct values, with negative result shown as a white point. (b) Pairwise SNP difference between the consensus genome of each sample when compared to the patient’s first sample. (c) Allele frequency of NA gene resistance mutations, presented as a percentage. Grey bars show Illumina data, and red bars Nanopore data. WT, wild-type. (d) Dates of haemodialysis and oseltamivir 30 mg IV doses.