1. Martin Wiedmann Cornell University E-mail: mw16@cornell.edu
hqSNP, wgMLST and the WGS alphabet soup: what epidemiologists need to know
Martin Wiedmann
Cornell University

2. Outline Review of genomes, genes, and evolution
Use of sequence data to assess relatedness of organisms
Data analysis approaches
wgMLST and hqSNP
Trees and how to interpret them

3. What is a SNP? Single Nucleotide Polymorphism (SNP)
ATGTTCCTC sequence
ATGTTGCTC reference
*phylogenetically informative differences
Insertion or Deletion (Indel)
ATGTTCCCTC sequence
ATGTTC-CTC reference
*differences not used in hqSNP analysis

4. Microbial evolution 101 – mechanisms of change
Point mutations
ACCCTCTAGTAGTAGCA
ACCATCTAGTAGTAGCA
ACCCTCTAGTAGTAGCA
1 SNP and one “genetic event”

5. Microbial evolution 101 – mechanisms of change
Insertion or deletion
ACCCTCTAGTAGTAGCA
ACCATCTAG TAGCA
ACCCTCTAGTAGTAGTAGCA
3 differences (?) and one “genetic event”

6. Microbial evolution 101 – mechanisms of change
Inversion
ACCCTCTAGTAGTAGCA
ACCATCTCGTAGTAGCA
ACCCTCTAGTAGTAGCA
Alignment:
ACCATCTCGTAGTAGCA
ACCCTCTAGTAGTAGCA
2 SNPs and one “genetic event”

7. Microbial evolution 101 – mechanisms of change
Horizontal gene transfer of homologous gene sequences
ACCCTCTAGTACTAGCATCC
TCCCTCTTGTCCTACCATCA
CTTGTCCTACCA
CTTGTCCTACCA
Alignment:
ACCCTCTAGTACTAGCATCC
ACCCTCTTGTCCTACCATCC
3 SNPs and 1 genetic event
ACCCTCTAGTACTAGCATCC
ACCCTCTTGTCCTACCATCC

8. Microbial evolution 101 – mechanisms of change
Transformation
Transduction

9. Case study – why does it matter
Human listeriosis outbreak in 2000 with 29 cases
Isolates show 1 SNP differences to food and human isolate from a single case linked to processing facility X in 1988
Epidemiology support that this facility was the source of the outbreak
Some analyses approaches that did not account for recombination would have shown that human isolates from show approx. 3,000 SNP differences to 1988 food isolate from facility X
Why: Large recombination event that introduces a large prophage (viruses inserted into the bacterial genome)

10. Outline Review of genomes, genes, and evolution
Use of sequence data to assess relatedness of organisms
Data analysis approaches
kSNP, wgMLST, and hqSNP
Trees and how to interpret them

11. Use of sequence data to assess relatedness of organisms
Differences in sequences can be used to assess relatedness of organisms and the likelihood of recent common ancestor
“Do the M. tuberculosis isolates from patient A and patient B share recent common ancestor”
Definition of “recent” becomes important – recent in years or generation times
Salmonella in a dry processing plant may stay dormant and rarely if ever multiply (or imagine anthrax spores in soil)
Salmonella in a chicken flock may multiply every 30 min (>7,500 times a year)
Assessing relationships of microbial isolates typically requires more information than just sequence data
Information on epidemiological relationships and other relevant data is essential

12. Outline Review of genomes, genes, and evolution
Use of sequence data to assess relatedness of organisms
Data analysis approaches
kSNP, wgMLST, hqSNP, and others
Trees and how to interpret them

13. Basics of WGS Analyses
Different ways to compare the genomes of 2 different isolates
Compare the genome small piece-by-small piece to find pieces that are different
Kmer based analyses
Use a high quality (reference) sequence or genome to identify differences
hqSNP analysis
Compare genomes on a gene-by-gene (locus-by-locus) basis
wgMLST analysis
All these analysis can provide an output that provides the “number of differences” or can be sued to build trees

14. Basics of WGS Analyses
Different ways to compare the genomes of 2 different isolates
Compare the genome small piece-by-small piece to find pieces that are different
Kmer based analyses
Use a high quality (reference) sequence or genome to identify differences
hqSNP analysis
Compare genomes on a gene-by-gene (locus-by-locus) basis
wgMLST analysis
All these analysis can provide an output that provides the “number of differences” or can be sued to build trees

15. What makes a SNP high quality (hq)?
Quality filtered Sequence Reads ready for analysis
Sequence reads
Sequence Reads
Apply a quality filter that filters out nucleotides in sequence reads for comparison based on sequence coverage and quality

16. The alphabet soup of analysis – Coverage
Coverage at 40x
Coverage at 5x
NGS generates 100,000 or more reads per one genome sequenced
Any single location on the genome can have zero to hundreds of sequence reads that cover the one region

17. What to call a SNP SNPs called based on:
ATGTTACTC
ATGTTCCTC
ATGTTTCTC
ATGTTCCTC ATGTTCCTC
ATGTTGCTC ATGTTGCTC reference
Is it a SNP?
SNPs called based on:
Quality
Coverage
Base frequency
The differences between the reference and compared genome are extracted and used to determine relatedness

18. -do no consider SNPs in this location
Where to call a SNP?
Not all SNP pipelines are equal – where you call SNPs will affect the total SNP count
SNPs relevant for phylogenetic analysis are vertically transmitted, not horizontally, so horizontal genetic elements like phages can be masked
Mobile
elements
genes
Raw reads
Mask mobile elements
-do no consider SNPs in this location
Only call SNPs in genes

19. How to report SNP data – keep it simple
Hi folks:
New Cluster:
Two isolates are 0 SNPs from each other:
E (SE77B52)
E (SE77B52)
New Cluster:
Two isolates are 2 SNPs from each other:
E (SE1B1)
I (SE1B1)

23. MDH00841
MDH00849

24. Caveats of hqSNP analyses
Advantages
Disadvantages
When to Use
Phylogenetically informative (build a tree consistent with evolution of the strains)
Requires a closely related reference genome – hqSNP analysis is problematic if reference genome is not closely related
Good for situations where a wgMLST database has not been developed and validated. May provide highest amount of resolution for strain comparison
SNP position can be identified on genome (gene affected can be identified)
Takes a while and requires a lot of computer power
Interpretation of data depends on genomes added – is not stable and does not lead to nomenclature

25. Basics of WGS Analyses
Different ways to compare the genomes of 2 different isolates
Compare the genome small piece-by-small piece to find pieces that are different
Kmer based analyses
Use a high quality (reference) sequence or genome to identify differences
hqSNP analysis
Compare genomes on a gene-by-gene (locus-by- locus) basis
wgMLST analysis
All these analysis can provide an output that provides the “number of differences” or can be used to build trees

26. Traditional MLST ~6-12 housekeeping genes; usually portion of gene
MLST.NET
~6-12 housekeeping genes; usually portion of gene
Developed in the area of Sanger sequencing, providing for improve discrimination over sequencing 1 gene
Targets selected to represent population structure, not as useful for outbreak detection
Schemes are available on international publically accessible databases
combination of 6-12 genes used to name a unique sequence type (i.e. MLST profile = ST1)

27. Whole genome multilocus sequence typing (MLST)
Database is built from gene content representing a diverse selection of the genus/species of the organism being compared
Each unique gene is referred to as a “locus” – a locus may include the entire gene or a piece of the gene
Any changes – SNP, insertions, deletions – equals a new allele call for a locus
New alleles are named sequentially when encountered- not based on sequence
2 SNPs
1 indel
Locus 1 ACTAGAGGGAAA ACTAGAGGCTAA ACT-GAGGGAAA
allele allele 2 allele 3

28. Whole genome multilocus sequence typing (MLST)
Allows for simpler analysis and clear naming of subtypes
Performs comparison on a gene by gene level
Isolate A
Isolate B
Isolate C
Locus 1 (20 nt) 1
Locus 2 (100nt) 8 12
Locus 3 (5000nt) 5 2
Etc.
Locus 2,005 (5nt) 4
wgMLST type A B

29. The alphabet soup of analysis - wgMLST
The allele calls at each locus are compared between isolates and differences are used to determine relatedness

30. “Allele Code” Pattern Naming in the Listeria Database
Pilot thresholds
10% = 300 alleles
5% = 150 alleles
2.5% = 75 alleles
1% = 30 alleles
0.5% = 15 alleles
0.25% = 7 alleles
Two isolate are the same:
Patient 1:
Patient 2:

31. The wgMLST “zip code” Two isolate are the same:
Patient 1:
Patient 2:
Three isolates; patient 3 differs by 1 to 7 alleles from 1 and 2
Patient 3:
Four isolates; patient 4 differs by 8 to 15 alleles from the others:
Patient 4:

32. How to report wgMLST data – keep it simple
Hi folks:
New Cluster:
Two isolates are 0 alleles from each other:
E (SE77B52)
E (SE77B52)
New Cluster:
Two isolates are 2 alleles from each other:
E (SE1B1)
I (SE1B1)

33. How to report wgMLST data – give me the ZIP codes
Looks like we may have a cluster
Patient 1:
Patient 2:
Patient 3:
Patient 4:
Patient 4:

34. MLST Analysis Faster than analyzing SNP differences
For WGS data, allele calls can be performed on short reads (“assembly free”) and assembled genomes (“assembly-based”)
If there is a conflict between the allele calls then no allele call is made

35. Advantages and Caveats of wgMLST analysis
Disadvantages
When to Use
Phylogenetically informative
Initial assignment of alleles is computationally costly (doing assemblies before calling alleles); CDCs system will call alleles directly from raw reads (~ 2 min); assemblies take about 2 h or perhaps longer; if there is a conflict between the allele calls then no allele call is made
Surveillance, especially for a distributed testing network
All virulence, serotyping, and antibiotic resistance genes can be pulled out as part of analysis
Comparing character data (allele numbers) rather than genetic data
Reference characterization
Neutralizes the effects of horizontal gene transfer (event is only counted once rather than many times for hqSNPs)
SNPs and indels treated equally
Accurate cluster detection
Allele calling is stable – data standardizable; directly comparable between laboratories; can lead to nomenclature based on allele calls, which can be used for communication and automated cluster detection; reproducibility not dependent on choice of reference strain; amenable to automated bioinformatics
Requires curation for allele calls
Need to communicate with partners using stable nomenclature

36. hqSNP versus MLST Analysis
Both analyses conducted from the same raw data (typically short read sequencing data)
For public health purposes, both correlate well
i.e the outermost branches of phylogenetic trees are almost identical
The two are not mutually exclusive
For some use cases MLST works better, others SNP works better

37. Interpreting analysis data – how to build trees using WGS analysis
Use WGS analysis to infer relatedness of isolates
For wgMLST: translate the number allele difference between isolates to a measure of similarity and use that to infer branch lengths and relatedness
For hqSNP analysis – translate nucleotide differences between isolates to relatedness
Can use substitution models to estimate the cost of changing from A>T, C>A, etc.
Thymine
Cytosine
adenine
guanine

38. How to report SNP data - trees1 2
1 ATATTCCGCAA
2 ATATTCCGCAA
3 ATATTGCGCAA
4 ACCTTGCGCTA 3 4 3 2 1

39. Building the tree
Isolate
Sequence A
ggagagtta B
ggatccccc C
ggattatta D
actgccggt
ancestor
actgaatta 6
Isolate B 1
ggatccccc
ggataatta 1 3
Isolate C
ggattatta
ggagaatta 1
Isolate A
actgaatta
ggagagtta 5
Isolate D
actgccggt
genetic change
Use the differences you identified by hqSNP or wgMLST to infer the relatedness or phylogeny

40. Reading the trees
Node
Most recent common ancestor (for isolate B and C) 6
Leaf
Taxa
Isolate B 1 1 3
Isolate C
Clade 1
Ancestral node
Terminal node
Isolate A
Outgroup/Root – related isolate (same PFGE pattern or 7-gene MLST) but not part of outbreak 5
Isolate D
genetic change

41. Trees, branches, and leaves – more than one way to draw a tree
Many different ways to display trees Branches that connect to the terminal node are the important branch lengths to indicate relatedness

42. Trees, branches and leaves – reading the trees
Difference between similarity and relatedness on the tree
Isolate A and C are more similar to each other than C and B are
Isolate C and B are more related to each other than C and A are 6
Isolate B 1
ggatccccc
ggataatta 1 3
Isolate C
ggattatta
ggagaatta 1
Isolate A
actgaatta
ggagagtta 5
Isolate D
actgccggt
genetic change

43. Trees, branches and leaves – what does it mean for my outbreak investigation
Epidemiologic data provides context to the tree – cannot rely on phylogenetic tree to identify outbreak source 5
spinach 1
ggatccccc
ggataatta 1 3
Stool
ggattatta
ggagaatta 1
kale
actgaatta
ggagagtta 5
stool
actgccggt
genetic change

44. wgMLST–based phylogenetic Tree
Crave Brothers
Minimum spanning tree (MST)
Unrooted
Depicts genomes in a network and branch lengths show relatedness of isolates (number of allele differences)
New subgroup
kale

45. 0-2 SNPs 0-1 SNPs 0 SNPs 1SNP 0-3 SNPs
MDH00219
MDH In-vivo, same as E
MDH Sporadic 4/19/01
MDH Sporadic 8/6/12
MDH Sporadic 5/14/01
MDH Sporadic 5/14/01
MDH Sporadic 7/11/00
MDH Sporadic 3/12/01
MDH Sporadic 8/23/00
MDH Sporadic 6/10/13
MDH00237 Sporadic 6/22/11
MDH Sporadic 5/7/11
MDH Sporadic 8/31/2000
MDH Sporadic 12/7/2001
MDH Sporadic 6/10/13
MDH Sporadic 8/22/2000
MDH Sporadic 4/30/2001
MDH Sporadic 6/11/2001
MDH00254
MDH00252
MDH00253
MDH00234
MDH Sporadic 6/21/2001
MDH Sporadic 7/16/2001
MDH Sporadic 7/7/2000
MDH Sporadic- Same time, PFGE, and MLVA as Outbreak 1
MDH00209
MDH00210
MDH00211
MDH In-vivo, same as E
MDH In-vivo, same as E
MDH00223
MDH00220
MDH00218
MDH Sporadic- Same PFGE and time as Outbreak 1
MDH Sporadic 10/17/01
MDH00227
MDH00230
MDH00251
MDH00229
MDH Sporadic 10/3/05
MDH Sporadic, same PFGE and time as Outbreak 5
MDH Sporadic 6/26/12
MDH00249
MDH00250
MDH00246-Sporadic 7/30/12
MDH OH Sample 1
MDH OH Sample 2
MDH Sporadic, same PFGE and time as Outbreak 5
MDH00239
MDH00242
MDH Environmental sample from Outbreak 5
MDH00238
MDH00240
Defined Outbreak Samples
Outbreak 1- Sept 2000
Outbreak 2- May 2001
Outbreak 3- Aug 2001
Outbreak 4- Nov 2003
Outbreak 5- Aug 2008
Outbreak 6- Spring 2014
Outbreak 7- Spring 2014
0-2 SNPs
1SNP
0 SNPs
0-1 SNPs
0-3 SNPs
Taylor et al. J Clin Micro Oct 2015.

46. Take Home Messages
Molecular epidemiology requires collaborations between epidemiologists and the lab
Microbial isolates can accumulate genetic differences through a variety of mechanisms (e.g., horizontal gene transfer)
The approach data analyses use to deal or not deal with these different evolutionary mechanisms can play an important role
hqSNP and wgMLST both address and account for horizontal gene transfer, but in different ways
Different organisms differ in their lifestyles and mechanisms of evolution
Need to know your epi and your bugs

47. Acknowledgments Centers for Disease Control and Prevention
Heather Carleton
Greg Armstrong
Peter Gerner-Smidt
John Besser
Integrated Food Safety Centers of Excellence

48. Questions