ESCMID Online Lecture by author

Transcription

1 Molecular typing as a tool for the control of MDR and XDR organisms. Whole genome sequencing - already here? Martin Llewelyn Reader, Consultant Brighton and Sussex Medical School, UK m.j.llewelyn@bsms.ac.uk

2 Disclosures Infectopharm Advisory board membership Astellas, Pfizer, Genentech - Advisory board membership Optimer and Merck Research funding 2

3 3 WHO Antimicrobial Resistance Global Report on Surveillance 2014

4 Antibiotic use Drivers and Interventions in AMR Patient susceptibility Iatrogenic HIV Societal Healthcare exposure Migration 4 Spellberg B NEJM 2013

5 Antibiotic use Drivers and Interventions in AMR Better diagnostics Patient susceptibility Iatrogenic HIV Societal Healthcare exposure Interruption of transmission Migration Outbreak detection 5 Spellberg B NEJM 2013

6 Antibiotic use Drivers and Interventions in AMR Better diagnostics High-throughput, Bench-top Whole-genome sequencing Patient susceptibility Iatrogenic HIV Societal Healthcare exposure Interruption of transmission Migration Outbreak detection 6 Spellberg B NEJM 2013

7 Classic Molecular Typing Techniques Ribotyping Restriction Pulsed-field gel Endonuclease Analysis electrophoresis Spa-typing 7

8 Is WGS just a YAYATM? (Yet Another YATM)? or the COMPASS we have been waiting for COMPASS: COMplete PAthogen Sequencing Solution* *Wellcome Trust/ Department of Health Health Innovation Challenge Fund project 8 Prof Derrick Crook, Modernising Medical Microbiology Consortium

9 Limitations of classic molecular typing in Reference tools control of AMR organisms Limited real-time utility Lack of portability Limits scale and translation of findings Multiple schemes for the same organisms Lack of resolution Even multi-locus sequence typing (MLST) Small genomic comparisons Poor discrimination between highly related isolates Bacterial genome S. aureus MLST compares ~0.1% of the genome 9

10 Mandatory reporting of MRSA bacteraemia introduced in 2001 Hospital targets to reduce rates introduced in 2004 Abrupt rise in rates at Brighton (BSUH) despite augmented infection control practice Price J et al Int J Med Micro 2009 Miller R et al J Hosp Infect % ST22 or ST36 95% ST22 or ST36 10

11 Extensive diversity among ST-22 strains before and during the outbreak Common ancestor in 2004 for almost all ST36 strains during outbreak Extensive diversity among ST36 strains before the outbreak and at Oxford 11

12 Whole-genome sequencing provides high resolution characterization Has the potential to discriminate individual nucleotide differences Are two isolates genetically identical? How many Single Nucleotide Polymorphisms / Variants (SNP)s differentiate them? SNP differences allow temporal relationships to be inferred If SNPs accumulate at a predictable, useful rate How long ago was their common ancestor? identify or refute potential transmission events 12

13 Transmission pathways Insights from whole-genome sequencing

14 Acquisition of M. abscessus among patients with cystic fibrosis M. abscessus infects up to 10% of patients with CF Increasingly common Resistant to most classes of antibiotics Associated with poor prognosis Source of acquisition unclear Three subspecies M. abscessus subsp abscessus M. abscessus subsp massiliense M. abscessus subsp bolletii 14

15 Phylogenetic tree of 168 M. abscessus isolates from 31 patients over 4 years at a single CF unit Two tight clusters of Subsp massiliense indicative of transmission between patients Subsp massiliense Differentiates subspecies Subsp abscessus Subsp bolletii Tight clustering within individuals Loose clustering between individuals suggesting independent acquisition of dominant clone Bryant JM et al Lancet

16 Acquisition of Clostridium difficile Major form of healthcare associated infection Dominant lineages by classical molecular typing (REA, Ribotyping, sequence typing) Falling Rate of CDI in England Government targets Isolation of symptomatic cases Environmental decontamination Antimicrobial stewardship 16

17 Sequenced 1233 /1250 consecutive CDI cases over 3.6 years Multiple samples from individuals to estimate molecular clock rate 0.74 ( ) SNPs / year/genome Specified subtypes as <10 SNPs apart (~10 years evolution) <25% of cases could be linked New subtypes identified consistently throughout Most cases acquired NOT from other cases but from a diverse hidden source Similar decline in rates of genetically linked and unlinked cases during the study period Falling rates likely due to reduced susceptibility rather than reduced transmission 17

18 Acquisition of S. aureus on intensive case Is acquisition mostly due to patient-to-patient transmission? 14 months, single ICU, all patients screened and followed up Isolates analysed by spa-typing and WGS 1109 admissions 185 importations 44 acquisitions 273 isolates 18

19 Maximum diversity within patients James R. Price et al. Clin Infect Dis. 2014;58: About 5 year About 5 years of evolution Molecular clock rate of 5-9 SNPs / year / genome 19

20 Maximum diversity within patients Contact prior to ICU admission? James R. Price et al. Clin Infect Dis. 2014;58: plausible patient to patient transmissions Minimum diversity between patients Compared with WGS, spa-typing Falsely identified 3 transmissions Failed to detect 2 acquisitions Failed to detect 4 transmissions 20

21 Whole-genome sequencing is transforming our understanding of how MDR organisms spread Evolution and spread of drug-resistant P. aeruginosa in CF patients Charts the global spread and evolution of ST239 MRSA Distinct human and animal evolution of MDR S. typhimyrium Highly relevant to developing strategies to combat spread of AMR 21

22 Outbreak investigations Do we need whole-genome sequencing? Could it really help?

23 Aims Outbreak investigations Termination of the outbreak Understanding its causes to prevent repetition Main tools Clinical epidemiology to link cases Supported by conventional typing Triggered primarily by a change in disease incidence / phenotype Inevitably delayed e.g. Brighton MRSA outbreak ribotype 027 hypervirulent C. difficile? Is it truly an outbreak? Is it increased incidence of unrelated infections? 23

24 Aims Outbreak investigations Termination of the outbreak Understanding its causes to prevent repetition Main tools Can t Clinical identify epidemiology outbreaks of clonal organisms Supported e.g. S. aureus, by conventional C. difficile, Acinetobacter typing baumannii Triggered by Limitations of conventional typing Only rules cases in /out Change in disease incidence (new disease / Can t phenotype) delineate Direction of transmission Nature of transmission e.g. chains / pulses Inevitably delayed eg. Brighton MRSA outbreak, ribotype hypervirulent C. difficile? Is it truly an outbreak? Is it increased incidence of unrelated infections? 24

25 WGS in outbreak investigation WGS-based outbreak investigation has potential To IDENTIFY and LEAD outbreak investigations Establishing links unsuspected on basis of clinical epidemiology Requires Understanding of diversity Within-host Cross-sectional Over time Between hosts Within and without outbreaks Informative diversity (e.g. adequate, not excessive, predictable) 25

26 Sequenced 390 M. tuberculosis strains from 254 patients To investigate genetic diversity Apply this retrospectively to assess community outbreaks Strains from the UK Health Protection Agency (PHE) archives selected to define four sample groups Cross-sectional within a patient at a point in time Longitudinal within a patient over time House-hold within a house-hold outbreak Community clusters defined by MIRU-VNTR *mycobacterial interspersed repetitive-unit-variable-number tandem repeat 26

27 27

28 Calculated molecular clock rate 0.5 ( ) SNPs / year/genome 28

29 > 12 SNPs indicates no recent common ancestor 6-12 SNPs equivocal About 10 years of evolution <5 SNPs indicates a recent common ancestor Calculated molecular clock rate 0.5 ( ) SNPs / year/genome 29

30 Analysis of 11 clusters defined by VNTR and clinical epidemiology <5 SNPS separating isolates from substance misusers with TB >30 SNPS separating 6 patients previously linked to a cluster of cases among recent immigrants 30

31 Sequenced 14 MRSA isolates Seven from outbreak patients (all ST22) Seven non-outbreak (three ST22) Clear delineation of outbreak and nonoutbreak strains Identified transmission of an ST1 strain not suspected with conventional approach One hypermutator isolated Defect in DNA repair mechanisms Distant from other outbreak strains Still clearly outbreak Reference ST22 isolate Non outbreak isolates Outbreak isolates Phylogenetic analysis of 10 MRSA 31 isolates of ST22

32 Special-care Baby Unit outbreak Application of whole-genome sequencing Identified additional cases not suspected by conventional approaches Established uncertain links Identified strain as PVL +ve Identified a transmission network outside SCBU Family doctors Children s emergency department Breast clinic Linked an acquisition to a nurse carrier Directed intervention Retrospective analysis Near to real-time analysis 32

33 80-week long outbreak of MDR A. baumannii pulsotype Returning military patient- 51 patients in total Initial investigation used antibiograms, pulsotyping Week 40 WGS introduced Characterized diversity Established thresholds to rule in / rule out transmission Revealed potential for dual infection Identified unsuspected links Burns theatre Specific beds Targeted interventions Terminated the outbreak 33

34 Successful use of WGS in investigation of outbreaks of other MDR organisms... Investigation of MDR E. coli outbreak on a neonatal unit in Australia Investigation of CPE outbreak on an ICU in the US. Investigation of MDR Enterobacter cloacae on a neonatal unit 34

35 What these studies tell us about applying WGS to outbreak detection Applicable to diverse organisms Mycobacteria S. aureus, C. difficile, A. baumannii Enterobacteriaciae Relies on informative diversity Established for key organisms Importance of hypermutator strains? Impact of latency / spores / environmental contamination? 35

36 Can individual labs apply WGS to outbreak investigation now? Current costs and turn-around times comparable to conventional typing technology ~100 Euros from organism to sequence Depending on throughput Validated assembly pipelines Commercial software solutions (e.g. CLC Bio) Off site bio-informatics technically easy Rapid rule of thumb outbreak detection using SNP differences 36

37 What will we do in the future? Current pathways are diverse, complex, laborious, slow. Didelot X Nat Rev Genet

38 Didelot X Nat Rev Genet 2012 What will we do in the future? Sequencing may be able to substantially replace them 38

39 This may be very close to reality Public Health England Modernizing Medical Microbiology - whole genome sequencing pilot for mycobacteria Aims to sequence 3000 mycobacterial genomes by August 2015 In preparation for deployment of accredited national service 39

40 Dublin Leeds Birmingham Oxford - Sequence Analysis Brighton Mycobacteria WGS pilot PHE - Server Clinical Metadata Cloud storage At local labs Mycobacterium grown on Bactec MGIT DNA extraction Library preparation Bench-top Sequencing Internationally France: Lille Germany: Borstal Canada: Vancouver 40

41 Compares diagnostic pathways in parallel Pankhurst, L et al., PHE conference, Warwick,

42 This pathway now processing 25% of all samples growing mycobacteria in England Reporting species resistance prediction nearest genetic matches PHE will sequence mycobacteria cultured by 2017 Status 42

43 Summary Use of WGS to better understand transmission is well established Potential in outbreak investigations established for certain organisms First real-life descriptions in outbreaks being reported WGS may be already there for mycobacteria nearly there for some others Main issues remain bioinformatic and logistic Potential to link data across wide geographical areas and provide resistance and virulence information gives WGS unique potential in diagnostic microbiology 43

44 Acknowledgements Authors of the cited publications Modernising Medical Microbiology esp Derrick Crook (Oxford) Tim Peto (Oxford) Sarah Walker(Oxford) John Paul (PHE, Brighton) Grace Smith (Birmingham) Mark Wilcox (Leeds) 44