The Whole Genome Sequencing Revolution Martin Wiedmann Gellert Family Professor of Food Safety Department of Food Science Cornell University, Ithaca, NY Phone:

Outline Subtyping for disease surveillance: from PFGE to WGS WGS challenges: when are two isolates the same or different? Can we find identical isolates in different locations? Looking in the future

PulseNet allows international outbreak detection and traceback – a hypothetical example Food isolate, deposited into PulseNet Human case

Whole Genome Sequencing It all started with the human genome project Sequencing of a bacterial genome is now feasible at costs of <$100/isolate Costs will continue to drop Commonly used platforms include Roche 454 Illumina HiSeq/MiSeq Applied Biosystems SOLiD Systems Life Technologies/Thermofisher Ion Torrent; PacBio RS Nanopore based systems (e.g., Oxford Nanopore MinION)

The genome sequence revolution

DNA sequencing- based subtyping Isolate 1AACATGCAGACTGACGATTCGACGTAGGCTAGACGTTGACTG Isolate 2AACATGCAGACTGACGATTCGTCGTAGGCTAGACGTTGACTG Isolate 3AACATGCAGACTGACGATTCGACGTAGGCTAGACGTTGACTG Isolate 4AACATGCATACTGACGATTCGTCGAAGGCTAGACGTTGACTG SNP: single nucleotide polymorphism

Challenges with use of PFGE as a subtyping method in outbreak investigations Two isolates may show the same PFGE type even though they are genetically distinct PFGE only interrogates small part of the genome Two isolates may show “slightly” (?? - the “3-band rule”) different PFGE patterns despite sharing a very recent common ancestor Could be due to lateral genes transfer, loss of plasmid, rearrangements, point mutations etc.

Xbal SpeI L Den Bakker et al AEM. Includes isolates form Salmonella outbreak linked to sausages (Rhode Island) and isolates from pistachios

Tip-dated maximum clade credibility tree based on SNP data for 47 Montevideo isolates

98 MLVA types Salmonella Enteritidis is most common cause of human salmonellosis – poorly resolved by current subtyping technologies. 52 PFGE types 163 combined MLVA-PFGE types

Full genome sequencing identified the following differences between these isolates: (i)28 single nucleotide polymorphisms (SNPs) and (ii)three indels, including a 33 kbp prophage that accounted for the observed difference in AscI PFGE patterns. Both isolates were found to harbor a 50 kbp putative mobile genomic island encoding translocation and efflux functions that has not been observed in other Listeria genomes. Gilmour et al. BMC Genomics 2010, 11:120

In addition, whole genome sequencing showed that 5 Listeria isolates collected in 2010 from the same facility were also closely related genetically to isolates from ill people.

Listeria Outbreaks and Incidence, Pre-PulseNet Early PulseNet Listeria Initiative No. outbreaks Incidence (per million pop) Era Outbreaks per year Median cases per outbreak WGS Data are preliminary and subject to change

March 2015: Listeriosis cases linked to Blue Bell ice cream

Outline Subtyping for disease surveillance: from PFGE to WGS WGS challenges: when are two isolates the same or different? Can we find identical isolates in different locations? Looking in the future

The challenge Identical bacteria (100% match over the whole genome) can be found in different places that can be potential sources of foodborne disease outbreaks

The theoretical background Bacteria divide asexually: Bacterial populations can be seen as large populations of “identical twins” Mutation rate during replication is low: extremes of the suggested mutation rates range from 2.25 × to 4.50 × per bp per generation – With a genome size of around 5 Million bp per bacterial genome (5 × 10 6 ) between approx. 450 and 9,000 generations are needed for a single SNP difference – Eyre et al. estimated evolutionary rate of 0.74 SNVs per successfully sequenced genome per year for C. difficile (N. Engl. J. Med. 2013) “Whole-genome sequencing … identified 13% of cases that were genetically related (≤2 SNVs) but without any evidence of plausible previous contact through a hospital, residential area, or family doctor.” – Unknown bacterial generation time in different environments complicates interpretation

2000 US outbreak - Environmental persistence of L. monocytogenes 1988: one human listeriosis case linked to hot dogs produced by plant X 2000: 29 human listeriosis cases linked to sliced turkey meats from plant X

Real world observations

In one case, isolates with < 3 SNP differences were found in retail delis in there different states

Conclusions Even with WGS, epidemiological data are still essential Number of SNP differences/allele differences that is meaningful differs by organism, strain, outbreak/cluster, and growth environment – Number of bacterial generations per calendar year can differ hugely (think dry environment versus active infection in an animal population) Best way to determine “meaningful” SNP differences is through combination of phylogenetic and epidemiological data

Looking in the future WGS will get cheaper and will be used more – STEC next, probably Salmonella Enteritidis after that – Detection of more clusters and outbreaks WGS database will grow rapidly with inclusion of environmental isolates – More outbreak will be linked to source by using WGS matches between food or environmental isolates and human isolates as stating point More broad application of WGS by private labs, maybe customers and consumers?

Conclusions WGS is a game changer and will significantly improve detection of outbreaks, adulteration, etc. – False alarms will occur though Pathogen detection in environments, by regulatory agencies, will lead to inclusion of WGS data in CDC/FDA/USDA databases (GenomeTrakr) – Environmental pathogen monitoring by industry will become even more important

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Analysis of genome wide SNPs (wgSNPs) Identifies all high confidence SNPs over whole genome (approx. 3 to 5 million nucleotides)

Whole genome multilocus sequence typing (MLST) Allows for simpler analysis and clear naming of subtypes Performs comparison on a gene by gene level Isolate AIsolate BIsolate C Gene 1111 Gene Gene 3552 Etc. Gene 1, wgMLST typeAAB