João André Carriço, PhDMicrobiology Institute/Institute for Molecular MedicineFaculty of Medicine, University of LisbonPortugal

Mayo Clinic, Rochester MS, 2 November 2015

CHOOSING THE RIGHT MICROBIAL TYPING METHOD: A QUANTITATIVE APPROACH

http://im.fm.ul.pthttp://imm.fm.ul.pthttp://www.joaocarrico.infoTwitter: @jacarrico

MICROBIAL TYPING

“Crude classifications and False generalizations are the curse of organized life”

George Bernard Shaw (1856 – 1950)

What is microbial typing: discriminating strains within a species/subspecies, for the identification of clones/lineages of interest

APPLICATIONS OF MICROBIAL TYPING

Bacterial PopulationGenetics

Pathogenesis and

NaturalHistory ofInfection

Surveillance ofInfectiousDiseases

Outbreak Investigation and Control

MICROBIAL TYPING METHODS

Genotypic – gel basedPulsed Field Gel Electrophoresis (PFGE )

Phenotypic : ATB Resistance profiles Serotyping Genotypic - Sequence based

Multilocus Sequence Typing (MLST)Multilocus VNTR Analysis (MLVA) Single Locus methods :

emm typing (Group B ,C & G Strep)

Spa typing (S aureus)

TRADITIONAL TYPING AND NGS

Chronicle of a Death Foretoldhttp://en.wikipedia.org/wiki/File:ChronicleOfADeathForetold.JPG

Next Generation Sequencing:

- Gene-by-gene: wgMLST, cgMLST,

MLST

- SNP approaches: comparison with reference strains

- Ability to recover most of the present sequence based typing information in a single experimental procedure

TYPE /CLONE/LINEAGE AND SUBTYPE CLASSIFICATIONS

Street market, Florence, Italy

Serotype :SerogroupSerotype

emm typing:emm type (95% similarity to prototype)emm subtypes (specific sequence)

Different typing method results are different partitions of a dataset

Spa typing:Spa typeBURP complex

PFGE :PFGE Type (cut-off 80% DICE/UPGMA)PFGE Subtype (cut-off 80% DICE/UPGMA)

MLST /MLVA/ cgMLST/SNP profile:Sequence TypeClonal Complex (eBURST/goeBURST) / cut-off on Minnimun Spanning Trees

TYPE /CLONE/LINEAGE AND SUBTYPE CLASSIFICATIONS

CHOOSING THE RIGHT METHOD

Struelens, M.J. et al, 1996. Clinical microbiology and infection, 2(1), pp.2–11.

Performance criteria:TypeabilityReproducibilityStabilityDiscriminatory powerEpidemiological concordanceTyping System concordance

Convinience Criteria:FlexibilitySpeedCost

Goal

COMPARING TYPING METHODS

Weissman S J et al. Appl. Environ. Microbiol. 2012;78:1353-1360

Conc

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MLS

T lo

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flmH sequences

The Hard way….

NEED FOR QUANTIFICATION AND STATISTICS

When you can measure what you are talking about and express it in numbers you know something about it. When you cannot measure it, when you cannot express it, your knowledge is of a meagre and unsatisfactory kind.

- Lord Kelvin 1861

COMPARING PARTITIONS FRAMEWORK

Use of three Coefficients :1) Simpson’s Index of Diversity 2) Adjusted Rand3) Adjusted Wallace

And the respective 95% confidence intervals

COMPARINGPARTITIONS WEBSITE

http://www.comparingpartitions.info

Copy/Paste from Excel

COMPARINGPARTITIONS WEBSITE

MEASURING DIVERSITY: SID

Simpson’s Index of Diversity

This index indicates the probability of two strains sampled randomly from a population belonging to two different types

Since it is a probability varies between 0 – 1.

Highly discriminatory methods are desired…

..but are they always needed?

Confidence intervals were defined for SID and should be used.

Simpson, 1948Hunter and Gaston, 1988Grundmann et al ,2001

Comparing SID’s 95% CIs

Null Hypothesis: The values under comparison are the same

COMPARING METHODS RESULTS

PFG

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s2

s3

s4

s5

s6

s7

Same Sequence Type?

Same PFGE cluster?

Y

N

Y N

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For each pair of isolates:

Seq

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ADJUSTED RAND

Overall concordance of two methods taking into account that the agreement between results could arise by chance alone.

Bi-directional agreement measureConfidence intervals by jackknife pseudo-values method.

CHANCE AGREEMENT ILLUSTRATION

Two possible random rearrangements…

CHANCE AGREEMENT: RAND VS ADJUSTED RAND

Without correction by chance agreement

CHANCE AGREEMENT: RAND VS ADJUSTED RAND

With correction by chance agreement

ADJUSTED WALLACE

Probability that if two strains share the same classification by a Method A they also share the same classification by Method B, corrected by chance agreement

Analytical confidence intervals.Jackknife pseudo values confidence intervals

COMPARING AR AND AW 95% CI

Null Hypothesis: The values under comparison are the same

OTHER APPLICATIONS FOR SID,AR AND AW

• Determination of the best set of markers for typing purposes : given dozens to hundreds or thousands of possible loci or SNPs is there a subset with enough discrimination to produce the same results as other typing method?

http://www.cidmpublichealth.org/pages/ausetts.html.

OTHER APPLICATIONS FOR SID,AR AND AW

• Determination of the best set of markers /typing methods for typing purposes for predicting a specific outcome or any associated metadata. Examples:

• Using AW to determine the which typing method better predicts a clinical outcome or prognosis.

• Using AW to determine association between alleles and Clonal Complexes (Weissman S J et al. Appl. Environ. Microbiol. 2012;78:1353-1360)

• Determining association between alleles or types and geographical location of sampling

• Determining the best combination of locus to predict a clinical outcome in order to design a fast RT-PCR method

CONCLUSIONS•The larger the sample size the more accurate can be the conclusions. Usually >50 is enough but >100 is better to get statistically significant results. (Severiano PlosOne 2011)

•Always use SID, Adjusted Rand and Adjusted Wallace to have an overall idea how the methods relate

•Confidence intervals give more information than the point estimates because they intrinsically take the sample size into consideration

•Don’t use coefficients that not corrected by chance agreement when comparing typing methods

ACKNOWLEDGEMENTS

Mário RamirezFrancisco Pinto Ana Severiano

Mramirez Lab / UMMI Members

Funding from Fundação para a Ciência e TecnologiaEU 7th Framework programme

www.comparingpartitions.info

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Registration and Abstract submission open at :

Plenary Sessions•Small scale microbial epidemiology - outbreaks•Large scale microbial epidemiology - surveillance•Bioinformatics for genome-based microbial epidemiology•Population genetics: pathogen emergence•Population dynamics: transmission networks and surveillance•Molecular epidemiology for global health and One Health

Parallel Sessions•Food and environmental pathogens•Microbial forensics•Viral evolution and surveillance•Fungal and yeast pathogens•Novel diagnostics and typing methodologies•Antimicrobial resistance: from mechanisms to impact in healthcare-associated infections•Microbial phylogenetic inference•Making sense of genomic data: bioinformatics tools

TO KNOW MORE:

For examples of usage see the list of references in:http://www.comparingpartitions.info/index.php?link=References