In March 2014, a molecular cluster of five multidrug-resistant Mycobacterium tuberculosis was detected by the Austrian National Reference Laboratory. An investigation was initiated to determine if transmission had occurred within Austria. Epidemiological links to Germany and Romania prompted a multi-national joint investigation, tracing the outbreak. The results were published by Fiebig and coworkers in 2017. Whole genome SNP analysis allowed for high resolution clustering of isolates. Whole genome sequencing further permitted simultaneous detection of resistance-causing variants. Using an improved variant detection pipeline, we identified novel variants undetected in the original study. We used functional analysis to explore if novel variants could be associated to antimicrobial resistance. Using the data published by Fiebig at al. in 2017, we demonstrate the use of CLC Microbial Genomics Module for tracing pathogen transmission during outbreaks and for the detection and functional analysis of resistance-causing variants. The applied user-friendly tools and preconfigured workflows ensure ease of use and reproducibility.

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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 overview
Microbiome 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
Introduction to Mycobacterium tuberculosis1
A case study2
Outbreak tracing3
Detecting antimicrobial resistance4
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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 A
VI, 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 B
Born 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
Introduction to Mycobacterium tuberculosis1
A case study2
Outbreak tracing3
Detecting antimicrobial resistance4
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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
detection
Phylogenetic
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
Introduction to Mycobacterium tuberculosis1
A case study2
Outbreak tracing3
Detecting antimicrobial resistance4
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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)?
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Original study New analysis
No. of
variants %
No. of
variants %
In congruence
with phenotype
61 81 30 63
Antimicrobials not
tested
8 11 14 29
In contrast with
phenotype
6 8 4 8
Total 75 48
Drug Locus
No. 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
Ethionamide
fabG1
promoter
0 10
Fluoroquinolones gyrA 1 1
Fluoroquinolones gyrB 0 1
Isoniazid
fabG1
promoter
0 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
DNA Gyrase Subunit A
Moxifloxacin
Reference model Variant model - Ala90Val
GyrA
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Variants in the context of drug-target 3D structure
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.
Reference model Variant model – Ser431Asn
RNA Polymerase Subunit B
Rifampicin
RpoB
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Variants in the context of drug-target 3D structure
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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