WHOLE GENOME SEQUENCING FOR STAPHYLOCOCCUS AUREUS. Claire Gordon MRC clinical research fellow Nuffield Department of Medicine, University of Oxford
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1 WHOLE GENOME SEQUENCING FOR STAPHYLOCOCCUS AUREUS Claire Gordon MRC clinical research fellow Nuffield Department of Medicine, University of Oxford
2 Staphylococcus aureus Remains a major healthcare associated pathogen Persistently carried by 20% of population, intermittently by 30% 1 Cause of outbreaks» approximately 500 putative S. aureus outbreaks referred to Public Health England for investigation per year Multidrug resistance» MRSA» VISA/VRSA 1 Werthhiem et al, 2005 Virulent strains» USA 300» Toxin producing
3 Whole Genome Sequencing Hypothesis: whole genome sequencing (WGS) can give us all the information we need to diagnose, treat and investigate illness due to S. aureus To date we have used WGS to study outbreaks / person to person spread predict antimicrobial resistance look for virulence factors
4 Extract DNA Fragmentation, labelling and cluster generation Assembly and analysis Sequencing by synthesis
5 OUTBREAKS
6 Outbreaks studies: standard tests Find which group the strains belong to MLST spa type phage typing SCCmec typing Pulsed field gel electrophoresis
7 PFGE : gold standard? Kazakova SV et al. N Engl J Med 2005;352:
8 Why use WGS? Single platform Better resolution Turnaround times approaching those of standard techniques May give additional information
9 Calibration
10 0 5 Pairwise difference, SNVs Distribution of pairwise differences for community and hospital outbreaks Community Hospital Setting No of pairs Mean pairwise difference (SNVs) 95% CI Hospital Community P=0.00
11 % of isolates included by each SNV distance Percentage of isolates included by SNV threshold Distance from nearest neighbour Distance from index SNV threshold
12 Case study: MRSA on ITU x x x x x x x Patient A (bay 1) Patient B (bay 1) Patient C (bay 2) Nurse1 Nurse2 air sample Dr Days
13 Using epidemiology x x x x x x x Patient A (bay 1) Patient B (bay 1) Patient C (bay 2) Nurse1 Nurse2 air sample Dr Days
14 Using standard typing (spa plus PFGE) EMRSA-15, pulsotype 15b1 EMRSA-16 EMRSA-15, pulsotype 15b1
15 Using WGS
16 Using WGS
17 First broken S. aureus outbreak Demonstrates diversity within same PFGE pulsotype Unidentified source of MRSA for patient A
18 Applying WGS to complex outbreaks 30/09/ /10/ /11/ /12/ /01/ /02/ /03/2012 STM-DW STM-BG STM-TV STM-PS STM-TC STM-HB STM-KM STM-EG STM-HC STM-CG STM-RB STM-LG STM-KW STM-HD SB-HG SB-VC SB-HG SAU-PW SAU-MS SAU-BE SAU-HW SAU-RS SAU-LC SAU-AW SAN-RW SAN-SC SAN-WS SAN-RW SAN-DW SAN-HJ SAN-EW SAN-LS SAN-FF QREC-JT QEND-EC MW-LB MW-DG MW-DE MW-MJ MW-JB MW-JD MW-JH MW-GR FRD-JC FRD-SJ FRD-AS FRD-DR FRD-SC FRD-JS FRD-IC DEAL-RF DEAL-JH DEAL-BC DEAL-AR DEAL-GW DEAL-RQ DEAL-FS Deal-RD DEAL-CT CSF-ML CSF-MS CSF-SB CDU-KB CDU-IM CDU-FU CDU-DL BIS-AG BIS-GS BIS-JP BIS-LM Ward Clusters
19 PFGE WGS
20 By time (light grey-> earliest)
21 By hospital
22 WGS in real time Illumina MiSeq 12-27hr turnaround time Bench top platform
23 RESISTOTYPING
24 Genotype vs phenotype Genotyping WGS de novo assembled contigs Blasted for catalogue of resistance determinants Phenotyping disc diffusion plus automated broth dilution E-test to resolve discrepancies Isolates n=491 bacteraemia and carriage isolates sourced from clinical collections in Oxford / Brighton
25 Overall categorical agreement >99% Total of 11 cryptic genotype / phenotype mismatches Penicillin Deletions / insertions in blaz causing frameshift mutations Rapid identification of novel variants non-synonymous mutations in fusa DNA sequence alignments showing single base blaz deletions in 4 penicillin susceptible isolates
26 Overall results Phenotype: resistant Phenotype: susceptible Error Rates Genotype Genotype VME ME Antimicrobial Susceptible Resistant Susceptible Resistant (%) (%) Penicillin Methicillin Ciprofloxacin Erythromycin Clindamycin Tetracycline Vancomycin n/a 0.0 Fusidic acid Trimethoprim Gentamicin Mupirocin Rifampicin Total
27 Overall results Phenotype: resistant Phenotype: susceptible Error Rates Genotype Genotype VME ME Antimicrobial Susceptible Resistant Susceptible Resistant (%) (%) Penicillin Methicillin Ciprofloxacin Erythromycin Clindamycin Tetracycline Vancomycin n/a 0.0 Fusidic acid Trimethoprim Gentamicin Mupirocin Rifampicin Total
28 Previous phenotyping studies Study Comparison no of isolates Categorical agreement (%) ME rate (%) VME rate (%) Ligozzi 2002 Vitek 2 vs agar dilution Fahr 2003 BD Phoenix vs broth dilution plus meca PCR Nonhoff 2005 Vitek 2 vs agar dilution Carroll 2006 BD Phoenix vs agar dilution Giani 2012 BD Phoenix vs broth dilution Bobenchik 2014 Vitek 2 vs broth dilution This study WGS vs combined disc diffusion / BD Phoenix
29 Previous phenotyping studies Study Comparison no of isolates Categorical agreement (%) ME rate (%) VME rate (%) Ligozzi 2002 Vitek 2 vs agar dilution Fahr 2003 BD Phoenix vs broth dilution plus meca PCR Nonhoff 2005 Vitek 2 vs agar dilution Carroll 2006 BD Phoenix vs agar dilution Giani 2012 BD Phoenix vs broth dilution Bobenchik 2014 Vitek 2 vs broth dilution This study WGS vs combined disc diffusion / BD Phoenix
30 Problems low frequency of resistance for some antimicrobials leading to inaccurate estimates of specificity Needs further testing with rare resistance determinants reflects resistance patterns in local population No VISA / daptomycin / linezolid resistance unknown / emerging resistance determinants
31 VIRULENCE
32 eta Virulence testing: presence / absence of known virulence genes etb etd lukpv.f lukpv.s lukm sea seb sec sed see seg enth sei sej seu selr sep tsst.1 chp sak scn virulence panel H H H H H H H H H H H H H H H H H H H H H
33 Identifying novel disease-associated mutations
34 Comparison of methods Manual Automated PCR In silico Platforms Disc diffusion, broth dilution Vitek, Phoenix, Microscan GeneXpert, IDI-MRSA, Nuclisens Easy-Q MiSeq, Ion Torrent Type of test Phenotypic Phenotypic Genotypic Genotypic No of samples Single Batch (approx 20) Single or batch Batch (currently 16) No of antimicrobials 4-6 per plate Approx Potentially unlimited Turnaround time hrs 4-24hrs 2-4hrs 27hrs
35 Current processing Resistance profile Speciation Rapid ID / MRSA PFGE for outbreaks Virulence profile, spa type
36 WGS workflow
37 Too expensive? Standard investigation WGS Culture Resistance testing Virulence testing Spa typing, PFGE Culture? Resistance profile Virulence profile Typing ~ 20 per isolate ~ 40 per isolate
38 Equipment High throughput Benchtop Palm top
39 Sequencing on your laptop?
40 Processing / bioinformatics
41 Mykrobe app: speciation and resistance prediction on a smartphone / tablet
42 Drag and drop raw data
43 Results in minutes
44
45 Turnaround times
46 Conclusions For S. aureus WGS has greater resolution than current standard typing for outbreak investigation WGS data reliably predicted antimicrobial resistance for majority of isolates tested, with overall error rates equivalent to current phenotyping tests Cost / turnaround time for WGS approaching those of standard typing
47 Enterobacteriacea, C. difficile Other organisms TB Viruses Fastidious organisms mykrobe.com
48 Acknowledgements Oxford Dr Tanya Golubchik Dr Richard Everitt John Finney Prof Tim Peto Prof Derrick Crook Prof Sarah Walker Marcus Morgan Dr David Eyre Mykrobe Phelim Bradley Dr Zam Iqbal Brighton Dr James Price Kevin Cole Dr John Paul Public Heath England Dr Bruno Pichon Prof Angela Kearns Prof Barry Cookson Outbreaks Dr Ginny Moore Dr Peter Wilson Dr James Nash
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