RESEARCH ARTICLE
Epidemiology of Mycobacterium tuberculosis
lineages and strain clustering within urban
and peri-urban settings in Ethiopia
Hawult TayeID1,2*, Kassahun Alemu2, Adane Mihret1, Sosina AyalewID1, Elena Hailu1,
James L. N. Wood3, Ziv Shkedy2,4, Stefan Berg5, Abraham AseffaID1, The ETHICOBOTS
consortium¶
1 Armauer Hansen Research Institute, Addis Ababa, Ethiopia, 2 Department of Epidemiology and
Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar,
Gondar, Ethiopia, 3 Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge,
Cambridge, United Kingdom, 4 Biostatistics and Bioinformatics, University of Hasselt, Hasselt, Belgium,
5 Bacteriology Department, Animal and Plant Health Agency, New Haw, United Kingdom
¶ Members of the ETHICOBOTS consortium are listed under Acknowledgments.
* hawultachew@gmail.com
Abstract
Background
Previous work has shown differential predominance of certain Mycobacterium tuberculosis
(M. tb) lineages and sub-lineages among different human populations in diverse geographic
regions of Ethiopia. Nevertheless, how strain diversity is evolving under the ongoing rapid
socio-economic and environmental changes is poorly understood. The present study inves-
tigated factors associated with M. tb lineage predominance and rate of strain clustering
within urban and peri-urban settings in Ethiopia.
Methods
Pulmonary Tuberculosis (PTB) and Cervical tuberculous lymphadenitis (TBLN) patients
who visited selected health facilities were recruited in the years of 2016 and 2017. A total of
258 M. tb isolates identified from 163 sputa and 95 fine-needle aspirates (FNA) were char-
acterized by spoligotyping and compared with international M.tb spoligotyping patterns reg-
istered at the SITVIT2 databases. The molecular data were linked with clinical and
demographic data of the patients for further statistical analysis.
Results
From a total of 258 M. tb isolates, 84 distinct spoligotype patterns that included 58 known
Shared International Type (SIT) patterns and 26 new or orphan patterns were identified.
The majority of strains belonged to two major M. tb lineages, L3 (35.7%) and L4 (61.6%).
The observed high percentage of isolates with shared patterns (n = 200/258) suggested a
substantial rate of overall clustering (77.5%). After adjusting for the effect of geographical
variations, clustering rate was significantly lower among individuals co-infected with HIV
and other concomitant chronic disease. Compared to L4, the adjusted odds ratio and 95%
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OPEN ACCESS
Citation: Taye H, Alemu K, Mihret A, Ayalew S,
Hailu E, Wood JLN, et al. (2021) Epidemiology of
Mycobacterium tuberculosis lineages and strain
clustering within urban and peri-urban settings in
Ethiopia. PLoS ONE 16(7): e0253480. https://doi.
org/10.1371/journal.pone.0253480
Editor: Md Jamal Uddin, Shahjalal University of
Science and Technology (SUST), BANGLADESH
Received: December 9, 2020
Accepted: June 6, 2021
Published: July 12, 2021
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
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editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0253480
Copyright: © 2021 Taye et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
confidence interval (AOR; 95% CI) indicated that infections with L3 M. tb strains were more
likely to be associated with TBLN [3.47 (1.45, 8.29)] and TB-HIV co-infection [2.84 (1.61,
5.55)].
Conclusion
Despite the observed difference in strain diversity and geographical distribution of M. tb line-
ages, compared to earlier studies in Ethiopia, the overall rate of strain clustering suggests
higher transmission and warrant more detailed investigations into the molecular epidemiol-
ogy of TB and related factors.
Introduction
Tuberculosis (TB) is a chronic infectious disease caused by species of the Mycobacterium tuber-
culosis complex (MTBC). Except for Mycobacterium tuberculosis (M. tb), which is the primary
cause of human TB, other members of the MTBC are believed to have adapted to different ani-
mal hosts and therefore they may have reduced fitness to cause human infection [1, 2]. Beside
environmental and socio-economic factors, the biology and epidemiology of human TB has
likely been shaped by the historical interaction between MTBC members and its host [2, 3].
The genetic variation between MTBC species contributes to the ambiguities concerning dis-
ease presentation, frequency of transmission and clinical progress [2, 4]. This is particularly
true for M. tb, where the interaction of genotypic variation among different strains with
human genetic polymorphism play a prominent role in the epidemiology of TB diseases [4–7].
The overall epidemiology of MTBC species is influenced by the environment, with its fre-
quency and distribution being dependent on social, economic, and ecological causes [4, 8].
Although, there are no well-established classical factors that are known to be strongly associ-
ated with disease phenotype, immunological studies have suggested that some M. tb strains
and lineages are more virulent and/or more infectious than others [9]. It has been stated that
some strains that belong to the modern MTBC Lineages are more capable of inducing higher
inflammatory response than lineages of the same clade (Haarlem, high; Beijing, low) [10].
However, difference in pathogenicity and lineage specific rate of transmission are important
only when considered together with the host genotype and geographical location [11].
Although, it is still challenging to investigate the influence of bacterial and host genotype on
the development of different forms of TB in humans, disease phenotype seems to be associated
with a bacterial genotype [2, 6]. According to other published reports, L4 seemed more likely
to be associated with Pulmonary TB (PTB) while L2 and L3 were linked with extra-pulmonary
TB (EPTB) disease, such as TB meningitis and TB in cervical Lymph Nodes (TBLN) [12–15].
Another comparative study showed that strains of the East African Indian (L3) and Euro-
American (L4) lineages were negatively associated with extra thoracic disease as compared to
strains of the East Asian lineage (L2) [16]. These studies thereby suggest that species diversity
and their interaction with host biology affects the pathophysiology and natural course of TB
disease [2, 17]. For example, a study conducted in Tanzania has shown that chronic signs of
TB disease, such as weight loss, have been more associated with L4 strains than with strains of
the Indo-Oceanic (L1) lineage [18]. In addition to factors associated with human genetics such
as ethnicity, biological and clinical determinants of an individual, such as HIV and body mass
index, have shown significant difference on disease phenotype and rate of transmission across
major M. tb Lineages [16, 19–21].
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Funding: This work was funded by the
Biotechnology and Biologic Sciences Research
Council, the Department for International
Development, the Economic & Social Research
Council, the Medical Research Council, the Natural
Environment Research Council and the Defence
Science & Technology Laboratory, under the
Zoonoses and Emerging Livestock Systems (ZELS)
program, ref: BB/L018977/1. SB was also partly
funded by the Department for Environment, Food
& Rural Affairs, United Kingdom, ref: TBSE3294.
The Armauer Hansen Research Institute is
supported by core funds from Norad and Sida.
Competing interests: The authors have declared
that no competing interests exist.
Different alternative molecular identification methods have been used to estimate rates of
disease transmission, which is generally inferred by comparing genotypic clustering between
patient isolates from a given epidemiological setting [10, 22]. In other words, successful trans-
mission of particular genotypes has been reflected through an increase in the frequency and
consistency of strain domination over time in defined populations [16, 23]. However, despite
recently developed advanced molecular diagnostic tools, both the nature of genotype varia-
tions and the characteristics of the host immune response to certain types of M. tb strains are
largely unknown in many TB high burden settings [24, 25]. Particularly in countries like Ethi-
opia, where there is high prevalence and high transmission rate and a diversified population of
bacterial species [26–29], molecular identification of the agents can be an important compo-
nent of the knowledge base required to improve on previous achievements of the national TB
control program. Taking all this into account, the present study investigated factors associated
with M. tb lineage predominance and rate of strain clustering within the context of urban and
peri-urban settings in Ethiopia.
Materials and methods
Study design and setting
A multi-centre health facility based cross-sectional study was conducted in Ethiopia during
2016 and 2017. As part of the Ethiopia Control of Bovine Tuberculosis Strategies (ETHICO-
BOTS) project, four hospitals, two private clinics, and fourteen health centers located in urban
and peri-urban areas, were purposively selected from four different regions of Ethiopia. Addis
Ababa was the largest study site and constituted of Addis Ababa city and the surrounding spe-
cial zone of Oromiya region while the remaining three study sites were located in the regional
urban cities of Mekele in Tigray, Gondar in Amhara, and Hawassa in Southern Nations
Nationalities, and Peoples’ region.
Study population
Recruitment of participants at selected health facilities was carried out according to the
national guideline standard case definition criteria. All presumed TB cases were initially con-
sidered as potential source of the study population. Then those patients clinically diagnosed
with PTB or TBLN were asked for informed consent and enrolled consecutively. Recruitment
of PTB cases was done at all selected governmental health facilities. TBLN patients were
enrolled from all four study sites; however they were only recruited from the Pathology Units
of three governmental hospitals and two private clinics because of lack of diagnostic facilities
and skilled professionals for fine-needle aspirate (FNA) cytology examination at governmental
health centers. Included cases from both groups were those eligible for first-line Anti-TB treat-
ment. Known MDR (multi drug resistant) TB cases and EPTB patients other than those with
TBLN were excluded in this study.
Data collection
Clinical and demographic information was collected from recruited TB cases using a pre-
tested structured questionnaire. Following the routine care service, consented PTB and TBLN
participants were requested to provide spot sputum and FNA samples, respectively. Care pro-
viders (nurses) working at directly observed therapy (DOT) centres collected sputum speci-
mens using sterile containers. FNA specimens were collected from the selected hospitals and
private clinics by experienced pathologists who performed FNA cytology examination as part
of their routine diagnostic service. According to the standard procedure, FNA collection was
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performed using a 21-gauge needle attached to a 10 ml syringe and specimens were collected
into cryo-tubes with sterile phosphate buffer saline (PBS). Samples were kept at -20˚C at
remote study sites until transported on ice boxes to the Armauer Hansen Research Institute
(AHRI) TB laboratory where the clinical samples were stored at -80˚C until processed for
mycobacterial culture. Clinical sample handling and laboratory procedures were performed
according to a previously published protocol [27].
Mycobacterial culturing
Samples collected in the study were processed and cultured for mycobacteria using standard
procedures established at the AHRI TB laboratory [27, 30]. Specimen samples were inoculated
on Lo¨wenstein-Jensen (LJ) medium slants supplemented with either glycerol or pyruvate and
incubated at 37˚C. The slopes were examined weekly for up to eight weeks for any visible
growth. Bacterial colonies identified as Acid-Fast Bacilli by ZN staining [27] were saved as fro-
zen stocks in 20% glycerol as well as heat-inactivated in 500μl distilled H2O at 80˚C for 60 min;
the latter samples were used for subsequent molecular identification.
Molecular identification techniques
All isolates were screened by Large Sequence Polymorphism (LSP) typing using conventional
PCR for examination of Region of Difference 9 (RD9) according to protocols by Berg et al.
(2009) [31]. Spoligotyping was performed according to Kamerbeek et al. (1997) [32], using a
non-commercial biodyne-C-membrane produced by the Animal & Plant Health Agency
(United Kingdom).
Genotype analysis and comparison with global databases
Spoligotype patterns were converted into binary and octal formats and compared with previ-
ously reported strains in the international SITVIT2 database [8] hosted by Institute Pasteur de
la Guadeloupe. Here, spoligotypes shared by more than one strain were designated as shared
types and were assigned a shared international type (SIT) number according to the SITVIT2
database, while patterns that were not recognized in the latest online version of the database
were labelled as “New” if the pattern was identified for more than one strain and “Orphan” if
the pattern was unique to only one strain. Further lineage classification for corresponding
nomenclature was done using the ‘Run TB-Lineage’ online tool from linked databases (http://
www.miru-vntrplus.org/MIRU/index.faces and http://tbinsight.cs.rpi.edu/run_tb_lineage.
html). Here, major lineages were predicted using a conformal Bayesian network (CBN) analy-
sis while knowledge based Bayesian network (KBBN) analysis was used to predict the corre-
sponding sub-lineages.
Data management and statistical analysis
All genotype outputs from the computer assisted analyses were imported to SPSS and merged
with clinical and demographic data. The final clean dataset was exported to STATA and R-
software to perform further statistical analysis. Two of the main outcome variables, clustering
rate and M. tb lineages, were categorized as binomial scale of measurement. In the first cate-
gory, “clustered” referred to two or more isolates sharing identical spoligotyping patterns
while isolates that did not have shared patterns was defined as “unique”. Here, three different
logistic regression analysis methods were performed to identify and compare factors associated
with strain clustering. The first Bivariable analysis was performed to estimate a crude (unad-
justed) odd ratio for each independent categorical variable while the second multivariable
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logistic regression analysis was used to estimate adjusted odd ratio (AOR with 95% CI) that
better reflect the likelihood of included variable associated with rate of strain clustering. The
third model (hierarchical logistic regression) was preferred to adjust for the effect of regional
variations, the first level factor that often attributed with strain clustering, where host-related
clinical factors and spoligotype-based M. tb lineage classification were considered as second
level factors. Variables included in the second model were reconsidered and used to compare
the corresponding adjusted estimates (AOR with 95% CI) generated from the third (Multi-
level) model which was done using STATA software with the recommended (melogit) com-
mand. The multivariable logistic regression was used to determine the clinical characteristics
or disease phenotypes associated with dominant M. tb lineage. In both cases, R-package Soft-
ware commands were used to perform bivariable and multivariable logistic regression. Before
running the multivariable logistic regression analysis, stepwise backward elimination tech-
nique was applied to select independent variables. Initially, all clinically relevant factor vari-
ables were included in the full model. Then using the specific statistical command (Step)
under R-studio, the software program automatically generated all possible alternative models
having lists of dependent and independent variables. Finally, according to the Likelihood
Ratio-test and to minimize the effect of confounding variables, a relatively better fitted model
with potential explanatory variables that has the lowest akaki information criteria (AIC) was
selected. Independent relationship of variables was decided based on different cut-off point for
statistical significance level (α: < 0.05; < 0.01 and< 0.001) and interpretation of key findings
was reported using the adjusted estimates (AOR with 95% CI).
Ethical considerations
This study was part of the ETHICOBOTS project, which obtained ethical clearance from the
Federal Ministry of Science and Technology (Ref. No: 301/001/2015), the AHRI/ALERT Ethics
Review Committee (Project Reg. No: PO46/14) and from University of Gondar Institutional
Review Board (Review number: O/V/P/RCS/04/45/2016). Support letters were obtained from
Regional State Health Bureaus and health facilities. Enrollment of study participants was done
after written informed consent was secured and signed agreements were received from all par-
ticipating health facilities. Detailed information about the risks and benefits of the study as
well as confidentiality of the research data was a prerequisite for study participation.
Results
Characteristics of the study population
This study examined a total of 258 TB patients (163 PTB and 95 TBLN cases) of which 145
(56.2%) were male and 113 (43.8%) were female, with a mean age of 32.2 (±12.9) years. Most
of these TB cases were from Gondar, 111/258 (43.0%), and Mekele, 61/258 (23.6%), in north-
ern Ethiopia while the remaining patients, 44/258 (17.1%) and 42/258 (16.3%), were from
Addis Ababa and Hawassa in central and southern Ethiopia, respectively. Farmers (80/258,
31.0%) and students (40/258, 15.5%) were the two most common occupations in the study
population. With regard to the medical history of the participants, 20/258 (7.8%) were co-
infected with HIV and 96/258 (37.2%) had at least one additional chronic concomitant disease
(Table 1).
Genetic diversity of Mycobacterium tuberculosis lineages
All 258 isolates provided in the S1 Table were genotyped by LSP as M. tb while being intact for
RD9. When the isolates were spoligotyped 84 different patterns were identified, of which 58
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SIT patterns were already recognized in the SITVIT2 database (accounting for 231/258
(89.5%) of the isolates). Among these patterns, 32 M. tb isolates were singletons while 25 desig-
nated shared patterns, each with 2 to 40 isolates, accounted for 85.7% (198/231) of all isolates
with identified SIT patterns. The remaining twenty five unique orphan patterns and two iso-
lates with a new shared spoligotype pattern (Table 2), representing 27 (10.5%) of the total iso-
lates, were not yet recognized by the SITVIT2 database. As presented in Table 3, over half of
the isolates 145/258 (56.2%) were represented by five of the dominant SIT patterns, including
SIT25 (n = 40), SIT149 (n = 36), SIT53 (n = 32), SIT26 (n = 17), and SIT37 (n = 11).
According to the CBN analysis, 97.3% of the total 258 isolates belonged to two major line-
ages, EA (61.6%) and EAI (35.7%). On the basis of SNP-based genome-wide phylogeny analy-
sis, these lineages are commonly known as L4 and L3, respectively [2]. The remaining 7/258
(2.7%) were represented by IO (L1) and AFRI (L7), each with three strains, and one with the
typical Beijing (L2) spoligotype pattern (Fig 1; S1 Table).
The alternative KBBN classification showed a predominance of the CAS (34.9%) sub-line-
age among strains defined as L3. T (15.9%), T3-ETH (15.1%) and Haarlem (10.9%) were the
most common sub-lineages of L4. There was a significant difference in geographical distribu-
tion between strain types; all LAM families of L4 (LAM, LAM3 and LAM5) were observed in
the northern part of the country (Gondar and Mekele). Similarly, the CAS families (L3), which
were highly dominant in the Gondar area, were rather rare around Hawassa. The Manu,
Table 1. Characteristics of the 258 study participants, 163 patients with pulmonary TB and 95 with cervical TB lymphadenitis, recruited at selected health facilities
located in urban and peri-urban areas of Ethiopia in the years 2016/17.
Patient characteristics PTB n (%) TBLN n (%) Total n (%) P-value of Chi-square test
Number of patients 163 (63.2%) 95 (37%) 258 (100%) -
Age group
< 35 years 105 (64.4) 61 (64.2) 166 (64.3) 0.298
� 35 years 58 (35.6) 34 (35.8) 92 (35.7)
Gender
Male 107 (65.6) 38 (40.0) 145 (56.2) 0.000
Female 56 (34.4) 57 (60.0) 113 (43.8)
Occupation
Farmer 46 (28.2) 34 (35.8) 80 (31.0) 0.087
Merchant 14 (8.6) 11 (11.6) 25 (9.7)
Employee 24 (14.7) 9 (9.5) 33 (12.8)
Student 24 (14.7) 16 (16.8) 40 (15.5)
House wife 20 (12.3) 17 (17.9) 37 (14.3)
Dairy worker 12 (7.4) 4 (4.2) 16 (6.2)
Others 23 (14.1) 4 (4.2) 27 (10.5)
Geographical location
Gondar 84 (51.5) 27 (28.4) 111 (43.0) 0.000
Hawassa 34 (20.9) 8 (8.4) 42 (16.3)
Mekele 40 (24.5) 21 (22.1) 61 (23.6)
Addis Ababa 5 (3.1) 39 (41.1) 44 (17.1)
HIV co-infection
No 145 (89) 93 (97.9) 238 (92.3) 0.010
Yes 18 (11) 2 (2.1) 20 (7.8)
Chronic concomitant disease
No 98 (60.1) 64 (67.4) 162 (62.8) 0.246
Yes 65 (39.9) 31 (32.6) 96 (37.2)
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Haarlem and T families (all of L4) accounted for the majority of strains identified in the
Hawassa region (Fig 2).
Factors associated with strain clustering and predominance
The overall clustering rate aggregated from 26 (25 SIT and one new) shared patterns was
77.5% (200/258). Our multivariable analysis (Table 4) showed that as compared to Gondar,
rate of clustering in Mekele and Hawassa was more than two and three fold higher, with
adjusted OR (95% CI) of 2.71 (1.16, 6.34) and 3.56 (1.09, 11.63), respectively. However, an
increased rate of M. tb transmission is generally inferred by comparing clustered genotyping
patterns of clinical isolates from a given epidemiological setting [10]. By contrast, cases with
Table 2. Descriptions of all orphan and new spoligotype patterns (n = 26) that were identified from 27 clinical samples collected from pulmonary TB and cervical
TB lymphadenitis patients recruited at selected health facilities in Ethiopia in the years of 2016/17.
No Spoligotype patterns of orphan or new strains Lineage classification based on # of isolates
Octal code Binary format (presence (black) or absence (white) of 43 spacers) KBBN CBN SNP-based prediction�
1 000001777020771 T1-RUS2 EA L4 1
2 037677560020771 H1 EA L4 1
3 101774000000000 ZERO EA L4 1
4 403000377760771 T1-RUS2 EA L4 1
5 477777757000771 H4-Ural-2 EA L4 1
6 503777740003171 CAS1-Delhi EAI L3 1
7 511777400003171 CAS EAI L3 1
8 555777437740171 T EA L4 1
9 603777700003771 CAS1-Delhi EAI L3 1
10 676777660760771 T EA L4 1
11 703737740003571 CAS1-Delhi EAI L3 1
12 703777700001171 CAS1-Delhi EAI L3 2
13 703777740001171 CAS1-Delhi EAI L3 1
14 703777740003171 CAS1-Delhi EAI L3 1
15 703777740003771 CAS1-Delhi EAI L3 1
16 703777747776771 Manu1 EA L4 1
17 711777740003171 CAS1-Delhi EAI L3 1
18 773777776000771 H3-Ural-1 EA L4 1
19 776737737760771 T3 EA L4 1
20 777000277760771 T3-ETH EA L4 1
21 777001777760771 T3-ETH EA L4 1
22 777737401760771 LAM5 EA L4 1
23 777737777760000 X2 EA L4 1
24 777777401760771 LAM EA L4 1
25 777777777420571 H3-Ural-1 EA L4 1
26 777777777600631 H3 EA L4 1
KBBN: knowledge based Bayesian network; CBN: conformal Bayesian network; SIT: shared international type; EA: Euro-American; EAI: East-African-Indian; IO:
Indio-Oceanic.
� Supported by SNP typing (Firdessa et al 2013)
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isolates of a unique pattern could be considered to have resulted from reactivation of latent
infection or were else presumably acquired outside of the study population [33]. Considering
that hierarchical logistic regression analysis was performed to minimize the observed hetero-
geneity due to geographical location. After controlling for the effect of regional variations
adjusted estimates generated from the final model showed that the rate of strain clustering was
inversely associated with TB-HIV co-infection and comorbidity with other chronic illnesses.
As shown in Table 4, TB-HIV co-infected individuals [0.16 (0.05, 0.47)] and those who had
any other concomitant chronic disease [0.46 (0.23, 0.91)] were less likely to have clustered
strains as compared to patients diagnosed with only TB disease.
A second multivariable analysis was performed in relation to the clinical characteristics of
the two most predominant lineages (L3 and L4). As shown in Table 5, in comparison to L4
strains of M. tuberculosis, the odds for TBLN cases infected with L3 was three and half fold
Table 3. Spoligotype descriptions of all registered SIT patterns with two or more isolates identified from 198 clinical samples collected from pulmonary TB and cer-
vical TB lymphadenitis patients recruited at selected health facilities in Ethiopia in the years of 2016/17.
Spoligotype patterns of shared SIT strains Lineage classification Shared isolates
SIT No Octal code Binary format (presence (black) or absence (white) of 43 spacers) KBBN CBN SNP-based Prediction�
4 000000007760771 T1-RUS2 EA L4 2 (0.8)
952 603777740003771 CAS1-Delhi EAI L3 3 (1.2)
1729 700000004177771 AFRI AFRI L7 2 (0.8)
21 703377400001771 CAS1-Kili EAI L3 5 (1.9)
2359 703677740003171 CAS1-Delhi EAI L3 4 (1.6)
2973 703701740003171 CAS1-Delhi EAI L3 2 (0.8)
1199 703701740003171 CAS1-Delhi EAI L3 2 (0.8)
25 703777740003171 CAS1-Delhi EAI L3 40 (15.5)
26 703777740003771 CAS1-Delhi EAI L3 17 (6.6)
1877 737377777760771 T EA L4 2 (0.8)
33 776177607760771 LAM3 EA L4 3 (1.2)
149 777000377760771 T3-ETH EA L4 36 (14.0)
504 777737737760771 T3 EA L4 2 (0.8)
726 777737747413771 EAI6-BGD1 IO L1 2 (0.8)
35 777737777420771 H3-Ural-1 EA L4 2 (0.8)
37 777737777760771 T3 EA L4 11 (4.3)
1688 777777403760771 LAM EA L4 2 (0.8)
41 777777404760771 Turkey EA L4 5 (1.9)
121 777777775720771 H3 EA L4 4 (1.6)
817 777777777420731 H3-Ural-1 EA L4 2 (0.8)
777 777777777420771 H3-Ural-1 EA L4 2 (0.8)
134 777777777720631 H3 EA L4 2 (0.8)
52 777777777760731 T2 EA L4 5 (1.9)
53 777777777760771 T EA L4 32 (12.4)
54 777777777763771 Manu2 EA L4 9 (3.5)
KBBN: knowledge based Bayesian network; CBN: conformal Bayesian network; SIT: shared international type; EA: Euro-American; EAI: East-African-Indian; IO:
Indio-Oceanic.
� Supported by SNP typing (Firdessa et al 2013)
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[3.47 (1.45, 8.29)] higher than PTB patients. Active TB disease due to L3 strains was signifi-
cantly associated with HIV-TB co-infection [2.84 (1.61, 5.55)], but less likely to be associated
with concomitant chronic disease [0.46 (0.25, 0.87)], as compared to L4.
Discussion
Despite the observed difference in strain diversity and distribution of M. tb lineages across
regions, high percentage of shared patterns suggested a substantial overall strain clustering
rate around urban and peri-urban settings in Ethiopia. Altogether, a predominance of known
SIT patterns resulted in an overall strain clustering rate of 77.5% in the current study, with a
range of 69–88% across the study regions (Table 4). That was significantly higher as compared
to earlier Ethiopian studies (2005–2018) reviewed by Mekonnen et al. (2019), with a pooled
clustering rate (95% CI) of 0.41 (0.32–0.50) [34]. Understandably, at national level, some pop-
ulation groups have likely contributed more to such TB incidence rate than other groups. Par-
ticularly, the risk of TB transmission around urban areas is known to be higher than among
sparsely populated societies and rural communities [24, 29]. Because of the simultaneously
Fig 1. Proportion of major Mycobacterium tuberculosis lineages circulating within peri-urban and urban areas in Ethiopia. ‘Others’ include L7 (AFRI), L2 (Beijing),
and L1 (IO).
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ongoing expansion of urbanization and emerging socio-economic conditions around urban
areas in Ethiopia (increasing population size and density e.g. through expanding slums, con-
gregation into condominiums, growing manufacturing and service sector), the pattern of TB
transmission among those living and working in the urban and peri-urban areas is postulated
to differ in strain diversity and clustering, compared to that of the general population [29], the
majority (85%) of which are rural communities. Despite previous achievements in reducing
national TB morbidity and mortality [35], summarized reports of data from the global burden
of TB diseases in the last two decades have shown a declined rate in reducing the prevalence
and mortality ratio in Ethiopia. Essentially, there has been a higher rate of new TB cases (inci-
dence) in the last few years than what was expected from the previous trend [35, 36].
Accordingly, a diverse range of strains of M. tb lineages, many previously not registered in
spoligotyping databases, continue to circulate and maintain a high rate of transmission of TB
in Ethiopia. Similarly, as would be expected, the observed diversified type of M. tb strain and
Fig 2. KBBN based classification of Mycobacterium tuberculosis sub-lineages circulating within peri-urban and urban areas in Ethiopia. H1, H3, H3-Ural-1 and
H4-Ural-2 were classified as ‘Haarlem’; ‘LAM’ include LAM3 and LAM5; Manu represent Manu1 and Manu2. ‘Others’ include the following types: T2, Turkey, T1-RUS2,
AFRI (Ethiopian), Beijing, EAI4-VNM, and EAI6-BGD1.
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lineage distribution in the current study closely matched with studies analyzed in the two most
recent TB reviews that showed specific lineage predominance across different geographical
locations in Ethiopia [29, 34]. This means, the same two major lineages, L4 and L3 (Fig 1),
were predominant [29, 30, 34], as were the five most common SIT patterns (Table 2) [14, 29,
37, 38]. As shown in Figs 1 and 2, the observed significant difference in proportions of strain
types across the four study sites, has also been noted from previous studies in Ethiopia [29,
34]. Those less prevalent M. tb lineages, which included the Ethiopian (L7), the Beijing (L2),
and the IO (L1) lineages, were identified from samples collected at sites located in the northern
regions (Gondar and Mekele). Strains of L7, which was first reported by Firdessa et al [14, 28,
37, 39] and that seem highly confined to Ethiopia, remain more prevalent in the north of the
country. The two SIT patterns (SIT1729 and SIT910) that we identified in this region are the
same as for those strains that were previously classified as L7 [8, 14].
Taking into account the observed geographical difference, the current study investigated
the contribution of bacterial genotype and host related factors associated with rate of strain
clustering. While comparing clustered genotyping patterns of the two most predominant M.
tb lineages, a relatively higher percentage of shared L3 patterns were identified as compared to
clustered patterns that belonged to L4. Despite limited discriminatory power of the spoligotyp-
ing method, an increased rate of M. tb transmission is generally inferred by comparing clus-
tered genotyping patterns of clinical isolates from a given epidemiological setting [10]. In
contrast, cases with isolates of a unique pattern could be considered to have resulted from
Table 4. Conventional and hierarchical (multi-level) logistic regression modeling methods were used to identify factors associated with strain clustering based on
spoligotyping.
Factor variables Proportion of cases n (%) Three logistic regression analyses
Bivariable Multivariable Hierarchical
Clustered Unique COR (95% CI) AOR (95% CI) AOR (95% CI)
Region
Gondar 77 (38.3) 34 (59.6) Ref Ref Level-I factor
Hawassa 37 (18.4) 5 (8.8) 3.17 (1.14, 8.79)� 3.56 (1.09, 11.63)�
Mekele 51 (25.4) 10 (17.5) 2.19 (0.99, 4.82) 2.71 (1.16, 6.34)�
Addis Ababa 36 (17.9) 8 (14.0) 1.93 (0.81, 4.59) 2.42 (0.84, 7.01)
Diagnosis
PTB 127 (63.2) 36 (63.2) Ref Ref Ref
TBLN 74 (36.8) 21 (36.8) 0.97 (0.53, 1.79) 0.52 (0.24, 1.15) 0.58 (0.27, 1.23)
HIV co-infection
No 191 (95.0) 47 (82.5) Ref Ref Ref
Yes 10 (5.0) 10 (17.5) 0.27 (0.11, 0.71)�� 0.16 (0.05, 0.50)�� 0.16 (0.05, 0.47)���
Co-morbidity of Chronic illness
No 134 (66.7) 28 (49.1) Ref Ref Ref
Yes 67 (33.3) 29 (50.9) 0.50 (0.27, 0.91)� 0.50 (0.25, 1.01) 0.46 (0.23, 0.91)�
Hemoptysis
No 167 (83.1) 42 (75.0) Ref Ref Ref
Yes 34 (16.9) 14 (25.0) 0.61 (0.30, 1.24) 0.50 (0.22, 1.16) 0.55 (0.24, 1.25)
TB lineage
L3 (EAI) 76 (37.8) 16 (28.1) Ref Ref Ref
L4 (EA) 121 (60.2) 38 (66.7) 0.69 (0.36, 1.32) 0.42 (0.20, 0.90)� 0.49 (0.23, 1.04)
Others 4 (2.0) 3 (5.3) 0.28 (0.06, 1.38) 0.25 (0.04, 1.48) 0.25 (0.04, 1.44)
EA, Euro-American; EAI, East Africa-India; The cut-off point for statistical significance (α) is represented by: < 0.05 = �; < 0.01 = ��; < 0.001 = ���
https://doi.org/10.1371/journal.pone.0253480.t004
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reactivation of latent infection or were else presumably acquired from outside of the study
population [2, 33, 40]. Indeed, diverse M. tb strains could be identified in the different regions
[2, 5, 8]. In spite of the fact that the molecular epidemiology of TB has shown remarkable dif-
ference across geographical locations, risk of transmission and TB disease progression is likely
to depend on the interactions of various factors related to strain type and host immunity [8].
Bacterial genetic difference has been shown to have an impact on the extent of TB transmis-
sion; thus strains from TB lineages referred to as ‘modern’ lineages (L2-L4) are assumed to be
more transmissible than other MTBC strains [2, 34]. It is interesting to note that after adjust-
ing for the effect of regional variations, the likelihood of clustering was significantly lower
among HIV co-infected patients and those who had any other concomitant chronic diseases.
A higher risk of primary exposure or an increased rate of TB transmission in endemic settings
has often been associated with the presence of more infectious PTB cases [41]. On the other
hand, poor host immunity has been linked with endogenous reactivation of latent infection
and could have greater contribution to the development of TBLN or disseminated TB [38].
However, as previously reported by others in several studies [14, 34, 37, 41], we also did not
Table 5. Results of logistic regression analysis exploring associations between clinical characteristics and active TB disease caused by L3 versus L4, the two most
dominant Mycobacterium tuberculosis lineages identified in the study.
Clinical characteristics Proportion of Cases: n (%) Bivariable analysis Multivariable analysis
Lineage 3 Lineage 4 COR (95% CI) P-value AOR (95% CI) P-value
Region
Addis Ababa 12 (13.0) 32 (20.1) Ref Ref
Gondar 54 (58.7) 53 (33.3) 2.77 (1.29, 5.95) 0.009 5.24 (2.03, 13.51) < 0.001
Hawassa 1 (1.1) 41 (25.8) 0.07 (0.01, 0.53) 0.010 0.11 (0.01, 0.95) 0.044
Mekele 25 (27.2) 33 (20.8) 2.02 (0.87, 4.69) 0.102 4.28 (1.52, 11.99) 0.006
Gender
Male 56 (60.9) 88 (55.3) Ref Ref
Female 36 (39.1) 71 (44.7) 0.79 (0.47, 1.33) 0.371 0.91 (0.48, 1.72) 0.781
Diagnosis
PTB 53 (57.6) 107 (67.3) Ref Ref
TBLN 39 (42.4) 52 (32.7) 1.5 (0.88, 2.55) 0.134 3.47 (1.45, 8.29) 0.005
HIV co-infection
No 81 (88.0) 151 (95.0) Ref Ref
Yes 11(12.0) 8 (5.0) 2.93 (1.09, 7.85) 0.033 2.84 (1.61, 5.55) 0.027
Comorbidity of Chronic illness
No 62 (67.4) 95 (59.7) Ref Ref
Yes 30 (32.6) 64 (40.3) 0.73 (0.43, 1.25) 0.252 0.46 (0.25, 0.87) 0.016
Taking prescribed Medication
No 55 (59.8) 117 (73.6) Ref Ref
Yes 37 (40.2) 42 (26.4) 1.86 (1.08, 3.21) 0.026 1.67 (0.83, 3.36) 0.152
Persistent Cough
No 19 (20.7) 32 (20.1) Ref Ref
Yes 73 (79.3) 127 (79.9) 0.94 (0.49, 1.78) 0.844 1.03 (0.41, 2.61) 0.944
Hemoptysis
No 74 (80.4) 129 (81.6) Ref Ref
Yes 18 (19.6) 29 (18.4) 1.08 (0.56, 2.08) 0.813 2.10 (0.90, 4.87) 0.085
Weight loss
No 12 (13.0) 27 (17.0) Ref Ref
Yes 80 (87.0) 132 (83.0) 1.37 (0.66, 2.86) 0.397 1.00 (0.41, 2.47) 0.997
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observe any difference in clustering rate with respect to site of infection. This might be because
of limited power of the study that could not control for all possible effects of confounding fac-
tors. Although, the differences in strain virulence and immunogenicity have been investigated
in experimental studies, whether this phenotypic variation plays a role in human disease
remains unclear [3, 6].
Therefore, it is believed that investigating the clinical epidemiology of dominant M. tb line-
ages among host populations would allow understanding of possible host-pathogen interac-
tion. In this regard, one of the findings that emerged from this study is that clinical factors,
which are often associated with host immunity, appeared to differ significantly between L3
and L4, the two most dominant lineages. According to the multivariate analysis (Table 5), the
likelihood of detecting L3 among TBLN cases and HIV co-infected patients was significantly
higher than for L4. However, a summary report generated from the updated version of the
international Mycobacterium tuberculosis spoligotyping global database has shown a higher
rate of CAS (L3) infection among HIV co-infected cases than other widely prevalent sub-line-
ages [8]. The observed discrepancy might be due to the interaction effect of sub-lineages or the
possibility of co-infection within the same host. Our analysis was performed based on major
M. tb lineage classification. Although it is often associated with host immunity, Oso´rio et al.
(2018) stated that due to selective advantage of extrinsic factors, within-host bacterial diversity
seems to contribute to difference in disease progression [4]. For example, certain groups of L4
strains are found to be more virulent in terms of disease severity and to display higher rates of
human-to-human transmission, but only at some specific geographical locations [2]. In favour
of that, and as compared to L4, the current study identified significantly lower rate of L3
strains among TB cases diagnosed with other concomitant chronic illnesses (Table 5). Cer-
tainly, any immune-compromised condition and HIV interferes with bacterial virulence
might lead to endogenous reactivation [20, 25, 41], suggesting that less virulent MTBC species
could progress to active TB disease in immune-compromised patients. For example, TB
patients infected with M. africanum were more likely to be older, HIV infected, and severely
malnourished than those infected with M. tb [42]. Although the mechanisms are not yet clear,
the influence of bacterial and host genotype on the development of different forms of TB in
humans is well documented. In this regard, the findings observed in this study seem to agree
with others that suggested a possible relationship between L3 and EPTB disease [12, 38]. Cor-
respondingly, a significantly higher rate of PTB was often associated with L4, while more
EPTB disease, such as TB meningitis and TBLN, was attributed to L3 [13, 15, 38].
Generally, because of a complex network related with many other proximal and distal
determinants, M. tb strain clustering or lineage specific effects on disease presentations may
not always be fully explained by some particular risk factors and it is difficult to quantify the
biological effect using numerical estimates [43]. As a result of that, most of the previously
reported epidemiological studies in humans have come up with inconsistent findings [2]. It is
known that heterogeneity is a defining feature of TB, which is certainly common in molecular
studies [43]. However, although the need for additional clinical evidence is obvious, disease
phenotypes can possibly be determined by genotype features of specific strains, suggesting that
different M. tb lineages could be more frequently present in specific clinical phenotypes and
disease presentations than in others [2].
Limitation
Spoligotyping has its limitations and may not truly detect ongoing changes (genetic differ-
ences) in a population and thereby not be the best tool for investigation of transmission net-
works [22]. Alternative molecular diagnostic tools, such as MIRU-VNTR and especially whole
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genome sequencing, have shown to have better discriminatory power for investigating strain
clustering and to confirm the ongoing rate of active TB disease transmission [14, 22]. Simi-
larly, the fairly small sample size, uneven representation of strains from the study sites, and fur-
ther categorization into different levels of factor variables, have reduced the power of our
statistical analysis. Hence, the numerical estimates may not truly imitate the biological interac-
tion or effect modification on host-related factors and specific M. tb lineages. Not only system-
atic and measurement errors, but the current study also recognized selection and recall bias
where selected isolates were subjected for spoligotyping based molecular analysis. However,
we have tried to minimize some of the anticipated measurement errors and known confound-
ing effects. For instance, alongside with internal quality control procedures for the identifica-
tion of lineages, SITVIT patterns were compared with alternative lineage classifications
generated from linked databases (KBBN and CBN) and further verified using SNP based pre-
dictions. In addition, the multivariate analysis has considered and used to adjust the expected
effect of regional variation on TB lineage predominance and related strain clustering.
Conclusion and recommendation
Despite differences in geographical variations, the overall clustering suggested higher trans-
mission of TB disease among human populations living around urban settings in Ethiopia.
This Spoligotyping-based investigation showed that the rate of strain clustering was relatively
higher among patients infected with L3 strains of M. tb as compared to L4. Regarding host-
related factors, strain clustering rate was inversely associated with patients diagnosed with
TB-HIV co-infection and comorbidity with other chronic illnesses. On the other hand, as
compared to M. tb L4, active TB disease due to L3 strains was three times higher among TBLN
patients and it was more likely to be associated with TB-HIV co-infection, while inversely asso-
ciated with other concomitant chronic disease.
Altogether, the current findings add up to previous indications and contribute to evidence
base on the continuous flux in the spectrum of TB infection and disease progression. Although
it is difficult to be conclusive on a fixed categorical relationship between strain sub-lineages
and disease type, as there is some other supportive evidence, disease phenotypes can possibly
be determined by genotypic features of specific strains. Considering the complex pathogenesis
of human TB disease and the interaction effect of other predisposing environmental factors, it
seems that active infection due to specific M. tb lineages might be associated with specific clini-
cal phenotypes and disease presentation.
Generally, considering the ongoing shift and heterogeneity of TB disease, clinical and pub-
lic health interventions should be alongside with molecular evidence for targeting high-risk
groups based on location, social determinants, disease comorbidities and related bacterial
strain predominance. However, as the dynamics of socioeconomic transformations exert pres-
sure on how people live and interact, large scale studies using advanced molecular techniques,
like whole genome sequencing, should further reveal the degree to which the genetic variation
influences disease epidemiology and phenotype in different population groups over time.
Supporting information
S1 Table. Spoligotype descriptions and lineage classifications of all clinical isolates
included in this study.
(XLS)
S1 File.
(DOCX)
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PLOS ONE | https://doi.org/10.1371/journal.pone.0253480 July 12, 2021 14 / 18
S1 Data.
(DTA)
Acknowledgments
We would like to forward our appreciation to supportive staff at the Armauer Hansen
Research Institute and all members of the ETHICOBOTS project who had a great contribution
to the success of this study. Besides, we would like to extend our acknowledgment to the Uni-
versity of Gondar and the academic staff of the public health institute. We also thank APHA
for providing with membranes for spoligotyping.
The members of the Ethiopia Control of Bovine Tuberculosis Strategies (ETHICOBOTS)
consortium are: Abraham Aseffa, Adane Mihret, Bamlak Tessema, Bizuneh Belachew, Eshco-
lewyene Fekadu, Fantanesh Melese, Gizachew Gemechu, Hawult Taye, Rea Tschopp, Shewit
Haile, Sosina Ayalew, Tsegaye Hailu, all from Armauer Hansen Research Institute, Ethiopia;
Rea Tschopp from Swiss Tropical and Public Health Institute, Switzerland; Adam Bekele, Chi-
lot Yirga, Mulualem Ambaw, Tadele Mamo, Tesfaye Solomon, all from Ethiopian Institute of
Agricultural Research, Ethiopia; Tilaye Teklewold from Amhara Regional Agricultural
Research Institute, Ethiopia; Solomon Gebre, Getachew Gari, Mesfin Sahle, Abde Aliy, Abebe
Olani, Asegedech Sirak, Gizat Almaw, Getnet Mekonnen, Mekdes Tamiru, Sintayehu Guta, all
from National Animal Health Diagnostic and Investigation Centre, Ethiopia; James Wood,
Andrew Conlan, Alan Clarke, all from Cambridge University, United Kingdom; Henrietta L.
Moore and Catherine Hodge, both from University College London, United Kingdom; Con-
stance Smith at University of Manchester, United Kingdom; R. Glyn Hewinson, Stefan Berg,
Martin Vordermeier, Javier Nunez-Garcia, all from Animal and Plant Health Agency, United
Kingdom; Gobena Ameni, Berecha Bayissa, Aboma Zewude, Adane Worku, Lemma Terfassa,
Mahlet Chanyalew, Temesgen Mohammed, Yemisrach Zeleke, all from Addis ababa Univer-
sity, Ethiopia.
Author Contributions
Conceptualization: Hawult Taye, Adane Mihret, James L. N. Wood, Stefan Berg, Abraham
Aseffa.
Data curation: Kassahun Alemu, Adane Mihret, Sosina Ayalew, James L. N. Wood, Stefan
Berg, Abraham Aseffa.
Formal analysis: Hawult Taye, Kassahun Alemu.
Funding acquisition: Adane Mihret, James L. N. Wood, Stefan Berg, Abraham Aseffa.
Investigation: Hawult Taye, Kassahun Alemu, Adane Mihret, Sosina Ayalew, Elena Hailu, Ste-
fan Berg, Abraham Aseffa.
Methodology: Hawult Taye, Kassahun Alemu, Adane Mihret, Sosina Ayalew, Elena Hailu,
James L. N. Wood, Ziv Shkedy, Stefan Berg, Abraham Aseffa.
Project administration: Hawult Taye, Adane Mihret, James L. N. Wood, Stefan Berg, Abra-
ham Aseffa.
Resources: Adane Mihret, James L. N. Wood, Stefan Berg, Abraham Aseffa.
Software: Hawult Taye, Kassahun Alemu.
Supervision: Kassahun Alemu, Ziv Shkedy, Abraham Aseffa.
PLOS ONE Spoligotyping based molecular study of Mycobacterium tuberculosis species
PLOS ONE | https://doi.org/10.1371/journal.pone.0253480 July 12, 2021 15 / 18
Validation: Hawult Taye, Kassahun Alemu, Adane Mihret, Sosina Ayalew, Elena Hailu, Stefan
Berg, Abraham Aseffa.
Visualization: Hawult Taye, Kassahun Alemu, Adane Mihret, Sosina Ayalew, Abraham
Aseffa.
Writing – original draft: Hawult Taye, Stefan Berg, Abraham Aseffa.
Writing – review & editing: Hawult Taye, Kassahun Alemu, Adane Mihret, Sosina Ayalew,
James L. N. Wood, Ziv Shkedy, Stefan Berg, Abraham Aseffa.
References
1. Brites D., Loiseau C., Menardo F., Borrell S., Boniotti M.B., Warren R., et al. A New Phylogenetic
Framework for the Animal-Adapted Mycobacterium tuberculosis Complex. Front Microbiol, 2018; 9: p.
2820. https://doi.org/10.3389/fmicb.2018.02820 PMID: 30538680
2. Coscolla M. Biological and Epidemiological Consequences of MTBC Diversity. Adv Exp Med Biol, 2017;
1019: p. 95–116. https://doi.org/10.1007/978-3-319-64371-7_5 PMID: 29116631
3. McHenry M.L., Bartlett J., Igo R.P. Jr., Wampande E.M., Benchek P., Mayanja-Kizza H., et al. Interac-
tion between host genes and Mycobacterium tuberculosis lineage can affect tuberculosis severity: Evi-
dence for coevolution? PLoS Genet, 2020; 16(4): p. e1008728. https://doi.org/10.1371/journal.pgen.
1008728 PMID: 32352966
4. Bastos H.N., Oso´rio N.S., Gagneux S., Comas I., and Saraiva M. The Troika host–pathogen–extrinsic
factors in tuberculosis: modulating inflammation and clinical outcomes. J Frontiers in immunology,
2018; 8: p. 1948. https://doi.org/10.3389/fimmu.2017.01948 PMID: 29375571
5. Gagneux S. Host-pathogen coevolution in human tuberculosis. Philos Trans R Soc Lond B Biol Sci,
2012; 367(1590): p. 850–9. https://doi.org/10.1098/rstb.2011.0316 PMID: 22312052
6. Yimer S.A., Kalayou S., Homberset H., Birhanu A.G., Riaz T., Zegeye E.D., et al. Lineage-Specific
Proteomic Signatures in the Mycobacterium tuberculosis Complex Reveal Differential Abundance of
Proteins Involved in Virulence, DNA Repair, CRISPR-Cas, Bioenergetics and Lipid Metabolism. Front
Microbiol, 2020; 11: p. 550760. https://doi.org/10.3389/fmicb.2020.550760 PMID: 33072011
7. Mekonnen D., Derbie A., Abeje A., Shumet A., Kassahun Y., Nibret E., et al. Genomic diversity and
transmission dynamics of M. tuberculosis in Africa: a systematic review and meta-analysis. 2019; 23
(12): p. 1314–1326.
8. Couvin D., David A., Zozio T., Rastogi N., and Evolution Macro-geographical specificities of the prevail-
ing tuberculosis epidemic as seen through SITVIT2, an updated version of the Mycobacterium tubercu-
losis genotyping database. J Infection, Genetics, 2019; 72: p. 31–43. https://doi.org/10.1016/j.meegid.
2018.12.030 PMID: 30593925
9. Ferraris D.M., Miggiano R., Rossi F., and Rizzi M.Mycobacterium tuberculosis Molecular Determinants
of Infection, Survival Strategies, and Vulnerable Targets. Pathogens, 2018; 7(1).
10. Reiling N., Homolka S., Walter K., Brandenburg J., Niwinski L., Ernst M., et al. Clade-specific virulence
patterns of Mycobacterium tuberculosis complex strains in human primary macrophages and aerogeni-
cally infected mice. mBio, 2013; 4(4). https://doi.org/10.1128/mBio.00250-13 PMID: 23900170
11. McHenry M.L., Bartlett J., Igo R.P. Jr, Wampande E.M., Benchek P., Mayanja-Kizza H., et al. Interac-
tion between host genes and Mycobacterium tuberculosis lineage can affect tuberculosis severity: Evi-
dence for coevolution? 2020; 16(4): p. e1008728. https://doi.org/10.1371/journal.pgen.1008728 PMID:
32352966
12. Drain P.K., Bajema K.L., Dowdy D., Dheda K., Naidoo K., Schumacher S.G., et al. Incipient and Sub-
clinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection. Clin Microbiol Rev,
2018; 31(4). https://doi.org/10.1128/CMR.00021-18 PMID: 30021818
13. Qian X., Nguyen D.T., Lyu J., Albers A.E., Bi X., and E.A. Graviss Risk factors for extrapulmonary dis-
semination of tuberculosis and associated mortality during treatment for extrapulmonary tuberculosis.
Emerg Microbes Infect, 2018; 7(1): p. 102. https://doi.org/10.1038/s41426-018-0106-1 PMID:
29872046
14. Firdessa R., Berg S., Hailu E., Schelling E., Gumi B., Erenso G., et al. Mycobacterial lineages causing
pulmonary and extrapulmonary tuberculosis, Ethiopia. Emerg Infect Dis, 2013; 19(3): p. 460–3. https://
doi.org/10.3201/eid1903.120256 PMID: 23622814
15. Krishnakumariamma K., Ellappan K., Muthuraj M., Tamilarasu K., Kumar S.V., and Joseph Molecular
diagnosis N.M., genetic diversity and drug sensitivity patterns of Mycobacterium tuberculosis strains
PLOS ONE Spoligotyping based molecular study of Mycobacterium tuberculosis species
PLOS ONE | https://doi.org/10.1371/journal.pone.0253480 July 12, 2021 16 / 18
isolated from tuberculous meningitis patients at a tertiary care hospital in South India. PloS one, 2020;
15(10): p. e0240257. https://doi.org/10.1371/journal.pone.0240257 PMID: 33017455
16. Pareek M., Evans J., Innes J., Smith G., Hingley-Wilson S., Lougheed K.E., et al. Ethnicity and myco-
bacterial lineage as determinants of tuberculosis disease phenotype. J Thorax, 2013; 68(3): p. 221–
229. https://doi.org/10.1136/thoraxjnl-2012-201824 PMID: 23019255
17. David S., Mateus A.R., Duarte E.L., Albuquerque J., Portugal C., Sancho L., et al. Determinants of the
Sympatric Host-Pathogen Relationship in Tuberculosis. PLoS One, 2015; 10(11): p. e0140625. https://
doi.org/10.1371/journal.pone.0140625 PMID: 26529092
18. Stavrum R., PrayGod G., Range N., Faurholt-Jepsen D., Jeremiah K., Faurholt-Jepsen M., et al.
Increased level of acute phase reactants in patients infected with modern Mycobacterium tuberculosis
genotypes in Mwanza, Tanzania. BMC Infect Dis, 2014; 14(1): p. 309. https://doi.org/10.1186/1471-
2334-14-309 PMID: 24903071
19. Blanco-Guillot F., Castañeda-Cediel M.L., Cruz-Hervert P., Ferreyra-Reyes L., Delgado-Sa´nchez G.,
Ferreira-Guerrero E., et al. Genotyping and spatial analysis of pulmonary tuberculosis and diabetes
cases in the state of Veracruz, Mexico. PLoS One, 2018; 13(3): p. e0193911. https://doi.org/10.1371/
journal.pone.0193911 PMID: 29534104
20. Fenner L., Egger M., Bodmer T., Furrer H., Ballif M., Battegay M., et al. HIV Infection Disrupts the Sym-
patric Host–Pathogen Relationship in Human Tuberculosis. PLoS Genet, 2013; 9(3). https://doi.org/10.
1371/journal.pgen.1003318 PMID: 23505379
21. Mo¨ller M., Kinnear C.J., Orlova M., Kroon E.E., van Helden P.D., Schurr E., et al. Genetic Resistance to
Mycobacterium tuberculosis Infection and Disease. Front Immunol, 2018; 9. https://doi.org/10.3389/
fimmu.2018.02219 PMID: 30319657
22. Meehan C.J., Moris P., Kohl T.A., Pečerska J., Akter S., Merker M., et al. The relationship between
transmission time and clustering methods in Mycobacterium tuberculosis epidemiology. EBioMedicine,
2018; 37: p. 410–416. https://doi.org/10.1016/j.ebiom.2018.10.013 PMID: 30341041
23. Borgdorff M. and Van Soolingen D. The re-emergence of tuberculosis: what have we learnt from molec-
ular epidemiology? J Clinical Microbiology Infection, 2013; 19(10): p. 889–901. https://doi.org/10.1111/
1469-0691.12253 PMID: 23731470
24. Mekonnen A., Merker M., Collins J.M., Addise D., Aseffa A., Petros B., et al. Molecular epidemiology
and drug resistance patterns of Mycobacterium tuberculosis complex isolates from university students
and the local community in Eastern Ethiopia. PLoS One, 2018; 13(9): p. e0198054. https://doi.org/10.
1371/journal.pone.0198054 PMID: 30222743
25. Suzana S., Shanmugam S., Uma Devi K.R., Swarna Latha P.N., and J.S. Michael Spoligotyping of
Mycobacterium tuberculosis isolates at a tertiary care hospital in India. Trop Med Int Health, 2017; 22
(6): p. 703–707. https://doi.org/10.1111/tmi.12875 PMID: 28374900
26. Bedewi Z., Worku A., Mekonnen Y., Yimer G., Medhin G., Mamo G., et al. Molecular typing of Mycobac-
terium tuberculosis complex isolated from pulmonary tuberculosis patients in central Ethiopia. BMC
Infect Dis, 2017; 17(1): p. 184. https://doi.org/10.1186/s12879-017-2267-2 PMID: 28249607
27. Berg S., Schelling E., Hailu E., Firdessa R., Gumi B., Erenso G., et al. Investigation of the high rates of
extrapulmonary tuberculosis in Ethiopia reveals no single driving factor and minimal evidence for zoo-
notic transmission of Mycobacterium bovis infection. BMC Infect Dis, 2015; 15: p. 112. https://doi.org/
10.1186/s12879-015-0846-7 PMID: 25886866
28. Yimer S.A., Norheim G., Namouchi A., Zegeye E.D., Kinander W., Tonjum T., et al. Mycobacterium
tuberculosis lineage 7 strains are associated with prolonged patient delay in seeking treatment for pul-
monary tuberculosis in Amhara Region, Ethiopia. J Clin Microbiol, 2015; 53(4): p. 1301–9. https://doi.
org/10.1128/JCM.03566-14 PMID: 25673798
29. Tulu B. and Ameni G. Spoligotyping based genetic diversity of Mycobacterium tuberculosis in Ethiopia:
a systematic review. J BMC infectious diseases, 2018; 18(1): p. 140. https://doi.org/10.1186/s12879-
018-3046-4 PMID: 29587640
30. Tilahun M., Ameni G., Desta K., Zewude A., Yamuah L., Abebe M., et al. Molecular epidemiology and
drug sensitivity pattern of Mycobacterium tuberculosis strains isolated from pulmonary tuberculosis
patients in and around Ambo Town, Central Ethiopia. PLoS One, 2018; 13(2): p. e0193083. https://doi.
org/10.1371/journal.pone.0193083 PMID: 29447273
31. Berg S., Firdessa R., Habtamu M., Gadisa E., Mengistu A., Yamuah L., et al. The burden of mycobacte-
rial disease in ethiopian cattle: implications for public health. PLoS One, 2009; 4(4): p. e5068. https://
doi.org/10.1371/journal.pone.0005068 PMID: 19352493
32. Kamerbeek J., Schouls L., Kolk A., van Agterveld M., van Soolingen D., Kuijper S., et al. Simultaneous
detection and strain differentiation of Mycobacterium tuberculosis for diagnosis and epidemiology. J
Clin Microbiol, 1997; 35(4): p. 907–14. https://doi.org/10.1128/jcm.35.4.907-914.1997 PMID: 9157152
PLOS ONE Spoligotyping based molecular study of Mycobacterium tuberculosis species
PLOS ONE | https://doi.org/10.1371/journal.pone.0253480 July 12, 2021 17 / 18
33. Kato-Maeda M., Metcalfe J.Z., and Flores L. Genotyping of Mycobacterium tuberculosis: application in
epidemiologic studies. Future Microbiol, 2011; 6(2): p. 203–16. https://doi.org/10.2217/fmb.10.165
PMID: 21366420
34. Mekonnen D., Derbie A., Chanie A., Shumet A., Biadglegne F., Kassahun Y., et al. Molecular epidemiol-
ogy of M. tuberculosis in Ethiopia: A systematic review and meta-analysis. Tuberculosis (Edinb), 2019;
118: p. 101858. https://doi.org/10.1016/j.tube.2019.101858 PMID: 31430694
35. Kyu H.H., Maddison E.R., Henry N.J., Ledesma J.R., Wiens K.E., Reiner R. Jr, et al. Global, regional,
and national burden of tuberculosis, 1990–2016: results from the Global Burden of Diseases, Injuries,
and Risk Factors 2016 Study. J The Lancet Infectious Diseases, 2018; 18(12): p. 1329–1349.
36. Deribew A., Deribe K., Dejene T., Tessema G.A., Melaku Y.A., Lakew Y., et al. Tuberculosis Burden in
Ethiopia from 1990 to 2016: Evidence from the Global Burden of Diseases 2016 Study. Ethiop J Health
Sci, 2018; 28(5): p. 519–528. https://doi.org/10.4314/ejhs.v28i5.2 PMID: 30607066
37. Nuru A., Mamo G., Worku A., Admasu A., Medhin G., Pieper R., et al. Genetic Diversity of Mycobacte-
rium tuberculosis Complex Isolated from Tuberculosis Patients in Bahir Dar City and Its Surroundings,
Northwest Ethiopia. Biomed Res Int, 2015; 2015: p. 174732. https://doi.org/10.1155/2015/174732
PMID: 26491657
38. Khandkar C., Harrington Z., Jelfs P.J., Sintchenko V., and Dobler C.C. Epidemiology of Peripheral
Lymph Node Tuberculosis and Genotyping of M. tuberculosis Strains: A Case-Control Study. PLoS
One, 2015; 10(7): p. e0132400. https://doi.org/10.1371/journal.pone.0132400 PMID: 26177546
39. Belay M., Ameni G., Bjune G., Couvin D., Rastogi N., and Abebe F. Strain diversity of Mycobacterium
tuberculosis isolates from pulmonary tuberculosis patients in Afar pastoral region of Ethiopia. Biomed
Res Int, 2014; 2014: p. 238532. https://doi.org/10.1155/2014/238532 PMID: 24734230
40. McIvor A., Koornhof H., and Kana B.D. Relapse, re-infection and mixed infections in tuberculosis dis-
ease. Pathog Dis, 2017; 75(3). https://doi.org/10.1093/femspd/ftx020 PMID: 28334088
41. Srilohasin P., Chaiprasert A., Tokunaga K., Nishida N., Prammananan T., Smittipat N., et al. Genetic
diversity and dynamic distribution of Mycobacterium tuberculosis isolates causing pulmonary and extra-
pulmonary tuberculosis in Thailand. J Clin Microbiol, 2014; 52(12): p. 4267–74. https://doi.org/10.1128/
JCM.01467-14 PMID: 25297330
42. de Jong B.C., Antonio M., and S. Gagneux Mycobacterium africanum—review of an important cause of
human tuberculosis in West Africa. PLoS Negl Trop Dis, 2010; 4(9): p. e744. https://doi.org/10.1371/
journal.pntd.0000744 PMID: 20927191
43. Trauer J.M., Dodd P.J., Gomes M.G.M., Gomez G.B., Houben R.M., McBryde E.S., et al. The impor-
tance of heterogeneity to the epidemiology of tuberculosis. J Clinical infectious diseases, 2018; 69(1):
p. 159–166.
PLOS ONE Spoligotyping based molecular study of Mycobacterium tuberculosis species
PLOS ONE | https://doi.org/10.1371/journal.pone.0253480 July 12, 2021 18 / 18