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Faculty of Sciences
Department of Biology
Unveiling the evolutionary history & molecular epidemiology of
Mycobacterium ulcerans
De onthulling van de evolutionaire geschiedenis en de moleculaire epidemiologie van
Mycobacterium ulcerans
Proefschrift voorgelegd tot het behalen van de graad van
Doctor in de Wetenschappen: Biologie
aan de Universiteit Antwerpen te verdedigen door
Koen Vandelannoote
Promotoren: Antwerpen, 2016
Prof. dr. Bouke de Jong
Prof. dr. Herwig Leirs
Co-Promotor:
Prof. dr. Dissou Affolabi
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Front cover image: Koen Vandelannoote Back cover image: Sophie Gryseels
Copyright © 2016 Universiteit Antwerpen - Faculteit Wetenschappen
Self-published by Koen Vandelannoote, Lode de Boningestraat 12, 2650 Edegem
(België).
All rights reserved. No part of this publication may be reproduced in any form by
print, photoprint, microfilm, electronic or any other means without written
permission from the publisher.
Alle rechten voorbehouden. Niets uit deze uitgave mag worden vermenigvuldigd
en/of openbaar gemaakt worden door middel van druk, fotokopie, microfilm,
elektronisch of op welke andere wijze ook zonder voorafgaande schriftelijke
toestemming van de uitgever.
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Para Luquinha
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Members of the Jury
Chairperson:
Prof. dr. Han Asard University of Antwerp (Belgium)
Secretary:
Prof. dr. Surbhi Malhotra Kumar University of Antwerp (Belgium)
Promotors:
Prof. dr. Bouke de Jong Institute of Tropical Medicine (Belgium)
Prof. dr. Herwig Leirs University of Antwerp (Belgium)
Co-Promotor:
Prof. dr. Dissou Affolabi University of Abomey-Calavi (Benin)
Members:
Prof. dr. Philippe Lemey University of Leuven (Belgium)
Prof. dr. Philip Supply CNRS & Genoscreen (France)
Em. Prof. dr. Françoise Portaels Institute of Tropical Medicine (Belgium)
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Contents
Abbreviations ..................................................................................................... 9
Summary ........................................................................................................... 11
Samenvatting .................................................................................................... 13
Chapter 1 .......................................................................................................... 15
1.1 Buruli ulcer ......................................................................................... 16
1.2 M. ulcerans genomic characteristics and evolutionary origin ........... 19
1.3 Etiology .............................................................................................. 21
1.4 Reservoir(s) and mode(s) of transmission ......................................... 22
1.5 BU transmission in Australia .............................................................. 25
1.6 Molecular phylogenetics and epidemiology ..................................... 27
1.7 Research Goals: breaking the Buruli enigma ..................................... 28
Chapter 2 .......................................................................................................... 31
2.1 Abstract .............................................................................................. 32
2.2 Introduction ....................................................................................... 32
2.3 Material and Methods ....................................................................... 35
2.4 Results ................................................................................................ 44
2.5 Discussion .......................................................................................... 52
2.6 Acknowledgements ........................................................................... 56
Chapter 3 .......................................................................................................... 57
3.1 Abstract .............................................................................................. 58
3.2 Introduction ....................................................................................... 59
3.3 Methods ............................................................................................. 61
3.4 Results ................................................................................................ 65
3.5 Discussion .......................................................................................... 70
3.6 Supporting Information ..................................................................... 72
3.7 Acknowledgments ............................................................................. 72
3.8 Funding Statement ............................................................................ 72
3.9 Data Availability ................................................................................. 73
Chapter 4 .......................................................................................................... 75
4.1 Abstract .............................................................................................. 76
4.2 Introduction ....................................................................................... 76
4.3 Methods ............................................................................................. 78
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4.4 Results & Discussion ........................................................................... 84
4.5 Conclusion .......................................................................................... 95
4.6 Supporting Information ...................................................................... 96
4.7 Data access ....................................................................................... 107
4.8 Acknowledgements .......................................................................... 107
Chapter 5......................................................................................................... 109
5.1 Abstract ............................................................................................ 110
5.2 Introduction ..................................................................................... 110
5.3 Materials and Methods .................................................................... 113
5.4 Results .............................................................................................. 118
5.5 Discussion ......................................................................................... 129
5.6 Supporting Information .................................................................... 132
5.7 Acknowledgements .......................................................................... 141
Chapter 6......................................................................................................... 143
6.1 Abstract ............................................................................................ 144
6.2 Author summary .............................................................................. 144
6.3 Introduction ..................................................................................... 145
6.4 Methods ........................................................................................... 146
6.5 Results .............................................................................................. 148
6.6 Discussion ......................................................................................... 153
6.7 Supporting Information .................................................................... 155
Chapter 7......................................................................................................... 157
Chapter 8......................................................................................................... 167
Acknowledgements ........................................................................................ 171
List of Publications .......................................................................................... 173
References ...................................................................................................... 175
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Abbreviations
aDNA: ancient DNA
BAPS: Bayesian Analysis of genetic Population Structure
BU: Buruli ulcer
DRC: Democratic Republic of Congo
EBSP: Extended Bayesian Skyline Plot
ESS: Effective Sample Size
FAO: Food and Agriculture Organization
GTR: Generalized Time Reversible
HPD: Highest Posterior Density
IME: Institut Médical Evangélique
Indel: insertion / deletion
IS: Insertion Sequence
ISE-SNP: Insertion Sequence Element - Single Nucleotide Polymorphism
ITM: Institute of Tropical Medicine
LJ: Löwenstein Jensen
MRCA: Most Recent Common Ancestor
ML: Maximum Likelihood
MP: Maximum Parsimony
NGS: Next Generation Sequencing
NJ: Neighbor-Joining
PNG: Papua New Guinea
PS: Path Sampling
qPCR: Quantitative Polymerase Chain Reaction
RC: Republic of the Congo
SNPs: Single Nucleotide Polymorphisms
SRA: Sequence Read Archive
SRTM: Shuttle Radar Topography Mission
WGS: Whole Genome Sequencing
WHO: World Health Organization
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Summary
Buruli ulcer is a neglected tropical disease of skin and subcutaneous tissue,
caused by infection with Mycobacterium ulcerans. Cases are reported around
the world, with rural wetlands of West- and Central African countries most
affected. Prevention and control of the disease is complicated by limited
understanding of the mode(s) of transmission of BU, probably involving
inoculation directly by a vector, or from a contaminated skin surface. While
direct human-to-human transmission does not occur, the potential
environmental reservoir(s), and the role of the human reservoir in sustaining
endemic BU, are controversial.
We redesigned a SNP-based (ISE-SNP) fingerprinting assay to gain
fundamental insights into the population structure and evolutionary history of
African M. ulcerans. Even though ISE-SNP genotyping resulted in higher
geographical resolution than previously achieved, the resolution was still too
limited to reconstruct evolutionary events on anything smaller than the
continental scale. Therefore we used state-of-the-art second and third
generation genome sequencing technologies and advanced statistical
approaches to understand the detailed evolutionary and spatio-temporal
history of African M. ulcerans. We also explored the use of the increased
resolution offered by low-cost genomics to distinguish disease relapses from
reinfections in patients with multiple BU episodes. African M. ulcerans was
found to be evolving entirely through clonal expansion and its genetic
diversity proved to be very restricted because of the pathogen’s slow
genome-wide substitution rate coupled with its relative recent origin.
Exploration of the genetic population structure revealed the existence of two
specific lineages within the African continent. We used temporal associations
and studied the past demographic history of M. ulcerans in a BU endemic
region to implicate the role of humans as a major reservoir in BU
transmission. In previous studies, M. ulcerans DNA has been detected in the
environment, possibly originating from BU patients with active, openly
discharging lesions. Transmission can then occur indirectly in the same
community water source, when the superficial skin surface of a naïve
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individual is contaminated, and the bacilli present on the contaminated skin
are subsequently inoculated subcutaneously through some form of
penetrating (micro)trauma.
Our observations on the role of humans as maintenance reservoir to sustain
new BU infections suggest that interventions in a region aimed at reducing the
human BU burden will at the same time break the transmission chains within
that region. Active case-finding programs and the early treatment of pre-
ulcerative infections with specific antibiotics will decrease the amounts of
mycobacteria shed into the environment, which may ultimately reduce
disease transmission in Africa.
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Samenvatting
Buruli ulcer (BU) is een verwaarloosde tropische ziekte veroorzaakt door
infectie met Mycobacterium ulcerans. De ziekte tast het huidoppervlak en de
onderhuidse weefsels aan. Hoewel er gevallen gemeld worden van over de
hele wereld zijn de rurale wetlands van West- en Centraal-Afrika het ergst
getroffen. Momenteel zijn BU preventie en -bestrijding gehinderd door een
beperkt begrip van de ziektetransmissie. Er wordt vermoed dat infectie kan
gebeuren na inoculatie via een vector of via het gecontamineerde
huidoppervlak. Hoewel overdracht van mens op mens niet voorkomt zijn
zowel potentiele reservoirs uit het milieu als de rol van het menselijke
reservoir in het onderhouden van een BU epidemie controversieel.
We herontwierpen een SNP-gebaseerde (ISE-SNP) fingerprinting assay om
fundamentele inzichten te verwerven in de populatiestructuur en de
evolutionaire geschiedenis van Afrikaanse M. ulcerans. Hoewel ISE-SNP
genotypering resulteerde in een ongezien hoge geografische resolutie, bleek
deze toch te beperkt om evolutionaire gebeurtenissen te reconstrueren op
een niveau kleiner dan de continentale schaal. Daarom hebben we gebruik
gemaakt van zowel state-of-the-art eerste en tweede generatie sequencing
technologieën, als geavanceerde statistische benaderingen om de
gedetailleerde evolutionaire en spatio-temporele geschiedenis van Afrikaanse
M. ulcerans te begrijpen. We verkenden eveneens het gebruik van de
aanzienlijk toegenomen resolutie gebracht door goedkope genomics om
herbesmetting te onderscheiden van herval in patiënten die verschillende BU-
episoden doormaakten. Afrikaanse M. ulcerans bleek volledig te evolueren
door middel van klonale expansie. Bovendien bleek de genetische diversiteit
vrij beperkt vanwege de trage genoomwijde substitutiesnelheid
gecombineerd met de relatief recente oorsprong van het pathogeen.
Onderzoek naar de genetische populatiestructuur onthulde het bestaan van
twee specifieke lineages binnen het Afrikaanse continent. We maakten
gebruik van temporele associaties en we bestudeerden daarnaast de
demografische geschiedenis van M. ulcerans in een BU endemisch gebied om
de mens te impliceren als een belangrijk reservoir in BU transmissie. In
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voorgaande studies werd in het milieu M. ulcerans DNA gedetecteerd dat
mogelijk afkomstig was van BU patiënten met actieve open etterende laesies.
Transmissie kan indirect optreden in hetzelfde gemeenschappelijke waterpunt
wanneer het huidoppervlak van een naïef individu wordt gecontamineerd, en
de bacillen aanwezig op de besmette huid vervolgens subcutaan worden
geïnoculeerd via een bepaalde vorm van penetrerend (micro)trauma.
Onze waarneming over de rol die de mens speelt als onderhoudsreservoir in
het tot stand brengen van nieuwe BU infecties suggereert dat interventies in
een gebied gericht op het reduceren van de menselijke BU ziektelast
tegelijkertijd transmissieketens zullen breken in dat gebied. Actieve case-
finding programma’s en vroege behandeling van laesies in het pre-ulceratieve
stadium met specifieke antibiotica zal het aantal mycobacteriën dat in de
omgeving uitgescheden wordt doen afnemen wat uiteindelijk verdere
besmettingen van de ziekte in Afrika zou doen afnemen.
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Chapter 1
General Introduction
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1.1 Buruli ulcer
Mycobacterium ulcerans is the causative agent of Buruli ulcer (BU), the third
most common mycobacterial disease in humans after tuberculosis and leprosy
[1]. BU is a slowly progressing necrotizing disease of the skin and
subcutaneous tissues and the affliction is recognized as one of 17 “neglected”
tropical diseases [2].
BU was first recognized and documented in 1948 in the Bairnsdale Region of
Victoria, Australia [3], where the disease until today is known as “Bairnsdale
ulcer”. However the most commonly used name of the disease originates
from the Buruli County in Uganda (presently the Nakasongola District) where
a large number of cases was described during the 1960s [4]. BU has gone
unnoticed for a long period of time, probably because it occurs in remote
communities that used to be poorly covered by national health surveillance
systems. Presently, the condition has been reported (but not always
microbiologically confirmed), in more than 30 countries spread over Africa,
the Americas, Asia, and Oceania [5] (Figure 1.1). However, Africa is by far the
worst affected continent with 2151 new cases reported to the WHO in 2014
[6]. The highest documented notification rates are found in the West African
nations of Côte d’Ivoire, Benin, and Ghana where in some specific areas the
number of BU cases may exceed those of tuberculosis and leprosy [7]. The
disease remains very uncommon in non-African countries. Nevertheless,
important disease foci located outside of Africa have persisted in Australia,
French Guiana, and Papua New Guinea [8]. Furthermore, a number of
imported cases have been reported in non-endemic European and American
countries after international travel [9, 10].
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Figure 1.1: BU endemic countries. The number of new cases reported to the WHO in 2014.
Image created using global health observatory data (WHO).
The disease manifests itself as painless, necrotizing cutaneous lesions that are
often located on the limbs and can take both ulcerative and non-ulcerative
forms. The non-ulcerative spectrum of the disease includes papules, nodules,
plaques, edema, and osteomyelitis. However, untreated infection with M.
ulcerans often leads to extensive destruction of the skin and soft tissues
through the formation of large ulcers, with characteristically undermined
edges and edema of the surrounding skin (Figure 1.2) [8]. Any age group can
be affected, yet incidence peaks in 5-15 year old children in Africa [7]. No
gender, ethnicity or social group is exempt.
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Figure 1.2: The spectrum of clinical BU disease. 1, A nodule (Photo: Kingsley Asiedu) 2, A
papule (Photo: John Hayman) 3, A plaque (Photo: Mark Evans) 4, Small ulcerative lesion; note
the characteristic yellowish-white necrotic base (cotton wool-like appearance) and the
undermined edge of the lesion (Photo: John Hayman) 5, Extensive ulceration on the left arm
(Photo: Kingsley Asiedu) 6, Extensive ulceration on the leg (Photo: Henri Assé) 7, Extensive
ulceration involving the chest and abdominal walls, the pubic region, the penis and the
scrotum; post excision and ready for grafting (Photo: Pius Agbenorku) 8, Non-ulcerative
oedema (Photo: Samuel Etuaful) 9, Amputation of the left leg (Photo: Linda Lehman) 10,
Contracture deformity of knee joint (Photo: Henri Assé) 11, An eye complication as a result of
BU (Photo: Kingsley Asiedu) 12, Bone lesion (osteomyelitis) visualized with an X-ray (Photo:
WHO) 13, Squamous cell carcinoma secondary to BU (Photo: Samuel Etuaful).
In Africa, BU affects mostly poor, remote rural communities [1, 7], where the
disease is known to have a serious impact on public health. While the
mortality is low, the morbidity is considerable, as without proper treatment
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the disease leads to scarring, contractures and other physical deformities
associated with stigma and a marked socioeconomic burden (Figure 1.2) [8].
Patients who present with advanced stages of the disease often require
extensive surgical intervention involving excision and skin grafting. This places
a lot of added burden on already stretched local health structures, as patients
often require extended hospitalization, with school dropouts and a reduced
workforce as major consequences for the affected communities [7]. Such
sequelae can be avoided by early case detection, as the early forms of the
disease can be treated relatively easily with a specific combined antibiotic
regimen (rifampicin and streptomycin) administered during 8 weeks, often
without need for surgery [11].
1.2 M. ulcerans genomic characteristics and evolutionary origin
The genome of the Ghanaian M. ulcerans strain Agy99 was sequenced in 2007
at the Pasteur Institute, Paris [12]. The chromosome contains 4160 genes and
771 pseudogenes and has a GC content of 65.7%. The Agy99 reference
genome is comprised of two circular replicons: a bacterial chromosome of
5.63 Mb and on average 1.9 copies of a giant circular virulence plasmid
pMUM001. This 174 kb megaplasmid encodes the genes required for the
biosynthesis of the endotoxin mycolactone [13]. This macrocyclic polyketide
molecule has cytotoxic, analgesic, and immunomodulatory properties that
cause the chronic ulcerative skin lesions with limited inflammation and thus
plays a key role in the pathogenesis of BU [14]. Recent major advances
identified the underlying molecular targets for mycolactone. First,
mycolactone prevents the co-translational translocation (and therefore
production) of many proteins that pass through the endoplasmic reticulum for
secretion or placement in cell membranes [15]. Second, it can target specific
scaffolding proteins, which control actin polymerization dynamics in adherent
cells and therefore lead to detachment and cell death [16]. Finally, the
hypoesthesia results from mycolactone eliciting signaling through type 2
angiotensin II receptors [17].
A recent genomic study of 35 M. marinum - M. ulcerans complex isolates
confirmed earlier indications [12] that all mycolactone-producing
mycobacteria evolved by a process of lateral gene transfer (pMUM001
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acquisition from another actinobacterium) and subsequent reductive
evolution from a common M. marinum-like progenitor [18-20]. Differences in
the extent of genome reduction along 3 deep branches of the complex
suggest that three major lineages have adapted to slightly different niche
environments [19]. The three lineages that are responding to different
environmental pressures can therefore be considered M. ulcerans ecovars.
Ecovar 1 encompasses globally distributed fish and frog isolates and isolates
that (rarely) cause BU in humans in the Americas [21]. Ecovar 2 is represented
by Japanese isolates from human BU cases. Finally, M. ulcerans disease
isolates from West and Central Africa belong to the third ecovar which
comprises a highly clonal group that is also responsible for BU in South East
Asia and Australia (Figure 1.3).
Figure 1.3: Neighbour joining phylogenetic tree based on 128,463 variable common
nucleotide positions across 34 sequenced isolates. The tree was rooted using M. tuberculosis
as an outgroup (not shown). The major clustering of isolates are M. marinum isolates (4) -
blue; Fish and frog isolates (5) - green; Japanese isolate (1) - Mu_8765; French Guiana isolate -
Mu_1G897; Australian isolates (10) - orange; African isolates (13) - red. The scale bars show
the median pairwise divergence for the set of isolates they span. The isolates that produce
mycolactone are highlighted in pale yellow. This image has been modified from [19].
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The reductive evolution of M. ulcerans has been mediated by the high-copy
number insertion sequence (IS) elements IS2404 and IS2606 [18-20]. As
IS2404 is present in the genome in approximately 200 copies [12], it is the
preferred target for the molecular detection of M. ulcerans by PCR in both
clinical and environmental samples [22]. For some M. ulcerans lineages a
second IS element, IS2606, is also present in a high copy number
(approximately 90 copies) [19]. IS elements code for a transposase that
catalyzes the enzymatic reaction allowing the elements to move along the
genome. Their widespread genome distribution and high copy number
indicate their potential to actively jump or duplicate in the genome and so, to
act as substrates for ongoing genome rearrangements. As such these IS
elements profoundly enhance mycobacterial genome plasticity by modifying
gene expression and inactivating genes [23]. As a direct result the M. ulcerans
Agy99 reference genome has accumulated 771 pseudogenes [12].
1.3 Etiology
The isolation of M. ulcerans in primary culture both from clinical and
environmental origins is particularly challenging as M. ulcerans is a slow
grower with an estimated generation time of 23 hours [24]. As a result,
cultures easily become contaminated by other less fastidious organisms that
outgrow M. ulcerans. To improve sensitivity, harsh decontamination methods
are employed that eliminate non mycobacterial cells prior to culture [25].
Nevertheless, the sensitivity of in vitro culture of M. ulcerans from clinical
specimens remains rather low and cultures can take several months to grow,
even under optimal conditions [26]. Since, furthermore, culture laboratories
are not easily accessible in most remote BU endemic regions of Africa,
samples from lesions often have to be stored for extended periods of time
prior to processing [27], furthermore decreasing culture sensitivity. These
difficulties present limitations for BU studies on molecular epidemiology,
treatment efficacy, and drug susceptibility where primary isolation of M.
ulcerans remains crucial.
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1.4 Reservoir(s) and mode(s) of transmission
A wide knowledge gap exists in our understanding of the mode(s) of
transmission and the environmental reservoir(s) of BU, complicating
prevention and control of the disease. Unlike leprosy and tuberculosis, which
are characterized by person-to-person transmission, BU is rarely, if ever,
contagious, and is considered a non-communicable disease [8]. A
characteristic of BU is its focal distribution pattern: even within endemic
regions, endemic and non-endemic communities may be separated by only a
couple of kilometers [28]. Epidemiological studies established early on that
the disease is related to rural wetlands, especially those with stagnant to
slow-flowing water [8]. People living or working close to these water bodies
are more at risk for contracting the disease in endemic areas [29, 30] which
suggests the presence of the mycobacterium in or near the watery
environment. A systematic review of all papers studying risk factors
associated with M. ulcerans infection throughout the world concluded
furthermore that (i) poor wound care, and (ii) failure to wear protective
clothing, were the only other two commonly identified risk factors in all eight
papers [31]. Review of these risk factors suggests that transmission of M.
ulcerans occurs through inoculation of mycobacteria into the skin after
(micro)trauma following contact with a contaminated environment. As such, it
is generally believed that M. ulcerans is an environmental mycobacterium,
which can initiate infection after being inoculated into the subcutaneous
tissues of the skin through some form of microtrauma [32, 33]. These
observations led to numerous studies that made use of polymerase chain
reaction (PCR) screening to detect M. ulcerans DNA in various different types
of environmental samples adjacent to, or within water bodies. Studies carried
out in Australia [34] and West Africa [35] during the late 90s were the first of
their kind to successfully detect M. ulcerans DNA, and they were soon
followed by a plethora of others. IS2404, was detected in several
environmental samples including water [36], fish [37], aquatic insects [35, 38,
39], biofilms [39], detritus [40], mollusks [41], and mosquitos [42]. However,
these findings need to be interpreted with caution as:
• The bacillary concentration (as quantitated by PCR) in IS2404 positive
samples proved nearly always insignificantly low, indicating that it was
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unlikely that M. ulcerans was multiplying in the tested specimens [36,
43, 44].
• PCR detects DNA, and not intact organisms. The death of an M.
ulcerans bacillum will lead to the release of its DNA into the
environment where it can stick to other substrates. Actual
confirmation of the presence of living M. ulcerans in IS2404 positive
specimens has proven problematic as numerous studies repeatedly
failed to isolate M. ulcerans from the environment [1, 24, 45]. There is
one notable exception; in 2008, a landmark paper was published by
Portaels et al. [46], which described the first isolation of M. ulcerans in
pure culture from an aquatic environment in Benin. The fully
characterized isolate was obtained from a water strider (Gerris sp.)
and required three rounds of passaging through mice followed by
culture. Although this result presented considerable technical
prowess, it has until this day not been successfully repeated.
• Early studies furthermore relied entirely on the detection of a single
genetic marker to identify M. ulcerans DNA: IS2404. As this target
proved unspecific [22], attempts were made to augment the specificity
of environmental screening by increasing the amount of tested
molecular targets with IS2606, and pMUM001 domains like
ketoreductase B (KR-B) and enoyl reductase (ER) [22, 39]. However,
the discovery [47] of distinct globally distributed M. ulcerans Ecovar 1
isolates that cause disease in fish and ectotherms and contain both
IS2404 and pMUM001 still severely complicate the identification of M.
ulcerans from the environment that is pathogenic to humans.
• A series of external quality assessment programs for PCR detection of
M. ulcerans in environmental specimens indicated furthermore that
false positives were common, especially in the earliest round,
jeopardizing the credibility of a substantial segment of this literature
[48].
• True IS2404 positive environmental samples might reflect only the
presence of M. ulcerans in the local environment and play no role in
human transmission. Very stringent criteria, building on Koch’s
postulates, exist in biomedical research for indicating the roles of living
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organisms as biologically significant reservoirs and/or vectors of a
pathogen [49].
In spite of these serious limitations, several organisms have been put forward
as “potential” vectors or reservoirs of M. ulcerans in Africa [38]. Furthermore,
published results on M. ulcerans DNA ubiquitously dispersed in the
environment [50] also spawned the notion that M. ulcerans exists
saprophytically as a free-living organism in the aquatic environment. This is
however unlikely, as M. ulcerans is relatively “fragile” compared to true
opportunistically pathogenic saprophytic mycobacteria (e.g. M. fortuitum and
M. marinum):
• M. ulcerans exhibits a narrow in vitro temperature tolerance: its
optimal growth temperature ranges between 30-32°C and the
mycobacterium is sensitive to temperatures of 37°C or higher [51, 52].
This temperature requirement favors development of BU lesions on
the skin and subcutaneous tissues of the limbs where the human body
temperature is generally lower.
• The bacterium is very sensitive to oxygen concentration in vitro: its a
microaerophile [53]. Anaerobic respiration is furthermore impossible
as M. ulcerans lacks nitrate and fumarate reductase systems present in
M. marinum [12].
• M. ulcerans is sensitive to light as it has lost the ability to produce
light-inducible carotenoids that protect M. marinum from incident
sunlight [12].
• The susceptibility of M. ulcerans to streptomycin and rifampicin also
suggests that it is not free-living, where it would have encountered
these naturally occurring antibiotics in the environment [54].
• A recent study identified an inverse correlation between salt tolerance
and host adaptation [55]. Very much like the obligatory pathogen M.
tuberculosis, M. ulcerans exhibited a low (3%) salt tolerance compared
to M. fortuitum (8%).
There is furthermore additional strong genetic evidence that unlike M.
marinum, M. ulcerans is a commensal, associated with another organism that
protects the mycobacterium against unfavorable physical parameters in the
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environment. In depth analysis of the M. ulcerans genome indicates it carries
six genomic signatures that are indicative of organisms that passed through an
evolutionary bottleneck and are adapting to a new ecological niche:
proliferation of insertion sequence elements, accumulation of pseudogenes,
chromosomal rearrangements, genome downsizing, a high degree of
relatedness (“clonality”), and acquisition of foreign genes (through plasmids
or bacteriophages) that confer a fitness advantage in the new niche [12]. M.
ulcerans shares these specific signatures with other bacteria like M. leprae
[56], Bordetella pertussis [57], and Yersinia pestis [58].
1.5 BU transmission in Australia
Australia is the only developed country in the world reporting significant local
transmission of M. ulcerans. Infection with M. ulcerans occurs infrequently in
the wet tropical northern state of Queensland, where the climate is
somewhat similar to the sub-Saharan African tropics [59]. However, more
than 80% of Australian cases of BU in the past 15 years have originated from
the temperate southeastern state of Victoria [60]. M. ulcerans DNA was
detected at low levels by qPCR in soil, sediment, water residue, aquatic plant
biofilm and terrestrial vegetation collected in the Point Lonsdale hotspot [22].
More importantly, again in Point Lonsdale, higher levels of M. ulcerans DNA
were detected in the feces of common ringtail and common brushtail
possums. Systematic testing of possum feces with adequate controls revealed
that M. ulcerans DNA could be detected in 41% of fecal samples collected in
Point Lonsdale compared with less than 1% of fecal samples collected from
non-endemic areas [61]. Until now, Australian researchers have not been able
to culture M. ulcerans from the strongly PCR positive possum feces. However,
capture and clinical examination of live possums in Point Lonsdale revealed
that from both ringtail and brushtail possums M. ulcerans could be cultured
from skin lesions [62]. As such, possum species have been indicated as a likely
reservoir in which M. ulcerans can multiply. Whole genome sequencing
revealed furthermore an extremely close genetic relationship between human
and possum M. ulcerans isolates [61].
In temperate Australia, at this point in time, M. ulcerans infection has also
been identified in other wild, domesticated, and zoo animals [24]. Laboratory-
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confirmed cases have been diagnosed in koalas (Phascolarctos cinereus), a
mountain brushtail possum (Trichosurus cunninghami), a long-footed potoroo
(Potorous longipes), alpacas (Vicugna pacos), horses, dogs, and a cat [62]. All
animal cases were identified in locations where human cases of BU have been
reported.
M. ulcerans DNA has furthermore been found in mosquitos captured during
an outbreak of BU in the state of Victoria [42]. In a particularly interesting
case, BU lesions developed on the ear of a child who only briefly visited an
outbreak area; the child’s mother suspected a mosquito bite as the initiating
event [42]. In a case control study performed in southeastern Australia, the
reporting of mosquito bites on the forearms and lower legs was associated
with increased risk, while the use of insect repellent was associated with a
reduced risk [63]. Moreover, over a period of seven years, BU notifications
were found to correlate with those of Ross River virus, another notifiable
“local” disease which is transmitted by mosquitos [64].
These investigations combined seem to suggest that in Victoria, Australia, BU
is a zoonosis transmitted from possums to humans, via a mosquito vector,
although definite proof is to date still lacking. It is very hard to translate these
findings to the endemic regions in the Afrotropic ecozone. In the temperate
state of Victoria people have less direct contact with the environment than in
tropical Africa (e.g. bathing, washing, cooking, and farming). Also, medical
health-seeking behavior differs substantially with Australians predominantly
presenting during the early pre-ulcerative onset of the disease. M. ulcerans
infections in domesticated and wild animals have never been reported in
Africa [36, 43, 65, 66]. Furthermore, marsupial mammals (possums) are not
endemic in the African continent. In addition, the results of studies looking
into mosquito related risk factors in Africa are contradictory: two studies [29,
67] found a decreased risk of infection with mosquito net use, while a third
study [30] found no association between bed net use and infection.
Additionally, BU lesion distribution proves not to be consistent with mosquito
biting patterns [68]. Finally, a recent field study in Benin found no trace of M.
ulcerans DNA in a total of 4322 mosquitoes and 5407 mosquito larvae,
indicating that mosquitoes are probably not involved in the ecology and
dissemination of M. ulcerans in Africa [69].
Page | 27
1.6 Molecular phylogenetics and epidemiology
The study of phylogenetics is essentially concerned with reconstructing the
phylogenetic tree that represents the evolutionary history of related genes,
individuals, populations or species. The phylogenetic tree is estimated from a
multiple sequence alignment of aligned homologous molecular sequences.
When a tree has been estimated from individuals sampled from the same
population, statistical properties of the tree can be used to learn more about
the population from which the sample was drawn. In particular, the size of the
population can be estimated using Kingsman’s so called n-coalescent.
Coalescent theory is today used to infer fundamental parameters driving
molecular evolution and population dynamics [70].
Pathogen molecular sequences sampled through space and time and
particular traits associated with those sequences (like sampling location) can
be analyzed using Bayesian statistical inference approaches [71]. The major
advantage of the Bayesian statistical framework is the fact that it takes into
account the uncertainty of the unknown genealogy when trying to understand
the processes that ultimately gave rise to that genealogy [72].
Heterochronous sampling times of the samples drawn from a pathogen
population represent the most important layer of information to incorporate
into Bayesian phylogenetic analysis as these dates allow calibration of
phylogenetic trees in calendar years [73]. This approach has revolutionized
phylogenetic inference as it allows measuring evolutionary histories in units of
time instead of genetic distance. When the relationship between sequence
divergence and time (better known as substitution rate) is matching in all
branches of a time-tree we can assume a strict molecular clock model [74].
However, many biological systems do not adhere to the strict molecular clock
model [73]. Therefore, when there are variations in the substitution rate of a
time-tree, a relaxed molecular clock model needs to be considered, as that
model will consider such variance [72].
During the phylogenetic reconstruction of a pathogen, a very important early
step is choosing the proper models that drive the pathogen’s evolution like
molecular clock, coalescent, and nucleotide substitution models. When
choosing from a set of potential models, the “best” one is the model that has
Page | 28
the flexibility to explain a specific dataset without being overly parameterized.
Different methods of model selection exist in Bayesian phylogenetics [75].
The theory of statistical phylogenetics and mathematical epidemiology were
integrated into phylodynamics; a relatively new field of study that offers a
framework to investigate the evolutionary dynamics of an epidemic [76]. In
phylodynamic analysis, genomic sequence data sampled through space and
time is investigated for mutations that accumulated in the genomes of a
pathogen during an epidemic. These mutations can represent the molecular
imprint of epidemiological processes that can otherwise not directly be
observed. This can give insights to establish: the origin of an epidemic, how
the epidemic spread historically, when outbreaks of a disease occurred in a
specific region, and how it might have been transmitted [71].
BEAST2 was used in this PhD thesis to date evolutionary events, determine
substitution rates, determine demographic histories, and produce time-trees
of M. ulcerans, as this software allows for inference of phylogenies with a
diverse set of molecular clock and population parameters in the face of
phylogenetic uncertainty [77]. Presently, the BEAST package is the most
widely used software for these inferences in RNA viruses [71] but also in
animal mitochondria [78]. Its use in bacteria has however been very limited to
date [79-82] as the main limitation to the application of such methods is the
low genetic diversity and extensive homologous recombination associated
with many bacterial populations.
1.7 Research Goals: breaking the Buruli enigma
After almost 70 years of study in Africa the mode of transmission and the non-
human reservoir(s) of BU are still largely unknown, even though various
hypotheses have been formulated. This is the reason why BU carries the
epithets: the “mysterious” and “enigmatic” disease. This knowledge gap in
large part is due to the clonal population structure of M. ulcerans and the
resulting lack of effective high resolution typing methods to discriminate
different strains of M. ulcerans to enable transmission pattern tracking.
Therefore, the aim of CHAPTER 2 was to apply a redesigned ISE-SNP
fingerprinting technique to a vast panel of M. ulcerans disease isolates and
Page | 29
clinical samples originating from multiple African disease foci in order to gain
fundamental insights into the population structure, evolutionary history, and
phylogeographic relationships of the pathogen.
Even though ISE-SNP genotyping resulted in the highest geographical
resolution of genotyping achieved at that time, the resolution was still too
limited to reconstruct evolutionary events on anything smaller than the
continental scale. However, the start of this millennium brought about the
development and application of the first genomic sequencing technologies
that permitted population genetics laboratories to study full genomic
variation rather than inferring relationships from a few relatively well-
characterized genes. Unfortunately, for most of the genomics era, population
genomics was not feasible for many research groups due to the expense,
expertise, and resources required to generate even one representative
genome sequence from a species. The development of second (e.g. Illumina)
and third (e.g. Pacific Biosciences) generation sequencing technologies moved
genomics out of the genome center and into the research laboratory, which
made it possible to perform the first sophisticated, comparative genomic
studies on African M. ulcerans. We aimed to use the considerably elevated
resolution afforded by comparative next-generation genomics for the first
time to explore the molecular epidemiology of BU at the continental
(CHAPTER 4), and at the smaller geographical “village scale” in a BU endemic
region of Ghana (CHAPTER 3) and one in the Democratic Republic of Congo
(CHAPTER 5). We also meant to explore the use of the increased resolution
offered by low-cost genomics to for the first time distinguish disease relapses
from reinfections in patients with multiple BU episodes (CHAPTER 6).
We believe our findings will make an important contribution to breaking the
Buruli enigma by offering new insights into the mycobacterial epidemiological
dynamics. It is our hope that this will contribute to paving the road towards
improved BU prevention and control.
Page | 30
Page | 31
Chapter 2
Insertion sequence element single nucleotide
polymorphism typing provides insights into
the population structure and evolution of
Mycobacterium ulcerans across Africa
This chapter is published as:
Koen Vandelannoote, Kurt Jordaens, Pieter Bomans, Herwig Leirs, Lies Durnez,
Dissou Affolabi, Ghislain Sopoh, Julia Aguiar, Delphin Mavinga Phanzu, Kapay Kibadi,
Sara Eyangoh, Louis Bayonne Manou, Richard Odame Phillips, Ohene Adjei, Anthony
Ablordey, Leen Rigouts, Françoise Portaels, Miriam Eddyani, Bouke C. de Jong.
Insertion sequence element single nucleotide polymorphism typing provides insights
into the population structure and evolution of Mycobacterium ulcerans across
Applied and Environmental Microbiology 2014 Feb;80(3):1197-209
Conceived and designed the experiments: KV, LR, FP,ME, BCdJ.
Performed the experiments: KV, PB, .
Analyzed the data: KV, KJ.
Contributed reagents/materials/analysis tools: KV, HL, LD, DA, GS, JA, DMP, KK, SE,
LBM, ROP, OA, AA, LR, FP, ME, BCdJ.
Wrote the paper: KV, KJ
Page | 32
2.1 Abstract
Buruli ulcer is an indolent, slowly progressing necrotizing disease of the skin
caused by infection with Mycobacterium ulcerans. In the present study we
applied a redesigned technique to a vast panel of M. ulcerans disease isolates
and clinical samples originating from multiple African disease foci (i) to gain
fundamental insights into the population structure and evolutionary history of
the pathogen and (ii) to disentangle the phylogeographic relationships within
the genetically conserved cluster of African M. ulcerans. Our analyses
identified 23 different African insertion sequence element single nucleotide
polymorphism (ISE-SNP) types which dominate in different Buruli ulcer
endemic areas. These ISE-SNP types appear to be the initial stages of clonal
diversification from a common, possibly ancestral, ISE-SNP type. ISE-SNP types
were found unevenly distributed over the greater West African hydrological
drainage basins. Our findings suggest that geographical barriers bordering the
basins to some extent prevented bacterial gene flow between basins and that
this resulted in independent focal transmission clusters associated with the
hydrological drainage areas. Different phylogenetic methods yielded two well
supported sister clades within the African ISE-SNP types. The ISE-SNP types
from the “pan-African clade” were found widespread throughout Africa while
the ISE-SNP types of the “Gabonese/Cameroonian clade” were much rarer
and found in a more restricted area, which suggested that the latter clade
evolved more recently. Additionally, the Gabonese/Cameroonian clade was
found to form a strongly supported monophyletic group with Papua New
Guinean ISE-SNP type 8, which was unrelated to other Southeast Asian ISE-
SNP types.
2.2 Introduction
Buruli ulcer (BU), is a slowly progressing necrotizing disease of skin and
subcutaneous tissue caused by infection with Mycobacterium ulcerans [2]. BU
is the third most common mycobacterial disease in humans, after tuberculosis
and leprosy, and the least understood of the three [83]. Even though the
infection affects all age groups, at least half of all cases occur in children under
age 15 [84]. More than 30 countries world-wide have reported (but not
always confirmed) this emerging disease, with the highest incidence in West
Page | 33
and Central Africa, where the disease occurs in foci among people living in
rural marshes, wetlands, and riverine areas [2, 85]. As proximity to these slow
flowing to stagnant water bodies is a known risk factor for M. ulcerans
infection [31] and as M. ulcerans DNA has been detected in a variety of
aquatic specimens [35, 36], it is generally believed that M. ulcerans is an
environmental mycobacterium which can initiate infection after micro-
traumata of the skin [32]. However, the exact mode of transmission and the
environmental reservoir(s) of M. ulcerans remain largely unknown [60] as (i)
culturing the slow growing mycobacterium from an environmental source is
particularly difficult [46] and (ii) the significance of the detection of M.
ulcerans DNA by PCR in environmental samples remains unclear in the disease
ecology of BU [35-37, 39, 86-89].
Multilocus sequence typing analyses [18] and subsequent whole-genome
comparisons [19] proved that M. ulcerans recently evolved from a
Mycobacterium marinum progenitor by acquisition of the virulence plasmid
pMUM001. This plasmid harbors genes required for the synthesis of the
macrocyclic polyketide toxin mycolactone [13], which has cytotoxic and
immunosuppressive properties that cause chronic ulcerative skin lesions with
limited inflammation and thus plays a key role in the pathogenesis of BU [14].
Both the acquisition of the plasmid and a reductive evolution [12, 20] led the
generalist M. marinum to become a highly specialized mycobacterium, more
adapted to a restricted environment such as that of a vertebrate host.
Analysis of the genome sequence suggests that this new niche is likely to be
an obscure, aerated, osmotically stable, extracellular environment where slow
growth, the loss of several immunogenic proteins and production of
mycolactone provided selective advantages [12, 19]. Many of the changes in
this evolutionary process were mediated by two insertion sequence elements
(ISE), IS2404 and IS2606, which are present in the M. ulcerans genome in ±
200 and ± 90 copies respectively [12]. These short, mobile genetic DNA
elements promote genetic rearrangements by modifying gene expression and
sequestering genes, profoundly affecting mycobacterial genome plasticity
[23]. Increased ISE numbers are expected as the aforementioned lifestyle shift
causes many loci to become excessive as they are no longer essential for the
survival in the new environment [90]. Subsequent whole-genome
Page | 34
comparisons [19] furthermore showed that the resulting niche-adapted
genomic signature was established in a M. ulcerans progenitor before its
intercontinental dispersal.
Deciphering the structure of pathogenic bacterial populations is instrumental
for the understanding of the epidemiology, global spread and evolutionary
history of bacterial infectious diseases. Moreover, understanding the
population structure allows for studying meaningful bacterial differences
affecting disease control, including public health interventions, such as
vaccination programs [91]. Differences in the ratio of genetic variation caused
by de novo mutations relative to recombination brings about a spectrum of
different bacterial population structures ranging from “clonal” (no
recombination) to “non-clonal” (where a lot of recombination of alleles
prevents the emergence of stable clones) [92]. The clonal population
structure of M. ulcerans has meant that conventional genetic fingerprinting
methods have largely failed to genetically differentiate clinical disease
isolates, complicating molecular analyses on the elucidation of the disease
ecology and the population structure and evolutionary history of the
pathogen [93]. However, in 2009, Käser et al. [94] identified single nucleotide
polymorphisms (SNPs) within M. ulcerans haplotype-specific IS2404 elements
MUL_2990 and MUL_3871, located respectively in region of difference RD1
and RD12 [95]. The identified SNPs differentiated multiple genotypes among
isolates originating from one region in Ghana, resulting in the highest
geographical resolution of genotyping achieved to date without the use of
whole genome sequencing. Given the apparent rarity of recombination in M.
ulcerans, ISE-SNP types should contain sufficient phylogenetic signal to
reconstruct recent evolutionary events on a continental scale. Hence, in the
present study we applied a redesigned form of the ISE-SNP typing technique
as described in Käser et al. [94] to a vast panel of M. ulcerans isolates
originating from multiple African disease foci to gain deeper insights into the
population structure and evolutionary history of the pathogen and continue
to disentangle the phylogeographic relationships within the genetically
conserved cluster of African M. ulcerans.
Page | 35
2.3 Material and Methods
A panel (n = 171) of 157 M. ulcerans clinical isolates and 14 clinical specimens
with a quantification cycle Cq (IS2404) ≤ 32 (see further) originating from
disease foci in 11 different African countries, was selected to assess the
polymorphisms in the RD1 and RD12-associated haplotype-specific copies of
IS2404 (Table 2.1 and 2.2). Clinical specimens consisted of tissue fragments
and swabs originating from ulcerated and non-ulcerated BU lesions. These
surplus samples had been collected for routine diagnostic purposes and for
re-checking for quality control. All isolates and specimens were selected from
the comprehensive mycobacterial collection of the Institute of Tropical
Medicine (ITM) and were chosen to maximize temporal and spatial diversity
within countries in which more than 20 isolates / specimens were available.
Isolates and specimens were processed and analyzed for bacterial
polymorphisms without use of any patient identifiers, except for country- and
village of origin if this information was available.
Based on conventional phenotypic and genotypic methods bacterial isolates
had previously been assigned to the species M. ulcerans. They had all tested
positive for IS2404 using primers that amplify all copies of IS2404 routinely
used for diagnostic PCR [96]. Mycobacterial isolates were maintained for
prolonged storage at ≤ 70°C in Dubos-broth enriched with growth supplement
and glycerol. They were recultured on solid Löwenstein-Jensen medium. DNA
was obtained by scraping 1-2 loopfuls of colonies into 400 μL of TE followed
by heat inactivation at 100°C for 5 min and subsequent centrifugation to
remove cellular debris. Clinical specimens were maintained (after
decontamination) at ≤ -18°C. The modified Boom DNA extraction procedure
was carried out on all clinical specimens as previously described [65].
As the original ISE-SNP typing method described by Käser et al. [94] resulted in
aspecific bands, short sequence reads and high background signals, we
redesigned and optimized primers and conditions for PCR and sequencing for
application directly on clinical specimens. Primer pair RD1_SENSE
(GGTGCTTAACGAAACGTGCTG) and RD1_ANTI_SENSE
(ACGGGCTATCTGGAGAACGA) was designed to amplify a fragment of 1431 bp
in RD1 that comprises IS2404 (MUL_2990) while the primer pair RD12_SENSE
Page | 36
(CGTTGGCGCGGTACAAGCTTCCCAA) and RD12_ANTI_SENSE
(GATGGTCGCGGTGCTGCTTGCCCT) was used to amplify a 1871 bp PCR product
in RD12 that comprises IS2404 (MUL_3871). Primers were designed with
Primer premier 6 (Premier Biosoft, California, US) and evaluated in silico with
Amplify 3.1.4 (Bill Engels, University of Wisconsin). The PCR design was
challenging as only haplotype specific copies of IS2404 (MUL_2990 and
MUL_3871) were to be amplified and because the relevant regions are of
considerable size (1730bp for RD1 / 1905bp for RD12). Although we reduced
the size of the amplicons in both assays (by 299bp for RD1 and by 48bp for
RD12), they still contained all the variable nucleotide positions described by
Käser et al. [94]. PCR reaction mixtures contained 1.0 U of HotStarTaq
polymerase (QIAGEN, Hilden, Germany), 3.0 μL 10X PCR buffer, 6.0 μL
Qsolution, 1.5 mM MgCl2, 200 μM of each dNTP, and 0.5 μM of each primer in
a total volume of 30 µL. PCR reactions were carried out on a Biometra
TProfessional Thermal cycler under the following conditions: an initial
denaturation step of 15 min at 95°C followed by 40 cycles of denaturation for
1 min at 95°C, annealing for 1 min at 65°C (RD1) or 70°C (RD12) and
elongation for 2 min at 72°C, and ending with a final elongation step of 10 min
at 72°C. PCR products were visualized with ethidium-bromide on 1% agarose
gels by electrophoresis (30 min, 100 V). PCR products were purified by
automated gel excision. Bi-directional sequencing was performed by the
Genetic Service Facility of the Flanders Institute for Biotechnology (GSF-VIB)
on an Applied Biosystems 3730 DNA Analyzer capillary sequencer using the
ABI PRISM BigDye Terminator cycle sequencing v3.1 kit using the PCR primers.
We estimated the bacterial load by a quantitative PCR (qPCR) for IS2404 as
described by Fyfe et al. [22] on a set of 122 clinical specimens to determine
the quantification cycle Cq (IS2404) below which the optimized genotyping
PCRs were always successful.
The sequences of RD1 and RD12 were concatenated to yield a 3278 bp
fragment and aligned in Clustal X v2.1 [97]. Sequences were trimmed to equal
length and all currently known ISE-SNP types (including 5 ISE-SNP types from
Papua New Guinea, Australia and Malaysia) [94] were added to this dataset.
We mapped SNPs according to the Agy99 bacterial reference chromosome
(GenBank accession no. NC_008611). We constructed a Neighbor-Joining (NJ)
Page | 37
tree based on p-distances between ISE-SNP types [98] in MEGA v5 [99].
Maximum Parsimony (MP) and Maximum Likelihood (ML) trees were
estimated in the same program using a heuristic search with the tree-
bisection-reconnection branch-swapping algorithm and random addition of
taxa. Relative branch support was evaluated with 1000 bootstrap replicates
[100] for the NJ and MP tree, and 200 for the ML tree. Phylogenetic trees for
ML analysis were inferred with the nucleotide substitution model selected
using jModelTest v0.1.1 [101]. Phylogenetic relationships were inferred with
ISE-SNP 28 (strain ITM_030524) from Papua New Guinea as outgroup, isolated
from a patient who had never travelled outside of the region around Yarapos
in the East Sepik Province (J Taylor, personal communication). Trees were
drawn using FigTree software [102].
A haplotype network was derived using the Median Joining algorithm after
processing the data with the reduced median method as implemented by
Network v4.6.1.0 with default settings [103].
The open source Geographic Information System “Quantum GIS” (QGIS) [104]
was used to generate the figure on the geographical distribution of African M.
ulcerans. The geographical location of the residence of BU patients at the time
of clinical visit was rendered as points. In the case where residence
information was missing we used the location of the hospital supplying the
sample. A modification of the QGIS Python plugin “Shift Points” was used to
modify this point shape file where point features with the same position
overlapped. Point displacement rendered such features in a circle around the
original “real” position. The river layer is translated from the “River-Surface
Water Body Network” dataset of the “African Water Resource database” of
the Food and Agriculture Organization (FAO) of the United Nations [105]. The
administrative borders of countries are rendered from the Global
Administrative Unit Layers dataset of FAO.
All statistical testing was performed in R v2.15.2 [106]. The correlation
between the number of isolates per country and the number of ISE-SNP types
per country was checked with a Spearman’s rank order correlation coefficient.
To examine the relation between ISE-SNP types and the greater West African
hydrological drainage basins a Fisher's exact test was used.
Page | 38
Table 2.1: Isolates used in this study; CDTUB: Centre de Dépistage et de traitement de l'Ulcère
de Buruli, CPC : Centre Pasteur du Cameroun, DRC: Democratic Republic of Congo, IME:
Institut Médical Evangélique, KCCR: Kumasi Centre for Collaborative Research in Tropical
Medicine, NCTC: National Collection of Type Cultures, PNLUB: Programme National de Lutte
contre l'Ulcère de Buruli, YOI : Year of isolation.
ISE-SNP
type Culture n°
Country of
Origin
First-level
Administrative
Division
Second-level
Administrative
Division
Third-level
Administrative
Division
Source YOI Remarks
1 ITM_940511 Ivory Coast Moyen-Cavally Duékoué Niambli ITM 1994
1 ITM_000483 Ivory Coast Moyen-Cavally Duékoué Niambli ITM 2000
1 ITM_000870 Ivory Coast Dix-Huit
Montagnes Zouan-Hounien Ouyatouo ITM 2000
1 ITM_063519 DRC Bas-Congo Cataractes /
Songololo
Luima / Cité
Songololo IME 2006
1 ITM_071924 Congo Kouilou Madingo-Kayes Loukouala ITM 2007 Originates from same
patient as 071925
1 ITM_071925 Congo Kouilou Madingo-Kayes Loukouala ITM 2007 Originates from same
patient as 071924
1 ITM_072398 DRC Bas-Congo Cataractes /
Songololo
Bamboma /
Mbanza-Manteke IME 2007
1 ITM_072401 DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna IME 2007
1 ITM_072732 DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna IME 2007
1 ITM_072733 DRC Bas-Congo Cataractes /
Songololo
Luima-Mayanga /
Ngombe IME 2007
1 ITM_072734 DRC Bas-Congo Cataractes /
Songololo
Bamboma /
Mbanza-Manteke IME 2007
1 ITM_072735 DRC Bas-Congo Cataractes /
Songololo Luima / Luvuvamu IME 2007
1 ITM_072840 DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna IME 2007
Originates from same
patient as 072841
1 ITM_072841 DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna IME 2007
Originates from same
patient as 072840
1 ITM_073453 DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna IME 2007
1 ITM_073459 Benin Kouffo Lalo Ahojinako CDTUB Lalo 2007
1 ITM_073463 DRC Bas-Congo Cataractes /
Songololo Luima / Kisonga IME 2007
1 ITM_073477 DRC Bas-Congo Cataractes /
Songololo
Luima / Cité
Songololo IME 2007
1 ITM_073478 Angola Malanje Marimba Kafufu / Luremo
(Kwango River) IME 2007
1 ITM_073479 DRC Bas-Congo Cataractes /
Songololo Luima / Kisonga IME 2007
1 ITM_082600 DRC Bas-Congo Cataractes /
Songololo Kilueka / Nzundu IME 2008
1 ITM_100140 DRC Bas-Congo Cataractes /
Songololo Lovo / Tole IME 2010
1 ITM_100141 DRC Bas-Congo Cataractes /
Songololo Mayanga / Mpelo IME 2010
Originates from same
patient as 100141
1 ITM_100142 DRC Bas-Congo Cataractes /
Songololo Luima / Luvuvamu IME 2010
Originates from same
patient as 100142
1 ITM_100832 DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna IME 2010
1 ITM_100833 DRC Bas-Congo Cataractes /
Songololo Mayanga / Mpelo IME 2010
1 ITM_032481 DRC Bas-Congo Cataractes /
Songololo
Luima / Nkondo-
Kiomba IME 2003
1 ITM_040149 Ghana Ashanti Asante Akim North Agogo Presbyterian
Hospital ITM 2003
1 ITM_991591 Togo Maritime Vo Anagali ITM 1999
1 ITM_050303 Congo Kouilou
ITM 1979 Originates from same
patient as 050304
Page | 39
ISE-SNP
type Culture n°
Country of
Origin
First-level
Administrative
Division
Second-level
Administrative
Division
Third-level
Administrative
Division
Source YOI Remarks
1 ITM_050304 Congo Kouilou
ITM 1979 Originates from same
patient as 050303
1 ITM_960658 Angola Bengo Dande Caxito ITM 1996 Originates from same
patient as 960657
1 ITM_960657 Angola Bengo Dande Caxito ITM 1996 Originates from same
patient as 960658
1 ITM_072662 Ghana Ashanti Asante Akim North Ananekrom KCCR 2007
1 ITM_072646 Ghana Ashanti Atwima Mponua Abofrom KCCR 2007
1 ITM_072651 Ghana Ashanti KMA Kaase KCCR 2007
1 ITM_120140 Cameroon Adamawa
Region Maya-Banyo Bankim / Mbondji II CPC 2011
1 ITM_030950 Benin Kouffo Lalo Adoukandji CDTUB Lalo 2003
1 ITM_030716 Benin Kouffo Lalo Tchito / Village
Aboeti CDTUB Lalo 2003
1 ITM_102686 Nigeria Oyo State Ibadan Ibadan ITM 2010
1 ITM_083232 Angola Lunda Norte Xa-Muteba (Kwango River) ITM 2008
1 ITM_000869 Ivory Coast Moyen-Cavally Duékoué Guezon ITM 2000
1 ITM_990007 Ivory Coast Haut-
Sassandra Issia Guetuzon II ITM 1998
1 ITM_991633 Ivory Coast Moyen-Cavally Duékoué Guezon ITM 1999
2 ITM_030791 Benin Kouffo Lalo Tchito / Gare CDTUB
Zagnanado 2003
2 ITM_970680 Benin Mono Houéyogbé Sahoué CDTUB Lalo 1997
2 ITM_022876 Benin Kouffo Lalo Tohou CDTUB Lalo 2002
2 ITM_021434 Benin Kouffo Klouékanmè Adjassagon CDTUB Lalo 2002
2 ITM_012596 Benin Mono Bopa Lobogo CDTUB Lalo 2001
2 ITM_071938 Benin Kouffo Lalo Tandji CDTUB Lalo 2007
4 ITM_5150 DRC Bandundu Kwilu
ITM 1962
5 ITM_940512 Benin Zou Ouinhi Ouokon CDTUB
Zagnanado 1994
5 ITM_010157 Benin Zou Zogbodomè Domè-
Houandougon
CDTUB
Zagnanado 2001
5 ITM_000951 Benin Zou Zogbodomè Domè-
Houandougon
CDTUB
Zagnanado 2000
5 ITM_970435 Benin Ouémé Bonou Bonou CDTUB
Zagnanado 1997
Originates from same
patient as 970301
5 ITM_970301 Benin Ouémé Bonou Bonou CDTUB
Zagnanado 1997
Originates from same
patient as 970435
5 ITM_000479 Benin Zou Zagnanado Zagnanado / Doga CDTUB
Zagnanado 2000
5 ITM_092100 Benin Zou Zagnanado Doga-Domè CDTUB
Zagnanado 2009
5 ITM_083865 Benin Zou Ouinhi Tohoue /
Hounnoumè
CDTUB
Zagnanado 2008
5 ITM_093013 Benin Zou Ouinhi Ouinhi /
Monzoungoudo
CDTUB
Zagnanado 2009
5 ITM_093695 Benin Zou Ouinhi Ouinhi /
Monzoungoudo
CDTUB
Zagnanado 2009
5 ITM_101300 Benin Zou Ouinhi Sagon / Adamè CDTUB
Zagnanado 2010
5 ITM_101302 Benin Zou Ouinhi Dasso / Bossa CDTUB
Zagnanado 2010
5 ITM_102554 Benin Zou Ouinhi Dasso / Agonkon CDTUB
Zagnanado 2010
5 ITM_081919 Benin Zou Ouinhi Dasso / Yaago &
Akantomè
CDTUB
Zagnanado 2008
Page | 40
ISE-SNP
type Culture n°
Country of
Origin
First-level
Administrative
Division
Second-level
Administrative
Division
Third-level
Administrative
Division
Source YOI Remarks
5 ITM_092997 Benin Zou Djidja Oungbègame CDTUB
Zagnanado 2009
5 ITM_080066 Benin Zou Ouinhi Sagon / Ayizè CDTUB
Zagnanado 2008
5 ITM_070381 Benin Zou Ouinhi Dasso / Yaago CDTUB
Zagnanado 2007
5 ITM_073151 Benin Zou Ouinhi Ouinhi /
Monzoungoudo
CDTUB
Zagnanado 2007
5 ITM_070131 Benin Zou Zagnanado Dovi-Dove / Tévedji CDTUB
Zagnanado 2007
5 ITM_092473 Benin Zou Ouinhi Tohoue /
Midjannangon
CDTUB
Zagnanado 2009
5 ITM_082549 Benin Zou Ouinhi Tohoue / Akassa CDTUB
Zagnanado 2008
5 ITM_090149 Benin Zou Zagnanado Dovi-Dove / Tévedji CDTUB
Zagnanado 2009
5 ITM_091800 Benin Zou Ouinhi Tohoue / Gangban CDTUB
Zagnanado 2009
5 ITM_083584 Benin Zou Ouinhi Ouinhi / Ahicon CDTUB
Zagnanado 2008
5 ITM_9146 Benin Zou Zagnanado Kpedekpo / Loko-
Alankpe
CDTUB
Zagnanado 1992
5 ITM_991721 Benin Atlantique Toffo Séhoué CDTUB
Zagnanado 1999
5 ITM_092472 Benin Atlantique Toffo Séhoué / Agaga CDTUB
Zagnanado 2009
5 ITM_070383 Benin Ouémé Dangbo Dékin CDTUB
Zagnanado 2007
5 ITM_070625 Nigeria Ogun State Yewa North Odja Odan CDTUB
Zagnanado 2007
5 ITM_061509 Benin Zou Zagnanado Zagnanado CDTUB
Zagnanado 2006
5 ITM_081676 Benin Plateau Adja-Ouere Tatonnoukon CDTUB
Zagnanado 2008
5 ITM_081681 Benin Plateau Issaba Onigbolo CDTUB
Zagnanado 2008
5 ITM_082696 Benin Ouémé Adjohoun Abato CDTUB
Zagnanado 2008
5 ITM_091801 Benin Zou Zogbodomè Kpokissa /
Hinzounmè
CDTUB
Zagnanado 2009
5 ITM_092101 Benin Ouémé Dangbo Gbéko CDTUB
Zagnanado 2009
5 ITM_093694 Benin Ouémé Dangbo Gbéko CDTUB
Zagnanado 2009
5 ITM_100126 Benin Zou Zogbodomè Kpokissa CDTUB
Zagnanado 2010
5 ITM_951009 Benin Zou Zagnanado
CDTUB
Zagnanado 1995
6 ITM_5151 DRC Maniema Kasongo
ITM 1972
7 ITM_970359 Ghana Ashanti Amansie West Manso-Afraso ITM 1997
7 ITM_970606 Ghana Ashanti Amansie West Yaw Kasakrom ITM 1997
7 ITM_970677 Ghana Ashanti Amansie West Manso Dominase ITM 1997
7 ITM_970678 Ghana Ashanti Asante Akim North Afrisre ITM 1997
7 ITM_970959 Ghana Ashanti Amansie West Manso-Afraso ITM 1997
7 ITM_970964 Ghana Ashanti Amansie West Offinho Asaman ITM 1997
7 ITM_971351 Ghana Ashanti Atwima Mponua Achiase ITM 1997
7 ITM_980063 Ghana Ashanti Atwima Mponua Achiase ITM 1998
7 ITM_940662 Ivory Coast Moyen-Cavally Duékoué Nanandi ITM 1994
7 ITM_990006 Ivory Coast Haut-
Sassandra Issia Guetuzon I ITM 1998
Page | 41
ISE-SNP
type Culture n°
Country of
Origin
First-level
Administrative
Division
Second-level
Administrative
Division
Third-level
Administrative
Division
Source YOI Remarks
7 ITM_990734 Ivory Coast Moyen-Cavally Duékoué Duékoué ITM 1999
7 ITM_991632 Ivory Coast Haut-
Sassandra Issia Bediegbeu ITM 1999
7 ITM_072634 Ghana Ashanti Asante Akim North Adoniem KCCR 2007
7 ITM_072652 Ghana Ashanti Atwima Mponua Achiase KCCR 2007
7 ITM_072654 Ghana Ashanti Atwima Mponua Achiase KCCR 2007
7 ITM_072657 Ghana Ashanti Atwima Mponua Achiase KCCR 2007
7 ITM_072658 Ghana Western
Region Wassa West Owusukrom KCCR 2007
7 ITM_072650 Ghana Ashanti Atwima
Nwabiagya Kyereyase KCCR 2007
7 ITM_072630 Ghana Central Upper Denkyira Nkotumso KCCR 2007
7 ITM_072656 Ghana Ashanti Atwima Mponua Abompe KCCR 2007
7 ITM_072655 Ghana Ashanti Atwima Mponua Sireso KCCR 2007
7 ITM_072653 Ghana Ashanti Atwima Mponua Amadaa KCCR 2007
7 ITM_072645 Ghana Ashanti Atwima Mponua Achiase KCCR 2007
12 ITM_072814 Benin Ouémé Dangbo Gbéko CDTUB
Zagnanado 2007
13 ITM_021433 Benin Kouffo Lalo Gnizoumè /
Hangbanou CDTUB Lalo 2002
13 ITM_022045 Benin Kouffo Lalo Adoukandji /
Yamontouhoué CDTUB Lalo 2002
13 ITM_022287 Benin Zou Agbangnizoun Kpota CDTUB Lalo 2002
13 ITM_022875 Benin Kouffo Lalo Gnizoumè CDTUB Lalo 2002
13 ITM_030717 Benin Kouffo Lalo Ahomadégbé CDTUB Lalo 2003
13 ITM_030718 Benin Kouffo Lalo Lalo CDTUB Lalo 2003
13 ITM_031892 Benin Kouffo Lalo Hlassamè CDTUB Lalo 2003
13 ITM_071804 Benin Kouffo Lalo Zalli CDTUB Lalo 2007
14 ITM_991590 Togo Maritime Vo Tchekpo Deve ITM 1999 Originates from same
patient as 000909
14 ITM_000909 Togo Maritime Vo Tchekpo Deve ITM 2000 Originates from same
patient as 991590
14 ITM_993354 Togo Maritime Vo Tchekpo Deve ITM 1999
14 ITM_042407 Togo Maritime Vo Kodji Kopé ITM 2004
15 ITM_070404 DRC Bas-Congo Cataractes /
Songololo
Kimpese / Cité-
Kimpese IME 2007
Originates from same
patient as 070123
15 ITM_070123 DRC Bas-Congo Cataractes /
Songololo
Kimpese / Cité-
Kimpese IME 2007
Originates from same
patient as 070404
15 ITM_092479 DRC Bas-Congo Cataractes /
Songololo
Kimpese / Cité-
Kimpese IME 2009
16 ITM_990008 Ivory Coast Haut-
Sassandra Issia Zakogbeu ITM 1998
18 ITM_070386 Nigeria Anambra State Ayamelum Ifite Ogwari ITM 2007
19 ITM_001211 Ivory Coast Dix-Huit
Montagnes Zouan-Hounien Zouan-Hounien ITM 2000
20 ITM_020279 Cameroon Centre Region Nyong-et-
Mfoumou Ayos CPC 2002
20 ITM_091067 Gabon Moyen-
Ogooué
Ogooue et des
Lacs Junkville ITM 2009
20 ITM_110450 Gabon Moyen-
Ogooué
Ogooue et des
Lacs Gravier ITM 2011
Page | 42
ISE-SNP
type Culture n°
Country of
Origin
First-level
Administrative
Division
Second-level
Administrative
Division
Third-level
Administrative
Division
Source YOI Remarks
22 ITM_020280 Cameroon Centre Region Nyong-et-
Mfoumou Akolo CPC 2002
22 ITM_120542 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga CPC 2011
23 ITM_021081 Cameroon Centre Region Nyong-et-
Mfoumou Obis CPC 2002
23 ITM_9102 Cameroon Centre Region
ITM 1970
23 ITM_9103 Cameroon Centre Region
ITM 1970
23 ITM_101500 Gabon Moyen-
Ogooué
Ogooue et des
Lacs
Lambaréné /
Adaghe ITM 2010
23 ITM_110893 Gabon Moyen-
Ogooué
Ogooue et des
Lacs Issac ITM 2011
23 ITM_120138 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga CPC 2010
23 ITM_120139 Cameroon Centre Region Nyong-et-
Mfoumou
Akonolinga /
Wouma CPC 2011
23 ITM_120141 Cameroon Centre Region Nyong-et-
Mfoumou Medjap CPC 2011
23 ITM_120142 Cameroon Centre Region Nyong-et-
Mfoumou
Akonolinga /
Ekolman CPC 2011
23 ITM_120534 Cameroon Centre Region Nyong-Et-Soo Bembé CPC 2011
23 ITM_120535 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga CPC 2011
23 ITM_120536 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga / Djo'o CPC 2011
23 ITM_120538 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga CPC 2011
23 ITM_120539 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga CPC 2011
23 ITM_120543 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga CPC 2011
23 ITM_120143 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga CPC 2011
24 ITM_051459 Uganda Northern
Region Adjumani Adjumani NCTC 2005
25 ITM_120537 Cameroon Centre Region Nyong-Et-Soo Edjom CPC 2011
26 ITM_120540 Cameroon Centre Region Nyong-et-
Mfoumou Akam-Engali CPC 2011
27 ITM_120541 Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga CPC 2011
Page | 43
Table 2.2: Clinical specimens used in this study; DRC: Democratic Republic of Congo, PNLUB:
Programme National de Lutte contre l'Ulcère de Buruli, YOI : Year of isolation.
ISE-
SNP
type
Sample n° Country of
Origin First-level Second-level Third-level Source YOI
1 BK121032 Nigeria Cross river
state Ogoja
TBL hospital
monaiya ITM 2012
1 BK120888 DRC Maniema Kibombo Likeri PNLUB 2012
1 BK120890 DRC Maniema Kasongo Samba / Malela PNLUB 2012
1 BK120891 DRC Maniema Kasongo Kankumba PNLUB 2012
1 BK065361 Nigeria Enugu State Igbo Eze North Nkpo Hamida ITM 2006
5 BK121025 Nigeria Ogun State Abeokuta North Abeokuta / Ijaye
State hospital ITM 2012
5 BK121026 Nigeria Ogun State Abeokuta North Abeokuta / Ijaye
State hospital ITM 2012
5 BK121031 Nigeria Ogun State Yewa South Oke-Odan / PHC
Oke-Odan ITM 2012
17 BK065369 Nigeria Ebonyi State Ohaozora Iburu ITM 2006
20 BK105250 Gabon Nyanga Douigni Moussamou
kougou ITM 2010
21 BK101660 Gabon Moyen-
Ogooué
Ogooue et des Lacs
Department
Lambaréné / Point
V ITM 2010
23 BK100901 Gabon Moyen-
Ogooué
Ogooue et des Lacs
Department
Lambaréné /
Bellevue ITM 2010
23 BK100900 Gabon Moyen-
Ogooué
Ogooue et des Lacs
Department Lambaréné / Isaac ITM 2010
Page | 44
2.4 Results
Primers and conditions for PCR and sequencing were redesigned and
optimized from those described by Käser et al. [94] for application directly on
clinical specimens (Figure 2.1). Isolates ITM_5150, ITM_5151 ITM_940511,
ITM_940512, ITM_960658, ITM_940662, ITM_970359, and ITM_970680 were
included in the panel to validate the redesigned assays. These isolates were
also included in the panel of Käser et al. [94] and gave identical genotypes as
with the redesigned assays.
Figure 2.1: The 1431 bp fragment of IS2404 (MUL_2990) in RD1 and the 1871 bp fragment of
IS2404 (MUL_3871) in RD12; L: GeneRuler 100 bp Plus DNA Ladder (Fermentas); 1,
ITM_951009; 2, ITM_072645; 3, ITM_072653; 4, ITM_072658; 5, No Template Control (NTC).
As our collection of African M. ulcerans isolates did not represent all endemic
countries and their different regions to the same extent owing to the low
sensitivity of culture, we fine-tuned the technique for application directly on
clinical specimens by adjusting individual PCR component concentrations and
optimizing the thermal PCR profile. We were thus able to deduce sequence
information of clinical samples with a modest bacterial load corresponding to
a Cq (IS2404) ≤ 32. Failure of PCR amplification for specimens with Cq (IS2404)
> 32 was caused by low mycobacterial DNA concentration.
Amplification and sequencing of IS2404 in MUL_2990 (RD1) and MUL_3871
(RD12) was successful on the entire collection of 157 (100%) clinical isolates
(Table 2.1 and 2.2). The optimized method also proved successful in all 14
clinical specimens analyzed with a Cq (IS2404) ≤ 32. A total of 75 (31 in
Page | 45
MUL_2990 (RD1), 44 in MUL_3871 (RD12)) variable nucleotide positions were
identified, including four insertions / deletions (indels) (Figure 2.2). This
resulted in 28 ISE-SNP types, of which 23 were found on the African continent.
Sixteen of these were newly identified types while the other seven
corresponded to the ISE-SNP types described by Käser et al. [94]. The Papua
New Guinean ISE-SNP type 28, used in the phylogenetic analyses as outgroup,
was also a novel type.
Figure 2.2: Sequence variation in two haplotype-specific concatenated IS2404 elements;
MUL_2990 (RD1) and MUL_3871 (RD12). Only variable nucleotides in the aligned sequences
are shown for all 28 ISE-SNP types. SNP position numbers are given according to Käser et al.
[94], with 1 corresponding to position 3313231 in RD1 and 1498 to position 4326896 in RD12
according to the Agy99 bacterial reference chromosome.
The Spearman's rank correlation coefficient showed a significant relationship
between the number of isolates per country and number of ISE-SNP types
identified per country (r[9] = 0.79, p < 0.01). We were able to identify all
African ISE-SNP types described by Käser et al. [94] except ISE-SNP type 3,
which was found in the Greater Accra Region of Ghana, a region not covered
by our panel. Some ISE-SNP types were common (types 1, 2, 5, 7, 23) while
others were represented by one isolate/clinical specimen only (types 4, 6, 12,
16, 17, 18, 19, 21, 24, 25, 26, and 27). Our panel included a number of linked
isolates originating from the same patient. In all eight occurrences (Table 2.1)
these linked isolates revealed the same ISE-SNP type. The geographical
distribution of all African ISE-SNP types is shown in Figure 2.3A. Most ISE-SNP
types had a distinct restricted geographical localization. For example, all 41
isolates of ISE-SNP type 5 were recovered from an area in West Africa with a
60 km radius. Other African ISE-SNP types were more widely dispersed. ISE-
Page | 46
SNP type 1 for instance, although also emerging in clusters in Central Africa,
was identified throughout Central and West Africa. Furthermore, some
regions harbored a multitude of different ISE-SNP types while others yielded
just one. In southern Benin, for example, the greatest variety of allelic
patterns is found with as much as five ISE-SNP types (viz. types 1, 2, 5, 12, and
13) circulating.
We found a strong relationship (Fisher’s exact test: p < 0.0001) between the
distribution of ISE-SNP types and the greater West African hydrological
drainage basins (Table 2.3) as shown in figure 2.3B. Hydrologically this region
can be divided into separate main drainage areas: the Mono, the Kouffo, the
Oueme, the Yewa, the Ogun, and the Togolese Coastal Rivers Basin. The main
rivers of these border-crossing basins all arise on the central west-African
plateau and form broad fertile richly inundated plains when they reach the
lowlands of the coastal regions, where BU-endemic areas are concentrated.
Here, the basins can be divided into an inland region drained by a network of
freshwater rivers and streams that discharges into a region of extensive
brackish-water swamps interconnected with lakes, narrow lagoons and
streams parallel to the coastline. Haplotype ISE-SNP 5 dominates in the BU-
endemic areas of the Oueme, the Yewa, and the Ogun Basins. The haplotype
is best represented along the Oueme and its last tributary, the Zou River,
where most Beninese BU cases are reported [107]. After the confluence, the
Oueme traverses over 1500 km² of floodplains after which the river discharges
into Lake Nokoue, Porto-Novo Lagoon, and the coastal lagoons of Nigeria
which are all interconnected by the numerous channels of the deltaic fan of
the Oueme River. Two other drainage units, the Ogun and the Yewa discharge
in this same system of lagoons and streams. So, although the basins are
separate drainage systems, they discharge into this collective interconnected
system, which could potentially explain the observed shared distribution of
haplotype ISE-SNP 5. Haplotypes ISE-SNP 2 and 13 dominate in the BU-
endemic areas of the Kouffo Basin [28, 108]. After draining the highly BU
endemic regions of the commune of Lalo, the Kouffo discharges via Lake
Aheme in the “western lagoonal complex“ that is in contact with the Gulf of
Guinea at “Bouche du Roi”. Although this system is part of a semi-continuous
line of narrow lagoons that runs behind the dunes along the entire coastal
Page | 47
strip until the Ghanaian border, it is not in contact with the interconnecting
drainage system of the Oueme Delta. The lower course of the Mono River
forms the border between Togo and Benin and discharges in the same
“western lagoonal system” as the Kouffo River. The Mono River basin
however has no known areas of BU-endemicity, despite similar riverine
habitats. Even further west, in southern Togo, three small coastal rivers (Boko,
Haho, and Zio) form a third small basin. The basin encompasses a couple of
BU endemic regions in which the Togolese haplotype ISE-SNP 14 is
represented.
Table 2.3: Distribution of ISE-SNP types over the hydrological drainage basins of southern
Benin, southern Togo, and southwestern Nigeria.
Coastal Rivers Basin Mono Kouffo Oueme Yewa Ogun
ISE-SNP type 1 1 0 3 0 0 0
ISE-SNP type 2 0 1 5 0 0 0
ISE-SNP type 5 0 0 0 37 2 2
ISE-SNP type 12 0 0 0 1 0 0
ISE-SNP type 13 0 0 8 0 0 0
ISE-SNP type 14 3 0 0 0 0 0
Page | 48
Figure 2.3: (A) The geographical distribution of African M. ulcerans. The location of residence
at the time of clinical visit of the individual BU patients was retrospectively correlated with
the ISE-SNP typing results. ISE-SNP types represented by only one clinical isolate or specimen
are depicted as numbers while more common ISE-SNP types are color coded. (B) The uneven
distribution of ISE-SNP types over the different greater hydrological drainage basins of West
Africa.
The NJ method yielded two well supported sister clades within the African ISE-
SNP types (Figure 2.4A). The first clade comprised ISE-SNP types 20 and 21
which circulate in different BU endemic regions of Cameroon and Gabon; this
clade had also a high bootstrap support for the MP and ML analyses. A second
“pan-African clade” comprised all other African ISE-SNP types (Figure 2.4B).
Support for other nodes within the “pan-African clade” was very low
Page | 49
(bootstrap values < 70%) except in the NJ analysis for (i) a clade of ISE-SNP
types 7 and 19 which circulate in Ghana and Ivory coast (ii) a clade of ISE-SNP
types 22, 23, 25, 26, and 27 which all circulate in Cameroon and neighboring
Gabon (iii) a modest support in the NJ analysis for a clade of ISE-SNP types 1,
2, 3, 4, 5, 12, 13, 14, 15, 16, 17, found throughout the continent.
Mycobacterium ulcerans haplotypes from Australia and South East Asia were
also included in the analysis as these, together with African haplotypes,
belong to the more virulent and distinct “classic” phylogenetic lineage [95,
109], relative to M. ulcerans isolates elsewhere. It is of particular interest that
ISE-SNP type 8 from Papua New Guinea forms a strongly supported
monophyletic group with ISE-SNP types 20 and 21 from Cameroon and Gabon
and is distinctly unrelated to other Southeast Asian clinical isolates, which
belong to ISE-SNP types 9, 10 and 28 (Figure 2.4A). In contrast, other ISE-SNP
types found in Papua New Guinea are related to Malaysian and Australian
clinical isolates.
Page | 50
Figure 2.4: A: Neighbor-joining tree showing the phylogenetic relationships between the 28
currently known ISE-SNP types of M. ulcerans with haplotype ISE-SNP 28 from Papua New
Guinea as outgroup. Bootstrap values (if >70%) for the Neighbor-Joining (NJ), Maximum
Likelihood (ML) and Maximum Parsimony (MP) analysis are given at the nodes as NJ/ML/MP.
ISE-SNP types belonging to “pan-African clade” and the “Gabonese/Cameroonian clade” are
highlighted in grey and red respectively. B: The geographical distribution of the pan-African
clade and the Gabonese/Cameroonian clade. The location of residence at the time of clinical
visit of the individual BU patients was retrospectively correlated with the ISE-SNP typing
results.
Page | 51
The phylogenetic network (Figure 2.5) showed that a number of African ISE-
SNP types are closely related and only differ in a single, or few, mutational
steps. However, other African ISE-SNP types are more distantly related and
even differ in a number of mutational steps that is similar to the number of
mutational steps between African and non-African types. Analogous with the
phylogenetic tree analysis, the “pan-African clade” is divided into two major
clusters. A first major cluster comprised the common Central and West
African ISE-SNP type 1 and several, closely related, yet rarer, ISE-SNP types.
The second cluster comprised ISE-SNP types 22, 23, 25, 26 and 27, which are
circulating in Cameroon and neighboring Gabon. The network also showed
several more distantly related African haplotypes of which most are relatively
rare (types 18, 19, 20, 21, and 24) and one (type 7) is common.
Page | 52
Figure 2.5: Phylogenetic network showing patterns of descent among the 28 currently know
ISE-SNP types of M. ulcerans in relation to their geographic origin. The network was derived
by using the Median Joining algorithm after processing the data with the reduced median
method as implemented by Network v.4.6.1.0. Each circle represents a unique ISE-SNP type,
and the size of the circle is proportional to the number of individuals sharing that type.
Numbers in boxes represent the number of mutational steps (if not given, then a single
mutational step). Position at which mutations occurred are given in Figure 1. Color codes
represent the country of origin as shown in the key.
2.5 Discussion
In this study, we applied an optimized ISE-SNP genotyping technique to a
comprehensive panel of isolates from all African countries that ever yielded
culture confirmed BU cases. This analysis, unparalleled in size and scope,
allowed us to assess the diversity and population structure across BU endemic
regions on a continental scale, and to explore the phylogenetic and
phylogeographic relationships within the genetically conserved cluster of
African M. ulcerans ISE-SNP types.
Analysis of polymorphisms in the RD1 and RD12 genomic regions, which have
been identified among the most variable of the M. ulcerans bacterial
chromosome [109], over our comprehensive sample panel spanning 11
endemic African countries, identified 23 different African ISE-SNP types. The
observed low level of polymorphisms [110], together with the characteristic
geographical restriction of most ISE-SNP types suggests a highly clonal
population structure of African M. ulcerans. This is in agreement with the
findings of Doig et al. [19], who found that clinical isolates from Ghana and
Benin were only separated by an average pairwise distance of 160 SNPs over
the entire 5.6 Mbp sequenced bacterial chromosome. This low sequence
diversity is in strong contrast with other pathogens like Helicobacter pylori,
where microevolution can be observed even within serial bacterial isolates
from individual humans with prolonged infection [111]. The genetic
conservation among African M. ulcerans might reflect a short evolutionary
history since its intra-continental dispersal but might also be explained by a
low mutation rate. Reliable estimates of mutation rates are required to
resolve these issues [110].
Page | 53
Closely related ISE-SNP types dominate in different BU endemic areas. The
identified SNPs describe a phylogenetic path wherein these individual ISE-SNP
types document the sequential accumulation of mutations from a common
root. If we assume that (i) an ancestral ISE-SNP type will be more
geographically dispersed than a more recently derived type and (ii) that the
geographical distribution of the ISE-SNP types is not explained by selective
effects, this common root node is represented here by ISE-SNP 1; the most
common type distributed over the entire continent. The different ISE-SNP
types thereby represent the initial stages of clonal diversification through de
novo mutations from this, possibly ancestral type, after its intra-continental
spread.
The unevenly distributed ISE-SNP types circulating within small regions of
West Africa are furthermore suggestive of the existence of independent
transmission clusters. We found a strong association between the distribution
of ISE-SNP types and the greater West African hydrological drainage basins.
Genetic differences between clinical isolates originating from two neighboring
drainage areas in Benin have previously been reported [19, 46]. It appears
that geographic barriers (eg. elevated regions and salt water) bordering these
hydrological basins, separated an ancestral genotype to a certain extent into
discontinuous parts by the formation of a physical barrier to bacterial gene
flow. Our data suggest that this resulted in differentiation by the slow
accumulation of point mutational changes of the original founder clone (ISE-
SNP 1) into different closely related types distributed over the various basins
(Figure 2.3B and Table 2.3). New ISE-SNP types derived from the founder type
did not easily spread but formed focal transmission clusters associated with
the hydrological drainage areas. Hence, BU infections in these areas probably
resulted from locally confined transmission of a single circulating clone, with
only occasional transfer of clones between basins. Our findings confirm a
study of Röltgen et al. [112], in which a number of M. ulcerans haplotypes
within the Densu hydrological basin of Ghana (with SNP typing based on
whole genome data) were differentiated, revealing similar focal transmission
clusters within the basin itself. Hence, our findings provide additional
evidence that both transmission and fine-grained evolutionary events play at
the local level and we consequently hypothesize that potential reservoirs have
Page | 54
a limited mobility. Such a scenario would correspondingly account for the
presence of endemic and non-endemic villages in close proximity (<10 km)
within the same drainage basin [108].
Our phylogenetic analyses did not result in a fully resolved phylogenetic tree
since most nodes had low bootstrap support. Nevertheless, there was support
for a “pan-African” and a “Gabonese / Cameroonian” sister clade. The ISE-SNP
types from the pan-African clade are found widespread throughout Africa
while the ISE-SNP types of the Gabonese/Cameroonian clade are much rarer,
and are found in a more restricted area (Figure 2.4), which suggests that the
latter clade evolved more recently. Alternatively, this may also be the result of
a sampling artifact; indeed the Spearman’s rank correlation indicated that the
higher the sampling effort per country the more ISE-SNP types are found.
However, the entirety of the Gabonese/Cameroonian region in itself was well
sampled, with five isolates/clinical samples belonging to the
Gabonese/Cameroonian clade and 25 isolates/clinical samples to the Pan
African clade (Table 2.1 and 2.2, Figure 2.3). Furthermore, the fact that we did
not encounter ISE-SNP types of the Gabonese/Cameroonian clade in
neighboring countries like DRC, where sampling was higher, also suggests that
the ISE-SNP types belonging to the Gabonese/Cameroonian clade are not only
rare but also have a limited distribution. Interestingly, the only ISE-SNP 1
isolate from Cameroon (ITM_120140) came from a patient from Bankim, a
district located along the Mapé river (Sanaga basin), while other studied
isolates all came from around the Nyong river basin. Bankim has been recently
identified as an additional BU endemic area in Cameroon. However, whether
BU was emerging in Bankim, or constitutes a newly recognized preexisting
disease focus, remains unclear [68, 113].
The Gabonese/Cameroonian clade was found to form a strongly supported
monophyletic group with Papua New Guinean ISE-SNP type 8, which is
distinctly unrelated to other ISE-SNP types found in Southeast Asia. Using a
different genotyping technique, the relatedness of a Papua New Guinean
clinical isolate (not included in this study) to African rather than to Southeast
Asian clinical isolates has been reported elsewhere [114]. The process
(historical events, restricted bacterial gene flow, etc.) that led to this inter-
continental association of ISE-SNP haplotypes remains elusive.
Page | 55
In this report we have analyzed a large collection of isolates representative of
the African M. ulcerans population to analyze its population structure
accurately and appropriately. The panel used in this study is, to our
knowledge, the most comprehensive one used so far. It covered disease foci
from all 11 well-documented BU-endemic countries ranging from West,
Central to East Africa. Six countries (Burkina Faso, Equatorial Guinea, Guinea,
Kenya, Liberia, and South Sudan) that have reported a limited number of BU
cases in the past [5] were not included in the study as we were unable to
include specimens, nor isolates, from them. Moreover, cases from Central
African Republic, Senegal and Sierra Leone were never confirmed by
laboratory tests [5]. Although we tried to maximize spatial diversity within our
panel some countries are better represented than others again due to the
limited availability of clinical isolates. We might have missed some ISE-SNP
types in these countries because there was a significant relationship between
the sampling effort per country and the amount of different ISE-SNP types
identified per country. Because of all these limitations we successfully
optimized the genotyping PCR technique for application directly on clinical
specimens, which allowed us to include clinical specimens from certain
geographical regions of Gabon and Nigeria of which no M. ulcerans isolates
were available (Table 2.2).
To our knowledge ISE-SNP typing currently yields the greatest resolution
within M. ulcerans, save for whole genome sequencing. The method may be
an easy, low cost, powerful, reliable, and reproducible tool for reference
laboratories to assist in the tracking of M. ulcerans ISE-SNP types of
epidemiological studies on a continental scale [10].
Because African M. ulcerans shows such low genetic variation, further studies
require a whole genome approach to comprehensively evaluate the genetic
diversity, the evolution, and the phylogenetic relatedness of African M.
ulcerans and to delineate the exact origin and spread of the pathogen at the
local and the continental level. It is specifically the paucity of genetic diversity
and the sequential order of the genetic changes that have occurred between
individual isolates that render M. ulcerans as such a promising model to reveal
evolutionary bacterial mechanisms. Furthermore, given the comprehensive
nature of full genome data, sequences could also serve in large scale micro-
Page | 56
epidemiological studies focusing on the elucidation of transmission pathways
and relevant reservoirs of M. ulcerans. Indeed, different studies in
mycobacterial genomics [19, 112, 115] already showed that, at the whole-
genome level, substantial genetic variation exists in African M. ulcerans, which
can be exploited for phylogenetically robust strain classification. In order to
capture as much diversity as possible and to minimize phylogenetic discovery
bias [116] in such impending large sequencing endeavors it will be desirable to
select representatives from all the central and radial ISE-SNP types defined in
this study.
2.6 Acknowledgements
The present study pays tribute to the extensive collection of M. ulcerans
isolates generated over decades by Emerita Prof. dr. Françoise Portaels and
her collaborators in endemic countries, who initiated and fueled research into
the pathogenesis, diagnosis and management of M. ulcerans disease.
Koen Vandelannoote was supported by a PhD-grant of the Flemish
Interuniversity Council - University Development Cooperation (Belgium).
Funding for this work was provided by the Stop Buruli Consortium supported
by the UBS Optimus Foundation, the European Community's Seventh
Framework Programme under grant agreement n° 241500 (BURULIVAC), the
European Commission (project no. INCO-CT-2005-051476-BURULICO), and the
Fund for Scientific Research Flanders (Belgium) (FWO grant n° G.0321.07N).
The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
We thank Tim Stinear for helpful discussions and a critical comments to the
manuscript. We thank Pim de Rijk, Krista Fissette, Elie Nduwamahoro, and
Anita Van Aerde (ITM) for their excellent technical assistance. We thank three
anonymous reviewers for their constructive and insightful comments, which
helped us to improve the manuscript
Page | 57
Chapter 3
Whole genome comparisons suggest random
distribution of Mycobacterium ulcerans
genotypes in a Buruli ulcer endemic region of
Ghana
This chapter is published as:
Anthony S. Ablordey, Koen Vandelannoote, Isaac A. Frimpong, Evans K. Ahortor,
Nana Ama Amissah, Miriam Eddyani, Lies Durnez, Françoise Portaels, Bouke C. de
Jong, Herwig Leirs, Jessica L. Porter, Kirstie M. Mangas, Margaret M. C. Lam, Andrew
Buultjens, Torsten Seemann, Nicholas J. Tobias, and Timothy P. Stinear
Whole genome comparisons suggest random distribution of Mycobacterium ulcerans
genotypes in a Buruli ulcer endemic region of Ghana
PLoS Neglected Tropical Diseases 2015 Mar 31;9(3):e0003681
Conceived and designed the experiments: ASA KV TPS.
Performed the experiments: ASA KV IAF EKA NAA JLP KMM MMCL NJT TPS. Analyzed
the data: ASA KV IAF NAA ME LD FP BCdJ HL AB NJT TPS.
Contributed reagents/materials/analysis tools: ME LD FP BCdJ HL TS.
Wrote the paper: ASA KV TPS.
Page | 58
3.1 Abstract
Efforts to control the spread of Buruli ulcer - an emerging ulcerative skin
infection caused by Mycobacterium ulcerans - have been hampered by our
poor understanding of reservoirs and transmission. To help address this issue,
we compared whole genomes from 18 clinical M. ulcerans isolates from a
30km² region within the Asante Akim North District, Ashanti region, Ghana,
with 15 other M. ulcerans isolates from elsewhere in Ghana and the
surrounding countries of Ivory Coast, Togo, Benin and Nigeria. Contrary to our
expectations of finding minor DNA sequence variations among isolates
representing a single M. ulcerans circulating genotype, we found instead two
distinct genotypes. One genotype was closely related to isolates from
neighbouring regions of Amansie West and Densu, consistent with the
predicted local endemic clone, but the second genotype (separated by 138
single nucleotide polymorphisms (SNPs) from other Ghanaian strains) most
closely matched M. ulcerans from Nigeria, suggesting another introduction of
M. ulcerans to Ghana, perhaps from that country. Both the exotic genotype
and the local Ghanaian genotype displayed highly restricted intra-strain
genetic variation, with less than 50 SNP differences across a 5.2 Mbp core
genome within each genotype. Interestingly, there was no discernible spatial
clustering of genotypes at the local village scale. Interviews revealed no
obvious epidemiological links among BU patients who had been infected with
identical M. ulcerans genotypes but lived in geographically separate villages.
We conclude that M. ulcerans is spread widely across the region, with
multiple genotypes present in any one area. These data give us new
perspectives on the behavior of possible reservoirs and subsequent
transmission mechanisms of M. ulcerans. These observations also show for
the first time that M. ulcerans can be mobilized, introduced to a new area and
then spread within a population. Potential reservoirs of M. ulcerans thus
might include humans, or perhaps M. ulcerans-infected animals such as
livestock that move regularly between countries.
Page | 59
3.2 Introduction
Buruli ulcer (BU) is a neglected tropical disease caused by infection
with Mycobacterium ulcerans. Each year 5000-6000 cases are reported from
15 of the 33 countries where BU cases have been reported, predominantly
from rural regions across West and Central Africa [117]. The disease involves
subcutaneous tissue and has several manifestations but necrotic skin ulcers
are a common presentation, caused by the proliferation of bacteria beneath
the dermis by virtue of a secreted bioactive lipid called mycolactone [14]. The
role of mycolactone in the natural ecology of M. ulcerans is not understood,
but it has been shown to possess several specific activities against mammalian
cells from activating actin polymerization, blocking secreted protein
translocation, to interacting with neuronal angiotensin type II receptors
causing hypoesthesia [15, 16]. These collective biological activities of
mycolactone, while diverse, might collectively help explain the tissue
destruction, lack of inflammation, and painlessness associated with BU. BU is
rarely fatal and early diagnosis followed by combined antibiotic therapy
(rifampicin and streptomycin) is key to preventing complications that can arise
from severe skin ulceration [11].
Epidemiological studies frequently link BU occurrence with low-lying and
wetland areas and human-to-human transmission seems rare, suggesting an
environmental source of the mycobacterium [29, 68, 113, 118-131].
Frustratingly however, the environmental reservoir(s) and mode(s) of
transmission of M. ulcerans remain unknown. M. ulcerans has the genomic
signature of a niche-adapted mycobacterium, indicating that it is unlikely to
be found free-living in diverse aquatic (or other) environments, but more
likely in close association with a host organism. In south eastern Australia,
native marsupials have been identified as both susceptible hosts and
reservoirs of M. ulcerans, with high numbers of the bacteria shed in the feces
of infected animals. Mosquitoes have also been found to harbor the bacteria
in this region and a zoonotic model of disease transmission has been
proposed involving possums, biting insects and humans [42, 61]. No such
animal reservoir has yet been identified in African BU endemic areas and
studies of BU lesion distribution are thought not consistent with mosquito
biting patterns [68, 89]. On the other hand, case-control studies in Cameroon
Page | 60
have shown that bed nets are protective, supporting a role for insects in
transmission [132].
A feature of M. ulcerans is the close correlation between genotype and the
geographic origin of a strain, but its restricted genetic diversity has limited the
application of traditional molecular epidemiological methods such as VNTR-
typing to discriminate between isolates at the village or even regional scales.
The advent of low cost genomics has opened up new possibilities to explore
and track the movement and spread of this pathogen within communities
[112, 133].
Agogo is the principal town of 30,000 inhabitants in the Asante Akim North
(AAN) district within the Ashanti region of Ghana and BU has been reported in
about half of the sixty-four communities in this district since mid-1975 [121].
The AAN district covers an area of 650 km2 in the forest belt of Ghana and it is
the third most endemic district in Ghana [134]. Five of the communities
(Ananekrom, Serebouso, Nshyieso, Serebuoso and Dukusen) in this district are
among the communities reported with the highest burden of the disease in
Ghana [134]. About 120 laboratory-confirmed new cases are reported
annually in this district [134]. Subsistence farming and petty trading are the
principal occupations of inhabitants of these endemic communities. People
generally live in simple dwellings constructed from local materials. Houses are
often close together with 3-5 households in a compound. Many inhabitants
raise animals such as goats, sheep, and pigs in the immediate vicinity of their
houses. Farming is the main occupation with some people engaged in fishing
and petty trading. Farms may be distant, ranging 5-20 km from a given
domicile. Fishing is usually undertaken close to home. Water sources are of
two types. Water for drinking and cooking is usually fetched from bore holes
fitted with mechanical pumps, within or near a village. Water for bathing and
domestic chores such as washing of clothes is drawn from local natural water
sources (rivers, streams, ponds). These natural sources are usually no more
than 500 metres from a given village.
In this study we sequenced and compared the genomes of 18 M. ulcerans
isolates obtained from 10 BU endemic villages in the AAN district and
uncovered genetic evidence supporting the introduction of a foreign clone
Page | 61
of M. ulcerans to this region. This observation indicates that M. ulcerans can
be mobilized and spread throughout a region, indicating that reservoirs of the
bacterium are themselves potentially highly mobile.
3.3 Methods
Ethics statement
M. ulcerans isolates were obtained from BU diagnostic samples, collected as
part of routine laboratory diagnosis. Ethical approval to interview patients and
use bacterial isolates resulting from diagnostic specimens for research was
obtained from the ethical review board of the Noguchi Memorial Institute for
Medical Research, University of Ghana, Legon, Accra, Ghana (FWA 00001824),
with written informed consent obtained from all adult patients or the
parents/guardians of the participating children.
Study site and case reporting
The study was carried out in ten endemic villages including Ananekrom,
Nshyieso, Serebouso, Dukusen, Afreserie, Afreserie OK, Baama,
Nysonyameye, Kwame Addo and Bebuso, in the Asante Akim North (AAN)
district of Ghana (Table 3.1). These are small villages and hamlets, 5 to 10 km
from each other with populations between 120-1500 inhabitants. Ananekrom
is the largest of these communities and is the closest (15 km) to the district
capital, Agogo. An asphalt road connects Agogo to Ananekrom, Dukusen and
Afriserie, while the other communities are located off this main road and are
connected to each other by unmade roads and foot-tracks. A community
health centre Ananefromh (near Ananekrom) is usually the first point of call
for patients seeking medical treatment. Patients suspected of having BU are
referred to the Agogo Presbyterian Hospital for diagnosis and treatment.
Patient information including name and place of residence were obtained
from hospital records and patients were visited in their homes for more
detailed interviews that included questions about possible travel to other BU
endemic areas outside the AAN district. GPS coordinates in the vicinity of each
patient’s residence were recorded in order to map the spatial distribution of
Page | 62
cases in the villages, based on the assumption that the patient acquired their
infection near their domicile.
Table 3.1: M. ulcerans isolate and DNA sequencing information. Notes: 1: parentheses
indicate alternate strain code, 2: expressed as latitude and longitude, 3: Democratic Republic
of the Congo, 4: reference genome
No. Strain ID1 Isolation date
Patient Age
(years)
Patient
Gender Village/Genotype District/ Country Location
2 Seq Plat. Avg cov. ENA Ref.
1 S15 2/02/2010 5 M Ananekrom/
Agogo-1 Ashanti/Ghana
6.91481, -
1.01658 SE PGM 106
SAMEA321257
8 This study
2 S43 9/05/2010 13 F Ananekrom/
Agogo-2 Ashanti /Ghana
6.91481, -
1.01658 SE PGM 120
SAMEA321257
0 This study
3 S38 23/06/2010 12 M Serebuso/ Agogo-
1 Ashanti /Ghana
6.94838, -
1.02373 SE PGM 104
SAMEA321258
0 This study
4 F64 8/09/2010 3 M Nsonyameye/
Agogo-1 Ashanti /Ghana
6.93744, -
0.96464 SE PGM 99
SAMEA321257
4 This study
5 F70 15/09/2010 32 F Baama/ Agogo-1 Ashanti /Ghana 6.96810, -
0.94013 SE PGM 61
SAMEA321258
2 This study
6 1510 (F79) 22/09/2010 28 F Bebuso/ Agogo-2 Ashanti /Ghana 6.88909, -
0.97209 PE Illumina 81
SAMEA321256
5 This study
7 F75 7/10/2010 18 F Afriserie OK/
Agogo-1 Ashanti /Ghana
7.01992, -
0.92325 SE PGM 68
SAMEA321258
1 This study
8 F85 20/10/2010 28 F Nshyieso/ Agogo-2 Ashanti /Ghana 6.95910, -
1.01860 SE PGM 71
SAMEA321256
6 This study
9 S77 24/11/2010 3 F Serebuso/ Agogo-
2 Ashanti /Ghana
6.94838, -
1.02373 SE PGM 83
SAMEA321257
1 This study
10 2610 (F92) 24/12/2010 14 F Nsonyameye/
Agogo-1 Ashanti /Ghana
6.93744, -
0.96464 PE Illumina 67
SAMEA321256
7 This study
11 F13 2/02/2011 8 F Bebosu Ado/
Agogo-1 Ashanti /Ghana
6.88909, -
0.97209 SE PGM 92
SAMEA321258
3 This study
12 F65 2/09/2011 28 F Ananekrom/
Agogo-2 Ashanti /Ghana
6.91481, -
1.01658 SE PGM 89
SAMEA321257
6 This study
13 F74 21/09/2011 38 M Dukusen/ Agogo-2 Ashanti /Ghana 6.97683, -
0.98278 SE PGM 46
SAMEA321257
2 This study
14 S72 12/12/2011 11 M Afriserie/ Agogo-1 Ashanti /Ghana 7.01992, -
0.92325 SE PGM 128
SAMEA321257
5 This study
15 612 (F36) 18/07/2012 37 M Ananekrom/
Agogo-1 Ashanti /Ghana
6.91481, -
1.01658 PE Illumina 97
SAMEA321256
8 This study
16 212 (F3) 8/02/2012 11 F Serebuso/ Agogo-
2 Ashanti /Ghana
6.94838, -
1.02373 PE Illumina 62
SAMEA321257
7 This study
17 712 (S24) 2/08/2012 40 F Wenamda/ Agogo-
2 Volta/Ghana
7.07738,
0.092016 PE Illumina 214
SAMEA321256
9 This study
18 412 (F37) 2/08/2012 12 M Ananekrom/
Agogo-1 Ashanti /Ghana
6.91481, -
1.01658 PE Illumina 162
SAMEA321258
4 This study
19 IC21 jul/00
Bondoukou Cote d’Ivoire 8.03333, -
2.8000 SE PGM 79
SAMEA321257
9 This study
20 IC38 jul/00
Dimbokro Cote d’Ivoire 6.64445, -
4.70540 SE PGM 63
SAMEA321257
3 This study
21 ITM000909 2000
Tchekpo Deve Maritime/Togo 6.48434,
1.369718 SE PGM 45
[135]
22 ITM991591 1999
Anagali Maritime/Togo 6.48434,
1.369718 SE PGM 55
[135]
23 ITM102686 2010
M Ibadan Oyo State/Nigeria 7.50194,
3.982327 SE PGM 51
[135]
24 ITM5151 1971
Maniema/DRC3
-4.18655,
26.43937 SE PGM 51
[135]
25 NM14.01 2001
Densu/Ghana 5.72532, -
0.31075 PE Illumina 111
[19]
26 NM43.02 2002
Densu/Ghana 5.69881, -
0.38597 PE Illumina 112
[19]
27 NM49.02 2002
Densu/Ghana 5.70209, -
0.29818 PE Illumina 114
[19]
28 NM54.02 2002
Densu/Ghana 5.6568, -
0.32419 PE Illumina 73
[19]
29 NM33.04 2004
Amansie West/Ghana 6.68712, -
1.62197 PE Illumina 84
[19]
30 Agy994 aug/99 5 F
Amansie West/Ghana
6.68712, -
1.62197 Sanger
[12]
31 ITM980535 1998 12 F Djigbé Atlantique/Benin 6.885076,
2.362017 PE Illumina 257
[19]
Page | 63
No. Strain ID1 Isolation date
Patient Age
(years)
Patient
Gender Village/Genotype District/ Country Location
2 Seq Plat. Avg cov. ENA Ref.
32 ITM000945 2000 21 F Hwegoudo Atlantique/Benin 6.7234,
2.377314 PE Illumina 231
[19]
33 ITM001506 2000 6 F Wokon Zou/Benin 7.099851,
2.464233 PE Illumina 233
[19]
Culture isolation and identification of M. ulcerans from patients
The isolates examined in this study are listed in Table 3.1 and were recovered
from fine needle aspirates (FNA) or swabs, obtained from pre-ulcerative
lesions and ulcers respectively. Specimens were stored in transport medium
and PBS and transported in cool boxes to the Noguchi Memorial Institute for
Medical Research (NMIMR) for diagnosis [136, 137]. Tubes containing swabs
were vortexed in 3 ml of transport medium for 30 sec and the swabs
removed. A volume of 250μl of the transport medium from either specimen
type was transferred into 1.5 ml microfuge tubes and decontaminated using
the oxalic acid method as previously described [138]. The pellets were
resuspended in 100 μl phosphate buffered-saline (PBS) and 100 μl volume of
the decontaminated sample was inoculated onto Löwenstein Jensen (LJ)
slopes and incubated at 33°C. The cultures were observed weekly for growth.
Suspected M. ulcerans colonies were harvested and DNA extracted as
described above [139]. The DNA extract was tested with the IS2404 PCR for
the identification of M. ulcerans [140]. Colonies positive for IS2404 were
suspended in 1 ml of Middlebrook 7H9 broth and stored at -80°C. All 18
bacterial samples analyzed were selected from this stored collection and were
subcultured on LJ medium and DNA for whole genome sequencing was
extracted from resulting growth as described [139]. The isolation date refers
to the date when colonies became visible on LJ medium following primary
cultivation.
Genome sequencing and analysis
DNA sequencing was performed using two methods. The Ion Torrent Personal
Genome Machine was employed, with a 316 chip and 200bp single-end
sequencing chemistry (Life Technologies). Genomic libraries for Ion Torrent
sequencing were prepared using Ion Express, with size selection using the
Pippin Prep (Sage Sciences) and emulsion PCR run using a One-Touch
Page | 64
instrument (Life Technologies). The Illumina MiSeq was also used, with
Nextera XP library preparation and 2x250 bp sequencing chemistry. Read data
for the study isolates have been deposited in the European Nucleotide
Archive (ENA) under accession ERA401876. Prior to further analysis, reads
were filtered to remove those containing ambiguous base calls, any reads less
than 50 nucleotides in length, and containing only homopolymers. All reads
were furthermore trimmed removing residual ligated Nextera adaptors and
low quality bases (less than Q10) at the 3' end. Resulting sequence Fastq
sequence read files from either platform were subjected to read-mapping to
the M. ulcerans Agy99 reference genome (Genbank accession
number CP000325) using Bowtie2 v2.1.0 [141] with default parameters and
consensus calling to identify SNPs (indels excluded) using Nesoni v0.109, a
Python utility that uses the reads from each genome aligned to the core
genome to construct a tally of putative differences at each nucleotide position
(including substitutions, insertions, and deletions)
(www.bioinformatics.net.au). Those positions in the Agy99 reference genome
that were covered by at least 3 reads from every isolate defined a core
genome. Note that the pMUM001 plasmid (required for mycolactone
synthesis) was not included in the reference genome [13]. Testing of the
plasmid sequences revealed less than 10 polymorphic sites among the
genomes under investigation and the highly repetitive sequence structure of
the mycolactone genes impaired unambiguous read-mapping. An unpaired t
test with Welch’s correction was used to assess the differences between
mean nucleotide pairwise identities for different groups of genomes. The null
hypothesis (no difference between means) was rejected for p<0.01. The
inputs for subsequent phylogenomic analyses were the nucleotide sequence
alignments of the concatenated variable nucleotide positions for the core
genome among all isolates. A maximum-likelihood (ML) phylogeny was
inferred using RAxML v 7.2.8, with the GTR model of nucleotide substitution
(plugin within Geneious v 8.0.4). We performed 1000 rapid pseudo-replicate
bootstrap analyses to assess support for the ML phylogeny. We used
Consensus-Tree-Builder (Geneious v8.0.4) to collapse nodes in the tree with
bootstrap values below a set threshold of 70%. The resulting phylogenomic
tree was exported in Newick format and visualized using FigTree v1.4.0
(tree.bio.ed.ac.uk/software/figtree). A haplotype network was derived using
Page | 65
the median-joining algorithm as implemented in SplitsTree v4.13.1 [103, 142].
A correction to the source attribution of the M. ulcerans Agy99 reference
genome was also made in the course of this study, where it was realized that
this isolate was actually obtained from a patient attending St Martin’s
Hospital in Agroyesum (Amansie West) and not the Ga District Hospital as
originally published [12] (K. Asiedu and J., Hayman pers comms), thereby
explaining the inconsistent geographic clustering reported in previous
molecular epidemiological studies [19, 112, 143].
3.4 Results
Genome sequence comparisons of 18 M. ulcerans isolates from Agogo
region
Eighteen M. ulcerans isolates were randomly selected for whole genome
sequencing. The isolates represented 20% (total of 92 isolates from 2010-
2012) of all culture-confirmed BU cases referred to the Agogo Presbyterian
Hospital between 2010 and 2012 (Table 3.1). There were no differences in
colony phenotype or growth characteristics among the isolates. The DNA
sequence reads for each genome were mapped to the M. ulcerans Agy99
reference sequence. Sequencing and read-mapping summary statistics are
given in Table 3.1. In addition to the 18 Agogo isolates sequenced here, 15
other genomes (including some previously described) were included in
comparisons making a total of 33 isolates (Table 3.1). These additional
genomes were from M. ulcerans isolates in other regions of Ghana and from
surrounding countries to provide appropriate genetic context for interpreting
the diversity and evolution of M. ulcerans isolates from around Agogo. Read-
mapping and SNP identification revealed 320 variable nucleotide positions
across a 5.2Mb core genome for the 33 isolates. A phylogeny was inferred
from this alignment, showing the clustering typical of M. ulcerans genotypes
with geographic origin (Figure 3.1). A separate SNP alignment was performed
taking the genome sequences for only the 18 isolates from the Agogo region,
and 10 of them (called Agogo-1) clustered with isolates from the neighboring
district of Amansie West and also the Ivory Coast, the country which borders
this region to the west (Figure 3.1). This close relationship is indicative of a
local clone that has spread and persisted within the greater region for some
Page | 66
time. Unexpectedly however, this analysis also revealed the presence of a
second distinct M. ulcerans genotype co-circulating with Agogo-1. This second
genotype (called Agogo-2) was substantially more diverse from all other
Ghanaian M. ulcerans genotypes (138 SNPs), suggesting the re-introduction
of M. ulcerans to the Agogo region, potentially from a source outside Ghana
(Figure 3.1, Table 3.S1). The intra-genotype variation within either cluster was
low. The mean nucleotide pairwise identity was 94.7% (SEM ± 0.4) for Agogo-
1 versus 97.2% (SEM ± 0.4) for Agogo 2. The mean pairwise nucleotide
identity was significantly lower for Agogo-2 genomes compared with Agogo-1
(p<0.001).
Figure 3.1: Genetic relationship among the 33 M. ulcerans isolates used in this study. A
maximum-likelihood consensus phylogeny was inferred based on whole genome alignments
of each of the isolates against the M. ulcerans Agy99 reference genome. The alignment file
from pairwise comparisons of the resulting 320 variable nucleotide positions was used as
input for RaxML. Nodes with less than 70% bootstrap support (1000 replicates) were
collapsed.
Page | 67
A likely foreign origin for Agogo-2
To investigate the possible origin of the Agogo-2 isolates we compared SNP
profiles among our panel of M. ulcerans genomes from across West and
Central Africa. The closest match obtained was to isolate ITM102686,
obtained from a patient originating from Ibadan, Nigeria, with 29 SNPs
different when only this genome was compared to the Agogo-2 cluster. This
close association may indicate that Nigeria was the source of the Agogo-2
cluster. Some circumspection is needed when interpreting these data, as only
two M. ulcerans genomes were sampled from countries east of Benin. There is
however a compelling patient history behind this isolate to support Nigeria as
the correct origin. The Caucasian patient, a long-term resident in Ibadan and
an employee of a non-government organisation, believes he was infected on
an Ibadan golf course, when he was bitten by black biting flies (his description
suggests they may have been moth flies [Psychodidae]) that began plaguing
the course when ground works started adjacent to a lake on the course. The
patient developed a painful ulcer on the site of the insect bites. A couple of
months later he developed a second ulcer on an adjacent site on the same
limb that was microbiologically diagnosed as a Buruli ulcer. This patient
history, combined with the documented cases of BU in Ibadan, with cases
occurring around the Ibadan University campus and other nearby institutions
[144], support Ibadan as the likely origin of M. ulcerans isolate ITM102686.
M. ulcerans genotype clustering breaks down at a local scale
We next explored the distribution of M. ulcerans genotypes in the Agogo
region at the village scale and observed no obvious pattern or relationship
between genotype, patient, strain and village (Figure 3.2). There is complete
intermixing of Agogo-1 and Agogo-2 clusters amongst the population.
Median-joining-network analysis suggested the independent radiation of the
two clusters throughout the region (Figure 3.2). Furthermore, within either
cluster there was a broad distribution of cluster subtypes across the region.
For example, isolates F70 and S38 (Agogo-1) have identical SNP profiles but
the patients came from Baama and Serebouso, villages separated by 10-15
km. Similarly isolates F74 and 1510 (Agogo-2), came from patients who live in
two different villages (Figure 3.2). Patient interviews did not identify any
Page | 68
travel histories or other epidemiological links that might explain these
distribution patterns. An 11-year old girl from Serebouso was the third child
within her family to have BU (isolate 212, November 2012), eight years after
two of her siblings had the disease. The family of this child lived very close to
that of another BU patient, a 3 year-old infant (isolate S77, February 2010).
Both isolates belonged to the Agogo-2 cluster but their genome sequence
differed in nine nucleotide positions, a significant amount of genetic variation
given that S77 shares a near identical genotype with F74 and 1510. Again, we
could not identify any specific activity or travel history such as attending a
common community event that was shared by Agogo-2 genotype patients.
These data suggest that (i) the disease is acquired locally, (ii) multiple M.
ulcerans genotypes are circulating simultaneously within the local region and
(iii) a single clone can have the propensity to spread through a region. Further
support for local acquisition of infection comes from observations of infants
with no travel history with BU such as a locally-born 2-year old infant from
Ananekrom identified over the time of this study.
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Figure 3.2: Micro-molecular epidemiology of BU in the Asante Akim North District revealed by
M. ulcerans whole genome sequence comparisons. (A) Median-joining network graph
showing the genetic relationship between 18 M. ulcerans clinical isolates comprising the
Agogo-1 and Agogo-2 genotypes (shaded), inferred from whole genome sequence
alignments. Node sizes in the graph are proportional to the frequency of genotype occurrence
and have been colour-coded accordingly. Edges are labelled in red with the number of
mutational steps between each node. (B) Map of Asante Akim North District study area,
showing the location of endemic villages and the origin of each of the 18 BU cases, with a
coloured circle corresponding with the genotype displayed in the network graph in (A). The
number “2” within some coloured circles indicates an Agogo-2 genotype.
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3.5 Discussion
The clonal population structure of M. ulcerans has made identifying and
comparing genetic variation in isolates at anything less than a continental
scale very difficult. Here we have used the high resolution afforded by
comparative genomics to explore the molecular epidemiology of BU at the
regional and village scale. Like recent studies using a single polymorphic
genetic locus or whole genome sequence comparisons to assess M. ulcerans
genetic diversity across a range of African countries, we found a highly
significant relationship between the genotype of an isolate and its geographic
origin at a national and regional scale [19, 135]. These repeated observations
indicate that M. ulcerans, when introduced to an area, remains localized and
isolated for a sufficiently long period to allow mutations to become fixed in
the bacterial population and a local genotype to evolve. It is reasonable to
infer therefore, that the environmental reservoirs of the bacterium in these
areas are also likely to be somewhat localized and isolated.
However, the current study has shown for the first time how this focal
distribution pattern breaks down at a local scale with the presence of identical
genotypes appearing concurrently in separate areas of the same district.
There was no discernible distribution pattern for either the Agogo-1 or Agogo-
2 genetic clusters, with both M. ulcerans genotypes appearing at the same
times and within the same villages across the region. Interestingly, there were
several examples of isolates with identical genome sequences (e.g. isolates
F74, 1510 or F85, F65) that were obtained from patients living in four
different villages, each separated by distances in excess of 10km (Figure 3.2).
There are several potential explanations for these patterns. The bacteria (or a
vector spreading the bacteria) may be widely distributed across the region
and infections are being acquired locally, or it may be that people are
traveling and becoming infected from a common point source. Patient
interviews and travel histories did not reveal any common activity that might
explain a point-source transmission scenario, although the long incubation
time for this disease (4-months) is likely to make recall of any such events
unreliable [145]. However, on balance the former scenario seems most likely,
and we suggest that each genotype of M. ulcerans has now spread equally
widely across the region. If this assumption is correct, then the lack of genetic
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variation among isolates suggests that the spread of M. ulcerans throughout
the region has occurred relatively rapidly, with insufficient time elapsed for
mutations to accumulate. Reliable mutation rates for M. ulcerans have not
been established and some solid data here would allow inferences regarding
the time particular clones have been extant within a population.
To our knowledge, this is the first report to employ whole genome sequencing
to explore the molecular epidemiology of BU at a local scale. A previous study
utilizing high-resolution SNP assays to explore M.ulcerans genetic variation
did uncover some suggestion of local genotype clustering and a recent report
used VNTR to examine the link between human and environmental sources
of M. ulcerans [66, 112]. However such approaches rely on variable
nucleotides that have been defined from a limited reference genome set. If
this reference genome set does not represent the genetic variation of the
isolates under investigation then data analysis can be flawed, with
phenomena such as long-branch attraction and phylogenetic discovery bias
confounding analyses [146]. Whole genome sequencing and comparisons of
all isolates under investigation as in our study here overcomes the potential
weaknesses of targeted SNP-based typing. SNP-typing could however be
employed to classify patient samples as Agogo-1 and Agogo-2 genotypes
without relying on sequencing of cultured isolates, as culture sensitivity is only
around 30%, depending on transport duration. Future studies could thus
search for clinical phenotypes between these two distinct bacterial genotypes,
although no differences were observed in pathology or treatment outcomes
among the patients associated with this study.
There are interesting parallels between M. ulcerans and Mycobacterium
leprae, the causative agent of leprosy, where genomics has shown that the
leprosy bacillus is another example of a niche-adapted, highly clonal, zoonotic
mycobacterial pathogen, with the potential to spread from environment-to-
human [56, 147-149]. Mycobacterium tuberculosis might also be considered in
a similar context, with genomic population analysis also suggesting
interactions among genetically distinct M. tuberculosis lineages [150, 151].
One potential issue arising from this study is the risk of incorrectly attributing
Nigeria as the origin M. ulcerans genome sequence ITM102686, as it
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represents only one isolate. While the patient history makes a persuasive
argument for Ibadan as the source of the infection, additional M. ulcerans
isolates are clearly required from patients in different BU endemic regions of
Nigeria and surrounding countries, to further explore the relationship and
disease transmission patterns we propose here.
Regardless of the precise origin of Agogo-2 isolates, the data presented here
suggest that M. ulcerans can be introduced into a region and then be spread
extensively. How might M. ulcerans be imported into a region? We speculate
that movements of people or perhaps animals between countries could be
one likely means, where infected individuals with BU lesions that can contain
very high bacterial burdens might inadvertently contaminate aquatic
environments during bathing or other water contact activities. Now is the
time to undertake more intensive and extensive whole-genome M. ulcerans
sequencing surveys across West Africa, to assess the extent of genotype
admixture such as we’ve revealed here. Enriching our genome data will also
inform other research programs that are identifying reservoirs of M. ulcerans,
leading to the new knowledge required to design interventions and stop the
spread of BU.
3.6 Supporting Information
Table 3.S1: Summary of the 127 variable nucleotides that differentiate the
Agogo-1 and Agogo-2 clusters, with predicted CDS consequences, based on
whole genome sequence comparisons.
� Object too large to print. Available online (http://tinyurl.com/h9xpemc).
3.7 Acknowledgments
We thank Conor Meehan for critical review of the analysis methods and
Laurent Marsollier for the provision of bacterial isolates.
3.8 Funding Statement
This study was supported in part by the Stop Buruli Initiative
(www.stopburuli.org, UBS Optimus Foundation, Zurich, Switzerland) and the
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Flemish Interuniversity Council -University Development Cooperation
(www.vliruos.be, VLIR-UOS). TPS was supported by a Fellowship from the
National Health and Medical Research Council of Australia
(www.nhmrc.gov.au). KV was supported by a VLADOC PhD scholarship of
VLIR-UOS (Belgium). The funders had no role in study design, data collection
and analysis, decision to publish or preparations of the manuscript.
3.9 Data Availability
All relevant data are within the paper and its Supporting Information files. All
DNA sequence files are available from the European Nucleotide Archive (ENA)
under accession ERA401876.
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Page | 75
Chapter 4
Population genomics aligns the spread of
Mycobacterium ulcerans and Buruli ulcer with
the scramble for Africa.
Koen Vandelannoote, Conor J. Meehan, Miriam Eddyani, Dissou Affolabi, Delphin
Mavinga Phanzu, Sara Eyangoh, Kurt Jordaens, Françoise Portaels, Kirstie Mangas,
Torsten Seemann, Laurent Marsollier, Estelle Marion, Annick Chauty, Jordi Landier,
Arnaud Fontanet, Herwig Leirs, Timothy P. Stinear, Bouke C. de Jong
Designed research: KV, CM, ME, FP, HL, TPS, BJ
Performed research: KV, KM, TPS
Contributed new reagents or analytic tools: KV, CM, DA, DP, SE, TS, LM, EM, AC, JL,
AF, TPS
Analysed data: KV, CM, TPS
Wrote the paper: KV, CM, TPS, KJ, BJ
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4.1 Abstract
Buruli ulcer (BU) is an insidious neglected tropical disease. Cases are reported
around the world but the rural regions of West and Central Africa are most
affected. How BU is transmitted and spreads has remained a mystery, even
though the causative agent, Mycobacterium ulcerans, has been known for
more than 70 years. Here, using the tools of population genomics, we
reconstruct the evolutionary history of M. ulcerans by comparing 165 isolates
spanning 48 years and representing 11 endemic countries across Africa. The
genetic diversity of African M. ulcerans was found to be restricted due to the
bacterium’s slow substitution rate coupled with its relatively recent origin. We
identified two specific M. ulcerans lineages within the African continent, and
showed that M. ulcerans lineage Mu_A1 existed in Africa for several hundreds
of years, unlike lineage Mu_A2, which was introduced much more recently
during the early 20th century. Additionally, we observed that specific M.
ulcerans epidemic Mu_A1 clones were introduced during the same time
period in the three hydrological basins that were well covered in our panel.
The estimated time span of the introduction events coincides with the Neo-
imperialism period, during which time the European colonial powers divided
the African continent among themselves. Using this temporal association, and
in the absence of a known BU reservoir or –vector on the continent, we
postulate that the so-called “Scramble for Africa” played a significant role in
the spread of the disease across the continent through the displacement of
BU-infected humans.
4.2 Introduction
Buruli ulcer (BU) is a slowly progressing necrotizing disease of skin and
subcutaneous tissue caused by the pathogen Mycobacterium ulcerans [2]. BU
is considered a neglected tropical disease, and in some highly endemic areas it
is more prevalent than the most notorious mycobacterial diseases,
tuberculosis and leprosy [83]. Even though BU can affect all age groups, the
majority of cases occur in children under age 15 [84]. On the African
continent, the first detailed clinical descriptions of ulcers caused by M.
ulcerans have been attributed to Sir Albert Cook, a missionary physician who
worked in Uganda in 1897 [152]. Since this first description BU was reported
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in 16 Sub-Saharan African countries over the course of the 20th
and the early
21th
century. Today, more than 30 countries worldwide have reported the
emerging disease, although the highest incidence by far is still observed in
impoverished, rural communities of West and Central Africa [5].
BU is known to occur primarily in foci around rural marshes, wetlands, and
riverine areas [2, 85]. As proximity (but not contact) to these slow flowing or
stagnant water bodies is a known risk factor for M. ulcerans infection [31], it is
generally believed that M. ulcerans is an environmental mycobacterium that
can initiate infection after a micro-trauma of the skin [32, 33]. Indeed, M.
ulcerans DNA has been detected in a variety of aquatic specimens [35, 36], yet
the significance of the detection of M. ulcerans DNA by PCR in environmental
samples remains unclear in the disease ecology of BU [35-37, 39, 86-89]. This
is largely due to the fact that definite evidence for the presence of viable M.
ulcerans in potential environmental reservoirs is lacking owing to the
challenge of culturing the slow growing mycobacterium from non-clinical,
environmental sources [46]. Consequently, the mode of transmission and
non-human M. ulcerans reservoir(s) remain poorly understood [153]. As until
today no animal reservoir for M. ulcerans has been identified in the Afrotropic
ecozone [89], we hypothesize that humans with active, openly discharging BU
lesions may play a pivotal role in the spread of the bacterium.
Multilocus sequence typing analyses [18] and subsequent whole-genome
comparisons [19] indicated that M. ulcerans evolved from a Mycobacterium
marinum progenitor by acquisition of the virulence plasmid pMUM001. This
plasmid harbors genes required for the synthesis of the macrocyclic
polyketide toxin mycolactone [13], which has cytotoxic and
immunosuppressive properties that can cause chronic ulcerative skin lesions
with limited inflammation and thus plays a key role in the pathogenesis of BU
[14]. Both the acquisition of the plasmid and a reductive evolution [12, 20]
suggested that a generalist proto-M. marinum became a specialized
mycobacterium, more adapted to a restricted environment, perhaps within a
vertebrate host. Analysis of the genome suggests that this new niche is likely
to be protected from sunlight, non-anaerobic, osmotically stable, and an
extracellular environment where slow growth, the loss of several
immunogenic proteins, and production of the immunosuppressive molecule,
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mycolactone, provide selective advantages [12, 19]. The evolution of M.
ulcerans has been mediated by the insertion sequence element (ISE) IS2404,
which is present in the M. ulcerans genome in ~200 copies [12]. For some M.
ulcerans lineages a second ISE, IS2606, is also present in a high copy number
(~90 copies). These short, mobile genetic DNA elements promote genetic
rearrangements by modifying gene expression and sequestering genes,
profoundly enhancing mycobacterial genome plasticity [23]. Increased ISE
numbers are a signature for bacteria that have passed through an
evolutionary bottleneck and undergone a lifestyle shift to a new niche,
causing loss of genetic loci that are no longer required for the survival in the
new environment [90]. Subsequent whole-genome comparisons showed that
this “niche-adapted” genomic signature was established in a M. ulcerans
progenitor before its intercontinental dispersal [19].
The clonal population structure of M. ulcerans has meant that conventional
genetic fingerprinting methods have largely failed to differentiate clinical
disease isolates, complicating molecular analyses on the elucidation of the
disease ecology, the population structure, and the evolutionary history of the
pathogen [93]. Whole genome sequencing (WGS) is currently replacing
conventional genotyping methods for M. ulcerans [19, 154-156]. Hence, in the
present study we sequenced and compared the genomes of 165 M. ulcerans
disease isolates originating from multiple African disease foci to gain deeper
insights into the population structure and evolutionary history of the
pathogen, and to untangle the phylogeographic relationships within the
genetically conserved cluster of African M. ulcerans.
4.3 Methods
Bacterial isolates and sequencing
We analyzed a panel of 165 M. ulcerans disease isolates originating from
disease foci in 11 African countries that had been cultured between 1964 and
2012 (Table 4.S1). Isolates were chosen to maximize temporal and spatial
diversity within countries in which more than 20 isolates were available
(Figure 4.S1). Even though most well-documented BU endemic countries were
well represented, we were unable to include isolates from several African
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countries (Equatorial Guinea, Kenya, Liberia, Sierra Leone, and South Sudan),
that have reported, if not isolated, (a limited number of) BU cases in the past
[5]. Two isolates from Papua New Guinea (PNG) were included as out-groups
to root the African phylogenetic tree. PNG M. ulcerans was specifically chosen
as it (together with African and other Southeast Asian M. ulcerans) belongs to
the more virulent and distinct “classic” phylogenetic lineage [109], relative to
M. ulcerans isolates elsewhere.
Permission for the study was obtained from the ITM Institutional Review
Board. Isolates were processed and analyzed without use of any patient
identifiers, except for country and village of origin if this information was
available. Based on conventional phenotypic and genotypic methods,
bacterial isolates had previously been assigned to the species M. ulcerans
[157]. Mycobacterial isolates were maintained for prolonged storage at ≤-
70°C in Dubos broth enriched with growth supplement and glycerol. DNA was
obtained by harvesting the growth of three Löwenstein-Jensen (LJ) slants
followed by heat inactivation, mechanical disruption, enzymatic digestion and
DNA purification on a Maxwell 16 automated platform [156].
Index-tagged paired-end sequencing-ready libraries were prepared from
gDNA extracts with the Nextera XT DNA Library Preparation Kit. Genome
sequencing was performed on Illumina HiSeq 2000 and Miseq DNA
sequencers according to the manufacturers’ protocols with 150bp, 250bp or
300bp paired-end sequencing chemistry. Sequencing statistics are provided in
Table 4.S1. The quality of raw Illumina reads was investigated with FastQC
v0.11.3 [158].
Prior to further analysis, reads were cleaned with clip, a tool in the Python
utility toolset Nesoni v0.130 [159]. Reads were filtered to remove those
containing ambiguous base calls, any reads <50 nucleotides in length, and
reads containing only homopolymers. All reads were further trimmed to
remove residual ligated Nextera adaptors and low quality bases (<Q10) at the
3' end. The total amount of read-pairs kept after clipping and their average
read length are summarized for all isolates in Table 4.S1.
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Read mapping and SNP / large deletion detection
Read mapping and SNP detection were performed using the Snippy v2.6
pipeline [160]. The Burrows-Wheeler Aligner (BWA) v0.7.12 [161] was used
with default parameters to map clipped read-pairs to two M. ulcerans Agy99
reference genomes: the M. ulcerans Agy99 bacterial chromosome (Genbank:
CP000325) and the M. ulcerans pMUM001 plasmid (Genbank: BX649209).
Due to the unreliability of read mapping in mobile repetitive regions all ISE
elements (IS2404 and IS2606) and all (plasmid-encoded) polyketide synthase
genes were masked in these reference genomes (397 kb / 5,63 Mb - 7% of
Agy99, 118 kb / 174 kb - 67% of pMUM001). After read mapping to M.
ulcerans Agy99 and pMUM001, average read depths were determined with
SAMtools v1.2 [162] and are summarized for all isolates in Table 4.S1. SNPs
were subsequently identified using the variant caller FreeBayes v0.9.21 [163],
with a minimum depth of 10 and a minimum variant allele proportion of 0.9.
Snippy was used to pool all identified SNP positions called in at least one
isolate and interrogate all isolates of the panel at that position. As such a
multiple sequence alignment of “core SNPs” was generated.
The number of reads mapping to unique regions of the plasmid and the
bacterial chromosome were used to roughly infer plasmid copy number. This
was achieved by calculating the ratio of the mode of read depth of all
positions in the plasmid to that of the chromosome: Mo(read depth unique
positions plasmid):Mo(read depth unique positions chromosome) [82].
Large (>1500 bp) chromosomal deletions were detected with Breseq v.0.27.1
[164], a reference-based alignment pipeline that has been specifically
optimized for microbial genomes. Breseq was used with Bowtie2 v.2.2.6 [141]
to map clipped read-pairs to Agy99 and the resulting missing coverage
evidence was used to detect large deleted chromosomal regions.
Population genetic analysis
Bayesian model-based inference of the genetic population structure was
performed using the “Clustering with linked loci” module [165] in BAPS v.6.0
[166]. This particular module takes potential linkage within the employed
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molecular information into consideration, which is advisable when performing
genetic mixture analysis on SNP data from a haploid organism. A
concatenation of the core-SNP alignment of both the bacterial chromosome
and the plasmid was loaded as a sequential BAPS formatted file. This entry
was complemented with a “linkage map” file that differentiated two linkage
groups (bacterial chromosome & plasmid). The optimal number of genetically
diverged BAPS-clusters (K) was estimated in our data by running the
estimation algorithm with the prior upper bound of K varying in the range of
3-20. Since the algorithm is stochastic, the analysis was run in 20 replicates for
each value of K as to increase the probability of finding the posterior optimal
clustering with that specific value of K.
QGIS v.2.10 [104] was used to generate the figures on the geographical
distribution of African M. ulcerans. The residence of BU patients at the time of
their clinical visit was represented as points. In the case where residence
information was missing we used the location of the hospital supplying the
sample. The administrative borders of countries were obtained from the
Global Administrative Unit Layers dataset of FAO.
Detection of recombination
Evidence for recombination between different BAPS-clusters was assessed
using several methods as studies show that no single method is optimal,
whereas multiple approaches may maximize the chances of detecting
recombination events [167]. A whole genome alignment was constructed with
Snippy using default parameters. Recombination was assessed using the
pairwise homoplasy index test Φw [168] (with significance set at 0.05), as
implemented in Splitstree v 4.13.1 [169] on the whole genome alignment. We
used BRAT-NextGen [170] to detect recombination events within our isolate
panel using the whole genome alignment. BRAT-NextGen was specifically
developed to detect homologous recombinant segments among a group of
closely related bacteria over the process of their diversification and has been
shown to be highly accurate when applied to mycobacteria [171]. The
estimation of recombination was started with a partitioning of the whole
genome alignment into 5kb segments and running a clustering analysis
separately on each of these segments. The alpha hyper-parameter was
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estimated with default settings. The proportion of shared ancestry (PSA) tree
was cut at 0.15 differentiating a total set of 4 clusters for all taxa.
Recombination profiles were calculated with 100 iterations, at which stage
parameter estimations had successfully converged. Significance (p<0.05) of
each putative recombinant segment was determined with 100 pseudo
replicate permutations.
Maximum-likelihood phylogenetic analysis
A maximum-likelihood (ML) phylogeny was estimated ten times from the SNP
alignment using RAxML v8.2.0 [172] under a plain generalized time reversible
(GTR) model (no rate heterogeneity) with likelihood calculation correction for
ascertainment bias using the Stamatakis method [173]. Identical sequences
were removed before the RAxML runs. For each run we performed 10,000
rapid bootstrap analyses to assess support for the ML phylogeny. The tree
with the highest likelihood across the ten runs was selected. We used
TreeCollapseCL v4 [174] to collapse nodes in the tree with bootstrap values
below a set threshold of 70% [175] to polytomies while preserving the length
of the tree. Phylogenetic relationships were inferred with the two PNG strains
as outgroups. Root-to-tip distances were extracted from the ML phylogeny
using TreeStat v1.2 [176]. The relationship between root-to-tip distances and
tip dates [177] was determined using linear regression analysis in R v3.2.0
[178].
Bayesian phylogenetic analysis
We used BEAST2 v2.2.1 [77] to date evolutionary events, determine the
substitution rate and produce a time-tree of African M. ulcerans, as this
approach allows for inference of phylogenies with a diverse set of molecular
clock and population parameters [72]. Path sampling (PS) [75] was used to
determine the best clock and population model priors by computing the
marginal likelihoods of competing models of evolution, as this method has
been shown to outperform other methods of model selection [75, 179]. We
compared three clock models (strict, uncorrelated exponential relaxed, and
uncorrelated log-normal relaxed) in combination with two demographic
coalescent models (constant and exponential). The required number of steps
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in PS analysis was determined by running one of the more complex models
(uncorrelated log-normal relaxed clock / constant coalescent tree prior) with a
different amount of steps, starting from 100 steps until 400 using increments
of 50. As no difference in marginal likelihood estimates was observed after
100 steps, each model was run for 100 path steps, each with 200 million
generations, sampling every 20,000 MCMC generations and with a burn-in of
30%. Likelihood log files of all individual steps were inspected with Tracer v1.6
[180] to see whether the chain length produced an effective sample size (ESS)
larger than 400, indicating sufficient sampling. Marginal likelihoods of the
models were then used to calculate natural log Bayes factors (LBF=2*(ln
mL(model1)-mL(model2)), which evaluate the relative merits of competing
models (see also 4.SI1 text).
The best clock/demographic model (UCLD relaxed clock with a constant
coalescent tree prior - see also 4.SI1 text) was then used to infer a genome
scale African M. ulcerans time-tree under the GTR substitution model and
with tip-dates defined as the year of isolation (Table 4.S1). Analysis was
performed in BEAST2 using a total of 10 independent chains of 200 million
generations, with samples taken every 20,000 MCMC generations. Log files
were inspected for convergence and mixing with Tracer v1.6. LogCombiner
v2.2.1 [77] was then used to combine log and tree files of the independent
BEAST2 runs, after having removed a 30% burn-in from each run. Thus,
parameter medians and 95% highest posterior density (HPD) intervals were
estimated from over 1.6 billion visited MCMC generations. To ensure prior
parameters were not over-constraining the calculations, the entire analysis
was furthermore run while sampling only from the prior. Finally, we also
checked for the robustness of our findings under different priors, as states of
low mutation rate and large troot are hard to distinguish from otherwise
identical states of large mutation rate and smaller troot [181]. This additional
analysis was required as the tree prior and the clock prior interact when
adding sequence data, and the strength of this interaction is not visible when
sampling exclusively from the prior.
TreeAnnotator was used to summarize the posterior sample of time-trees so
as to produce a maximum clade credibility tree with the posterior estimates
of node heights visualized on it (posterior probability limit ≥ 0.8).
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A permutation test was used to assess the validity of the temporal signal in
the data. This was undertaken by performing 20 additional BEAST2 runs (of
200 million MCMC generations each) with identical substitution (GTR), clock
(uncorrelated log-normal relaxed) and demographic models (constant
coalescent) but with tip dates randomly reassigned to sequences. This random
“null set” of tip-date and sequence correlations was then compared with the
substitution rate estimate of the genuine tip-date and sequence correlations
[82, 177].
Discrete phylogeographic analysis.
To assess the geospatial distribution of African M. ulcerans through time, an
additional BEAST2 analysis was performed. In this analysis the posterior
probability distribution of the location state (geographic region) of each node
in the tree was inferred, in addition to the parameters described above (tree
topology, evolutionary & demographic model). Sampled isolates were
associated with fixed discrete location states and a discrete BSSVS geospatial
model [182] was subsequently used to reconstruct the ancestral location
states of internal nodes in the tree from these isolate regions. To prevent loss
of the signal in the data by considering too many discrete regions compared
to the number of isolates, we limited the amount of discrete regions by
merging neighboring countries (similarly as carried out in [183, 184]. As such
we differentiated 5 regions: Ivory Coast (20 isolates), Ghana-Togo (25
isolates), Benin-Nigeria (65 isolates), Cameroon-Gabon (24 isolates), and
Angola-DRCongo-Congo (30 isolates). Ten independent chains were run for
200 million generations, with subsamples recorded from the posterior every
20,000 MCMC generations. LogCombiner was then used to combine tree files
of the independent BEAST2 runs, after having removed a 30% burn-in from
each run. TreeAnnotator was used as described above to summarize the
posterior sample of time-trees.
4.4 Results & Discussion
In order to understand how and when M. ulcerans has spread across Africa we
sequenced the genomes of 165 African isolates that were obtained between
1964 and 2012 and spanned most of the known endemic areas of BU in 11
Page | 85
different African countries (Figure 4.S1). This collection captured as much
diversity as possible within Africa while minimizing the phylogenetic discovery
bias implicit to SNP typing [112, 146]. Resulting sequence reads were mapped
to the Ghanaian M. ulcerans Agy99 reference genome and, after excluding
mobile repetitive IS elements and small insertion-deletions (indels), we
detected a total of 9,193 SNPs uniformly distributed across the M. ulcerans
chromosome with approximately 1 SNP per 613 bp (0.17% nucleotide
divergence) (Figure 4.S2). Similarly, a total of 81 SNPs were identified in the
non-repetitive regions of pMUM001, which resulted in a very comparable
nucleotide divergence of 0.14%. The maximum chromosomal genetic distance
between two African isolates was 5,157 SNPs.
The population structure of M. ulcerans in Africa
Large DNA deletions are excellent evolutionary markers since they are very
unlikely to occur independently in different lineages but rather are the result
of unique irreversible events in a common progenitor [185]. We explored the
distribution of large chromosomal deletions (relative to Agy99) and identified
differential genome reduction that supports the existence of two specific M.
ulcerans lineages within the African continent, hereafter referred to as
Lineage Africa I (Mu_A1) and Lineage Africa II (Mu_A2).
SNP-based exploration of the genetic population structure agreed with the
above deletion analysis and subdivided the African M. ulcerans population
into four major clusters. Clusters 1 to 3 constitute Mu_A1 while BAPS-cluster
4 corresponds to Mu_A2. The composition of these clusters is detailed in
Table 4.S1. Cluster 1 circulates throughout the African continent and
represents the majority of the isolates, n=136 (82.4%). This cluster is also the
most genetically diverse with an intra-cluster average pairwise SNP difference
(SNPΔ) of 171 SNPs (SD=73). Clusters 2, 3, and 4 were considerably smaller,
encompassing 20 (12.1%), 1 (0.6%) and 8 (4.8%) isolates respectively. Cluster
2 circulates in different regions of Cameroon and neighboring Gabon and
corresponds to an SNPΔ of 64 SNPs (SD=54). Cluster 3 (1 isolate) was only
found in Uganda. Finally, Cluster 4 (which encompasses Mu_A2 entirely) has a
SNPΔ of 81 SNPs (SD=41) and is common in Gabon (40%, n=5), but quite rare
in Cameroon (5%, n=19) and Benin (8%, n=59).
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African M. ulcerans evolves through clonal expansion, not recombination
Ignoring recombination when analyzing evolving bacterial pathogens can be
misleading as the process has the potential to both distort phylogenetic
inference and create a false signal of apparent mutational evolution by
(horizontally) introducing additional divergence between heterochronously
sampled disease isolates [186]. The pairwise homoplasy index (Φw) did not
find statistically significant evidence for recombination (p= 0.1545) between
different BAPS-clusters. Correspondingly, BRAT-NextGen did not detect any
recombined segments in any isolate, supporting a strongly clonal population
structure for M. ulcerans that is evolving by vertically inherited mutations.
Phylogenetic analysis reveals strong geographical restrictions on M. ulcerans
dispersal
A phylogeny was reconstructed from the chromosomal SNP alignment using
both maximum-likelihood (RAxML) and Bayesian (BEAST2) approaches. Figure
4.S3 shows a well-supported ML-phylogeny that resolved the two major
African lineages Mu_A1 and Mu_A2 and distinguished between the 4 BAPS-
clusters within the African panel. Both the lineages and the BAPS-clusters had
100% bootstrap support and a Bayesian posterior probability of 1 (BEAST2
tree – Figure 4.1). The genome-based phylogeny was consistent with
previously constructed phylogenies based on discriminating ISE-SNP markers
even though these previous trees suffered from low branch support [135].
The tree also indicated that Mu_A1 is much more widely dispersed within the
African continent than Mu_A2.
We identified an unambiguous relationship between the genotype of an
isolate and its geographical origin. This is illustrated by the explicit regional
clustering of M. ulcerans within the phylogenetic tree, indicating significant
geographical structure in the African mycobacterial population. For instance,
all strains isolated from patients living in the hydrological basin of the Kouffo
River of southern Benin clustered together in a “basin-specific” clade in the
Bayesian phylogeny (Figure 4.1). Our observations confirm and extend
previous data showing geographical subdivisions [19, 112, 135, 154, 155], and
indicate that when M. ulcerans is introduced in a particular area, it remains
Page | 87
isolated and localized for a sufficient amount of time to allow mutations to
become fixed in that population. As such, a local genotype that is associated
with that area is allowed to evolve.
We also identified a strong association between the distribution of particular
genotypes and hydrological drainage areas. It appears that the borders of
hydrological basins (consisting of elevated regions, and salt water) also form a
barrier to bacterial spread. For instance, the isolates of the Kouffo Basin are
distinct from isolates originating from the neighboring Oueme Basin, and are
in fact more related to isolates from Ghana and Ivory Coast (Figure 4.1).
Page | 88
Page | 89
Figure 4.1: Bayesian maximum clade credibility phylogeny for African M. ulcerans. The tree
was visualized and colored in Figtree v1.4.2 [102]. Branches are color coded according to their
branch specific substitution rate (legend at top). Branches defining major lineages are
annotated on the tree. Tip labels are color coded according to their respective BAPS-clusters
(the best visited BAPS partitioning scheme of our sample yielded a natural log marginal
likelihood of -95857). Divergence dates (mean estimates and their respective 95% HDP) are
indicated in green for major nodes. Note 95% HDP intervals grow larger closer to the root of
the tree as increasingly less timing calibration information is available the further one goes
back in time. Geographically localized clonal expansions associated with four particular
hydrological basins (Congo, Kouffo, Oueme, and Nyong) are highlighted with boxes and their
corresponding t(MRCA) & 95% HDP are specified in green.
A central role for the mycolactone producing plasmid
All sequenced isolates carried the pMUM001 plasmid. By comparing read
depths of all plasmid and chromosome positions we roughly estimated an
average pMUM001 copy number of 1.3 copies per cell (range 0.4– 1.7). This
asserted the central role of the mycolactone producing plasmid in the
evolution of African M. ulcerans. The plasmid ML-tree (built with the
discovered 81 SNPs) closely matches the topology and relative branch lengths
of the chromosome ML-tree (Figure 4.S3A), which is consistent with co-
evolution of the plasmid with the host-chromosome, stable maintenance of
the plasmid, and absence of transfer of plasmid variants between host
bacteria.
The substitution rate of African M. ulcerans is remarkably low
The major objective of this study was to estimate the rate of evolution of M.
ulcerans in order to estimate the temporal dynamics of the spread of the
pathogen across Africa. Like Mycobacterium tuberculosis [184], M. ulcerans
does not exhibit a strict molecular clock with substitution mutations occurring
at a fixed regular rate, complicating temporal inferences. To overcome this
limitation, we used a Bayesian approach with a relaxed molecular clock model
to infer the evolutionary dynamics of the African mycobacterial population
(See also 4.SI1 text). As such, a genome scale African M. ulcerans time-tree
was inferred (Figure 4.1) while also providing estimates of nucleotide
substitution rates and divergence times for key M. ulcerans clades.
Page | 90
We estimated a mean genome wide substitution rate of 6.32E-8 per site per
year (95% HPD interval [3.90E-8 - 8.84E-8]), corresponding to the
accumulation of 0.33 SNPs per chromosome per year (95% HPD interval [0.20
- 0.46]) (excluding IS elements). To test the validity of the discovered temporal
signal in the data we performed 20 permutation tests. This produced a null set
of 20 “randomized” substitution rate distributions, which were significantly
different (Wilcoxon test, p<2.2E-16) to the substitution rate estimate of the
genuine tip-date and sequence correlation (Figure 4.S4). This clearly indicated
that the tip dates were informative and could provide sufficient calibrating
information for the analysis [82, 177]. The estimated genome wide
substitution rate is lower than the estimate for Clostridium difficile (1.88E-7)
[183] and Shigella sonnei (6.0E-7) [82] yet slightly higher than that of M.
tuberculosis (2.6E-9), the bacterium with the slowest rate currently described
[184]. The analysis also indicated that the genealogy has undergone very
moderate rate variation, with a 2.8-fold difference between the slowest
(3.18E-8) and the fastest (8.87E-8) evolving branches. Rate accelerations and
decelerations are found interspersed in the time-tree (Figure 4.1). The
observed slight rate variation is probably attributable to fluctuations in the
number of bacterial replication cycles per time unit, changes in selection
pressures through time, or combinations of these factors.
M. ulcerans has existed in Africa for centuries and was recently re-
introduced
African Mu_A2 strains were found to form a very strongly supported
(posterior probability = 1) monophyletic group with two PNG strains that were
included in the analysis as outgroup, indicating a closer relationship with
strains from PNG than African Mu_A1 stains. The Bayesian analysis (Figure
4.1) demonstrated furthermore that lineage Mu_A1 has been endemic in the
African continent for hundreds of years (tMCRA(Mu_A1) = 68 B.C. (95% HPD
1093 B.C. - 719 A.D.)). Conversely, Lineage Mu_A2 was introduced much more
recently in the African continent (tMCRA(Mu_A2) = 1800 A.D. (95% HPD 1689
A.D. - 1879 A.D.)), explaining why the lineage is less common and more
geographically restricted. The estimated time span of the Mu_A2 introduction
event coincides with a historical event of particular interest: the period of
Neo-imperialism (late 19th - early 20th century). During this period the
Page | 91
European powers divided the African continent among themselves through
the invasion, colonization and annexation of territory. In the absence of a
known alternative reservoir, nor vector, this specific temporal association
implies a human mediated Mu_A2 introduction event, whether through the
introduction of M. ulcerans bacteria within diseased humans, or an alternative
reservoir or vector.
Recent introduction of M. ulcerans in the Congo, Kouffo, Oueme and Nyong
basins
The time-tree of African M. ulcerans also reveals evidence of the likely role
that the so-called “Scramble for Africa” played in the spread of endemic
Mu_A1 clones in three hydrological basins (Congo, Oueme & Nyong) that are
particularly well covered by our isolate panel (Figure 4.1) [187]. Since, to our
knowledge, no epidemiological studies were conducted in these hydrological
basins until the late 1900s, whether BU was a newly introduced, versus an old
expanding illness in these regions [5] had remained unclear to date. Close
inspection of the time-tree revealed that, similar to the Mu_A2 introduction
event, the basin-specific introduction events coincide with the start of colonial
rule (Table 4.1).
To situate the historical model we are suggesting here, it is important to note
that inhabitants of the three regions of interest have long exploited the river
and forest ecologies prior to the arrival of the European colonial powers [188].
Many of the basin’s inhabitants relied on natural resources for their survival
and as such, were continuously exposed to the lentic environments. However,
it was only after the start of colonial rule that the basin associated epidemic
Mu_A1 clones were introduced, presumably through the introduction of a M.
ulcerans reservoir or vector. However, given the fact that no vector or
reservoir species is known in the Afrotropic ecozone other than Homo sapiens
[36, 65, 66], we postulate that it was the arrival of displaced BU-infected
humans that played a pivotal role in the observed spread of M. ulcerans.
Colonialism was commonly violent and introduced significant socio-economic
changes in the three basins that often involved population displacement. In all
likelihood, displaced BU-infected humans were not directly infecting other
humans as human-to-human transmission of M. ulcerans is extremely rare
Page | 92
[189]. Humans were nevertheless in all probability an important reservoir as
displaced BU-infected patients with active, openly discharging lesions could
contaminate the environment during water contact activities by shedding
concentrated clumps of mycobacteria. Transmission could occur indirectly in
the same community water source, when the superficial skin surface of a
naïve individual was contaminated, and the bacilli present on the
contaminated skin were subsequently inoculated subcutaneously through
some form of penetrating (micro)trauma [190]. Alternatively, we cannot rule
out the possibility that the displaced BU-infected patients were instead source
reservoirs for mechanical transmission via an (unknown) kind of biting aquatic
insect vector. In spite of this uncertainty, since tMCRA of the three hydrological
basins did not predate colonization, it seems likely that M. ulcerans was
introduced after the instigation of colonial rule through an influx of BU
infected humans.
A fourth noteworthy hydrological basin is that of the Beninese Kouffo River.
The timing of its basin specific introduction event (1977 A.D. (95% HPD 1959
A.D. - 1988 A.D.)) is much more recent than the three previously discussed
basins. Notably, the first BU cases from this region were identified and treated
in 1977 [191], concurrent with the estimated introduction event.
Page | 93
Table 4.1: Timing of introduction events of four selected epidemic lineage Mu_A1 clones in
their respective hydrological basin. a
Founding of the Belgian Congo Free State; b
Kingdom of
Dahomey annexed into the French colonial empire; c German Empire claimed the colony of
Kamerun and began a steady push inland; d Based on interviews and observations of healed
lesions in the villages of the Songololo territory it was believed that M. ulcerans infections
already existed in the area in 1935 [120]. Abbreviations: HDP, highest probability density
interval; t(MRCA), time to most recent common ancestor.
Hydrological
Basin Endemic hotspot
Approx. start of
colonial rule Mean t(MRCA)
95% HPD
t(MRCA)
First reported
cases
Congo Songololo Territory 1885a 1905 1855-1941 1961
d [192]
Nyong Between Ayos and
Akonolinga 1884
c 1901 1848-1937 1969 [193, 194]
Oueme Southeastern Benin 1892b 1890 1835-1932 1988 [195]
Kouffo Southeastern Benin 1892b 1977 1959-1988 1977 [191]
The historic spread of M. ulcerans lineage Mu_A1
The phylogeographic analysis also offered new insights on the geospatial
spread of M. ulcerans lineage Mu_A1 through time (Figure 4.2). Uganda,
Cameroon, and Gabon occupy basal branches in the Mu_A1 time-tree
indicating that the bacterium has been extant in these regions for the longest
time. The lineage subsequently expanded from these regions into West and
Central Africa. Ancestral state reconstruction analysis indicated that this
expansion most likely occurred from the region that encompasses Benin and
Nigeria (posterior probability = 0.91). From there Mu_A1 spread further west
(into Togo, Ghana and Ivory Coast) and back east (into Congo, DRC and
Angola).
In other bacterial pathogens, the discipline of microbial phylogeography has
also proved to be a powerful means of investigating not only the spread of
microbes but also the movement of their hosts. For example, interesting
associations were found between the genotypes of Helicobacter pylori strains,
their places of origin, and the migration and ethnicity of their human hosts
[196]. Additionally, the spread of M. tuberculosis and M. leprae also reflects
the migrations of early humans [56, 184].
Page | 94
Comparable as in these landmark studies, the phylogeographic approach
applied here is limited in the respect that it studies a sample of the
mycobacterial population from which it then infers information about the
entire population through various statistical methods. Even though our isolate
panel originates from all African disease foci that have ever yielded positive
M. ulcerans cultures, the spatial coverage of disease isolates is moderately
restricted to specific geographical areas, which might have confounded our
interpretation of the historic spread of African M. ulcerans.
Page | 95
Figure 4.2: Geospatial distribution of African M. ulcerans through time. A Bayesian maximum
clade credibility phylogeny is drawn for lineage Mu_A1 with branches colour coded according
to their most likely location state (legend at top). Pie charts indicate location state posterior
probability distributions of major nodes. The amount of location states was limited to five by
merging the disease isolates of certain neighboring countries. The genetically distinct
Ugandan singleton node (which represents its own BAPS-cluster) was omitted from the
analysis as multiple isolates are required per cluster. Divergence dates (mean estimates and
their respective 95% HDP) are indicated in green for nodes that fall outside of the time scale.
A number of oversampled localized clonal expansions are collapsed in the tree with the size of
their representing cartoon proportional to the amount of collapsed taxa. The tips of the tree
are connected to the location of residence of patients from whom the isolate was grown.
4.5 Conclusion
Here we reconstructed the population structure and evolutionary history of
African M. ulcerans using the molecular and bioinformatics tools of modern
population genomics. The genetic diversity of M. ulcerans proved restricted
because of its slow substitution rate coupled with its recent origin. Sequence
types appear to be maintained in geographically separated subpopulations
that are associated with hydrological drainage areas. Our data show for the
first time that the spread of M. ulcerans across Africa is a relatively modern
phenomenon and one that has escalated since the late 19th and early 20th
centuries. Using temporal associations, this work implicates human-induced
changes and activities behind the expansion of BU in Africa. We propose that
humans with actively infected, openly discharging BU lesions inadvertently
contaminated aquatic environments during water contact activities and thus
played the pivotal role in the spread of the mycobacterium. Our observations
on the role of humans as potential maintenance reservoir to sustain new BU
infections suggests that interventions in a region aimed at reducing the
human BU burden will at the same time break the transmission chains within
that region. Active case-finding programs, improved disease surveillance, and
the early treatment of pre-ulcerative infections with specific antibiotics will
decrease the amounts of mycobacteria shed into the environment and may as
a result reduce disease transmission. Our findings are supported by the
observed decline of BU incidence recorded in some areas which profited from
both improved BU surveillance and early treatment [197].
Page | 96
4.6 Supporting Information
Figure 4.S1: Distribution of BU in Africa by country, as of 2016. Relative endemicity is denoted
as high (red), moderate (yellow), and low (green); dots denote countries with suspected BU
cases. Stars represent the residence of BU patients from whom M. ulcerans disease isolates
were grown at the time of clinical visit. In the case where residence information was missing
we used the location of the hospital supplying the sample. Five countries (Equatorial Guinea,
Kenya, Liberia, Sierra Leone, and South Sudan) that have reported a limited number of BU
cases in the past [5] were not included in the study due to a lack of isolates.
Page | 97
Figure 4.S2: Distribution of SNPs identified compared to the Ghanaian M. ulcerans Agy99
reference genome. The Y-axis corresponds to SNP counts per 10,000bp window, the dashed
line indicates the average rate of 16 SNPs per 10,000bp (or 1 SNP per 613 bp window).
Page | 98
Page | 99
Figure 4.S3: Maximum-likelihood phylogeny of African M. ulcerans based on 9,193 SNP
differences detected across the whole core genome of 167 sequenced isolates. Branches
defining major lineages are annotated on the tree. Nodes in the tree with bootstrap support
below a set threshold of 70% were collapsed to polytomies, while preserving the length of the
tree. The tree was rooted with the two PNG strains as outgroup. Branches are color coded
according to their respective BAPS-clusters as indicated in the legend. Branches defining
major lineages are further annotated on the tree. Tip labels are color coded according to
country of isolation. Inset A: Maximum-likelihood phylogeny of the plasmid which was
constructed from 81 SNPs identified in the non-repetitive regions of pMUM001. The topology
and relative branch lengths of the plasmid tree match those of the chromosomal tree. Inset B:
Linear regression analysis of year of isolation vs root-to-tip distances extracted from
chromosomal ML-phylogeny. Linear regression lines (with 95% CI) and Spearman's rank
correlation coefficients are indicated separately for each lineage.
Page | 100
Figure 4.S4: Comparison of Bayesian estimates of nucleotide substitution rates for real and
randomized tip dates. Filled squares & circles represent mean estimates, while bars indicate
values of the 95% highest probability density (HDP) interval. The estimate obtained using the
real tip date associations (circle) is shown to the far right while estimates from random
associations (squares) are shown to the left. All randomized data sets were analyzed in
BEAST2 using identical model settings as used in the analysis of the real tip date data. Note
the y-axis is on the log scale.
Page | 101
Table 4.S1: M. ulcerans isolate and DNA sequencing information. 1: Democratic Republic of
the Congo
Isolate n° Lineage BAPS
cluster YOI Supplier Country of Origin
Administrative
Division First-
level
Administrative
Division Second-
level
Administrative Division
Third-level Latitude Longitude
Coverage
(x)
Average
read
length
(bp)
Sequencing Platform
ITM_072814 Mu_A1 BAPS-1 2007 CDTUB
Zagnanado Benin Ouémé Dangbo Gbéko 6.606738 2.454321 44 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_940512 Mu_A1 BAPS-1 1994 CDTUB
Zagnanado Benin Zou Ouinhi Ouokon 7.099851 2.464233 63 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_010157 Mu_A1 BAPS-1 2001 CDTUB
Zagnanado Benin Zou Zogbodomey Domè-Houandougon 7.100052 2.300224 39 135
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_000951 Mu_A1 BAPS-1 2000 CDTUB
Zagnanado Benin Zou Zogbodomey Domè-Houandougon 6.906959 2.453384 73 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_970435 Mu_A1 BAPS-1 1997 CDTUB
Zagnanado Benin Ouémé Bonou Bonou 6.906959 2.453384 83 138
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_970301 Mu_A1 BAPS-1 1997 CDTUB
Zagnanado Benin Ouémé Bonou Bonou 6.906959 2.453384 58 138
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_092100 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Zou Zagnanado Doga-Domè 7.202393 2.336118 50 131
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_083865 Mu_A1 BAPS-1 2008 CDTUB
Zagnanado Benin Zou Ouinhi Tohoue / Hounnoumè 6.974698 2.419704 92 115
Illumina MiSeq (PE 2 x
250 bp)
ITM_093013 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Zou Ouinhi Ouinhi / Monzoungoudo 7.073294 2.527386 83 243
Illumina MiSeq (PE 2 x
250 bp)
ITM_093695 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Zou Ouinhi Ouinhi / Monzoungoudo 7.073294 2.527386 108 244
Illumina MiSeq (PE 2 x
250 bp)
ITM_101300 Mu_A1 BAPS-1 2010 CDTUB
Zagnanado Benin Zou Ouinhi Sagon / Adamè 7.157755 2.425868 111 241
Illumina MiSeq (PE 2 x
250 bp)
ITM_101302 Mu_A1 BAPS-1 2010 CDTUB
Zagnanado Benin Zou Ouinhi Dasso / Bossa 6.997382 2.454390 116 243
Illumina MiSeq (PE 2 x
250 bp)
ITM_102554 Mu_A1 BAPS-1 2010 CDTUB
Zagnanado Benin Zou Ouinhi Dasso / Agonkon 6.970885 2.438508 97 240
Illumina MiSeq (PE 2 x
250 bp)
ITM_081919 Mu_A1 BAPS-1 2008 CDTUB
Zagnanado Benin Zou Ouinhi
Dasso / Yaago &
Akantomè 7.051987 2.415819 58 103
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_092997 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Zou Djidja Oungbègame 7.278137 2.023043 55 245
Illumina MiSeq (PE 2 x
250 bp)
ITM_080066 Mu_A1 BAPS-1 2008 CDTUB
Zagnanado Benin Zou Ouinhi Sagon / Ayizè 7.166206 2.500697 94 129
Illumina MiSeq (PE 2 x
250 bp)
ITM_070381 Mu_A1 BAPS-1 2007 CDTUB
Zagnanado Benin Zou Ouinhi Dasso / Yaago 6.989340 2.461358 77 127
Illumina MiSeq (PE 2 x
250 bp)
ITM_073151 Mu_A1 BAPS-1 2007 CDTUB
Zagnanado Benin Zou Ouinhi Ouinhi / Monzoungoudo 7.073294 2.527386 80 133
Illumina MiSeq (PE 2 x
250 bp)
ITM_070131 Mu_A1 BAPS-1 2007 CDTUB
Zagnanado Benin Zou Zagnanado Dovi-Dove / Tévedji 7.097947 2.409703 98 111
Illumina MiSeq (PE 2 x
250 bp)
ITM_092473 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Zou Ouinhi Tohoue / Midjannangon 6.967335 2.419675 76 244
Illumina MiSeq (PE 2 x
250 bp)
ITM_082549 Mu_A1 BAPS-1 2008 CDTUB
Zagnanado Benin Zou Ouinhi Tohoue / Akassa 7.051987 2.415819 51 118
Illumina MiSeq (PE 2 x
250 bp)
ITM_090149 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Zou Zagnanado Dovi-Dove / Tévedji 7.097947 2.409703 86 231
Illumina MiSeq (PE 2 x
250 bp)
ITM_083584 Mu_A1 BAPS-1 2008 CDTUB
Zagnanado Benin Zou Ouinhi Ouinhi / Ahicon 7.074330 2.505618 317 184
Illumina MiSeq (PE 2 x
250 bp)
ITM_991721 Mu_A1 BAPS-1 1999 CDTUB
Zagnanado Benin Atlantique Toffo Séhoué 6.902940 2.254384 71 133
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_092472 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Atlantique Toffo Séhoué / Agaga 6.925428 2.270933 39 130
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_070383 Mu_A1 BAPS-1 2007 CDTUB
Zagnanado Benin Ouémé Dangbo Dékin 6.557344 2.451185 44 135
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_070625 Mu_A1 BAPS-1 2007 CDTUB
Zagnanado Nigeria Ogun State Yewa North Odja Odan 6.896438 2.845491 62 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_081676 Mu_A1 BAPS-1 2008 CDTUB
Zagnanado Benin Plateau Adja-Ouere Tatonnoukon 6.910516 2.538600 72 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_081681 Mu_A1 BAPS-1 2008 CDTUB
Zagnanado Benin Plateau Issaba Onigbolo 7.170837 2.650620 66 139
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_082696 Mu_A1 BAPS-1 2008 CDTUB
Zagnanado Benin Ouémé Adjohoun Abato 6.738905 2.484311 62 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_091801 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Zou Zogbodomey Kpokissa / Hinzounmè 7.000827 2.399765 41 130
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_092101 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Ouémé Dangbo Gbéko 6.606738 2.454321 54 131
Illumina HiSeq 2000
(PE 2 x 150 bp)
Page | 102
Isolate n° Lineage BAPS
cluster YOI Supplier Country of Origin
Administrative
Division First-
level
Administrative
Division Second-
level
Administrative Division
Third-level Latitude Longitude
Coverage
(x)
Average
read
length
(bp)
Sequencing Platform
ITM_093694 Mu_A1 BAPS-1 2009 CDTUB
Zagnanado Benin Ouémé Dangbo Gbéko 6.606738 2.454321 90 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_100126 Mu_A1 BAPS-1 2010 CDTUB
Zagnanado Benin Zou Zogbodomey Kpokissa 7.000827 2.399765 76 138
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_951009 Mu_A1 BAPS-1 1995 CDTUB
Zagnanado Benin Zou Zagnanado NA 7.209312 2.341557 60 139
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_021433 Mu_A1 BAPS-1 2002 CDTUB Lalo Benin Kouffo Lalo Gnizoumè / Hangbanou 6.941835 1.957969 60 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_022045 Mu_A1 BAPS-1 2002 CDTUB Lalo Benin Kouffo Lalo Adoukandji /
Yamontouhoué 6.917139 1.959558 75 138
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_022287 Mu_A1 BAPS-1 2002 CDTUB Lalo Benin Zou Agbangnizoun Kpota 7.023358 1.973188 59 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_022875 Mu_A1 BAPS-1 2002 CDTUB Lalo Benin Kouffo Lalo Gnizoumè 6.943016 1.942924 64 135 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_030717 Mu_A1 BAPS-1 2003 CDTUB Lalo Benin Kouffo Lalo Ahomadégbé 6.834510 2.001984 54 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_030718 Mu_A1 BAPS-1 2003 CDTUB Lalo Benin Kouffo Lalo Lalo 6.929381 1.888870 57 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_031892 Mu_A1 BAPS-1 2003 CDTUB Lalo Benin Kouffo Lalo Hlassamè 6.902215 1.941018 40 128 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_071804 Mu_A1 BAPS-1 2007 CDTUB Lalo Benin Kouffo Lalo Zalli 6.980859 1.926673 61 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_030791 Mu_A1 BAPS-1 2003 CDTUB
Zagnanado Benin Kouffo Lalo Tchito / Gare 6.919671 2.046356 56 130
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_970680 Mu_A1 BAPS-1 1997 CDTUB Lalo Benin Mono Houéyogbé Sahoué 6.538114 1.807039 68 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_021434 Mu_A1 BAPS-1 2002 CDTUB Lalo Benin Kouffo Klouékanmè Adjassagon 7.001751 1.951723 46 135 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_012596 Mu_A1 BAPS-1 2001 CDTUB Lalo Benin Mono Bopa Lobogo 6.624900 1.907600 81 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_071938 Mu_A1 BAPS-1 2007 CDTUB Lalo Benin Kouffo Lalo Tandji 6.943208 1.970882 59 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_000909 Mu_A1 BAPS-1 1999 ITM Togo Maritime Vo Tchekpo Deve 6.484348 1.369718 49 142 Illumina MiSeq (PE 2 x
300 bp)
ITM_070123 Mu_A1 BAPS-1 2006 IME DRC1 Bas-Congo
Cataractes /
Songololo Kimpese / Cité-Kimpese -5.56652 14.43447 72 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_092479 Mu_A1 BAPS-1 2009 IME DRC Bas-Congo Cataractes /
Songololo Kimpese / Cité-Kimpese -5.563273 14.445951 67 132
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_000483 Mu_A1 BAPS-1 2000 ITM Ivory_Coast Moyen-Cavally Duékoué Niambli 6.746215 -7.278825 150 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_000870 Mu_A1 BAPS-1 2000 ITM Ivory_Coast Dix-Huit
Montagnes Zouan-Hounien Ouyatouo 6.872745 -8.109516 45 133
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_063519 Mu_A1 BAPS-1 2006 IME DRC Bas-Congo Cataractes /
Songololo Luima / Cité Songololo -5.6969 14.05914 56 135
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_071924 Mu_A1 BAPS-1 2007 ITM Congo Kouilou Madingo-Kayes Loukouala -4.433392 11.700150 53 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_071925 Mu_A1 BAPS-1 2007 ITM Congo Kouilou Madingo-Kayes Loukouala -4.433392 11.700150 54 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072398 Mu_A1 BAPS-1 2006 IME DRC Bas-Congo Cataractes /
Songololo
Bamboma / Mbanza-
Manteke -5.46421 13.78163 65 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072401 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna -5.76785 13.92162 74 138
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072732 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna -5.76916 13.92802 44 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072733 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Luima / Ngombe -5.39494 14.12631 57 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072734 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo
Palabala / Km 70
Vemadiyo -5.68719 13.89086 40 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072735 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Luima / Luvuvamu -5.70311 14.1483 40 135
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072840 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna -5.76805 13.91836 83 138
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072841 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna -5.76805 13.91836 52 138
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_073453 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Luima / Kisonga -5.75556 13.98184 67 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_073459 Mu_A1 BAPS-1 2007 CDTUB Lalo Benin Kouffo Lalo Ahojinako 6.737059 1.978464 84 139 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_073463 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Songololo / Luvituku -5.67491 14.01439 74 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
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Isolate n° Lineage BAPS
cluster YOI Supplier Country of Origin
Administrative
Division First-
level
Administrative
Division Second-
level
Administrative Division
Third-level Latitude Longitude
Coverage
(x)
Average
read
length
(bp)
Sequencing Platform
ITM_073477 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo
Bamboma / Mbanza-
Manteke -5.46071 13.78244 48 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_073478 Mu_A1 BAPS-1 2007 IME Angola Malanje Marimba Kafufu / Luremo (Kwango
River) -8.096696 17.503546 47 135
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_073479 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Luima / Kisonga -5.75556 13.98184 50 138
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_082600 Mu_A1 BAPS-1 2007 IME DRC Bas-Congo Cataractes /
Songololo Kimpese / Nzundu -5.41166 14.49669 41 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_100140 Mu_A1 BAPS-1 2009 IME DRC Bas-Congo Cataractes /
Songololo Kimpese / Tole -5.70385 14.39294 51 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_100141 Mu_A1 BAPS-1 2009 IME DRC Bas-Congo Cataractes /
Songololo Luima / 5km -5.73816 14.04989 69 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_100142 Mu_A1 BAPS-1 2009 IME DRC Bas-Congo Cataractes /
Songololo Luima / 5km -5.73816 14.04989 48 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_100832 Mu_A1 BAPS-1 2009 IME DRC Bas-Congo Cataractes /
Songololo Palabala / Nkamuna -5.76913 13.92102 56 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_100833 Mu_A1 BAPS-1 2009 IME DRC Bas-Congo Cataractes /
Songololo Mayanga / Mpelo -5.29764 14.03725 54 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_032481 Mu_A1 BAPS-1 2003 IME DRC Bas-Congo Cataractes /
Songololo Luima / Nkondo -5.60384 14.13927 89 133
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_040149 Mu_A1 BAPS-1 2003 ITM Ghana Ashanti Asante Akim North Agogo Presbyterian
Hospital 6.799841 -1.083548 60 133
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_991591 Mu_A1 BAPS-1 1999 ITM Togo Maritime Vo Anagali 6.500000 1.366667 79 144 Illumina MiSeq (PE 2 x
300 bp)
ITM_050303 Mu_A1 BAPS-1 1979 ITM Congo Kouilou NA NA -4.432895 12.299497 59 131 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_960658 Mu_A1 BAPS-1 1996 ITM Angola Bengo Dande Caxito -8.565601 13.669590 55 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_960657 Mu_A1 BAPS-1 1996 ITM Angola Bengo Dande Caxito -8.565601 13.669590 79 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072646 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Mponua Abofrom 6.616723 -1.986334 74 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072651 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti KMA Kaase 6.644303 -1.632192 55 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_120140 Mu_A1 BAPS-1 2011 CPC Cameroon Adamawa
Region Maya-Banyo Bankim / Mbondji II 6.084052 11.486537 78 139
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_030950 Mu_A1 BAPS-1 2003 CDTUB Lalo Benin Kouffo Lalo Adoukandji 6.818579 1.986384 70 132 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_030716 Mu_A1 BAPS-1 2003 CDTUB Lalo Benin Kouffo Lalo Tchito / Village Aboeti 6.917797 2.023378 67 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_102686 Mu_A1 BAPS-1 2010 ITM Nigeria Oyo State Ibadan Ibadan 7.387891 3.920866 56 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_083232 Mu_A1 BAPS-1 2008 ITM Angola Lunda Norte Xa-Muteba (Kwango River) -8.522231 17.746601 56 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_000869 Mu_A1 BAPS-1 2000 ITM Ivory_Coast Moyen-Cavally Duékoué Guezon 6.733249 -7.107404 48 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_990007 Mu_A1 BAPS-1 1998 ITM Ivory_Coast Haut-Sassandra Issia Guetuzon II 6.746001 -6.929517 59 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_991633 Mu_A1 BAPS-1 1999 ITM Ivory_Coast Moyen-Cavally Duékoué Guezon 6.733249 -7.107404 94 139 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_990008 Mu_A1 BAPS-1 1998 ITM Ivory_Coast Haut-Sassandra Issia Zakogbeu 6.782380 -6.814253 81 139 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_070386 Mu_A1 BAPS-1 2007 ITM Nigeria Anambra State Ayamelum Ifite Ogwari 6.609054 6.951123 68 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_5151 Mu_A1 BAPS-1 1972 ITM DRC Maniema Kasongo NA -4.186555 26.439372 59 145 Illumina MiSeq (PE 2 x
250 bp)
ITM_970359 Mu_A1 BAPS-1 1997 ITM Ghana Ashanti Amansie West Manso-Afraso 6.343930 -1.984469 46 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_970606 Mu_A1 BAPS-1 1997 ITM Ghana Ashanti Amansie West Yaw Kasakrom 6.450216 -1.791701 64 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_970677 Mu_A1 BAPS-1 1997 ITM Ghana Ashanti Amansie West Manso Dominase 6.464442 -1.893037 78 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_970678 Mu_A1 BAPS-1 1997 ITM Ghana Ashanti Asante Akim North Afrisre 7.050181 -0.962048 69 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_970959 Mu_A1 BAPS-1 1997 ITM Ghana Ashanti Amansie West Manso-Afraso 6.343930 -1.984469 81 139 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_970964 Mu_A1 BAPS-1 1997 ITM Ghana Ashanti Amansie West Offinho Asaman 6.373639 -1.999730 65 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_971351 Mu_A1 BAPS-1 1997 ITM Ghana Ashanti Atwima Mponua Achiase 6.599467 -2.029728 76 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
Page | 104
Isolate n° Lineage BAPS
cluster YOI Supplier Country of Origin
Administrative
Division First-
level
Administrative
Division Second-
level
Administrative Division
Third-level Latitude Longitude
Coverage
(x)
Average
read
length
(bp)
Sequencing Platform
ITM_980063 Mu_A1 BAPS-1 1998 ITM Ghana Ashanti Atwima Mponua Achiase 6.599467 -2.029728 66 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_940662 Mu_A1 BAPS-1 1994 ITM Ivory_Coast Moyen-Cavally Duékoué Nanandi 6.706187 -7.106573 43 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_990006 Mu_A1 BAPS-1 1998 ITM Ivory_Coast Haut-Sassandra Issia Guetuzon I 6.741000 -6.940000 73 139 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_990734 Mu_A1 BAPS-1 1999 ITM Ivory_Coast Moyen-Cavally Duékoué Duékoué 6.683885 -7.313537 44 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_991632 Mu_A1 BAPS-1 1999 ITM Ivory_Coast Haut-Sassandra Issia Bediegbeu 6.67837 -6.82040 112 139 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072634 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Asante Akim North Adoniem 6.884259 -0.974580 86 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072652 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Mponua Achiase 6.599467 -2.029728 73 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072654 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Mponua Achiase 6.599467 -2.029728 84 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072657 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Mponua Achiase 6.599467 -2.029728 40 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072658 Mu_A1 BAPS-1 2007 KCCR Ghana Western Region Wassa West Owusukrom 5.295855 -1.995595 51 135 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072650 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Nwabiagya Kyereyase 6.703054 -1.837654 78 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072630 Mu_A1 BAPS-1 2007 KCCR Ghana Central Upper Denkyira Nkotumso 6.001297 -1.918974 45 135 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072656 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Mponua Abompe 6.532913 -2.045321 47 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072655 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Mponua Sireso 6.619944 -2.302405 65 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072653 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Mponua Amadaa 6.645268 -1.991520 53 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_072645 Mu_A1 BAPS-1 2007 KCCR Ghana Ashanti Atwima Mponua Achiase 6.599467 -2.029728 78 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_001211 Mu_A1 BAPS-1 2000 ITM Ivory_Coast Dix-Huit
Montagnes Zouan-Hounien Zouan-Hounien 6.919888 -8.204394 57 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_020279 Mu_A2 BAPS-4 2002 CPC Cameroon Centre Region Nyong-et-
Mfoumou Ayos 3.903651 12.536624 51 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_091067 Mu_A2 BAPS-4 2009 ITM Gabon Moyen-Ogooué Ogooue et des Lacs Junkville -0.777504 10.192464 48 131 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_110450 Mu_A2 BAPS-4 2011 ITM Gabon Moyen-Ogooué Ogooue et des Lacs Gravier -0.629682 10.446592 88 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_020280 Mu_A1 BAPS-2 2002 CPC Cameroon Centre Region Nyong-et-
Mfoumou Akolo 3.833876 12.182044 39 133
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_021081 Mu_A1 BAPS-2 2002 CPC Cameroon Centre Region Nyong-et-
Mfoumou Obis 3.983916 12.467150 52 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_9103 Mu_A1 BAPS-2 1970 ITM Cameroon Centre Region NA NA 3.782691 12.249357 59 131 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_101500 Mu_A1 BAPS-2 2010 ITM Gabon Moyen-Ogooué Ogooue et des Lacs Lambaréné / Adaghe -0.703731 10.240318 70 138 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_110893 Mu_A1 BAPS-2 2011 ITM Gabon Moyen-Ogooué Ogooue et des Lacs Issac -0.487290 9.925157 73 136 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_120141 Mu_A1 BAPS-2 2011 CPC Cameroon Centre Region Nyong-et-
Mfoumou Medjap 3.600827 11.983612 90 139
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_120142 Mu_A1 BAPS-2 2011 CPC Cameroon Centre Region Nyong-et-
Mfoumou Akonolinga / Ekolman 3.773791 12.243754 51 137
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_051459 Mu_A1 BAPS-3 1964 NCTC Uganda Northern
Region Adjumani Adjumani 3.381441 31.783100 119 136
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_112512 Mu_A1 BAPS-1 2011 CDTUB
Zagnanado Nigeria Ondo State Ifedore Ilara-Mokin 7.348108 5.110065 187 127
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_121398 Mu_A1 BAPS-1 2012 CDTUB
Zagnanado Nigeria Ogun State Ewekoro NA 6.934564 3.218125 39 120
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_020585 Mu_A1 BAPS-1 2001 CDTUB
Zagnanado Benin Mono Bopa Lobogo / Lobogo 6.624900 1.907600 48 118
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_052188 Mu_A1 BAPS-1 2005 CDTUB
Zagnanado Benin Zou Agbangnizoun Sahe / Gbozoun 7.052104 1.956116 98 121
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_060218 Mu_A1 BAPS-1 2005 CDTUB
Zagnanado Benin Zou Agbangnizoun Sahe / Gbozoun 7.052104 1.956116 87 122
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_121000 Mu_A1 BAPS-1 2012 CDTUB
Zagnanado Benin Kouffo Klouékanmey Ahogbeya / Ahogbeya 7.026145 1.910315 52 120
Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_032343 Mu_A2 BAPS-4 2003 CDTUB
Zagnanado Benin Ouémé Porto-Novo
Porto-Novo / Kpota
Sandodo 6.472724 2.620146 57 118
Illumina HiSeq 2000
(PE 2 x 150 bp)
Page | 105
Isolate n° Lineage BAPS
cluster YOI Supplier Country of Origin
Administrative
Division First-
level
Administrative
Division Second-
level
Administrative Division
Third-level Latitude Longitude
Coverage
(x)
Average
read
length
(bp)
Sequencing Platform
ITM_091405 Mu_A2 BAPS-4 2009 CDTUB
Zagnanado Benin Zou Zogbodomey Kpokissa / Kpokissa 7.015763 2.381552 102 181
Illumina MiSeq (PE 2 x
300 bp)
ITM_051510 Mu_A2 BAPS-4 2005 CDTUB
Zagnanado Benin Zou Zogbodomey Kpokissa / Dehounta 6.999813 2.400830 53 165
Illumina MiSeq (PE 2 x
300 bp)
Mu_12-29 Mu_A1 BAPS-2 2012 CPC Cameroon NA NA NA NA NA 117 231 Illumina MiSeq (PE 2 x
250 bp)
Mu_08-51 Mu_A1 BAPS-2 2008 CPC Cameroon Centre Region Nyong-et-
Mfoumou Eboa 3.756858 12.250822 115 232
Illumina MiSeq (PE 2 x
250 bp)
Mu_12-79 Mu_A1 BAPS-2 2012 CPC Cameroon Centre Region Nyong-et-
Mfoumou Ndjong Medjap 3.655066 12.173620 114 227
Illumina MiSeq (PE 2 x
250 bp)
Mu_08-47 Mu_A1 BAPS-2 2008 CPC Cameroon Centre Region Nyong-et-
Mfoumou Djoudjoua 3.677862 12.018625 112 228
Illumina MiSeq (PE 2 x
250 bp)
Mu_11-30 Mu_A1 BAPS-2 2011 CPC Cameroon Centre Region Nyong-et-
Mfoumou Mengou 3.895708 12.149926 99 222
Illumina MiSeq (PE 2 x
250 bp)
Mu_11-210 Mu_A1 BAPS-2 2011 CPC Cameroon Centre Region Nyong-et-
Mfoumou Ngonanga 3.705152 12.233626 99 244
Illumina MiSeq (PE 2 x
250 bp)
Mu_12-98 Mu_A1 BAPS-2 2012 CPC Cameroon Centre Region Nyong-et-
Mfoumou Djoudjoua 3.677862 12.018625 95 133
Illumina HiSeq 2000
(PE 2 x 150 bp)
Mu_08-186 Mu_A1 BAPS-2 2008 CPC Cameroon Centre Region Nyong-et-
Mfoumou Djoudjoua 3.677862 12.018625 92 244
Illumina MiSeq (PE 2 x
250 bp)
Mu_11-33 Mu_A1 BAPS-2 2011 CPC Cameroon Centre Region Nyong-et-
Mfoumou Medoumou 3.624172 12.052166 91 244
Illumina MiSeq (PE 2 x
250 bp)
Mu_11-263 Mu_A1 BAPS-2 2011 CPC Cameroon Centre Region Nyong-et-
Mfoumou Nkoldja'a 3.697345 12.115912 88 242
Illumina MiSeq (PE 2 x
250 bp)
Mu_12-53 Mu_A1 BAPS-2 2012 CPC Cameroon Centre Region Nyong-et-
Mfoumou Djoudjoua 3.677862 12.018625 86 134
Illumina HiSeq 2000
(PE 2 x 150 bp)
Mu_11-20 Mu_A1 BAPS-2 2011 CPC Cameroon NA NA NA NA NA 77 137 Illumina HiSeq 2000
(PE 2 x 150 bp)
ITM_121474 Mu_A1 BAPS-1 2012 ITM Nigeria Cross river state Ogoja TBL hospital monaiya 6.661395 8.797635 48 139 Illumina MiSeq (PE 2 x
300 bp)
ITM_111662 Mu_A1 BAPS-2 2011 ITM Gabon Moyen-Ogooué Ogooue et des Lacs Lambaréné / Isaac -0.703731 10.240318 47 142 Illumina MiSeq (PE 2 x
300 bp)
ITM_940659 Mu_A1 BAPS-1 1994 ITM Ivory_Coast NA NA NA NA NA 39 141 Illumina MiSeq (PE 2 x
300 bp)
ITM_940815 Mu_A1 BAPS-1 1994 ITM Ivory_Coast NA NA NA NA NA 62 142 Illumina MiSeq (PE 2 x
300 bp)
ITM_940816 Mu_A1 BAPS-1 1994 ITM Ivory_Coast NA NA NA NA NA 43 142 Illumina MiSeq (PE 2 x
300 bp)
ITM_940821 Mu_A1 BAPS-1 1994 ITM Ivory_Coast NA NA NA NA NA 46 138 Illumina MiSeq (PE 2 x
300 bp)
ITM_980341 Mu_A1 BAPS-1 1998 ITM Ivory_Coast NA NA NA NA NA 52 140 Illumina MiSeq (PE 2 x
300 bp)
ITM_980342 Mu_A1 BAPS-1 1998 ITM Ivory_Coast NA NA NA NA NA 49 143 Illumina MiSeq (PE 2 x
300 bp)
ITM_990012 Mu_A1 BAPS-1 1998 ITM Ivory_Coast NA NA NA NA NA 45 141 Illumina MiSeq (PE 2 x
300 bp)
ITM_990321 Mu_A1 BAPS-1 1998 ITM Ivory_Coast NA NA NA NA NA 45 136 Illumina MiSeq (PE 2 x
300 bp)
ITM_991770 Mu_A1 BAPS-1 1999 ITM Ivory_Coast NA NA NA NA NA 40 140 Illumina MiSeq (PE 2 x
300 bp)
ITM_993355 Mu_A1 BAPS-1 1999 ITM Togo Maritime NA NA 6.348805 1.405662 49 141 Illumina MiSeq (PE 2 x
300 bp)
Mu_282-5 Mu_A2 BAPS-4 2011 CDTUB Pobé Benin Ouémé Bonou Dame Wogon 6.946733 2.418495 84 135 Illumina HiSeq 2000
(PE 2 x 150 bp)
Mu_283-8 Mu_A2 BAPS-4 2011 CDTUB Pobé Benin Ouémé Bonou Dame Wogon 6.946733 2.418495 75 135 Illumina HiSeq 2000
(PE 2 x 150 bp)
Mu_V71_200
5 Mu_A2 BAPS-4 2005 VIDRL Papua New Guinea NA NA NA NA NA 44 182
Illumina MiSeq (PE 2 x
300 bp)
ITM_9537 Mu_A2 BAPS-4 1971 ITM Papua New Guinea NA NA NA NA NA 39 156 Illumina MiSeq (PE 2 x
300 bp)
Page | 106
Supplemental Text 4.SI1: The search for a molecular clock to date
phylogenetic events:
Several robust approaches exist to date bacterial phylogenies. Ideally,
molecular clocks are calibrated using temporal information from ancient DNA
(aDNA) sequences [198]. When aDNA is not available, “short-term” mutation
rates estimated from molecular epidemiological data [199] or animal infection
models [200] can be used. Since none of the above approaches are currently
possible for M. ulcerans a short-term molecular clock was estimated here
using correlations between phylogenetic divergence and isolation times of
heterochronous disease isolates.
Maximum likelihood phylogenetic analysis (Figure 4.S3B) revealed a very weak
(Mu_A1 ρ=0.013 and Mu_A2 ρ=0.34) correlation between root-to-tip branch
lengths and dates of isolation of the sequenced isolates. This observation is
indicative of the lack of rapid strict-clock-like evolution in which substitution
mutations occur at a fixed regular rate. The finding is in line with studies of
other bacterial species with notably slower clock rates that also failed to find
significant correlations between isolation time and phylogenetic divergence
[183, 184, 201].
Since M. ulcerans conflicted with strict clock-like molecular evolution we
subsequently used a Bayesian approach that allowed for the use of relaxed
molecular clock models (BEAST2) to infer the evolutionary dynamics of the
African mycobacterial population. PS analysis was used to select the best of
three clock models and the better of two demographic coalescent tree priors.
The model with the highest marginal likelihood was the UCLD relaxed clock
with a constant coalescent tree prior. Since this model was also one of the
more parameter-rich models we calculated LBF’s, which evaluate the relative
merits of competing models. A difference of more than 10 LBF units (which is
considered as very strong evidence against a competing model [202]) was
used as the threshold for accepting a more parameter-rich model. This
confirmed the UCLD relaxed clock model with a constant coalescent tree prior
to be the best model and thus was used for all subsequent BEAST2 runs.
Page | 107
Table: Natural log marginal likelihoods, Natural log Bayes factors and model probabilities of 6
competing models of molecular evolution of African M. ulcerans. Abbreviations: LBF, Natural
log Bayes factor; LmL, Natural log marginal likelihood; UCED, Uncorrelated exponential
distribution; UCLD; Uncorrelated log-normal distribution.
Clock model Coalescent tree prior LmL LBF Model probability
UCLD relaxed Constant -52551,97 0 0,99997
UCLD relaxed Exponential -52557,15 -10,36 3,17E-05
Strict Constant -52594,84 -85,74 5,80E-38
UCED relaxed Constant -52594,91 -85,88 5,04E-38
Strict Exponential -52601,46 -98,98 1,03E-43
UCED relaxed Exponential -52602,69 -101,44 8,81E-45
4.7 Data access
Read data for the study isolates have been deposited in the NCBI Sequence
Read Archive (SRA) under BioProject accession PRJNA313185.
4.8 Acknowledgements
KV was supported by a PhD-grant of the Flemish Interuniversity Council -
University Development Cooperation (Belgium). BdJ & CM were supported by
the European Research Council-INTERRUPTB starting grant (nr.311725). TPS
was supported by a fellowship from the National Health and Medical Research
Council of Australia (1105525).
Funding for this work was provided by the Department of Economy, Science
and Innovation of the Flemish Government, the Stop Buruli Consortium
supported by the UBS Optimus Foundation, and the Fund for Scientific
Research Flanders (Belgium) (FWO grant n° G.0321.07N). The computational
resources used in this work were provided by the HPC core facility CalcUA and
VSC (Flemish Supercomputer Center), funded by the University of Antwerp,
the Hercules Foundation and the Flemish Government - department EWI.
Aspects of the research in Cameroon and Benin were funded by the Raoul
Follereau Fondation France. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Page | 108
We thank Rodenbrandt Rex for helpful discussions and critical comments to
the manuscript. We thank Pim de Rijk, Wim Mulders, Krista Fissette, Elie
Nduwamahoro, and Cécile Uwizeye for their excellent technical assistance.
Page | 109
Chapter 5
The population size of Mycobacterium
ulcerans in the Congo river basin reflects the
intensity of control efforts: is the human
reservoir important?
Koen Vandelannoote, Delphin Mavinga Phanzu, Kapay Kibadi, Miriam Eddyani, Conor
J. Meehan, Kurt Jordaens, Herwig Leirs, Françoise Portaels, Timothy P. Stinear, Simon
R. Harris, Bouke C. de Jong
The population size of Mycobacterium ulcerans in the Congo river basin reflects the
intensity of control efforts: is the human reservoir important?
Designed research: KV, DMP, ME, CM, FP, TPS, SRH, BCJ.
Performed research: KV, DMP, KK.
Contributed new reagents or analytic tools: KV, DMP, KK, ME, CM, KJ, HL, FP, TPS,
SRH, BCJ.
Analysed data: KV, DMP.
Wrote the paper: KV.
Page | 110
5.1 Abstract
Buruli ulcer (BU) is a slowly progressing necrotizing disease of skin and
subcutaneous tissue caused by infection with the pathogen Mycobacterium
ulcerans. After almost 70 years of study in Africa, the mode of transmission
and the non-human reservoir(s) of BU are still largely unknown. The vastly
greater resolution offered by genomics is opening up new possibilities to
explore the pathogen’s cryptic epidemiology and disease ecology. In this
study, we aimed to use comparative second and third generation genomics to
explore the molecular epidemiology of BU at the continental scale, and at the
smaller geographical “village scale” in a BU endemic region of The Democratic
Republic of Congo. We used both temporal associations and the study of the
mycobacterial demographic history to estimate the contribution of humans as
a reservoir in BU transmission. We identified a relationship between the
observed past population dynamics of M. ulcerans from the Songololo
Territory and the timing of health policy changes managing the BU epidemic in
that region. We propose that humans with actively infected, openly
discharging BU lesions inadvertently contaminate community water sources
during bathing/wading and as such indirectly expose naïve individuals to the
etiological agent.
5.2 Introduction
Buruli ulcer (BU) is a slowly progressing necrotizing disease of skin and
subcutaneous tissue caused by the pathogen Mycobacterium ulcerans [2]. In
BU patients, early diagnosis followed by 8 weeks of treatment with a
combined antibiotic regimen (rifampicin and streptomycin) is key to
preventing complications that can arise from severe skin ulcerations [11]. BU
is a neglected tropical disease, and in some highly endemic areas it is more
prevalent than the most notorious mycobacterial diseases, tuberculosis and
leprosy [83]. Despite the fact that more than 30 countries worldwide have
reported BU, the highest incidence by far is still observed in impoverished,
rural communities of West and Central Africa [5], with 2151 new cases
reported to the WHO in 2014 [6].
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M. ulcerans evolved from a Mycobacterium marinum progenitor by
acquisition of the virulence plasmid pMUM001 [18]. This plasmid harbors
genes required for the biosynthesis of the macrocyclic polyketide toxin
mycolactone [13]. This toxin plays a key role in the pathogenesis of BU as it
has cytotoxic, analgesic, and immunomodulatory properties that can cause
painless chronic ulcerative skin lesions with limited inflammation [14]. The
acquisition of the virulence plasmid also introduced insertion sequence (IS)
elements IS2404 and IS2606 into the bacterial chromosome. These IS
elements subsequently expanded extensively in the genome, promoting
genetic rearrangements by modifying gene expression and sequestering
genes, profoundly enhancing mycobacterial genome plasticity [23].
BU epidemiology is characterized by its patchy focal distribution within
endemic countries [5]. Disease foci are known to primarily occur around low-
lying rural marshes, wetlands, and riverine areas [83]. As living or working
close to these slow flowing or stagnant water bodies is a known risk factor for
M. ulcerans infection [31], and as human-to-human transmission is rare, it is
generally believed that M. ulcerans is an environmental mycobacterium that
can infect humans through introduction via a micro-trauma of the skin [32,
33]. Indeed, M. ulcerans DNA has been detected in a variety of aquatic
specimens [35, 36], yet the significance of the detection of M. ulcerans DNA
by PCR in environmental samples remains unclear in the disease ecology of BU
[35-37, 86, 203]. Indeed, environmental sampling surveys show M. ulcerans
DNA can be present in the environment, but the concentration of bacteria in
positive samples is nearly always insignificantly low, indicating that it is
unlikely that M. ulcerans was multiplying in the tested specimens [36, 44].
Furthermore, the frequency of positive sample occurrence is nearly always
low [43]. Finally, definite evidence for the presence of viable M. ulcerans in
potential environmental reservoirs is lacking owing to the strenuous challenge
of culturing the slow growing mycobacterium from non-clinical,
environmental sources [46]. The combination of these limitations clearly
indicates how difficult it is to detect evidence of M. ulcerans in aquatic
environments. As a direct result the mode of transmission and non-human M.
ulcerans reservoir(s) remain poorly understood in Africa [153].
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In Victoria, Australia, BU is a considered a zoonosis transmitted from possums
to humans, possibly via a mosquito vector [42, 61-64], although definite proof
is to date still lacking. It is very hard to translate these findings to the endemic
regions in the Afrotropic ecozone. Marsupial mammals like possums are not
endemic in the African continent. Furthermore, M. ulcerans infections in
domesticated and wild animals have never been reported in Africa [36, 65,
66]. Additionally, the results of case-control studies looking into mosquito
related risk factors in Africa are contradictory [29, 30, 67]. A recent extensive
environmental survey in Benin also found no trace of M. ulcerans DNA in
mosquitos [69].
As M. ulcerans has the genome signature of a “niche-adapted”
mycobacterium it is unlikely to be found free-living in various aquatic or
terrestrial environments and is rather more likely living in close association
with a host organism [12]. A recent study used temporal associations to
implicate human-induced changes and activities in the spread of BU across
Africa during the period of Neo-imperialism (late 19th - early 20th century)
(CHAPTER 4). African lineage Mu_A2 was found to be introduced to the
continent during this specific time period. Additionally, close inspection of the
M. ulcerans time-tree revealed that introduction of BU in three well sampled
disease foci coincided with the instigation of colonial rule. Since these disease
foci were inhabited prior to the arrival of the European powers and since it
was only after the start of colonial rule that the specific epidemic clones were
introduced, the study posited that - in the absence of a known BU reservoir or
vector on the African continent [89] - displaced humans with actively infected,
openly discharging BU lesions inadvertently contaminated aquatic
environments during water contact activities and as such spread the
mycobacterium.
The first BU case in the Democratic Republic of the Congo (DRC) was reported
in 1950, in the Kwilu region of the Bandundu Province [204]. Since this first
description, microbiologically confirmed cases have been identified in the
provinces of Bandundu, Katanga, Kinshasa, Kongo Central, Maniema and
South-Kivu [205]. The main endemic focus of BU in the country is located in
the Songololo Territory of the Kongo Central Province and encompasses the
highly endemic rural health zones of Kimpese and Nsona-Mpangu. The
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General Reference Hospital of the Institut Médical Evangélique (IME) in
Kimpese receives most of the hospitalized BU patients of this endemic
hotspot. A 2008 study [206] estimated the overall BU prevalence of the
Songololo Territory at 3.3/1000 population, with considerable variation (0.0 to
27.5/1000 population) among the 40 health areas of the territory. Since no
epidemiological studies were conducted in the Territory until the 1960s and
1970s [120, 207, 208], it remained unclear whether BU was a newly
introduced, versus an old expanding illness in the region. Prior to 2002, BU
control in DRC suffered from decades of neglect and conflict affecting the vast
nation’s health and sanitation infrastructure [5].
A better understanding of the transmission and the disease dynamics of M.
ulcerans infection could have a direct impact on the development of effective
and appropriate control strategies against the disease. However, conventional
genetic fingerprinting methods have largely failed to differentiate clinical
disease isolates in this monomorphic organism [93], leading to their
replacement with whole genome sequencing (WGS) [19, 154-156] (CHAPTER
4). The vastly greater resolution offered by genomics is opening up new
possibilities to explore the pathogen’s cryptic epidemiology and disease
ecology. In this study, we have sequenced and compared the genomes of 179
M. ulcerans strains isolated from patients in the Democratic Republic of the
Congo (DRC), The Republic of the Congo (RC) and Angola over a 52 year period
to investigate the microevolution and population dynamics of this pathogen
during its establishment in the region.
5.3 Materials and Methods
Study sites
The study covered all BU foci in DRC, RC and Angola that have ever yielded
positive M. ulcerans cultures. The endemic foci of DRC are located in the
provinces of Bandundu, Kongo Central (previously known as Bas-Congo), and
Maniema. The vast majority of isolates originated from the Songololo
Territory of the Kongo Central Province. Isolates from the low endemic health
zone of Tshela, northwest in the Kongo Central province are also included.
Isolates from the Maniema province originated from the historical BU focus of
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the Kasongo territory [209] which was recently assessed to be still active
[210]. Finally, the province of Bandundu is represented by a recently
discovered endemic focus along the Kwango River, a tributary of the Congo
River that forms the border between Angola and DRC [211].
Bacterial isolates and sequencing
We analyzed a panel of 179 M. ulcerans strains originating from disease foci in
DRC, RC and Angola that had been isolated between 1966 and 2014 (Table
5.S1). In addition to the isolates sequenced here, 144 other African genomes
(described in CHAPTER 4) were included to provide appropriate genetic
context for interpreting the diversity and evolution of Central African M.
ulcerans.
Permission for the study was obtained from the ITM Institutional Review
Board (Belgium) and the Ethics Committee of the Public Health School of the
University of Kinshasa (DRC). All patients provided written informed consent
for the collection of samples and subsequent analyses, except when cultures
were analyzed retrospectively, after they had been collected for diagnostic
purposes, as part of routine clinical care. Isolates were processed and
analyzed without use of any patient identifiers, except for approximate
residence location if this information was available.
Based on conventional phenotypic and genotypic methods, bacterial isolates
had previously been assigned to the species M. ulcerans [157]. Mycobacterial
isolates were maintained for prolonged storage at ≤-70°C in Dubos broth
enriched with OAD growth supplement and glycerol.
Index-tagged paired-end sequencing-ready libraries were prepared from
gDNA extracts with the Nextera XT DNA Library Preparation Kit. Genome
sequencing was performed on an Illumina HiSeq 2000 sequencer according to
the manufacturers’ protocols with 100bp or 150bp paired-end sequencing
chemistry. Sequencing statistics are provided in Table 5.S1. The quality of raw
Illumina reads was investigated with FastQC v0.11.3 [158]. Read data for the
study isolates have been deposited in the NCBI Sequence Read Archive (SRA)
under BioProject accession XXX. Prior to further analysis, reads were cleaned
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with clip, a tool in the Python utility toolset Nesoni v0.130 [159]. Reads were
filtered to remove those containing ambiguous base calls, any reads <50
nucleotides in length, and reads containing only homopolymers. All reads
were further trimmed to remove residual ligated Nextera adaptors and low
quality bases (<Q10) at the 3' end. The total amount of read-pairs kept after
clipping and their average read length are summarized for all isolates in Table
5.S1.
For isolate Mu_ITM032481, intact, pure, high-molecular-weight gDNA was
obtained by harvesting the growth of 10 Löwenstein-Jensen (LJ) slants
followed by heat inactivation (80°C – 1h), enzymatic digestion (Proteinase K,
lysozyme and RNAse) and purification with the Genomic DNA Buffer Set
(Qiagen, cat. no. 19060) and 100/G Genomic-tips (Qiagen, cat. no. 10243).
This gDNA sample was submitted to the Duke “Sequencing and Genomic
Technologies Shared Resource” for sequencing on a Pacific Biosciences RSII
instrument. Libraries of 15 to 20 kb were constructed and sequenced on 3
SMRT cells using P5-C3 chemistry. This yielded 895 Mbp from a total of
161,629 subreads. The average subread length was 5,536 bp with a
sequencing depth of 160X. Data were analyzed using SMRT Analysis v2.3.0
(Pacific Biosciences). The continuous long reads (CLR) were assembled de
novo using the PacBio Hierarchical Genome Assembly Process 3 (HGAP.3) and
polished using Quiver as previously described [212]. This resulted in a single
contig that was circularized and subsequently annotated using Prokka v1.11
[213]. The annotated closed genome was then manually curated and
visualized using Artemis v.16 [214] and Geneious v9.0.5 [215]. The Congolese
M. ulcerans Mu_ITM032481 bacterial reference chromosome sequence
received strain name SGL03 and was submitted to GenBank with accession
number XXX.
Read mapping and SNP detection
Read mapping and SNP detection were performed using the Snippy v3.0
pipeline [160]. The Burrows-Wheeler Aligner (BWA) v0.7.12 [161] was used
with default parameters to map clipped read-pairs to the new Congolese
SGL_03 reference genome. Due to the unreliability of read mapping in mobile
repetitive regions all ISE elements (IS2404 and IS2606) were hard masked in
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these reference genomes (0.398 Mb / 5.625 Mb, i.e. 7% of SGL03). After read
mapping to M. ulcerans SGL03, average read depths were determined with
SAMtools v1.2 [162] and are summarized for all isolates in Table 5.S1. SNPs
were subsequently identified using the variant caller FreeBayes v0.9.21 [163],
with a minimum depth of 10 and a minimum variant allele proportion of 0.9.
Snippy was used to pool all identified SNP positions called in at least one
isolate and interrogate all isolates of the panel at that position. As such a
multiple sequence alignment of “core SNPs” was generated.
Population genetic analysis
Bayesian model-based inference of the genetic population structure was
performed using the “Clustering with linked loci” module [165] in BAPS v.6.0
[166]. The optimal number of genetically diverged BAPS-clusters (K) was
estimated in our data by running the estimation algorithm with the prior
upper bound of K varying in the range of 1-20. Since the algorithm is
stochastic, the analysis was run in 20 replicates for each value of K as to
increase the probability of finding the posterior optimal clustering with that
specific value of K.
On the assumption that patients were infected near their residences, the
latitude and longitude coordinates of a location in the vicinity of patients’
residences at the time of the first clinical visit were collected, including for
retrospective isolates, by using handheld GPS devices (Garmin eTrex 20).
When exact residence locations were missing we used the latitude and
longitude of the village center. QGIS v.2.14.1 [104] was used to generate the
figures of the geographical distribution of Congolese M. ulcerans. The QGIS
Python plugin “Points displacement” was used to modify point shape files,
where point features with the same position overlapped. Point displacement
rendered such features in a circle around the original “real” position.
Geographical analysis of diversity and the overlaying of a phylogenetic tree
was performed with GenGIS v2.5.0 [216], based on the household GPS
coordinates of patients and whole-genome ML phylogenies of the
corresponding M. ulcerans isolates.
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Maximum-likelihood phylogenetic analysis
Maximum-likelihood (ML) phylogenies were estimated ten times from SNP
alignments using RAxML v8.2.4 [172] under a plain generalized time reversible
(GTR) model (no rate heterogeneity) with likelihood calculation correction for
ascertainment bias using the Stamatakis method [173]. Identical sequences
were removed before the RAxML runs. For each run we performed 10,000
rapid bootstrap analyses to assess support for the ML phylogeny. The tree
with the highest likelihood across the ten runs was selected. We used
TreeCollapseCL v4 [174] to collapse nodes in the tree with bootstrap values
below a set threshold of 70% to polytomies while preserving the length of the
tree.
Bayesian phylogenetic analysis
We used BEAST2 v2.4.0 [77] to date evolutionary events, determine the
substitution rate, estimate the demographic history, and produce a time-tree
of DRC M. ulcerans. An uncorrelated log-normal relaxed molecular clock [72]
was used with the extended Bayesian skyline demographic method [217] to
infer a genome scale Congolese M. ulcerans time-tree under the GTR
substitution model and with tip-dates defined as the year of isolation (Table
5.S1) (CHAPTER_4). Analysis was performed in BEAST2 using a total of 10
independent chains of 200 million generations, with samples taken every
20,000 MCMC generations. Log files were inspected in Tracer v1.6 [180] for
convergence, proper mixing, and to see whether the chain length produced an
effective sample size (ESS) for all parameters larger than 400, indicating
sufficient sampling. LogCombiner v2.4.0 [77] was then used to combine log
and tree files of the independent BEAST2 runs, after having removed a 30%
burn-in from each run. Thus, parameter medians and 95% highest posterior
density (HPD) intervals were estimated from 70,000 sampled MCMC
generations. To ensure prior parameters were not over-constraining the
calculations, the entire analysis was also run while sampling only from the
prior, and the resulting parameter distributions were compared in Tracer.
TreeAnnotator v2.4.0 [77] was used to summarize the posterior sample of
time-trees in order to as to produce a maximum clade credibility tree with the
posterior estimates of node heights visualized on it.
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The estimated timing of population increases and decreases is dependent on
the estimated substitution rate. A potential source of error when estimating
the substitution rate is that tip dates alone, rather than the link of tip dates
associated with sequence data might be driving the results, especially when
the sequence data lacks temporal phylogenetic information [218]. Therefore,
a permutation test was used to assess the validity of the temporal signal in
the data. This was undertaken by performing 20 additional BEAST2 runs (of
200 million MCMC generations each) with identical substitution (GTR), clock
(uncorrelated log-normal relaxed) and demographic models (extended
Bayesian skyline) but with tip dates randomly reshuffled to sequences [219].
This random “null set” of tip-date and sequence correlations was then
compared with the substitution rate estimate of the genuine tip-date and
sequence correlations (CHAPTER 4) [82, 220].
5.4 Results
Genome sequence comparisons of 178 M. ulcerans isolates from Central
Africa
To understand the dynamics and timing of the spread of M. ulcerans across
Central Africa, we sequenced the genomes of 179 clinical isolates that were
obtained between 1966 and 2014 and spanned most of the known endemic
areas of BU in the DRC, RC and Angola (Table 5.S1). Using PacBio reads, a new
complete, closed DRC M. ulcerans reference genome was assembled that
received the strain name SGL03 (“Songololo Territory 2003”). The strain
originated from a patient (Male / 10 years old) from the hamlet Nkondo-
Kiombia (Minkelo Health area) who presented with a severe disseminated
form of BU in 2003. The patient was BU confirmed with all four diagnostic
tests: Ziehl–Neelsen microscopy, IS2404 qPCR, culture and histopathology.
SGL03 comprises a single 5,625,184 bp (6422 bp smaller than Agy99) circular
bacterial chromosome with a G+C content of 65.5 %. Illumina sequence reads
of the sample panel were mapped to the newly assembled M. ulcerans SGL03
reference genome and, after excluding mobile repetitive IS elements and
small insertion-deletions (indels), we detected a total of 6655 SNPs uniformly
distributed across the M. ulcerans chromosome with approximately 1 SNP per
846 bp (0.12% nucleotide divergence) (Figure 5.S1).
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The population structure of M. ulcerans in Central Africa
A Bayesian maximum clade credibility phylogeny was inferred from a whole-
genome alignment of the isolates (Figure 5.1). Both known lineages of African
M. ulcerans were identified within the Central African isolate panel: 177/178
(99.4%) corresponded to lineage Africa I (Mu_A1) and 1/178 (0.6%)
corresponded to the uncommon lineage Africa II (Mu_A2). The single Mu_A2
isolate originated from a patient (F/40) from the hamlet Kilima in the
Songololo Territory (Nkamuna Health area).
The average pairwise SNP difference (SNPΔ) between Mu_A1 Central African
isolates was low indicating that the majority of the discovered diversity
derived from the large genetic distance between Mu_A1 and the single
Mu_A2 isolate from the region.
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Figure 5.1: Bayesian maximum clade credibility phylogeny for DRC, RC and Angolan M.
ulcerans. The tree was visualized and colored in Figtree v1.4.2 [102]. Branches are color coded
according to their branch specific substitution rate (legend at top). Branches defining major
lineages are annotated on the tree. Tip labels of Songololo Territory isolates are color coded
according to their respective BAPS-clusters. Divergence dates (mean estimates and their
respective 95% HDP) are indicated in black for major nodes.
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Phylogenetic analysis reveals strong geographical restrictions on M. ulcerans
dispersal at high-level geographical scales
Within an M. ulcerans phylogeny of the entire African continent (Figure 5.S2),
the single Songololo Mu_A2 isolate clusters together with a clade of 8 other
Mu_A2 isolates originating from Benin, Gabon, and Cameroon. Furthermore,
a distinct Mu_A1 isolate from RC (ITM_071925) clusters together with a small
clade of Nigerian and Cameroonian M. ulcerans. More importantly however,
all other 176 Mu_A1 isolates of the Central African panel form a strongly
supported (posterior probability = 1) monophyletic group within that
continental African phylogeny. Within that clade we can discern an
unambiguous relationship between the genotype of an isolate and its
geographical origin. This is illustrated by the clear spatial clustering of M.
ulcerans from the different endemic BU foci within the Bayesian phylogeny.
For instance, all 123 isolates of the endemic BU focus of the Songololo
Territory form a strongly supported monophyletic group (posterior probability
= 1) (Figure 5.2). The Songololo Territory isolates are unrelated to the 4
isolates form the neighboring Tshela Territory (northwest in the Kongo Central
province) which form a separate monophyletic group (posterior probability =
1) (Figure 5.2).
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Figure 5.2: Phylogeography of DRC, RC and Angolan M. ulcerans. A Maximum-likelihood
phylogeny is drawn for lineage Mu_A1 with branches color coded according to BU disease
focus (legend bottom right). The ML phylogeny is based on 1373 SNP differences detected
across the whole core genome of 135 sequenced isolates with GPS data. Nodes in the tree
with bootstrap support below a set threshold of 70% were collapsed to polytomies, while
preserving the length of the tree. The green clade formed by 123 isolates from the Songololo
Territory disease focus is collapsed in the tree. The tips of the tree are connected to the
location of residence of patients from whom the strain was isolated. The administrative
borders of countries were obtained from the Global Administrative Unit Layers dataset of
FAO. The river layer was translated from the River-Surface Water Body Network data set of
the African Water Resource database of FAO.
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The clustering of M. ulcerans genotypes ends at low-level geographical
scales
We then explored the geographical distribution of M. ulcerans genotypes at a
lower geographical scale: that of the Songololo Territory. The territory covers
an area of 8190 km² and is limited to the north by the Congo River, to the east
by the Kwilu-Ngongo health zone, to the south by the northern border of
Angola, and to the west by the Sekebanza health zone (Figure 5.S3). The
Songololo Territory has the highest case notifications, yet the limits of this
endemic focus are somewhat extended into the above mentioned bordering
health zones. The 123 Songololo isolates originating from 123 unique patients
are spread rather evenly over the territory and the majority of Health Areas
with a “modest” to “high” BU burden are well represented (Figure 5.S3).
Bayesian model-based inference of the genetic population structure revealed
the existence of six BAPS-clusters within the territory (Figure 5.3). The six
clusters occur somewhat intermixed, as in some regions of the territory,
multiple clusters are found circulating simultaneously. In the Health Area of
Lovo for instance, up to 5 different clusters are co-circulating (BAPS 1, 2, 3, 4,
5). The clusters are however distributed differently over the study region:
clusters 2, 4 and 5 are found rather widely dispersed, while cluster 1, 3 and 6
are more geographically restricted (Figure 5.3). Cluster 1 (n=20) is found
almost exclusively in the east of the Territory while cluster 3 (n=31) is
localized in the west (Figure 5.S4). Cluster 6, finally, is uncommon (n=4) and
found solely in the south-west. Moreover, within the clusters there are some
distinct cluster subtypes, which occasionally also have a limited distribution
across the region. For example, one specific sub-cluster of BAPS cluster 2
consist of 7 isolates that all originate from a 90 km² zone covering the
neighboring health areas of Mukimbungu and Kasi (Figure 5.S4). However,
other sub-clusters can be far more broadly distributed; with the extreme
example of identical genomes identified in different BU patients separated by
large geographical distances (Figure 5.3; I-X). A total of ten such genomes that
were identified multiple times in the Songololo Territory can be discerned
(Table 5.1). The average geographical distance between the domiciles of
patients identified with an identical genome is 17,3 ± 18,1 km.
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Table 5.1: Identical genomes identified in different BU patients of the
Songololo Territory. YOI: Year Of Isolation; Not Applicable: “-“
Isolate 1
(YOI)
Isolate 2
(YOI)
Isolate 3
(YOI)
Geographical
distance (km)
Years between
isolation dates
Identical Genome Set I ITM102560
(2009)
ITM131951
(2008)
ITM141716
(2013)
26,6 5
Identical Genome Set II ITM130328
(2011)
ITM130330
(2011)
- 56,5 0
Identical Genome Set III ITM130336
(2012)
ITM131959
(2013)
- 0,0 1
Identical Genome Set IV ITM081364
(2007)
ITM082600
(2007)
- 0,1 0
Identical Genome Set V ITM141715
(2013)
ITM141729
(2014)
- 37,2 1
Identical Genome Set VI ITM072731
(2007)
ITM141740
(2014)
- 18,7 7
Identical Genome Set VII ITM112015
(2011)
ITM112016
(2011)
- 6,0 0
Identical Genome Set VIII ITM073463
(2007)
ITM141700
(2013)
- 11,2 6
Identical Genome Set IX ITM081935
(2007)
ITM110809
(2009)
- 5,7 2
Identical Genome Set X ITM141709
(2013)
ITM141717
(2013)
- 11,4 0
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Figure 5.3: Phylogeography of the Songololo Territory BU disease focus. A Maximum-
likelihood phylogeny is drawn for lineage Mu_A1. The ML phylogeny is based on 684 SNP
differences detected across the whole core genome of 123 sequenced isolates from the
Songololo Territory with GPS data. Branches are color coded according to their respective
BAPS-clusters as indicated in the legend. Nodes in the tree with bootstrap support below a set
threshold of 70% were collapsed to polytomies, while preserving the length of the tree. The
location of residence of patients from whom the isolate was grown is colored according to the
BAPS-cluster the corresponding isolate belonged to. Identical genomes identified in different
patients are interconnected by the green curves, which are annotated with Roman numerals.
The background map was created using elevation data from the Shuttle Radar Topography
Mission (SRTM). The river layer (Congo river and its tributaries) was digitized from declassified
Soviet military topographic maps xb33-13, xb33-14, xb33-15, xb33-16, and xb33-17 (scale
1:200k) and xb33-1, and xb33-1 (scale 1:500k).
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The Central African mutation rate of M. ulcerans is similar to the one
inferred on a continental scale
A genome scale Central African M. ulcerans time-tree was inferred (Figure 5.1)
while also providing estimates of nucleotide substitution rates and divergence
times for key M. ulcerans clades. In this process, a molecular clock was
estimated using correlations between phylogenetic divergence and isolation
times of heterochronous disease isolates. We estimate a mean genome wide
substitution rate of 4.38E-8 per site per year (95% HPD interval [2.83E-8 -
6.03E-8]), corresponding to the accumulation of 0.23 SNPs per chromosome
per year (95% HPD interval [0.15 – 0.32]) (excluding IS elements). The analysis
also indicates that the genealogy has undergone very moderate substitution
rate variation, with a 6-fold difference between the slowest (1.22E-08) and
the fastest (8.06E-08) evolving branches. Rate accelerations and decelerations
are found interspersed in the time-tree (Figure 5.1). When we estimated the
substitution rate by informing the prior distribution of the substitution rate in
the Bayesian analysis with that of a previous estimate (CHAPTER 4) we obtain
a similar result (4.56E-8 per site per year (95% HPD interval [2.53E-8 - 6.01E-
8])).
The Bayesian analysis (Figure 5.1) indicates that lineage Mu_A1 has been
endemic in the Central Africa for hundreds of years (tMCRA(Mu_A1) = 1372
A.D.(95% HPD 913 - 1776)). The time-tree of Central African M. ulcerans also
reveals the timing of the BU introduction event in the Songololo Territory:
tMCRA(Songololo)= 1865 A.D. (95% HPD 1803 - 1915). Finally, the time-tree
also indicates that the separated “eastern” (tMCRA (BAPS-1)=1941 A.D. (95%
HPD 1908 - 1969)) and “western” (tMCRA (BAPS-3)=1922 A.D. (95% HPD 1885
- 1954)) Songololo clusters have most likely remained segregated over a
timespan of half a century.
The demographic history of M. ulcerans in the Songololo Territory
The reconstruction of the demographic history of M. ulcerans in the Songololo
Territory involved the estimation of the phylogeny and the inference of the
mycobacterial population size (Ne.τ) at different points along the timescale of
that phylogeny. There were sufficient event times informing the population
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dynamics between 1880 and 2014. Inspection of the extended Bayesian
skyline plot (EBSP) (Figure 5.4) suggested that the M. ulcerans population size
remained stable until the early 1980s, after which it increased slightly during
the course of the nighties, until it reached a peak around 2004. This was
followed by a small decline that persisted until 2014.
We identified a relationship between the observed past population dynamics
of M. ulcerans from the Songololo Territory and the timing of health policy
changes managing the BU epidemic in that region. More than 500 cases of BU
had been reported before 1980, after which there was a 20 year long “silent”
period in the scientific literature during which no cases were reported [5].
During this period the hospital lost the majority of its specialized personnel,
which was partially due to the political situation in DRC at that time. This lead
to IME’s lowest recorded (all-cause) admission rate of 4,5 patients/year
between 1989 and 1999 [221]. Later, in 2002, a national BU program was
started (Program National de Lutte contre L’Ulcère de Buruli - PNLUB) and
during 2002-2004 an apparent resurgence of BU was reported in the
Songololo Territory [222]. Since the end of 2004 the General Reference
Hospital of the Institut Médical Evangélique (IME) in Kimpese launched a
specialized BU program (sponsored by American Leprosy Missions), offering
free-of-charge treatment and supplementary aid. Since the start of the BU
control project a strong increase was noted in the number of notified BU
cases, including those admitted to IME hospital [223].
We checked for two factors that could bias the reconstruction of the
mycobacterial population size over time: population structure and lack of
temporal information in the alignment. To test whether our results were
influenced by any of these factors we conducted extensive resampling and
randomization experiments. To test the validity of the discovered temporal
signal in the data we performed 20 date-randomization tests. The estimate of
the substitution rate passed this test as its mean did not fall within the 95%
HPD intervals of rate estimates obtained using replicate data sets in which the
sampling times were randomized (Figure 5.S5). This indicated the spread of
sampling times was sufficient to allow the substitution rate to be estimated
reliably.
Page | 128
Figure 5.4: The demographic history of M. ulcerans in the Songololo Territory. Extended
Bayesian Skyline plot; the central dotted line represents the median mycobacterial population
size (Ne.τ) with its 95% central posterior density (CPD) interval represented by the upper and
lower lines. Note the y-axis is on the log scale. The EBSP method was implemented in a
framework in which the phylogeny, the demographic history and all other parameters of the
model of molecular evolution were co-estimated in a single analysis. As a result the resulting
plot of the population history includes credibility intervals that represent the combined
phylogenetic and coalescent uncertainty. Ne reflects the number of individuals that contribute
offspring to the next generation and will always be smaller than the “real” population size. τ
represents the generation time.
Page | 129
5.5 Discussion
The demographic history of a pathogen population leaves a “signature” in the
genomes of modern representatives of that population [224]. Reconstructing
this history can allow us to gain valuable insights into the processes that drove
past population dynamics [76]. We recognized a relationship between the
mycobacterial population dynamics and the timing of health policy changes
managing the BU epidemic in the Songololo Territory. In the Territory, the
mycobacterial population size increased in the period without BU control
activities, as reflected by lack of notified BU cases, with a subsequent subtle
decline when BU control activities resumed. To test whether our results were
not biased we conducted extensive resampling and randomization
experiments and used a wide range of parameter settings. All additional
analyses confirmed our results. The bacterial population size increased prior
to the start of a national BU program in the country during a period of
decreased attention to BU that resulted in the loss of specialized personnel.
After the start of the program and the implementation of free-of-charge
treatment, a strong increase was noted in the number of admitted BU cases
which concorded with a detected inflection - perhaps a small drop - of the
mycobacterial population size. These observations suggest that control
strategies at the public health level may have decreased the size of the human
M. ulcerans reservoir and that this reservoir is important in sustaining new
infections. These analyses furthermore indicate that even if other
environmental reservoirs exists, the number of M. ulcerans infections will
decrease, even if only human cases are treated.
The M. ulcerans phylogeography revealed one almost exclusively
predominant sublineage of Mu_A1 that arose in Central Africa and
proliferated in the different endemic foci of DRC, Angola, and RC during the
Age of Discovery (15th - 18th century). The principal sublineage of Mu_A1 was
introduced into the Songololo Territory around 1865 (95% HPD 1803-1915)
and, over the subsequent century (1865-1974), it established itself and
evolved in six distinct clusters across the territory. This timing is consistent
with in-depth interviews with former patients and observations of healed
lesions that had suggested that M. ulcerans infections already occurred in the
Songololo Territory in 1935 and probably even earlier [120]. The genome-
Page | 130
based time-tree of Central African M. ulcerans thus revealed that the
Songololo Territory became endemic during the time when the region was
being colonized by Belgium. Early during the Belgian occupation, the
Songololo territory was developed heavily to link the oceanic harbor of
Matadi by rail with Kinshasa, where the Congo River becomes navigable,
opening up the entire interior of DRC for economic exploitation [225]. The
Songololo Territory was already inhabited long before the arrival of the
European colonizers. The Kongo People are believed to have settled at the
mouth of the Congo River before 500 BC, as part of the larger Bantu migration
[226]. However, our data reveal that it was only after the start of colonial rule
that the epidemic Songololo M. ulcerans clone was introduced, possibly
through the arrival of displaced BU-infected humans.
Similar to recent studies that used comparative genomics to investigate the
microevolution of M. ulcerans during its establishment in a continent
(CHAPTER 4) or region [154, 155], the genotypes in Central Africa show strong
spatial segregation (Figure 5.2). This was illustrated by the explicit regional
clustering of M. ulcerans from the different endemic BU foci (Songololo,
Tshela, Kwango) within the phylogenies. This clustering of cases within
endemic foci reflects a common source of infection within the disease focus.
These repeated findings indicate that when M. ulcerans is introduced in a
particular area, it remains isolated, resulting in a localized clonal expansion
associated with that area. Inspection of the time tree shows that a clonal
complex associated with an endemic focus often has been pervasive in that
region for a considerable time; in the case of Songololo around 150 years.
Even within the Songololo Territory we observed that the separated eastern
(tMCRA (BAPS-1)=1941) and western (tMCRA (BAPS-2)=1922) Songololo
clusters have most likely remained segregated over a timespan of half a
century, indicating that M. ulcerans spreads relatively slowly between
neighboring regions. This also indicates that environmental reservoirs of the
mycobacterium in that region had to remain localized and relatively isolated.
Unlike the larger geographic scale data, at smaller geographical scales
genotypes start to intermingle. Firstly, four Songololo BAPS clusters were
found to be co-circulating. Moreover, we observed even completely identical
genomes originating from patients living in villages separated by distances of
Page | 131
on average 17 km, similarly to the findings of a recent studies [154, 156]. We
believe the observed “breakup” of the focal distribution pattern at smaller
geographical scales can be explained by the determined low substitution rate
that corresponds to the accumulation of just 0.23 SNPs per bacterial
chromosome per year. The slow substitution rate severely limits the
accumulation of point mutations and as such lowers the resolving power of
the genomes. This explains why (in the most extreme case), over a period of
five years an identical genome was discovered in three patients who lived in
three different villages separated by 26 km: insufficient time has elapsed for
point mutations to accumulate.
Finally, an old debate relating to the role of Angolan refugees on a resurge of
BU in the Songololo territory [227] can be settled. In the aftermath of the
Angolan civil war (which ended in 2002) BU was frequently diagnosed in
Angolan refugees who lived in refugee camps located in the Songololo
Territory. As cases have been reported in Angola [228] the possibility existed
that these patients were infected in Angola and re-introduced BU in the
region. Most of these Angolans however had already lived in DRC for several
years before their diagnosis, and some young Angolan patients were born in
DRC and had never visited Angola. Analysis of the phylogenies shows that no
typical Angolan genotypes were detected in Songololo, indicating that the
refugees were in all likelihood infected in the DRC.
In conclusion, in the present study we used both temporal associations and
the study of the mycobacterial demographic history in an endemic focus to
implicate human-induced changes and activities over (recent) historical scales
behind the expansion of BU in Central Africa. We propose that humans with
actively infected, openly discharging BU lesions inadvertently contaminate
aquatic environments during water contact activities and thus play the pivotal
role in the spread of the mycobacterium. A total of 74% of BU patients
identified during a cross-sectional study [206] of the Songololo Territory had
ulcerative lesions (49% category I, 31% category II, and 20 % category III)
indicating a high percentage of patients might be shedding bacteria into the
environment potentially infecting others. Based on these conclusions we
suggest that in BU-affected areas, chains of transmission will be broken and
the spread of disease stopped through improved disease surveillance, active
Page | 132
case-finding programs and early treatment of pre-ulcerative infection. This
view is supported by the decline of BU incidence recorded in some areas
which profited from both improved BU surveillance and early treatment [197].
Future longitudinal micro-epidemiological studies involving comparative
genomics and SNP-typing of clinical and environmental samples could possibly
provide even deeper insights into the transmission pathways of M. ulcerans.
We anticipate that the SNPs described here will provide useful genetic
markers for future M. ulcerans transmission pathway tracing in the Songololo
Territory.
5.6 Supporting Information
Figure 5.S1: Distribution of SNPs identified in the entire sequenced sample panel (Mu_A1 &
Mu_A2) compared to the Congolese M. ulcerans SNG03 reference genome. The Y-axis
corresponds to SNP counts per 10,000bp window, the dashed line indicates the average rate
of 11.8 SNPs per 10,000 bp (or 1 SNP per 846 bp window).
Page | 133
Figure 5.S2: The position of Central African isolates in the continental African M. ulcerans
tree. Bayesian maximum clade credibility phylogeny drawn for lineage Mu_A1 and Mu_A2 for
the 179 isolates sequenced in this study plus 144 African M. ulcerans isolates sequenced
previously (total n=323). DRC, RC, and Angolan isolates are highlighted in pink in the
phylogeny. Branches defining major lineages are annotated on the tree.
Page | 134
Figure 5.S3: Sampling effort and the distribution of the BU disease burden in the Health zones
and Health areas of the Kongo Central province of RDC. The distribution of all BU cases per
Health zone (top) or Health area (bottom) was determined from all disease notifications
reported since the start of the national BU program (PNLUB) in 2002 until 2014. The red
crosses denote the General Reference Hospitals of the Institut Médical Evangélique (IME) in
Kimpese and that of Nsona-Mpangu. Yellow points represent the residence of BU patients
from whom M. ulcerans disease isolates were grown at the time of clinical visit.
Page | 135
Figure 5.S4: Detailed view of the phylogeography of BAPS clusters 1 (“eastern”) and 3
(“western”) and sub cluster Mukimbungu-Kasi of the Songololo Territory BU disease focus.
For clarity, not all connecting lines are plotted. The distribution of isolates and the overlaying
of the phylogenetic tree was performed with GenGIS v2.5.0, based on the household GPS
coordinates of each patient and the whole-genome ML phylogeny (same as in Figure 5.3) of
their corresponding M. ulcerans isolates.
Page | 136
Figure 5.S5: Comparison of Bayesian estimates of nucleotide substitution rates for real and
randomized tip dates. Filled squares & circles represent mean estimates, while bars indicate
values of the 95% highest probability density (HDP) interval. The estimate obtained using the
real tip date associations (circle) is shown to the far right while estimates from random
associations (squares) are shown to the left. All randomized data sets were analyzed in
BEAST2 using identical model settings as used in the analysis of the real tip date data. Note
the y-axis is on the log scale.
Page | 137
Table 5.S1: M. ulcerans isolate and DNA sequencing information. 1: Democratic Republic of
the Congo
Isolate n° Lineage BAPS cluster YOI Country of Origin Latitude Longitude Coverage (x) Average read length (bp)
ITM030221 Mu_A1 BAPS_1 2002 DRC1
-5.38942 14.30403 63 95
ITM030719 Mu_A1 BAPS_1 2002 DRC -5.42357 14.48067 56 95
ITM030952 Mu_A1
2002
66 96
ITM031677 Mu_A1 BAPS_1 2003 DRC -5.49655 14.69358 60 95
ITM032481 Mu_A1 BAPS_2 2003 DRC -5.60384 14.13927 89 138
ITM040358 Mu_A1 BAPS_2 2003 DRC -5.73894 14.04787 61 95
ITM041706 Mu_A1
2003 DRC -4.722099 13.041641 61 95
ITM050303 Mu_A1
1966 Congo -4.432895 12.299497 59 138
ITM050715 Mu_A1 BAPS_1 2004 DRC -5.55598 14.45739 71 91
ITM060976 Mu_A1
2005 Congo -4.761918 11.868028 52 95
ITM060977 Mu_A1 BAPS_2 2005 DRC -5.09303 14.07327 52 95
ITM062479 Mu_A1
2006
52 95
ITM063519 Mu_A1 BAPS_3 2006 DRC -5.6969 14.05914 56 139
ITM070118 Mu_A1 BAPS_4 2006 DRC -5.55598 14.45739 65 95
ITM070121 Mu_A1 BAPS_6 2006 DRC -5.76913 13.92102 66 95
ITM070122 Mu_A1 BAPS_2 2006 DRC -5.50590 14.52313 67 95
ITM070123 Mu_A1 BAPS_1 2006 DRC -5.56652 14.43447 72 139
ITM070124 Mu_A1 BAPS_3 2006 DRC -5.67882 13.96647 69 95
ITM071491 Mu_A1 BAPS_2 2006 DRC -5.68600 13.88951 53 95
ITM071499 Mu_A1
2006 Angola
69 96
ITM071910 Mu_A1 BAPS_5 2006 DRC -5.46310 13.78095 59 95
ITM071912 Mu_A1 BAPS_2 2007 DRC -5.09275 14.02277 62 96
ITM071913 Mu_A1 BAPS_2 2007 DRC -5.12371 14.01776 57 95
ITM071925 Mu_A1
2007 Congo -4.433392 11.700150 54 140
ITM072398 Mu_A1 BAPS_5 2006 DRC -5.52036 13.90937 65 140
ITM072399 Mu_A1 BAPS_2 2006 DRC -5.76827 13.92186 60 95
ITM072400 Mu_A1 BAPS_6 2007 DRC -5.76913 13.92102 60 95
ITM072401 Mu_A1 BAPS_3 2007 DRC -5.76785 13.92162 74 140
ITM072731 Mu_A1 BAPS_2 2007 DRC -5.15632 14.18982 56 95
ITM072732 Mu_A1 BAPS_3 2007 DRC -5.76854 13.91985 44 140
ITM072733 Mu_A1 BAPS_2 2007 DRC -5.39494 14.12631 57 140
ITM072734 Mu_A1 BAPS_2 2007 DRC -5.68719 13.89086 40 140
ITM072735 Mu_A1 BAPS_2 2007 DRC -5.70311 14.1483 40 139
ITM072737 Mu_A1 BAPS_2 2006 DRC -5.68680 13.89025 62 95
ITM072840 Mu_A1 BAPS_2 2007 DRC -5.76805 13.91836 83 140
ITM073461 Mu_A1 BAPS_2 2007 DRC -5.75556 13.98184 66 92
ITM073463 Mu_A1 BAPS_2 2007 DRC -5.67491 14.01439 74 139
ITM073477 Mu_A1 BAPS_2 2007 DRC -5.46071 13.78244 48 139
ITM073676 Mu_A1 BAPS_3 2007 DRC -5.76855 13.92024 63 95
ITM080059 Mu_A1 BAPS_1 2007 DRC -5.70821 14.04022 59 95
ITM080060 Mu_A1 BAPS_2 2007 DRC -5.77422 13.94787 59 95
ITM080062 Mu_A1 BAPS_3 2007 DRC -5.77508 14.16654 63 91
ITM080064 Mu_A1 BAPS_2 2007 DRC -5.56748 14.12015 65 95
ITM080098 Mu_A1
2007 Angola -8.515200 17.828573 65 95
Page | 138
Isolate n° Lineage BAPS cluster YOI Country of Origin Latitude Longitude Coverage (x) Average read length (bp)
ITM080285 Mu_A1 BAPS_4 2007 DRC -5.75498 14.43446 58 95
ITM080288 Mu_A1 BAPS_4 2007 DRC -5.75519 14.43433 52 95
ITM081018 Mu_A1 BAPS_1 2007 DRC -5.73305 14.45571 57 95
ITM081364 Mu_A1 BAPS_5 2007 DRC -5.41084 14.49715 57 95
ITM081433 Mu_A1
2007 DRC -4.722099 13.041641 62 95
ITM081434 Mu_A1
2007 DRC -4.722099 13.041641 67 95
ITM081436 Mu_A1
2007 DRC -4.722099 13.041641 59 95
ITM081935 Mu_A1 BAPS_2 2007 DRC -5.75556 13.98184 62 95
ITM081937 Mu_A1 BAPS_2 2007 DRC -5.76938 13.93092 72 96
ITM082001 Mu_A1
2008 DRC
68 95
ITM082494 Mu_A1 BAPS_1 2007 DRC -5.56121 14.44862 64 95
ITM082495 Mu_A1 BAPS_4 2007 DRC -5.56121 14.44862 81 96
ITM082498 Mu_A1 BAPS_5 2007 DRC -5.41089 14.49713 68 95
ITM082499 Mu_A1 BAPS_4 2008 DRC -5.81782 14.19031 64 95
ITM082575 Mu_A1
2008 DRC
66 95
ITM082600 Mu_A1 BAPS_5 2007 DRC -5.41166 14.49669 41 139
ITM083232 Mu_A1
2008 Angola -9.418594 18.432287 56 140
ITM083545 Mu_A1
2008 DRC
67 95
ITM083546 Mu_A1
2008 DRC
63 95
ITM083547 Mu_A1
2008 DRC
72 95
ITM083663 Mu_A1
2008 DRC
82 96
ITM083664 Mu_A1
2008 DRC
83 96
ITM083665 Mu_A1
2008 DRC
80 96
ITM090058 Mu_A1
2008 DRC
65 95
ITM090059 Mu_A1 BAPS_3 2007 DRC -5.76785 13.92162 47 93
ITM090244 Mu_A1 BAPS_3 2008 DRC -5.37190 14.37605 58 95
ITM090245 Mu_A1 BAPS_4 2008 DRC -5.72645 14.39808 58 95
ITM090250 Mu_A1
2008 DRC
67 96
ITM090252 Mu_A1
2008
66 96
ITM090253 Mu_A1 BAPS_2 2008 DRC -5.56121 14.44862 64 95
ITM091058 Mu_A1
2008 DRC
72 96
ITM091059 Mu_A1
2008 DRC
78 96
ITM091155 Mu_A1 BAPS_3 2008 DRC -5.72898 14.44607 87 96
ITM091795 Mu_A1 BAPS_2 2008 DRC -5.64428 14.18763 83 96
ITM092479 Mu_A1
2009
UNKNOWN UNKNOWN 67 138
ITM092522 Mu_A1
2008
66 95
ITM100140 Mu_A1 BAPS_2 2009 DRC -5.70385 14.39294 90 139
ITM100831 Mu_A1 BAPS_2 2009 DRC -5.73816 14.04989 70 95
ITM100833 Mu_A1 BAPS_3 2009 DRC -5.29764 14.03725 54 140
ITM101864 Mu_A1
2009 DRC -4.825456 15.179014 68 95
ITM102560 Mu_A1 BAPS_3 2009 DRC -5.73978 14.04599 86 96
ITM102561 Mu_A1 BAPS_3 2009 DRC -5.73978 14.04599 63 95
ITM102562 Mu_A1 BAPS_2 2010 DRC -5.51255 13.90563 57 95
ITM102563 Mu_A1
2010
81 96
ITM102564 Mu_A1 BAPS_3 2010 DRC -5.701165 14.038949 66 96
ITM102565 Mu_A1 BAPS_2 2009 DRC -5.38319 14.51654 57 95
ITM110183 Mu_A1 BAPS_3 2009 DRC -5.09500 14.02389 68 96
Page | 139
Isolate n° Lineage BAPS cluster YOI Country of Origin Latitude Longitude Coverage (x) Average read length (bp)
ITM110798 Mu_A1 BAPS_5 2008 DRC -5.25116 14.14875 70 96
ITM110799 Mu_A1 BAPS_3 2008 DRC -5.51917 13.90905 82 96
ITM110800 Mu_A1 BAPS_3 2008 DRC -5.67940 13.96586 90 96
ITM110801 Mu_A1 BAPS_2 2009 DRC -5.67933 13.96667 80 96
ITM110804 Mu_A1 BAPS_2 2011 DRC -5.68124 13.91436 82 96
ITM110805 Mu_A1
2008 DRC
59 95
ITM110807 Mu_A1 BAPS_3 2009 DRC -5.40057 14.11641 70 96
ITM110808 Mu_A1 BAPS_2 2009 DRC -5.76894 13.92052 66 96
ITM110809 Mu_A1 BAPS_2 2009 DRC -5.74082 14.03083 72 96
ITM110810 Mu_A1 BAPS_5 2010 DRC -5.47175 14.46689 75 96
ITM110811 Mu_A1 BAPS_1 2010 DRC -5.41990 14.47082 79 96
ITM110812 Mu_A1
2010 DRC
81 96
ITM110813 Mu_A1 BAPS_1 2010 DRC -5.55463 14.46311 89 96
ITM110891 Mu_A1 BAPS_3 2010 DRC -5.6969 14.05914 81 96
ITM112014 Mu_A1 BAPS_1 2011 DRC -5.45031 14.46770 58 95
ITM112015 Mu_A1 BAPS_2 2011 DRC -5.14118 14.03652 63 95
ITM112016 Mu_A1 BAPS_2 2011 DRC -5.09462 14.02354 81 96
ITM120108 Mu_A1
2011
82 96
ITM120109 Mu_A1
2011
73 96
ITM130324 Mu_A1 BAPS_2 2011 DRC -5.73737 14.04630 74 96
ITM130325 Mu_A1
2011 DRC
87 96
ITM130326 Mu_A1 BAPS_2 2011 DRC -5.76861 13.92081 79 96
ITM130327 Mu_A1 BAPS_2 2011 DRC -4.910070 14.166960 88 96
ITM130328 Mu_A1 BAPS_1 2011 DRC -5.54598 13.95093 70 96
ITM130329 Mu_A1
2011
72 96
ITM130330 Mu_A1 BAPS_1 2011 DRC -5.47032 14.45564 70 96
ITM130333 Mu_A1 BAPS_4 2007 DRC -5.60976 14.08427 67 96
ITM130334 Mu_A1 BAPS_1 2012 DRC -5.56121 14.44862 67 96
ITM130335 Mu_A1 BAPS_3 2011 DRC -5.75556 13.98184 70 96
ITM130336 Mu_A1 BAPS_1 2012 DRC -5.50590 14.52313 64 96
ITM130337 Mu_A1 BAPS_2 2012 DRC -5.42084 13.82826 81 96
ITM130338 Mu_A1 BAPS_2 2012 DRC -5.37215 13.78772 75 96
ITM130340 Mu_A2
2013 DRC -5.76894 13.93317 73 96
ITM130341 Mu_A1 BAPS_2 2013 DRC -5.76913 13.92102 70 96
ITM130342 Mu_A1 BAPS_6 2012 DRC -5.75556 13.98184 66 96
ITM130343 Mu_A1 BAPS_2 2012 DRC -5.09502 14.02358 67 96
ITM130347 Mu_A1 BAPS_6 2012 DRC -5.74139 14.03020 70 96
ITM130348 Mu_A1 BAPS_4 2012 DRC -5.47705 13.78943 67 96
ITM131942 Mu_A1
2012
86 96
ITM131943 Mu_A1 BAPS_5 2012 DRC -5.70424 14.39377 79 96
ITM131944 Mu_A1 BAPS_2 2012 DRC -5.74140 14.03005 73 96
ITM131945 Mu_A1 BAPS_2 2012 DRC -5.75556 13.98184 73 96
ITM131947 Mu_A1 BAPS_1 2012 DRC -5.59428 14.44455 74 96
ITM131948 Mu_A1 BAPS_3 2012 DRC -5.68378 13.91524 65 96
ITM131950 Mu_A1 BAPS_1 2012 DRC -5.50590 14.52313 73 96
ITM131951 Mu_A1 BAPS_3 2008 DRC -5.58945 13.94445 77 96
ITM131953 Mu_A1 BAPS_3 2012 DRC -5.68112 13.91754 87 96
Page | 140
Isolate n° Lineage BAPS cluster YOI Country of Origin Latitude Longitude Coverage (x) Average read length (bp)
ITM131954 Mu_A1 BAPS_2 2012 DRC -5.38895 13.85555 72 96
ITM131955 Mu_A1
2013 DRC -6.479574 16.811128 68 96
ITM131957 Mu_A1 BAPS_1 2013 DRC -5.50590 14.52313 76 96
ITM131959 Mu_A1 BAPS_1 2013 DRC -5.50590 14.52313 75 96
ITM131960 Mu_A1 BAPS_3 2013 DRC -5.75556 13.98184 80 96
ITM131961 Mu_A1 BAPS_2 2013 DRC -5.50388 13.87502 72 96
ITM131963 Mu_A1 BAPS_3 2008 DRC -5.58674 13.95612 71 96
ITM131964 Mu_A1
2013 DRC
81 96
ITM141700 Mu_A1 BAPS_2 2013 DRC -5.76605 13.96978 118 125
ITM141701 Mu_A1 BAPS_3 2013 DRC -5.77753 14.16762 120 125
ITM141706 Mu_A1
2013 DRC
120 125
ITM141707 Mu_A1
2013 DRC
120 125
ITM141709 Mu_A1 BAPS_2 2013 DRC -5.61116 14.08526 113 125
ITM141710 Mu_A1 BAPS_2 2013 DRC -5.60220 14.13882 118 125
ITM141711 Mu_A1
2013 DRC
129 125
ITM141712 Mu_A1
2013 DRC
128 125
ITM141715 Mu_A1 BAPS_3 2013 DRC -5.37203 13.78832 138 125
ITM141716 Mu_A1 BAPS_3 2013 DRC -5.79074 14.05777 124 125
ITM141717 Mu_A1 BAPS_2 2013 DRC -5.62628 14.18642 138 125
ITM141719 Mu_A1
2013 DRC
131 125
ITM141722 Mu_A1 BAPS_3 2013 DRC -5.52901 14.07047 128 125
ITM141723 Mu_A1
2013 DRC
133 125
ITM141724 Mu_A1 BAPS_1 2013 DRC -5.72464 14.47332 118 125
ITM141725 Mu_A1 BAPS_2 2013 DRC -5.74056 14.04931 132 125
ITM141727 Mu_A1
2013 DRC
121 125
ITM141728 Mu_A1 BAPS_3 2013 DRC -5.58575 13.95549 141 125
ITM141729 Mu_A1 BAPS_3 2014 DRC -5.68276 13.91298 142 125
ITM141732 Mu_A1
2013 DRC
131 125
ITM141733 Mu_A1
2013 DRC
141 125
ITM141736 Mu_A1 BAPS_3 2013 DRC -5.76594 13.97003 121 125
ITM141737 Mu_A1 BAPS_2 2013 DRC -5.74126 14.05073 118 125
ITM141738 Mu_A1 BAPS_4 2013 DRC -5.70475 14.39368 118 125
ITM141739 Mu_A1
2013 DRC
125 125
ITM141740 Mu_A1 BAPS_2 2014 DRC -5.14118 14.03652 121 125
ITM141741 Mu_A1
2014 DRC
105 125
ITM141742 Mu_A1
2014 DRC
108 125
ITM5122 Mu_A1
1962
59 96
ITM5151 Mu_A1
1972 DRC -4.186555 26.439372 59 158
ITM5152 Mu_A1
1974
65 96
ITM5153 Mu_A1
1975
63 96
ITM960657 Mu_A1
1996 Angola -8.565601 13.669590 79 140
Page | 141
5.7 Acknowledgements
KV was supported by a PhD-grant of the Flemish Interuniversity Council -
University Development Cooperation (Belgium). BdJ & CM were supported by
the European Research Council-INTERRUPTB starting grant (nr.311725).
Funding for this work was provided by the Department of Economy, Science
and Innovation of the Flemish Government, and the Stop Buruli Consortium
supported by the UBS Optimus Foundation. The computational resources
used in this work were provided by the HPC core facility CalcUA and VSC
(Flemish Supercomputer Center), funded by the University of Antwerp, the
Hercules Foundation and the Flemish Government - department EWI. The
funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
We thank Wim Mulders, Krista Fissette, Elie Nduwamahoro, and Cécile
Uwizeye for their excellent technical assistance.
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Chapter 6
A Genomic Approach to Resolving Relapse
versus Reinfection among Four Cases of
Buruli Ulcer
This chapter is published as:
Miriam Eddyani , Koen Vandelannoote, Conor J. Meehan, Sabin Bhuju, Jessica L.
Porter, Julia Aguiar, Torsten Seemann, Michael Jarek, Mahavir Singh, Françoise
Portaels, Timothy P. Stinear, Bouke C. de Jong
A Genomic Approach to Resolving Relapse versus Reinfection among Four Cases of
Buruli Ulcer.
PLoS Neglected Tropical Diseases 2015 Nov 30;9(11):e0004158.
Conceived and designed the experiments: ME, FP, BCdJ.
Performed the experiments: ME, SB, JLP
Analyzed the data: ME, KV, CJM, SB.
Contributed reagents/materials/analysis tools: JA, MJ, MS.
Wrote the paper: ME, KV, CJM, TS, TPS, BCdJ.
Page | 144
6.1 Abstract
Background: Increased availability of Next Generation Sequencing (NGS)
techniques allows, for the first time, to distinguish relapses from reinfections
in patients with multiple Buruli ulcer (BU) episodes.
Methodology: We compared the number and location of single nucleotide
polymorphisms (SNPs) identified by genomic screening between four pairs of
Mycobacterium ulcerans isolates collected at the time of first diagnosis and at
recurrence, derived from a collection of almost 5000 well characterized
clinical samples from one BU treatment center in Benin.
Principal Findings: The findings suggest that after surgical treatment - without
antibiotics - the second episodes were due to relapse rather than reinfection.
Since specific antibiotics were introduced for the treatment of BU, the one
patient with a culture available from both disease episodes had M. ulcerans
isolates with a genomic distance of 20 SNPs, suggesting the patient was most
likely reinfected rather than having a relapse.
Conclusions: To our knowledge, this study is the first to study recurrences in
M. ulcerans using NGS, and to identify exogenous reinfection as causing a
recurrence of BU. The occurrence of reinfection highlights the contribution of
ongoing exposure to M. ulcerans to disease recurrence, and has implications
for vaccine development.
6.2 Author summary
We compared the whole genomes of four pairs of Mycobacterium ulcerans
isolates collected at the time of first diagnosis and at recurrence, derived from
a collection of almost 5000 well characterized clinical samples from one BU
treatment center in Benin. Our findings suggest that after surgical treatment -
without antibiotics - the second episodes were due to relapse rather than
reinfection. Since specific antibiotics were introduced for the treatment of BU,
the one patient with a culture available from both disease episodes had M.
ulcerans isolates with a larger genomic distance, suggesting that the patient
was most likely reinfected rather than having a relapse. To our knowledge,
this study is the first to assess recurrences in M. ulcerans using whole
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genomes, and to identify exogenous reinfection as causing a recurrence of BU.
The occurrence of reinfection highlights the contribution of ongoing exposure
to M. ulcerans to disease recurrence, and has implications for vaccine
development.
6.3 Introduction
Buruli ulcer (BU) is a neglected necrotizing skin and bone disease caused by
the enigmatic pathogen Mycobacterium ulcerans that occurs in riverine
regions of West and Central Africa.
The clonal nature of M. ulcerans has complicated molecular analyses of the
epidemiology of the pathogen, as genotyping methods with sufficient
resolution have been lacking [93]. Using insertion sequence element single
nucleotide polymorphism (ISE-SNP) typing, a technique in which two relatively
small regions (1,431 and 1,871 bp) of the M. ulcerans genome are screened
for polymorphisms, strains could be distinguished to the level of the river
basin in West Africa [135]. However, such molecular genotyping techniques
lack sufficient resolution to distinguish relapse from reinfection with an
unrelated exogenous strain of M. ulcerans among BU recurrences. The World
Health Organization (WHO) defines a relapse as a recurrence of BU within one
year after termination of antibiotic treatment [229]. A recurrence that
appears after that period is consequently considered a reinfection. The
contribution of relapses versus reinfections to BU recurrences and their
biological basis are to date unknown.
Until the routine use of antibiotics (rifampicin and streptomycin) was
advocated by the WHO in 2004, surgery was the mainstay of BU therapy. After
surgical treatment only, a recurrence rate of 6% was reported in Benin [107,
230]. Higher recurrence rates were reported in Ghana (16%-35%) [231, 232],
Ivory Coast (17%) [233], Uganda (20%) [234], and Australia (32%) [235]. When
specific antibiotics are used very few, if any, recurrences are observed [11,
236, 237].
The introduction of Next Generation Sequencing (NGS) now allows for the
first time to distinguish relapses from reinfections in patients with multiple BU
episodes. Similar studies have been conducted for Mycobacterium
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tuberculosis [238, 239] and other monomorphic bacterial infections, such as
Clostridium difficile [240]. In the present study we compared the number and
location of SNPs identified by genomic screening between four pairs of M.
ulcerans isolates collected at the time of first diagnosis and at recurrence,
derived from a collection of almost 5000 well characterized clinical samples
from one BU treatment center in Zagnanado, Benin, between 1989 and 2010.
6.4 Methods
Study design and participants
We defined an episode as a clinical suspicion of BU and a recurrence as the
presence of two episodes separated by at least six months. Four such patients
were identified for this study.
We compared the number of SNP differences separating the paired isolates
and a random selection of 36 isolates from 36 patients living in the same
geographical area and diagnosed with BU in the same time frame (1998-2008)
as the patients with multiple episodes. We also compared genomic
relatedness between six patients with two M. ulcerans cultures isolated from
the same disease episode. This genetic background helped to avoid
misclassifying any second episodes with similar M. ulcerans strains prevalent
in the patient’s environment as relapses rather than reinfections. As such a
total of 58 M. ulcerans isolates obtained from 46 patients was included in this
study (Figure 6.1).
Figure 6.1: Flow chart outlining the M. ulcerans isolates included in this analysis.
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Genome sequencing and analysis
DNA was obtained by harvesting the growth of three Löwenstein-Jensen
slants followed by heat inactivation, mechanical disruption, enzymatic
digestion and DNA purification on a Maxwell 16 automated platform, a
technique modified from Käser et al. [241].
Whole genome sequencing of the isolates was performed using an Illumina
HiSeq 2000 DNA sequencer and an Illumina MiSeq DNA sequencer with
Nextera XT or TruSeq (Illumina Inc., San Diego, CA, USA) library preparation
and 2x36bp 2x100bp 2x150bp 2x250bp sequencing paired-end chemistry.
Sequencing statistics are provided in Table 6.S1.
The sequencing data analysis was done using the Nesoni software [159].
Firstly, reads were filtered to remove those containing ambiguous base calls,
any reads <50 nucleotides in length, and reads containing only
homopolymers. All reads were furthermore trimmed removing residual
ligated Nextera of TruSeq adaptors and low quality bases (<Q10) at the 3' end.
Average read lengths after clipping are summarized for all isolates in Table
6.S1. Bowtie2 v2.1.0 [141] was used with default parameters to map clipped
sequence reads sets to the Ghanaian M. ulcerans Agy99 reference genome
(Genbank accession number: CP000325). Due to the unreliability of read
mapping in repetitive regions, all ISE elements (IS2404 and IS2606) were hard
masked in this reference genome. Average read depths after mapping to M.
ulcerans Agy99 are summarized for all isolates in Table 6.S1.
We compared the number and location of SNPs between isolates collected at
baseline and at recurrence. At each of the loci called as a variant in any read
set, Nesoni was used to generate a multi-way summary of consensus allele
calls at the corresponding locus in all other read sets of the investigated panel.
By concatenating all these loci a multiple SNP sequence alignment was
generated containing all 282 variant loci across the Agy99 reference
chromosome sequence. A maximum likelihood (ML) phylogenetic tree was
constructed from this alignment using RAxML v8.0.19 [242] under a GTR
model of evolution (no rate heterogeneity) and with an ascertainment bias
likelihood correction for SNP data. The resulting tree was visualized in Figtree
v1.4.0 [102] with nodes of interest highlighted. A haplotype network was
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derived using the median joining algorithm [103] as implemented within
SplitsTree v.4.13.1 [142] with default settings. This network was subsequently
visualized with Hapstar v.0.7 [243].
The open source geographic information system Quantum GIS (QGIS v1.8.0)
[244] was used to generate the illustration of the geographical distribution of
all included M. ulcerans genomes in Figure 6.2. The geographical locations of
the residences of BU patients at the time of their first consultation are shown.
The river layer (Ouémé river and its tributaries) was digitized from declassified
Soviet military topographic maps b31-03, b31-09, and b31-15 (scale 1:200k).
The administrative borders of African countries were rendered from the
Global Administrative Unit Layers data set of FAO [245].
Ethics
This retrospective study on stored isolates was approved by the Institutional
Review Board of the Institute of Tropical Medicine.
Accession Numbers
Read data for the study isolates have been deposited in the NCBI Sequence
Read Archive (SRA) under accession n° PRJNA296792.
6.5 Results
Among the 4951 clinically BU suspected patients who consulted the BU
treatment centre of Gbemotin in Zagnanado in southern Benin between 1989
and 2010, we identified 100 who presented with multiple BU episodes (Figure
6.3). A majority of 93 patients had two disease episodes while 7 had three
episodes. Twenty recurrence patients received streptomycin and/or
rifampicin during their first BU episode. The distribution of patients that had
received (partially) effective antibiotics is shown in the Figure 6.S1. Only for
seven of the 100 recurrence patients were we able to successfully culture
isolates from each of two or three disease episodes, owing to the limited
sensitivity of culture for isolation of M. ulcerans from skin biopsies [136].
These mycobacterial isolates were stored at ≤70°C in Dubos broth enriched
with growth supplement and glycerol. However, paired cultures were found
to be viable for only four of these seven patients. The first two patients of
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these four patients each had three paired isolates while the other two
patients each had two (Table 1).
Figure 6.3: Flow chart outlining the patients contributing isolates to this analysis. SM:
streptomycin; RMP: rifampicin.
Table 6.1: Details on the four patients with recurrent BU. PCN: penicillin; GM: gentamycin;
CXA: cloxacillin, SM: streptomycin, RMP: rifampicin.
ID Patient A Patient B Patient C Patient D
Gender M F M M
Age at first consultation 12y 9y 18y 10y
Time between two episodes 9.5 months 7 months 22,5 months 9 months
Episode 1 Location right lower limb left lower limb left upper limb left back/flank
Date of presentation 7-Apr-1999 10-Jul-2000 27-May-2002 1-Mar-2004
Treatment PCN GM CXA GM CXA GM CXA SM RMP
surgery surgery surgery surgery
Cultures stored 1 1 1 1
ITM992421 ITM001249 ITM021902 ITM041698
Episode 2 Location right lower limb left lower limb left lower limb right buttock
Cultures stored 2 2 1 1
ITM000562 ITM010548 ITM041716 ITM051516
ITM000563 ITM010623
SNP difference 1 0 0 20
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We assessed the paired M. ulcerans isolates of four patients (letter-coded
from A to D) with two BU episodes each (Table 6.1). Each of these 4 patients
presented for the first time at the BU treatment centre of Gbemotin between
1999 and 2004. Patients A & B had lesions at the same location during both
episodes, while patients C & D had lesions at another body site. The time
between the diagnoses of both episodes ranged from seven months to almost
two years. All patients underwent surgery, while patient D also received
specific antibiotics, which were introduced for the routine management of BU
in 2004 [246]. Comparative genomic analysis identified two patients (B & C)
with no detectable genetic differences (0 SNPs) between isolates originating
from two disease episodes. Patient A had one SNP difference between his first
episode isolate and both second episode isolates although at different
positions, suggesting that micro-evolution took place. For patient D however
the isolates of both disease episodes were differentiated from each other by
no less than 20 SNPs, which were found distributed throughout the genome.
This is a genetic distance similar to that observed between different isolates
from the same geographic region as illustrated in the haplotype network
(Figure 6.2B) and the phylogenetic tree (red nodes, Figure 6.S2). The
difference in SNPs between control patients varied from 0 to 53 SNPs. To
exclude cross-contamination of the M. ulcerans culture obtained from the
second episode of patient D, the genome obtained from a strongly positive
biopsy that was treated on the same day as the biopsy of patient D was
sequenced as well and found to differ by 21 SNPs.
There were 2 clusters of 5 and 4 patients having identical M. ulcerans
genomes, who lived in an area of respectively 70 km² (4.7-27 km between
villages) and 14 km² (3-18 km between villages). Four patients with multiple
M. ulcerans isolates from the same BU episode had identical paired genomes
while one such patient differed in 1 SNP and another one in 6 SNPs between
the paired genomes with respectively 8 and 7 days between the times of
sampling of the biopsies from which the M. ulcerans cultures were isolated
(Figure 6.2B).
Among the total of 24 substitutions that were identified in patient A and
patient D, four occurred in intergenic regions, while 20 were found in coding
sequences resulting in 3 synonymous changes (i.e. ’silent’ changes) and 17
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non-synonymous changes (i.e. resulting in a change in amino acid), in genes
encoding proteins with various functions (Table 6.S2).
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Figure 6.2: (A): Geographical origin of the 58 isolates included in the study. Patients of special
interest are colored accordingly. The size of the dots is proportional to the number of patients
originating from a specific village varying from 1 to 3. The Ouémé river and its tributaries, and
the BU treatment center of Zagnanado are shown as well. (B): Median-Joining network
showing patterns of descent among the 58 studied M. ulcerans isolates. Each circle represents
a unique genotype, and the size of the circle is proportional to the number of individuals
sharing that type. Every edge represents a single mutational step (or SNP) separating the
sampled and hypothetical genotypes. Genotypes obtained from patients of special interest
are colored accordingly.
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6.6 Discussion
This study is the first to study recurrences in M. ulcerans using NGS, and to
identify exogenous reinfection as causing a recurrence of BU. In relapses,
paired isolates are genetically more related to each other than to isolates
from other patients living in the same region and infected during the same
time-period. In reinfections on the other hand, paired isolates are potentially
not more related to each other than to isolates from other patients living in
the same region and infected during the same time-period.
Our results suggest that the second BU episode of patients A, B and C was
most likely due to relapses. The second BU episode of the 4th patient was
probably due to a reinfection. This patient is also the only one who had
received specific antibiotics during his first BU episode. We can however not
be entirely certain that patients A, B and C were not infected a second time by
an identical M. ulcerans strain, as we identified other genetically identical
clusters among patients living in the same area and time period. As the mode
of transmission of M. ulcerans remains enigmatic, detailed investigation of
these genetic clusters may provide leads to a common point source of
exposure.
To our knowledge, this study is the first using NGS to assess recurrences in M.
ulcerans, which is important to understand BU epidemiology. In BU, when
disease re-occurs within one year after the end of treatment it is assumed to
be a relapse which is considered the primary endpoint in several studies [11,
236, 237] and in patient management. This study for the first time indicates
that exogenous reinfection plays a role in recurrence of BU. However, the
restricted phylogeny presented here based on four patients should be
interpreted with some circumspection because of the limited sample size,
which is due to the overall low recurrence rate at this treatment center,
combined with the low sensitivity of in vitro culture of M. ulcerans.
In other mycobacterial infections, most notably infection with M. tuberculosis,
the number of different SNPs between relapse and reinfection pairs can be
large, with a clear distinction between pairs with a small difference (≤6 SNPs),
classified as probably relapses, and those with a large difference (≥1306
SNPs), deemed to be reinfections [238]. Epidemiologically linked M.
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tuberculosis populations have been reported with a mean SNP difference of
3.4 demonstrating high genomic stability [247]. Walker and colleagues [248]
reported that in patients with an epidemiological link the divergence between
their M. tuberculosis genomes generally does not exceed 14 SNPs, with most
patients having fewer than 5 SNP differences during one disease episode.
During recurrences of Clostridium difficile infection, paired isolates ≤2 SNPs
apart were considered relapses while paired isolates >10 SNPs apart were
considered reinfections [240]. In the present study we detected only one
genomic difference between the relapse pairs, reaffirming the high genomic
stability previously reported for M. ulcerans [249].
Apparent reinfections could theoretically result from differential sampling of
an initially mixed infection. Patient D could possibly have had a simultaneous
infection by two M. ulcerans strains although the results from the 6 patients
with multiple isolates from one BU episode suggest that different M. ulcerans
strains causing a single disease episode is, at best, an infrequent occurrence.
The same was observed among four Cameroonian BU patients with multiple
isolates from one BU episode [250]. This suggests mixed infection would have
been an unlikely explanation for the genetic differences between the paired
isolates of patient D.
The occurrence of reinfection highlights the contribution of ongoing exposure
to M. ulcerans to disease recurrence. Since the delay between relapse (7, 9.5,
and 22.5 months) and reinfection (9 months) episodes overlap, the use of NGS
on cultured isolates is required to distinguish these two scenarios. BU
recurrences within a period of one year after antibiotic treatment that are
considered relapses by WHO [246] may therefore also be reinfections.
Since BU transmission is probably not human-to-human and the mode of
transmission from the environment is not yet clarified, epidemiological links in
support of transmission routes are speculative at best. However, we expect to
be able to determine a SNP-threshold which can be interpreted as an
epidemiological link consistent with a common source of infection in an
ongoing study with a greater number of M. ulcerans genomes from the
Ouémé river valley. The definition of transmission clusters could help to
unravel the enigmatic transmission routes of M. ulcerans.
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NGS of paired M. ulcerans strains collected from patients with multiple
episodes of BU has sufficient resolution to distinguish relapse from
reinfection. Our results on a small number of patients suggest that after
surgical treatment without antibiotics the second episodes were due to
relapse rather than reinfection. Since specific antibiotics were introduced for
the treatment of BU, the only patient with a culture available from both
disease episodes had M. ulcerans isolates with a greater genetic distance,
suggesting this patient was most likely reinfected.
6.7 Supporting Information
Table 6.S1: Sequencing statistics of the study isolates.
� Object too large to print. Available online (http://tinyurl.com/zpwrfdh).
Table 6.S2: Description of substitutions in patients A and D.
� Object too large to print. Available online (http://tinyurl.com/zpwrfdh).
Figure 6.S1: Proportion of clinical suspects that had either not received any specific antibiotics
or (partially) effective antibiotics (rifampicin, streptomycin, amikacin or clarithromycin) during
their first disease episode stratified by delay between episodes (6 months: cutoff in this study;
12 months: WHO cutoff).
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Figure 6.S2: Unrooted phylogenetic SNP tree: RAxML was used to build a maximum likelihood
tree from the genomic SNP data with 282 variable positions among 58 genomes. Nodes of
interest are colored according to subject. The scale indicates expected number of
substitutions per site.
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Chapter 7
General Discussion
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Deciphering the structure of pathogenic bacterial populations is instrumental
for understanding the evolutionary history, spread, and epidemiology of
bacterial infectious diseases. Additionally, a better understanding of disease
transmission and the disease dynamics can have a direct impact on the
development of effective control strategies against the spread of the disease.
This is why, in this PhD thesis, we have used Bayesian phylogenetics and
phylogeography as key methodologies to understand M. ulcerans evolution
and spread using genomic sequence data. Major advances in these fields were
made over the last decade through the development of new statistical models
and rapid advances in low-cost next-generation sequencing technologies.
Whereas 15 years ago it would have taken hundreds of individuals months of
work and millions of Euro to obtain a single bacterial genome sequence,
today, the same sequence can be obtained by a single individual in a couple of
weeks for a few hundred Euro. This made it possible in this PhD thesis, to
perform the first large scale comparative genomic studies on African M.
ulcerans.
The genetic diversity of African M. ulcerans proved to be very restricted
because of the pathogen’s slow genome-wide substitution rate coupled with
its relatively recent origin. As no evidence for recombination was identified
using various comprehensive genome-wide analyses, African M. ulcerans was
found to be evolving entirely through clonal expansion. As a result, the clonal
population structure of African M. ulcerans evolves exclusively by the
accumulation of vertically inherited mutations. Furthermore, the virulence
plasmid pMUM001 was found to be entirely co-evolving with the bacterial
chromosome.
Throughout the work presented in this thesis we repeatedly observed how
genotypes of M. ulcerans show strong spatial segregation at higher
geographical scales, confirming and extending previous data showing
geographical subdivisions [19, 112, 135, 154, 155]. These repeated findings
indicate that when M. ulcerans is introduced in an area it remains localized
and isolated for a sufficient amount of time to allow new mutations to
accumulate in the mycobacterial population of that area. As a direct result a
local genotype associated with the region evolves. This clustering of cases
within endemic foci reflects a common source of infection within foci. We
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observed that clonal complexes associated with particular disease foci usually
have been pervasive in these foci for a considerable amount of time; in the
case of the Songololo Territory hotspot around 150 years. This indicates that
an M. ulcerans outbreak spreads relatively slowly, which consequentially also
means that M. ulcerans reservoirs and/or (potential) vectors should also
remain relatively localized and isolated for extended amounts of time within a
disease focus.
On the other hand, we also observed that M. ulcerans genotype clustering
breaks down at lower geographical scale as, at the village level, genotypes
started to intermingle both in Ghana and DRC. Furthermore, in both studies,
various BU patients were identified who had been infected by identical M.
ulcerans genotypes but lived in geographically separated villages and
developed BU in different years. In the Ghanaian study, no obvious
epidemiological links (e.g. travel histories) were revealed in interviews with
patients who had been infected with identical genotypes. We believe this
breakup of the focal distribution of genotypes can be explained by M.
ulcerans’ low substitution rate: insufficient time has elapsed to allow for the
accumulation of discriminatory mutations in the genomes. This complicates
the elucidation of chains of transmission of BU in both DRC and Ghana.
Differential genome reduction analysis and SNP-based exploration of the
genetic population structure revealed the existence of two specific lineages
within the African continent. Phylogeographic analysis indicated that
dominant lineage Mu_A1 has been endemic in the African continent for
hundreds of years, during which time it established itself in different endemic
foci in West and Central Africa. Conversely, lineage Mu_A2 was found to be
less widespread and is rarer, and introduced into Africa much more recently.
We used temporal associations and studied the past demographic history of
M. ulcerans in a BU endemic region to implicate the role of humans as a major
reservoir in BU transmission. African lineage Mu_A2 was found to be
introduced during the period of Neo-imperialism. Additionally, detailed
phylogeographic analysis revealed that introduction of BU in three well
sampled disease foci coincided with the instigation of colonial rule. These
regions were already inhabited long before the arrival of the European
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colonizers, when inhabitants were constantly exposed to the lentic
environments in their search for natural resources. However, it was only after
the start of colonial rule that the M. ulcerans clones associated with the
present disease foci were introduced, probably through the arrival of
displaced BU infected human hosts. Furthermore, we identified a relationship
between the past mycobacterial population dynamics of M. ulcerans from the
Songololo Territory and the timing of health policy changes in managing the
BU epidemic in that region. The inverse association we identified over time
between BU control activities and the size of the human M. ulcerans reservoir,
while no proof of causality, nevertheless suggests an impact. Conversely, the
human reservoir appears to be important to sustain new infections. These
combined observations suggest that humans with actively infected, open,
discharging BU lesions can indirectly cause new infections.
In Africa, humans are currently the only known reservoir of M. ulcerans as M.
ulcerans infections in domesticated and wild animals have never been
reported in the African tropics [36, 65, 66]. In all likelihood, humans are not
directly infecting other humans as human-to-human transmission of M.
ulcerans is extremely rare [189]. Humans are nevertheless in all probability an
important reservoir as BU patients with active, openly discharging lesions
contaminate the environment during water contact activities by shedding
concentrated clumps of mycobacteria. Transmission can occur indirectly in the
same community water source, when the superficial skin surface of a naïve
individual is contaminated, and the bacilli present on the contaminated skin
are subsequently inoculated subcutaneously through some form of
penetrating (micro)trauma. This trauma may be as slight as a hypodermic
injection or as serious as a gunshot or a landmine wound [32]. Alternatively,
the inoculation of bacteria can occur through deep bites of transiently
contaminated insects [32], or even scorpions [32] and snakes [251].
Experiments with a Guinea pig infection model showed that passive
application of M. ulcerans onto a preexisting superficial abrasion was
insufficient as the mycobacteria needed to be inoculated at least
intradermally to establish infection [190]. Interestingly, M. marinum, the
closest relative of M. ulcerans, occasionally also causes human infection (fish
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tank granulomas) by inoculation through small skin lesions which are often
not remembered by the patient because of the long incubation period [252].
Our observations on the role of humans as potential maintenance reservoir to
sustain new BU infections suggests that interventions in a region aimed at
reducing the human BU burden will at the same time break the transmission
chains within that region. Active case-finding programs and the early
treatment of pre-ulcerative infections with specific antibiotics will decrease
the amounts of mycobacteria shed into the environment and may as a result
reduce disease transmission. Our findings are also supported by the observed
decline of BU incidence recorded in some areas which profited from both
improved BU surveillance and early treatment [197].
However, our findings do not allow us to definitely determine whether
humans are a spillover or a maintenance host species for M. ulcerans [253]. In
a maintenance host an infection can persist through intra-species
transmission alone while in a spillover host, infection will not persist
indefinitely unless there is occasional reinfection from another species.
Nevertheless, our analyses of the demographic history of M. ulcerans from
the Songololo Territory imply that even if other environmental reservoirs
exist, the number of BU notifications can decrease when only human cases
are treated.
In South Eastern Australia (Victoria), BU is considered a zoonosis transmitted
from possums to humans, potentially via a mosquito vector, although to date
definite proof is still lacking [61]. Along these lines, Fyfe et al. [61] drew an
interesting parallel between the epidemiology of BU in Victoria and
leptospirosis, the most commonly identified bacterial zoonosis worldwide
[254]. Similar to BU, leptospirosis is not known to spread directly between
humans. The disease is transmitted through contact with water, food, or soil
contaminated with urine from animals (e.g. rodents) infected with bacteria of
the genus Leptospira. These bacteria can enter the human body through skin
or mucous membranes, especially if the skin is cut or scratched.
Unlike temperate Victoria [24], in tropical North Eastern Australia
(Queensland) M. ulcerans infection has never been identified in wild,
domesticated, nor zoo animals. To date, there are no (potential) vectors
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and/or non-human reservoir species known in the region. The epidemiology
of BU in Queensland resembles BU in the African continent with a static
endemic area (Daintree River catchment) that has seen little change since the
1950’s [255].
In Australia, the role of humans in indirectly establishing new BU infections is
probably negligible, as Australians predominantly seek treatment in the early
pre-ulcerative onset of the disease. As a result, patients cannot contaminate
aquatic environments. Additionally, Australians are not dependent on
communal water sources for day-to-day activities like bathing, washing, and
cooking, which furthermore decreases indirect transmission. However, the
close genetic relationship between M. ulcerans from Africa and Australia
suggests that findings from studies of BU transmission in Australia may find
corollaries in African BU endemic settings. For example, more mammal
reservoirs might still remain to be identified that, in a scenario where all BU
cases have been treated in a region, could still spill over the infection to the
human population. The current absence of evidence of a non-human reservoir
of M. ulcerans in Africa is not the evidence of its absence.
A number of comprehensive environmental sampling surveys show that M.
ulcerans DNA can be present in African aquatic environments. However, in
these surveys, the frequency of positive sample occurrence is usually very
low, and furthermore, the bacillary concentration in PCR positive
environmental samples is nearly always insignificantly low [36, 43, 44].
Additionally, actual confirmation of the presence of living M. ulcerans in PCR
positive specimens is extremely difficult to achieve [46]. All of these
limitations combined clearly indicate how difficult it is to detect evidence of
viable M. ulcerans in aquatic environments. These difficulties have raised the
suspicion that M. ulcerans might not have a maintenance reservoir in African
aquatic environments [253]. Nevertheless, our findings in this PhD thesis on
the role of humans in indirectly establishing new BU infections indicate that
future detailed, controlled environmental sampling surveys in Africa should
move away from the commonly applied “fishing expedition” approach and
instead, have an increased focus on community water sources where BU
patients are known to have water contact activities like bathing or washing
[256]. By determining the relative concentrations of M. ulcerans DNA among
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the different sample types collected around these community water sources,
researches might find a gradient of M. ulcerans DNA that could lead to the
discovery of the first, non-human reservoir of M. ulcerans in Africa. This
approach in environmental surveys has been successfully applied in Australia
to establish possum species as reservoirs of M. ulcerans [257].
In this PhD thesis, we documented a case of a patient who suffered two
different BU episodes, separated by a period of nine months, caused by
reinfection with an unrelated exogenous strain. Paired isolates originating
from the two episodes of this particular patient were not more related to
each other than two isolates from other patients living in the same region and
infected during the same time period. The occurrence of this reinfection case
clearly highlighted the contribution of ongoing exposure to M. ulcerans. On
the other hand, in three other studied recurrence cases, the second BU
episode was determined to be most likely due to a relapse, as paired isolates
from the different episodes of the three patients were more related to each
other than to isolates from other patients living in the same region, and
infected during the same time-period. Interestingly, all three relapse patients
were treated during a time period (<2004) when surgery was the mainstay of
BU therapy and the use of specific antibiotics (rifampicin and streptomycin)
was not yet advocated by the WHO.
Until today, ISE-SNP genotyping offers the highest geographical resolution of
genotyping achieved, save for methods that rely on WGS. In this thesis, this
method allowed us to gain some fundamental insights into the population
structure and evolutionary history of M. ulcerans that were confirmed in
subsequent genomic work. Furthermore, ISE-SNP typing led to the discovery
of “rare” lineage Mu_A2. In the ISE-SNP technique, two relatively small (<1,9
kb) polymorphic regions of the M. ulcerans genome are screened for
polymorphisms. However, WGS queries nearly the entire genome for
polymorphisms, and consequently, its approach offers vastly greater
geographical genotyping resolution while minimizing phylogenetic discovery
bias [146] to an absolute minimum. As a result, in this thesis, WGS offered the
possibility to comprehensibly study the spread and the evolutionary history of
M. ulcerans at the lower geographical “village” level. In the face of the vastly
greater resolution offered by WGS, a future for ISE-SNP typing can still be
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envisaged in its use as an easy, reliable, fast, low tech means to allow
epidemiological tracking of M. ulcerans on a continental scale. The technique
could as such still be applied to trace cases reported after international travel
[9, 10]. An additional advantage of the technique is that, in the case an isolate
failed to grow in culture, the method can be applied directly on clinical
specimens (like swabs or a biopsies) with even very modest mycobacterial
loads. ISE-SNP typing relies solely on widely available conventional PCR
methodology and classical Sanger sequencing and hence it does not require
access to state-of-the-art sequencing equipment and know-how to perform
complex computational data analysis. However, in the future, it will become
ever harder to justify the consumable cost of ISE-SNP typing (+/-30€) against
that of WGS (+/- 70€) in the face of the great difference in resolution.
Some important caveats need to be considered for the various performed
phylogeographic analyses, in particular pertaining to sampling and weak
temporal signals:
A first major limitation we encountered in this PhD thesis was that we studied
a sample of the population from which we then tried to infer information
about the entire population through various statistical methods. The isolate
panels used in this thesis are, to our knowledge, the most comprehensive
ones studied so far in the literature. Even though all well-documented BU
endemic countries were well represented, we were unable to acquire isolates
from several African countries (Equatorial Guinea, Kenya, Liberia, Sierra
Leone, and South Sudan) that have reported (albeit a limited number of) BU
cases in the past [5]. As a result, the spatial coverage of disease isolates is
moderately restricted to specific geographical areas, which might have
restricted our interpretation of the historic spread of African M. ulcerans.
A second important caveat is related to the weak temporal signal. M. ulcerans
molecular sequences sampled through space and time were analyzed in this
thesis using statistical inference approaches. We used a simple linear
regression of the root-to-tip distances of a ML-tree as a function of sampling
times of a heterochronous isolate panel to give an exploratory insight into the
temporal signal present in our panels [219]. The difficulties we encountered
while identifying a linear regression indicated the temporal signal in our data
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was rather weak. Nonetheless, in such a situation, relaxed molecular clocks
can perform reasonably well, even though additional attention is required to
avoid that the increased impact of the tree prior in the analysis leads to
incorrect temporal estimates. As a result, we conducted extensive resampling
and randomization experiments, which confirmed that the tip dates
associated with isolates were informative.
Several alternative approaches to date bacterial phylogenies exist that might
be considered to confirm the genome-wide substitution rate identified here.
Ideally, molecular clocks are calibrated using temporal information from
ancient DNA sequences. In a particularly noteworthy Bayesian dating analysis,
Bos et al. [198] used radiocarbon dates obtained from 1000 year old Peruvian
human skeletons as tip-date calibrations for the M. tuberculosis genomes that
were reconstructed from the studied archeological remains. Alternatively,
bacterial phylogenies of M. tuberculosis have been calibrated with temporal
information from human mitochondrial genome data which to a certain
extent overlap in tree topology as tuberculosis is spread from human to
human [184]. As to date there are no known archaeological remains of BU
infected humans, and as BU is a non-communicable disease, yet other
approaches could be considered to confirm the identified genome-wide
substitution rate. Alternative “short-term” mutation rates can be estimated
from molecular epidemiological data or animal infection models. Walker et al.
[199] studied within-host longitudinal diversity between paired isolates of 30
tuberculosis patients to estimate the genome wide substitution rate of M.
tuberculosis using a coalescent-based ML approach. We envisaged doing a
similar analysis for the BU epidemic of southern Benin (CHAPTER 6) but we
identified insufficient patients that fit the inclusion criteria (three from a total
of 4951). Since the introduction of specific antibiotics in the management of
BU in Africa (2004), the amount of patient relapses decreased significantly,
likely impeding similar future investigations. As a result, the only remaining
approach that could still be applied is studying longitudinal diversity between
paired isolates obtained from an animal infection model. Ford et al. [200]
used WGS to compare the accumulation of mutations in M. tuberculosis
isolated from macaques with different tuberculosis disease states. As a result
authors were able to determine the amount of mutations that accumulated
Page | 166
per day of infection. We can envisage a similar experiment using pigs (Sus
scrofa) as experimental infection model for BU as the skin of pigs has striking
structural and physiological similarities with human skin and infection leads to
the development of lesions that closely resemble human BU lesions [258].
Page | 167
Chapter 8
Future Perspectives
Page | 168
The collective efforts of a handful of research teams around the world will
need to continue if we ever are to understand the exact means by which BU is
spreading. Various research lines should be further explored to potentially
shed some more light on this “mystery disease”:
In the future, attempts can be made to obtain M. ulcerans fingerprint patterns
directly from DNA extracts, especially from environmental specimens with
modest to high bacterial loads. Sequenced genomes from an endemic focus
can be used to identify a minimal set of specific “cluster-defining” SNPs. Real-
time PCR based SNP-assays can be developed for this minimal set. The SNP
profiles of environmental specimens can be matched with those of clinical
isolates recovered from patients within the same endemic area in order to
make inferences of potential transmission routes. The development of these
real-time PCR assays, when combined with the ability to rapidly type M.
ulcerans strains, could be essential for understanding the ecology of M.
ulcerans disease.
In this thesis we focused on M. ulcerans from the continent that is worst
affected by BU: Africa. In future work it would be interesting to study the
evolutionary history, phylogeography, and inter-continental spread of M.
ulcerans on a global scale by enriching our genome data further with M.
ulcerans strains from the Americas, Asia, and Oceania.
By virtue of their repetitive nature, IS elements are difficult to infer accurately
using the relatively short reads produced by second generation sequencing
technologies like Illumina sequencing. As a result, in our analyses, 7% of the
bacterial chromosome was ignored. Closing the genome of the new Congolese
reference sequence “SGL03” proved that state-of-the art sequence
technologies like Pacific Biosciences Single Molecule, Real-Time (SMRT)
sequencing can easily span these regions with single reads. This could
significantly increase the geographical genotyping resolution of future
comparative genomics studies even further.
Finally, the ability to interrogate full microbial genomes practically in real time
is changing the face of bacterial population analyses. As next-generation
sequencing technology permeates microbial population genetics further, not
only will new life be infused into long-standing questions like the ones
Page | 169
discussed here, but a more elaborate understanding of microbial diversity and
function will inevitably arise, and from this, entirely new questions will
emerge.
Page | 170
Page | 171
Acknowledgements
Acknowledgements are quite commonplace in academic communication and
virtually obligatory in dissertation writing as they lead to a sense of closure at
the end of a lengthy and demanding research period. The acknowledgements
section is as a result the means of publicly recognizing the role of mentors, the
sacrifices of loved ones, and sharing the relief of completing the PhD process.
However, I feel expressing thanks to others is an entirely personal affair that
shouldn’t necessarily be put in writing for all to read. I believe I gave personal
thanks to all who provided me with love, advice, and guidance throughout the
preparation of my dissertation, and to all who have helped to shape the
accompanying text. Thank you for all your assistance and contributions. I will
forever be indebted to you for being there for me.
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Page | 173
List of Publications
� Eddyani M, Vandelannoote K, Meehan CJ, Bhuju S, Porter JL, Aguiar J, Seemann T, Jarek
M, Singh M, Portaels F, Stinear TP, de Jong BC. A Genomic Approach to Resolving Relapse
versus Reinfection among Four Cases of Buruli Ulcer. PLoS Negl Trop Dis. 2015 Nov
30;9(11):e0004158.
� Ablordey AS, Vandelannoote K, Frimpong IA, Ahortor EK, Amissah NA, Eddyani M, Durnez
L, Portaels F, de Jong BC, Leirs H, Porter JL, Mangas KM, Lam MM, Buultjens A, Seemann
T, Tobias NJ, Stinear TP. Whole genome comparisons suggest random distribution of
Mycobacterium ulcerans genotypes in a Buruli ulcer endemic region of Ghana. PLoS Negl
Trop Dis. 2015 Mar 31;9(3):e0003681.
� Amissah NA, Gryseels S, Tobias NJ, Ravadgar B, Suzuki M, Vandelannoote K, Durnez L,
Leirs H, Stinear TP, Portaels F, Ablordey A, Eddyani M. Investigating the role of free-living
amoebae as a reservoir for Mycobacterium ulcerans. PLoS Negl Trop Dis. 2014 Sep
4;8(9):e3148.
� Barletta F, Vandelannoote K, Collantes J, Evans CA, Arévalo J, Rigouts L. Standardization
of a TaqMan-based real-time PCR for the detection of Mycobacterium tuberculosis-
complex in human sputum. Am J Trop Med Hyg. 2014 Oct;91(4):709-14.
� Vandelannoote K, Jordaens K, Bomans P, Leirs H, Durnez L, Affolabi D, Sopoh G, Aguiar J,
Phanzu DM, Kibadi K, Eyangoh S, Manou LB, Phillips RO, Adjei O, Ablordey A, Rigouts L,
Portaels F, Eddyani M, de Jong BC. Insertion sequence element single nucleotide
polymorphism typing provides insights into the population structure and evolution of
Mycobacterium ulcerans across Africa. Appl Environ Microbiol. 2014 Feb;80(3):1197-209.
� Bayonne Manou LS, Portaels F, Eddyani M, Book AU, Vandelannoote K, de Jong BC.
[Mycobacterium ulcerans disease (Buruli ulcer) in Gabon: 2005-2011]. Med Sante Trop.
2013 Oct-Dec;23(4):450-7. (Article in French)
� Gryseels S, Amissah D, Durnez L, Vandelannoote K, Leirs H, De Jonckheere J, Silva MT,
Portaels F, Ablordey A, Eddyani M. Amoebae as potential environmental hosts for
Mycobacterium ulcerans and other mycobacteria, but doubtful actors in Buruli ulcer
epidemiology. PLoS Negl Trop Dis. 2012;6(8):e1764.
� Affolabi D, Sanoussi N, Vandelannoote K, Odoun M, Faïhun F, Sopoh G, Anagonou S,
Portaels F, Eddyani M. Effects of decontamination, DNA extraction, and amplification
procedures on the molecular diagnosis of Mycobacterium ulcerans disease (Buruli ulcer).
J Clin Microbiol. 2012 Apr;50(4):1195-8.
� Vandelannoote K, Durnez L, Amissah D, Gryseels S, Dodoo A, Yeboah S, Addo P, Eddyani
M, Leirs H, Ablordey A, Portaels F. Application of real-time PCR in Ghana, a Buruli ulcer-
endemic country, confirms the presence of Mycobacterium ulcerans in the environment.
FEMS Microbiol Lett. 2010 Mar;304(2):191-4
Page | 174
Page | 175
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