Applications of Whole Genome Sequencing (WGS) to Food Safety – Perspective from a reference laboratory
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http://tiny.cc/faowgsworkshop Applications of genome sequencing technology on food safety management- United Kingdom. Presentation from the FAO expert workshop on practical applications of Whole Genome Sequencing (WGS) for food safety management - 7-8 December 2015, Rome, Italy.
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Applications of Whole Genome Sequencing (WGS) to Food Safety – Perspective from a reference laboratory
- 1. Applications of Whole Genome Sequencing to Food Safety – Perspective from a reference laboratory Dr Tim Dallman Gastrointestinal Bacteria Reference Unit 7th December 2015
- 2. “PHE are a governmental organisation working with national and local government, industry and the NHS to protect and improve the nation’s health” - Microbiological Characterisation - Epidemiological Investigation - ExpertAdvice
- 3. 3 All isolates of Salmonella & STEC in England & Wales referred centrally to PHE Colindale, London All cases of STEC in England & Wales followed with an ESQ DAY1 – Isolate receipted and grown overnight in broth DAY2 – High throughput extraction and submission to central sequencing facility DAY3 -> DAY6 – Quantification, Normalisation, Library Prep, Sequencing DAY7 – Automated bioinformatics & Validation HiSeq 2500 high-throughput machines
- 4. Public Health Questions • What clone is it? e.g. O157, O26, Typhimurium, Typhi etc • Have we seen this strain before? • How pathogenic is this strain to humans? • How resistant to treatment may this infection be? 4
- 5. 5 • WGS Laboratory Result Clone Virulence Fine Type
- 6. How do we assess similarity between genomes? 6 Genomic insight into an ongoing outbreak of Shigella flexneri in men who have sex with men Strain 1 Strain 2 Ref G T A G Strain 1 G G A A Strain 2 A G G G Ref Strain 1 Strain 2 0.5 Clone specific reference
- 7. 7 Presentation title - edit in Header and Footer7 Challenges: • Many CCs • Hundreds of strains a week • Rapid, parallel, hands-off analysis • Real time cluster detection SnapperDb Sample FASTQs CC11 – O157db db db db db CC17 – O103 CC245 – S. flexneri CC21 – O26 CC442 – O146 … 30 mins - parallel 2 minutes https://github.com/PHE-GIDIS/
- 8. Typing – SNP SnapperDb - Outputs • SNP alignments • Annotated variants • Genetic distance between all isolates 8 Sequencing Reads QC IDENTIFICATION CHARACTERISATION Speciate Antimicrobial resistance Virulence factors TYPING SNP
- 13. 13 Presentation title - edit in Header and Footer http://benfry.com/zipdecode/
- 14. 14 Presentation title - edit in Header and Footer http://benfry.com/zipdecode/
- 16. 16 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 1 2 3 4 5 6 7 8 ProportionofPais SNP Distance Same Person Household Epi Linked N=129, STEC O157:H7 Dallman 2015, CID
- 17. 17 0 50 100 150 200 250 300 350 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 Frequencyofpairs SNP Distance > 30 days 7 to 30 days <7 days Common Source Threshold (CST) Dallman 2015, CID
- 18. Have we identified previously unidentified links? 18 • 160 / 334 (2012) strains fell within 5 SNP CST • Comprises 53 clusters – 20 clusters household or same patient – 33 community clusters • 20 / 33 clusters were NOT identified through PH investigations carried out of the time.
- 19. 19 0 50 100 150 200 250 300 350 400 450 0 2 4 6 8 10 12 14 Averagepairwiseresidentialdistance(km) Cluster Size WGS Linked Cluster Epi Linked Cluster
- 20. Outbreak – O157:H7 18.35.397.765.1482.X • 40 cases found within a 5SNP cluster • 18.35.397.765.1482 – (MODE SNP = 0, MAX SNP = 2) • stx2a • AMR fully susceptible (other than tellurite) 20
- 21. What’s the neighborhood like? 18.35.397.765.1482 Dallman, Microbial Genomics 2015
- 23. 23
- 24. 24
- 25. In depth interviews & further investigations • Revealed 2 supermarket chains and several specific salad products implicated • One common ingredient • Food trace-back information indicates 3 possible growers of this ingredient including 1 in the region similar strains are enriched in • Sampling on-going……… 25
- 27. WGS led surveillance • Identify linked strains with unprecedented sensitivity and specificity • Provide context about strains • Native / Imported • Geographical signals • Virulence potential • Need global databases – clinical, animal, food NCBI BioProject PRJNA248042 27
- 29. Questions to the enteric reference laboratory? • “Is my isolate related to anything else?” real-time forensic national surveillance • So we have an outbreak! Does this new isolate need to be followed up? Is it in or out!? rapid frontline confirmation • How far down the rabbit hole does this thing go!? cross border collaboration 29
- 30. Salmonella Enteritidis 14B Heartlands Hospital Birmingham
- 31. 31 Illumina – sequencing by synthesis 98% Accuracy 100-300 bp read length 1.5 billion reads per flow cell 27 hrs
- 32. 32 Oxford Nanopore – Nanopore sequencing
- 33. Rapid Frontline Conformation • minION sequencer deployed at hospital with > 30 cases infected • Using real-time sequence analysis could we identify if the hospital outbreak was the same strain as the national outbreak? • 2 samples – 1 Outbreak / 1 Sporadic • Every 10 mins – extract minION reads and attempt to identify species, serogroup and strain level resolution 33
- 34. Species ID Align minION reads to taxa specific genes (MetaPhlA2) using LAST. S.enterica identified in under 30 mins Serogroup ID Align minION reads to S.enterica reference genome. Phylogenetic placement with pplacer Within 50 mins we could identify the strains as serotype Enteritidis
- 35. Outbreak ID Alignment of minION reads to Salmonella Enteritidis reference genome. Phylogenetic placement with pplacer Within 100 minutes the outbreak strains was unambiguously part of the national 14b cluster (RED) and the sporadic cases (BLUE) was indeed sporadic Quick et al – Genome Biology 2015
- 36. Inns et al – Eurosurveillance 2015
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- 40. Association between phylogeny and distribution network • Measure distance between cases on phylogeny and traceback network • Test association between distance matrices using a non-parametric Mantel test • Adjust for cases from same point source outbreak as a covariate Salmonella NGS at PHE
- 41. Can we explain this association? • Trace back in UK – All outbreaks involve eggs from same German supplier • Cases in Austria, Germany, France and Luxemburg • Trace back investigations lead to sampling of egg production premises in Germany • Company has 4 separate egg production plants supplied with layer flocks from single supplier • European isolates sequenced – cases / eggs 41
- 42. 0.02 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * ** * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * Clade 1 – 12 egg isolates + traceback (all site A) Clade 2 – 2 egg isolates + traceback (all site B) Clade 3 – 0 egg isolates, 0 traceback to site A or B 1 2 3
- 44. Acknowledgements 44 PHE Elizabeth de Pinna, Satheesh Nair, Kathie Grant, Phil Ashton, Claire Jenkins, Martin Day, Neil Perry Cath Arnold and sequencing team, Jonathan Green, Anthony Underwood and bioinformatics team, Jeremy Hawker, Lisa Byrne Imperial College Thibaut Jombart University of Birmingham Nic Loman, Josh Quick Roslin Institute David Gally Robert Koch Institut Wolfgang Rabsch, Sandra Simon