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Use of Whole-Genome Sequencing in the Investigation of a Nosocomial Influenza Virus Outbreak

Use of Whole-Genome Sequencing in the Investigation of a Nosocomial Influenza Virus Outbreak Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 Use of Whole-Genome Sequencing in the Investigation of a Nosocomial Influenza Virus Outbreak Catherine F Houlihan, Dan Frampton, R Bridget Ferns, Jade Raffle, Paul Grant, Myriam Reidy, Leila Hail, Kirsty Thomson, Frank Mattes, Zisis Kozlakidis, Deenan Pillay, Andrew Hayward, Eleni Nastouli Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 The Journal of Infectious Diseases BRIEF REPORT in healthcare staff in England (49.5% in January 2016)  com- Use of Whole-Genome Sequencing bined with reduced vaccine efficacy raises the possibility that in the Investigation of a Nosocomial healthcare staff are involved in nosocomial transmission [2]. Influenza Virus Outbreak Commonly, nosocomial transmission of influenza virus within the healthcare setting has been identified through tra- 1 1 1,4 1 Catherine F. Houlihan, Dan Frampton, R. Bridget Ferns, Jade Raffle, 5 6 6 7 Paul Grant, Myriam Reidy, Leila Hail, Kirsty Thomson, ditional molecular diagnostic methods (ie, real-time poly- 5 1,8 1 Frank Mattes, Zisis Kozlakidis, Deenan Pillay, merase chain reaction [PCR] analysis and reverse transcription 2,8 3,5 Andrew Hayward, and Eleni Nastouli PCR [RT-PCR]) for the detection of viral species, combined 1 2 Division of Infection and Immunity, Institute of Epidemiology and Health Care, and with data collected on patient and staff movement within the Department of Population, Policy, and Practice, Great Ormond Street Institute of Child Health, University College London (UCL), National Institute for Health Research hospital. Patients who have overlapped in time and space and 5 6 Biomedical Research Centre, Department of Clinical Virology, Infection Control who have evidence of infection with the same viral type and, Service, and Department of Blood Diseases, UCL Hospitals National Health Service Foundation Trust, and Department of Infectious Disease Informatics, Farr Institute of if available, subtype are assumed to have transmitted to each Health Informatics Research, London, United Kingdom other. Direct Sanger sequencing methods have more recently Traditional epidemiological investigation of nosocomial trans- been used to identify possible clusters of nosocomial influenza mission of influenza involves the identification of patients who virus infection [4], but next-generation sequencing (NGS) of have the same influenza virus type and who have overlapped the whole virus genome, which may oer in ff creased discrim- in time and place. This method may misidentify transmission inatory capacity, has been infrequently reported. We describe November where it has not occurred or miss transmission when it has. an outbreak of influenza A  virus infection, during early 2016, We used influenza virus whole-genome sequencing (WGS) to on a hematology/oncology ward in a National Health Service investigate an outbreak of influenza A virus infection in a hema- (NHS) hospital in London, in which timely use of whole-ge- tology/oncology ward and identified 2 separate introductions, nome sequencing (WGS) would have identified the presence or one of which resulted in 5 additional infections and 79 bed- absence of nosocomial transmission and allowed a more tar- days lost. Results from WGS are becoming rapidly available and geted infection control response. may supplement traditional infection control procedures in the investigation and management of nosocomial outbreaks. CLINICAL CASES Keywords. Influenza; nosocomial; sequencing; transmission Two patients with hematological/oncological malignancies (patients A  and B; Figure  1), who had each been admitted Nosocomial transmission of influenza A virus is of significant for 2 and 5 weeks, respectively, on the north side of ward 1, a concern since infection in individuals who are immunocom- hematology/oncology ward in a London NHS hospital, devel- OA-CC-BY promised, immunosuppressed, at extremes of age, or pregnant oped coryzal symptoms with fever in the same 24-hour period. have an increased risk of severe illness, morbidity and death Combined nose and throat swab specimens were collected from [1, 2]. e Th risk of nosocomial acquisition of influenza virus each patient at symptom onset (day 0), as per the hospital’s pol- is high since patients commonly share open bays before viral icy, and were tested using multiplex RT-PCR analysis involving respiratory infection is diagnosed, and significant healthcare a standard panel of 6 respiratory viruses, including influenza costs are associated with hospital ward closures due to influ- A and B viruses. Results were available on day 1, and influenza enza virus outbreaks. Influenza can also be asymptomatic, and A virus was detected in samples from both patients. Nosocomial prolonged shedding has been demonstrated in those who are transmission was not assumed to have occurred between these immunocompromised [3]. Low rates of influenza vaccination patients, since they were in separate positive-pressure rooms. e m Th echanism was therefore uncertain. On day 1, a further patient (patient C; Figure  1) on the south side of the ward, developed symptoms and tested positive for influenza A virus. Received 7 March 2018; editorial decision 29 May 2018; accepted 4 June 2018; published Since this south-side patient had no clear link to patients A and online June 5, 2018. Correspondence: Eleni Nastouli, MD, Department of Clinical Virology, UCLH 60 Whitfield St, B on the north side, it was assumed that an outbreak was not W1T 4EU, UK (e.nastouli@ucl.ac.uk). occurring, and the ward was not closed. Over the next 48 hours, The Journal of Infectious Diseases 2018;218:1485–89 a further 2 patients (patients D and E) tested positive for influ- © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons enza A virus (on days 2 and 3, respectively), and the ward was Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted closed on day 3. Testing of 1 further symptomatic patient and reuse, distribution, and reproduction in any medium, provided the original work is properly cited. DOI: 10.1093/infdis/jiy335 screening of all asymptomatic patients on day 4 identified 2 BRIEF REPORT • JID 2018:218 (1 November) • 1485 Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 North Side South Side A F BC D E G 0 1 23 4 E B Day of outbreak, indicating when patients became symptomatic. All symptomatic patients had fever (>38°C) and coryza. Figure  1. Map of the ward. Rectangles are side rooms, and squares indicate bays with 4 beds. Letters indicate patients and are colored green or purple according to whether influenza virus sequences are considered part of the same transmission cluster. Patient D is colored black since the genome coverage was considered of sufficient depth. Patient G was asymptomatic but tested positive for influenza virus A on day 4. further patients (patient F, who was symptomatic, and patient and a modified 8-segment PCR method [6]. Fifty-microliter G, who was asymptomatic). A visiting relative of patient B later reactions containing 10.0  µL of RNA, a final concentration of revealed that they had coryzal symptoms that preceded and 1× SuperScript III One-Step RT-PCR buffer, 0.1  mM of each continued throughout the outbreak. This relative tested positive primer, and 1.0 µL of SuperScript III RT/Platinum Taq high-fi- for influenza A virus 1 week aer t ft he first patients were tested. delity enzyme were prepared. RT-PCR thermal cycling condi- A total of 7 patients developed influenza A  virus infection tions were as described elsewhere [6]. Two PCR amplicons were on this ward. Of these, patient E required a prolonged period generated; equal volumes of both were combined, and the total in intensive care. Thirteen patients who were contacts of the DNA concentration was determined using the Qubit HS DNA patients described above were screened, had negative test assay (Invitrogen). results, and commenced prophylaxis, in keeping with Public Library preparations were generated using the Nextera XT Health England guidance and the NHS trust policy [5]. The DNA sample preparation kit (Illumina) according to manufac- ward was closed for 6 days and resulted in 79 bed-days lost. Five turer’s instructions, using 1 ng of input DNA. Tagged PCR sam- healthcare workers developed influenza-like illness and were ples were purified with 30 µL of AMPure XP beads (catalog no. not permitted to attend work, as per the NHS trust’s occupa- A63881; Agencourt). Sample library normalization and MiSeq tional health policy. They were not tested for influenza virus. sample loading were carried out according to the Nextera XT protocols. Pooled normalized samples, including a Phi-X con- MATERIALS AND METHODS trol at a final concentration of 0.1 pM, were loaded onto a Samples from the 7 patients on the same ward, 9 samples that were Mi-Seq reagent kit V3 600 cycle (Illumina) and sequenced on submitted to the same laboratory on the same days from the same a MiSeq (Illumina). hospital, and a sample from the symptomatic relative who had tested Consensus genomes were generated from short reads, using positive for influenza A  virus on the basis of standard in-house ICONIC’s in-house de novo assembly pipeline, applying a read RT-PCR were sequenced using the Mi-Seq (Illumina) platform. depth cutoff of ≥20 reads to the final sequences. Phylogenetic Amplification of influenza A  virus RNA was performed analysis was undertaken first by separately aligning each set of using the SuperScript III One-Step RT-PCR kit (Invitrogen) segments using MAFFT [7] and then concatenating the coding 1486 • JID 2018:218 (1 November) • BRIEF REPORT Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 regions within Aliview [8]. Maximum-likelihood phyloge- depth, resulting in a reliable sequence for NS1 alone (segment netic trees were inferred for each alignment, using RAxML [9]. 8). Despite this limitation, when a reliable sequence from this Phylogenies were inferred under a general time-reversible sub- patient’s sample was included in a maximum-likelihood phy- stitution model with rate heterogeneity among sites modeled logenetic tree analysis, samples from patients B, C, D, E, F, and under a 4-category discrete approximation of a gamma distri- G clustered tightly with high bootstrap support (>95%), with bution. Branch support was assessed through nonparametric almost identical sequences (Figure  2). The same result was bootstrapping of 1000 pseudoreplicates. Direct linkage of whole obtained when the sample from patient D was excluded from virus genomes was considered to have statistical support if the the phylogenetic analysis (Supplementary Figure 1). observed number of mutations between them occurred within er Th e was no evidence of transmission between patient the 95% confidence interval of the expected number (given the A  and patient B, who both had influenza A  virus detected by mutation rate, interval between sample collection, genome, and RT-PCR analysis on day 1. es Th e 2 patients were likely infected pair-wise alignment lengths), and they clustered with respective via separate nosocomial transmissions. Incidentally, a further bootstrap values ≥95% (Supplementary Methods). probable (unrecognized) nosocomial transmission was identi- fied on a separate ward (in patients H and I; Figure 2). RESULTS DISCUSSION Seven samples from 7 hematology/oncology patients, 1 sam- ple from a visiting relative of patient B, and 9 samples from Using WGS, we illustrated the dynamics of an influenza virus other patients in the same hospital tested positive for influenza outbreak on a hematology/oncology ward, clarifying that 2 A virus, according to multiplex RT-PCR analysis. WGS was suc- patients (patients A and B) who were initially considered to be cessful for 15 of 17 samples, with a median read depth across index cases, based on findings of traditional epidemiological all samples of 800 (interquartile range, 600–5000). Attempts methods, were not linked and that one of these individuals was to sequence the sample from the relative of patient A  were not involved in the outbreak. e Th use of traditional epidemi- unsuccessful. The sample from patient D failed to generate ological infection control measures erroneously linked these 2 sequence across all 8 segments of the genome with sufficient unrelated infections and missed spread of the infection from the Ward 1Patient ADay 0 Ward 2Patient ODay 6 Ward 4Patient NDay 6 A and EPatient JDay 5 OPD Patient KDay -1 Ward 4Patient HDay 2 Ward 4Patient IDay 4 A and EPatient MDay 3 Ward 3Patient PDay 6 OPD Patient LDay 2 Ward 1Patient EDay 3 Ward 1Patient BDay 0 Ward 1 Patient DDay 2 Ward 1Patient FDay 4 Ward 1Patient CDay 1 Ward 1Patient GDay 4 2.0E–4 Figure 2. Maximum-likelihood tree derived from a genomic alignment of sequences generated during the influenza outbreak on ward 1. Tips are colored according to loca- tion within the hospital and whether the patients are considered part of the same transmission cluster (green, ward 1, linked; blue, ward 1, unlinked; red, elsewhere in the same hospital, unlinked). Tips in black indicate insufficient genome coverage (only the sequence for NS-1 was available). Bootstrap support percentages from 1000 replicates are shown for each node. A and E, Accident and Emergency Department; OPD, Out Patient Department. BRIEF REPORT • JID 2018:218 (1 November) • 1487 Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 north to the south side of the ward, leading to a delay in infec- virus sequencing) will, in the near future, be available 24 hours tion control action. Had WGS data been available in real time, aer s ft ample receipt. the cross-ward transmission would have been identified on day Two large studies from an epidemiological surveillance unit 1 or 2, and the ward would have been closed. Identification of in Germany and a retrospective analysis of nosocomial trans- the separate source of influenza A virus introduction (ie, patient missions in care facilities in Canada have confirmed nosoco- A) would additionally have led to a separate investigation and mial influenza virus transmission where it was thought to have instigation of infection control procedures specific to this occurred, using WGS [13, 14]. Although it could be argued introduction. that confirmation of a suspected transmission has high cost e Th integration of WGS data with epidemiological analysis with limited benefit, we have shown that WGS can demon- in nosocomial infections could identify common pathways of strate separate introductions and allow tailored infection con- transmission on wards, such as communal areas, bays, shared trol approaches. Further, the use of NGS during outbreaks of patient equipment, healthcare sta, o ff r visitors interacting with highly infectious diseases with significant consequences and the each other and other patients. Early identification of path- sharing of data with public health teams have been instrumental ways of transmission could prevent further nosocomial cases in understanding transmission, highlighted in the recent Ebola of influenza. Interventions would include earlier initiation of and Zika outbreaks. ward closure; prophylaxis; emphasis on influenza vaccination of At this hospital, during the 2016 influenza season, WGS staff and, if they are unwell, treatment; enhanced equipment or allowed early identification of the predominant circulating room cleaning; and the limiting of visitor-visitor or visitor-pa- influenza A virus subtype as H1N1, rather than H3N2, and led tient contact. Prophylaxis has been shown to reduce the risk of to a local policy change from the recommendation of oseltami- symptomatic influenza virus infection in immunocompetent vir as first-line treatment to zanamivir. Public Health England and immunocompromised adults by up to 80% [10]. Staff vac- altered the national guideline in a similar way, 1 month later cination rates on this ward were extremely low at the time of [5]. Moving forward, the use of WGS would allow a more the outbreak: 25% and 55% of staff working on the north and timely and accurate shift in treatment recommendation, and south ends, respectively, had received the seasonal influenza individualized antiviral treatment for influenza would be vaccine (Figure  1). Analysis of the effectiveness of the World possible. Health Organization–recommended seasonal influenza vac- We have described an outbreak of influenza in a hematology/ cination against the 2009 pandemic influenza A(H1N1) virus oncology ward in which real-time use of WGS may have mit- circulating at the time has been reported as 65% overall [11]. igated propagation of the outbreak and certainly would have Demonstration of the nosocomial outbreak described on this increased understanding of it. The use of WGS during the influ- ward led anecdotally to increased vaccine uptake by sta.ff enza season, either for all hospitalized patients or specifically We achieved full-length genome sequencing for all but 2 iso- for patients who develop influenza during an inpatient stay, lates in this case, allowing more-accurate linkage of infections, may identify transmission where it was not suspected and allow compared with the accuracy achieved via limited sequencing rapid implementation of infection control procedures. of the hemagglutinin or neuraminidase genes. One of these 2 Supplementary Data failed attempts at sequencing may be attributable to sampling Supplementary materials are available at e Th Journal of Infectious the relative of patient B close to the end of their illness, when the Diseases online. Consisting of data provided by the authors to quantity and quality of viral RNA is less robust. benefit the reader, the posted materials are not copyedited and NGS has been used to identify common sources of nos- are the sole responsibility of the authors, so questions or com- ocomial outbreaks of norovirus and methicillin-resistant ments should be addressed to the corresponding author. Staphylococcus aureus, but it has been used less frequently to identify transmission of respiratory viruses, including influenza Notes virus [12]. Possible transmission between patients can be iden- tified from relatively small amounts of viral RNA in specimens. Financial support. This work was supported by the Health However, bioinformatics analysis, equipment, and consumables Innovation Challenge Fund T5-344 (Infection Response costs have limited the implementation of this technology into Through Virus Genomics), a parallel funding partnership routine clinical care. These limitations, as well as reduction in between the Department of Health and the Wellcome Trust. turnaround times, are improving with time and recognition Potential coni fl cts of interest. All authors: No reported con- of the clinical usefulness of NGS. WGS is now available with flicts of interest. All authors have submitted the ICMJE Form small, lightweight, and easily transportable devices, such as the for Disclosure of Potential Conflicts of Interest. Conflicts that MinION (Oxford Nanopore). It is expected that NGS results for the editors consider relevant to the content of the manuscript recognized pathogens with established pipelines (ie, influenza have been disclosed. 1488 • JID 2018:218 (1 November) • BRIEF REPORT Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 References read next-generation sequencing. Phylosophical Trans R Soc 2013; 368:20120205. 1. Louie JK, Winter K, Jean C, et  al. Factors associated with 7. Katoh K, Standley DM. MAFFT multiple sequence align- death or hospitalization due to pandemic 2009 influ- ment software version 7: improvements in performance and enza A  (H1N1) infection in California. JAMA 2009; usability. Mol Biol Evol 2013; 30:772–80. (17):1896–902. 8. Larsson A. AliView: a fast and lightweight alignment viewer 2. Public Health England (PHE). PHE weekly national and editor for large datasets. Bioinformatics 2014; 30:3276–8. influenza report; 2015. https://www.gov.uk/government/ 9. Stamatakis A. RAxML version 8: a tool for phylogenetic anal- uploads/system/uploads/attachment_data/file/490370/ ysis and post-analysis of large phylogenies. Bioinformatics Weekly_report_current_wk_1_7Jan2016.pdf. Accessed 2014; 30:1312–3. February 1, 2018. 10. Jefferson T, Ma J, Doshi P, et  al. Neuraminidase inhibitors 3. Lehners N, Tabatabai J, Prifert C, et al. Long-term shedding for preventing and treating influenza in adults and children of influenza virus, parainfluenza virus, respiratory syncy- (Review). Cochrane 2015; CD008965. tial virus and nosocomial epidemiology in patients with 11. Chambers C, Skowronski D, Sabaiduc S, Winter A, hematological disorders. PLoS One 2016; 11:e0148258. Dickinson J. Interim estimates of 2015/16 vaccine ective- doi:10.1371/journal.pone.0148258. ness against influenza A(H1N1)pdm09, Canada, February 4. Jonges M, Rahamat-Langendoen J, Meijer A, Niesters HG, 2016. Eurosurveillance 2016; 21:30168. Koopmans M. Sequence-based identification and charac- 12. Seong MW, Cho SI, Park H, et  al. Genotyping influenza terization of nosocomial influenza A(H1N1)pdm09 virus virus by next-generation deep sequencing in clinical speci- infections. J Hosp Infect 2012; 82:187–93. mens. Ann Lab Med 2016; 36:255–8. 5. Public Health England. Antiviral prescribing when 13. MacFadden DR, McGeer A, Athey T, et  al. Use of genome A(H1N1) pdm09 influenza virus is the dominant circulat- sequencing to define institutional influenza outbreaks, ing strain. Update letter, January 2016. 2016. https://assets. Toronto, Ontario, Canada, 2014–15. Emerg Infect Dis 2018; publishing.ser vice.gov.uk/government/uploads/system/ 24:492–7. uploads/ attachment_data/file/648758/PHE_guidance_ 14. Meinel DM, Heinzinger S, Eberle U, Ackermann N, antivirals_influenza_201718_FINAL.pdf . Accessed February Schönberger K, Sing A. Whole genome sequencing identifies 1, 2018. influenza A H3N2 transmission and oer ff s superior resolu- 6. Watson SJ, Welkers MRA, Depledge DP, et  al. Viral popu- tion to classical typing methods. Infection 2018; 46:69–76. lation analysis and minority-variant detection using short BRIEF REPORT • JID 2018:218 (1 November) • 1489 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Infectious Diseases Oxford University Press

Use of Whole-Genome Sequencing in the Investigation of a Nosocomial Influenza Virus Outbreak

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Oxford University Press
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Copyright © 2022 Infectious Diseases Society of America
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0022-1899
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DOI
10.1093/infdis/jiy335
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Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 Use of Whole-Genome Sequencing in the Investigation of a Nosocomial Influenza Virus Outbreak Catherine F Houlihan, Dan Frampton, R Bridget Ferns, Jade Raffle, Paul Grant, Myriam Reidy, Leila Hail, Kirsty Thomson, Frank Mattes, Zisis Kozlakidis, Deenan Pillay, Andrew Hayward, Eleni Nastouli Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 The Journal of Infectious Diseases BRIEF REPORT in healthcare staff in England (49.5% in January 2016)  com- Use of Whole-Genome Sequencing bined with reduced vaccine efficacy raises the possibility that in the Investigation of a Nosocomial healthcare staff are involved in nosocomial transmission [2]. Influenza Virus Outbreak Commonly, nosocomial transmission of influenza virus within the healthcare setting has been identified through tra- 1 1 1,4 1 Catherine F. Houlihan, Dan Frampton, R. Bridget Ferns, Jade Raffle, 5 6 6 7 Paul Grant, Myriam Reidy, Leila Hail, Kirsty Thomson, ditional molecular diagnostic methods (ie, real-time poly- 5 1,8 1 Frank Mattes, Zisis Kozlakidis, Deenan Pillay, merase chain reaction [PCR] analysis and reverse transcription 2,8 3,5 Andrew Hayward, and Eleni Nastouli PCR [RT-PCR]) for the detection of viral species, combined 1 2 Division of Infection and Immunity, Institute of Epidemiology and Health Care, and with data collected on patient and staff movement within the Department of Population, Policy, and Practice, Great Ormond Street Institute of Child Health, University College London (UCL), National Institute for Health Research hospital. Patients who have overlapped in time and space and 5 6 Biomedical Research Centre, Department of Clinical Virology, Infection Control who have evidence of infection with the same viral type and, Service, and Department of Blood Diseases, UCL Hospitals National Health Service Foundation Trust, and Department of Infectious Disease Informatics, Farr Institute of if available, subtype are assumed to have transmitted to each Health Informatics Research, London, United Kingdom other. Direct Sanger sequencing methods have more recently Traditional epidemiological investigation of nosocomial trans- been used to identify possible clusters of nosocomial influenza mission of influenza involves the identification of patients who virus infection [4], but next-generation sequencing (NGS) of have the same influenza virus type and who have overlapped the whole virus genome, which may oer in ff creased discrim- in time and place. This method may misidentify transmission inatory capacity, has been infrequently reported. We describe November where it has not occurred or miss transmission when it has. an outbreak of influenza A  virus infection, during early 2016, We used influenza virus whole-genome sequencing (WGS) to on a hematology/oncology ward in a National Health Service investigate an outbreak of influenza A virus infection in a hema- (NHS) hospital in London, in which timely use of whole-ge- tology/oncology ward and identified 2 separate introductions, nome sequencing (WGS) would have identified the presence or one of which resulted in 5 additional infections and 79 bed- absence of nosocomial transmission and allowed a more tar- days lost. Results from WGS are becoming rapidly available and geted infection control response. may supplement traditional infection control procedures in the investigation and management of nosocomial outbreaks. CLINICAL CASES Keywords. Influenza; nosocomial; sequencing; transmission Two patients with hematological/oncological malignancies (patients A  and B; Figure  1), who had each been admitted Nosocomial transmission of influenza A virus is of significant for 2 and 5 weeks, respectively, on the north side of ward 1, a concern since infection in individuals who are immunocom- hematology/oncology ward in a London NHS hospital, devel- OA-CC-BY promised, immunosuppressed, at extremes of age, or pregnant oped coryzal symptoms with fever in the same 24-hour period. have an increased risk of severe illness, morbidity and death Combined nose and throat swab specimens were collected from [1, 2]. e Th risk of nosocomial acquisition of influenza virus each patient at symptom onset (day 0), as per the hospital’s pol- is high since patients commonly share open bays before viral icy, and were tested using multiplex RT-PCR analysis involving respiratory infection is diagnosed, and significant healthcare a standard panel of 6 respiratory viruses, including influenza costs are associated with hospital ward closures due to influ- A and B viruses. Results were available on day 1, and influenza enza virus outbreaks. Influenza can also be asymptomatic, and A virus was detected in samples from both patients. Nosocomial prolonged shedding has been demonstrated in those who are transmission was not assumed to have occurred between these immunocompromised [3]. Low rates of influenza vaccination patients, since they were in separate positive-pressure rooms. e m Th echanism was therefore uncertain. On day 1, a further patient (patient C; Figure  1) on the south side of the ward, developed symptoms and tested positive for influenza A virus. Received 7 March 2018; editorial decision 29 May 2018; accepted 4 June 2018; published Since this south-side patient had no clear link to patients A and online June 5, 2018. Correspondence: Eleni Nastouli, MD, Department of Clinical Virology, UCLH 60 Whitfield St, B on the north side, it was assumed that an outbreak was not W1T 4EU, UK (e.nastouli@ucl.ac.uk). occurring, and the ward was not closed. Over the next 48 hours, The Journal of Infectious Diseases 2018;218:1485–89 a further 2 patients (patients D and E) tested positive for influ- © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons enza A virus (on days 2 and 3, respectively), and the ward was Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted closed on day 3. Testing of 1 further symptomatic patient and reuse, distribution, and reproduction in any medium, provided the original work is properly cited. DOI: 10.1093/infdis/jiy335 screening of all asymptomatic patients on day 4 identified 2 BRIEF REPORT • JID 2018:218 (1 November) • 1485 Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 North Side South Side A F BC D E G 0 1 23 4 E B Day of outbreak, indicating when patients became symptomatic. All symptomatic patients had fever (>38°C) and coryza. Figure  1. Map of the ward. Rectangles are side rooms, and squares indicate bays with 4 beds. Letters indicate patients and are colored green or purple according to whether influenza virus sequences are considered part of the same transmission cluster. Patient D is colored black since the genome coverage was considered of sufficient depth. Patient G was asymptomatic but tested positive for influenza virus A on day 4. further patients (patient F, who was symptomatic, and patient and a modified 8-segment PCR method [6]. Fifty-microliter G, who was asymptomatic). A visiting relative of patient B later reactions containing 10.0  µL of RNA, a final concentration of revealed that they had coryzal symptoms that preceded and 1× SuperScript III One-Step RT-PCR buffer, 0.1  mM of each continued throughout the outbreak. This relative tested positive primer, and 1.0 µL of SuperScript III RT/Platinum Taq high-fi- for influenza A virus 1 week aer t ft he first patients were tested. delity enzyme were prepared. RT-PCR thermal cycling condi- A total of 7 patients developed influenza A  virus infection tions were as described elsewhere [6]. Two PCR amplicons were on this ward. Of these, patient E required a prolonged period generated; equal volumes of both were combined, and the total in intensive care. Thirteen patients who were contacts of the DNA concentration was determined using the Qubit HS DNA patients described above were screened, had negative test assay (Invitrogen). results, and commenced prophylaxis, in keeping with Public Library preparations were generated using the Nextera XT Health England guidance and the NHS trust policy [5]. The DNA sample preparation kit (Illumina) according to manufac- ward was closed for 6 days and resulted in 79 bed-days lost. Five turer’s instructions, using 1 ng of input DNA. Tagged PCR sam- healthcare workers developed influenza-like illness and were ples were purified with 30 µL of AMPure XP beads (catalog no. not permitted to attend work, as per the NHS trust’s occupa- A63881; Agencourt). Sample library normalization and MiSeq tional health policy. They were not tested for influenza virus. sample loading were carried out according to the Nextera XT protocols. Pooled normalized samples, including a Phi-X con- MATERIALS AND METHODS trol at a final concentration of 0.1 pM, were loaded onto a Samples from the 7 patients on the same ward, 9 samples that were Mi-Seq reagent kit V3 600 cycle (Illumina) and sequenced on submitted to the same laboratory on the same days from the same a MiSeq (Illumina). hospital, and a sample from the symptomatic relative who had tested Consensus genomes were generated from short reads, using positive for influenza A  virus on the basis of standard in-house ICONIC’s in-house de novo assembly pipeline, applying a read RT-PCR were sequenced using the Mi-Seq (Illumina) platform. depth cutoff of ≥20 reads to the final sequences. Phylogenetic Amplification of influenza A  virus RNA was performed analysis was undertaken first by separately aligning each set of using the SuperScript III One-Step RT-PCR kit (Invitrogen) segments using MAFFT [7] and then concatenating the coding 1486 • JID 2018:218 (1 November) • BRIEF REPORT Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 regions within Aliview [8]. Maximum-likelihood phyloge- depth, resulting in a reliable sequence for NS1 alone (segment netic trees were inferred for each alignment, using RAxML [9]. 8). Despite this limitation, when a reliable sequence from this Phylogenies were inferred under a general time-reversible sub- patient’s sample was included in a maximum-likelihood phy- stitution model with rate heterogeneity among sites modeled logenetic tree analysis, samples from patients B, C, D, E, F, and under a 4-category discrete approximation of a gamma distri- G clustered tightly with high bootstrap support (>95%), with bution. Branch support was assessed through nonparametric almost identical sequences (Figure  2). The same result was bootstrapping of 1000 pseudoreplicates. Direct linkage of whole obtained when the sample from patient D was excluded from virus genomes was considered to have statistical support if the the phylogenetic analysis (Supplementary Figure 1). observed number of mutations between them occurred within er Th e was no evidence of transmission between patient the 95% confidence interval of the expected number (given the A  and patient B, who both had influenza A  virus detected by mutation rate, interval between sample collection, genome, and RT-PCR analysis on day 1. es Th e 2 patients were likely infected pair-wise alignment lengths), and they clustered with respective via separate nosocomial transmissions. Incidentally, a further bootstrap values ≥95% (Supplementary Methods). probable (unrecognized) nosocomial transmission was identi- fied on a separate ward (in patients H and I; Figure 2). RESULTS DISCUSSION Seven samples from 7 hematology/oncology patients, 1 sam- ple from a visiting relative of patient B, and 9 samples from Using WGS, we illustrated the dynamics of an influenza virus other patients in the same hospital tested positive for influenza outbreak on a hematology/oncology ward, clarifying that 2 A virus, according to multiplex RT-PCR analysis. WGS was suc- patients (patients A and B) who were initially considered to be cessful for 15 of 17 samples, with a median read depth across index cases, based on findings of traditional epidemiological all samples of 800 (interquartile range, 600–5000). Attempts methods, were not linked and that one of these individuals was to sequence the sample from the relative of patient A  were not involved in the outbreak. e Th use of traditional epidemi- unsuccessful. The sample from patient D failed to generate ological infection control measures erroneously linked these 2 sequence across all 8 segments of the genome with sufficient unrelated infections and missed spread of the infection from the Ward 1Patient ADay 0 Ward 2Patient ODay 6 Ward 4Patient NDay 6 A and EPatient JDay 5 OPD Patient KDay -1 Ward 4Patient HDay 2 Ward 4Patient IDay 4 A and EPatient MDay 3 Ward 3Patient PDay 6 OPD Patient LDay 2 Ward 1Patient EDay 3 Ward 1Patient BDay 0 Ward 1 Patient DDay 2 Ward 1Patient FDay 4 Ward 1Patient CDay 1 Ward 1Patient GDay 4 2.0E–4 Figure 2. Maximum-likelihood tree derived from a genomic alignment of sequences generated during the influenza outbreak on ward 1. Tips are colored according to loca- tion within the hospital and whether the patients are considered part of the same transmission cluster (green, ward 1, linked; blue, ward 1, unlinked; red, elsewhere in the same hospital, unlinked). Tips in black indicate insufficient genome coverage (only the sequence for NS-1 was available). Bootstrap support percentages from 1000 replicates are shown for each node. A and E, Accident and Emergency Department; OPD, Out Patient Department. BRIEF REPORT • JID 2018:218 (1 November) • 1487 Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 north to the south side of the ward, leading to a delay in infec- virus sequencing) will, in the near future, be available 24 hours tion control action. Had WGS data been available in real time, aer s ft ample receipt. the cross-ward transmission would have been identified on day Two large studies from an epidemiological surveillance unit 1 or 2, and the ward would have been closed. Identification of in Germany and a retrospective analysis of nosocomial trans- the separate source of influenza A virus introduction (ie, patient missions in care facilities in Canada have confirmed nosoco- A) would additionally have led to a separate investigation and mial influenza virus transmission where it was thought to have instigation of infection control procedures specific to this occurred, using WGS [13, 14]. Although it could be argued introduction. that confirmation of a suspected transmission has high cost e Th integration of WGS data with epidemiological analysis with limited benefit, we have shown that WGS can demon- in nosocomial infections could identify common pathways of strate separate introductions and allow tailored infection con- transmission on wards, such as communal areas, bays, shared trol approaches. Further, the use of NGS during outbreaks of patient equipment, healthcare sta, o ff r visitors interacting with highly infectious diseases with significant consequences and the each other and other patients. Early identification of path- sharing of data with public health teams have been instrumental ways of transmission could prevent further nosocomial cases in understanding transmission, highlighted in the recent Ebola of influenza. Interventions would include earlier initiation of and Zika outbreaks. ward closure; prophylaxis; emphasis on influenza vaccination of At this hospital, during the 2016 influenza season, WGS staff and, if they are unwell, treatment; enhanced equipment or allowed early identification of the predominant circulating room cleaning; and the limiting of visitor-visitor or visitor-pa- influenza A virus subtype as H1N1, rather than H3N2, and led tient contact. Prophylaxis has been shown to reduce the risk of to a local policy change from the recommendation of oseltami- symptomatic influenza virus infection in immunocompetent vir as first-line treatment to zanamivir. Public Health England and immunocompromised adults by up to 80% [10]. Staff vac- altered the national guideline in a similar way, 1 month later cination rates on this ward were extremely low at the time of [5]. Moving forward, the use of WGS would allow a more the outbreak: 25% and 55% of staff working on the north and timely and accurate shift in treatment recommendation, and south ends, respectively, had received the seasonal influenza individualized antiviral treatment for influenza would be vaccine (Figure  1). Analysis of the effectiveness of the World possible. Health Organization–recommended seasonal influenza vac- We have described an outbreak of influenza in a hematology/ cination against the 2009 pandemic influenza A(H1N1) virus oncology ward in which real-time use of WGS may have mit- circulating at the time has been reported as 65% overall [11]. igated propagation of the outbreak and certainly would have Demonstration of the nosocomial outbreak described on this increased understanding of it. The use of WGS during the influ- ward led anecdotally to increased vaccine uptake by sta.ff enza season, either for all hospitalized patients or specifically We achieved full-length genome sequencing for all but 2 iso- for patients who develop influenza during an inpatient stay, lates in this case, allowing more-accurate linkage of infections, may identify transmission where it was not suspected and allow compared with the accuracy achieved via limited sequencing rapid implementation of infection control procedures. of the hemagglutinin or neuraminidase genes. One of these 2 Supplementary Data failed attempts at sequencing may be attributable to sampling Supplementary materials are available at e Th Journal of Infectious the relative of patient B close to the end of their illness, when the Diseases online. Consisting of data provided by the authors to quantity and quality of viral RNA is less robust. benefit the reader, the posted materials are not copyedited and NGS has been used to identify common sources of nos- are the sole responsibility of the authors, so questions or com- ocomial outbreaks of norovirus and methicillin-resistant ments should be addressed to the corresponding author. Staphylococcus aureus, but it has been used less frequently to identify transmission of respiratory viruses, including influenza Notes virus [12]. Possible transmission between patients can be iden- tified from relatively small amounts of viral RNA in specimens. Financial support. This work was supported by the Health However, bioinformatics analysis, equipment, and consumables Innovation Challenge Fund T5-344 (Infection Response costs have limited the implementation of this technology into Through Virus Genomics), a parallel funding partnership routine clinical care. These limitations, as well as reduction in between the Department of Health and the Wellcome Trust. turnaround times, are improving with time and recognition Potential coni fl cts of interest. All authors: No reported con- of the clinical usefulness of NGS. WGS is now available with flicts of interest. All authors have submitted the ICMJE Form small, lightweight, and easily transportable devices, such as the for Disclosure of Potential Conflicts of Interest. Conflicts that MinION (Oxford Nanopore). It is expected that NGS results for the editors consider relevant to the content of the manuscript recognized pathogens with established pipelines (ie, influenza have been disclosed. 1488 • JID 2018:218 (1 November) • BRIEF REPORT Downloaded from https://academic.oup.com/jid/article/218/9/1485/5033093 by DeepDyve user on 13 July 2022 References read next-generation sequencing. Phylosophical Trans R Soc 2013; 368:20120205. 1. Louie JK, Winter K, Jean C, et  al. Factors associated with 7. 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Use of genome A(H1N1) pdm09 influenza virus is the dominant circulat- sequencing to define institutional influenza outbreaks, ing strain. Update letter, January 2016. 2016. https://assets. Toronto, Ontario, Canada, 2014–15. Emerg Infect Dis 2018; publishing.ser vice.gov.uk/government/uploads/system/ 24:492–7. uploads/ attachment_data/file/648758/PHE_guidance_ 14. Meinel DM, Heinzinger S, Eberle U, Ackermann N, antivirals_influenza_201718_FINAL.pdf . Accessed February Schönberger K, Sing A. Whole genome sequencing identifies 1, 2018. influenza A H3N2 transmission and oer ff s superior resolu- 6. Watson SJ, Welkers MRA, Depledge DP, et  al. Viral popu- tion to classical typing methods. Infection 2018; 46:69–76. lation analysis and minority-variant detection using short BRIEF REPORT • JID 2018:218 (1 November) • 1489

Journal

The Journal of Infectious Diseases – Oxford University Press

Published: Sep 22, 2018

Keywords: influenza a virus; medical oncology; orthomyxoviridae; patients' rooms; infections; hematology; influenza; disease outbreaks; whole genome sequencing; infectious disease prevention / control; nosocomial transmission

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