Next Generation Surveillance Systems integrating whole genome sequencing data into real-time detection and control (of TADs / EADs)

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Samantha Powers
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1 Next Generation Surveillance Systems integrating whole genome sequencing data into real-time detection and control (of TADs / EADs) Peter Durr Ivano Broz Kerryne Graham Jianning Wang Frank Wong Sam McCullough AUSTRALIAN ANIMAL HEALTH LABORATORY (AAHL) 17 th June 2015

2 Where Is Geelong? What is AAHL 2

3 Revolutions in (animal) disease diagnostics 1980s: The ELISA tests for antigens ( agents ) and antibodies 1990s: The gel-based ( conventional ) PCR for agents 2000s: quantitative (real-time) PCR 2010s: Portable and rapid testing quality diagnostics Multiple analyte testing (e.g. Luminex platform) high throughput whole genome sequencing ( next-gen sequencing NGS )

4 The potential and challenges of using NGS data for surveillance and control 4

5 The potential of using Next Gen data for TAD / EAD detection and control Whole genome sequencing (WGS) at reasonable cost Rapid turnaround hours of sample collection Assist in resolving the paths-of-transmission problem during an outbreak Where did the pathogen come from and how did it enter the country? Where was the failure in surveillance / quarantine system (national level) How did the farm get infected? Where was the failure in the outbreak control system (farm level)

6 The challenges (aka problems) of using NGS data for TAD / EAD detection and control 1. Maintaining sequence quality Farm-level paths-of-transmission studies often rely on a few SNPs (1-10) In sequence analysis variation and error are the same Therefore an erroneous base call will result in the wrong conclusion Farm A did not infect Farm B 2. Integration with covariate data Animal movement and transport network data Informal activity data (e.g. Google queries and Twitter feeds Wind speed and direction 3. Delivery of data to the disease control staff Rapid turn-around (24-28 hours of sample collection) Readily used for decision making and practical disease control

7 The bigger picture.. 7

8 Building an integrated molecular surveillance system for Australia s TAD / EAD response 8

The vision (2008) a molecular surveillance approach to outbreak investigations during EIDs Raw sequence data Sample test data Spatial data Disease reports Assembled (

9 The vision (2008) a molecular surveillance approach to outbreak investigations during EIDs Raw sequence data Sample test data Spatial data Disease reports Assembled ( annotated) sequences Quality molecular data Genomic epidemiology submission data, reports & analysis Demographic & movements data International databases (Genbank etc.) Delivery to user 9

A project and incremental approach The MOOVIT platform Movement On/Off Visualisation Tool Data mining of cattle movement database Focus on using the data The MARS platform Molecular Analysis And

10 A project and incremental approach The MOOVIT platform Movement On/Off Visualisation Tool Data mining of cattle movement database Focus on using the data The MARS platform Molecular Analysis And Reporting System Started in 2008 Whole of laboratory approach to sequence data management The TAPPAS platform Tool for Assessing Pest/Pathogen Airborne Spread From particle dispersion to pathogen / vector dispersion A one biosecurity approach aerial dispersion for crops more of a problem than for animals 10

11 MARS as a sequence data management system for the diagnostic workflow at AAHL (1) Genbank Consensus sequences*. fasta) Assembly (using various software tools) Diagnostic test, viral isolation etc Submission BLAST searches Report to clients Alignments Phylogenetic analyses Analyses output (*jpg, *.a2m, Nexus) 11

12 MARS as a sequence data management system for the diagnostic workflow at AAHL (2) Genbank Consensus sequences*. fasta) Uniprot taxonomy MARS sequence store Assembly (using various software tools) MARS sequencing request system Microstores Diagnostic test, viral isolation etc Submission Sample Manager BLAST searches Report to clients Alignments Phylogenetic analyses Analyses output (*jpg, *.a2m, Nexus) 12

13 MARS Raw sequence data management (1) 13

14 MARS Raw sequence data management (2) 14

15 MARS Raw sequence data management (3) 15

16 MARS My sequences data management (1) 16

17 MARS My sequences data management (2) 17

18 MARS My sequences data management (3) 18

19 The TAPPAS platform (1) 19

20 The TAPPAS platform (2) 20

21 The TAPPAS platform (3) 21

22 How might a big data approach assisted with the H5N2 outbreak investigations in North America? 22

23 A key knowledge gap in the HPAI epidemic in the USA - how farms are getting infected? Purported paths of transmission 1. Wild birds direct and indirect contact infected turkeys and layers For: H5N2 found in wild birds and proven mechanism in other outbreaks Against: Outbreaks occurring in large layer farms with good biosecurity 2. Infected farms virus aerosol plumes farms getting infected downwind For: Evidence for downwind spread from the Dutch H7N7 outbreak in 2003 Against: Evidence largely circumstantial 3. Infected farms human and vehicle traffic farms getting infected closeby and far away For: Proven method of spread in other epidemics Against: Outbreaks occurring on farms with good biosecurity 23

Algorithm to use BIG DATA approaches to rule out paths of transmission Analyse wind plume patterns from infected farms Wind plume patterns irregular or highly dispersed (Mostly) RULE OUT wind-borne

24 Algorithm to use BIG DATA approaches to rule out paths of transmission Analyse wind plume patterns from infected farms Wind plume patterns irregular or highly dispersed (Mostly) RULE OUT wind-borne transmission Analyse full genomes of nearby infected farms Sequences always (almost) 100% similar (Mostly) RULE OUT wild bird transmission Analyse truck GPS patterns on/off the farms GPS data available and no consistent pattern (Mostly) RULE OUT trucks and other vehicles Conventional epidemiological approaches 24

25 But.. It s complex 25

26 HYSPLIT output for a single run 26

27 Aggregated wind plumes using TAPPAS 27

28 Lessons learned / reflections / next steps 28

29 Lessons learned Generating data is no longer the problem using the data is the real challenge Challenge in separating signal from noise separating error from true variation separating background variation from important variation Rapid pace of technical change 2005: no NGS (or iphones or YouTube or Twitter ) Creating sustainable surveillance systems is (very) hard Technical political social institutional challenges (Almost) as wicked problem The need for a team approach TAPPAS 30+ people have worked on the project (too date!) 29

30 Next steps Need to bring all the 3 systems together EXODIS exotic disease information system Need for significant investment Maybe 5-10 million dollars Need for ongoing research Analysis tools Effective visualisation Desirability of a one biosecurity approach But. very challenging Desirability of international partnering Role for the Quads? Role for OIE / FAO? 30

31 Acknowledgements Institutions / companies Australian Biosecurity CRC for New and Emerging Diseases Bureau of Meteorology CSIRO IM&T Intersect Air Resources Laboratory (NOAA) OIE Individuals Rieks van Klinken Debbie Eagles Peter Daniels Paul de Barrro Martyn Jeggo James Watson Kurt Zuelke 31

32 Further reading on molecular surveillance systems 32

AAHL s Regional One-Health Activities

International partnerships Disease reservoirs Unique capability AAHL s Regional One-Health Activities Dr. Kurt Zuelke Director, Health & Biosecurity and AAHL 30 October 2015 6 th Asia Pacific Workshop