Mixed-Strain Mycobacterium tuberculosis Infections and the Implications for Tuberculosis Treatment and Control
SUMMARY
Numerous studies have reported that individuals can simultaneously harbor multiple distinct strains of Mycobacterium tuberculosis. To date, there has been limited discussion of the consequences for the individual or the epidemiological importance of mixed infections. Here, we review studies that documented mixed infections, highlight challenges associated with the detection of mixed infections, and discuss possible implications of mixed infections for the diagnosis and treatment of patients and for the community impact of tuberculosis control strategies. We conclude by highlighting questions that should be resolved in order to improve our understanding of the importance of mixed-strain M. tuberculosis infections.
INTRODUCTION
Despite the relative genetic conservation among Mycobacterium tuberculosis strains compared with many other bacterial species (91), numerous approaches for distinguishing mycobacterial isolates have been successfully developed. Early methods based on phage typing were capable of making only crude distinctions between mycobacterial strains. More recent developments (e.g., IS6110 restriction fragment polymorphism [RFLP] analysis, spoligotyping, and mycobacterial interspersed repetitive unit–variable-number tandem repeat [MIRU-VNTR] analysis), each based on the detection of differences in the numbers or locations of particular genetic sequences or signatures, permit differentiation with much greater resolution (63). In addition to the obvious public health applications of these tools for the identification of clusters of recent transmission, studies using molecular epidemiological methods have improved our understanding of the natural history of disease and how tuberculosis (TB) spreads in communities (72, 105).
Early molecular epidemiological studies conducted in settings where the incidence of tuberculosis was low found an unexpectedly high proportion of disease that was attributable to recent transmission (1, 89). These findings implied that control efforts that focus on reducing the transmission of disease might have a more important role than previously appreciated (93), even in settings where the incidence of tuberculosis is low. Similarly, studies in settings where the incidence of tuberculosis is high documented that the majority of cases occur as a consequence of recent transmission (103) and that many of these transmission events are taking place outside households (108). Similar studies have also illuminated the importance of nosocomial (34, 39, 69, 79, 81, 83), penitentiary (17, 102), and casual (54, 71) social settings in disease spread. These findings further underscore the epidemiological importance of diagnostic delays (due to both patient health-seeking and health care provider behaviors) and suggest that more rapid diagnosis and initiation of appropriate drug treatment can play an enormous role in interrupting transmission.
Molecular epidemiological studies also identified individuals with repeated episodes of disease due to reinfection, thus revealing the incomplete immunizing effect of previous M. tuberculosis infection (18, 107). These data confirm previously reported observational data (15, 45) and statistical arguments (4, 13, 96, 109) suggesting that previous exposure or disease does not confer complete protection against subsequent reinfection and have resulted in a paradigm shift in our understanding of the epidemiology of tuberculosis (62).
While reinfection is expected to occur most frequently in settings where the incidence of TB is high (since multiple exposures are most likely) (80, 101, 111), studies in some moderate- and low-incidence settings among individuals with repeated episodes of disease have found that reinfection also occurs at unexpectedly high rates (14). Several explanations may account for the reinfection documented among those with repeated episodes of tuberculosis disease in low-incidence areas. Previously diseased individuals may have particular biological characteristics (genetic or acquired immunodeficiency) or acquired or environmental risk factors (alcoholism or nutritional deficiencies) that render them more prone to the development of disease after (re)infection. It is also possible that in low-incidence settings, the risk of exposure is concentrated within “sociospatial pockets,” such that individuals infected once are likely to be infected multiple times (19). In other models that account for superinfection, because each infection is not immunizing, reinfection plays a dominant role in disease dynamics when the disease prevalence increases past a threshold (44).
The fact that previous exposure provides incomplete protection from subsequent infection may have important implications for the implementation of interventions (e.g., treatment of latent infection) (65) and for the prospect of developing effective preventive or therapeutic vaccines (44). However, the overall importance of reinfection within the context of the tuberculosis epidemic has been questioned (56). Ongoing doubt concerning the epidemiological importance of reinfection may reflect the fact that clear documentation of this phenomenon usually requires the collection of different strains from two disease episodes within an individual. This approach underestimates the frequency of reinfection since it cannot detect individuals who were repeatedly infected prior to developing a single episode of disease and will also misclassify reinfections as relapses when individuals are repeatedly infected with similar strains that are predominant in their community or social circle. Previously reported models suggested that a substantial fraction of first-time disease episodes is likely to be the result of reinfection and that this fraction is dependent on both the local prevalence of disease, which determines the force of infection that each individual experiences (19, 65), as well as the degree of protection afforded by a previous infection (44).
More recently, molecular studies that documented the phenomenon of polyclonal or “mixed” infections, in which multiple strains of M. tuberculosis are retrieved from an individual host at a single point in time, underscored the potential importance of reinfection in tuberculosis epidemics (Table 1). This phenomenon of mixed infections is not unique to M. tuberculosis and appears to occur with pathogens of all types (6). At present, accounts of mixed M. tuberculosis infections are accumulating for various geographic settings, but there have been few efforts to examine the public health implications of the fact that a single host can simultaneously harbor more than one distinct strain of M. tuberculosis. In this paper, we review existing evidence of mixed M. tuberculosis infections collected from many different epidemiological settings. We highlight why these studies, while very important for documenting that these types of infections occur in all settings, may greatly underestimate the frequency of mixed infections due to the limited sensitivity of current approaches for the detection of the presence of mixed infections. We review evidence of the importance of mixed infections for treatment outcomes of tuberculosis patients receiving standard regimens of combination therapy and discuss how mathematical models reveal the importance of mixed infections to the projected trajectory of tuberculosis epidemics and the effects of interventions. Throughout our review, we aim to identify areas where additional research is needed to clarify how mixed M. tuberculosis infections modify disease dynamics and control.
Table 1
| Reference (yr of publication) | Study population (location) | HIV status(es) of subject(s) | Method(s) used for detection of mixed-strain infection | No. (%) of mixed infections detectedb |
|---|---|---|---|---|
| 60 (1975) | 233 patients (Canada) | Not tested (predates HIV) | Phage typing | 33 (14.1) |
| 9 (1976) | 87 patients (US) | Not tested (predates HIV) | Phage typing | 3 (3.4) |
| 16 (1999) | 28 patients (Spain) | Only HIV positive | IS6110 RFLP; confirmed with DNA typing using PGRS with plasmid pTBN12 | 1 (3.6)c |
| 113 (1999) | 49 patients (US) | HIV-positive and HIV-negative patients | IS6110 RFLP; confirmed with hybridization with PGRS | 1 (2.0) |
| 76 (1999) | 1 patient (Austria) | HIV negative | Spoligotyping and IS6110RFLP | 1 case reported |
| 33 (2001) | 13 patients (South Africa) | HIV negative | Multiprobe RFLP analysis (IS6110, DR sequence, MTB484) | 2 (15.4) |
| 12 (2001) | 2 patients (US) | HIV negative | IS6110RFLP | 1 case reportedd |
| 78 (2002) | 131 patients (South Africa) | HIV negative | RFLP with IS6110 and DR probe | 3 (2.3) |
| 28 (2004) | 543 patients (India) | Not tested | RFLP with IS6110 and DR probes | 2 (0.4) |
| 112 (2004) | 186 patients (South Africa) | Not tested | PCR amplification using 2 overlapping primer sets to detect different lineages (Beijing, IS6110 and Rv2820; non-Beijing, Rv2819); also examined with spoligotyping | 35 (19) |
| 42 (2004) | 12 patients (Spain) | HIV-negative patient with mixed infection; remaining patients not tested | Spoligotyping; confirmed with IS6110RFLP | 1 (8.3) |
| 2 (2004) | 2 patients (Belgium) | Not tested | 12-locus MIRU-VNTR and IS6110RFLP | 1 case reported |
| 87 (2004) | 97 patients (Bangladesh) | Not tested | Spoligotyping; confirmed with IS6110RFLP and 13-locus MIRU-VNTR | 2 (2.1) |
| 106 (2005) | 48 patients (South Africa) | HIV negative, HIV positive, and HIV status not determined | IS6110 RFLP analysis and strain-specific PCR amplification | 5 (10.4) |
| 41 (2005) | 115 cultures (Spain) | Not tested | 12-locus MIRU-VNTR; confirmed by IS6110RFLP and spoligotyping | 3 (2.6) |
| 26 (2005) | 397 specimens (Uzbekistan and Turkmenistan) | Not tested | IS6110 fingerprinting and spoligotyping | 15 (3.8) |
| 86 (2006) | 199 patients (Georgia) | Not tested | IS6110RFLP; confirmed by 15-locus MIRU-VNTR | 22 (11.1) |
| 27 (2007) | 397 patients (Central Asia) | Not tested | IS6110RFLP | 20 (5.0) |
| 100 (2007) | 22 patients (Rwanda) | HIV positive (54.5%) and HIV negative (45.5%) | Spoligotyping and MIRU-VNTR | 1 (4.3) |
| 35 (2008) | 249 patients (China) | Not tested | 7-locus MIRU-VNTR | 14 (5.6)e |
| 3 (2008) | 17 patients (South Africa) | All patients tested (15 of 17) were HIV positive | Spoligotyping | 5 (29.4) |
| 48 (2009) | 7 patients (Uzbekistan)f | Not tested | MIRU-VNTR, spoligotyping, and IS6110RFLP | 4 (57.1) |
| 92 (2009) | 54 isolates (South Africa) | HIV positive (44%), HIV negative (22%), and unknown (34%) (7 of the 15 patients with mixed infection were HIV positive) | Spoligotyping followed by 15-locus MIRU-VNTR, IS6110RFLP | 10 (18.5)g |
| 66 (2009) | 56 patients (Kyrgyzstan) | HIV negative | IS6110 inverse PCR, 12-locus MIRU-VNTR, and spoligotyping | 8 (13.7) |
| 31 (2010) | 113 patients (Uganda) | 3 (37.5%) patients with mixed-strain infections and 13 (12.6%) patients with single-strain infections were HIV positive | 15-locus MIRU-VNTR | 8 (7.1) |
| 59 (2010) | 160 specimens (Malawi) | 37 of 59 patients tested (62.7%) were HIV positive, including both patients with mixed infections | PCR amplification using different primers to detect different lineages (Beijing, IS6110 insertion in Rv2920; Latin American-Mediterranean, IS6110 insertion at position 932204); confirmed with 24-locus MIRU-VNTR | 2 (2.8) |
| 49 (2010) | 185 patients (Taiwan) | 1 HIV-positive patient identified | PCR amplification using different primer sets to detect different lineages (Beijing, IS6110 and Rv2820; non-Beijing, Rv2819) | 21 (11.3) |
| 22 (2011) | 56 patients (South Africa) | 54 of 56 (96%) HIV positive, including all patients with mixed infections | Spoligotyping and 24-locus MIRU-VNTR | 5 (9) |
| 110 (2011) | 466 patients (Taiwan) | Not reported | PCR amplification using different primer sets to detect different lineages (Beijing, IS6110 and Rv2820; non-Beijing, Rv2819) | 14 (3.0) |
| 74 (2011) | 780 patients (Spain) | 5 of the 9 patients with mixed infection were HIV positive | 15-locus MIRU-VNTR and 24-locus MIRU-VNTR; confirmed by IS6110RFLPh | 9 (1.2) |
| 50 (2012) | 1,248 patients (Vietnam) | Not tested | IS6110RFLP, spoligotyping, and 15-locus MIRU-VNTR | 39 (3.1)i |
a
PGRS, polymorphic GC-rich sequence; DR, direct repeat.
b
Some studies used staged selection procedures, so the fraction of samples exhibiting multiple-strain infections may not be representative of the underlying population. The number (percent) of mixed infections was calculated among those patients for whom there was the potential to detect mixed strains.
c
The original study reported an additional nine patients with mixed infections for whom isolates collected at two different times were compared.
d
The original study reported an additional patient for whom isolates collected 15 days apart were compared.
e
Those authors used two or more alleles at a single MIRU locus to define mixed infections.
f
Patients were selected to undergo MIRU-VNTR analysis if they had documented heteroresistance. Among 35 patients studied, 7 patients had documented heteroresistance.
g
In the original paper, using two or more alleles at a single MIRU locus to define a mixed infection, the number mixed infections detected was 15 (27.8%). Ten of these samples had two or more alleles at two or MIRU loci. The original study reported data for 252 patients. Samples underwent MIRU-VNTR analysis if they had one of the two most common spoligotypes. This procedure may have led to either an underestimation (since strains with “mixed spoligotypes” would be excluded) or an overestimation (if the two most prevalent lineages are more likely to be involved in mixed infections) of the proportion of mixed infections.
h
The MIRU-VNTR methods used by those authors changed during the course of the study; as such, the sensitivity for the detection of mixed infections may not have been constant.
i
Thirty-nine patients had mixed infections, which were defined by those authors as isolates with both different RFLP patterns and different spoligotypes. Those authors also reported that 60 patients were found to have possible mixed infections, which were defined as double alleles at at least two loci in the MIRU-VNTR pattern. Of these 60 patients, 29 had mixed infections confirmed by RFLP and spoligotyping.
EVIDENCE OF MIXED INFECTION
References for this review were identified through searches of PubMed for articles published by December 2011, by use of the keywords “(mixed or multiple) AND strain AND tuberculosis.” Articles resulting from these searches and relevant references cited in those articles were reviewed. Articles published in English were retained if they reported evidence of at least one patient with more than one strain of M. tuberculosis on a single day. Depending on the method used to detect mixed strains, more than one sample or testing of more than one colony from the same day may have been required in order to meet this inclusion criterion.
Selected details of studies documenting mixed M. tuberculosis infection are provided in Table 1, and a more detailed account of studies can be found in Table S1 in the supplemental material; we also list additional studies that did not meet our strict inclusion criteria in Table S2 in the supplemental material.
CHALLENGES FOR THE DETECTION OF MIXED INFECTIONS
Figure 1 summarizes laboratory tools that have been used to identify mixed-strain M. tuberculosis infections, each of which has significant shortcomings. While false-positive results are possible (especially where the risk of cross-contamination is not minimized within the laboratory), the most significant obstacle is the limited sensitivity of these approaches; therefore, current estimates of the prevalence of mixed-strain infections serve as a lower bound, and the actual proportion of cases with mixed infections may be significantly larger. The detection of mixed infections is dependent on collecting a clinical specimen that contains multiple strains, handling the specimen such that evidence of individual strains remains intact, and using a typing method that can differentiate the strains. We discuss concerns related to each of these steps below.
Fig 1
Specimen Selection and Collection
A major impediment to the identification of mixed M. tuberculosis infections is that it is not usually possible to isolate mycobacteria from individuals in the latent stage of infection using current techniques because bacillary loads are low and the sites of infection are inaccessible (a notable exception is found for lung resection [33]). Thus, the majority of studies in which mixed M. tuberculosis isolates have been identified used sputum samples from individuals with either diagnosed or suspected pulmonary tuberculosis. The most sensitive methods for the detection of mixed infections appear to be able to detect minority strains constituting ∼1% of the bacteria within a sample (106). Given that the lung bacillary burden in active tuberculosis has been estimated to be around 108 to 1010 (43, 88), this means that minority strains may need to be present at extremely high numbers (107 to 109) at the time of sampling to be detected, assuming that the diagnostic specimen represents a random sample of bacteria present within the body.
The numbers of specimens examined differ among studies, but most often, a single sputum specimen is collected. Shamputa et al. found that increasing the number of sputum samples collected increased the likelihood of detection of mixed infections (86). Even if multiple sputum specimens are examined, it is likely that not all pulmonary lesions may be open to the airways at a given time, and thus, bacteria present within sputum may not adequately reflect the heterogeneity in underlying infections (7). The timing of sputum collection relative to the initiation of treatment is also likely to affect the sensitivity of tests for mixed infections (59, 78); ideally, pretreatment specimens would be collected, as heterogeneity should be more frequently detected in these unselected bacterial populations.
In several cases, mixed-strain M. tuberculosis infections have been identified by using specimens sampled from multiple sites (e.g., lung, spleen, blood, and lymph nodes) (9, 16, 74), including specimens collected during autopsy (22, 33). It is reasonable to assume that as the numbers and types of specimens included in studies are expanded, especially considering that some lineages may exhibit organ tropism (55), the sensitivity for the detection of mixed infections will increase.
Specimen Handling
Even when multiple distinct strains are initially present in clinical specimens collected for analysis, the probability of detecting mixed infections is affected by how specimens are handled before and after they enter the laboratory. In most cases of mixed infection, isolates will be present in differing frequencies within specimens, and minority variants may be preferentially lost, leading to underestimates of mixed infections. Delays from collection to laboratory testing will likely decrease the sensitivity of methods to detect mixed infections, as the minority strains present may be preferentially lost. Methods of specimen decontamination that have been optimized to avoid overgrowth by other organisms may not be ideal for facilitating the survival of these minority variants, as decontamination processes are known to substantially decrease the number of viable organisms (53). To our knowledge, there are no existing studies that have examined the effect of delays or different decontamination protocols on the yield of mixed-infection studies.
While most studies have employed a culture step to increase the mycobacterial population prior to attempts to detect mixed infections, the effect of culture on the ability to detect minority variants is not clear (59). Amplification-based methods that employ lineage-specific PCR primers to probe for infections with multiple M. tuberculosis lineages directly from sputum samples (i.e., without the use of culture) have successfully documented mixed infections (49, 59). When Martin et al. (61) compared direct methods and indirect methods that included culture, they observed that in many samples, the clonal composition changed significantly after culture, and in some cases, only one strain was detected after culture. Isolates may also exhibit marked differences in their abilities to grow and divide in different types of media (solid versus liquid medium and Lowenstein-Jensen [LJ] versus Middlebrook medium, etc.); thus, differing culture methods may result in different estimates of the prevalence of mixed infections (61).
Methods of collection of mycobacterial material from culture for typing also differ between studies and are likely to affect the frequency at which mixed strains are discovered. In some studies, multiple individual clones were isolated from different colonies on solid culture and separately analyzed for evidence of heterogeneity (35, 41, 87), whereas in other studies, “sweeps” across multiple colonies on solid cultures or samples from liquid cultures were collectively assessed for evidence of mixed infection (9). The choice of how to sample from cultures may be restricted by the culture approach used and the typing method to be employed.
Typing Methods
The method chosen for strain typing affects the likelihood of successfully detecting mixed infections (Fig. 1). Since the diversity of mycobacteria differs among settings (40), it is difficult to quantify the operating characteristics of these tests for mixed infections. However, each approach has clear limitations that depend on the limits of detection and the discriminatory capacity of each test. Tests based on mycobacteriophages are capable of only a gross classification of strains and are expected to exhibit poor sensitivity for the detection of mixed infections (90). IS6110-based approaches for the detection of mixed infections in a single isolate can be somewhat subjective since they require the identification of multiple strains based on different intensities of banding patterns, which may be difficult to reliably discern (30). The detection of mixed infections by IS6110 analysis also requires that at least 10% of mycobacterial DNA originate from the minority strain. Some PCR-based approaches have utilized primers specific only at the level of lineages; as such, in these studies, it was not possible to detect mixed infections that occurred as a result of infection with multiple strains within the same lineage or sublineage (59, 106, 112), and lineages for which primers are not included would be missed. Heteroresistance, which occurs as the result of sporadic resistance-conferring mutations arising throughout the course of infection (38) in clonal disease (and, thus, is not a true mixed infection as we have defined here), will also be missed unless specific methods to detect sequence diversity in resistance-related genes are employed (94).
MIRU-VNTR-based methods, currently the most widely used method for the detection of mixed infections, examine heterogeneity only within a limited set of loci, which necessarily limits sensitivity. Furthermore, it is difficult to draw clear distinctions between clonal heterogeneity (defined as clonal diversity occurring as the result of mutation events within the infected host) and mixed infections (defined as diversity occurring as the result of multiple infections) based on differences in MIRU-VNTR patterns (22, 73). There are vast differences between the rapidity with which these two mechanisms produce within-host diversity; clonal heterogeneity involves the sporadic appearance of sequential single-nucleotide polymorphisms through mutation, while mixed infection allows a host to acquire, wholesale, an entirely new M. tuberculosis genome. Furthermore, the rate of acquisition of mixed infections depends on the force of infection in the community and the diversity of strains in that environment (i.e., mixed infections depend on population-level events), whereas clonal heterogeneity depends entirely on within-host processes.
Whole-genome sequencing of M. tuberculosis bacteria collected from individuals offers resolution that is not possible with other typing methods. The ability to distinguish highly related but genetically distinct strains will soon enable better estimates of the true extent of mixed-strain infections. These data will also facilitate the study of microevolution events and the functional impact of previous infection on superinfection and the progression to active disease (37). The application of these methods is currently limited by the cost, the complexity of data processing and analysis, and the limitations of short sequencing reads to account for repeat regions (84). However, the rapidly falling costs of sequencers, the emergence of new-generation sequencers with longer reads, and the development of new tools for bioinformatic analysis will help to address these challenges in the very near future.
EFFECTS OF MIXED INFECTION
Despite the limited sensitivity of current methods for the detection of mixed infections, researchers have been able to identify deleterious effects of mixed infections for individuals who are affected and have begun to explore the potential consequences for disease dynamics and control.
Individual-Level Effects
For some individuals with mixed-strain tuberculosis, the presence of multiple strains will result in poor treatment outcomes (5, 75, 98). van Rie et al. (106) described a clear example of the negative individual-level impact of a mixed infection by using stored specimens collected from a series of patients with multidrug-resistant (MDR) tuberculosis. Using strain-specific PCR to detect mixed infections, those researchers identified patients who harbored both MDR and susceptible strains; detailed treatment data allowed them to describe how the MDR strain was able to persist and grow during treatment with first-line regimens and, upon a switch to second-line regimens, how the susceptible strain was able to reemerge. In addition to demonstrating relative fitness deficits of MDR strains in these patients, that study found that mixed infections with strains of different resistance phenotypes could compromise treatment outcomes using standard combination treatment regimens. Since the presence of underlying resistance (even monoresistance) erodes treatment success and increases the risk of acquired resistance with standardized combination therapy (58), we expect that mixed infections with resistant strains may be associated with poor outcomes in other settings as well. Furthermore, that study, from South Africa, showed that apparent cases of sequential reinfection may be more accurately categorized as mixed infection using methods with a greater sensitivity for the detection of polyclonality. The within-host “unmasking” of MDR strains after first-line therapy may be considered a novel mechanism by which drug-resistant disease may occur. From a programmatic perspective, the unmasking of underlying drug-resistant strains that arose through transmission would result in a misclassification of these events as occurrences of acquired drug resistance, thereby unfairly judging the quality of treatment programs. Since national tuberculosis programs in the vast majority of settings with a high tuberculosis burden have a limited capacity to perform mycobacterial culture (with even more restrictions for the use of drug susceptibility testing), the likelihood that tuberculosis patients receiving therapy will have follow-up drug susceptibility testing performed during treatment is low. Accordingly, the presence of an occult mixed infection at the time of the initiation of therapy in areas where drug resistance is prevalent or emerging poses a threat for most treatment programs.
In addition to the ability of multiple-strain infections to compromise the effectiveness of treatment, it is also possible that superinfection may occur some time after the primary infection, initiating disease progression and the endogenous reactivation of the primary infection (33). This implies that the primary infection is unable to confer protection against a secondary infection. A similar conclusion was drawn from mouse model experiments after sequential infections with different M. tuberculosis strains (67, 77) or repeated infections with the same strain (47). Although the role of reinfection in the pathogenic mechanism of progression of tuberculosis is not well understood, it has been suggested that a secondary “infection” with the vaccine strain Mycobacterium bovis BCG may enhance the risk of developing tuberculosis in patients who probably had a previous M. tuberculosis infection (36). This implies that the vaccination process (equivalent to a secondary infection) altered the immune state of the host, thereby allowing M. tuberculosis (equivalent to the primary infection) to actively replicate and cause disease (68, 97). Alternatively, the expression of resuscitation-promoting factors (RPFs) may be responsible for stimulating the growth of a previously quiescent infection (11, 29, 32, 46, 52, 70, 82). Deciphering these mechanisms may have important implications for the understanding of protective immunity and the development of new vaccines.
Data from animal models suggest that mycobacterial strains exhibit complicated within-host dynamics. Elegant studies using fish, frogs, and mice found that superinfecting mycobacteria preferentially invade existing granulomas (24, 25). These findings demonstrate complex interactions between the pathogen and host, whereby the pathogen “tricks” the host macrophages into transporting the bacilli to an environment where replication may readily occur. However, in a mixed-infection study, the presence of a hypervirulent strain was unable to alter the growth rate of the coinfecting hypovirulent strain (7). Similarly, the overabundance of a strain that induced a strong Th1 response was unable to control the growth of the hypervirulent strain. These findings have important implications for vaccine development, as it has long been thought that strains which induce a Th1 cytokine profile are good vaccine candidates (7). The above-described studies did not exclude the possibility of synergy or antagonism between infecting strains, as the strains used in the above-described study did not reflect the population structure of M. tuberculosis in community settings.
The relationship between HIV-associated immune compromise and the risk of harboring multiple strains of M. tuberculosis has not been well elucidated. In a study in Uganda using MIRU-VNTR analysis to detect mixed infections in 113 smear- and culture-positive patients, a higher proportion of patients with mixed-strain infections were HIV positive than patients with single-strain infections (37.5% versus 16.6%). In theory, we expect that impaired cell-mediated immunity might increase the probability of harboring multiple infections, but the shorter delay between infection and disease and the higher risk of extrapulmonary disease could theoretically offset the probability of either acquiring or detecting multiple-strain infections when they are present.
Population-Level Effects
Investigations of the potential population-level effects of mixed infections require the use of mathematical models. While all mathematical models of tuberculosis are limited by uncertainties in the natural history of tuberculosis infection and disease (which affect both model structure and parameterization), models to explore the consequences of mixed-strain M. tuberculosis infections require additional features that are difficult to model given our limited understanding of this phenomenon. Examples of additional areas of uncertainty that are introduced into models that consider mixed infections include mechanisms of strain interaction/competition within coinfected hosts, the strain specificity of the immune responses, and the impact of previous infections on the likelihood and consequences of reinfection. Nevertheless, models can play an essential role in helping to prioritize the questions that must be answered to improve our ability to understand the population-level consequences of mixed-strain infections and may offer new qualitative insights even when we have substantial uncertainty about model structure and parameterization.
For example, it is clear from the simplest mathematical models of the dynamics of multistrain pathogens that when strains are in strict competition with one another for susceptible hosts (defined where each host can be infected by only a single strain), only the strain with the highest reproductive number will persist. Relaxing these models to allow that hosts may simultaneously harbor more than one strain can substantially expand the potential behaviors of the system, allowing for the long-term persistence of strains that would have been outcompeted in simpler models (23, 114). This diversity-promoting effect of mixed infections is similar to the promotion of diversity in metapopulation models in ecology. In contrast to simple resource-based ecological models, in which the species making the most effective use of a resource excludes competing species, metapopulation models have the flexibility to allow for differences in the relative ability of plant species to grow within a patch (i.e., the ability of strains to expand within a host) and the relative ability of plants to be dispersed between patches (i.e., the ability of strains to be transmitted between hosts). This can permit the stable coexistence of species (99). A recent mathematical model that allowed for differences in the abilities of M. tuberculosis strains to compete within and between hosts determined that models that allow for mixed infections can permit the long-term persistence of relatively less transmissible drug-resistant strains if these strains benefitted from some competitive advantage within coinfected hosts (85).
Mixed infections may also affect the projected population-level impact of antituberculosis interventions. For example, Colijn et al. (23) used a simple mathematical model to demonstrate how mixed infections expand the conditions under which the widespread use of isoniazid preventive therapy (IPT) selects for isoniazid resistance at the population level. A model of the potential effects of new tuberculosis vaccines found that mixed infections, by facilitating the coexistence of multiple strain types, could be expected to erode the expected benefits of a new vaccine that does not adequately cover all circulating strain types, since the vaccine would be more likely to lead to strain replacement (20).
CONCLUSIONS
Previous reviewers have questioned the overall importance of mixed infections for individuals with tuberculosis or for the dynamics of tuberculosis within communities (10). In this paper, we present an overview of studies that documented mixed-strain infections, enumerate challenges for the detection of mixed infections, and discuss the individual-level and potential population-level implications of this phenomenon.
The low sensitivity of most current approaches for the detection of mixed infections constitutes a serious obstacle to our ability to understand their importance. Our inability to reliably document existing mixed infections stems from challenges with specimen collection (we may not retrieve all strains when obtaining our clinical sample), specimen handling (minority variants present in the clinical sample may be lost during transit, decontamination, culture, or sampling), and typing (the strain typing method may not be sensitive enough to pick up rare variants or may not have adequate discriminatory power).
Despite these serious limitations, investigators have documented that a substantial minority of tuberculosis patients (10 to 20%) in certain settings are infected with multiple strains; this strongly suggests that mixed infections are very common. Given existing, though admittedly limited, evidence of the effect of mixed infections on treatment outcomes (particularly when strains with different drug resistance phenotypes are involved) and the model-based arguments for the importance of mixed infections in tuberculosis dynamics and control, we believe that mixed infections are an important subject for future research.
Proximal practical questions of interest include assessing which methods of specimen collection and processing are most sensitive for documenting mixed infections. Extensions of current collection methods, including the collection of 24-h sputum samples or the collection of strains from multiple anatomical sites, may result in clinical samples that better reflect the within-host population structure of mycobacteria. Decontamination and culture steps, while necessary prerequisites for many of the current typing methods, may also erode test sensitivity, and new methods that can type strains directly from sputum samples will be valuable. Deep sequencing, which we expect to be used routinely for the detection of mixed infections in the near future, is likely to displace many other methods and should generate better data to understand many aspects of within-host diversity (37). New animal studies of mixed infection can identify whether certain strains are more capable of superinfection than others and clarify whether previous infection shapes the host immune response in such a way that makes reinfection easier (47). The relevant scale of strain interactions for both individual- and population-level consequences of mixed infections is not known; direct competition between individual organisms likely arises by local interactions within a single granuloma (24), whereas more indirect forms of competition may occur as a result of interactions between strains confined to different granulomas or organs (33). Studies of these types of interactions would allow for a more complete understanding of strain competition and even the potential for strain synergy.
Despite the limitations of current methods of detection and unanswered questions about effects, the current data suggest that mixed infections are likely to have an important impact on accurate disease diagnosis, the effective treatment of individuals, and the control of tuberculosis in populations. New studies aiming to measure the prevalence of mixed infections that use methods of specimen collection and handling that are likely to sample and protect minority strains in combination with the most sensitive typing methods are needed to provide better estimates of how common these mixed infections are. Laboratory, animal model, and epidemiological studies that focus on the within-host complexity of M. tuberculosis infection and disease can contribute complementary information that improves our ability to effectively combat this pathogen. For example, an understanding of the strengths and mechanisms of within-host strain interactions will allow us to better project the trajectories of tuberculosis epidemics and the expected diversity of pathogen populations (8, 23, 85), understand the effects of the scaling up of existing interventions (e.g., IPT) (21), and evaluate the potential effectiveness of novel vaccines (20, 64).
ACKNOWLEDGMENTS
T.C. received support through award number DP2OD006663 from the Office of the Director, U.S. National Institutes of Health. I.A. receives support from the United Kingdom National Institute for Health Research through a senior research fellowship (award number SR-2011-04-001).
We thank Maria Kempner for assistance with an initial literature search while we were exploring this topic.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the Office of the Director of the U.S. NIH or the NIH.
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Author Bios
Division of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA
Department of Epidemiology and Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
Ted Cohen, M.D., M.P.H., D.P.H., is an epidemiologist whose research focuses on the transmission dynamics of infectious diseases. Dr. Cohen's primary research focus is on the emergence and control of drug-resistant tuberculosis. He uses a combination of mathematical modeling techniques and traditional methods for epidemiological analysis in his research and is currently working on research projects in South America and sub-Saharan Africa. Dr. Cohen has carried out studies that document mixed infections and evaluate the potential population-level effects of mixed infections. Dr. Cohen is an Assistant Professor in the Department of Epidemiology at Harvard School of Public Health and an Assistant Professor in Medicine in the Division of Global Health Equity at Brigham and Women's Hospital. Dr. Cohen holds an M.P.H. from the University of North Carolina at Chapel Hill, an M.D. from the Duke University School of Medicine, and a D.P.H. from the Harvard School of Public Health.
Paul D. van Helden
DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/MRC Centre for Molecular and Cellular Biology, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
Paul D. van Helden, Ph.D., is the Director of the DST/NRF Centre of Excellence for Biomedical Tuberculosis Research at Stellenbosch University. Dr. van Helden is one of the world's most prolific TB researchers. He started working on tuberculosis in about 1990 and in that time has focused the attention of the department mainly on this disease. In July 2009, Dr. van Helden was featured in an article on Thompson Reuters' Sciencewatch, citing him as the fourth-highest-ranked scientist in the world in the field of tuberculosis. He has been the recipient of a number of awards, including Stellenbosch University's Vice Chancellors award for Excellence in Research, Gold Medals by the SASBMB and the Academy of Science of South Africa, the Silver medal of the MRC, and the NSTF award for outstanding contribution to Science.
Douglas Wilson
Department of Medicine, Edendale Hospital, Pietermaritzburg, University of KwaZulu-Natal, Durban, South Africa
Douglas Wilson, M.B.Ch.B., is a clinician-researcher at Edendale Hospital, KwaZulu-Natal, South Africa. He, along with Dr. Cohen, has conducted research documenting mixed-strain M. tuberculosis infection in South Africa. Dr. Wilson has also published on the diagnosis of tuberculosis and the treatment of tuberculosis in patients with HIV coinfection.
Caroline Colijn
Department of Mathematics, Imperial College London, London, United Kingdom
Caroline Colijn, M.E.S., Ph.D., is an applied mathematician and Lecturer in Biomathematics at the Imperial College London. Dr. Colijn works on mathematical modeling applied to epidemiology. She is interested in coinfecting pathogens and the interplay between coinfection, competition between pathogen variants, and pathogen evolution. She is also working on mathematical and computational models of bacterial metabolism, with applications to M. tuberculosis and Bordetella pertussis. Dr. Colijn, along with Dr. Cohen, has evaluated the potential population-level consequences of mixed infections. Dr. Colijn earned her master of Environmental Studies from York University and her Ph.D. in applied mathematics from the University of Waterloo.
Megan M. McLaughlin
Division of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA
Megan M. McLaughlin, M.P.H., is a Research Assistant in the Division of Global Health Equity at Brigham and Women's Hospital. She conducts research in collaboration with clinicians at the nonprofit organization Partners in Health, where her focus has been on the evaluation of treatment outcomes and adverse effects in patients treated for multidrug-resistant tuberculosis and HIV coinfection in Lesotho. Ms. McLaughlin earned her M.P.H. from Yale University.
Ibrahim Abubakar
Department of Medicine, Norwich Medical School, University of East Anglia, Norwich, and Tuberculosis Section, Health Protection Agency, London, United Kingdom
Ibrahim Abubakar, M.B.B.S., M.Sc., Ph.D., D.P.H. F.R.C.P., is a Clinical Professor and co-Director of the Centre for Infectious Disease Epidemiology at University College London. He is also a Consultant Infectious Disease Epidemiologist with responsibility for national tuberculosis epidemiology and surveillance at the Health Protection Agency, London, United Kingdom. His research interests include the epidemiology of tuberculosis, HIV, and hepatitis, evaluation of public health interventions and diagnostics, and the use of molecular epidemiology to inform the control of infectious disease. He holds an M.B.B.S. from Ahmadu Bello University, an M.Sc. from the London School of Hygiene and Tropical Medicine, a D.P.H. from the University of Cambridge, and a Ph.D. from the University of East Anglia.
Robin M. Warren
DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/MRC Centre for Molecular and Cellular Biology, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
Robin M. Warren, Ph.D., is an Associate Professor in the Department of Medical Biochemistry at Stellenbosch University. His research interests focus on the disease dynamics of the M. tuberculosis epidemic in two vastly different communities, the evolution of the M. tuberculosis genome, the global spread of defined M. tuberculosis strains, the development of new epidemiological markers, the diagnosis of MDR tuberculosis, the population structure of M. tuberculosis strains in the host, and the identification of mechanisms leading to drug resistance. Dr. Warren has published over 160 papers and holds 5 patents. He is a core member of the DST/NRF Centre of Excellence for Biomedical Tuberculosis Research. Dr. Warren is a worldwide authority on mixed-strain M. tuberculosis infections. He, along with Dr. Paul van Helden, was among the first researchers to document mixed-strain M. tuberculosis infections, using DNA fingerprinting and spoligotyping, and to establish their role in compromising an individual's response to treatment.
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2012. Mixed-Strain Mycobacterium tuberculosis Infections and the Implications for Tuberculosis Treatment and Control. Clin Microbiol Rev 25:.
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