1.
Oksana Lukjancenko, PhD
Research group of Genomics Epidemiology
National Food Institute,
Technical University of Denmark
www.compare-europe.eu
www.genomicepidemiology.org
Global surveillance
One World – One Health
9th GMI meeting
25th May 2016
Rome, Italy

2.
DTU Food, Technical University of Denmark
• Dynamics of common infectious diseases are changing
– Demographic change, population density, AMR, etc.
• New diseases emerge frequently
– Deforestation, population growth, health system inequalities,
travel, trade, climate change
• Effects are difficult to predict due to complexity of problems
– Rapid flexible response
• Public health and clinical response depend on global capacity for
disease surveillance
– Rapid sharing, comparison and analysis of data from multiple
sources and using multiple methodologies
Infectious disease situation 2015

3.
Clinical research response to ID outbreaks usually
fragmented and too late
3
Infectedpatients
Public Health response
Preclinical research response
time
clinical research
response

4.
Clinical research response to ID outbreaks with
improved detection and sharing of data
4
Infectedpatients
Public Health response
Preclinical research response
time
clinical research response

5.
DTU Food, Technical University of Denmark
• Real-time sharing data on occurrences of all infectious agents
including AMR data
• Tools for automatically detections of related clusters in time and
space
• Possibilities to observe trends in clones and species as well as
resistance, virulence, and other epidemiological markers
• Ability to rapidly compare between all types of data
What is needed!
There can be no real-time surveillance without real-time data sharing

6.
DTU Food, Technical University of Denmark
The Surveillance Pyramid
Population
exposures
Person becomes ill
Person seeks care
Specimen obtained
Lab tests for organism
Culture-confirmed case
Reported to health unit

7.
DTU Food, Technical University of Denmark
Monitoring large health populations

8.
DTU Food, Technical University of Denmark
Metagenomics analysis –
Quantification of all bacterial and virus including
AMR genes for surveillance
Nordahl Petersen T et al. 2015. Sci Rep.

9.
DTU Food, Technical University of Denmark
Metagenomics analysis –
Quantification of all bacterial and virus including
AMR genes for surveillance
Nordahl Petersen T et al. 2015. Sci Rep.

10.
DTU Food, Technical University of Denmark
Disease hotspot surveillance -
Slumcity of Kibera in Nairobi, Kenya

11.
DTU Food, Technical University of Denmark
Disease hotspot surveillance -
Slumcity of Kibera, Nairobi, Kenya
• Monitoring the vulnerable populations of Kibera
– Collected 2 sewage samples every day for 3 months
• Demonstrate the application of using a metagenomics approach
– to detect potential disease outbreaks
– to develop corresponding intervention and prevention
strategies
• Apply a temporal metagenomics analysis to identify and quantify
human pathogens including bacteria and associated antimicrobial
resistance, virus, and parasites
– correlate with the disease trends from collected syndromic
surveillance data and visits to the clinic
• Currently working with EBI to share data
– PRJEB13833 - Kibera Sewage Project

12.
DTU Food, Technical University of Denmark
Disease hotspot surveillance -
Slumcity of Kibera, Nairobi, Kenya
Number of Clinic Visits
Numberofcases
10
20
30
40
50
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
Week_25
Week_26
Week_27
Week_28
Week_29
Week_30
Week_31
Week_32
Week_33
Week_34
Week_35
Number of Detected Pathogens
Numberofcases
0
1
2
3
4
5 ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Week_25
Week_26
Week_27
Week_28
Week_29
Week_30
Week_31
Week_32
Week_33
Week_34
Week_35
Reported Fever Syndrome
Numberofcases
10
20
30
40
50
60
70
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Week_25
Week_26
Week_27
Week_28
Week_29
Week_30
Week_31
Week_32
Week_33
Week_34
Week_35
Reported Diarrhea Syndrome
Numberofcases
0
2
4
6
8
●
● ●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
Week_25
Week_26
Week_27
Week_28
Week_29
Week_30
Week_31
Week_32
Week_33
Week_34
Week_35
● Site 9
● Site 10
Fractionofthereads(%)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.005
0.010
0.015
0.020
0.025
0.0
0.2
0.4
0.6
0.8
1.0
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.00
0.02
0.04
0.06
0.08
0.10
Aeromonas
● ●
●
●
● ●
●
●
●
● ●
●
●
● ●
●
●
●
●
Clostridium difficile
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Klebsiella pneumoniae
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ● ●
Shigella boydii
●
● ●
●
●
●
●
● ●
● ● ●
●
●
● ● ● ● ●
Shigella sonnei
● ● ● ●
●
● ●
●
● ● ● ●
●
● ● ● ● ● ●
Week_25_Mon
Week_25_Wed
Week_26_Mon
Week_26_Wed
Week_27_Mon
Week_27_Wed
Week_28_Mon
Week_28_Wed
Week_29_Mon
Week_29_Wed
Week_30_Mon
Week_30_Wed
Week_31_Mon
Week_31_Wed
Week_32_Mon
Week_32_Wed
Week_33_Mon
Week_33_Wed
Week_34_Mon
Week_34_Wed
Week_35_Mon
Week_35_Wed
0e+00
2e−04
4e−04
6e−04
8e−04
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.001
0.002
0.003
0.004
0.005
0.00
0.01
0.02
0.03
0.04
0.010
0.015
0.020
0.025
0.030
0.035
Campylobacter coli
●
●
● ●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
Enterococcus
●
●
●
●
●
●
● ●
●
●
●
●
●
● ●
●
●
●
●
Listeria monocytogenes
●
●
●
●
● ● ●
●
●
●
●
●
● ●
●
●
●
●
●
Shigella dysenteriae
● ●
●
●
●
●
● ● ● ● ● ●
●
● ● ● ● ● ●
Vibrio cholerae
●
● ●
●
● ●
●
●
●
●
●
●
●
● ●
● ●
●
●
Week_25_Mon
Week_25_Wed
Week_26_Mon
Week_26_Wed
Week_27_Mon
Week_27_Wed
Week_28_Mon
Week_28_Wed
Week_29_Mon
Week_29_Wed
Week_30_Mon
Week_30_Wed
Week_31_Mon
Week_31_Wed
Week_32_Mon
Week_32_Wed
Week_33_Mon
Week_33_Wed
Week_34_Mon
Week_34_Wed
Week_35_Mon
Week_35_Wed
0.00
0.01
0.02
0.03
0.04
0.05
1
2
3
4
5
6
7
0.1
0.2
0.3
0.4
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.00
0.01
0.02
0.03
0.04
0.05
Campylobacter jejuni
● ● ● ●
● ● ● ● ● ● ● ●
●
● ● ● ● ●
●
Escherichia coli
●
●
●
●
●
●
●
●
●
●
● ●
●
●
● ●
●
●
●
Salmonella enterica
●
● ●
● ● ● ●
●
● ●
●
●
●
● ●
●
●
●
●
Shigella flexneri
●
● ●
●
●
●
●
● ● ● ● ●
●
● ● ● ● ● ●
Yersinia enterocolitica
●
●
●
●
●
●
●
●
●
●
●
● ●
● ●
●
●
●
●
Week_25_Mon
Week_25_Wed
Week_26_Mon
Week_26_Wed
Week_27_Mon
Week_27_Wed
Week_28_Mon
Week_28_Wed
Week_29_Mon
Week_29_Wed
Week_30_Mon
Week_30_Wed
Week_31_Mon
Week_31_Wed
Week_32_Mon
Week_32_Wed
Week_33_Mon
Week_33_Wed
Week_34_Mon
Week_34_Wed
Week_35_Mon
Week_35_Wed
Site
● Site 9
● Site 10
● VF−F
● VF−T
Site 9
Site 10
Site
● 9
10

13.
DTU Food, Technical University of Denmark
Global sewage surveillance - 2016

14.
Global sewage surveillance - 2016

15.
DTU Food, Technical University of Denmark
• Information about presence and distribution of (pathogenic) bacteria,
virus and parasites on a global scale
• A proof-of-concept of large-scale population surveillance using state-of-
the-art technologies, metagenomics
– Provide better and faster detection and control of health risks
– Potentially reduce morbidity and mortality through rapid disease
detection
– Reduce development of antimicrobial resistance.
– Improve treatment outcome and minimize disease spread
• Sample processing - Samples are divided into fractions
– 250 ml for DNA (bacteria / virus / parasites) & RNA (virus)extraction
– 250 ml for bacterial plasmid purification
– 150 - 400 ml for Residue analysis
• PRJEB13831 - Global Sewage Project (Currently working with EBI to
share data among COMPARE partners before release)
Global sewage surveillance - 2016

16.
DTU Food, Technical University of Denmark
Copenhagen according to sewage -
2016
“Real time” sharing of data: PRJEB13832 - Copenhagen Sewage
Project (public – instant release of data)

17.
DTU Food, Technical University of Denmark
• Project start: 23-11-2015
• Samples are collected weekly - 80 samples till 02-05-2016
– 3 sewage treatment plants:
• Avedøre (12 samples)
• Damhusåen (35 samples)
• Lynetten (33 samples)
• Samples are picked up every two weeks and brought to DTU and
processed within a week (turnaround time 3 weeks)
– 250 ml for DNA (bacteria / virus / parasites) & RNA
(virus)extraction
– 250 ml for bacterial plasmid purification
• Sequenced in-house by MiSeq
– The sequences are uploaded to EBI directly after sequencing
Copenhagen according to sewage -
2016

18.
DTU Food, Technical University of Denmark
• WGS/NGS is rapidly entering diagnostic and public health, with
near real time data generation
• Metagenomic sequencing is superior to conventional and other
genomic methods for quantification of AMR and pathogens
– Need for better databases
• Bottleneck at level of bioinformatics and data sharing
– Need for infrastructure and agreements to meet the coming
demand
• Novel sites should be explored for sampling for example large
healthy populations e.g. hotspots, mass gatherings, cities etc..
• Metagenomic data are complex
– Perspectives to combine with advanced mathematical
modelling for predictions
Conclusions

19.
Thank you for your attention
Oksana Lukjancenko, PhD
Rene S. Hendriksen, PhD
Research group of Bacterial Genomics and Antimicrobial Resistance
WHO Collaborating Centre for Antimicrobial Resistance in Food borne Pathogens
European Union Reference Laboratory for Antimicrobial Resistance
National Food Institute, Technical University of Denmark
rshe@food.dtu.dk