http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/ Real-Time Genome Sequencing of Resistant Bacteria Provides Precision Infection Control in an Institutional Setting. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.

1. Real-Time Genome Sequencing of
Resistant Bacteria Provides
Precision Infection Control in an
Institutional Setting
Dag Harmsen
University Hospital Münster, Germany

2. Antibiotic Resistance: a Global Concern
“The problem is so serious that it threatens the achievements
of modern medicine. A post-antibiotic era—in which common
infections and minor injuries can kill—is a very real possibility
for the 21st century.”
www.who.int

3. Setting
MRSA, VRE, carbapenem-resistant Gram-
negative bacteria (4MDR-GN) are isolated on
every ward
carbapenem-susceptible but ß-lactame +
quinolone-resistant Gram-negative bacteria
(3MDR-GN) are isolated only on risk wards

4. Real-time WGS of Multidrug Resistant
(MDR) Bacteria – an Interventional Study
• Study goals
– Interval I: Is prospective real-time WGS technical
feasible on a daily basis and are the results available
within a timeframe for infection control interventions?
– Interval II: What is the real transmission rate of MDR
bacteria – effects of intervention?
– Is WGS-based surveillance cost-effective?

5. Study Timeline
Documentation of
– turn-around time (TAT) from sample entry to completed
sequence (incl. potential repeated sequencing)
– epidemiological data
Interval I
prospective real-time
WGS of MRSA, VRE,
MDR E. coli, MDR P.
aeruginosa
Interval II
prospective real-time
WGS of all MDR
bacteria
Intervention*
change of infection
control procedures
*Managing board decided after Interval I to retain and expand WGS to all MDR
bacterial species and to cost with institutional resources

6. WGS Methods
• Overnight broth culture of pure culture
• DNA extraction: Qiagen MagAttract HMW Kit
• Library prep: Illumina Nextera XT;
aiming for 100x coverage
• 250 bp paired-end protocol (v2 chemistry) on a
single Illumina MiSeq

7. Quality Control and Data nalysis
• Sequencing run QC
– cluster density and Q30 value above manufacturer´s
specifications
• Data analysis
– de novo assembly (CLCbio GWB; Velvet)
– SeqSphere+ software (Ridom) for extraction of cgMLST
targets using species specific cgMLST schemes
• Sequence quality / sample
– % good cgMLST targets ≥ 95% → control of the whole
procedure (lab & bioinformatics)

8. Interval I:
Prospective Real-time WGS is Feasible
• In total 645 MDR bacteria sequenced
– 412 MRSA
– 102 MDR E. coli
– 79 VRE
– 52 MDR P. aeruginosa
• 58 runs (2-3 runs / week); one run failed due to
low Q30 value (59%)
• Mean 13 samples / run
• 561 (87.0%) samples were immediately
successfully sequenced

9. Interval I: Summary of Sequencing Results
& TAT (n = 645 MDR bacteria)
Organism
(total no.)
Mean % of
successfully
extracted cgMLST
targets (total no.
targets/scheme*)
No. (%) of isolates
that required repeated
sequencing
Mean (SD) turn-
around time of all
samples without
repeaters in days
Mean (SD) turn-
around time of all
samples including
failed samples in days
S. aureus
(412) 98.5 (1861) 38 (9.2) 4.4 (1.6) 5.0 (2.6)
E. coli
(102) 99.2 (2325) 11 (10.8) 4.4 (1.4) 5.3 (3.0)
E. faecium
(79) 97.2 (2018) 20 (25.3) 4.1 (1.5) 6.2 (4.6)
P. aeruginosa
(52) 97.8 (3842) 15 (28.8) 4.8 (1.8) 6.8 (4.0)
Total 98.4 84 (13.0) 4.4 (1.5) 5.3 (3.2)
*for S. aureus see Leopold et al. 2014. JCM 52: 2365, PubMed ; for the other pathogens
preliminary schemes were applied
SD, standard deviation
Mellmann et al., submitted

10. Interval I: WGS-based Typing of All MRSA
Exhibiting 71 Different spa Types
Epidemic curve of all 412 MRSA
(each box = single isolate)
Mellmann et al., submitted

11. Interval I: Diversity of LA-MRSA of
spa Type t011 (n=66)
Minimum-spanning tree
based on allelic profiles
based on 1,861 target
genes, pairwise ignoring
missing data (mean missing
targets: 35); cluster
threshold ≤ 6 differing alleles
transmission unlikely
transmission possible
Mellmann et al., submitted

12. Interval I:
Cost Calculation for Sequencing
• Prerequisites
– 645 isolates, overall including all repeats 752 samples
sequenced
– 13 samples / run
• Included costs for
– Reagents
– Staff (experienced technician, E9 TVL)
– Full depreciation of MiSeq and computer hardware
(over 3 years, 1500 samples p.a.) and software licenses

13. Interval I:
Costs for Sequencing
Organism (total no.)
€ / sample
(incl. VAT)
€ / isolate
(incl. 16.6% repeated
samples; incl. VAT)
% of
overall
costs
Reagents 122.26 142.54 70.4
Staff 16.89 19.69 9.7
Miseq and hardware
depreciation,
software licenses
33.54 40.27 19.9
Total 173.68 202.49
Mellmann et al., submitted

14. Interval I: Conclusions
• WGS on a routine basis is feasible
• Mean measured TAT of 4.4 to 5.3 days influence
decisions to implement or change extraordinary
infection control measures
• Transmission events are rare in our setting

15. Intervention and Interval II
• Change of infection control procedures (11 Jul 14)
– Stopping of preemptive isolation of patients on risk
wards (adult ICUs, adult & children cancer wards)
carrying Gram-negative MDR resistant against
Piperacillin + 3rd Gen. Cephalosporins +
Fluorquinolones but susceptible against Carbapenems
(i. e. 3MDR-GN)
• Interval II: prospective WGS of all MDR bacteria
(15 Oct 14 – 15 Apr 15)

16. Interval II:
Sequenced MDR Bacteria
• In total 598 isolates sequenced
– MRSA (n=325)
– E. coli (n=120)
– VRE (n=56)
– P. aeruginosa (n=49)
– K. pneumoniae (n=25)
– E. cloacae complex (n=11)
– A. baumannii, E. aerogenes, P. mirabilis (each n=2)
– C. freundii, E. faecalis, H. alvei, K. oxytoca,
M. morganii, S. marcescens (each n=1)
n=550

17. Transmission Rates of MRSA and 3MDR-
GN E. coli During the Two Study Intervals
Study interval
Pathogen
(no. of isolates /
no. of total patient
cases / no. of
cases at risk
wards)
No. of genotypic
clusters
(maximal
distance for
cluster
recognition)
Epidemiological assessment of
genotypic clusters
Total no. of
cases involved
in probable
transmissions
(%)
No. of cases at risk
wards with changed
infection control
procedures for 3MDR-
GN (%) during interval II
Interval I
MRSA
(412 / 397 / 68)
32 (≤ 6 alleles)
8 clusters with probable transmissions
16 clusters with unlikely transmissions
8 times same patient but different
colony morphology / phenotype
23 (5.8%) 15 (22.1%)
E. coli
(102 / 86 / 51)
13 (≤ 10 alleles)
1 cluster with probable transmission
1 cluster with unlikely transmissions
11 times same patient but different
cases / colony morphology / phenotype
2 (2.3%) 2 (3.9%)
Interval II
MRSA
(325 / 325 / 57)
15 (≤ 6 alleles)
6 clusters with probable transmissions
9 clusters with unlikely transmissions
14 (4.3%) 6 (10.5%)
E. coli
(120 / 120 / 45)
8 (≤ 10 alleles)
1 cluster with probable transmissions
7 clusters with unlikely transmissions
6 (5.0%) 0 (0%)
Mellmann et al., submitted

18. Cost-efficiency in Our Hospital Setting?
- Assumptions -
• Beds in multi-bed rooms are blocked if a patient is
positive for MDR – these indirect costs are even
higher than direct costs, i.e. contact precautions
– Herr et al. (ICHE 24: 673, 2003; PubMed) calculated 371.95 €
for both for a German non-ICU surgical ward in year
2000
• Mean % occupied beds
– 85.0 % (2013)
– 85.3 % (2014)

19. WGS-based Surveillance is Cost-effective
• Interval I
– Overall sequencing costs: 130,608.84 €
– Costs for isolation calculated for blocked beds only for
52 cases with 3MDR-GN at risk wards (mean residence
time in isolation: 19.7 days): 323,871.74 €
• Interval II
– Overall sequencing costs: 111,371.88 €
– Avoided costs due to avoided isolation of 56 cases with
3MDR-GN at risk wards (mean residency time after
MDR detection: 17.9 days): 317,180.37 €
– Overall savings: 205.808,49 €
Mellmann et al., submitted

20. Laboratory Workflow Improvements
• Manual DNA extraction (Köser et al. 2013. JAC 69: 1275, PubMed)
and library preparation starting from a pure culture
• 50% dilution of library reagents (≈ Baym et al. 2015. PLoS One
10: e0131262, PubMed)
• After finishing of pipelines, the technician does QC
of runs and samples (failed samples are then
repeated immediately)

21. Bioinformatic Workflow Improvements
• SeqSphere+ pipelines automatically de novo
assembles the data and triggers cluster early
warnings
– 3 pipelines on independent hardware systems (≥ 32 GB
RAM, 4-10 cores, all Windows 7/2008 server)
simultaneously monitor the MiSeq for new data and
feed into the same database (Windows 2008 Server) to
accelerate data analysis
• A physician checks results and correlates typing
data with epi-data → triggering of infection control
measures in case of likely transmission events

22. Summary
• Transmissions are quite rare; exclusions of
transmissions are most common situations
• Rule-out majority of isolates easy with NGS; rule-
in requires extra epidemiologic information
• WGS-based surveillance is cost effective
• Patients´ benefits after translation of our findings
to routine management of bacterial infections
– No general preemptive isolation of patients with 3MDR-
GN (only in suspected cluster situations)
– Avoiding the negative effects of isolation increase
quality of patient care (Saint et al. 2003. AJIC 31: 354, PubMed; Tarzi et
al. 2001. JHI 49: 250, PubMed)
– Increased control of standard hygiene measures

23. Acknowledgements
• Inst. Hygiene, Univ. Hosp. Münster, Germany
– U. Keckevoet, I. Höfig, T. Boeking, S. Bletz , A.
Mellmann
• Dept. Periodontology, Univ. Hosp. Münster,
Germany
– A. Schultes, K. Prior
• Ridom GmbH, Münster
– J. Rothgänger
European Union’s Seventh
Framework Programme for research

24. Real-Time Genome Sequencing of
Resistant Bacteria Provides
Precision Infection Control in an
Institutional Setting
Dag Harmsen
University Hospital Münster, Germany

25. Interval I: Reasons for Sequencing Failures
Organism
(total no.)
Reasons for sequencing failure (no. of samples)
S. aureus
(412)
low coverage* (22)
sequencing run failure (12)
primary base calling failure (4)
E. coli
(102)
low coverage* (10)
sequencing run failure (1)
E. faecium
(79)
low coverage* (14)
mixed culture (5)
sequencing run failure (1)
P. aeruginosa
(52)
low coverage* (10)
sequencing run failure (5)
Total
low coverage* (56)
sequencing run failure (19)
mixed culture (5)
primary base calling failure (4)
* The low coverage led to a failure to achieve ≥ 95 % successfully extracted cgMLST
targets.
Mellmann et al., submitted