Tuberculosis is an important public health threat around the globe and is particularly common in developing countries. It is difficult to control the spread of the disease because the bacteria that cause it can spread when an infected individual coughs or sneezes. It may take years for an infected individual to develop symptoms of tuberculosis so it can be hard to trace the source of an outbreak, and people infected with HIV are particularly susceptible to the disease.

The bacterium that causes the majority of cases of tuberculosis is called Mycobacterium tuberculosis. There are several different varieties or ‘lineages’ of M. tuberculosis, and it is thought that they may vary in their ability to spread and cause disease. However, the results of previous studies have been inconsistent and there also seems to be a lot of variation between strains within the same lineage.

In this study, Guerra-Assunção et al. used an approach called whole genome sequencing alongside more traditional methods to study the spread of tuberculosis in Malawi. They sequenced the genomes of every available sample of M. tuberculosis collected from patients in the Karonga district of Malawi over a 15-year period. This produced high-quality DNA sequence data about the bacteria responsible for almost 1700 cases of disease.

Using this massive amount of data, Guerra-Assunção et al. constructed networks that showed how the bacteria had spread in the community. This revealed that there were differences between the ability of the various M. tuberculosis lineages to cause disease and to spread in communities. For example, lineage 1 was less likely than the other lineages to cause disease soon after infecting an individual and was less able to spread.

The data also show that the proportion of cases of disease due to recent infection declined substantially during the 15-year period. This indicates that the tuberculosis and HIV control programmes in the area have been successful.

Guerra-Assunção et al.'s findings show that it is possible to understand how tuberculosis is transmitted on a large scale. The next challenge is to understand why the lineages differ in their ability to cause disease and spread between individuals.

Despite the huge global burden of tuberculosis, the factors influencing transmission remain poorly understood. Compared to other bacteria, the genome of Mycobacterium tuberculosis is stable and genetic variation was thought to be limited, but with increased sequencing, greater diversity has been recognized (Homolka et al., 2010). Based on the genotype, M. tuberculosis has seven lineages: three ‘ancient’ (lineage-1 and two Mycobacterium africanum lineages), and three ‘modern’ (lineages-2, 3, 4) (Comas et al., 2009), and one intermediate (lineage-7), recently described in Ethiopia (Firdessa et al., 2013). The lineages may vary in propensity to transmit and cause disease (Thwaites et al., 2008; Homolka et al., 2010; Parwati et al., 2010; Gagneux, 2012), but results are inconsistent and there is considerable strain-to-strain variation within lineages (Portevin et al., 2011; Mathema et al., 2012).

Lineage-2 (Beijing) strains are associated with increasing spread and drug resistance in some areas but not others (European Concerted Action on New Generation Genetic Markers, 2006), and with a lower (Click et al., 2012) or higher (Kong et al., 2007) proportion of extrapulmonary tuberculosis. M. africanum has been associated with lower virulence (de Jong et al., 2008), and lineage-1 with faster sputum smear conversion (Click et al., 2013). In low incidence settings, lineage is often associated with immigrant sub-groups, and while host–pathogen co-evolution has been suggested, it is difficult to disentangle the effects of lineage and host susceptibility on pathogenesis (Reed et al., 2009; Gagneux, 2012; Pareek et al., 2013).

Since the 1990s, methods such as RFLP based on the insertion element IS6110 (van Embden et al., 1993) have been used to distinguish clusters of patients with shared DNA-fingerprint patterns, suggesting recent transmission (Small et al., 1994), but within the clusters, these methods cannot distinguish who transmitted to whom. Whole genome sequencing provides far greater resolution, and if data are collected in a whole population over several years, single nucleotide polymorphisms (SNPs) can be used to construct transmission networks (Bryant et al., 2013; Walker et al., 2013, 2014). In low-incidence settings small numbers of SNPs have been found between epidemiologically linked patients (Kato-Maeda et al., 2013), although the maximum SNP difference to ‘confirm’ a link is not yet established (Perez-Lago et al., 2014). No population-based study to-date has applied long-term large-scale whole genome sequencing in a high prevalence area (Luo et al., 2014; Walker et al., 2014), it is much more challenging to interpret transmission networks when there are many possible sources of infection. Yet understanding transmission in high prevalence areas would have the greatest public health benefit.

As part of the Karonga Prevention Study in Malawi, we assess transmission using whole genome sequencing in the whole district over 15 years. We show decreasing transmission over time and marked variation between M. tuberculosis lineages 1–4 which are unconfounded by host differences.

Between September 1995 and September 2010, there were 2332 person-episodes of culture-confirmed tuberculosis in Karonga District. Whole genome sequences that passed quality control were available for 1687 (72%). The distribution of patients with and without sequences available was very similar by age, sex, and HIV status. The proportion with sequences available was the highest in 2002–2006 (82%) and was higher in those with smear-negative pulmonary disease (78%) than in those with smear-positive disease (71%) and extra-pulmonary disease (67%).

The phylogenetic tree is shown in Figure 1. Most M. tuberculosis strains (68%) were lineage-4, with 16% lineage-1, 4% lineage-2, and 12% lineage-3 (Table 1). Lineage-4 strains were more common in the earlier years. Lineage-1 strains were more common in HIV-positive and older patients and less common in recurrent tuberculosis. Lineage-2 strains were more common in younger patients and were all drug sensitive. Lineage-3 strains were associated with recurrent tuberculosis and with isoniazid resistance. There was no association between lineage and having been born or recently resident outside the district. The associations of lineage with HIV status and recurrent tuberculosis persisted after adjusting for age, sex, and year. The association between lineage and recurrent tuberculosis was also present when restricted to those with drug-sensitive strains, and the association between lineage and isoniazid resistance was also present when restricted to those with first episode tuberculosis.

Figure 1

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SNP-based linkage thresholds

Figure 2 shows the SNP distances between all possible pairs of samples in the data set (including more than one per individual in some cases). Peaks corresponding to large numbers of SNPs represent comparisons between lineages. On the basis of the distribution, we chose cut-offs at 5 and 10 SNPs for distinguishing links. Similar figures were drawn for the mutation rate (Figure 2—figure supplement 1). We have previously shown that patients with relapse had up to 8 SNPs difference (Guerra-Assuncao et al., 2014), and these cut-offs are similar to those used in other studies (Bryant et al., 2013; Walker et al., 2013).

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Transmission network

To construct the transmission network, we included links of up to 10 SNPs difference. We included one sample per person-episode of disease and excluded extra-pulmonary cases as they cannot transmit. Example clusters are shown in Figure 3, and the full transmission network in Figure 3—figure supplement 1. Overall, after excluding relapses (recurrences with ≤10 SNPs difference from the initial episode), 66% of patients were in clusters with at least one other patient. Clusters ranged in size from 2 to 36 (Figure 4A), with 23% of patients in clusters of 10 or more. The size of the clusters varied by lineage (Figure 4B): compared to lineage-4 (the commonest lineage), lineage-2 and lineage-3 strains were more likely to be clustered and in larger clusters and lineage-1 strains were less likely to be clustered and were in smaller clusters. The median cluster size and interquartile range (IQR) for lineages 1–4 were 3 (1, 6), 13 (7, 24), 7 (2, 22), and 3 (1, 8), respectively. The p-values for differences between lineages were similar if non-clustered strains were excluded.

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This is the largest whole genome sequencing study of M. tuberculosis transmission to-date, and the first to use a network approach. We show that this approach is feasible and that with long-term, population-wide data, important inferences can be made about transmission. In this population, although lineage-4 has been present for longer (Glynn et al., 2010), lineages are not now associated with area of birth or recent residence, so differences by lineage are unlikely to be confounded by associations with host sub-populations.

The mutation rates in this study are consistent with those from other settings (Bryant et al., 2013; Walker et al., 2013) and in vitro (Ford et al., 2013). This is the largest study to measure between-patient mutation rates. Although the confidence intervals on the estimate are narrow, there is considerable variation as others have found. The measure assumes the correct source has been identified and uses the time interval between dates of when the disease was first diagnosed or specimen collection as necessarily crude approximations of the time since divergence of the samples from their common ancestor. Furthermore, there is a bottleneck on transmission: most infections probably arise from one or very few organisms (Lurie, 1964), which may be minority strains in the first case. The within-patient estimate of mutation rate does not have the same measurement problems and gave a consistent result.

We showed striking differences between the lineages in cluster size, the proportion of disease due to recent transmission and transmissibility. Lineage-1 formed the smallest clusters, with the largest SNP differences. Patients with lineage-1 strains were the least likely to have disease due to recent transmission and were less likely to transmit and cause new cases than those with lineages 2 or 3. These observations suggest a lower propensity to cause disease, which may explain lineage-1's association with HIV infection, if it is less likely to cause active disease in those who are not immunosuppressed. Lineage-1 strains have been associated with lower virulence in animal models (Narayanan et al., 2008; Reiling et al., 2013).

Lineage-2 formed large clusters with small SNP differences. It had the highest proportion of disease due to recent transmission and the highest proportion of transmissions. Increased virulence in lineage-2 has been suggested previously (Parwati et al., 2010), often in association with drug resistance, but in this population all lineage-2 strains were drug sensitive. Despite these associations that suggest higher virulence and transmissibility, the proportion of cases due to lineage-2 did not increase over the period. We have previously reported that lineage-2 was first detected in this area in 1991, initially increased, and then plateaued from around 2000 (Glynn et al., 2005a, 2010). This may explain the lower proportion of lineage-2 strains in the oldest age group. The high proportion of linked cases with lineage-2 could reflect few imported (and therefore unlinked) cases, although there was no association between lineage and immigration.

In contrast, lineage-3 increased as a proportion of tuberculosis cases over time. It was associated with an intermediate proportion of disease due to recent infection and high transmission. In this population, it is also associated with relapse. Lineage-4 had smaller cluster sizes than lineages 2 and 3. It remains the most common lineage in this population, although the proportion has fallen over time.

Over the period of the study, the proportion of cases due to recent transmission decreased from 46% to 30%, and the proportion of cases transmitting and giving rise to new cases of tuberculosis also fell markedly. This correlates with a reduction in tuberculosis incidence over this period (Mboma et al., 2013). It suggests a considerable success of the tuberculosis and HIV control programmes, despite the potential for M. tuberculosis transmission in antiretroviral clinics.

We found no association with HIV infection in the proportion of disease due to recent infection (in contrast to our findings with RFLP in the earlier period [Houben et al., 2009, 2010]) or in transmissibility. Social clustering of HIV-infected individuals may increase the opportunities for transmission to susceptible individuals who manifest disease, balancing out any decreased transmissibility. The change from our earlier findings could be due to the reduced transmission in the population and to the increasing use of isoniazid prophylaxis and antiretroviral therapy in HIV-positive individuals.

In this study, we had high quality whole genome sequence data on 72% of culture-positive patients over 15 years. While this is a high proportion, links will be missed, and the best link found may not be the correct one (especially when there are multiple patients with identical strains). Missing links will lead to underestimation of the proportion of disease due to recent transmission and of transmissions. The missing and wrongly attributed links are likely to be randomly distributed, leading to non-differential misclassification of linkage, and underestimation of associations with lineage and other factors.

This large, long-term study provides strong evidence for differences in transmission patterns and virulence between the M. tuberculosis lineages, particularly high transmissibility and virulence for lineages 2 and 3 and low transmissibility and virulence for lineage-1, which are unrelated to drug resistance, HIV infection, or host sub-population.

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Author details

1. JA Guerra-Assunção

   Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   JG-A, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. AC Crampin

   1. Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
   2. Karonga Prevention Study, Malawi, Malawi

   Contribution

   ACC, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. RMGJ Houben

   Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   RH, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. T Mzembe

   Karonga Prevention Study, Malawi, Malawi

   Contribution

   TM, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-3998-3951

5. K Mallard

   Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   KM, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. F Coll

   Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   FC, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. P Khan

   Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   PK, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. L Banda

   Karonga Prevention Study, Malawi, Malawi

   Contribution

   LB, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. A Chiwaya

   Karonga Prevention Study, Malawi, Malawi

   Contribution

   AC, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. RPA Pereira

    Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    RP, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. R McNerney

    Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    RMN, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. PEM Fine

    Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    PF, Conception and design, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

13. J Parkhill

    Wellcome Trust Sanger Institute, Hinxton, United Kingdom

    Contribution

    JP, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

14. TG Clark

    Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    TGC, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

15. JR Glynn

    Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    JRG, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    judith.glynn@lshtm.ac.uk

    Competing interests

    The authors declare that no competing interests exist.

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