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. 2019 Oct 30;69(10):1649-1656.
doi: 10.1093/cid/ciz020.

Whole-genome Sequencing Provides Data for Stratifying Infection Prevention and Control Management of Nosocomial Influenza A

Sunando Roy  1 , John Hartley  2 , Helen Dunn  2 , Rachel Williams  1 , Charlotte A Williams  1 , Judith Breuer  1   2   3
Affiliations

Whole-genome Sequencing Provides Data for Stratifying Infection Prevention and Control Management of Nosocomial Influenza A

Sunando Roy et al. Clin Infect Dis. .

Abstract

Background: Influenza A virus causes annual epidemics in humans and is associated with significant morbidity and mortality. Haemagglutinin (HA) and neuraminidase (NA) gene sequencing have traditionally been used to identify the virus genotype, although their utility in detecting outbreak clusters is still unclear. The objective of this study was to determine the utility, if any, of whole-genome sequencing over HA/NA sequencing for infection prevention and control (IPC) in hospitals.

Methods: We obtained all clinical samples from influenza (H1N1)-positive patients at the Great Ormond Street Hospital between January and March 2016. Samples were sequenced using targeted enrichment on an Illumina MiSeq sequencer. Maximum likelihood trees were computed for both whole genomes and concatenated HA/NA sequences. Epidemiological data was taken from routine IPC team activity during the period.

Results: Complete genomes were obtained for 65/80 samples from 38 patients. Conventional IPC analysis recognized 1 outbreak, involving 3 children, and identified another potential cluster in the haemato-oncology ward. Whole-genome and HA/NA phylogeny both accurately identified the previously known outbreak cluster. However, HA/NA sequencing additionally identified unrelated strains as part of this outbreak cluster. A whole-genome analysis identified a further cluster of 2 infections that had been previously missed and refuted suspicions of transmission in the haemato-oncology wards.

Conclusions: Whole-genome sequencing is better at identifying outbreak clusters in a hospital setting than HA/NA sequencing. Whole-genome sequencing could provide a faster and more reliable method for outbreak monitoring and supplement routine IPC team work to allow the prevention of transmission.

Keywords: infection control; influenza; next-generation sequencing; transmission; whole genome.

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Figures

Figure 1.
Figure 1.
Maximum likelihood trees for (A) whole-genome and (B) HA/NA concatenated genes. The strains sequenced in this study are color-coded by individual patients. Bootstrap support >70% at nodes is highlighted using a black circle. The vaccine strain used in the formulation is highlighted in the box. Abbreviations: HA, haemagglutinin; NA, neuraminidase.
Figure 2.
Figure 2.
Pairwise SNV between strains in the study. The blue bars highlight variations within an individual patient; the red bars highlight variations between confirmed outbreak sequences; the green bars highlight variations between epidemiologically unrelated sequences within the hospital; and the purple bars highlight variations between strains circulating in Europe in the same season. Whole-genome SNV (A) and (B) HA/NA concatenated SNV. Abbreviations: HA, haemagglutinin; NA, neuraminidase; SNV, single nucleotide variations.
Figure 3.
Figure 3.
Variable sites highlighted across the genome between related (red) and unrelated (green) influenza A strains. The different segments and their respective boundaries are shown on top. Abbreviations: HA, haemagglutinin; MA, matrix protein; NA, neuraminidase; NP, nucleoprotein; NS, nonstructural protein; PA, PB1, PB2, polymerase protein.
Figure 4.
Figure 4.
A, Table with dates of sampling for patients indirectly linked to cluster 2. B, Popart analysis of putative transmission chain of patients using a median joining network. Nodes with no labels are inferred nodes. The notches on each link between 2 nodes represent the number of changes between the 2 nodes at the whole-genome level.
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