Introduction

Tuberculosis (TB) is a chronic infectious disease caused by species of the Mycobacterium tuberculosis 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 animal 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 disease 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 frequency 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 associated 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].

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 transmission 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 variations 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 Ethiopia, 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 component 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

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 northern 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).

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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.

https://doi.org/10.1371/journal.pone.0253480.t001

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 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 designated 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 isolates with a new shared spoligotype pattern (Table 2), representing 27 (10.5%) of the total isolates, 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).

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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.

https://doi.org/10.1371/journal.pone.0253480.t002

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Table 3. Spoligotype descriptions of all registered SIT patterns with two or more isolates identified from 198 clinical samples collected from pulmonary TB and cervical TB lymphadenitis patients recruited at selected health facilities in Ethiopia in the years of 2016/17.

https://doi.org/10.1371/journal.pone.0253480.t003

According to the CBN analysis, 97.3% of the total 258 isolates belonged to two major lineages, EA (61.6%) and EAI (35.7%). On the basis of SNP-based genome-wide phylogeny analysis, 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).

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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).

https://doi.org/10.1371/journal.pone.0253480.g001

The alternative KBBN classification showed a predominance of the CAS (34.9%) sub-lineage 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 distribution 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, Haarlem and T families (all of L4) accounted for the majority of strains identified in the Hawassa region (Fig 2).

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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.

https://doi.org/10.1371/journal.pone.0253480.g002

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 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 heterogeneity 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.

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Table 4. Conventional and hierarchical (multi-level) logistic regression modeling methods were used to identify factors associated with strain clustering based on spoligotyping.

https://doi.org/10.1371/journal.pone.0253480.t004

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 [3.47 (1.45, 8.29)] higher than PTB patients. Active TB disease due to L3 strains was significantly 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.

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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.

https://doi.org/10.1371/journal.pone.0253480.t005

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 population groups have likely contributed more to such TB incidence rate than other groups. Particularly, 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 ongoing expansion of urbanization and emerging socio-economic conditions around urban areas in Ethiopia (increasing population size and density e.g. through expanding slums, congregation 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 (incidence) 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 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 spoligotyping method, an increased rate of M. tb transmission is generally inferred by comparing clustered 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 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 difference 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 transmission; 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 adjusting 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 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 factors. 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 lineages among host populations would allow understanding of possible host-pathogen interaction. 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-lineages [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, Osório 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). Certainly, 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]. Correspondingly, 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 differences) in a population and thereby not be the best tool for investigation of transmission networks [22]. Alternative molecular diagnostic tools, such as MIRU-VNTR and especially whole 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]. Similarly, the fairly small sample size, uneven representation of strains from the study sites, and further 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 interaction or effect modification on host-related factors and specific M. tb lineages. Not only systematic 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 confounding effects. For instance, alongside with internal quality control procedures for the identification of lineages, SITVIT patterns were compared with alternative lineage classifications generated from linked databases (KBBN and CBN) and further verified using SNP based predictions. 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 transmission 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 associated 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 clinical phenotypes and disease presentation.

Generally, considering the ongoing shift and heterogeneity of TB disease, clinical and public 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 pressure 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

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 University 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, Eshcolewyene 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, Chilot 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; Constance 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 University, Ethiopia.