Multilocus sequence-based approaches to the characterisation of isolates of pathogens

1. Multilocus sequence-based approaches to the characterisation of isolates of pathogens Brian G Spratt

2. Strain characterisation for global epidemiology Disease is a global problem - need methods that allow simple and unambiguous international comparisons between strains Many methods less than ideal for global epidemiology - comparing patterns of DNA fragments With many of these molecular typing methods (e.g. PFGE), variation often accumulates too rapidly for global epidemiology Difficulties in comparing strains from different countries and decades, particularly when method used in different laboratories Can standardise PFGE methodology and have database of all PFGE patterns in a central database and can compare pattern of new strain via the web (e.g. “PulseNet”)

3. Still a need for better typing systems Allowing labs to communicate - to have a common typing language Recent move away from methods that compare DNA fragment patterns Need digital data – unambiguous data Need to harness power of internet Sequence data Single nucleotide polymorphisms (SNPs) Repeat length polymorphisms (VNTRs)

4. Multilocus Sequence Typing (MLST) Chromosomal DNA Amplify ≈ 450-bp internal fragments of seven house- keeping genes Sequence the seven gene fragments Each different sequence at a locus is given a different allele number The allele numbers at the seven MLST loci gives the allelic profile of the isolate Compare the allelic profile of isolate to those of all isolates within a central database on the web via the internet (www.mlst.net)

5. Dendrogram based on allelic mismatches between the allelic profiles 1.0 Clonal complex 0.8 0.6 Linkage Distance 0.4 0.2 0.0 Strain 8 Strain 4 Strain 5 Strain 2 Strain 9 Strain 7 Strain 3 Strain 6 Strain 1 Strain 10 Strain 12 Strain 11 Allelic Profile Strain 1 1 5 3 7 2 8 4 Strain 2 11 15 30 12 22 11 20 Clone Strain 3 6 5 9 1 2 13 17 Strain 4 9 10 1 19 12 18 14 Strain 5 11 15 30 12 22 11 20 Strain 6 1 5 3 7 2 8 4 Strain 7 20 11 29 9 21 13 11 9 15 13 27 22 18 14 Strain 8 Strain 9 20 11 29 9 21 13 11 11 15 30 12 22 11 20 Strain 10 1 5 3 5 2 8 4 Strain 11 Strain 12 11 15 30 12 22 11 20

6. No universally appropriate molecular typing method Choice of typing method depends on the question and Depends on the biology of the pathogen

7. Short term or local epidemiological questions Outbreak of disease in school, community or hospital Clusters of linked cases of gonorrhoea in city Treatment failure vs re-infection in individual patient Longer term or global epidemiological questions Relationships between MRSA, or penicillin-resistant pneumococci, from worldwide sources Post-vaccine surveillance Relationship between human and animal isolates May need different methods for local and global epidemiology MLST designed for global epidemiology although, for some pathogens, may have uses for local epidemiology

8. No universally appropriate molecular typing method Choice of typing method depends on the question and Depends on the biology of the pathogen

9. Bacterial pathogens have very variable biology Isolates causing a disease may be uniform or diverse Genomes of isolates may diversify slowly or rapidly

10. Sequence-based approaches MLST provides a good approach for many pathogens Not appropriate for some pathogens – notably the so-called select agents – anthrax, plague, tularaemia, glanders - which are all essentially monoclonal species (distinctive clones of a ‘Mother’ species inappropriately raised to species status) Also not TB, typhoid, whooping cough e.g. glanders – B. mallei as a monoclonal ‘species’

12. B. mallei 23 isolates B. pseudomallei

13. Bacterial pathogens have very variable biology Isolates causing a disease may be uniform or diverse Genomes of isolates may diversify slowly or rapidly - Other extreme – H. pylori

14. Highly diverse species (High mutation rate and high recombination rate)

15. Bacterial pathogens have very variable biology Fortunately, many species of public health concern are intermediate between the monoclonal species, such as B. mallei or B. anthracis, and the non-clonal species, such as H. pylori Enough sequence variation in house-keeping genes to allow vast number of strains (clones) to be distinguished and Rate of diversification not too rapid – can identify clones and clonal complexes

16. MLST developed for pathogen that is a challenge for typing – the meningococcus 0.7 N. meningitidis ET-5 (ST32) clonal complex. Not found before 1970. Extensive diversification in 30 years Rapid diversification, but still get clones * 0.6 0.5 * 0.4 MLST works very well for meningococci Genetic distance 0.3 0.2 0.1 ST32 ST35 0.0 ST35 complex ST32 complex (ET-5 complex)

17. High MLST defines genotypes (no clones) MLST gives some resolution within clones MLST gives good resolution within clones MLST identifies clones Sequence diversity High Low MLST inappropriate Low Diversification rate

18. Multilocus Sequence Typing (MLST) MLST databases and automation software – public health needs? Exploring and displaying relatedness between isolates Multiantigen Sequence Typing (MAST)

20. Imperial Site (David Aanensen)

21. Oxford Site (Martin Maiden)

22. Berlin Site (Mark Achtman)

23. MLST databases No such thing as a perfect database Need a database appropriate to your interests (Public Health/Research) MLST database – the reference database of STs, allelic profiles and allele sequences Over-riding function – official assignment of STs and alleles However, all MLST databases have a representative set of strains with some basic epidemiological information However, be careful – over time they tend to focus on new STs as people may not submit isolates of STs that are already common in the database

24. MLST databases Need your own database(s) May need to link databases e.g. HPA, SCIEH and CDC Network of linked private databases – linked to MLST database How to achieve this – local Bionumerics – with mirrored MLST database One central database with private secure sub-databases How to ensure strains are submitted to reference MLST database

25. Automation High-throughput analysis of isolates for Public Health Labs etc. Automation – robotics within the laboratory Software improvements – making it much easier to analyse large block of new strains Ideally, from sequencer to ST assignment without any manual steps Automated editing of sequences, automated allele and ST assignment (David Aanensen) – avoiding error-prone procedures, particularly cut-and-paste

26. Visualising relationships among isolates Clustering – dendrograms Differences in allelic profiles Concatenated sequences Minimum spanning trees eBURST etc

27. Burkholderia pseudomallei/thailandensis MLST Differ at all seven MLST loci – no resolution 1.0 B. pseudomallei B. thailandensis Allelic profiles 0.8 0.6 Linkage Distance 0.4 0.2 ST51 ST70 ST32 ST37 ST46 ST60 ST40 0.0 B. mallei

28. Clustering based of DNA sequences Can use DNA sequences rather than allelic profiles Join sequences of loci end-to end (concatenate) ~ 3,500 bp Less reliable than using allelic profiles, unless evolutionary change at MLST loci is predominantly due to mutation (i.e. low impact of recombination) Can be useful: Relatedness between species e.g. used for non-typeable presumptive pneumococci – are they really pneumococci or something closely-related but clearly distinct Relationships among Burkholderia species

29. Concatenated sequences Neighour-joining tree Allelic profiles ST5 ST57 ST10 ST23 ST17 ST38 ST64 ST18 B. mallei ST2 ST40 ST1 ST6 ST11 ST12 B. pseudomallei ST7 ST26 ST27 ST59 ST4 ST72 ST62 ST63 ST25 ST9 ST22 ST33 ST34 ST44 ST45 ST14 ST35 ST37 ST36 B. pseudomallei ST24 ST39 ST43 ST42 ST20 ST41 ST28 ST65 ST61 ST19 ST60 ST3 ST8 ST15 ST16 ST53 ST46 ST47 ST49 ST50 ST48 ST51 100 ST55 ST56 ST58 ST13 ST71 ST66 ST67 ST29 ST21 ST30 ST68 ST69 ST31 ST32 ST70 100 ST52 ST54 ST73 ST74 B. thailandensis 96 ST75 ST79 ST76 ST80 ST77 B. cepacia g’var III ST78 0.005 1.0 0.8 0.6 0.4 0.2 0.0 Linkage Distance B. thailandensis Godoy et al., 2003

30. Visualising relationships among isolates Difficult to see overall clustering in whole database using dendrograms – thousands of isolates

32. Visualising relationships among isolates Difficult to see overall clustering in whole database using dendrograms – thousands of isolates Scaleable dendrograms help Working to get better outputs for dendrograms

33. http://haemophilus.mlst.net/

34. Dendrograms useful as visual and show clustering of genotypes Can we get a reliable intra- species phylogeny? or Do we really have a network? Can we only hope to look at recent events that have occurred as clones diversify into clonal complexes?

35. Congruence between trees for different house-keeping loci* E. coli 90% (Reid et al. 2000) (Feil et al, 2003) S. aureus 55% (Meats et al, 2003) H. influenzae 43% Increasing impact of recombination (Feil et al, 1999) N. meningitidis 7% (Feil et al, 2001) 0% S. pyogenes (Feil et al, 2000) S. pneumoniae 0% * % of the 42 pair-wise tree comparisons that show significant congruence With Eddie Holmes (Oxford)

36. Reliable intra-species phylogeny??? or Can only explore recent diversification? • Fortunately, in many cases interested in recent events • emergence of antibiotic resistance • emergence of new virulent strains • changes due to introduction of a vaccine Dendrogram provides a very poor representation of recent evolutionary events Need better way of exploring recent evolution

37. Beyond trees 0.6 0.5 0.4 0.3 clonal complex 0.2 Linkage Distance 0.1 0.0 No information about ancestry or patterns of evolutionary descent

38. ST11 Clonal complex ST2 ST10 ST3 ST2 ST1 Emerging clone ST9 ST4 ST1 ST5 ST8 eBURST ST6 ST7 Divides bacterial population into non- overlapping groups (clusters of related strains – clonal complexes) Area of circles = prevalence of ST ST2 Double locus variant (DLV) ST11 ST3 Predicts founding ST in each group Single locus variant (SLV) ST10 Predicted founder of the group is placed in centre with radial links to all of its SLVs and on to DLVs etc ST4 Founding genotype ST1 ST9 Displays most parsimonious patterns of descent of all STs in group from the founder ST5 ST8 JAVA™ applet produced by Bao Li ST6 ST7 Feil et al., 2004. J. Bacteriol. 186: 1518-1530.

39. Streptococcus pneumoniae 0.5 Bootstrap = 100% 0.4 82 285 84 189 0.3 83 282 Linkage Distance 714 ST81 326 0.2 * 286 551 633 85 634 0.1 0.0 ST81 (Spain23F-1 clone) SLVs of ST81

40. Neisseria meningitidis One of each ST 0.8 0.7 ST8 complex (A4) 0.6 ST11 complex (ET-37) 0.5 0.4 Linkage Distance 0.3 0.2 0.1 0.0

41. Neisseria meningitidis ST11 complex Bootstrap for ST11 = 100% ST11 ST11 Blue = founder Yellow = subgroup founder Size of circle is prevalence Links to MLST databases eBURST default settings (6/7 shared alleles)

42. Neisseria meningitidis One of each ST 0.8 Related complexes?? 0.7 ST8 complex (A4) 0.6 ST11 complex (ET-37) 0.5 0.4 Linkage Distance 0.3 0.2 0.1 0.0

43. Neisseria meningitidis ST8 ST11 eBURST relaxed setting (5/7 shared alleles) Fig 8

44. Neisseria meningitidis One representative of each Sequence Type (ST) Lineage 3 Group B Group A

45. Lineage 3 re-interpreted by eBURST Group B Group A Possible founder Feil et al. 2004

46. Streptococcus pneumoniae population snapshot Clonal complexes Map on serotypes drug resistance etc Feil et al. J. Bacteriol. 2004

47. S. aureus population snapshot 15 1 36 30 5 22 247 45 8 239 Map on SCCmec types etc

48. eBURST Provides a hypothesis about ancestry and patterns of descent Conservative - much more so than minimum spanning trees New features in next release – auto-editing of eBURST diagrams Re-drawing of clonal complex based on an alternative founder Shows SLV and DLV links of any ST on eBURST diagrams on request etc. eBURST links to MLST databases

49. eburst

50. Identifying ‘sexual networks’ by molecular methods Need new molecular typing systems for bacterial STIs Appropriate molecular typing method could recognise sexual contacts and groups of individuals within the same sexual network Need highly discriminatory typing system that uses genetic variation that accumulates rapidly (very different from MLST etc) With correct level of discrimination, and correct speed of evolutionary change at the ‘typing loci’, isolates from a large city will typically be different in genotype, unless they are from sexual contacts, or are within a group of individuals who share sexual links - the same sexual network Need to obtain almost all isolates from the city (multiple clinics) to get the clusters of identical genotypes