WHOLE GENOME SEQUENCING FOR STAPHYLOCOCCUS AUREUS

Claire Gordon MRC clinical research fellow

Nuffield Department of Medicine, University of Oxford

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

• Virulent strains

» USA 300

» Toxin producing

1Werthhiem et al, 2005

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

Extract DNA Fragmentation, labelling and cluster generation

Sequencing by synthesis Assembly and analysis

OUTBREAKS

Outbreaks studies: standard tests

• Find which group the strains belong to

• MLST

• spa type

• phage typing

• SCCmec typing

• Pulsed field gel electrophoresis

PFGE : gold standard?

Kazakova SV et al. N Engl J Med 2005;352:468-475.

Why use WGS?

• Single platform

• Better resolution

• Turnaround times approaching those of standard techniques

• May give additional information

Calibration

Setting No of pairs Mean pairwise

difference (SNVs) 95% CI

Hospital 363 2.96 2.72-3.19

Community 53 7.30 5.79-8.82

P=0.00

05

10

15

20

25

Pair

wis

e d

iffe

rence

, S

NV

s

Community Hospital

Distribution of pairwise differences for community and hospital outbreaks

SNV threshold

% o

f is

ola

tes

incl

ud

ed b

y ea

ch S

NV

dis

tan

ce

Percentage of isolates included by SNV threshold

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12

Distance from nearest neighbour

Distance from index

Case study: MRSA on ITU

4 9 14 19 24 29 34 39 44 49 54

Patient A (bay 1)

Patient B (bay 1)

Patient C (bay 2)

Nurse1

Nurse2

air sample

Dr

x

x

x

x

xx

x

Days

Using epidemiology

4 9 14 19 24 29 34 39 44 49 54

Patient A (bay 1)

Patient B (bay 1)

Patient C (bay 2)

Nurse1

Nurse2

air sample

Dr

x

x

x

x

xx

x

Days

Using standard typing (spa plus PFGE)

EMRSA-16

EMRSA-15, pulsotype 15b1

EMRSA-15, pulsotype 15b1

69

20

9

13 35

43

Using WGS

69

20

9

13 35

43

Using WGS

• First “broken” S. aureus outbreak

• Demonstrates diversity within same PFGE pulsotype

• Unidentified source of MRSA for patient A

Applying WGS to complex outbreaks 30/09/2011 30/10/2011 29/11/2011 29/12/2011 28/01/2012 27/02/2012 28/03/2012

STM-DWSTM-BGSTM-TVSTM-PSSTM-TCSTM-HBSTM-KMSTM-EGSTM-HCSTM-CGSTM-RBSTM-LG

STM-KWSTM-HD

SB-HGSB-VCSB-HG

SAU-PWSAU-MSSAU-BE

SAU-HWSAU-RSSAU-LC

SAU-AWSAN-RWSAN-SC

SAN-WSSAN-RWSAN-DW

SAN-HJSAN-EW

SAN-LSSAN-FF

QREC-JTQEND-EC

MW-LBMW-DGMW-DEMW-MJMW-JBMW-JDMW-JHMW-GRFRD-JCFRD-SJ

FRD-ASFRD-DRFRD-SCFRD-JSFRD-IC

DEAL-RFDEAL-JHDEAL-BCDEAL-AR

DEAL-GWDEAL-RQDEAL-FSDeal-RDDEAL-CTCSF-MLCSF-MSCSF-SB

CDU-KBCDU-IMCDU-FUCDU-DLBIS-AGBIS-GSBIS-JP

BIS-LM

Ward

Clusters

PFGE WGS

By time (light grey-> earliest)

By hospital

WGS in real time

• Illumina MiSeq

• 12-27hr turnaround time

• Bench top platform

RESISTOTYPING

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

• Overall categorical agreement >99%

• Total of 11 cryptic genotype / phenotype mismatches

• Rapid identification of novel variants

• non-synonymous mutations in fusA

• Penicillin • Deletions / insertions in blaZ

causing frameshift mutations

DNA sequence alignments showing single base blaZ deletions in 4

penicillin susceptible isolates

Overall results

Phenotype: resistant Phenotype: susceptible Error Rates

Genotype Genotype VME ME

Antimicrobial Susceptible Resistant Susceptible Resistant (%) (%)

Penicillin 2 398 84 3 0.5 3.4

Methicillin 0 55 432 0 0.0 0.0

Ciprofloxacin 2 64 421 0 3.0 0.0

Erythromycin 1 80 404 2 1.2 0.5

Clindamycin 1 76 2 0 1.3 0.0

Tetracycline 0 18 467 2 0.0 0.4

Vancomycin 0 0 491 0 n/a 0.0

Fusidic acid 1 39 445 0 2.6 0.0

Trimethoprim 0 2 200 1 0.0 0.5

Gentamicin 1 2 484 0 33.3 0.0

Mupirocin 0 2 485 0 0.0 0.0

Rifampicin 0 5 482 0 0.0 0.0

Total 8 741 4397 8 1.1 0.2

Overall results

Phenotype: resistant Phenotype: susceptible Error Rates

Genotype Genotype VME ME

Antimicrobial Susceptible Resistant Susceptible Resistant (%) (%)

Penicillin 2 398 84 3 0.5 3.4

Methicillin 0 55 432 0 0.0 0.0

Ciprofloxacin 2 64 421 0 3.0 0.0

Erythromycin 1 80 404 2 1.2 0.5

Clindamycin 1 76 2 0 1.3 0.0

Tetracycline 0 18 467 2 0.0 0.4

Vancomycin 0 0 491 0 n/a 0.0

Fusidic acid 1 39 445 0 2.6 0.0

Trimethoprim 0 2 200 1 0.0 0.5

Gentamicin 1 2 484 0 33.3 0.0

Mupirocin 0 2 485 0 0.0 0.0

Rifampicin 0 5 482 0 0.0 0.0

Total 8 741 4397 8 1.1 0.2

Previous phenotyping studies

Study Comparison no of

isolates

Categorical

agreement

(%)

ME rate

(%)

VME rate

(%)

Ligozzi 2002 Vitek 2 vs agar dilution 100 94-100 0 0

Fahr 2003 BD Phoenix vs broth dilution

plus mecA PCR 116 97.6 1.2 1.7

Nonhoff 2005 Vitek 2 vs agar dilution 273 - 1.5 0.7

Carroll 2006 BD Phoenix vs agar dilution 232 98.2 0.3 0.4

Giani 2012 BD Phoenix vs broth dilution 95 98 1.3 2.1

Bobenchik 2014 Vitek 2 vs broth dilution 134 98.9 0.1 1.4

This study WGS vs combined disc

diffusion / BD Phoenix 491 98.8 0.2 1.1

Previous phenotyping studies

Study Comparison no of

isolates

Categorical

agreement

(%)

ME rate

(%)

VME rate

(%)

Ligozzi 2002 Vitek 2 vs agar dilution 100 94-100 0 0

Fahr 2003 BD Phoenix vs broth dilution

plus mecA PCR 116 97.6 1.2 1.7

Nonhoff 2005 Vitek 2 vs agar dilution 273 - 1.5 0.7

Carroll 2006 BD Phoenix vs agar dilution 232 98.2 0.3 0.4

Giani 2012 BD Phoenix vs broth dilution 95 98 1.3 2.1

Bobenchik 2014 Vitek 2 vs broth dilution 134 98.9 0.1 1.4

This study WGS vs combined disc

diffusion / BD Phoenix 491 98.8 0.2 1.1

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

VIRULENCE

Virulence testing: presence / absence of known virulence genes

virulence panel

eta

etb

etd

lukP

V.f

lukP

V.S

lukM

sea

seb

sec

sed

see

seg

entH

sei

sej

seu

selR

sep

tsst.1

chp

sak

scn

H123300156

H123300157

H123300158

H123300159

H123320274

H123400081

H123400082

H123420083

H123420084

H130300166

H131620047

H123420080

H123420088

H123420082

H123420085

H123420086

H123420081

H123420087

H123420089

H123420090

H123700148

0

20

40

60

80

100

Identifying novel disease-associated mutations

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 anti-

microbials

4-6 per plate

Approx 20

1-2

Potentially

unlimited

Turnaround

time 18-24 hrs 4-24hrs 2-4hrs 27hrs

Current processing

Resistance profile

Speciation

Rapid ID / MRSA

Virulence profile, spa type

PFGE for outbreaks

WGS workflow

Too expensive?

• Standard investigation • Culture

• Resistance testing

• Virulence testing

• Spa typing, PFGE

• WGS • Culture?

• Resistance profile

• Virulence profile

• Typing

~£20 per isolate

~£40 per isolate

Equipment

High throughput

Benchtop

Palm top

Sequencing on your laptop?

Processing / bioinformatics

Mykrobe app: speciation and resistance prediction on a smartphone / tablet

Drag and drop raw data

Results in minutes

Turnaround times

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

Other organisms

• Enterobacteriacea, C. difficile

• TB

• Viruses

• Fastidious organisms mykrobe.com

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