high resolution outbreak tracing and resistance detection using whole genome sequencing in the case...

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High resolution outbreak tracing and resistance detection using whole genome sequencing in the case of a Mycobacterium tuberculosis outbreak

Winnie Ridderberg, Ph.D.

Senior Scientist, Microbial Genomics

High resolution outbreak tracing and resistance detection using WGS in the case of a M. tuberculosis outbreak 1

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Outline

Introduction to Mycobacterium tuberculosis1

A case study2

Outbreak tracing3

Detecting antimicrobial resistance4


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Mycobacterium tuberculosis – Infection

Tuberculosis (TB) is one of the deadliest diseases worldwide

• One third of the world’s population has been infected with Mycobacterium tuberculosis

• In 2015, 10.4 million became sick with TB and 1.8 million died from TB-related causes worldwide

Clinical manifestations of tuberculosis

• Pulmonary TB

• Extrapulmonary TB affecting organs other than the lungs

◦ Pleura, lymph nodes, abdomen, skin, bones or meninges

• Latent TB infection or TB disease

Antimicrobial resistance

• Multi-drug resistance (MDR)

◦ Resistance to at least isoniazid and rifampicin

• Extensive drug resistance (XDR)

◦ MDR plus resistance to a fluoroquinolone and an injectable antibiotic (amikacin, kanamycin,

or capreomycin)


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Mycobacterium tuberculosis – Genetics

M. tuberculosis strains are highly clonal

• They have a nucleotide identity of 99.9%

• They display very little recombination

• The main mechanism of antimicrobial resistance is acquiring mutations affecting binding

pocket and drug-target interactions


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Combatting the TB epidemic

6

Classic methods

New methods:

focus on whole

genome

• Contact investigation

• Phenotypic methods

• Molecular methods

◦ Spoligo-typing

◦ 24-MIRU-VNTR

• Antimicrobial susceptibility testing

• Core-genome MLST www.cgmlst.org

• NGS–based whole genome analysis

• To control the spread of TB, particularly MDR/XDR-TB, surveillance is key

• For correct and effective treatment, understanding the resistance mechanisms is vital

High resolution outbreak tracing and resistance detection using WGS in the case of a M. tuberculosis outbreak

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Combatting the TB epidemic

Challenges

in whole

genome

sequencing

Transmission

at maximum

resolution

Linking

mutations to

resistance

Accurate

detection of

resistance

mutations

Evaluating

unknown

mutations


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Microbial Genomics Pro Suite

Features overviewMicrobiome profiling

• 16S and whole metagenome

• Taxonomic profiling

• Functional profiling

• Comparative analytics

NGS-based isolate typing

• Taxonomy

• AM resistance

• Epidemiology

• Molecular phylogeny

Bx

De Novo Assembly

• Genome and metagenome

• PacBio

• Gene finding

• Annotation

Pathogen typing

AMR typing

Epidemiolgy

Food safety

Microbiome analysis


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Outline


A case study2

Outbreak tracing3



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Case study

• Fiebig and co-workers, 2017: Surveillance and outbreak report

• Describes the investigation of a molecular cluster of multi-drug-resistant TB

across Austria, Germany and Romania

Fiebig, L., Kohl, T.A., Popovici, O., Mühlenfeld, M., Indra, A., Homorodean,

D.,Chiotan, D., Richter, E., Rüsch-Gerdes, S., Schmidgruber, B., Beckert, P., Hauer,

B.,Niemann, S., Allerberger, F. and Haas, W (2017) A joint cross-border investigation

of a cluster of multidrug-resistant tuberculosis in Austria, Romania and Germany in

2014 using classic, genotyping and whole genome sequencing methods: lessons

learnt. Euro Surveill. 22,1–11


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Case study

Five MDR-TB VNTR A

IV, V – Diagnosed

in June 2013

I, II, III – Diagnosed

between 2010–12

Born in Austria

Originate from the same

city in Romania

Live in city 1

I, II – Moved to city 1

III – Moved to city 2

III had a

sister with

MDR-TB in

Germany

Austria

Three MDR-TB VNTR AVI, VII, VIII –

Diagnosed in 2011

VI – Sister of III

VII

Originate from

Romania

Born in Africa

Germany

VIII

Five MDR-TB analyzed

IX, X, XI, XII, XIII –

Diagnosed

between 2004–14

X, XI – VNTR A

IX – VNTR BBorn and live in

Romania

Romania

XII, XIII – VNTR C

Five patients

originate from

the same city

in Romania

I, II, III, VI, VII


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Case study

Patient ID Sex Age group

(years)

Country of

residence

Country of

birth

Month and year

of current TB

diagnosis

Year of

previous TB

diagnosis

Site of disease

I Female 30–39 Austria Romania March 2010 2001 Pulmonary

II Male 50–59 Austria Romania January 2011 Pulmonary

III Female 30–39 Austria Romania March 2012 1998 and 2003 Pulmonary

IV Male 40–49 Austria Austria June 2013 Pulmonary

V Male 50–59 Austria Austria June 2013 Pulmonary

VI Female 30–39 Germany Romania December 2011 Pulmonary

VII Female 30–39 Germany Romania May 2011 Pulmonary

VIII Male 30–39 Germany Nigeria July 2011 Extrapulmonary

IX Male 40–49 Romania Romania January 2004 Pulmonary

X Male 50–59 Romania Romania December 2011 2011 Pulmonary

XI Male 30–39 Romania Romania January 2014 Pulmonary

XII Male 20–29 Romania Romania December 2013 Pulmonary

XIII Female 60–69 Romania Romania January 2014 2004 Pulmonary


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Outline


A case study2

Outbreak tracing3



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Outbreak tracing

Whole genome sequencing for transmission analysis

WGS for transmission analysis

• Bacteria evolve throughout the progression of an outbreak

◦ Genome-wide comparison of mutations offers highest resolution for clustering

and source tracking

• Methods

◦ Genome-wide variant (SNP, SNVs) detection

◦ Phylogenetic analysis via SNP trees

Whole

genome

sequencing

Variant

detectionPhylogenetic

analysis


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Outbreak tracing

Automated workflows for outbreak tracing

• Preconfigured workflow “map to

specified reference”

• Output from the variant detection tool

was used for SNP-tree calculation


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Case study results

Outbreak tracing

• Analysis is based on 673 variant positions


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Case study results

398

9

4

1

46

34

28

22

6

12

20112012

2011

2014

2010

2004

2011

2013

2014

2011

2013

2011

2013

VNTR“C”

VNTR“B”

VNTR“A”

Both born and living in Romania

All originate from the same

Romanian city, but live in three

countries

Sisters

Three patients from the

same Austrian city

Whole genome sequencing and SNP analysis provide higher resolution outbreak

investigation compared to genotyping methods.


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Outline


A case study2

Outbreak tracing3



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Detecting antimicrobial resistance

Mutation detection

• SNVs and InDels

• Local realignment

◦ Reduced FP InDel calling

• Low Frequency Variant Detection

tool

• Optional: further variant filtering

after variant detection

◦ Reduced FN rate

Reference database

• 1459 variants (SNPs and InDels)

• 31 loci

• 15 drugs

Sources for variant database

• Coll et al. (2015)

◦ TBDreaMDB

◦ MUBII–TB–DB

◦ Recent literature to include additional variants and loci

• Additional variants described by Miotto et al. (2014) and

Allana et al. (2017)


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Results

Antimicrobial resistance

• Detected resistance causing variants

• Total 123 variants

• 75 from original study

• 48 new

How do you evaluate novel variant calls (missing in the database)?


Original study New analysis

No. of

variants %

No. of

variants %

In congruence

with phenotype61 81 30 63

Antimicrobials not

tested8 11 14 29

In contrast with

phenotype6 8 4 8

Total 75 48

Drug LocusNo. of variants

detected in paper

No. of additional

detected variants

Capreomycin tlyA 0 2

Capreomycin rrs 2 4

Clofazimide RV0678 0 1

Ethambutol embB 15 1

Ethambutol embC 0 1

Ethambutol embR 0 2

Ethionamide ethA 5 0

EthionamidefabG1

promoter0 10

Fluoroquinolones gyrA 1 1

Fluoroquinolones gyrB 0 1

IsoniazidfabG1

promoter0 10

Isoniazid katG 13 5

Kanamycin eis promoter 0 6

Kanamycin rrs 2 4

Para-aminosalisylic acid thyA 1 0

Pyrazinamide pncA 11 8

Rifampicin rpoB 14 2

Rifampicin rpoC 0 4

Streptomycin gidB 11 0

Streptomycin rpsL 2 0

Streptomycin rrs 2 4

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Evaluating unknown mutations

Link variants to 3D protein structure (novel variants not found in literature)

• Visualization of amino acid alterations

on 3D models

• Evaluate the effect of variants on the

drug-target interaction or protein

function


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Variants in the context of drug-target 3D structure

Link variants to 3D protein structure

DNA gyrase

• Target of fluoroquinolones

• Involved in supercoiling of DNA, binding

DNA and introducing transient double-

stranded breaks

• Fluoroquinolones bind to the enzyme-DNA

complex

• Mutation of the target region alter target

structure and the binding affinity for the

drug

DNAGyraseSubunitAMoxifloxacin

Referencemodel Variantmodel- Ala90Val

GyrA


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Link variants to 3D protein structure

RNA polymerase

• Target of rifamycin

• Rifamycin binds within the DNA/RNA

channel physically blocking elongation

• Resistance arises from amino acid

alterations in the binding site, decreasing the

affinity for the drug

• Sensitive variant detection reveals more mutations with likely causal effects.

• 3D structure to assess impact of mutations on drug-target interaction.

Referencemodel Variantmodel– Ser431Asn

RNAPolymeraseSubunitBRifampicin

RpoB


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More examples

RpoB KatG RpsL PncA

GyrA PncA ThyA RpoC

KatG TlyA EmbR Rv0678


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Summary

• Outbreak tracing

◦ High resolution outbreak analysis

◦ Preconfigured workflows for data QC, mapping and variant detection

◦ SNP tree for visualization of outbreak isolates and associated metadata

• Resistance detection

◦ Optimized variant detection

◦ Filtering against known resistance causing variants

◦ 3D visualization for qualification of undescribed variants


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