• Drug resistance;
  • molecular epidemiology;
  • phylogeography;
  • population dynamics;
  • risk factors;
  • transmission;
  • tuberculosis


  1. Top of page
  2. Abstract
  3. Introduction
  4. Molecular Typing Methods
  5. Molecular Epidemiological Insights from the Last Two Decades
  6. Open Questions
  7. Transparency Declaration
  8. References

Tuberculosis (TB) has re-emerged over the past two decades: in industrialized countries in association with immigration, and in Africa owing to the human immunodeficiency virus epidemic. Drug-resistant TB is a major threat worldwide. The variable and uncertain impact of TB control necessitates not only better tools (diagnostics, drugs, and vaccines), but also better insights into the natural history and epidemiology of TB. Molecular epidemiological studies over the last two decades have contributed to such insights by answering long-standing questions, such as the proportion of cases attributable to recent transmission, risk factors for recent transmission, the occurrence of multiple Mycobacterium tuberculosis infection, and the proportion of recurrent TB cases attributable to re-infection. M. tuberculosis lineages have been identified and shown to be associated with geographical origin. The Beijing genotype is strongly associated with multidrug resistance, and may have escaped from bacille Calmette–Guérin-induced immunity. DNA fingerprinting has quantified the importance of institutional transmission and laboratory cross-contamination, and has helped to focus contact investigations. Questions to be answered in the near future with whole genome sequencing include identification of chains of transmission within clusters of patients, more precise quantification of mixed infection, and transmission probabilities and rates of progression from infection to disease of various M. tuberculosis lineages, as well as possible variations in vaccine efficacy by lineage. Perhaps most importantly, dynamics in the population structure of M. tuberculosis in response to control measures in high-prevalence areas should be better understood.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Molecular Typing Methods
  5. Molecular Epidemiological Insights from the Last Two Decades
  6. Open Questions
  7. Transparency Declaration
  8. References

Tuberculosis (TB) had an estimated global incidence of approximately 8.7 million cases in 2011, and caused 1.4 million deaths [1]. In Europe, although TB rates are declining, the Millennium Development Goal of 50% reduction by 2015 as compared with 1990 will not be reached [2]. In the 1980s, TB had been in strong decline for decades in industrialized countries, and plans were made for TB elimination in these countries [3, 4]. In the early 1990s, this optimism was shown to be unfounded. Drug-resistant TB emerged in New York [5], and a molecular epidemiological study showed the importance of ongoing transmission in that setting [6]. Moreover, it was shown that ongoing transmission was responsible for a large proportion of TB cases in San Francisco [7]. On a global scale, the Global Burden of Disease Study showed that TB was among the top ten causes of mortality and healthy life-years lost [8, 9]. In Africa, TB incidence increased steeply as a result of the human immunodeficiency virus (HIV) epidemic; conventional control measures were unable to control TB there [10]. Finally, the rapid emergence of drug-resistant TB in eastern Europe and beyond is of major concern [11, 12].

At present, the global decline in TB incidence is estimated to be approximately 2% per year [1], despite wide adoption of the Stop TB strategy and previous predictions that this would lead to a much stronger declining incidence of 5–10% per year [13, 14]. In order to reach the goal of TB elimination by the year 2050, an annual decline in the order of 15% in the incidence of TB would be needed. The variable and uncertain impact of current TB control measures [15, 16] emphasizes the urgent need to obtain better tools for the diagnosis, treatment and prevention of TB, including more sensitive point-of-care diagnostics, shorter drug regimens, and more effective vaccines. Moreover, we need to better understand TB epidemiology in various settings. Molecular tools have proven to be extremely useful in gaining a better understanding of TB epidemiology over the past two decades. This article summarizes the major findings and provides suggestions for further research in the light of new opportunities emerging today with rapid technical developments.

Molecular Typing Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Molecular Typing Methods
  5. Molecular Epidemiological Insights from the Last Two Decades
  6. Open Questions
  7. Transparency Declaration
  8. References

Molecular markers for Mycobacterium tuberculosis have been developed since the early 1990s; they reveal different levels of genetic polymorphism, and consequently have different applications (Table 1). The technical aspects and characteristics of the typing methodologies for M. tuberculosis complex isolates have been described previously [17]. Spoligotyping has the advantage of being simple and cheap, and is, to a large extent, capable of identifying M. tuberculosis complex strains the at (sub)species and genotype family levels [18]. Restriction fragment length polymorphism (RFLP) typing with IS6110 as a marker has a higher resolution than spoligotyping. The relatively high rate of change of IS6110 banding patterns allows distinction between M. tuberculosis complex stains [17].

Table 1. Suitability of genetic markers in different applications
Typing methodTransmissionPhylogenyStability
  1. RFLP, restriction fragment length polymorphism; SNP, single-nucleotide polymorphism; VNTR, variable-number tandem-repeat.

IS6110 RFLP++++±
Genomic deletions+++++++

Variable-number tandem-repeat (VNTR) typing allows strain typing as well as reasonably robust phylogenetic analysis, and this typing method is therefore being increasingly used [19] (Fig. 1). The resolution of VNTR typing is similar to that of RFLP. Limitations of VNTR typing for studying the phylogeny of M. tuberculosis are that mutations may be bi-directional and thus may undo themselves over time, and the possibility of convergent evolution: the same arrangement in repeats may occur independently in genetically unrelated strains. Methods that do not suffer from these limitations are based on the detection of large sequence polymorphisms [20] and single-nucleotide polymorphisms [21]. Two breakthrough studies were that of Brosch in 2002, using the former application [20], and that of Herschberg in 2008, using the latter [21]. Both large deletions and single-nucleotide polymorphisms are extremely unlikely to occur independently in different strains, given the low DNA sequence variation in M. tuberculosis, and the limited — although perhaps not absent [22] — role of horizontal gene transfer. The resolution of DNA fingerprinting was enhanced by multispacer typing, based on sequencing several intergenic regions, which were selected on the basis of complete genome sequence analysis [23]. Whole genome sequencing (WGS) is considered to be the maximum-resolution typing method [24-26]. It is obvious RFLP and VNTR typing clusters can often be subdivided with this technique, and that it adds to the overall resolution of typing. Whether this technique can be used as a frontline typing technique has yet to be explored.


Figure 1. Principle of variable-number tandem-repeat (VNTR) typing of Mycobacterium tuberculosis. The number of tandem repeats at each examined locus can differ, and hence the length of the PCR product generated can also differ. By determining the length of the PCR product, the number of tandem repeats present can be deduced. As this is done for 24 loci in the genome of M. tuberculosis, the standard VNTR pattern is a numerical code of 24 numbers.

Download figure to PowerPoint

Molecular Epidemiological Insights from the Last Two Decades

  1. Top of page
  2. Abstract
  3. Introduction
  4. Molecular Typing Methods
  5. Molecular Epidemiological Insights from the Last Two Decades
  6. Open Questions
  7. Transparency Declaration
  8. References

Recent transmission

Before 1990, it was generally believed that, in low-incidence countries, most TB cases were attributable to endogenous reactivation of latent infection, and only a small proportion, in the order of 10%, would derive from recent transmission [3, 27]. On the basis of this assumption, progress towards elimination in low-incidence countries was predicted, as this would depend mainly on the prevalence of latent infection in older age cohorts and the natural replacement of this high-prevalence group by younger, less infected age cohorts [3]. Two landmark studies in the 1990s overthrew this assumption on the basis of RFLP typing. In New York and San Francisco, >30% of TB cases were attributed to recent infection on the basis of clustering (different patients whose isolates had the same RFLP pattern) [6, 7].

The initial studies on DNA fingerprinting raised questions about the suitability of RFLP clustering for measuring recent transmission. For instance, in a rural population in Arkansas, it proved impossible to identify recent epidemiological links between the majority of clustered cases, in particular among the elderly [28]. On the other hand, with intensive follow-up in The Netherlands, an epidemiological link was either demonstrated or probable in up to 85% of clustered cases [29].

There are multiple reasons for an imperfect correspondence between RFLP clustering and epidemiological contact information [30]. First, confirmation of contacts is expected to have limited sensitivity. Given the long incubation period of TB, recent transmission has been defined as transmission in the past 2–5 years [31, 32]. Epidemiological confirmation of all instances of airborne transmission over such a long period is a priori unlikely. Second, the intensity of efforts to identify epidemiological contact is likely to be important, and may be limited and variable under routine conditions. Third, although the rate of change of RFLP patterns supports its use for studying recent transmission [33], the RFLP pattern does not change exactly after the arbitrarily defined period of recent transmission, so some cases with identical fingerprints may be linked over longer periods, and some cases linked through recent transmission may not have isolates with identical fingerprints. Fourth, immigrants may introduce strains with (nearly) identical DNA fingerprints, which may not reflect recent transmission in the study area, but rather transmission or common strains in the country of origin. Fifth, sampling in time, space or at random was shown to lead to underestimation of clustering [34, 35]. In particular, cases in small clusters would run the risk of being misclassified as non-clustered. This precludes the use of clustering as an indicator of recent transmission in studies using small sampling fractions, e.g. national sample surveys. However, as sampling in space and time is unavoidable, and complete DNA fingerprinting results for all eligible TB cases are rarely obtained, this also served as a warning to interpret clustering statistics cautiously [31]. Finally, the interpretation of clustering depends on factors such as the age distribution of TB cases (clustering may overestimate recent transmission in the elderly and underestimate it in the young) and the TB trend over time [36]. In a meta-analysis, a large variation in clustering between studies was observed, and was indeed explained in part by study duration, sampling fraction, occurrence of strains with low copy numbers, and TB incidence [37].

Risk factors for TB attributable to recent transmission

Risk factors for TB attributable to recent transmission include male sex, being a young adult, being native (vs. foreign-born), urban residence, alcohol and drug abuse, being homeless, being exposed in crowded settings, including prisons, and having pulmonary tuberculosis [6, 7, 38-42]. HIV and multidrug-resistant TB (MDR-TB) were found to be risk factors in some settings, but not in others [40]. As risk factors were identified relative to the risk of TB not attributable to recent infection, care needs to be taken in the interpretation. For instance, the elderly in low-incidence countries have a much higher risk of TB attributable to remote infection than the young, so the proportion of TB in that age group attributable to recent infection may be expected to be smaller than among the young [36]. However, young age is also, in absolute terms, a risk factor for recently transmitted TB. For instance, in The Netherlands, the vast majority of TB cases attributed to recent transmission were found among young secondary cases resulting from recent transmission from a young index case [43, 44].

Sampling bias might affect not only the clustering proportion but also the identification of risk factors for clustering. A mathematical model suggested that ORs for clustering would be underestimated as a result of sampling bias [35]. However, a recent study showed that this sampling bias was very limited, unless extremely small samples were taken [45]. Risk factors for clustering can thus be used to identify priority groups for contact investigations and intensified case-finding [46, 47].

Focusing contact investigations

With the introduction of molecular typing, it was hoped that the technique would contribute to improving contact investigations and outbreak detection. Indeed, DNA fingerprinting was shown to lead to the identification of epidemiological links, in particular in ‘non-traditional' settings, including bars and churches [48]. Moreover, unsuspected outbreaks have been detected frequently [38, 49-52]. Molecular typing may also contribute to targeting contact investigations based on the characteristics of the first two cases of a cluster [46].

Conversely, RFLP typing has been used to identify some of the limitations of conventional contact investigations to identify recent transmission. For instance, molecular epidemiological findings suggested that contact investigations may be inadequate to prevent disease if contact occurs outside the household or close relatives or friends [49]. In Rotterdam, molecular typing identified widespread transmission from multiple sources among drug users, thus showing the limitations of contact investigation in his high-risk population without molecular typing, and leading to an active case-finding programme [47, 53]. In various settings, a substantial proportion of household contacts were infected with a different strain than the index case: 30% in California [54], and 54% in Cape Town [55]; before molecular typing became available, this would have been attributed to transmission within the household. It is expected that replacing RFLP typing with the much faster VNTR typing method will further help in the targeting of contact investigations.

Nosocomial transmission

Among the first applications of RFLP typing was the identification of outbreaks of TB among hospitalized HIV-infected patients [56, 57]. Recognition of this risk has led to the inclusion of infection control as one of the ‘three I's’ for the control of TB among HIV-infected patients, the other two being intensified TB case-finding among HIV-infected patients and isoniazid preventive therapy.

Wereas nosocomial transmission of M. tuberculosis and Mycobacterium bovis is hazardous for HIV-infected patients [58], the risk to non-HIV-infected health workers and patients appears to be variable [59, 60], perhaps depending on differences between settings in patient populations and infection control practices. The risk of nosocomial transmission was highlighted by molecular epidemiological studies, but other approaches have also made an important contribution to this knowledge. In particular, an in vivo air-sampling model with exposure of guinea pigs demonstrated the high variability in infectiousness between patients [61, 62] and the impact of various control measures [63].

Laboratory cross-contamination

False-positive cultures as a result of laboratory cross-contamination were demonstrated early on in the application of DNA fingerprinting [64]. Positive cultures attributed to laboratory cross-contamination were reported to contribute up to 3% of culture-positive cases [59, 65]. Changing from the relatively slow RFLP typing to the much faster VNTR typing should help in the early identification of laboratory cross contamination [66]. This is important, as laboratory cross-contamination may lead to false or delayed diagnosis and unnecessary health risks and costs associated with treatment and hospital admission [67].

Recurrent TB

Before the advent of molecular tools, the risk of re-infection after curative treatment of TB was unclear. Quantifying this phenomenon is important for various reasons, including a better understanding of the role of acquired protective immunity and hence the prospects for more effective vaccines. Styblo suggested that the decline in incidence of TB among the elderly in The Netherlands over the course of the 20th century was attributable to a declining risk of TB caused by re-infection [14]. Given the high annual risk of tuberculous infection at the beginning of the 20th century (>10% before 1910), the prevalence of latent infection was extremely high in these birth cohorts from adulthood onwards. The decreasing TB incidence in these birth cohorts over the years might be explained by a declining rate of disease attributable to re-infection. However, other authors did not consider re-infection to be important [68, 69], and an alternative hypothesis might explain the declining TB rates among the elderly as well, as rates of reactivation from latent infection to disease might have declined over time, e.g. as a result of better nutrition. That reactivation rates vary strongly between settings was shown by a study from Hong Kong, which estimated that the rate of reactivation among elderly men was approxumately 17 times higher in Hong Kong than in the UK [70].

A landmark study from Cape Town provided direct evidence of the importance of re-infection as a cause of recurrent TB after curative treatment [71]. Among 16 patients with recurrent TB, 12 (75%) had a strain with a different RFLP pattern from that during the first episode, suggesting an extremely important role for re-infection in recurrent TB in a high-incidence setting. In this study, HIV results were not available, but the HIV prevalence was believed to be low. In studies measuring HIV status, recurrent TB resulting from re-infection was particularly common among HIV-infected patients [72, 73]. The risk among HIV-infected patients was lowered by antiretroviral therapy [74]. A study on recurrent TB among HIV-infected children showed that recurrence was common, affecting 10% of children, and was attributable to both relapse and re-infection [75]. In low-incidence settings, on the other hand, recurrent TB was much less common, and was rarely attributable to re-infection [76].

A later study from Cape Town suggested that some individuals may be particularly susceptible to TB, as the incidence of recurrent TB attributed to re-infection was higher than the incidence of a first episode of TB in the same population [77]. This finding has since been confirmed elsewhere, both for HIV-infected and for HIV-uninfected individuals [78, 79].

Although recurrent TB resulting from re-infection may have limited relevance to TB control activities [80], it calls into question the role of protective immunity [81]. Further immunological studies are needed to determine the role of protective immunity in TB and the implications for vaccine development [82].

Multiple infection

In the early studies, RFLP patterns were generally interpreted as being derived from one strain, as, almost invariably, the intensities of all bands were equal, and mixtures of different bacterial populations reflected in two subsets of bands with different intensities were hardly observed. This was remarkable, because, in the 1990s, a significant proportion of TB patients in western countries already came from high-prevalence areas, where the probability of multiple infection may be considerable. In The Netherlands, where DNA fingerprinting has been conducted since 1993, the only indication of two mixed RFLP patterns with different intensities was traced back to long-term laboratory cross-contamination in a peripheral laboratory [83]. A systematic search for RFLP patterns with single ‘vague’ (low-intensity) bands suggested that mixed infection might indeed occur [84]. However, in RFLP analysis of single colonies from such isolates, the vague bands disappeared, and bacteria of individual colonies either had a normal-intensity band at the position of the vague band in the parental strains, or no band at all. Therefore, transpositions of IS6110 in the genome of M. tuberculosis, and thus genetic drift in a part of the bacterial population, was a likely explanation. In purposely composed mixtures of strains with different RFLP patterns, it became clear that the limit for detection of a second strain was approximately 10% ‘foreign’ DNA [84]. Similarly, mixtures of drug-resistant and susceptible strains have been recognized [85].

Mixed infection with different stains has also been identified with molecular techniques. Whereas, in low-incidence countries, the probability of multiple infection is expected to be low, in high-incidence countries this risk may be high. For instance, if the annual risk of infection were 4%, as has been observed in Cape Town, South Africa [86], it can be calculated that, at age 35 years, 24% of individuals would have escaped infection, 35% would have been infected once, and 41% would have been infected more than once, unless prior infection protected against re-infection. Evidence of multiple strains involved in TB disease has emerged in recent years. Of TB patients in Cape Town, 19% were infected with both a Beijing strain and a non-Beijing strain [87]. Two studies in Taiwan found that, among TB patients, 3% and 11%, respectively, were infected with a Beijing strain and a non-Beijing strain [88, 89]. In Malawi, 3% of patients were infected with strains of the LAM and non-LAM lineages [90]. This suggests that multiple infections are rather common among TB patients in high-prevalence settings. Owing to methodological limitations (multiple infection is demonstrated with genotype-specific PCR testing), the extent of multiple infection contributing to disease is likely to have been underestimated in studies thus far. On the other hand, given the risk of contamination in PCR, one could also argue that the problem of mixed infections is overestimated. Therefore, more research is needed to better clarify this important issue. Both recurrent TB after re-infection and multiple infections call into question the role of protective adaptive immunity and the possibilities of developing effective vaccines [81, 82].

Incubation period

It has been known for a long time that the incubation period of TB may range from a few months to many years [91, 92]. The measurement of this was challenging, because determining the moment of infection is difficult, and a long follow-up is required, with a low risk per person necessitating large cohorts, and re-infection may occur during follow-up.

Some decades ago, follow-up studies were performed among contacts of infectious TB patients in the USA [91] and in a control group of a bacille Calmette–Guérin (BCG) vaccination trial among adolescents in the UK [92]. Among those developing disease within 10 years, 50% did so within 2 years in the former studies and 82% in the latter. No risk factors for short incubation periods were identified.

In a recent molecular epidemiological study in The Netherlands, the incubation period distribution was determined among 1095 secondary cases attributed to 688 source cases whose isolates had identical RFLP patterns and for whom epidemiological contact had been reported [93]. Of those developing TB within 15 years, 62% did so within 2 years. Risk factors for short incubation periods were young age, male sex, extrapulmonary TB, and not having had previous TB or preventive therapy [93]. The latter two risk factors appear to be consistent with some role for adaptive protective immunity.

Drug-resistant TB

MDR-TB was an important problem during the re-emergence of TB in New York in the early 1990s [5], and is recognized as a serious threat in eastern Europe and Central Asia [11, 94, 95]. Since 2006, extensively drug-resistant TB (XDR-TB) has been recognized as a global problem [12, 96, 97], with an extremely high mortality among HIV-positive individuals [98, 99]. Recently, reports on totally drug-resistant TB have emerged in Iran and India [100, 101]. Methods to control MDR-TB are known: first, its emergence needs to be prevented by appropriate treatment of drug-susceptible TB [102]; and second, if present, MDR-TB needs to be treated adequately to prevent transmission, death, and the development of XDR-TB [12].

The prospects for the control of MDR-TB are unclear. An important uncertainty is the reproductive fitness of drug-resistant strains [103, 104]. One might expect drug-resistant strains to have increased reproductive fitness, as resistant cases are likely to be infectious for a longer time than susceptible ones, and because drug-resistant TB may occur preferentially among certain risk groups, such as HIV-infected individuals [105]. On the other hand, as was reported in the 1950s and 1960s, on the basis of experiments in guinea pigs, the fitness of M. tuberculosis might be impaired if underlying mutations impact on the ability to withstand exposure to oxygen radicals [106, 107]. However, this will depend on the specific mutations. For instance, some mutations in the katG gene, such as the S315T mutation, will only reduce the expression of katalase/peroxidase, whereas others will stop its expression entirely [108]. If drug resistance-conferring mutations reduce virulence, this effect may be undone through compensatory evolution [104, 109].

The incidence of drug-resistant TB has declined in some settings [110], sometimes even in the absence of specific control measures for drug-resistant TB [111], suggesting reduced reproductive fitness. However, this does not appear to apply to all forms [112]. The ability to preserve fitness while becoming resistant may be associated with particular genotypes, such as the Beijing strain [113-115]. For instance, a large proportion of recently transmitted MDR-TB/XDR-TB strains in the European Union are of the Beijing genotype [116, 117].

RFLP clustering has been used to compare the relative fitness of drug-resistant and drug-susceptible strains. Isoniazid-resistant strains were less likely to be clustered [38, 118], but not if resistance was attributable to the katG gene S315T mutation [108, 119]. Moreover, a wider comparison suggested that the relative fitness of drug-resistant strains varies between settings [103]. Overall, the reproductive fitness is likely to depend both on biological factors—such as loss of virulence and compensatory evolution—and on factors associated with the setting—such as speed and completeness of case detection, quality of drugs and drug regimens used, and systems to ensure treatment compliance [112].

Beijing genotype

The Beijing genotype was described in 1995 as the predominant genotype in the Beijing region [120], and since then in various Asian countries [e.g. [121]]. It was initially recognized on the basis of the characteristic IS6110 RFLP and spoligotyping pattern; later, the definition was refined [122]. Mokrousov et al. [123] introduced the distinction between typical and atypical Beijing strains, and this facilitated studies on the evolutionary development of this genotype family [124, 125]. For instance, it has been suggested that the success of the more recent typical Beijing strain may be attributable to its ability to circumvent immune protection after BCG vaccination [124].

The emergence of Beijing strains was reported in various settings [113, 115, 126], e.g. in Vietnam [121], where it was associated with young age, in the Canary Islands [127], where an outbreak and fast spread were documented, in South Africa, where a strong increase was seen among young children [128], and in The Netherlands, where the incidence increased in association with immigration and among young natives [129]. The lineage is observed all over the world, and is associated with drug resistance in various settings [113, 115, 126], including in eastern Europe, New York (where a side branch of the Beijing lineage was known under the name ‘W’ family or W strain [96]), and in South Africa [130].

Recently, a correlation was shown between MDR-TB and the Beijing genotype in Colombia [131]. This may be alarming, as these strains have hardly been found in Latin America in the past. The strong association of the Beijing genotype with MDR-TB/XDR-TB in eastern Europe is reflected in the European Union, where the largest number of clustered patients with MDR-TB/XDR-TB were infected with one type of Beijing genotype strain [116, 117]. Nearly half of the MDR-TB/XDR-TB cases included in European surveillance were in clusters, and 85% of the transmitted cases were Beijing isolates not distinguishable with RFLP and VNTR typing. This is remarkable, because, of the susceptible isolates in Europe, only 6–7% are of this genotype.

Although various reasons for the emergence of the Beijing genotype have been proposed, including escape from BCG vaccination, an increased ability to acquire drug resistance without loss of fitness, and an increased virulence, further research is needed [114, 124]. If Beijing strains do indeed have selective advantages over other M. tuberculosis strains and have been emerging for a few decades, the time of divergence should be short. On the basis of WGS of three typical and three atypical Beijing strains from China, Vietnam, and South Africa, the typical Beijing strains from this widespread geographical area appeared to be genetically highly conserved, whereas the more ancestral atypical strains were much more diverse [17, 132] (Fig. 2). The 53 mutations that separate all typical Beijing strains from the atypical strains were, for the large part, traced to regulatory regions of the genome, and may influence the overall protein expression in typical strains. Recently, it has been found that some Beijing strains have a much higher mutation frequency, leading to rifampicin resistance [133]. Moreover, a higher dose of rifampicin was needed to achieve 100% killing of Beijing genotype bacteria, suggesting that Beijing bacteria have higher intrinsic resistance against this drug [133].


Figure 2. Mutations in the regulatory network are associated with the recent clonal expansion of a dominant subclone of the Mycobacterium tuberculosis Beijing genotype. The hypothetical phylogenetic tree of the Beijing genotype strains of M. tuberculosis is shown. The atypical Beijing strains are genetically diverse. The typical strains presumably gained a selective advantage over the atypical strains, and started to spread recently. The currently isolated typical Beijing strains from a widespread geographical area are highly clonal, which may be related to an enhanced capacity to circumvent bacille Calmette–Guérin-induced immunity or to withstand treatment with antituberculosis drugs.

Download figure to PowerPoint


The first lineage of M. tuberculosis to be found was the Beijing genotype described in 1995 [120]. It is considered to be one of the six main lineages distributed globally [134]. The first definition of Beijing strains was based on their specific spoligotyping and IS6110 RFLP patterns [120], although both markers have serious limitations for studying the phylogeny of the M. tuberculosis complex. Insertion sequence IS6110 is, in fact, a mobile genomic element that utilizes preferential insertion sites, thus favouring convergent evolution. Nevertheless, IS6110 RFLP patterns, to a large degree, group M. tuberculosis isolates into genotype families, and this characterization is valuable for identifying, for instance, Beijing genotype strains [122]. Spoligotyping also has been used extensively to study the phylogeography of the M. tuberculosis complex, and a huge database representing >39 000 isolates from 122 countries provided the first insights into the distribution of genotype families worldwide [18]. Spoligotyping offers insufficient resolution in some genotype families, and convergent evolution has been noted in offspring of well-characterized strains [135].

Although VNTR typing was initially seen a strain typing method, several studies have shown that the VNTR pattern is also a valuable phylogenetic marker [136], even though convergent evolution may occur occasionally [137].

The distribution of the six M. tuberculosis lineages appears to differ significantly by geographical area, with the largest variability in Africa [134]. This is reflected in the names used by Gagneux et al. (Indo-Oceanic, East Asian, East African-Indian, Euro-American, West African lineage I, and West African lineage II) [134]. The association between M. tuberculosis lineages and geographical areas has been observed among isolates from recent immigrants in low-incidence countries [138, 139], and is also emerging from many recent publications on lineage distributions in different geographical areas. These geographical associations are likely to be attributable, at least in part, to historical migration patterns and perhaps the origin of humankind, as described for Helicobacter pylori [140]. It is interesting that Mycobacterium canettii, which is believed to be closely linked to the common ancestor of the M. tuberculosis complex [20, 141, 142], has its epicentre in the Horn of Africa, the geographical area where humankind presumably started its spread over the world.

There are various possible explanations for the association between lineage and geographical area. First, the spread of M. tuberculosis may have represented a series of population bottlenecks (founder effect). Moreover, co-evolution between the human host and M. tuberculosis may have played a role [21, 138]. In San Francisco, TB transmission was more common within than between ethnic groups [138], but this association may have been the result of social mixing rather than host–pathogen co-evolution.

It has been shown that polymorphisms in human susceptibility genes are associated with the clinical presentation and genotypes of M. tuberculosis infecting patients [143, 144]. Overall, the evidence for the role of the genotype of M. tuberculosis in transmissibility, pathogenicity and virulence in various human populations is limited, and is more extensive for some genotypes, such as the Beijing genotype [114], than for others.

Prospects of TB elimination and impact of immigration

In the late 1980s, TB elimination was expected to be achived within decades in various low-incidence countries [3, 4]. Since then, progress has been slower than foreseen, owing to four main factors: temporary neglect of TB control; the emergence of HIV; increasing human migration; and the development of resistance against anti-TB drugs. The impact of neglect of TB control was most clearly observed in New York, where TB notification rates nearly tripled from 1978 to 1992, and then showed a 20% decline between 1992 and 1994 after the re-strengthening of control [110]. At around the same time, the impact of the HIV epidemic on TB epidemiology became evident. HIV-infected individuals have a strongly increased risk of progressing from infection to disease [145], and HIV has led to a strongly increased TB incidence in Africa [146]. Fortunately, the HIV epidemic in industrialized countries did not evolve into a generalized epidemic, but remained restricted to high-risk populations. Furthermore, the risk of TB in HIV-infected individuals was reduced after the introduction of highly active antiretroviral therapy in the 1990s [147].

Progress towards TB elimination was slowed down by immigration from high-incidence areas [148]. In New York, most cases of TB among immigrants were attributed to reactivation of latent infection [39]. In The Netherlands, a molecular epidemiological study showed a strong decline in the incidence of TB attributable to reactivation among the native population, from 170 cases in 1995 to 91 cases in 2005, more or less as predicted in 1990 [44]. The decline in the number of index cases among foreign-born individuals was much less (from 250 to 222 cases). The risk of transmission from immigrants to the native population is generally low [149-151]. However, although the absolute risk is low, the proportion of secondary cases among the native population in The Netherlands attributed to foreign-born index cases increased from 29% in 1995 to 50% in 2005 [44].

Earlier studies and surveillance thus suggested that existing control programmes should be maintained for as long as the disease is not eliminated, that surveillance is of vital importance, and that most TB in low-incidence countries is found among the foreign-born. Molecular epidemiological studies have helped to quantify transmission from the foreign-born to the native population, and can thus be used to predict progress towards elimination. In order to accelerate progress towards TB elimination in low-incidence countries, these countries need to maintain programmes for TB control, use new tools as they become available, expand the use of preventive therapy in those with latent infection (primarily the elderly and the foreign-born), consider expanding screening for TB infection and disease [152], and support global TB control, as this is expected to be most effective in the long term, and may even be cost-effective in the short term [153].

Open Questions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Molecular Typing Methods
  5. Molecular Epidemiological Insights from the Last Two Decades
  6. Open Questions
  7. Transparency Declaration
  8. References

Although great progress has been made in the molecular epidemiology of TB over the last two decades, many open questions remain.

It should be investigated whether WGS can be developed into a practical tool to cover a large part of the laboratory diagnosis. In principle, identification, indicative drug susceptibility testing and epidemiological typing can all be performed with this single technique [17]. The first studies on the value added by WGS to current typing techniques have shown that separate transmission chains within RFLP/VNTR typing clusters can be distinguished on the basis of infrequent mutations occurring in the offspring of bacteria [21, 24, 25]. For a wider epidemiological application of this technique, it is important to determine the rate of change of the M. tuberculosis genome. WGS should be able to predict resistance patterns in M. tuberculosis, although more resistance-conferring mutations need to be identified [154]. Moreover, techniques are needed to study mixed populations of bacteria with different resistance profiles [155].

With wider application of DNA fingerprinting, advances in WGS, and the development of new tools for TB control and the implementation of trials to assess these new tools, we may be able to answer further important questions in TB control. These include identification of chains of transmission within clusters of patients with identical RFLP/VNTR patterns [24] (Fig. 3), identification of determinants of genomic stability [24, 156], improved quantification of mixed infection [157, 158], determination of the transmission probabilities and rates of progression from infection to disease and clinical presentation of various M. tuberculosis strains or lineages in various human populations [109, 159-161], identification of correlates of immunological protection depending on host and the M. tuberculosis lineage [109, 134, 138], improved insights into the prospects for the control of drug-resistant TB [112], determination of the validity of new tests to diagnose latent M. tuberculosis infection and TB dependent on lineage [134], determination of clinical presentation and the probability of treatment failure or relapse in association with the causative M. tuberculosis strain [134, 162, 163], and the efficacy of BCG and new vaccines in relation to the causative M. tuberculosis strains [124, 134].


Figure 3. Stability of the Mycobacterium tuberculosis genome in a chain of transmission spanning 14 years. In order to examine the highest resolution of DNA typing, the strain that caused the Harlingen outbreak in The Netherlands in 1993 was subjected to whole genome sequencing. The sequence of an isolate in 1993 was compared with that of an isolate of the same strain in 2006, after it had been passed on through four patients to a fifth patient.

Download figure to PowerPoint

But perhaps the most important research question in the molecular epidemiology of TB concerns the dynamics in the population structure of M. tuberculosis in high-prevalence areas and what genetic factors underlie the ongoing selection. If the worldwide TB epidemic is changing into an epidemic of drug-resistant TB, the previous success in control will be reversed, and the plague of humankind will have reinvented itself.

Transparency Declaration

  1. Top of page
  2. Abstract
  3. Introduction
  4. Molecular Typing Methods
  5. Molecular Epidemiological Insights from the Last Two Decades
  6. Open Questions
  7. Transparency Declaration
  8. References

No financial support was received for this review. The authors have no conflict of interest to declare.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Molecular Typing Methods
  5. Molecular Epidemiological Insights from the Last Two Decades
  6. Open Questions
  7. Transparency Declaration
  8. References
  • 1
    World Health Organization. Global tuberculosis report 2012. Geneva: WHO, 2012.
  • 2
    European Centre for Disease Prevention and Control/WHO Regional Office for Europe. Tuberculosis surveillance and monitoring in Europe 2013. Stockholm: European Centre for Disease Prevention and Control, 2013.
  • 3
    Styblo K. The elimination of tuberculosis in The Netherlands. Bull Int Union Tuberc Lung Dis 1990; 65: 4955.
  • 4
    Dowdle WR. A strategic plan for the elimination of tuberculosis in the United States. MMWR 1989; 38(suppl 3): 125.
  • 5
    Frieden TR, Sterling T, Pablos-Mendez A, Kilburn JO, Cauthen GM, Dooley SW. The emergence of drug-resistant tuberculosis in New York City. N Engl J Med 1993; 328: 521526.
  • 6
    Alland D, Kalkut GE, Moss AR et al. Transmission of tuberculosis in New York City. An analysis by DNA fingerprinting and conventional epidemiologic methods. N Engl J Med 1994; 330: 17101716.
  • 7
    Small PM, Hopewell PC, Singh SP et al. The epidemiology of tuberculosis in San Francisco. A population-based study using conventional and molecular methods. N Engl J Med 1994; 330: 17031709.
  • 8
    Murray CJ, Lopez AD. Mortality by cause for eight regions of the world: global burden of disease study. Lancet 1997; 349: 12691276.
  • 9
    Murray CJ, Lopez AD. Regional patterns of disability-free life expectancy and disability-adjusted life expectancy: global burden of disease study. Lancet 1997; 349: 13471352.
  • 10
    De Cock KM, Chaisson RE. Will DOTS do it? A reappraisal of tuberculosis control in countries with high rates of HIV infection. Int J Tuberc Lung Dis 1999; 3: 457465.
  • 11
    Espinal MA, Laszlo A, Simonsen L et al. Global trends in resistance to antituberculosis drugs. World Health Organization–International Union against Tuberculosis and Lung Disease Working Group on Anti-tuberculosis Drug Resistance Surveillance. N Engl J Med 2001; 344: 12941303.
  • 12
    Gandhi NR, Nunn P, Dheda K et al. Multidrug-resistant and extensively drug-resistant tuberculosis: a threat to global control of tuberculosis. Lancet 2010; 375: 18301843.
  • 13
    Dye C, Garnett GP, Sleeman K, Williams BG. Prospects for worldwide tuberculosis control under the WHO DOTS strategy. Directly observed short-course therapy. Lancet 1998; 352: 18861891.
  • 14
    Styblo K. Epidemiology of tuberculosis. Selected papers 24. The Hague: KNCV Tuberculosis Foundation, 1992.
  • 15
    Lonnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: the role of risk factors and social determinants. Soc Sci Med 2009; 68: 22402246.
  • 16
    Dye C, Lonnroth K, Jaramillo E, Williams BG, Raviglione M. Trends in tuberculosis incidence and their determinants in 134 countries. Bull World Health Organ 2009; 87: 683691.
  • 17
    Schurch AC, van Soolingen D. DNA fingerprinting of Mycobacterium tuberculosis: from phage typing to whole-genome sequencing. Infect Genet Evol 2012; 12: 602609.
  • 18
    Brudey K, Driscoll JR, Rigouts L et al. Mycobacterium tuberculosis complex genetic diversity: mining the fourth international spoligotyping database (SpolDB4) for classification, population genetics and epidemiology. BMC Microbiol 2006; 6: 23.
  • 19
    Supply P, Allix C, Lesjean S et al. Proposal for standardization of optimized mycobacterial interspersed repetitive unit-variable-number tandem repeat typing of Mycobacterium tuberculosis. J Clin Microbiol 2006; 44: 44984510.
  • 20
    Brosch R, Gordon SV, Marmiesse M et al. A new evolutionary scenario for the Mycobacterium tuberculosis complex. Proc Natl Acad Sci USA 2002; 99: 36843689.
  • 21
    Hershberg R, Lipatov M, Small PM et al. High functional diversity in Mycobacterium tuberculosis driven by genetic drift and human demography. PLoS Biol 2008; 6: e311.
  • 22
    Namouchi A, Didelot X, Schöck U, Gicquel B, Rocha EP. After the bottleneck: genome-wide diversification of the Mycobacterium tuberculosis complex by mutation, recombination, and natural selection. Genome Res 2012; 22: 721734.
  • 23
    Djelouadji Z, Arnold C, Gharbia S, Raoult D, Drancourt M. Multispacer sequence typing for Mycobacterium tuberculosis genotyping. PLoS ONE 2008; 18: 3.
  • 24
    Schurch AC, Kremer K, Daviena O et al. High-resolution typing by integration of genome sequencing data in a large tuberculosis cluster. J Clin Microbiol 2010; 48: 34033406.
  • 25
    Gardy JL, Johnston JC, Ho Sui SJ et al. Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. N Engl J Med 2011; 364: 730739.
  • 26
    Walker TM, Ip CL, Harrell RH et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect Dis 2013; 13: 137146.
  • 27
    Horwitz O. Disease, cure, and death: epidemiologic and clinical parameters for chronic diseases illustrated by a model—tuberculosis. Am J Epidemiol 1973; 97: 148159.
  • 28
    Braden CR, Templeton GL, Cave MD et al. Interpretation of restriction fragment length polymorphism analysis of Mycobacterium tuberculosis isolates from a state with a large rural population. J Infect Dis 1997; 175: 14461452.
  • 29
    Van Deutekom H, Hoijng SP, de Haas PE et al. Clustered tuberculosis cases: do they represent recent transmission and can they be detected earlier? Am J Respir Crit Care Med 2004; 169: 806810.
  • 30
    Jasmer RM, Hahn JA, Small PM et al. A molecular epidemiologic analysis of tuberculosis trends in San Francisco, 1991–1997. Ann Intern Med 1999; 130: 971978.
  • 31
    Glynn JR, Bauer J, de Boer AS et al. Interpreting DNA fingerprint clusters of Mycobacterium tuberculosis. European Concerted Action on Molecular Epidemiology and Control of Tuberculosis. Int J Tuberc Lung Dis 1999; 3: 10551060.
  • 32
    Borgdorff MW, van der Werf MJ, de Haas PE, Kremer K, van Soolingen D. Tuberculosis elimination in the Netherlands. Emerg Infect Dis 2005; 11: 597602.
  • 33
    de Boer AS, Borgdorff MW, de Haas PE, Nagelkerke NJ, van Embden JD, van Soolingen D. Analysis of rate of change of IS6110 RFLP patterns of Mycobacterium tuberculosis based on serial patient isolates. J Infect Dis 1999; 180: 12381244.
  • 34
    Glynn JR, Vynnycky E, Fine PE. Influence of sampling on estimates of clustering and recent transmission of Mycobacterium tuberculosis derived from DNA fingerprinting techniques. Am J Epidemiol 1999; 149: 366371.
  • 35
    Murray M. Sampling bias in the molecular epidemiology of tuberculosis. Emerg Infect Dis 2002; 8: 363369.
  • 36
    Vynnycky E, Nagelkerke N, Borgdorff MW, van Soolingen D, van Embden JD, Fine PE. The effect of age and study duration on the relationship between ‘clustering’ of DNA fingerprint patterns and the proportion of tuberculosis disease attributable to recent transmission. Epidemiol Infect 2001; 126: 4362.
  • 37
    Houben RM, Glynn JR. A systematic review and meta-analysis of molecular epidemiological studies of tuberculosis: development of a new tool to aid interpretation. Trop Med Int Health 2009; 14: 892909.
  • 38
    van Soolingen D, Borgdorff MW, de Haas PE et al. Molecular epidemiology of tuberculosis in the Netherlands: a nationwide study from 1993 through 1997. J Infect Dis 1999; 180: 726736.
  • 39
    Geng E, Kreiswirth B, Driver C et al. Changes in the transmission of tuberculosis in New York City from 1990–1999. N Engl J Med 2002; 346: 14531458.
  • 40
    Nava-Aguilera E, Andersson N, Harris E et al. Risk factors associated with recent transmission of tuberculosis: systematic review and meta-analysis. Int J Tuberc Lung Dis 2009; 13: 1726.
  • 41
    Rodwell TC, Kapasi AJ, Barnes RF, Moser KS. Factors associated with genotype clustering of Mycobacterium tuberculosis isolates in an ethnically diverse region of southern California, United States. Infect Genet Evol 2012; 12: 19171925.
  • 42
    Love J, Sonnenberg P, Glynn JR et al. Molecular epidemiology of tuberculosis in England, 1998. Int J Tuberc Lung Dis 2009; 13: 201207.
  • 43
    Borgdorff MW, Nagelkerke NJ, de Haas PE, van Soolingen D. Transmission of Mycobacterium tuberculosis depending on the age and sex of source cases. Am J Epidemiol 2001; 154: 934943.
  • 44
    Borgdorff MW, van den Hof S, Kremer K et al. Progress towards tuberculosis elimination: secular trend, immigration and transmission. Eur Respir J 2010; 36: 339347.
  • 45
    Borgdorff MW, van den Hof S, Kalisvaart N, Kremer K, van Soolingen D. Influence of sampling on clustering and associations with risk factors in the molecular epidemiology of tuberculosis. Am J Epidemiol 2011; 174: 243251.
  • 46
    Kik SV, Verver S, van Soolingen D et al. Tuberculosis outbreaks predicted by characteristics of first patients in a DNA fingerprint cluster. Am J Respir Crit Care Med 2008; 178: 96104.
  • 47
    de Vries G, van Hest RA, Richardus JH. Impact of mobile radiographic screening on tuberculosis among drug users and homeless persons. Am J Respir Crit Care Med 2007; 176: 201207.
  • 48
    McNabb SJ, Kammerer JS, Hickey AC et al. Added epidemiologic value to tuberculosis prevention and control of the investigation of clustered genotypes of Mycobacterium tuberculosis isolates. Am J Epidemiol 2004; 160: 589597.
  • 49
    Cronin WA, Golub JE, Lathan MJ et al. Molecular epidemiology of tuberculosis in a low- to moderate-incidence state: are contact investigations enough? Emerg Infect Dis 2002; 8: 12711279.
  • 50
    Ruddy MC, Davies AP, Yates MD et al. Outbreak of isoniazid resistant tuberculosis in north London. Thorax 2004; 59: 279285.
  • 51
    Aspler A, Chong H, Kunimoto D et al. Sustained intra- and inter-jurisdictional transmission of tuberculosis within a mobile, multi-ethnic social network: lessons for tuberculosis elimination. Can J Public Health 2010; 101: 205209.
  • 52
    Allix-Beguec C, Supply P, Wanlin M, Bifani P, Fauville-Dufaux M. Standardised PCR-based molecular epidemiology of tuberculosis. Eur Respir J 2008; 31: 10771084.
  • 53
    de Vries G, van Hest RA. From contact investigation to tuberculosis screening of drug addicts and homeless persons in Rotterdam. Eur J Public Health 2006; 16: 133136.
  • 54
    Behr MA, Hopewell PC, Paz EA, Kawamura LM, Schecter GF, Small PM. Predictive value of contact investigation for identifying recent transmission of Mycobacterium tuberculosis. Am J Respir Crit Care Med 1998; 158: 465469.
  • 55
    Verver S, Warren RM, Munch Z et al. Proportion of tuberculosis transmission that takes place in households in a high-incidence area. Lancet 2004; 363: 212214.
  • 56
    Guerrero A, Cobo J, Fortun J et al. Nosocomial transmission of Mycobacterium bovis resistant to 11 drugs in people with advanced HIV-1 infection. Lancet 1997; 350: 17381742.
  • 57
    Moro ML, Gori A, Errante I et al. An outbreak of multidrug-resistant tuberculosis involving HIV-infected patients of two hospitals in Milan, Italy. Italian Multidrug-Resistant Tuberculosis Outbreak Study Group. AIDS 1998; 12: 10951102.
  • 58
    Samper S, Iglesias MJ, Rabanaque MJ et al. Systematic molecular characterization of multidrug-resistant Mycobacterium tuberculosis complex isolates from Spain. J Clin Microbiol 2005; 43: 12201227.
  • 59
    Frieden TR, Woodley CL, Crawford JT, Lew D, Dooley SM. The molecular epidemiology of tuberculosis in New York City: the importance of nosocomial transmission and laboratory error. Tuber Lung Dis 1996; 77: 407413.
  • 60
    French AL, Welbel SF, Dietrich SE et al. Use of DNA fingerprinting to assess tuberculosis infection control. Ann Intern Med 1998; 129: 856861.
  • 61
    Escombe AR, Moore DA, Gilman RH et al. The infectiousness of tuberculosis patients coinfected with HIV. PLoS Med 2008; 5: e188.
  • 62
    Escombe AR, Oeser C, Gilman RH et al. The detection of airborne transmission of tuberculosis from HIV-infected patients, using an in vivo air sampling model. Clin Infect Dis 2007; 44: 13491357.
  • 63
    Escombe AR, Oeser CC, Gilman RH et al. Natural ventilation for the prevention of airborne contagion. PLoS Med 2007; 4: e68.
  • 64
    Small PM, McClenny NB, Singh SP, Schoolnik GK, Tompkins LS, Mickelsen PA. Molecular strain typing of Mycobacterium tuberculosis to confirm cross-contamination in the mycobacteriology laboratory and modification of procedures to minimize occurrence of false-positive cultures. J Clin Microbiol 1993; 31: 16771682.
  • 65
    Jasmer RM, Roemer M, Hamilton J et al. A prospective, multicenter study of laboratory cross-contamination of Mycobacterium tuberculosis cultures. Emerg Infect Dis 2002; 8: 12601263.
  • 66
    Martin A, Herranz M, Lirola MM, Fernandez RF, Bouza E, Garcia de Viedma D. Optimized molecular resolution of cross-contamination alerts in clinical mycobacteriology laboratories. BMC Microbiol 2008; 8: 30.
  • 67
    de Boer AS, Blommerde B, de Haas PE et al. False-positive Mycobacterium tuberculosis cultures in 44 laboratories in The Netherlands (1993 to 2000): incidence, risk factors, and consequences. J Clin Microbiol 1993; 2002: 40044009.
  • 68
    Blower SM, McLean AR, Porco TC et al. The intrinsic transmission dynamics of tuberculosis epidemics. Nat Med 1995; 1: 815821.
  • 69
    Blower SM, Small PM, Hopewell PC. Control strategies for tuberculosis epidemics: new models for old problems. Science 1996; 273: 497500.
  • 70
    Vynnycky E, Borgdorff MW, Leung CC, Tam CM, Fine PE. Limited impact of tuberculosis control in Hong Kong: attributable to high risks of reactivation disease. Epidemiol Infect 2008; 136: 943952.
  • 71
    van Rie A, Warren R, Richardson M et al. Exogenous reinfection as a cause of recurrent tuberculosis after curative treatment. N Engl J Med 1999; 341: 11741179.
  • 72
    Sonnenberg P, Murray J, Glynn JR, Shearer S, Kambashi B, Godfrey-Faussett P. HIV-1 and recurrence, relapse, and reinfection of tuberculosis after cure: a cohort study in South African mineworkers. Lancet 2001; 358: 16871693.
  • 73
    Crampin AC, Mwaungulu JN, Mwaungulu FD et al. Recurrent TB: relapse or reinfection? The effect of HIV in a general population cohort in Malawi. AIDS 2010; 24: 417426.
  • 74
    Houben RM, Glynn JR, Mboma S et al. The impact of HIV and ART on recurrent tuberculosis in a sub-Saharan setting. AIDS 2012; 26: 22332239.
  • 75
    Schaaf HS, Krook S, Hollemans DW, Warren RM, Donald PR, Hesseling AC. Recurrent culture-confirmed tuberculosis in human immunodeficiency virus-infected children. Pediatr Infect Dis J 2005; 24: 685691.
  • 76
    Jasmer RM, Bozeman L, Schwartzman K, Burman WJet al. Recurrent tuberculosis in the United States and Canada: relapse or reinfection? Am J Respir Crit Care Med 2004; 170: 13601366.
  • 77
    Verver S, Warren RM, Beyers N et al. Rate of reinfection tuberculosis after successful treatment is higher than rate of new tuberculosis. Am J Respir Crit Care Med 2005; 171: 14301435.
  • 78
    Glynn JR, Murray J, Bester A, Nelson G, Shearer S, Sonnenberg P. High rates of recurrence in HIV-infected and HIV-uninfected patients with tuberculosis. J Infect Dis 2010; 201: 704711.
  • 79
    Lahey T, Mackenzie T, Arbeit RD et al. Recurrent tuberculosis risk among HIV-infected adults in Tanzania with prior active tuberculosis. Clin Infect Dis 2013; 56: 151158.
  • 80
    Lambert ML, Hasker E, Van Deun A, Roberfroid D, Boelaert M, Van der Stuyft P. Recurrence in tuberculosis: relapse or reinfection? Lancet Infect Dis 2003; 3: 282287.
  • 81
    Chiang CY, Riley LW. Exogenous reinfection in tuberculosis. Lancet Infect Dis 2005; 5: 629636.
  • 82
    Nagelkerke NJ, de Vlas SJ, Mahendradhata Y, Ottenhoff TH, Borgdorff M. Tuberculosis (Edinb) 2006; 86: 4146.
  • 83
    Van Duin JM, Pijnenburg JE, van Rijswoud CM, de Haas PE, Hendriks WD, van Soolingen D. Investigation of cross contamination in a Mycobacterium tuberculosis laboratory using IS6110 DNA fingerprinting. Int J Tuberc Lung Dis 1998; 2: 425429.
  • 84
    de Boer AS, Kremer K, Borgdorff MW, de Haas PE, Heersma HF, van Soolingen D. Genetic heterogeneity in Mycobacterium tuberculosis isolates reflected in IS6110 restriction fragment length polymorphism patterns as low-intensity bands. J Clin Microbiol 2000; 38: 44784484.
  • 85
    Rinder H, Mieskes KT, Loscher T. Heteroresistance in Mycobacterium tuberculosis. Int J Tuberc Lung Dis 2001; 5: 339345.
  • 86
    Kritzinger FE, Den BS, Verver S et al. No decrease in annual risk of tuberculosis infection in endemic area in Cape Town, South Africa. Trop Med Int Health 2009; 14: 136142.
  • 87
    Warren RM, Victor TC, Streicher EM et al. . Patients with active tuberculosis often have different strains in the same sputum specimen. Am J Respir Crit Care Med 2004; 169: 610614.
  • 88
    Wang JY, Hsu HL, Yu MC et al. Mixed infection with Beijing and non-Beijing strains in pulmonary tuberculosis in Taiwan: prevalence, risk factors, and dominant strain. Clin Microbiol Infect 2011; 17: 12391245.
  • 89
    Huang HY, Tsai YS, Lee JJ et al. Mixed infection with Beijing and non-Beijing strains and drug resistance pattern of Mycobacterium tuberculosis. J Clin Microbiol 2010; 48: 44744480.
  • 90
    Mallard K, McNerney R, Crampin AC et al. Molecular detection of mixed infections of Mycobacterium tuberculosis strains in sputum samples from patients in Karonga District, Malawi. J Clin Microbiol 2010; 48: 45124518.
  • 91
    Ferebee SH. Controlled chemoprophylaxis trials in tuberculosis. A general review. Bibl Tuberc 1970; 26: 28106.
  • 92
    Sutherland I. The ten year incidence of clinical tuberculosis following ‘conversion’ in 2550 individuals aged 14 to 19 at the time of conversion. TSRU Progress Report 1968. The Hague: KNCV Tuberculosis Foundation, 1968.
  • 93
    Borgdorff MW, Sebek M, Geskus RB, Kremer K, Kalisvaart N, van Soolingen D. The incubation period distribution of tuberculosis estimated with a molecular epidemiological approach. Int J Epidemiol 2011; 40: 964970.
  • 94
    Niemann S, Rusch-Gerdes S, Richter E. IS6110 fingerprinting of drug-resistant Mycobacterium tuberculosis strains isolated in Germany during 1995. J Clin Microbiol 1997; 35: 30153020.
  • 95
    Viljanen MK, Vyshnevskiy BI, Otten TF et al. Survey of drug-resistant tuberculosis in northwestern Russia from 1984 through 1994. Eur J Clin Microbiol Infect Dis 1998; 17: 177183.
  • 96
    Centers for Disease Control and Prevention. Emergence of Mycobacterium tuberculosis with extensive resistance to second-line drugs—worldwide, 2000–2004. MMWR 2006; 55: 301305.
  • 97
    Dheda K, Warren RM, Zumla A, Grobusch MP. Extensively drug-resistant tuberculosis: epidemiology and management challenges. Infect Dis Clin North Am 2010; 24: 705725.
  • 98
    Gandhi NR, Moll A, Sturm AW et al. Extensively drug-resistant tuberculosis as a cause of death in patients co-infected with tuberculosis and HIV in a rural area of South Africa. Lancet 2006; 368: 15751580.
  • 99
    Gandhi NR, Shah NS, Andrews JR et al. HIV coinfection in multidrug- and extensively drug-resistant tuberculosis results in high early mortality. Am J Respir Crit Care Med 2010; 181: 8086.
  • 100
    Velayati AA, Masjedi MR, Farnia P et al. Emergence of new forms of totally drug-resistant tuberculosis bacilli: super extensively drug-resistant tuberculosis or totally drug-resistant strains in Iran. Chest 2009; 136: 420425.
  • 101
    Udwadia ZF, Amale RA, Ajbani KK, Rodrigues C. Totally drug-resistant tuberculosis in India. Clin Infect Dis 2012; 54: 579581.
  • 102
    Dye C, Williams BG. Criteria for the control of drug-resistant tuberculosis. Proc Natl Acad Sci USA 2000; 97: 81808185.
  • 103
    Dye C, Williams BG, Espinal MA, Raviglione MC. Erasing the world's slow stain: strategies to beat multidrug-resistant tuberculosis. Science 2002; 295: 20422046.
  • 104
    Borrell S, Gagneux S. Infectiousness, reproductive fitness and evolution of drug-resistant Mycobacterium tuberculosis. Int J Tuberc Lung Dis 2009; 13: 14561466.
  • 105
    Moss AR, Alland D, Telzak E et al. A city-wide outbreak of a multiple-drug-resistant strain of Mycobacterium tuberculosis in New York. Int J Tuberc Lung Dis 1997; 1: 115121.
  • 106
    Gagneux S, Long CD, Small PM, Van T, Schoolnik GK, Bohannan BJ. The competitive cost of antibiotic resistance in Mycobacterium tuberculosis. Science 2006; 312: 19441946.
  • 107
    Gagneux S, Burgos MV, Deriemer K et al. Impact of bacterial genetics on the transmission of isoniazid-resistant Mycobacterium tuberculosis. PLoS Pathog 2006; 2: e61.
  • 108
    van Soolingen D, de Haas PE, van Doorn HR, Kuijper E, Rinder H, Borgdorff MW. Mutations at amino acid position 315 of the katG gene are associated with high-level resistance to isoniazid, other drug resistance, and successful transmission of Mycobacterium tuberculosis in the Netherlands. J Infect Dis 2000; 182: 17881790.
  • 109
    Borrell S, Gagneux S. Strain diversity, epistasis and the evolution of drug resistance in Mycobacterium tuberculosis. Clin Microbiol Infect 2011; 17: 815820.
  • 110
    Frieden TR, Fujiwara PI, Washko RM, Hamburg MA. Tuberculosis in New York City—turning the tide. N Engl J Med 1995; 333: 229233.
  • 111
    Deriemer K, Garcia-Garcia L, Bobadilla-Del-valle M et al. Does DOTS work in populations with drug-resistant tuberculosis? Lancet 2005; 365: 12391245.
  • 112
    Dye C. Doomsday postponed? Preventing and reversing epidemics of drug-resistant tuberculosis. Nat Rev Microbiol 2009; 7: 8187.
  • 113
    Glynn JR, Whiteley J, Bifani PJ, Kremer K, van Soolingen D. Worldwide occurrence of Beijing/W strains of Mycobacterium tuberculosis: a systematic review. Emerg Infect Dis 2002; 8: 843849.
  • 114
    Parwati I, van Crevel R, van Soolingen D. Possible underlying mechanisms for successful emergence of the Mycobacterium tuberculosis Beijing genotype strains. Lancet Infect Dis 2010; 10: 103111.
  • 115
    European Concerted Action on New Generation Genetic Markers and Techniques for the Epidemiology and Control of Tuberculosis. Beijing/W genotype Mycobacterium tuberculosis and drug resistance. Emerg Infect Dis. 2006; 12: 736743.
  • 116
    Devaux I, Kremer K, Heersma H, van Soolingen D. Clusters of multidrug-resistant Mycobacterium tuberculosis cases. Europe. Emerg Infect Dis 2009; 15: 10521060.
  • 117
    Devaux I, Manissero D, Fernandez de la Hoz K, Kremer K, van Soolingen D; EuroTB network. Surveillance of extensively drug-resistant tuberculosis in Europe, 2003–2007. Euro Surveill 2010; 15: 19518.
  • 118
    Burgos M, Deriemer K, Small PM, Hopewell PC, Daley CL. Effect of drug resistance on the generation of secondary cases of tuberculosis. J Infect Dis 2003; 188: 18781884.
  • 119
    Metcalfe JZ, Kim EY, Lin SY et al. Determinants of multidrug-resistant tuberculosis clusters, California, USA, 2004–2007. Emerg Infect Dis 2010; 16: 14031409.
  • 120
    van Soolingen D, Qian L, de Haas PE et al. Predominance of a single genotype of Mycobacterium tuberculosis in countries of east Asia. J Clin Microbiol 1995; 33: 32343238.
  • 121
    Anh DD, Borgdorff MW, Van LN et al. Mycobacterium tuberculosis Beijing genotype emerging in Vietnam. Emerg Infect Dis 2000; 6: 302305.
  • 122
    Kremer K, Glynn JR, Lillebaek T et al. Definition of the Beijing/W lineage of Mycobacterium tuberculosis on the basis of genetic markers. J Clin Microbiol 2004; 42: 40404049.
  • 123
    Mokrousov I, Narvskaya O, Otten T et al. Phylogenetic reconstruction within Mycobacterium tuberculosis Beijing genotype in northwestern Russia. Res Microbiol 2002; 153: 629637.
  • 124
    Kremer K, van-der-Werf MJ, Au BK et al. Vaccine-induced immunity circumvented by typical Mycobacterium tuberculosis Beijing strains. Emerg Infect Dis 2009; 15: 335339.
  • 125
    Schurch AC, Kremer K, Warren RM et al. Mutations in the regulatory network underlie the recent clonal expansion of a dominant subclone of the Mycobacterium tuberculosis Beijing genotype. Infect Genet Evol 2011; 11: 587597.
  • 126
    Bifani PJ, Mathema B, Kurepina NE, Kreiswirth BN. Global dissemination of the Mycobacterium tuberculosis W-Beijing family strains. Trends Microbiol 2002; 10: 4552.
  • 127
    Caminero JA, Pena MJ, Campos-Herrero MI et al. Epidemiological evidence of the spread of a Mycobacterium tuberculosis strain of the Beijing genotype on Gran Canaria Island. Am J Respir Crit Care Med 2001; 164: 11651170.
  • 128
    Cowley D, Govender D, February B et al. Recent and rapid emergence of W-Beijing strains of Mycobacterium tuberculosis in Cape Town, South Africa. Clin Infect Dis 2008; 47: 12521259.
  • 129
    Borgdorff MW, de Haas P, Kremer K, van Soolingen D. Mycobacterium tuberculosis Beijing genotype, the Netherlands. Emerg Infect Dis 2003; 9: 13101313.
  • 130
    Johnson R, Warren RM, van der Spuy GD et al. Drug-resistant tuberculosis epidemic in the Western Cape driven by a virulent Beijing genotype strain. Int J Tuberc Lung Dis 2010; 14: 119121.
  • 131
    Ferro BE, Nieto LM, Rozo JC, Forero L, van Soolingen D. Multidrug-resistant Mycobacterium tuberculosis, Southwestern Colombia. Emerg Infect Dis 2011; 17: 12591262.
  • 132
    Schurch AC, Kremer K, Hendriks AC et al. SNP/RD typing of Mycobacterium tuberculosis Beijing strains reveals local and worldwide disseminated clonal complexes. PLoS ONE 2011; 6: e28365.
  • 133
    de Steenwinkel JE, ten Kate MT, de Knegt GJ et al. Drug susceptibility of Mycobacterium tuberculosis Beijing genotype and association with MDR TB. Emerg Infect Dis 2012; 18: 660663.
  • 134
    Gagneux S, Small PM. Global phylogeography of Mycobacterium tuberculosis and implications for tuberculosis product development. Lancet Infect Dis 2007; 7: 328337.
  • 135
    Schurch AC, Kremer K, Kiers A, Boeree MJ, Siezen RJ, van Soolingen D. Preferential deletion events in the direct repeat locus of Mycobacterium tuberculosis. J Clin Microbiol 2011; 49: 13181322.
  • 136
    Wirth T, Hildebrand F, Allix-Beguec C et al. Origin, spread and demography of the Mycobacterium tuberculosis complex. PLoS Pathog 2008; 4: e1000160.
  • 137
    Reyes JF, Chan CH, Tanaka MM. Impact of homoplasy on variable numbers of tandem repeats and spoligotypes in Mycobacterium tuberculosis. Infect Genet Evol 2012; 12: 811818.
  • 138
    Gagneux S, Deriemer K, Van T et al. Variable host–pathogen compatibility in Mycobacterium tuberculosis. Proc Natl Acad Sci USA 2006; 103: 28692873.
  • 139
    Baker L, Brown T, Maiden MC, Drobniewski F. Silent nucleotide polymorphisms and a phylogeny for Mycobacterium tuberculosis. Emerg Infect Dis 2004; 10: 15681577.
  • 140
    Falush D, Wirth T, Linz B et al. Traces of human migrations in Helicobacter pylori populations. Science 2003; 299: 15821585.
  • 141
    van Soolingen D, Hoogenboezem T, de Haas PE et al. A novel pathogenic taxon of the Mycobacterium tuberculosis complex, Canetti: characterization of an exceptional isolate from Africa. Int J Syst Bacteriol 1997; 47: 12361245.
  • 142
    Somoskovi A, Dormandy J, Parsons LM et al. Sequencing of the pncA gene in members of the Mycobacterium tuberculosis complex has important diagnostic applications: identification of a species-specific pncA mutation in ‘Mycobacterium canettii’ and the reliable and rapid predictor of pyrazinamide resistance. J Clin Microbiol 2007; 45: 595599.
  • 143
    Caws M, Thwaites G, Dunstan S et al. The influence of host and bacterial genotype on the development of disseminated disease with Mycobacterium tuberculosis. PLoS Pathog 2008; 4: e1000034.
  • 144
    van Crevel R, Parwati I, Sahiratmadja E et al. Infection with Mycobacterium tuberculosis Beijing genotype strains is associated with polymorphisms in SLC11A1/NRAMP1 in Indonesian patients with tuberculosis. J Infect Dis 2009; 200: 16711674.
  • 145
    Selwyn PA, Hartel D, Lewis VA et al. A prospective study of the risk of tuberculosis among intravenous drug users with human immunodeficiency virus infection. N Engl J Med 1989; 320: 545550.
  • 146
    Lawn SD, Zumla AI. Tuberculosis. Lancet 2011; 378: 5772.
  • 147
    Badri M, Wilson D, Wood R. Effect of highly active antiretroviral therapy on incidence of tuberculosis in South Africa: a cohort study. Lancet 2002; 359: 20592064.
  • 148
    Svensson E, Millet J, Lindqvist A, Olsson M, Ridell M, Rastogi N. Impact of immigration on tuberculosis epidemiology in a low-incidence country. Clin Microbiol Infect 2011; 17: 881887.
  • 149
    Sails AD, Barrett A, Sarginson S et al. Molecular epidemiology of Mycobacterium tuberculosis in East Lancashire 2001–2009. Thorax 2011; 66: 709713.
  • 150
    Dahle UR, Eldholm V, Winje BA, Mannsaker T, Heldal E. Impact of immigration on the molecular epidemiology of Mycobacterium tuberculosis in a low-incidence country. Am J Respir Crit Care Med 2007; 176: 930935.
  • 151
    Vanhomwegen J, Kwara A, Martin M et al. Impact of immigration on the molecular epidemiology of tuberculosis in Rhode Island. J Clin Microbiol 2011; 49: 834844.
  • 152
    Liu Y, Weinberg MS, Ortega LS, Painter JA, Maloney SA. Overseas screening for tuberculosis in US-bound immigrants and refugees. N Engl J Med 2009; 360: 24062415.
  • 153
    Schwartzman K, Oxlade O, Barr RG et al. Domestic returns from investment in the control of tuberculosis in other countries. N Engl J Med 2005; 353: 10081020.
  • 154
    Sandgren A, Strong M, Muthukrishnan P, Weiner BK, Church GM, Murray MB. Tuberculosis drug resistance mutation database. PLoS Med 2009; 6: e2.
  • 155
    Saunders NJ, Trivedi UH, Thomson ML, Doig C, Laurenson IF, Blaxter ML. Deep resequencing of serial sputum isolates of Mycobacterium tuberculosis during therapeutic failure due to poor compliance reveals stepwise mutation of key resistance genes on an otherwise stable genetic background. J Infect 2011; 62: 212217.
  • 156
    van Deutekom H, Supply P, de Haas PE et al. Molecular typing of Mycobacterium tuberculosis by mycobacterial interspersed repetitive unit–variable-number tandem repeat analysis, a more accurate method for identifying epidemiological links between patients with tuberculosis. J Clin Microbiol 2005; 43: 44734479.
  • 157
    Sandegren L, Groenheit R, Koivula T et al. Genomic stability over 9 years of an isoniazid resistant Mycobacterium tuberculosis outbreak strain in Sweden. PLoS ONE 2011; 6: e16647.
  • 158
    Schurch AC, Kremer K, Kiers A et al. The tempo and mode of molecular evolution of Mycobacterium tuberculosis at patient-to-patient scale. Infect Genet Evol 2010; 10: 108114.
  • 159
    de Jong BC, Hill PC, Aiken A et al. Progression to active tuberculosis, but not transmission, varies by Mycobacterium tuberculosis lineage in The Gambia. J Infect Dis 2008; 198: 10371043.
  • 160
    Verhagen LM, van den Hof S, van Deutekom H et al. Mycobacterial factors relevant for transmission of tuberculosis. J Infect Dis 2011; 203: 12491255.
  • 161
    Click ES, Moonan PK, Winston CA, Cowan LS, Oeltmann JE. Relationship between Mycobacterium tuberculosis phylogenetic lineage and clinical site of tuberculosis. Clin Infect Dis 2012; 54: 211219.
  • 162
    Buu TN, Huyen MN, van Soolingen D et al. The Mycobacterium tuberculosis Beijing genotype does not affect tuberculosis treatment failure in Vietnam. Clin Infect Dis 2010; 51: 879886.
  • 163
    Lan NT, Lien HT, Tung LB, Borgdorff MW, Kremer K, van Soolingen D. Mycobacterium tuberculosis Beijing genotype and risk for treatment failure and relapse, Vietnam. Emerg Infect Dis 2003; 9: 16331635.