Skip to main content
Microbiology Spectrum logoLink to Microbiology Spectrum
. 2019 Jul 26;7(4):10.1128/microbiolspec.gpp3-0067-2019. doi: 10.1128/microbiolspec.gpp3-0067-2019

Mycobacterium tuberculosis Metabolism

Gabriel T Mashabela 1,2, Timothy J de Wet 3,4, Digby F Warner 5,6
Editors: Vincent A Fischetti7, Richard P Novick8, Joseph J Ferretti9, Daniel A Portnoy10, Miriam Braunstein11, Julian I Rood12
PMCID: PMC10957194  PMID: 31350832

ABSTRACT

Mycobacterium tuberculosis is the cause of tuberculosis (TB), a disease which continues to overwhelm health systems in endemic regions despite the existence of effective combination chemotherapy and the widespread use of a neonatal anti-TB vaccine. For a professional pathogen, M. tuberculosis retains a surprisingly large proportion of the metabolic repertoire found in nonpathogenic mycobacteria with very different lifestyles. Moreover, evidence that additional functions were acquired during the early evolution of the M. tuberculosis complex suggests the organism has adapted (and augmented) the metabolic pathways of its environmental ancestor to persistence and propagation within its obligate human host. A better understanding of M. tuberculosis pathogenicity, however, requires the elucidation of metabolic functions under disease-relevant conditions, a challenge complicated by limited knowledge of the microenvironments occupied and nutrients accessed by bacilli during host infection, as well as the reliance in experimental mycobacteriology on a restricted number of experimental models with variable relevance to clinical disease. Here, we consider M. tuberculosis metabolism within the framework of an intimate host-pathogen coevolution. Focusing on recent advances in our understanding of mycobacterial metabolic function, we highlight unusual adaptations or departures from the better-characterized model intracellular pathogens. We also discuss the impact of these mycobacterial “innovations” on the susceptibility of M. tuberculosis to existing and experimental anti-TB drugs, as well as strategies for targeting metabolic pathways. Finally, we offer some perspectives on the key gaps in the current knowledge of fundamental mycobacterial metabolism and the lessons which might be learned from other systems.

INTRODUCTION

The notion that metabolic capacity might correlate with lifestyle has largely been confirmed by comparative genomics analyses of different bacterial species: free-living bacteria tend to have larger, more complex genomes than pathogens and symbionts (1), with obligate intracellular organisms often representing the extreme limit of small genome size (2). This correlation exists also in Mycobacterium, a genus which includes environmental organisms as well as a select group of animal and human pathogens—primarily of the Mycobacterium tuberculosis complex (MTBC) (3)—whose genome sizes and compositions relate broadly to habitat and host range (4, 5). Most notable among these from a public health perspective is M. tuberculosis, the causative agent of tuberculosis (TB), an airborne infectious disease which continues to result in over 10 million new cases and more than 1.5 million deaths globally each year (6). This is despite the widespread use of a neonatal anti-TB vaccine (7) and the existence of an effective combination chemotherapy (8), the insufficiency of which points to systemic failures in TB diagnosis and treatment (9, 10), as well as the complexities encountered in eliminating an organism that has coevolved with its human host (11) and, throughout modern history, has proven adept at exploiting urbanization (12), economic disparity (13, 14), societal upheaval (15), and incarceration (1618) in maintaining a global prevalence characterized by high-burden endemic regions (6).

M. tuberculosis is an obligate human pathogen; the bacillus can infect other animals, but evidence for zoonotic transmission (back) to humans from these “spillover” hosts (11) is scarce (19). Consistent with its near exclusive association with a single “maintenance host” (11), the M. tuberculosis genome has undergone significant downsizing during its evolution from a generalist ancestor. Nevertheless, M. tuberculosis retains a large metabolic repertoire, much of which is shared with other mycobacteria with very different lifestyles (2022); moreover, horizontal gene transfer during the early evolution of the MTBC has enabled the acquisition of additional functions that are critical for pathogenicity (2325). In other words, the organism has harnessed, adapted, and even augmented the metabolic pathways of its environmental ancestor in evolving the capacity to persist and propagate within its obligate human host (21, 22, 26).

The development of specialist M. tuberculosis sublineages from more generalist precursors (11, 27)—in some cases through the inactivation of certain genes (28)—appears, therefore, to signal natural evolution driven by key structural changes in human population demographics and geographies. These include the shift to urbanization, the introduction and widespread use of anti-TB drugs and, possibly, BCG vaccination (29). Greater host population density and the emergence of comorbidities such as diabetes and HIV might additionally have favored the selection of more virulent strains (30, 31), though this inference is not always clear-cut (32). A common theme, however, is that gain or loss of different metabolic functions appears to be a major factor in the modern evolution of the M. tuberculosis bacillus (21, 22, 28, 3336).

We noted previously (37) that the topic “M. tuberculosis metabolism” is very broad, encompassing all the dynamic processes and chemical transformations which enable bacillary cellular function and physiology, including those involved in maintaining structural integrity, energy production, generation of biomass, and elimination of waste. It is not possible, therefore, to cover these in any detail in a single review. Instead, this article will attempt to situate M. tuberculosis metabolism within the framework of an intimate host-pathogen coevolution while providing some insights into the special challenges faced in elucidating metabolic function (as distinct from metabolic capacity) in a pathogen with no known reservoir outside its obligate human host. Where relevant, we will highlight recent advances in our understanding of different aspects of the metabolism of M. tuberculosis, focusing primarily on those which represent unusual adaptations or departures from the model intracellular pathogens; that is, the key observations implicating specific metabolic functions in mycobacterial pathogenicity and the host-pathogen interaction. In addition, given the strong interest in the development of new (shorter) combination regimens for both drug-susceptible and drug-resistant TB, we will consider the impact of metabolism on the susceptibility of M. tuberculosis to existing and experimental anti-TB drugs. As with other summaries of this subject, the reader is advised not to consider this a standalone piece. Rather, any of a number of specialist reviews should be consulted for further details on those aspects covered only superficially here, as well as for different perspectives on the key gaps in current knowledge of fundamental mycobacterial metabolism and the implications thereof for TB prevention and new TB drug development (for a list of suggested readings, please see Table 1).

TABLE 1.

Selected readings

Metabolic pathway Recommended reviews
Cell wall biosynthesis and maintenance 253, 254, 329
Energy metabolism and respiration 230, 231
Central carbon metabolism 26, 66
Nitrogen metabolism 330333
Sulfur metabolism 334, 335
Metallobiology 275, 336
Cofactors (molybdenum, coenzyme A, riboflavin, coenzyme F420, cytochrome P450, vitamin B12, etc.) 145, 337340
Nucleic acid metabolism 341343
PE/PPE proteins 77

MYCOBACTERIAL METABOLISM AND HUMAN TB DISEASE

Although the precise duration of the M. tuberculosis-human coevolution remains subject to contention (11)—estimates range from fewer than 6,000 years (38) to around 70,000 years (39)—there is general acceptance that the interaction extends across millennia. It was striking, therefore, when the first whole-genome sequence of M. tuberculosis revealed the genetic potential for synthesis of all essential amino acids, vitamins, and cofactors, with a disproportionate number of pathways dedicated to lipid and fatty acid metabolism (40). This large metabolic capacity (often conflated with “metabolic flexibility” [4143]) represents a key departure from many other obligate pathogens which tend to rely on the host to complement their limited (and often diminishing) metabolic repertoires (44) and is critical to understanding the defining characteristics of this organism and the disease it causes (26, 4548). Such characteristics include (i) long-term coevolution of M. tuberculosis with humans (38, 39), leading to sympatric associations of specific lineages with geographically distinct human populations (11, 49), (ii) the ability to persist for months to decades in a subclinical state (in some cases characterized by undetected disease [50]) which might develop later to cause active TB (26, 47, 5153), (iii) the inertness to clearance by the host immune response or eradication by antibiotics (54, 55), (iv) the requirement for extended-duration combination therapy to effect symptom-free cure (8, 56) and, linked to that, (v) the difficulties inherent in developing new drugs and combination regimens to accelerate therapy (57, 58), and (vi) the capacity to acquire—and adapt to—drug resistance mutations that might otherwise be expected to incur a crippling fitness cost, particularly where sequential mutations confer multidrug resistance (5961). Each of these characteristics reinforces the durability of M. tuberculosis in preserving its special niche within humans and, by implication, the challenges inherent in disrupting this association, whether by vaccines, chemotherapy, or other interventions (Fig. 1).

FIGURE 1.

FIGURE 1

The metabolic capacity of M. tuberculosis is critical to understanding key features of mycobacterial pathogenicity, which include (i) long-term coevolution with humans, (ii) the ability to persist for months to decades in a subclinical state, (iii) inertness to clearance by the host immune response or eradication by antibiotics, (iv) the requirement for extended duration combination therapy to effect symptom-free cure and, linked to that, (v) the difficulties inherent in developing new drugs and combination regimens to accelerate therapy, as well as (vi) the capacity to acquire—and adapt to—multiple drug resistance mutations without incurring a crippling fitness cost.

Pathogenicity and Metabolism

That physiology and pathogenicity cannot be separated in an obligate pathogen such as M. tuberculosis seems axiomatic (26, 62). However, the relevance of this precept to efforts to counter TB might be less evident and so bears restating: an understanding of mycobacterial metabolic capacity supports the inference that M. tuberculosis is a “survival machine,” able to exploit its human host as both a source of nutrients for increased biomass and a reservoir for long-term persistence (26, 37, 63). Reinforcing this premise is the observation that the infecting population does not necessarily drive a metabolic program aimed at maximizing growth rate (64, 65) or biomass (26, 37, 63, 66). Instead, as observed by others (66), the frequent requirement for bacilli to exit the standard bacterial cell cycle owing to the pressures imposed by the host immune response and, in the modern history of M. tuberculosis, antimycobacterial chemotherapy, demands the capacity to maintain viability in a state of rapidly reactive and reversible metabolic quiescence for extended periods.

While evidence exists for decades-long survival of bacilli in an asymptomatic host (67, 68), most cases of clinical disease are thought to arise within 1 to 2 years of infection (53, 69). During this period, those bacilli which survive the attempts at clearance by the host’s innate immunity generally maintain a state of paucibacillary, asymptomatic infection in which host lipids and mycobacterial antigens are accumulated within alveoli in the lung apices. There is a spread of bacilli which, depending on the preferred model, is thought to occur via bronchi (69) or via the lymphatics (70). And, although most new lesions regress spontaneously, necrosis can result in caseous pneumonia, a heterogenous outcome which can be paucibacillary or multibacillary, with or without significant neutrophil involvement. Importantly, this manifestation—known as post-primary TB disease—depends on the acquisition of systemic immunity by the infected host. Its sequelae are considered the key determinant of disease outcome: softening and fragmentation of the caseous pneumonia triggers its expulsion by coughing, in turn leaving a hole which can become the site of active bacillary growth and the primary source of organisms for transmission; in contrast, those sites which are not removed by coughing are sources of ongoing inflammation, ultimately leading to fibrocaseous disease (69). Though central to disease pathogenesis, these events in post-primary TB, in particular, the development during latent infection of an asymptomatic obstructive lobular pneumonia, have not been adequately studied, owing to the unavailability of suitable clinical specimens and the failure of animal models to recapitulate many of the key features. Inevitably, mycobacterial metabolism under these conditions, too, remains very poorly understood.

The quiescent state adopted by M. tuberculosis is distinct from “true dormancy,” a very specific physiological and morphological condition often involving sporulation, for which the bacillus appears to lack the necessary genetic machinery (71). Physiologically, though, it is interesting to note that a deficiency in α-crystallin—a major TB antigen (72), bacterial protectant, and chaperone protein which has been implicated in long-term bacillary survival (55)—is associated with hypervirulence in vivo (73). This seems to imply a disturbance of the natural host-pathogen interaction where a genetic lesion has deprived the bacillus of its natural capacity for persistence. The observation, noted previously (37), that mutations in regulatory genes are often associated with hypervirulence (7476) appears to buttress this conclusion. So, too, does recent evidence (28) implicating disrupted secretion of the large class of Mycobacterium-specific PPE-MPTR and PE_PGRS proteins (whose functions remain largely unknown [77]) in enhanced growth and virulence of modern Beijing lineage strains in vivo. The inferred centrality of a conserved host-pathogen interaction in TB pathogenesis is reinforced in the “hyper conservation” of T cell epitopes (39, 78), a feature which might be integral to the development of post-primary TB (69, 79).

At an epidemiological level, the inference that M. tuberculosis is hard-wired for persistence is supported by the fact that the number of active TB cases globally (∼10 million) (6) is dwarfed by the estimated annual incidence (thought to exceed tens of millions [53]) and global prevalence of infections (estimated at ∼25% of the world’s population [80] but subject to contention [53]). As Rhee and colleagues have cogently argued, this entails that the bacillus must occupy a range of physiological states book-ended by “unrestrained replication” and “perpetual quiescence” (26), extremes which are incompatible with the natural history of the species (11, 49). In turn, the notion that M. tuberculosis cellular function defaults to persistence immediately suggests the corollary: host status, whether genetic (81), immunological (82), nutritional (83, 84), environmental (85, 86), or a combination of some or all of these, is critical in determining who develops, and succumbs to, TB disease (a seemingly obvious conclusion, perhaps, but one which reinforces the potential pitfalls in attempting to define—and quantify—mycobacterial “virulence” as a solely bacterial property).

The intimate, dynamic, and durable interaction between M. tuberculosis and humans defines the framework within which any new anti-TB interventions must be considered. That is, tackling any single aspect—for example, drug regimens to treat active cases, novel preventative chemotherapies or vaccines to forestall new disease, or strategies to block transmission—is unlikely to prove sufficient on its own (87). Importantly, it also underscores an acknowledged limitation of many studies which aim to elucidate fundamental aspects of M. tuberculosis metabolism: namely, that the potential disconnect separating bacillary metabolic functions active during host infection (whether in the presence or absence of manifest TB disease) versus those actuated—and therefore described—in vitro and/or in vivo in the various disease models risks a growing phenomenology of metabolic capacity distinct from direct evidence of metabolic function in the context of the complete biological system which defines TB (37, 88).

Investigating Metabolism in Vitro

While the majority of genome-wide mutagenesis studies recognize the importance of, and even exploit, the applied in vitro conditions (or infection model) in inferring conditional essentiality (8993), the impact of growth conditions on experimental observations has often been ignored in fundamental mycobacteriology. This is despite an increasing number of reports of the profound impact of the growth medium and various additives on the described phenotypes (9497, 344). Aside from auxotrophy (92, 98, 99), perhaps the most illustrative example of how even apparently absolute concepts such as essentiality (100) can be condition-dependent is provided by the paradoxical tolerance of cell wall-targeting antibiotics in cell wall-deficient L-form bacteria (101), a phenotype which demonstrates the potential for wholesale loss of a fundamental macromolecular structure under the appropriate conditions—in that case, in vivo residence in a suitably osmoprotective intracellular environment.

Similarly, practical considerations tend to favor the use of culture conditions that enable optimal (that is, the most rapid) growth. For M. tuberculosis, this generally means aerobic incubation in standard medium (usually Middlebrook 7H9 base or equivalent) comprising glycerol and glucose as the primary carbon sources (102). The disconnect between in vitro and in vivo models used in experimental investigations of mycobacterial metabolism and those environments which are likely to prevail in the natural human host is not unique to M. tuberculosis (103). Therefore, in addition to appreciating that logarithmic aerobic growth represents just another condition and that mycobacterial physiology in this state will necessarily influence assessments of gene essentiality or metabolic pathway vulnerability, there is a pressing need to develop experimental systems that target slow-growing and nongrowing states, perhaps in the context of more advanced cellular scaffolds (104). Though often performed in the context of TB drug discovery, the application of advanced analytical technologies to determine the precise molecular content (proteins, lipids, metabolites) of a number of environments, including macrophage organelles (105), lipid droplets (106), and tissue samples from animal models and patients (107, 108), represents an exciting area of development and might be complemented by recent advances in generating more complex tissue culture models (109).

Many researchers have made pioneering efforts to develop other complex models of M. tuberculosis growth and survival, and the strategies adopted to model environments that are far less favorable for unchecked generation of biomass have yielded important insights into metabolic function under the applied conditions, as well as revealing condition-selective antimycobacterial compounds. These include manipulating the pH of growth media, the inclusion of peroxide and/or nitric oxide stresses, oxygen and nutrient deprivation, and combinations thereof (110115), many of which have been applied in screening programs aimed at identifying compounds with condition-specific activities or those able to inhibit survival of nonreplicating bacilli (116, 117). Again, while each is intuitively attractive, the difficulties inherent in translating inferences derived from these in vitro systems to human TB disease remain daunting; the field urgently requires a proof-of-concept demonstration of enhanced therapeutic efficacy achieved using a novel small molecule whose development has been prioritized owing to its condition-selective activity in one of more in vitro models.

Moving to More Complex Models

Like for many other pathogens, investigations of cellular functions in the context of the host-pathogen interface of M. tuberculosis have tended to focus primarily on the effectors of immunity (host) or virulence (bacillus), with the metabolic consequences of infection often ignored (44). Systems biology and its tools, including the analysis of labeled metabolites (118122) and other mass spectrometry-based technologies (123127), have eroded this bias, enabling key insights into the biochemical compositions and metabolic fluxes which characterize M. tuberculosis bacilli under different conditions and in different infection models (128). These and related approaches have also been used to elucidate the regulators of metabolic adaptation and function (129131), the mechanisms of action of existing and experimental anti-TB drugs (43, 131134), in assigning functions to proteins of unknown or predicted function (133) and in functional genomics of clinical M. tuberculosis isolates (36). Except for sputum bacilli, though, the number of studies directly investigating M. tuberculosis metabolic function and physiological state(s) in situ during host infection remains very small (135137).

In some cases, results from experimental systems in vitro and in vivo have been combined with analyses of human TB pathology to construct sophisticated models of mycobacterial metabolism (138, 139). Though compelling, the in vivo relevance of these is largely untested; for this reason, determining the metabolic and physiological states adopted by the tubercle bacillus in the infected human host represents the ultimate, though daunting, scientific goal. Knowledge that the functions of approximately 40% of the M. tuberculosis proteome remains predicted or obscure (140, 141)—so, too, the speculative reasons the bacillus possesses numerous examples of apparently redundant or duplicate pathways (142149)—exacerbates the size of the task. Moreover, growing evidence indicating key functional differences between (and even among different laboratory stocks of) the laboratory strain, M. tuberculosis H37Rv, clinical M. tuberculosis isolates, and other species of the MTBC (3335, 150, 151), implies that diverse phenotypes will inevitably be recorded when different species, and even strains (152), are tested in the same assay. That is, while prevailing approaches might enable some generalizable insights into core M. tuberculosis metabolism, the next phase demands innovative approaches to study bacilli of different genotypes in the context of natural host infection (153). Here, understanding the features which differentiate infection outcomes in sympatric versus allopatric hosts (154) might provide some important insights (151).

Of course, the fact that the diversity which characterizes M. tuberculosis lineages and strains is matched by the genetic and phenotypic variety of host immune cells further complicates these analyses. For example, different macrophages and macrophage-like cell lines are commonly explored to understand mycobacterial function in the intracellular environment (155); for TB, these are usually immortalized human and mouse cell lines (e.g., THP-1s, J774s), mouse bone marrow-derived macrophages, and human peripheral blood mononuclear cells (156), with some recent reports proposing alternative, more complex, systems (109, 157). Macrophages themselves are functionally diverse, however, and follow different developmental trajectories and respond to a variety of activation signals (158). Consequently, there are significant risks inherent in a rigid dependence on standard models, a pitfall underscored by recent observations that alveolar macrophages might offer a more favorable intracellular environment for M. tuberculosis survival and dissemination than interstitial macrophages (159, 160). Moreover, evidence that a shift to aerobic glycolysis in infected cells can enable proliferation of intracellular bacilli (161, 162) requires that caution should be exercised in drawing conclusions about mycobacterial metabolic function from experiments utilizing the different macrophage lines. To enable rapid anabolism, proliferating cells (as is the case for the various cell lines) naturally drive a strong Warburg metabolism which is independent of intracellular infection but must inevitably impact the metabolic response of the infecting bacillus (161). A further complication is that this effect can be species-specific, with recent evidence highlighting differential metabolic responses in human versus mouse macrophages (163). In this light, the proposal that intracellular pathogens might have evolved to take advantage of specific immune cell subsets locked into promicrobial metabolic programs (44) seems apposite. Therefore, special emphasis must be placed on the need for much greater functional and ontogenic information about the host cells in which bacilli preferentially reside during infection. And this should be coupled with a better understanding of the extent to which immunometabolic responses of host cells to M. tuberculosis are cell-type specific, host species specific, and/or strain specific (45, 161).

M. tuberculosis Metabolism and the TB Disease Life Cycle

Other than during transient periods of transmission, when bacilli are released into the environment in droplet aerosols, the entire M. tuberculosis life cycle occurs within the infected host (26, 164). This concept—encapsulated in the notion of humans as the “maintenance host” (11)—is critical to understanding M. tuberculosis pathogenicity: the ability to infect, persist, cause disease, and trigger production of infectious aerosols demands the capacity for survival and propagation in different environments and under a variety of stresses. Current knowledge of M. tuberculosis infection reinforces this notion. Most experimental models, ranging from intramacrophage systems in vitro (165, 166) through small animals (159, 167, 168) to nonhuman primates (NHPs) (169, 170), indicate that bacilli are able to replicate rapidly immediately after infection, with evidence of early dissemination in NHPs (170) until host-mediated killing results in a decline in replication rates and total bacillary load. Even then, it appears from the mouse model (167) that replication might continue in discrete microenvironments throughout infection (68), a possibility recapitulated in genomic data from bacilli isolated from NHPs which revealed the acquisition of chromosomal mutations during subclinical disease (171). Moreover, although its relevance to in vivo infection remains to be established, the observation that M. tuberculosis replicates rapidly inside dead macrophages (172) further argues that the transient growth rates (and fates) of infecting bacilli in different lesions in the same host are likely to be dynamic and unlinked.

The heterogeneity which characterizes host infection supports a model in which infecting bacilli must establish an early foothold within the new host before seeding a number of discrete lesions, the fate of which appears to be determined for each lesion at a local level, independent of events in other microenvironments (173). For M. tuberculosis as a species, each new infection represents a potential dead-end if transmission is prevented. Therefore, long-term survival within a host—and by any mechanism—appears to represent the best hedge against this risk. Loss of immune control at any stage during the host’s lifetime might then allow for stimulation of the destructive pathology that could enable (or enhance) infectious aerosol release, renewing the infection cycle.

This is the lens through which mycobacterial metabolism must be viewed. Transient opportunity for generation of biomass means that preservation of viability under varied and multiple stresses represents the driving metabolic imperative. While contrary to popular views which conflate bacterial virulence with rapid growth, the enforced (and sustained) quiescence which likely characterizes human M. tuberculosis infections is a feature of most bacteria in their preferred niches, whether pathogens or not (103). The apparently “surprising” retention of a multifunctional metabolism in a successful obligate pathogen appears then to be just the opposite: metabolic flexibility is, instead, precisely the attribute which enables M. tuberculosis to occupy its preferred environment—the human host—without recourse to a separate (environmental) reservoir. For M. tuberculosis, the absence of any competing microorganisms within its “ultra-narrow ecologic niche” (66) additionally ensures a privileged, albeit transiently hostile, space in which replication can occur subject to prevailing conditions, largely determined by the host immune response. Again, this highlights the need for greater understanding of the metabolic functions which ensure prolonged viability under nonreplicating persistence. Here, it is interesting to consider recent work with other bacteria which has demonstrated the conditional essentiality of many genes, including a large number of proteins of unknown function, in long-term fitness under growth arrest (174, 175), as well as the potential for selective targeting of pathogens under these conditions (176).

The example of pat mutants, engineered strains of M. tuberculosis which achieve population numbers similar to wild-type bacilli in vivo but do not cause the same degree of pathology (177), has been cited as evidence of the fact that disease outcome is not necessarily linked to the bacillary population size (37). Rather, it is the interplay between the host response and the infecting M. tuberculosis population—and the antigens it produces (69)—which determines infection outcomes (178). This tenet is perhaps best captured in the aphorism that M. tuberculosis is necessary, but not sufficient, for TB disease (82, 179).

Again, evidence from pioneering studies in NHPs reinforces this conclusion. For both diseased and clinically latent monkeys, the population of culturable organisms commonly numbers around 2 × 105 bacilli per lesion prior to the onset of the adaptive immune response and stabilizes at a median of ∼102 bacilli per lesion during active disease (169). That is, the total numbers of bacilli present in active versus latent disease can overlap. Moreover, while there can be microenvironments in which the mycobacterial population achieves large numbers during active disease, the total bacillary burden generally seems much lower than might be expected given the extent of pathology (180). This observation is reiterated in human studies (69, 82) and contrasts with recent conclusions from an unrelated infectious disease model which demonstrated that infection outcome was directly attributable to bacterial load (181).

It is important, though, to differentiate TB disease in HIV-infected versus HIV-uninfected individuals. The enhanced propensity for extrapulmonary disease and TB bacteremia in HIV-TB cases argues that infection outcomes in the context of HIV-mediated immunodeficiency might correlate much more closely with the mycobacterial burden (182). Moreover, while the precise cause of death in disseminated TB disease in HIV-infected individuals remains speculative (183, 345), for pulmonary TB in individuals who are not immune-suppressed, it is clear that most patients ultimately succumb to advanced pathology (signaling a systemic failure in TB diagnosis and treatment [184]), a distinction which reinforces the intimate interplay between host and pathogen in the context of a functional immune response (185). Therefore, to consider TB disease as a single entity and, by extension, to categorize M. tuberculosis metabolic states (and, by implication, metabolic vulnerabilities) according to a single framework that assumes a handful of possible infection outcomes (51, 52) imposes a conceptual constraint that, albeit practically appealing, is potentially crippling in its failure to accommodate the complexity inherent in an infectious organism adapted to persistence, not virulence.

TB as Polymicrobial Infection of Humans

The continual acquisition of new sequence data from different mycobacterial species and strains has enabled comparative genomics analyses to determine the evolutionary histories of the different members of the MTBC with greater resolution (3). Importantly, when key genetic differences are considered against the backdrop of the very specific host tropisms of the different MTBC members, it becomes increasingly evident that laboratory-determined virulence is not only insufficient to understand pathogenicity, but likely inappropriate. As noted elsewhere (186), each species has diverged from a common ancestor through coevolution with its own cognate host, resulting in genetically related but phenotypically distinct pathogens specifically suited to hosts with disparate social and biological features.

M. tuberculosis causes TB, a disease which manifests primarily as damage of the lungs, yet extrapulmonary forms are also known which affect other organs and sites, including the pleura, abdomen, genitourinary tract, meninges, skin, joints, and bones (187). Lymph nodes, too, are often cited among lists of “extrapulmonary” sites, but this modifier is likely inaccurate given significant evidence that infection of lymph nodes constitutes a critical event in TB pathogenesis (70, 188, 189). Also distinct from extrapulmonary TB is the observation that M. tuberculosis can occupy diverse anatomical and cellular niches during latent host infection (190). Even in “simple” cases of pulmonary TB, infecting M. tuberculosis bacilli are not restricted to specific environments but can be found in multiple different host compartments, including cellular and necrotic granulomas and the inner surfaces of open cavities (58, 191193). In each of these, bacilli can be sequestered within host cells or located in extracellular niches (194), reinforcing the suggestion that TB disease might usefully be considered a polymicrobial infection (195).

The occupation of diverse host microenvironments—in many cases, by paucibacillary populations—with independent disease trajectories constitutes a major obstacle to elucidating the metabolic adaptations demanded of bacilli located within (or transitioning between) the different niches (26). From a pharmacological perspective, this uncertainty presents a clear challenge to developing approaches that will ensure optimal drug delivery to bacilli in dynamic and disparate physical loci and metabolic states (57, 58, 196) and to understanding which (sub)populations must be controlled (or eliminated) for favorable therapeutic outcomes (55, 189, 197199). For drug discovery, such uncertainty undermines efforts to identify unequivocally the pathways and functions which are critical to mycobacterial survival and so might represent viable antibiotic targets, that is, the organism’s “metabolic vulnerabilities.” In addition, it renders ambiguous the potential utility of compounds known to be selectively active against either proliferating or quiescent organisms, growing or subsisting on different nutrient sources (conversely, such molecules are of unquestionable utility as probes of the distinct metabolic states adopted by M. tuberculosis in those environments). Therefore, for those wanting (simply) to understand the link between bacillary metabolism and host disease, the lack of granularity informing available descriptions of in situ metabolism poses unresolved challenges regarding the assays and approaches—available and required—that might best elucidate mycobacterial metabolic function in vitro, in infection models, and in clinical disease.

Immunometabolism and Pathometabolism at the Host-Pathogen Interface

Like all intracellular pathogens, the bacillus must adapt its metabolism to the host cell, whose own metabolic status might correspondingly be altered as a consequence of infection (200). Though poorly understood, this interplay between immunometabolism (201) and pathometabolism (202) is critical to infection outcomes (45). There is increasing evidence that successful intracellular pathogens such as M. tuberculosis have evolved to exploit the reciprocal determinism linking host cellular function and metabolic status (161). Ergo, this interplay might offer a solution to the apparent paradox that the very immune cells designed to eliminate the invading pathogen can function as a preferred niche (44). Consistent with their role as early responders, innate immune cells appear much more vulnerable to infection than adaptive immune cells, reinforcing the inference that organisms such as M. tuberculosis have adapted to the tightly regulated responses of innate immune cells to enable long-term intracellular survival. The observation that a core transcriptional profile unites species and lineages across the MTBC in response to intracellular infection (151) strengthens this conclusion and is supported by the presence of metabolic enzymes among the hyperconserved T-cell epitopes in M. tuberculosis (203). That is, through millennia of coevolution, the bacillus is likely to have adapted to the complex network of metabolic and immune alterations and signals triggered in its host.

The corollary also appears to hold true: in response to M. tuberculosis infection, some host macrophages shift to a “Warburg-like” (161) program of aerobic glycolysis (162, 204207) in which oxidative phosphorylation is reduced and cytosolic glycolysis and the mitochondrial tricarboxylic acid cycle are redirected primarily to the biosynthesis of fatty acids. In an example of “bipartite metabolism” (44), these are utilized as energy sources by the intracellular bacillus (120) without imposing excessive metabolic stress on the host macrophage, which is able to drive an anabolic program utilizing the available glucose. Notably, a metabolic shift in the infected macrophage has been implicated in the accumulation of intracellular lipid bodies characteristic of foamy macrophages. In turn, this phenotype has been closely linked with mycobacterial persistence and disease progression (208211), though recent observations suggest that it might be more nuanced (212). There is also evidence that altered metabolism at the site of M. tuberculosis infection influences cytokine production (213), reinforcing the connection between cellular metabolic state and immune function. Here, it is useful to consider the key observation that, at a metabolic level, the host-pathogen interaction is likely to possess emergent properties: specifically, the metabolic responses of host cells to living bacteria are likely to be more subtle and complex than the sum of metabolic responses to individual bacterial components (161).

Given the major recent impact of positron emission tomography and computed tomography imaging in providing unprecedented insights into TB disease pathogenesis and heterogeneity, both within and between individuals in different disease models and human TB patients (50, 169, 180, 189), it is worth noting here that the technique utilizes a [18F]fluoro-2-deoxy-2-d-glucose ([18F]-FDG) probe which exploits the enhanced glucose utilization of inflammatory cells. Recent work investigating the potential for alternative probes to provide cell-specific information ([18F]-FDG is taken up by all glycolytically active cells and does not distinguish subsets) has revealed the dynamic nature of granuloma composition and metabolism over the course of infection (214), again reinforcing the critical concept that lesional progression with an individual does not follow a fixed temporal program (173).

M. TUBERCULOSIS METABOLISM

As noted at the outset, it is not possible to cover all aspects of mycobacterial metabolism within a single review. Instead, this and subsequent sections will provide a very brief update on research supporting four key concepts which are critical to understanding M. tuberculosis metabolic function and regulation, namely, (i) metabolic and respiratory flexibility, (ii) the capacity for cocatabolism of different nutrient sources, (iii) the potential for metabolic anticipation, and (iv) the heavy investment in cell wall metabolism.

Central Metabolism

“Not a picky eater, at least not for carbon and nitrogen sources” (Luiz Pedro De Carvalho, Twitter, 1 March 2019). Despite its levity, this declaration, made recently in promotion of research demonstrating the utilization by M. tuberculosis of amino acids as nitrogen sources (215), perhaps best summarizes a sizeable literature on (mostly in vitro) mycobacterial metabolic function (see Table 1 for selected references). Unsurprisingly, the predominant focus over the past 2 decades has been on carbon utilization, and consistent with this bias, this aspect of mycobacterial metabolism has been extensively reviewed (26, 45). Among the central tenets emerging from this work are that M. tuberculosis can cocatabolize different carbon sources (216), an observation which signals a key departure from classic bacterial metabolism in which diauxic growth regulated via carbon catabolite repression constitutes the dominant model.

A number of subsequent studies have reinforced carbon cocatabolism as critical to M. tuberculosis cellular function and pathogenicity, identifying specific roles for fructose-1,6-bisphosphate aldolase (217) and pyruvate kinase (218) in enabling this capacity. Moreover, the persistence defect observed in an M. tuberculosis mutant lacking both glucokinase homologues—in other words, functionally depleted of glycolytic potential—supports the conclusion that both carbohydrates and fatty acids are required for long-term survival in vivo (219). More recent research has extended the capacity for cocatabolism to nitrogen sources in vitro (215) and in an intracellular model (220), strongly suggesting the generalizability of this principle to mycobacterial metabolism. In this context, recent evidence of the power of techniques such as cryo-electron microscopy in revealing cytoplasmic organization and the presence of microcompartments in different prokaryotes (221, 222) suggests the intriguing possibility of investigating the potential that cocatabolism is spatially regulated.

At least one codicil to the inferred absence of catabolite repression in M. tuberculosis might exist in the observation that cholesterol-dependent growth inhibition cannot be alleviated by the provision of glucose as an alternative carbon source (223). This implies that preferential utilization of cholesterol—predicted to be a feature of M. tuberculosis pathogenicity (224)—might represent a critical constraint to mycobacterial metabolic flexibility (223). In some ways, this phenomenon is reminiscent of the very well-described differential (often poor) growth of clinical M. tuberculosis isolates in specialist laboratory medium following isolation from patient samples, including sputum (54, 225). In turn, it highlights the fact that, while M. tuberculosis H37Rv is the workhorse of experimental mycobacteriology, this strain has become adapted to growth in laboratory media through decades of serial passage in vitro (152).

The concept that growth arrest represents a key stress response in M. tuberculosis and, importantly, might be triggered even when the organism retains the metabolic capacity to grow (65) is also central to understanding mycobacterial pathogenicity. This preservation strategy appears to be enabled, at least partially, by the capacity for “anticipation” of future events, an example of which was provided by the observation that M. tuberculosis exposed to hypoxia remodels its own cell envelope to generate building blocks for later reentry into the cell cycle (226). Notably, this capacity is enabled by the catabolism of trehalose mycolates, from which the bacillus generates pentose phosphate intermediates that are incorporated into peptidoglycan biosynthesis much later, when environmental conditions improve, that is, contingent on re-aeration at an indeterminate future time. This illustrates an inherent capacity for preservation that might be hard-wired through long-term adaptation to predictable host environments and stimuli (227). Reinforcing this idea, very recent work has demonstrated that multiple stresses induce expression of the small regulatory RNA, MrsI, which functions to preserve intrabacillary iron reserves, perhaps in anticipation of iron deprivation in the infected macrophage (228). Although not involving metabolic anticipation per se, the observation that utilization of nitrate as the sole nitrogen source requires a functional proteasome (229) is analogous in demonstrating the very tight connection between metabolic pathway utilization and regulation of cellular macromolecular synthesis and recycling, perhaps offering insights into the organism’s intrinsic responses to in vivo nutrient availabilities.

Respiration

Like all mycobacteria, M. tuberculosis is an obligate aerobe. Therefore, survival in growth-inhibitory hypoxic environments demands sufficient energy generation for maintenance functions, while aerobic conditions enable full metabolic activity for bacillary replication (230). From comparative genomics of different mycobacteria, it is apparent that, whereas major biosynthetic pathways are reasonably conserved, occurring within core orthologous clusters, the respective complements of genes involved in energy metabolism are more diverse. This observation is consistent with the adaptation of individual species to specific environmental and host niches and, perhaps, replication rates (21, 22).

M. tuberculosis possesses multiple branched pathways for electron transfer from a variety of low-potential reductants to oxygen during aerobic growth (230) and to alternative electron acceptors under hypoxic conditions (231). The potential flexibility suggested by the expanded complement of primary dehydrogenases and oxidoreductases (232)—the function and regulation of which remain poorly understood (230)—is, however, tempered by the observation that aerobic respiration in M. tuberculosis appears to depend on a single lipoquinone, menaquinone, and two terminal respiratory oxidases, the aa3-type cytochrome c oxidase and the cytochrome bd menaquinol oxidase (231). Moreover, evidence suggests the obligatory coupling of ATP generation to the electron transport chain and the membrane-bound F1F0-ATP synthase, irrespective of the oxygen concentration or the proton motive force (230). And, whereas some bacteria are able to utilize ATP to energize the cytomembrane via coupling of ATP hydrolysis to proton pumping, the M. tuberculosis F1F0 ATP synthase lacks ATP hydrolysis activity (233). These limitations are unusual for mycobacteria and have been exploited in the development of the diarylquinoline bedaquiline (Sirturo), the first new FDA-approved anti-TB drug in over 40 years, whose initial use has been restricted to multidrug-resistant TB pending the results of ongoing phase 2 and phase 3 trials (234, 235).

As noted elsewhere (230), the M. tuberculosis genome encodes expanded complements of both electron donors and electron acceptors whose roles in pathogenicity are largely unexplored. It is perhaps no exaggeration to suggest that, of all the core metabolic functions, respiration and energy metabolism are those which have been most prone to very selective investigation given the practical difficulties associated with manipulating the bacillus under variable conditions of hypoxia and anoxia, the default reliance on standard, glucose-based growth media in vitro, and the general use of mouse models which recapitulate only certain aspects of TB pathology (236). A salient example is provided by the likely contribution of the assimilatory/respiratory nitrate reductase to bacillary survival under various stresses, a notion supported by strong functional and comparative genomics evidence that the acquisition of molybdopterin biosynthetic and salvage genes (molybdenum cofactor is essential for multiple enzymes in carbon, sulfur, and nitrogen metabolism, including nitrate reductase [145, 237]) differentiates the pathogenic M. tuberculosis from precursor (nonpathogenic) mycobacteria (22).

The predicted capacity of infecting bacilli to subsist under different conditions and in various microenvironments offering very different substrates for energy generation argues for further research into the consequences of disrupted mycobacterial bioenergetics for host infection outcomes (238, 239). Certainly, early clinical reports of the benefits of bedaquiline for multidrug-resistant and extensively drug-resistant TB are very encouraging (234, 235). Moreover, in addition to highlighting the possible benefits of targeting components of the oxidative phosphorylation machinery for new TB drug development, knowledge about the precise mechanism of action of this compound has proved very instructive. By binding the atpE-encoded F1F0 oligomeric c-ring subunits, bedaquiline effectively functions as a nonclassic uncoupler: exposure of bacilli to the drug stimulates respiration owing to the electroneutral uncoupling of proton transport and ATP synthesis (240, 241). This is important because it suggests the potential to target the proton motive force, a property that appears to be shared by known antimycobacterial compounds (242), as well as verapamil (243). However, caution is required given recent observations that inhibition of energy metabolism might have unintended consequences in some combinations (244).

Cell Wall Homeostasis

Consistent with its dominant role in the biology of M. tuberculosis, the cell wall has been the subject of intensive study for decades (Table 1): as a primary site of the host-pathogen interface (245247), as a target of—and impermeability barrier to—many current and experimental antimycobacterial drugs (248251), and as a major determinant of the robustness and durability of the bacillus throughout its life cycle (252). The molecular composition and arrangement of the mycobacterial cell envelope, classified as Gram positive but comprising an outer membrane and a periplasmic space, is quite well understood and broadly comprises three distinct layers (251, 253, 254). Exterior to the plasma membrane, a peptidoglycan layer links covalently to a mycolyl-arabinogalactan layer which is decorated with noncovalently linked lipids and lipoglycans such as the phosphatidylinositol mannosides, phthiocerol dimycocerosates, phenolic glycolipids, and lipoarabinomannan, which have been investigated for their roles in mycobacterial pathogenicity (255) and as diagnostic markers (256, 257). This outer membrane or “mycomembrane” is, in turn, surrounded by a loosely attached capsular layer predominantly comprising polysaccharides and proteins (95, 253).

Less well understood is how the construction, maintenance, and remodeling of the cell envelope are regulated and accomplished in concert with the mycobacterial cell cycle, particularly in vivo in different host microenvironments (258, 259). Here, an emerging theme is the application of advanced, live-cell and time-lapse imaging in combination with elegant fluorescent bioreporters, in most cases fluorophore-linked analogs of natural cell wall precursors which are incorporated into the cell wall without significant impairment of mycobacterial growth (260265). Moreover, advances in mass spectrometry have enabled the cataloguing of cell wall lipids, fatty acids, and proteins with ever greater resolution (266), including under different growth conditions (267), in response to drug treatment (268), and in drug resistance and drug tolerance (94, 269).

Accessing Nutrients from the Host

The number of M. tuberculosis organisms required to initiate successful host infection remains unknown. Though deep sequencing suggests the possibility for transmission of multiple genotypes (270), most historical estimates are that a single bacillus might be adequate (271). This prediction has been reinforced most convincingly in NHP models in which the elegant use of genetic “barcodes” has shown that individual lesions can arise from a single seeding M. tuberculosis genotype (169, 170). Whatever the number, proliferation depends on the ability of the invading pathogen to assimilate host nutrients for energy consumption and macromolecular synthesis. Precisely which host-derived nutrients M. tuberculosis accesses, where this occurs, and how are subjects of ongoing investigations (41, 272277). A core paradox in M. tuberculosis research is that, whereas the promiscuity in the nutrients and cofactors potentially assimilated from the host seems to complement M. tuberculosis’s autarkic metabolism (41), the same promiscuity is difficult to reconcile with the inferred impermeability of the mycobacterial cell wall. This apparent incongruity has driven the search for potential transport and porin proteins in the mycobacterial outer membrane (278, 279).

An intriguing mechanism for nutrient acquisition was proposed in a series of studies characterizing the tuberculosis necrotizing toxin (TNT), an enzyme which intracellular M. tuberculosis utilizes to kill the host macrophage and gain access to cytoplasmic nutrients (280283). Synthesizing multiple lines of experimental evidence, the prevailing model holds that bacilli contained within the macrophage phagolysosome deploy CpnT, a bifunctional protein comprising an N-terminal pore-forming domain and C-terminal NAD+ glycohydrolase (TNT), via the mycobacterial ESX-1 type VII secretion system (284) to effect macrophage death. Notably, release of TNT into the macrophage cytoplasm (the glycohydrolase is pH sensitive, and so, inactive in the acidified phagolysosome) is dependent on the ability of the other ESX-1 substrate, EsxB (also known as ESAT-6/CFP-10), to puncture the phagolysosomal membrane, therefore requiring the neat coordination of mycobacterial effectors to ensure that intraphagosomal bacilli (which, from the inserted CpnT N-terminal domain, now possess the membrane pores necessary for maximal nutrient uptake) are poised to take advantage of the inactivated host cell. In an intriguing twist, parallel work has suggested that, contrary to the connotations of its explosive moniker, the mycobacterial toxin might deliver a “blast without power” (285), resulting in necrotic cell death that might deliberately limit immunogenicity. Given the implied importance of this mechanism to the ability of M. tuberculosis to exploit the host macrophage, perhaps while minimizing host immune detection, it would be interesting to know whether the enhanced growth in dead macrophages described recently (172) also depends on functional TNT and, moreover, whether loss of TNT impacts pathogenicity in vivo.

MYCOBACTERIAL METABOLISM AND NEW TB DRUG DISCOVERY

We noted previously (286) that investigations of core metabolic processes in M. tuberculosis are generally framed within the context of drug discovery—whether looking specifically for new active molecules or vulnerable targets or attempting to elucidate mechanisms of action or to reveal reasons for compound failure or bypass. Examining the current TB drug pipeline, it is evident that significant progress has been made in expanding not only the number of different mechanistic classes represented in the most promising drug-target pairs, but also in validating new high-value targets for future discovery programs (134, 287). Nevertheless, the challenges for new TB drug discovery remain significant. Compound permeation (288), xenobiotic metabolism (289), and efflux (290292) are three major innate mechanisms of drug resistance, all of which are poorly understood and correspondingly difficult to demonstrate or quantify. Impermeability of the complex mycobacterial cell envelope is often cited as a major confounder to new TB drug development. Though intuitively attractive, the evidence to support this conclusion remains scarce. In contrast, the formidable capacity of M. tuberculosis for biotransformation of xenobiotics is much better established (289) and, with the mostly theoretical contribution of efflux (293), might represent the major innate barrier to antibiotic activity (294).

The increasing appreciation of the organism’s capacity for biotransformation has exposed the lack of rapid, medium-throughput strategies to measure metabolism of xenobiotics (289). Similarly, despite the frequency with which the inactivity of many compounds against M. tuberculosis is assumed to be a function of poor or failed permeation, proving so remains enormously difficult. The inability to determine quickly and reliably whether a compound—and its analogs in a structure-activity relationship analysis—has lost activity owing to differential (lost) target engagement, inactivation via metabolic transformation, extrusion by efflux, or failure to penetrate severely undermines medicinal chemistry efforts. Without complete information about the fate of the applied molecule, efforts to optimize physicochemical properties toward a drug-like lead are based almost entirely on experience, intuition, and empiricism (i.e., that which works is clearly evident in the inhibition of bacillary growth in a specific assay, usually an MIC determination in vitro [295]). The scarcity of techniques to enable rapid determination of the intrabacillary accumulation and metabolism of compounds, perhaps even independent of inhibitory activity, therefore presents a significant obstacle to rational drug development and suggests that strategies employed in other model systems (296, 297) might be usefully adapted to this end.

Recent work elucidating the impact of cell wall antibiotics on membrane fluidity and biogenesis (298, 299), as well as the observation that many compounds with antimycobacterial activity function as protonophores (242, 243)—in some cases, in addition to interacting directly with their cellular target activity (300)—and so operate at the cytoplasmic membrane (the site of many so-called promiscuous targets, including DprE1, QcrB, and MmpL3 [250, 301]), reinforces the sense that outer membrane impermeability might not present the major obstacle to drug efficacy. This impression will, however, require confirmation via the use of advanced metabolomics approaches to elucidate the (differential) penetration of different compounds into the mycobacterial cytoplasm (302).

Efflux, too, presents a potentially sizeable but largely undefined obstacle. The M. tuberculosis genome encodes more than 40 predicted efflux pumps, only a handful of which have been implicated in innate drug resistance (232, 292, 303, 304). Recent evidence that the antihypertensive verapamil does not function as an efflux pump inhibitor but instead disrupts membrane energetics in M. tuberculosis (243) reinforces the perils inherent in making inferences from phenotypic observations in the absence of compelling mechanistic insight. Even ostensibly clear-cut drug-target interactions can have surprising locations (305), while the mechanism of resistance does not always correlate with the mechanism of action (306, 307).

From a metabolic perspective, incomplete understanding of M. tuberculosis metabolism in its human host can have very practical implications for TB drug discovery. For example, two separate studies elucidating the guaB2-encoded mycobacterial inosine monophosphate dehydrogenase (IMPDH) as the target of related compound series took very different views of the potential of their respective molecules for advancement as candidate drugs and, by implication, the attractiveness of de novo purine biosynthesis as an anti-TB target. The first (149) propounded IMPDH as “vulnerable” and, consequently, worth pursuing. In contrast, challenging pharmacokinetics, coupled with the observation that concentrations of guanine in resected lung tissue from TB patients achieve levels in excess of those required to allow bypass of IMPDH through salvage, prompted the authors of the second study to conclude the opposite. They argued that IMPDH might be “essential” but is “not vulnerable” (107), thus consigning GuaB2 and its chemical inhibitors to red flag status for new TB drug development, at least in their estimation (270). These conflicting views reinforce the fact that, even where a molecular catalogue of individual tissues, lesions, or microcompartments is available, it might not necessarily enable an unequivocal prediction of what the bacillus is able to access and assimilate from its surroundings. That is, the availability of any nutrient must be distinguished from its accessibility to the infecting M. tuberculosis population. Of course, when prioritizing resources for new drug candidates, it is prudent to assume that all nutrients, if present, are (potentially) accessible. The risk that salvage might overcome antibiotic-mediated nutrient starvation therefore ensures that the bar is set very high for new TB drug development (308).

Harnessing Metabolism to Potentiate Drug Activity

Earlier sections highlighted some of the ways in which different intrinsic metabolic functions contribute to the innate resistance of M. tuberculosis to antibiotics, reinforcing a theme which has been discussed extensively elsewhere (65, 309, 310). Increasingly, however, knowledge of metabolic function is being exploited not only to circumvent innate resistance mechanisms, but also to understand the metabolic functions which might subvert antibiotic efficacy so as to identify novel countermeasures (64, 311, 312). Recent work highlighting the contribution of altered propionate metabolism to the emergence of drug resistance in M. tuberculosis (313) provides a very important example of the potential for metabolic deficiency to confer a tolerant phenotype that enables the acquisition and fixation of genetic drug resistance. Moreover, the growing number of studies demonstrating altered metabolic function in drug-resistant organisms, including M. tuberculosis (269, 314), suggests that this phenomenon might even be more widespread.

A corollary to the above is that metabolism might also be harnessed to improve drug action. For example, the inferred mechanism of action of the repurposed agent, clofazimine, which triggers a redox cycling mechanism that generates toxic reactive oxygen species (315), reinforces the notion that bacilli might be trapped in a futile and ultimately destructive metabolism. Notably, by combining clofazimine with other compounds targeting components of the mycobacterial electron transport chain, Steyn and colleagues demonstrated the potential to enhance killing of M. tuberculosis in an intracellular model (43). In an analogous example of this approach, inactivation of the essential maltosyltransferase, GlgE, was shown to trigger an amplification loop which resulted in rapid mycobacterial death owing to self-poisoning by maltose 1-phosphate accumulation (316). Though the precise mechanism differs in each case, these observations suggest the potential for combination regimens based on drug targets and/or mechanisms of action which incorporate inhibition of growth (target/drug A) and prevention of ordered metabolic shutdown (target/drug B); that is, combinations designed to prevent metabolic escape. This strategy is distinct from both metabolic bypass (in which an alternative, perhaps less efficient or autonomous, mechanism exists to enable generation of the same essential metabolite, e.g., via scavenging from the host) and metabolic buffering (in which partially redundant mechanisms enable the organism to maintain homeostasis). Instead, it entails eliminating the organism’s inherent capacity for surviving through replicative and metabolic quiescence and so might add a further element to the three models for antimycobacterial drug synergy proposed previously (65). A recent report describing the identification of chemical inhibitors of the stringent response enzyme, RelMtb, a (p)ppGpp synthetase, provides a cogent demonstration of the potential of this therapeutic approach (317).

To some extent, this strategy overlaps with the idea, summarized in a recent review, of developing “metabolism-targeted adjuvant therapies” to prevent antibiotic tolerance (311). In support of that proposal, the authors cite the targeted disruption of regulatory mechanisms, for example, through the identification of artemisinin analogs as inhibitors of the mycobacterial DosRST system (318), an ∼50-gene regulon that has been implicated in antibiotic tolerance in infection models in vitro and in vivo (319). In addition, they provide several illustrations of the potential to achieve or enhance antimycobacterial activity by interfering with bacillary metabolism. These include the direct inhibition of specific “tolerance/persistence” effectors such as isocitrate lyase (320) or nicotinamide adenine dinucleotide synthetase, NadE (321). Of particular interest, though, is the proposition that stimulating bacillary respiration and energy metabolism, for example, by providing a glycolytic substrate together with an alternative terminal electron acceptor such as fumarate (322), can increase efficacy of known antibacterial agents against apparently recalcitrant subpopulations. This concept not only reinforces the impact of metabolic state on antibiotic efficacy (an obvious conclusion), but it also suggests the potential to manipulate bacillary metabolism artificially through the deliberate provision of nutrients which might drive a defined metabolic program that ensures susceptibility to the applied agent. That is, it opens the door for a combination comprising an (inhibitory) antibiotic and a (stimulatory) metabolite.

FUTURE PERSPECTIVES

This review has highlighted the complications inherent in working with an infectious pathogen whose preferred anatomical target (i.e., the human lung) is difficult to access. The result is that most studies, rather than revealing mycobacterial metabolic function in the context of TB disease, have tended to elucidate the metabolic capacity of the bacillus, either in vitro in different growth media and under various stresses thought to mimic the intrahost environment or in vivo in experimental models of infection (37). Those attempts, much fewer in number, to investigate M. tuberculosis pathogenicity (including metabolism) in situ or closer to the site of disease have predominantly relied on bacilli captured in sputum or bronchoalveolar lavage fluid, lymph node or other biopsies, or lung resections of treated patients. One consequence is that the potential distinction between primary and post-primary TB as separate disease entities (and therefore likely invoking different mycobacterial metabolic functions) has been obscured (69, 79).

As noted above, determining the metabolic and physiological states adopted by the tubercle bacillus in the infected human host represents the ultimate goal and, though still a significant challenge, seems increasingly attainable with the development of new technologies. Recent advances such as the ability to obtain transcriptional data from very-low-abundance RNA (258, 323) and at the single-cell level (324) promise to provide much greater insight into the exchange of stimuli and responses that occurs between M. tuberculosis and its host cell during intracellular infection and which shapes the metabolic adaptation of the invading bacilli to the available nutrients in this and other microenvironments. Exciting advances in other bacterial models, including the combined application of RNA profiling with stable isotope labelling (325, 326), reinforce the sense that elucidating the growth rates—and metabolic states—of even slow-growing pathogens such as M. tuberculosis will become feasible (327).

A recent systems pharmacology study (198) suggests another approach to this problem, namely, the potential to combine empirical data derived from in vitro and in vivo experiments—ideally including clinical samples (197, 328)—to generate a complex mathematical model that simulates disease progression within a single locus. In that case, a shift was made from the M. tuberculosis-infected macrophage as the “unit of infection” (155) to the granuloma in modeling the efficacies of different fluoroquinolone antibiotics as anti-TB agents. Their approach enabled a compelling demonstration of the capacity to incorporate both host and bacillary factors in building the model and, at the same time, raised the possibility that this strategy could be adapted to (or informed by) metabolomic, proteomic, and lipidomic data. Notably, the study also highlighted the emergent properties of a complex system comprising infecting bacilli and different types of host immune cells (the variety was necessarily limited to a handful of T cell subsets and macrophage phenotypes owing to the demands of computational modeling). Again, this suggests the potential to enable key insights into the dominant metabolic pathways active in different lesions, how these might influence infection trajectories and pathological outcomes, and how they might be exploited for improved chemotherapy.

ACKNOWLEDGMENTS

We thank Valerie Mizrahi and Richard Haynes for critical review of the manuscript and members of the MMRU for many discussions of key ideas. We apologize to those authors whose work was not cited here owing to space limitations.

Research on different aspects of mycobacterial metabolism in the Molecular Mycobacteriology Research Unit is enabled by financial support from the Department of Science and Technology (DST) and National Research Foundation (NRF) of South Africa, the South African Medical Research Council, the University of Cape Town, the U.S. National Institute of Child Health and Human Development (NICHD) U01HD085531-02, and the U.S. National Institute of Allergy and Infectious Diseases (NIAID) R21AI115993. We also gratefully acknowledge the support of the Research Council of Norway (INTPART, AMR-PART) for work on antimicrobial resistance in M. tuberculosis.

Contributor Information

Gabriel T. Mashabela, SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, DST/NRF Centre of Excellence for Biomedical TB Research, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa Current address: Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, University of Stellenbosch, South Africa.

Timothy J. de Wet, SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, DST/NRF Centre of Excellence for Biomedical TB Research, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa Department of Integrative Biomedical Sciences, University of Cape Town, South Africa.

Digby F. Warner, SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, DST/NRF Centre of Excellence for Biomedical TB Research, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa Wellcome Centre for Infectious Disease Research in Africa, University of Cape Town, South Africa.

Vincent A. Fischetti, The Rockefeller University, New York, NY

Richard P. Novick, Skirball Institute for Molecular Medicine, NYU Medical Center, New York, NY

Joseph J. Ferretti, Department of Microbiology & Immunology, University of Oklahoma Health Science Center, Oklahoma City, OK

Daniel A. Portnoy, Department of Molecular and Cellular Microbiology, University of California, Berkeley, Berkeley, CA

Miriam Braunstein, Department of Microbiology and Immunology, University of North Carolina-Chapel Hill, Chapel Hill, NC.

Julian I. Rood, Infection and Immunity Program, Monash Biomedicine Discovery Institute, Monash University, Melbourne, Australia

REFERENCES

  • 1.Moran NA. 2002. Microbial minimalism: genome reduction in bacterial pathogens. Cell 108:583–586 10.1016/S0092-8674(02)00665-7. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 2.Martínez-Cano DJ, Reyes-Prieto M, Martínez-Romero E, Partida-Martínez LP, Latorre A, Moya A, Delaye L. 2015. Evolution of small prokaryotic genomes. Front Microbiol 5:742. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Chiner-Oms Á, Comas I. 2019. Large genomics datasets shed light on the evolution of the Mycobacterium tuberculosis complex. Infect Genet Evol Feb 26:S1567-1348(19)30027-9 10.1016/j.meegid.2019.02.028. [DOI] [PubMed] [Google Scholar]
  • 4.Galagan JE. 2014. Genomic insights into tuberculosis. Nat Rev Genet 15:307–320 10.1038/nrg3664. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 5.Orgeur M, Brosch R. 2018. Evolution of virulence in the Mycobacterium tuberculosis complex. Curr Opin Microbiol 41:68–75 10.1016/j.mib.2017.11.021. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 6.WHO. 2018. Global tuberculosis report 2018. https://www.who.int/tb/publications/global_report/en/.
  • 7.Dockrell HM, Smith SG. 2017. What have we learnt about BCG vaccination in the last 20 years? Front Immunol 8:1134 10.3389/fimmu.2017.01134. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Dobbs TE, Webb RM. 2017. Chemotherapy of tuberculosis. Microbiol Spectr 5:10.1128/microbiolspec.TNMI7-0040-2017 10.1128/microbiolspec.TNMI7-0040-2017. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 9.GBD Tuberculosis Collaborators. 2018. The global burden of tuberculosis: results from the Global Burden of Disease Study 2015. Lancet Infect Dis 18:261–284 10.1016/S1473-3099(17)30703-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Naidoo P, Theron G, Rangaka MX, Chihota VN, Vaughan L, Brey ZO, Pillay Y. 2017. The South African tuberculosis care cascade: estimated losses and methodological challenges. J Infect Dis 216(suppl_7):S702–S713 10.1093/infdis/jix335. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Gagneux S. 2018. Ecology and evolution of Mycobacterium tuberculosis. Nat Rev Microbiol 16:202–213 10.1038/nrmicro.2018.8. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 12.Ortblad KF, Salomon JA, Bärnighausen T, Atun R. 2015. Stopping tuberculosis: a biosocial model for sustainable development. Lancet 386:2354–2362 10.1016/S0140-6736(15)00324-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Andrews JR, Basu S, Dowdy DW, Murray MB. 2015. The epidemiological advantage of preferential targeting of tuberculosis control at the poor. Int J Tuberc Lung Dis 19:375–380 10.5588/ijtld.14.0423. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Foster N, Vassall A, Cleary S, Cunnama L, Churchyard G, Sinanovic E. 2015. The economic burden of TB diagnosis and treatment in South Africa. Soc Sci Med 130:42–50 10.1016/j.socscimed.2015.01.046. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 15.Eldholm V, Pettersson JH, Brynildsrud OB, Kitchen A, Rasmussen EM, Lillebaek T, Rønning JO, Crudu V, Mengshoel AT, Debech N, Alfsnes K, Bohlin J, Pepperell CS, Balloux F. 2016. Armed conflict and population displacement as drivers of the evolution and dispersal of Mycobacterium tuberculosis. Proc Natl Acad Sci USA 113:13881–13886 10.1073/pnas.1611283113. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Stuckler D, Basu S, McKee M, King L. 2008. Mass incarceration can explain population increases in TB and multidrug-resistant TB in European and central Asian countries. Proc Natl Acad Sci USA 105:13280–13285 10.1073/pnas.0801200105. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Baussano I, Williams BG, Nunn P, Beggiato M, Fedeli U, Scano F. 2010. Tuberculosis incidence in prisons: a systematic review. PLoS Med 7:e1000381 10.1371/journal.pmed.1000381. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Altice FL, Azbel L, Stone J, Brooks-Pollock E, Smyrnov P, Dvoriak S, Taxman FS, El-Bassel N, Martin NK, Booth R, Stöver H, Dolan K, Vickerman P. 2016. The perfect storm: incarceration and the high-risk environment perpetuating transmission of HIV, hepatitis C virus, and tuberculosis in Eastern Europe and Central Asia. Lancet 388:1228–1248 10.1016/S0140-6736(16)30856-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Supply P, Brosch R. 2017. The biology and epidemiology of Mycobacterium canettii. Adv Exp Med Biol 1019:27–41 10.1007/978-3-319-64371-7_2. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 20.Stinear TP, Seemann T, Harrison PF, Jenkin GA, Davies JK, Johnson PD, Abdellah Z, Arrowsmith C, Chillingworth T, Churcher C, Clarke K, Cronin A, Davis P, Goodhead I, Holroyd N, Jagels K, Lord A, Moule S, Mungall K, Norbertczak H, Quail MA, Rabbinowitsch E, Walker D, White B, Whitehead S, Small PL, Brosch R, Ramakrishnan L, Fischbach MA, Parkhill J, Cole ST. 2008. Insights from the complete genome sequence of Mycobacterium marinum on the evolution of Mycobacterium tuberculosis. Genome Res 18:729–741 10.1101/gr.075069.107. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Malhotra S, Vedithi SC, Blundell TL. 2017. Decoding the similarities and differences among mycobacterial species. PLoS Negl Trop Dis 11:e0005883 10.1371/journal.pntd.0005883. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.McGuire AM, Weiner B, Park ST, Wapinski I, Raman S, Dolganov G, Peterson M, Riley R, Zucker J, Abeel T, White J, Sisk P, Stolte C, Koehrsen M, Yamamoto RT, Iacobelli-Martinez M, Kidd MJ, Maer AM, Schoolnik GK, Regev A, Galagan J. 2012. Comparative analysis of Mycobacterium and related Actinomycetes yields insight into the evolution of Mycobacterium tuberculosis pathogenesis. BMC Genomics 13:120 10.1186/1471-2164-13-120. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Levillain F, Poquet Y, Mallet L, Mazères S, Marceau M, Brosch R, Bange FC, Supply P, Magalon A, Neyrolles O. 2017. Horizontal acquisition of a hypoxia-responsive molybdenum cofactor biosynthesis pathway contributed to Mycobacterium tuberculosis pathoadaptation. PLoS Pathog 13:e1006752 10.1371/journal.ppat.1006752. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Supply P, Marceau M, Mangenot S, Roche D, Rouanet C, Khanna V, Majlessi L, Criscuolo A, Tap J, Pawlik A, Fiette L, Orgeur M, Fabre M, Parmentier C, Frigui W, Simeone R, Boritsch EC, Debrie AS, Willery E, Walker D, Quail MA, Ma L, Bouchier C, Salvignol G, Sayes F, Cascioferro A, Seemann T, Barbe V, Locht C, Gutierrez MC, Leclerc C, Bentley SD, Stinear TP, Brisse S, Médigue C, Parkhill J, Cruveiller S, Brosch R. 2013. Genomic analysis of smooth tubercle bacilli provides insights into ancestry and pathoadaptation of Mycobacterium tuberculosis. Nat Genet 45:172–179 10.1038/ng.2517. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Bottai D, Stinear TP, Supply P, Brosch R. 2014. Mycobacterial pathogenomics and evolution. Microbiol Spectr 2:MGM2-0025-2013. 10.1128/microbiolspec.MGM2-0025-2013. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 26.Ehrt S, Schnappinger D, Rhee KY. 2018. Metabolic principles of persistence and pathogenicity in Mycobacterium tuberculosis. Nat Rev Microbiol 16:496–507 10.1038/s41579-018-0013-4. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Stucki D, Brites D, Jeljeli L, Coscolla M, Liu Q, Trauner A, Fenner L, Rutaihwa L, Borrell S, Luo T, Gao Q, Kato-Maeda M, Ballif M, Egger M, Macedo R, Mardassi H, Moreno M, Tudo Vilanova G, Fyfe J, Globan M, Thomas J, Jamieson F, Guthrie JL, Asante-Poku A, Yeboah-Manu D, Wampande E, Ssengooba W, Joloba M, Henry Boom W, Basu I, Bower J, Saraiva M, Vaconcellos SEG, Suffys P, Koch A, Wilkinson R, Gail-Bekker L, Malla B, Ley SD, Beck HP, de Jong BC, Toit K, Sanchez-Padilla E, Bonnet M, Gil-Brusola A, Frank M, Penlap Beng VN, Eisenach K, Alani I, Wangui Ndung’u P, et al. 2016. Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages. Nat Genet 48:1535–1543 10.1038/ng.3704. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Ates LS, Dippenaar A, Ummels R, Piersma SR, van der Woude AD, van der Kuij K, Le Chevalier F, Mata-Espinosa D, Barrios-Payán J, Marquina-Castillo B, Guapillo C, Jiménez CR, Pain A, Houben ENG, Warren RM, Brosch R, Hernández-Pando R, Bitter W. 2018. Mutations in ppe38 block PE_PGRS secretion and increase virulence of Mycobacterium tuberculosis. Nat Microbiol 3:181–188 10.1038/s41564-017-0090-6. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 29.Warner DF, Koch A, Mizrahi V. 2015. Diversity and disease pathogenesis in Mycobacterium tuberculosis. Trends Microbiol 23:14–21 10.1016/j.tim.2014.10.005. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 30.Fenner L, Egger M, Bodmer T, Furrer H, Ballif M, Battegay M, Helbling P, Fehr J, Gsponer T, Rieder HL, Zwahlen M, Hoffmann M, Bernasconi E, Cavassini M, Calmy A, Dolina M, Frei R, Janssens J-P, Borrell S, Stucki D, Schrenzel J, Böttger EC, Gagneux S, Swiss HIV Cohort and Molecular Epidemiology of Tuberculosis Study Groups. 2013. HIV infection disrupts the sympatric host-pathogen relationship in human tuberculosis. PLoS Genet 9:e1003318 10.1371/journal.pgen.1003318. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Koch AS, Brites D, Stucki D, Evans JC, Seldon R, Heekes A, Mulder N, Nicol M, Oni T, Mizrahi V, Warner DF, Parkhill J, Gagneux S, Martin DP, Wilkinson RJ. 2017. The influence of HIV on the evolution of Mycobacterium tuberculosis. Mol Biol Evol 34:1654–1668 10.1093/molbev/msx107. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Albanna AS, Reed MB, Kotar KV, Fallow A, McIntosh FA, Behr MA, Menzies D. 2011. Reduced transmissibility of East African Indian strains of Mycobacterium tuberculosis. PLoS One 6:e25075 10.1371/journal.pone.0025075. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Rose G, Cortes T, Comas I, Coscolla M, Gagneux S, Young DB. 2013. Mapping of genotype-phenotype diversity among clinical isolates of Mycobacterium tuberculosis by sequence-based transcriptional profiling. Genome Biol Evol 5:1849–1862 10.1093/gbe/evt138. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Chiner-Oms Á, González-Candelas F, Comas I. 2018. Gene expression models based on a reference laboratory strain are poor predictors of Mycobacterium tuberculosis complex transcriptional diversity. Sci Rep 8:3813 10.1038/s41598-018-22237-5. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Tsolaki AG, Hirsh AE, DeRiemer K, Enciso JA, Wong MZ, Hannan M, Goguet de la Salmoniere YO, Aman K, Kato-Maeda M, Small PM. 2004. Functional and evolutionary genomics of Mycobacterium tuberculosis: insights from genomic deletions in 100 strains. Proc Natl Acad Sci USA 101:4865–4870 10.1073/pnas.0305634101. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Oeyas O, Borrel S, Trauner A, Zimmermann M, Feldmann J, Gagneux S, Stelling J, Sauer U, Zampieri M. 2019. Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis. bioRxiv 10.1101/555763:555763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Warner DF. 2015. Mycobacterium tuberculosis metabolism. Cold Spring Harb Perspect Med 5:a021121 10.1101/cshperspect.a021121. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Bos KI, Harkins KM, Herbig A, Coscolla M, Weber N, Comas I, Forrest SA, Bryant JM, Harris SR, Schuenemann VJ, Campbell TJ, Majander K, Wilbur AK, Guichon RA, Wolfe Steadman DL, Cook DC, Niemann S, Behr MA, Zumarraga M, Bastida R, Huson D, Nieselt K, Young D, Parkhill J, Buikstra JE, Gagneux S, Stone AC, Krause J. 2014. Pre-Columbian mycobacterial genomes reveal seals as a source of New World human tuberculosis. Nature 514:494–497 10.1038/nature13591. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Comas I, Coscolla M, Luo T, Borrell S, Holt KE, Kato-Maeda M, Parkhill J, Malla B, Berg S, Thwaites G, Yeboah-Manu D, Bothamley G, Mei J, Wei L, Bentley S, Harris SR, Niemann S, Diel R, Aseffa A, Gao Q, Young D, Gagneux S. 2013. Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat Genet 45:1176–1182 10.1038/ng.2744. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, Gordon SV, Eiglmeier K, Gas S, Barry CE III, Tekaia F, Badcock K, Basham D, Brown D, Chillingworth T, Connor R, Davies R, Devlin K, Feltwell T, Gentles S, Hamlin N, Holroyd S, Hornsby T, Jagels K, Krogh A, McLean J, Moule S, Murphy L, Oliver K, Osborne J, Quail MA, Rajandream MA, Rogers J, Rutter S, Seeger K, Skelton J, Squares R, Squares S, Sulston JE, Taylor K, Whitehead S, Barrell BG. 1998. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 393:537–544 10.1038/31159. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 41.Berney M, Berney-Meyer L, Wong KW, Chen B, Chen M, Kim J, Wang J, Harris D, Parkhill J, Chan J, Wang F, Jacobs WR Jr. 2015. Essential roles of methionine and S-adenosylmethionine in the autarkic lifestyle of Mycobacterium tuberculosis. Proc Natl Acad Sci USA 112:10008–10013 10.1073/pnas.1513033112. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Beste DJ, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P, Klamt S, Kierzek AM, McFadden J. 2007. GSMN-TB: a Web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol 8:R89 10.1186/gb-2007-8-5-r89. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Lamprecht DA, Finin PM, Rahman MA, Cumming BM, Russell SL, Jonnala SR, Adamson JH, Steyn AJ. 2016. Turning the respiratory flexibility of Mycobacterium tuberculosis against itself. Nat Commun 7:12393 10.1038/ncomms12393. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Eisenreich W, Rudel T, Heesemann J, Goebel W. 2017. To eat and to be eaten: mutual metabolic adaptations of immune cells and intracellular bacterial pathogens upon infection. Front Cell Infect Microbiol 7:316 10.3389/fcimb.2017.00316. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Russell DG, Huang L, VanderVen BC. 2019. Immunometabolism at the interface between macrophages and pathogens. Nat Rev Immunol 19:291–304 10.1038/s41577-019-0124-9. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Boshoff HI, Barry CE III. 2005. Tuberculosis: metabolism and respiration in the absence of growth. Nat Rev Microbiol 3:70–80 10.1038/nrmicro1065. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 47.Barry CE III, Boshoff HI, Dartois V, Dick T, Ehrt S, Flynn J, Schnappinger D, Wilkinson RJ, Young D. 2009. The spectrum of latent tuberculosis: rethinking the biology and intervention strategies. Nat Rev Microbiol 7:845–855 10.1038/nrmicro2236. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Pai M, Behr MA, Dowdy D, Dheda K, Divangahi M, Boehme CC, Ginsberg A, Swaminathan S, Spigelman M, Getahun H, Menzies D, Raviglione M. 2016. Tuberculosis. Nat Rev Dis Primers 2:16076 10.1038/nrdp.2016.76. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 49.Gagneux S. 2012. Host-pathogen coevolution in human tuberculosis. Philos Trans R Soc Lond B Biol Sci 367:850–859 10.1098/rstb.2011.0316. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Esmail H, Lai RP, Lesosky M, Wilkinson KA, Graham CM, Coussens AK, Oni T, Warwick JM, Said-Hartley Q, Koegelenberg CF, Walzl G, Flynn JL, Young DB, Barry CE III, O’Garra A, Wilkinson RJ. 2016. Characterization of progressive HIV-associated tuberculosis using 2-deoxy-2-[18F]fluoro-d-glucose positron emission and computed tomography. Nat Med 22:1090–1093 10.1038/nm.4161. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Drain PK, Bajema KL, Dowdy D, Dheda K, Naidoo K, Schumacher SG, Ma S, Meermeier E, Lewinsohn DM, Sherman DR. 2018. Incipient and subclinical tuberculosis: a clinical review of early stages and progression of infection. Clin Microbiol Rev 31:e00021-18 10.1128/CMR.00021-18. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Lin PL, Flynn JL. 2018. The end of the binary era: revisiting the spectrum of tuberculosis. J Immunol 201:2541–2548 10.4049/jimmunol.1800993. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Behr MA, Edelstein PH, Ramakrishnan L. 2018. Revisiting the timetable of tuberculosis. BMJ 362:k2738 10.1136/bmj.k2738. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Chengalroyen MD, Beukes GM, Gordhan BG, Streicher EM, Churchyard G, Hafner R, Warren R, Otwombe K, Martinson N, Kana BD. 2016. Detection and quantification of differentially culturable tubercle bacteria in sputum from patients with tuberculosis. Am J Respir Crit Care Med 194:1532–1540 10.1164/rccm.201604-0769OC. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Malherbe ST, Shenai S, Ronacher K, Loxton AG, Dolganov G, Kriel M, Van T, Chen RY, Warwick J, Via LE, Song T, Lee M, Schoolnik G, Tromp G, Alland D, Barry CE III, Winter J, Walzl G, Lucas L, Spuy GV, Stanley K, Thiart L, Smith B, Du Plessis N, Beltran CG, Maasdorp E, Ellmann A, Choi H, Joh J, Dodd LE, Allwood B, Koegelenberg C, Vorster M, Griffith-Richards S, Griffith-Richards S, Catalysis TB–Biomarker Consortium. 2016. Persisting positron emission tomography lesion activity and Mycobacterium tuberculosis mRNA after tuberculosis cure. Nat Med 22:1094–1100 10.1038/nm.4177. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Kerantzas CA, Jacobs WR Jr. 2017. Origins of combination therapy for tuberculosis: lessons for future antimicrobial development and application. MBio 8:e01586-16 10.1128/mBio.01586-16. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Dartois V. 2014. The path of anti-tuberculosis drugs: from blood to lesions to mycobacterial cells. Nat Rev Microbiol 12:159–167 10.1038/nrmicro3200. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Dartois V, Barry CE III. 2013. A medicinal chemists’ guide to the unique difficulties of lead optimization for tuberculosis. Bioorg Med Chem Lett 23:4741–4750 10.1016/j.bmcl.2013.07.006. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Kodama C, Lange B, Olaru ID, Khan P, Lipman M, Seddon JA, Sloan D, Grandjean L, Ferrand RA, Kranzer K. 2017. Mycobacterium tuberculosis transmission from patients with drug-resistant compared to drug-susceptible TB: a systematic review and meta-analysis. Eur Respir J 50:1701044 10.1183/13993003.01044-2017. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 60.Eldholm V, Norheim G, von der Lippe B, Kinander W, Dahle UR, Caugant DA, Mannsåker T, Mengshoel AT, Dyrhol-Riise AM, Balloux F. 2014. Evolution of extensively drug-resistant Mycobacterium tuberculosis from a susceptible ancestor in a single patient. Genome Biol 15:490 10.1186/s13059-014-0490-3. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Becerra MC, Huang C-C, Lecca L, Bayona J, Contreras C, Calderon R, Yataco R, Galea J, Zhang Z, Atwook S, Cohen T, Mitnick CD, Farmer P, Murray M. 2018. Resistance at no cost: the transmissibility and potential for disease progression of drug-resistant M. tuberculosis. bioRxiv 10.1101/475764:475764. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Warner DF, Mizrahi V. 2008. Physiology of Mycobacterium tuberculosis, p 53–69. In Kaufmann SHE, van Helden P, Rubin E, Britton WJ (ed), Handbook of Tuberculosis, vol 1. Wiley-Blackwell, Hoboken, NJ. [PubMed] [Google Scholar]
  • 63.Ehrt S, Rhee K. 2013. Mycobacterium tuberculosis metabolism and host interaction: mysteries and paradoxes. Curr Top Microbiol Immunol 374:163–188 10.1007/82_2012_299. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 64.Baek SH, Li AH, Sassetti CM. 2011. Metabolic regulation of mycobacterial growth and antibiotic sensitivity. PLoS Biol 9:e1001065 10.1371/journal.pbio.1001065. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Baer CE, Rubin EJ, Sassetti CM. 2015. New insights into TB physiology suggest untapped therapeutic opportunities. Immunol Rev 264:327–343 10.1111/imr.12267. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Baughn AD, Rhee KY. 2014. Metabolomics of central carbon metabolism in Mycobacterium tuberculosis. Microbiol Spectr 2:MGM2-0026-2013 10.1128/microbiolspec.MGM2-0026-2013. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 67.Lillebaek T, Dirksen A, Baess I, Strunge B, Thomsen VO, Andersen AB. 2002. Molecular evidence of endogenous reactivation of Mycobacterium tuberculosis after 33 years of latent infection. J Infect Dis 185:401–404 10.1086/338342. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 68.Lillebaek T, Norman A, Rasmussen EM, Marvig RL, Folkvardsen DB, Andersen AB, Jelsbak L. 2016. Substantial molecular evolution and mutation rates in prolonged latent Mycobacterium tuberculosis infection in humans. Int J Med Microbiol 306:580–585 10.1016/j.ijmm.2016.05.017. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 69.Hunter RL. 2016. Tuberculosis as a three-act play: a new paradigm for the pathogenesis of pulmonary tuberculosis. Tuberculosis (Edinb) 97:8–17 10.1016/j.tube.2015.11.010. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Behr MA, Waters WR. 2014. Is tuberculosis a lymphatic disease with a pulmonary portal? Lancet Infect Dis 14:250–255 10.1016/S1473-3099(13)70253-6. [DOI] [PubMed] [Google Scholar]
  • 71.Rittershaus ES, Baek SH, Sassetti CM. 2013. The normalcy of dormancy: common themes in microbial quiescence. Cell Host Microbe 13:643–651 10.1016/j.chom.2013.05.012. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Castro-Garza J, García-Jacobo P, Rivera-Morales LG, Quinn FD, Barber J, Karls R, Haas D, Helms S, Gupta T, Blumberg H, Tapia J, Luna-Cruz I, Rendon A, Vargas-Villarreal J, Vera-Cabrera L, Rodríguez-Padilla C. 2017. Detection of anti-HspX antibodies and HspX protein in patient sera for the identification of recent latent infection by Mycobacterium tuberculosis. PLoS One 12:e0181714 10.1371/journal.pone.0181714. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Hu Y, Movahedzadeh F, Stoker NG, Coates AR. 2006. Deletion of the Mycobacterium tuberculosis alpha-crystallin-like hspX gene causes increased bacterial growth in vivo. Infect Immun 74:861–868 10.1128/IAI.74.2.861-868.2006. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Parish T, Smith DA, Kendall S, Casali N, Bancroft GJ, Stoker NG. 2003. Deletion of two-component regulatory systems increases the virulence of Mycobacterium tuberculosis. Infect Immun 71:1134–1140 10.1128/IAI.71.3.1134-1140.2003. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Shimono N, Morici L, Casali N, Cantrell S, Sidders B, Ehrt S, Riley LW. 2003. Hypervirulent mutant of Mycobacterium tuberculosis resulting from disruption of the mce1 operon. Proc Natl Acad Sci USA 100:15918–15923 10.1073/pnas.2433882100. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Smith LJ, Bochkareva A, Rolfe MD, Hunt DM, Kahramanoglou C, Braun Y, Rodgers A, Blockley A, Coade S, Lougheed KEA, Hafneh NA, Glenn SM, Crack JC, Le Brun NE, Saldanha JW, Makarov V, Nobeli I, Arnvig K, Mukamolova GV, Buxton RS, Green J. 2017. Cmr is a redox-responsive regulator of DosR that contributes to M. tuberculosis virulence. Nucleic Acids Res 45:6600–6612 10.1093/nar/gkx406. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Delogu G, Brennan MJ, Manganelli R. 2017. PE and PPE genes: a tale of conservation and diversity. Adv Exp Med Biol 1019:191–207 10.1007/978-3-319-64371-7_10. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 78.Lindestam Arlehamn CS, Paul S, Mele F, Huang C, Greenbaum JA, Vita R, Sidney J, Peters B, Sallusto F, Sette A. 2015. Immunological consequences of intragenus conservation of Mycobacterium tuberculosis T-cell epitopes. Proc Natl Acad Sci USA 112:E147–E155 10.1073/pnas.1416537112. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Hunter RL Jr. 2018. The pathogenesis of tuberculosis: the early infiltrate of post-primary (adult pulmonary) tuberculosis: a distinct disease entity. Front Immunol 9:2108 10.3389/fimmu.2018.02108. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Houben RM, Dodd PJ. 2016. The global burden of latent tuberculosis infection: a re-estimation using mathematical modelling. PLoS Med 13:e1002152 10.1371/journal.pmed.1002152. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Abel L, Fellay J, Haas DW, Schurr E, Srikrishna G, Urbanowski M, Chaturvedi N, Srinivasan S, Johnson DH, Bishai WR. 2018. Genetics of human susceptibility to active and latent tuberculosis: present knowledge and future perspectives. Lancet Infect Dis 18:e64–e75 10.1016/S1473-3099(17)30623-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Elkington P, Tebruegge M, Mansour S. 2016. Tuberculosis: an infection-initiated autoimmune disease? Trends Immunol 37:815–818 10.1016/j.it.2016.09.007. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Odone A, Houben RMGJ, White RG, Lönnroth K. 2014. The effect of diabetes and undernutrition trends on reaching 2035 global tuberculosis targets. Lancet Diabetes Endocrinol 2:754–764 10.1016/S2213-8587(14)70164-0. [DOI] [PubMed] [Google Scholar]
  • 84.Jeon CY, Murray MB. 2008. Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS Med 5:e152 10.1371/journal.pmed.0050152. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Namasivayam S, Sher A, Glickman MS, Wipperman MF. 2018. The microbiome and tuberculosis: early evidence for cross talk. MBio 9:e01420-18 10.1128/mBio.01420-18. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Lönnroth K, Castro KG, Chakaya JM, Chauhan LS, Floyd K, Glaziou P, Raviglione MC. 2010. Tuberculosis control and elimination 2010-50: cure, care, and social development. Lancet 375:1814–1829 10.1016/S0140-6736(10)60483-7. [DOI] [PubMed] [Google Scholar]
  • 87.Dye C, Williams BG. 2010. The population dynamics and control of tuberculosis. Science 328:856–861 10.1126/science.1185449. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 88.Russell DG. 2011. Mycobacterium tuberculosis and the intimate discourse of a chronic infection. Immunol Rev 240:252–268 10.1111/j.1600-065X.2010.00984.x. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Griffin JE, Gawronski JD, Dejesus MA, Ioerger TR, Akerley BJ, Sassetti CM. 2011. High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism. PLoS Pathog 7:e1002251 10.1371/journal.ppat.1002251. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Rengarajan J, Bloom BR, Rubin EJ. 2005. Genome-wide requirements for Mycobacterium tuberculosis adaptation and survival in macrophages. Proc Natl Acad Sci USA 102:8327–8332 10.1073/pnas.0503272102. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Sassetti CM, Rubin EJ. 2003. Genetic requirements for mycobacterial survival during infection. Proc Natl Acad Sci USA 100:12989–12994 10.1073/pnas.2134250100. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Zhang YJ, Reddy MC, Ioerger TR, Rothchild AC, Dartois V, Schuster BM, Trauner A, Wallis D, Galaviz S, Huttenhower C, Sacchettini JC, Behar SM, Rubin EJ. 2013. Tryptophan biosynthesis protects mycobacteria from CD4 T-cell-mediated killing. Cell 155:1296–1308 10.1016/j.cell.2013.10.045. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Dhar N, McKinney JD. 2010. Mycobacterium tuberculosis persistence mutants identified by screening in isoniazid-treated mice. Proc Natl Acad Sci USA 107:12275–12280 10.1073/pnas.1003219107. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Larrouy-Maumus G, Marino LB, Madduri AV, Ragan TJ, Hunt DM, Bassano L, Gutierrez MG, Moody DB, Pavan FR, de Carvalho LP. 2016. Cell-envelope remodeling as a determinant of phenotypic antibacterial tolerance in Mycobacterium tuberculosis. ACS Infect Dis 2:352–360 10.1021/acsinfecdis.5b00148. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Sani M, Houben EN, Geurtsen J, Pierson J, de Punder K, van Zon M, Wever B, Piersma SR, Jiménez CR, Daffé M, Appelmelk BJ, Bitter W, van der Wel N, Peters PJ. 2010. Direct visualization by cryo-EM of the mycobacterial capsular layer: a labile structure containing ESX-1-secreted proteins. PLoS Pathog 6:e1000794 10.1371/journal.ppat.1000794. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Pethe K, Sequeira PC, Agarwalla S, Rhee K, Kuhen K, Phong WY, Patel V, Beer D, Walker JR, Duraiswamy J, Jiricek J, Keller TH, Chatterjee A, Tan MP, Ujjini M, Rao SP, Camacho L, Bifani P, Mak PA, Ma I, Barnes SW, Chen Z, Plouffe D, Thayalan P, Ng SH, Au M, Lee BH, Tan BH, Ravindran S, Nanjundappa M, Lin X, Goh A, Lakshminarayana SB, Shoen C, Cynamon M, Kreiswirth B, Dartois V, Peters EC, Glynne R, Brenner S, Dick T. 2010. A chemical genetic screen in Mycobacterium tuberculosis identifies carbon-source-dependent growth inhibitors devoid of in vivo efficacy. Nat Commun 1:57 10.1038/ncomms1060. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Savvi S, Warner DF, Kana BD, McKinney JD, Mizrahi V, Dawes SS. 2008. Functional characterization of a vitamin B12-dependent methylmalonyl pathway in Mycobacterium tuberculosis: implications for propionate metabolism during growth on fatty acids. J Bacteriol 190:3886–3895 10.1128/JB.01767-07. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Sampson SL, Mansfield KG, Carville A, Magee DM, Quitugua T, Howerth EW, Bloom BR, Hondalus MK. 2011. Extended safety and efficacy studies of a live attenuated double leucine and pantothenate auxotroph of Mycobacterium tuberculosis as a vaccine candidate. Vaccine 29:4839–4847 10.1016/j.vaccine.2011.04.066. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Vilchèze C, Copeland J, Keiser TL, Weisbrod T, Washington J, Jain P, Malek A, Weinrick B, Jacobs WR Jr. 2018. Rational design of biosafety level 2-approved, multidrug-resistant strains of Mycobacterium tuberculosis through nutrient auxotrophy. MBio 9:e00938-18 10.1128/mBio.00938-18. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Rancati G, Moffat J, Typas A, Pavelka N. 2018. Emerging and evolving concepts in gene essentiality. Nat Rev Genet 19:34–49 10.1038/nrg.2017.74. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 101.Kawai Y, Mickiewicz K, Errington J. 2018. Lysozyme counteracts β-lactam antibiotics by promoting the emergence of L-form bacteria. Cell 172:1038–1049.e10 10.1016/j.cell.2018.01.021. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Larsen MH, Biermann K, Jacobs J, William R. 2007. Laboratory maintenance of Mycobacterium tuberculosis. Curr Protoc Microbiol 6:10A.1.1–10A.1.8. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 103.Bergkessel M, Basta DW, Newman DK. 2016. The physiology of growth arrest: uniting molecular and environmental microbiology. Nat Rev Microbiol 14:549–562 10.1038/nrmicro.2016.107. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Bielecka MK, Tezera LB, Zmijan R, Drobniewski F, Zhang X, Jayasinghe S, Elkington P. 2017. A bioengineered three-dimensional cell culture platform integrated with microfluidics to address antimicrobial resistance in tuberculosis. MBio 8:e02073-16 10.1128/mBio.02073-16. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Wagner D, Maser J, Lai B, Cai Z, Barry CE III, Höner Zu Bentrup K, Russell DG, Bermudez LE. 2005. Elemental analysis of Mycobacterium avium-, Mycobacterium tuberculosis-, and Mycobacterium smegmatis-containing phagosomes indicates pathogen-induced microenvironments within the host cell’s endosomal system. J Immunol 174:1491–1500 10.4049/jimmunol.174.3.1491. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 106.Menon D, Singh K, Pinto SM, Nandy A, Jaisinghani N, Kutum R, Dash D, Prasad TSK, Gandotra S. 2019. Quantitative lipid droplet proteomics reveals Mycobacterium tuberculosis induced alterations in macrophage response to infection. ACS Infect Dis 5:559–569 10.1021/acsinfecdis.8b00301. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Park Y, Pacitto A, Bayliss T, Cleghorn LA, Wang Z, Hartman T, Arora K, Ioerger TR, Sacchettini J, Rizzi M, Donini S, Blundell TL, Ascher DB, Rhee K, Breda A, Zhou N, Dartois V, Jonnala SR, Via LE, Mizrahi V, Epemolu O, Stojanovski L, Simeons F, Osuna-Cabello M, Ellis L, MacKenzie CJ, Smith AR, Davis SH, Murugesan D, Buchanan KI, Turner PA, Huggett M, Zuccotto F, Rebollo-Lopez MJ, Lafuente-Monasterio MJ, Sanz O, Diaz GS, Lelièvre J, Ballell L, Selenski C, Axtman M, Ghidelli-Disse S, Pflaumer H, Bösche M, Drewes G, Freiberg GM, Kurnick MD, Srikumaran M, Kempf DJ, Green SR, et al. 2017. Essential but not vulnerable: indazole sulfonamides targeting inosine monophosphate dehydrogenase as potential leads against Mycobacterium tuberculosis. ACS Infect Dis 3:18–33 10.1021/acsinfecdis.6b00103. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Sarathy JP, Zuccotto F, Hsinpin H, Sandberg L, Via LE, Marriner GA, Masquelin T, Wyatt P, Ray P, Dartois V. 2016. Prediction of drug penetration in tuberculosis lesions. ACS Infect Dis 2:552–563 10.1021/acsinfecdis.6b00051. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Tezera LB, Bielecka MK, Chancellor A, Reichmann MT, Shammari BA, Brace P, Batty A, Tocheva A, Jogai S, Marshall BG, Tebruegge M, Jayasinghe SN, Mansour S, Elkington PT. 2017. Dissection of the host-pathogen interaction in human tuberculosis using a bioengineered 3-dimensional model. eLife 6:e21283 10.7554/eLife.21283. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Vandal OH, Nathan CF, Ehrt S. 2009. Acid resistance in Mycobacterium tuberculosis. J Bacteriol 191:4714–4721 10.1128/JB.00305-09. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Baker JJ, Johnson BK, Abramovitch RB. 2014. Slow growth of Mycobacterium tuberculosis at acidic pH is regulated by phoPR and host-associated carbon sources. Mol Microbiol 94:56–69 10.1111/mmi.12688. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Gold B, Nathan C. 2017. Targeting phenotypically tolerant Mycobacterium tuberculosis. Microbiol Spectr 5:TBTB2-0031-2016 10.1128/microbiolspec.TBTB2-0031-2016. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113.Early J, Ollinger J, Darby C, Alling T, Mullen S, Casey A, Gold B, Ochoada J, Wiernicki T, Masquelin T. 2018. Identification of compounds with pH-dependent bactericidal activity against Mycobacterium tuberculosis. ACS Infect Dis 5:272–280. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 114.Deb C, Lee CM, Dubey VS, Daniel J, Abomoelak B, Sirakova TD, Pawar S, Rogers L, Kolattukudy PE. 2009. A novel in vitro multiple-stress dormancy model for Mycobacterium tuberculosis generates a lipid-loaded, drug-tolerant, dormant pathogen. PLoS One 4:e6077 10.1371/journal.pone.0006077. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115.Gold B, Warrier T, Nathan C. 2015. A multi-stress model for high throughput screening against non-replicating Mycobacterium tuberculosis. Methods Mol Biol 1285:293–315. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 116.Warrier T, Martinez-Hoyos M, Marin-Amieva M, Colmenarejo G, Porras-De Francisco E, Alvarez-Pedraglio AI, Fraile-Gabaldon MT, Torres-Gomez PA, Lopez-Quezada L, Gold B, Roberts J, Ling Y, Somersan-Karakaya S, Little D, Cammack N, Nathan C, Mendoza-Losana A. 2015. Identification of novel anti-mycobacterial compounds by screening a pharmaceutical small-molecule library against nonreplicating Mycobacterium tuberculosis. ACS Infect Dis 1:580–585 10.1021/acsinfecdis.5b00025. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 117.Grant SS, Kawate T, Nag PP, Silvis MR, Gordon K, Stanley SA, Kazyanskaya E, Nietupski R, Golas A, Fitzgerald M, Cho S, Franzblau SG, Hung DT. 2013. Identification of novel inhibitors of nonreplicating Mycobacterium tuberculosis using a carbon starvation model. ACS Chem Biol 8:2224–2234 10.1021/cb4004817. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118.Beste DJ, Bonde B, Hawkins N, Ward JL, Beale MH, Noack S, Nöh K, Kruger NJ, Ratcliffe RG, McFadden J. 2011. 13C metabolic flux analysis identifies an unusual route for pyruvate dissimilation in mycobacteria which requires isocitrate lyase and carbon dioxide fixation. PLoS Pathog 7:e1002091 10.1371/journal.ppat.1002091. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 119.Beste DJ, Nöh K, Niedenführ S, Mendum TA, Hawkins ND, Ward JL, Beale MH, Wiechert W, McFadden J. 2013. 13C-flux spectral analysis of host-pathogen metabolism reveals a mixed diet for intracellular Mycobacterium tuberculosis. Chem Biol 20:1012–1021 10.1016/j.chembiol.2013.06.012. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 120.Lee W, VanderVen BC, Fahey RJ, Russell DG. 2013. Intracellular Mycobacterium tuberculosis exploits host-derived fatty acids to limit metabolic stress. J Biol Chem 288:6788–6800 10.1074/jbc.M112.445056. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 121.Marrero J, Rhee KY, Schnappinger D, Pethe K, Ehrt S. 2010. Gluconeogenic carbon flow of tricarboxylic acid cycle intermediates is critical for Mycobacterium tuberculosis to establish and maintain infection. Proc Natl Acad Sci USA 107:9819–9824 10.1073/pnas.1000715107. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 122.Watanabe S, Zimmermann M, Goodwin MB, Sauer U, Barry CE III, Boshoff HI. 2011. Fumarate reductase activity maintains an energized membrane in anaerobic Mycobacterium tuberculosis. PLoS Pathog 7:e1002287 10.1371/journal.ppat.1002287. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 123.Loots T. 2014. An altered Mycobacterium tuberculosis metabolome induced by katG mutations resulting in isoniazid resistance. Antimicrob Agents Chemother 58:2144–2149 10.1128/AAC.02344-13. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 124.du Preez I, Loots DT. 2013. New sputum metabolite markers implicating adaptations of the host to Mycobacterium tuberculosis, and vice versa. Tuberculosis (Edinb) 93:330–337 10.1016/j.tube.2013.02.008. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 125.Rhee K. 2013. Minding the gaps: metabolomics mends functional genomics. EMBO Rep 14:949–950 10.1038/embor.2013.155. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 126.Banaei-Esfahani A, Nicod C, Aebersold R, Collins BC. 2017. Systems proteomics approaches to study bacterial pathogens: application to Mycobacterium tuberculosis. Curr Opin Microbiol 39:64–72 10.1016/j.mib.2017.09.013. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 127.Nandakumar M, Prosser GA, de Carvalho LP, Rhee K. 2015. Metabolomics of Mycobacterium tuberculosis. Methods Mol Biol 1285:105–115 10.1007/978-1-4939-2450-9_6. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 128.Zimmermann M, Kogadeeva M, Gengenbacher M, McEwen G, Mollenkopf HJ, Zamboni N, Kaufmann SHE, Sauer U. 2017. Integration of metabolomics and transcriptomics reveals a complex diet of Mycobacterium tuberculosis during early macrophage infection. mSystems 2:e00057-17 10.1128/mSystems.00057-17. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 129.Galagan JE, Minch K, Peterson M, Lyubetskaya A, Azizi E, Sweet L, Gomes A, Rustad T, Dolganov G, Glotova I, Abeel T, Mahwinney C, Kennedy AD, Allard R, Brabant W, Krueger A, Jaini S, Honda B, Yu WH, Hickey MJ, Zucker J, Garay C, Weiner B, Sisk P, Stolte C, Winkler JK, Van de Peer Y, Iazzetti P, Camacho D, Dreyfuss J, Liu Y, Dorhoi A, Mollenkopf HJ, Drogaris P, Lamontagne J, Zhou Y, Piquenot J, Park ST, Raman S, Kaufmann SH, Mohney RP, Chelsky D, Moody DB, Sherman DR, Schoolnik GK. 2013. The Mycobacterium tuberculosis regulatory network and hypoxia. Nature 499:178–183 10.1038/nature12337. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 130.Nakedi KC, Calder B, Banerjee M, Giddey A, Nel AJM, Garnett S, Blackburn JM, Soares NC. 2018. Identification of novel physiological substrates of Mycobacterium bovis BCG protein kinase G (PknG) by label-free quantitative phosphoproteomics. Mol Cell Proteomics 17:1365–1377 10.1074/mcp.RA118.000705. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 131.Carette X, Platig J, Young DC, Helmel M, Young AT, Wang Z, Potluri LP, Moody CS, Zeng J, Prisic S, Paulson JN, Muntel J, Madduri AVR, Velarde J, Mayfield JA, Locher C, Wang T, Quackenbush J, Rhee KY, Moody DB, Steen H, Husson RN. 2018. Multisystem analysis of Mycobacterium tuberculosis reveals kinase-dependent remodeling of the pathogen-environment interface. MBio 9:e02333-17 10.1128/mBio.02333-17. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 132.Zampieri M, Szappanos B, Buchieri MV, Trauner A, Piazza I, Picotti P, Gagneux S, Borrell S, Gicquel B, Lelievre J, Papp B, Sauer U. 2018. High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. Sci Transl Med 10:eaa13973 10.1126/scitranslmed.aal3973. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 133.Jansen RS, Rhee KY. 2017. Emerging approaches to tuberculosis drug development: at home in the metabolome. Trends Pharmacol Sci 38:393–405 10.1016/j.tips.2017.01.005. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 134.Ballinger E, Mosior J, Hartman T, Burns-Huang K, Gold B, Morris R, Goullieux L, Blanc I, Vaubourgeix J, Lagrange S, Fraisse L, Sans S, Couturier C, Bacqué E, Rhee K, Scarry SM, Aubé J, Yang G, Ouerfelli O, Schnappinger D, Ioerger TR, Engelhart CA, McConnell JA, McAulay K, Fay A, Roubert C, Sacchettini J, Nathan C. 2019. Opposing reactions in coenzyme A metabolism sensitize Mycobacterium tuberculosis to enzyme inhibition. Science 363:eaau8959 10.1126/science.aau8959. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 135.Kim MJ, Wainwright HC, Locketz M, Bekker LG, Walther GB, Dittrich C, Visser A, Wang W, Hsu FF, Wiehart U, Tsenova L, Kaplan G, Russell DG. 2010. Caseation of human tuberculosis granulomas correlates with elevated host lipid metabolism. EMBO Mol Med 2:258–274 10.1002/emmm.201000079. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 136.Barrios-Payán J, Saqui-Salces M, Jeyanathan M, Alcántara-Vazquez A, Castañon-Arreola M, Rook G, Hernandez-Pando R. 2012. Extrapulmonary locations of mycobacterium tuberculosis DNA during latent infection. J Infect Dis 206:1194–1205 10.1093/infdis/jis381. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 137.Ufimtseva E, Eremeeva N, Petrunina E, Umpeleva T, Karskanova S, Bayborodin S, Vakhrusheva D, Kravchenko M, Skornyakov S. 2018. Ex vivo expansion of alveolar macrophages with Mycobacterium tuberculosis from the resected lungs of patients with pulmonary tuberculosis. PLoS One 13:e0191918 10.1371/journal.pone.0191918. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 138.Kavvas ES, Seif Y, Yurkovich JT, Norsigian C, Poudel S, Greenwald WW, Ghatak S, Palsson BO, Monk JM. 2018. Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions. BMC Syst Biol 12:25 10.1186/s12918-018-0557-y. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 139.Garay CD, Dreyfuss JM, Galagan JE. 2015. Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis. BMC Syst Biol 9:57 10.1186/s12918-015-0206-7. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 140.Mazandu GK, Mulder NJ. 2012. Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins. Int J Mol Sci 13:7283–7302 10.3390/ijms13067283. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141.Satta G, Lipman M, Smith GP, Arnold C, Kon OM, McHugh TD. 2018. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential? Clin Microbiol Infect 24:604–609 10.1016/j.cmi.2017.10.030. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 142.DeJesus MA, Gerrick ER, Xu W, Park SW, Long JE, Boutte CC, Rubin EJ, Schnappinger D, Ehrt S, Fortune SM, Sassetti CM, Ioerger TR. 2017. Comprehensive essentiality analysis of the Mycobacterium tuberculosis genome via saturating transposon mutagenesis. MBio 8:302133-16 10.1128/mBio.02133-16. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 143.Fivian-Hughes AS, Houghton J, Davis EO. 2012. Mycobacterium tuberculosis thymidylate synthase gene thyX is essential and potentially bifunctional, while thyA deletion confers resistance to p-aminosalicylic acid. Microbiology 158:308–318 10.1099/mic.0.053983-0. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 144.Muñoz-Elías EJ, McKinney JD. 2005. Mycobacterium tuberculosis isocitrate lyases 1 and 2 are jointly required for in vivo growth and virulence. Nat Med 11:638–644 10.1038/nm1252. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 145.Williams M, Mizrahi V, Kana BD. 2014. Molybdenum cofactor: a key component of Mycobacterium tuberculosis pathogenesis? Crit Rev Microbiol 40:18–29 10.3109/1040841X.2012.749211. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 146.Vilchèze C, Weinrick B, Leung LW, Jacobs WR Jr. 2018. Plasticity of Mycobacterium tuberculosis NADH dehydrogenases and their role in virulence. Proc Natl Acad Sci USA 115:1599–1604 10.1073/pnas.1721545115. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 147.Dawes SS, Warner DF, Tsenova L, Timm J, McKinney JD, Kaplan G, Rubin H, Mizrahi V. 2003. Ribonucleotide reduction in Mycobacterium tuberculosis: function and expression of genes encoding class Ib and class II ribonucleotide reductases. Infect Immun 71:6124–6131 10.1128/IAI.71.11.6124-6131.2003. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148.Billig S, Schneefeld M, Huber C, Grassl GA, Eisenreich W, Bange FC. 2017. Lactate oxidation facilitates growth of Mycobacterium tuberculosis in human macrophages. Sci Rep 7:6484 10.1038/s41598-017-05916-7. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 149.Singh V, Donini S, Pacitto A, Sala C, Hartkoorn RC, Dhar N, Keri G, Ascher DB, Mondésert G, Vocat A, Lupien A, Sommer R, Vermet H, Lagrange S, Buechler J, Warner DF, McKinney JD, Pato J, Cole ST, Blundell TL, Rizzi M, Mizrahi V. 2017. The inosine monophosphate dehydrogenase, GuaB2, is a vulnerable new bactericidal drug target for tuberculosis. ACS Infect Dis 3:5–17 10.1021/acsinfecdis.6b00102. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 150.Carey AF, Rock JM, Krieger IV, Chase MR, Fernandez-Suarez M, Gagneux S, Sacchettini JC, Ioerger TR, Fortune SM. 2018. TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities. PLoS Pathog 14:e1006939 10.1371/journal.ppat.1006939. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 151.Homolka S, Niemann S, Russell DG, Rohde KH. 2010. Functional genetic diversity among Mycobacterium tuberculosis complex clinical isolates: delineation of conserved core and lineage-specific transcriptomes during intracellular survival. PLoS Pathog 6:e1000988 10.1371/journal.ppat.1000988. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 152.Ioerger TR, Feng Y, Ganesula K, Chen X, Dobos KM, Fortune S, Jacobs WR Jr, Mizrahi V, Parish T, Rubin E, Sassetti C, Sacchettini JC. 2010. Variation among genome sequences of H37Rv strains of Mycobacterium tuberculosis from multiple laboratories. J Bacteriol 192:3645–3653 10.1128/JB.00166-10. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 153.Fenhalls G, Stevens L, Moses L, Bezuidenhout J, Betts JC, Helden Pv P, Lukey PT, Duncan K. 2002. In situ detection of Mycobacterium tuberculosis transcripts in human lung granulomas reveals differential gene expression in necrotic lesions. Infect Immun 70:6330–6338 10.1128/IAI.70.11.6330-6338.2002. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 154.Gagneux S, DeRiemer K, Van T, Kato-Maeda M, de Jong BC, Narayanan S, Nicol M, Niemann S, Kremer K, Gutierrez MC, Hilty M, Hopewell PC, Small PM. 2006. Variable host-pathogen compatibility in Mycobacterium tuberculosis. Proc Natl Acad Sci USA 103:2869–2873 10.1073/pnas.0511240103. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 155.VanderVen BC, Huang L, Rohde KH, Russell DG. 2016. The minimal unit of infection: Mycobacterium tuberculosis in the macrophage. Microbiol Spectr 4:TBTB2-0025-2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 156.Keiser TL, Purdy GE. 2017. Killing Mycobacterium tuberculosis in vitro: what model systems can teach us. Microbiol Spectr 5:TBTB2-0028-2016 10.1128/microbiolspec.TBTB2-0028-2016. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 157.Woo M, Wood C, Kwon D, Park KP, Fejer G, Delorme V. 2018. Mycobacterium tuberculosis infection and innate responses in a new model of lung alveolar macrophages. Front Immunol 9:438 10.3389/fimmu.2018.00438. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 158.Ginhoux F, Schultze JL, Murray PJ, Ochando J, Biswas SK. 2016. New insights into the multidimensional concept of macrophage ontogeny, activation and function. Nat Immunol 17:34–40 10.1038/ni.3324. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 159.Huang L, Nazarova EV, Tan S, Liu Y, Russell DG. 2018. Growth of Mycobacterium tuberculosis in vivo segregates with host macrophage metabolism and ontogeny. J Exp Med 215:1135–1152 10.1084/jem.20172020. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 160.Cohen SB, Gern BH, Delahaye JL, Adams KN, Plumlee CR, Winkler JK, Sherman DR, Gerner MY, Urdahl KB. 2018. Alveolar macrophages provide an early Mycobacterium tuberculosis niche and initiate dissemination. Cell Host Microbe 24:439–446.e4 10.1016/j.chom.2018.08.001. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 161.Escoll P, Buchrieser C. 2018. Metabolic reprogramming of host cells upon bacterial infection: why shift to a Warburg-like metabolism? FEBS J 285:2146–2160 10.1111/febs.14446. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 162.Shi L, Eugenin EA, Subbian S. 2016. Immunometabolism in tuberculosis. Front Immunol 7:150 10.3389/fimmu.2016.00150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 163.Vijayan V, Pradhan P, Braud L, Fuchs HR, Gueler F, Motterlini R, Foresti R, Immenschuh S. 2019. Human and murine macrophages exhibit differential metabolic responses to lipopolysaccharide: a divergent role for glycolysis. Redox Biol 22:101147 10.1016/j.redox.2019.101147. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 164.Turner RD, Chiu C, Churchyard GJ, Esmail H, Lewinsohn DM, Gandhi NR, Fennelly KP. 2017. Tuberculosis infectiousness and host susceptibility. J Infect Dis 216(Suppl 6):S636–S643 10.1093/infdis/jix361. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 165.Rohde KH, Veiga DF, Caldwell S, Balázsi G, Russell DG. 2012. Linking the transcriptional profiles and the physiological states of Mycobacterium tuberculosis during an extended intracellular infection. PLoS Pathog 8:e1002769 10.1371/journal.ppat.1002769. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 166.Mouton JM, Helaine S, Holden DW, Sampson SL. 2016. Elucidating population-wide mycobacterial replication dynamics at the single-cell level. Microbiology 162:966–978 10.1099/mic.0.000288. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 167.Gill WP, Harik NS, Whiddon MR, Liao RP, Mittler JE, Sherman DR. 2009. A replication clock for Mycobacterium tuberculosis. Nat Med 15:211–214 10.1038/nm.1915. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 168.Orme IM, Ordway DJ. 2016. Mouse and guinea pig models of tuberculosis. Microbiol Spectr 4:TBTB2-0002-2015. [DOI] [PubMed] [Google Scholar]
  • 169.Lin PL, Ford CB, Coleman MT, Myers AJ, Gawande R, Ioerger T, Sacchettini J, Fortune SM, Flynn JL. 2013. Sterilization of granulomas is common in active and latent tuberculosis despite within-host variability in bacterial killing. Nat Med 20:75–79. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 170.Martin CJ, Cadena AM, Leung VW, Lin PL, Maiello P, Hicks N, Chase MR, Flynn JL, Fortune SM. 2017. Digitally barcoding Mycobacterium tuberculosis reveals in vivo infection dynamics in the macaque model of tuberculosis. MBio 8:e00312-17 10.1128/mBio.00312-17. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 171.Ford CB, Lin PL, Chase MR, Shah RR, Iartchouk O, Galagan J, Mohaideen N, Ioerger TR, Sacchettini JC, Lipsitch M, Flynn JL, Fortune SM. 2011. Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat Genet 43:482–486 10.1038/ng.811. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 172.Mahamed D, Boulle M, Ganga Y, McArthur C, Skroch S, Oom L, Catinas O, Pillay K, Naicker M, Rampersad S, Mathonsi C, Hunter J, Wong EB, Suleman M, Sreejit G, Pym AS, Lustig G, Sigal A. 2017. Intracellular growth of Mycobacterium tuberculosis after macrophage cell death leads to serial killing of host cells. eLife 6:e22028. 10.7554/eLife.22028. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 173.Cadena AM, Fortune SM, Flynn JL. 2017. Heterogeneity in tuberculosis. Nat Rev Immunol 17:691–702 10.1038/nri.2017.69. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 174.Basta DW, Bergkessel M, Newman DK. 2017. Identification of fitness determinants during energy-limited growth arrest in Pseudomonas aeruginosa. MBio 8:e01170-17 10.1128/mBio.01170-17. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 175.Pechter KB, Yin L, Oda Y, Gallagher L, Yang J, Manoil C, Harwood CS. 2017. Molecular basis of bacterial longevity. MBio 8:e01726-17 10.1128/mBio.01726-17. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 176.Spero MA, Newman DK. 2018. Chlorate specifically targets oxidant-starved, antibiotic-tolerant populations of Pseudomonas aeruginosa biofilms. MBio 9:e01400-18 10.1128/mBio.01400-18. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 177.Hingley-Wilson SM, Sambandamurthy VK, Jacobs WR Jr. 2003. Survival perspectives from the world’s most successful pathogen, Mycobacterium tuberculosis. Nat Immunol 4:949–955 10.1038/ni981. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 178.Ernst JD. 2012. The immunological life cycle of tuberculosis. Nat Rev Immunol 12:581–591 10.1038/nri3259. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 179.Nagelkerke NJ, de Vlas SJ, MacDonald KS, Rieder HL. 2004. Tuberculosis and sexually transmitted infections. Emerg Infect Dis 10:2055–2056 10.3201/eid1011.030785. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 180.Lin PL, Rodgers M, Smith L, Bigbee M, Myers A, Bigbee C, Chiosea I, Capuano SV, Fuhrman C, Klein E, Flynn JL. 2009. Quantitative comparison of active and latent tuberculosis in the cynomolgus macaque model. Infect Immun 77:4631–4642 10.1128/IAI.00592-09. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 181.Duneau D, Ferdy JB, Revah J, Kondolf H, Ortiz GA, Lazzaro BP, Buchon N. 2017. Stochastic variation in the initial phase of bacterial infection predicts the probability of survival in D. melanogaster. eLife 6:e28298 10.7554/eLife.28298. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 182.Kerkhoff AD, Barr DA, Schutz C, Burton R, Nicol MP, Lawn SD, Meintjes G. 2017. Disseminated tuberculosis among hospitalised HIV patients in South Africa: a common condition that can be rapidly diagnosed using urine-based assays. Sci Rep 7:10931 10.1038/s41598-017-09895-7. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 183.Bisson GP, Zetola N, Collman RG. 2015. Persistent high mortality in advanced HIV/TB despite appropriate antiretroviral and antitubercular therapy: an emerging challenge. Curr HIV/AIDS Rep 12:107–116 10.1007/s11904-015-0256-x. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 184.Getnet F, Demissie M, Assefa N, Mengistie B, Worku A. 2017. Delay in diagnosis of pulmonary tuberculosis in low-and middle-income settings: systematic review and meta-analysis. BMC Pulm Med 17:202 10.1186/s12890-017-0551-y. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 185.Stek C, Allwood B, Walker NF, Wilkinson RJ, Lynen L, Meintjes G. 2018. The immune mechanisms of lung parenchymal damage in tuberculosis and the role of host-directed therapy. Front Microbiol 9:2603 10.3389/fmicb.2018.02603. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 186.Behr MA, Gordon SV. 2015. Why doesn’t Mycobacterium tuberculosis spread in animals? Trends Microbiol 23:1–2 10.1016/j.tim.2014.11.001. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 187.Qian X, Nguyen DT, Lyu J, Albers AE, Bi X, Graviss EA. 2018. Risk factors for extrapulmonary dissemination of tuberculosis and associated mortality during treatment for extrapulmonary tuberculosis. Emerg Microbes Infect 7:1–14 10.1038/s41426-018-0106-1. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 188.Wolf AJ, Desvignes L, Linas B, Banaiee N, Tamura T, Takatsu K, Ernst JD. 2008. Initiation of the adaptive immune response to Mycobacterium tuberculosis depends on antigen production in the local lymph node, not the lungs. J Exp Med 205:105–115 10.1084/jem.20071367. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 189.Ganchua SKC, Cadena AM, Maiello P, Gideon HP, Myers AJ, Junecko BF, Klein EC, Lin PL, Mattila JT, Flynn JL. 2018. Lymph nodes are sites of prolonged bacterial persistence during Mycobacterium tuberculosis infection in macaques. PLoS Pathog 14:e1007337 10.1371/journal.ppat.1007337. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 190.Mayito J, Andia I, Belay M, Jolliffe DA, Kateete DP, Reece ST, Martineau AR. 2019. Anatomic and cellular niches for Mycobacterium tuberculosis in latent tuberculosis infection. J Infect Dis 219:685–694 10.1093/infdis/jiy579. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 191.Eum SY, Kong JH, Hong MS, Lee YJ, Kim JH, Hwang SH, Cho SN, Via LE, Barry CE III. 2010. Neutrophils are the predominant infected phagocytic cells in the airways of patients with active pulmonary TB. Chest 137:122–128 10.1378/chest.09-0903. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 192.Lowe DM, Redford PS, Wilkinson RJ, O’Garra A, Martineau AR. 2012. Neutrophils in tuberculosis: friend or foe? Trends Immunol 33:14–25 10.1016/j.it.2011.10.003. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 193.O’Garra A, Redford PS, McNab FW, Bloom CI, Wilkinson RJ, Berry MP. 2013. The immune response in tuberculosis. Annu Rev Immunol 31:475–527 10.1146/annurev-immunol-032712-095939. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 194.Kaplan G, Post FA, Moreira AL, Wainwright H, Kreiswirth BN, Tanverdi M, Mathema B, Ramaswamy SV, Walther G, Steyn LM, Barry CE III, Bekker LG. 2003. Mycobacterium tuberculosis growth at the cavity surface: a microenvironment with failed immunity. Infect Immun 71:7099–7108 10.1128/IAI.71.12.7099-7108.2003. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 195.Evangelopoulos D, McHugh TD. 2015. Improving the tuberculosis drug development pipeline. Chem Biol Drug Des 86:951–960 10.1111/cbdd.12549. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 196.Tanner L, Denti P, Wiesner L, Warner DF. 2018. Drug permeation and metabolism in Mycobacterium tuberculosis: prioritising local exposure as essential criterion in new TB drug development. IUBMB Life 70:926–937 10.1002/iub.1866. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 197.Prideaux B, Via LE, Zimmerman MD, Eum S, Sarathy J, O’Brien P, Chen C, Kaya F, Weiner DM, Chen PY, Song T, Lee M, Shim TS, Cho JS, Kim W, Cho SN, Olivier KN, Barry CE III, Dartois V. 2015. The association between sterilizing activity and drug distribution into tuberculosis lesions. Nat Med 21:1223–1227 10.1038/nm.3937. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 198.Pienaar E, Sarathy J, Prideaux B, Dietzold J, Dartois V, Kirschner DE, Linderman JJ. 2017. Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach. PLOS Comput Biol 13:e1005650 10.1371/journal.pcbi.1005650. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 199.Gillespie SH. 2016. The role of moxifloxacin in tuberculosis therapy. Eur Respir Rev 25:19–28 10.1183/16000617.0085-2015. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 200.Hoffmann E, Machelart A, Song OR, Brodin P. 2018. Proteomics of Mycobacterium infection: moving towards a better understanding of pathogen-driven immunomodulation. Front Immunol 9:86 10.3389/fimmu.2018.00086. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 201.O’Neill LA, Kishton RJ, Rathmell J. 2016. A guide to immunometabolism for immunologists. Nat Rev Immunol 16:553–565 10.1038/nri.2016.70. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 202.Eisenreich W, Heesemann J, Rudel T, Goebel W. 2015. Metabolic adaptations of intracellullar bacterial pathogens and their mammalian host cells during infection (“pathometabolism”). Microbiol Spectr 3:MBP-0002-2014 10.1128/microbiolspec.MBP-0002-2014. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 203.Comas I, Chakravartti J, Small PM, Galagan J, Niemann S, Kremer K, Ernst JD, Gagneux S. 2010. Human T cell epitopes of Mycobacterium tuberculosis are evolutionarily hyperconserved. Nat Genet 42:498–503 10.1038/ng.590. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 204.Gleeson LE, Sheedy FJ, Palsson-McDermott EM, Triglia D, O’Leary SM, O’Sullivan MP, O’Neill LA, Keane J. 2016. Cutting edge: Mycobacterium tuberculosis induces aerobic glycolysis in human alveolar macrophages that is required for control of intracellular bacillary replication. J Immunol 196:2444–2449 10.4049/jimmunol.1501612. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 205.Shi L, Salamon H, Eugenin EA, Pine R, Cooper A, Gennaro ML. 2016. Infection with Mycobacterium tuberculosis induces the Warburg effect in mouse lungs. Sci Rep 5:18176 10.1038/srep18176. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 206.Appelberg R, Moreira D, Barreira-Silva P, Borges M, Silva L, Dinis-Oliveira RJ, Resende M, Correia-Neves M, Jordan MB, Ferreira NC, Abrunhosa AJ, Silvestre R. 2015. The Warburg effect in mycobacterial granulomas is dependent on the recruitment and activation of macrophages by interferon-γ. Immunology 145:498–507 10.1111/imm.12464. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 207.Lachmandas E, Beigier-Bompadre M, Cheng SC, Kumar V, van Laarhoven A, Wang X, Ammerdorffer A, Boutens L, de Jong D, Kanneganti TD, Gresnigt MS, Ottenhoff TH, Joosten LA, Stienstra R, Wijmenga C, Kaufmann SH, van Crevel R, Netea MG. 2016. Rewiring cellular metabolism via the AKT/mTOR pathway contributes to host defence against Mycobacterium tuberculosis in human and murine cells. Eur J Immunol 46:2574–2586 10.1002/eji.201546259. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 208.Peyron P, Vaubourgeix J, Poquet Y, Levillain F, Botanch C, Bardou F, Daffé M, Emile JF, Marchou B, Cardona PJ, de Chastellier C, Altare F. 2008. Foamy macrophages from tuberculous patients’ granulomas constitute a nutrient-rich reservoir for M. tuberculosis persistence. PLoS Pathog 4:e1000204 10.1371/journal.ppat.1000204. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 209.Russell DG, Cardona PJ, Kim MJ, Allain S, Altare F. 2009. Foamy macrophages and the progression of the human tuberculosis granuloma. Nat Immunol 10:943–948 10.1038/ni.1781. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 210.Singh V, Jamwal S, Jain R, Verma P, Gokhale R, Rao KV. 2012. Mycobacterium tuberculosis-driven targeted recalibration of macrophage lipid homeostasis promotes the foamy phenotype. Cell Host Microbe 12:669–681 10.1016/j.chom.2012.09.012. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 211.Singh V, Kaur C, Chaudhary VK, Rao KV, Chatterjee S. 2015. M. tuberculosis secretory protein ESAT-6 induces metabolic flux perturbations to drive foamy macrophage differentiation. Sci Rep 5:12906 10.1038/srep12906. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 212.Knight M, Braverman J, Asfaha K, Gronert K, Stanley S. 2018. Lipid droplet formation in Mycobacterium tuberculosis infected macrophages requires IFN-γ/HIF-1α signaling and supports host defense. PLoS Pathog 14:e1006874 10.1371/journal.ppat.1006874. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 213.Lachmandas E, Rios-Miguel AB, Koeken VACM, van der Pasch E, Kumar V, Matzaraki V, Li Y, Oosting M, Joosten LAB, Notebaart RA, Noursadeghi M, Netea MG, van Crevel R, Pollara G. 2018. Tissue metabolic changes drive cytokine responses to Mycobacterium tuberculosis. J Infect Dis 218:165–170 10.1093/infdis/jiy173. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 214.Mattila JT, Beaino W, Maiello P, Coleman MT, White AG, Scanga CA, Flynn JL, Anderson CJ. 2017. Positron emission tomography imaging of macaques with tuberculosis identifies temporal changes in granuloma glucose metabolism and integrin α4β1-expressing immune cells. J Immunol 199:806–815 10.4049/jimmunol.1700231. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 215.Agapova A, Serafini A, Petridis M, Hunt DM, Garza-Garcia A, Sohaskey CD, de Carvalho LPS. 2019. Flexible nitrogen utilisation by the metabolic generalist pathogen Mycobacterium tuberculosis. eLife 8:e41129 10.7554/eLife.41129. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 216.de Carvalho LP, Fischer SM, Marrero J, Nathan C, Ehrt S, Rhee KY. 2010. Metabolomics of Mycobacterium tuberculosis reveals compartmentalized co-catabolism of carbon substrates. Chem Biol 17:1122–1131 10.1016/j.chembiol.2010.08.009. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 217.Puckett S, Trujillo C, Eoh H, Marrero J, Spencer J, Jackson M, Schnappinger D, Rhee K, Ehrt S. 2014. Inactivation of fructose-1,6-bisphosphate aldolase prevents optimal co-catabolism of glycolytic and gluconeogenic carbon substrates in Mycobacterium tuberculosis. PLoS Pathog 10:e1004144 10.1371/journal.ppat.1004144. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 218.Noy T, Vergnolle O, Hartman TE, Rhee KY, Jacobs WR Jr, Berney M, Blanchard JS. 2016. Central role of pyruvate kinase in carbon co-catabolism of Mycobacterium tuberculosis. J Biol Chem 291:7060–7069 10.1074/jbc.M115.707430. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 219.Marrero J, Trujillo C, Rhee KY, Ehrt S. 2013. Glucose phosphorylation is required for Mycobacterium tuberculosis persistence in mice. PLoS Pathog 9:e1003116 10.1371/journal.ppat.1003116. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 220.Borah K, Beyss M, Theorell A, Wu H, Basu P, Mendum T, Noh K, Beste D, McFadden J. 2019. A mixed nitrogen diet and compartmentalized utilization for Mycobacterium tuberculosis replicating in host cells: results of a systems-based analysis. bioRxiv 10.1101/542480. [DOI] [Google Scholar]
  • 221.Oikonomou CM, Jensen GJ. 2017. A new view into prokaryotic cell biology from electron cryotomography. Nat Rev Microbiol 15:128 10.1038/nrmicro.2016.195. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 222.Kerfeld CA, Aussignargues C, Zarzycki J, Cai F, Sutter M. 2018. Bacterial microcompartments. Nat Rev Microbiol 16:277–290 10.1038/nrmicro.2018.10. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 223.VanderVen BC, Fahey RJ, Lee W, Liu Y, Abramovitch RB, Memmott C, Crowe AM, Eltis LD, Perola E, Deininger DD, Wang T, Locher CP, Russell DG. 2015. Novel inhibitors of cholesterol degradation in Mycobacterium tuberculosis reveal how the bacterium’s metabolism is constrained by the intracellular environment. PLoS Pathog 11:e1004679 10.1371/journal.ppat.1004679. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 224.Crowe AM, Workman SD, Watanabe N, Worrall LJ, Strynadka NCJ, Eltis LD. 2018. IpdAB, a virulence factor in Mycobacterium tuberculosis, is a cholesterol ring-cleaving hydrolase. Proc Natl Acad Sci USA 115:E3378–E3387 10.1073/pnas.1717015115. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 225.Dartois V, Saito K, Warrier T, Nathan C. 2016. New evidence for the complexity of the population structure of Mycobacterium tuberculosis increases the diagnostic and biologic challenges. Am J Respir Crit Care Med 194:1448–1451 10.1164/rccm.201607-1431ED. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 226.Eoh H, Wang Z, Layre E, Rath P, Morris R, Branch Moody D, Rhee KY. 2017. Metabolic anticipation in Mycobacterium tuberculosis. Nat Microbiol 2:17084 10.1038/nmicrobiol.2017.84. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 227.Mitchell A, Romano GH, Groisman B, Yona A, Dekel E, Kupiec M, Dahan O, Pilpel Y. 2009. Adaptive prediction of environmental changes by microorganisms. Nature 460:220–224 10.1038/nature08112. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 228.Gerrick ER, Barbier T, Chase MR, Xu R, François J, Lin VH, Szucs MJ, Rock JM, Ahmad R, Tjaden B, Livny J, Fortune SM. 2018. Small RNA profiling in Mycobacterium tuberculosis identifies MrsI as necessary for an anticipatory iron sparing response. Proc Natl Acad Sci USA 115:6464–6469 10.1073/pnas.1718003115. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 229.Becker SH, Jastrab JB, Dhabaria A, Chaton CT, Rush JS, Korotkov KV, Ueberheide B, Darwin KH. 2019. The Mycobacterium tuberculosis Pup-proteasome system regulates nitrate metabolism through an essential protein quality control pathway. Proc Natl Acad Sci USA 116:3202–3210 10.1073/pnas.1819468116. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 230.Cook GM, Hards K, Dunn E, Heikal A, Nakatani Y, Greening C, Crick DC, Fontes FL, Pethe K, Hasenoehrl E, Berney M. 2017. Oxidative phosphorylation as a target space for tuberculosis: success, caution, and future directions. Microbiol Spectr 5:TBTB2-0014-2016 10.1128/microbiolspec.TBTB2-0014-2016. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 231.Cook GM, Hards K, Vilchèze C, Hartman T, Berney M. 2014. Energetics of respiration and oxidative phosphorylation in mycobacteria. Microbiol Spectr 2:MGM2-0015-2013 10.1128/microbiolspec.MGM2-0015-2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 232.Black PA, Warren RM, Louw GE, van Helden PD, Victor TC, Kana BD. 2014. Energy metabolism and drug efflux in Mycobacterium tuberculosis. Antimicrob Agents Chemother 58:2491–2503 10.1128/AAC.02293-13. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 233.Haagsma AC, Driessen NN, Hahn MM, Lill H, Bald D. 2010. ATP synthase in slow- and fast-growing mycobacteria is active in ATP synthesis and blocked in ATP hydrolysis direction. FEMS Microbiol Lett 313:68–74 10.1111/j.1574-6968.2010.02123.x. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 234.Lange C, Chesov D, Heyckendorf J, Leung CC, Udwadia Z, Dheda K. 2018. Drug-resistant tuberculosis: an update on disease burden, diagnosis and treatment. Respirology 23:656–673 10.1111/resp.13304. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 235.Tiberi S, du Plessis N, Walzl G, Vjecha MJ, Rao M, Ntoumi F, Mfinanga S, Kapata N, Mwaba P, McHugh TD, Ippolito G, Migliori GB, Maeurer MJ, Zumla A. 2018. Tuberculosis: progress and advances in development of new drugs, treatment regimens, and host-directed therapies. Lancet Infect Dis 18:e183–e198 10.1016/S1473-3099(18)30110-5. [DOI] [PubMed] [Google Scholar]
  • 236.Lenaerts A, Barry CE III, Dartois V. 2015. Heterogeneity in tuberculosis pathology, microenvironments and therapeutic responses. Immunol Rev 264:288–307 10.1111/imr.12252. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 237.Williams MJ, Kana BD, Mizrahi V. 2011. Functional analysis of molybdopterin biosynthesis in mycobacteria identifies a fused molybdopterin synthase in Mycobacterium tuberculosis. J Bacteriol 193:98–106 10.1128/JB.00774-10. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 238.Iqbal IK, Bajeli S, Akela AK, Kumar A. 2018. Bioenergetics of mycobacterium: an emerging landscape for drug discovery. Pathogens 7:E24 10.3390/pathogens7010024. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 239.Hards K, Cook GM. 2018. Targeting bacterial energetics to produce new antimicrobials. Drug Resist Updat 36:1–12 10.1016/j.drup.2017.11.001. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 240.Hards K, Robson JR, Berney M, Shaw L, Bald D, Koul A, Andries K, Cook GM. 2015. Bactericidal mode of action of bedaquiline. J Antimicrob Chemother 70:2028–2037. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 241.Hards K, McMillan DGG, Schurig-Briccio LA, Gennis RB, Lill H, Bald D, Cook GM. 2018. Ionophoric effects of the antitubercular drug bedaquiline. Proc Natl Acad Sci USA 115:7326–7331 10.1073/pnas.1803723115. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 242.Feng X, Zhu W, Schurig-Briccio LA, Lindert S, Shoen C, Hitchings R, Li J, Wang Y, Baig N, Zhou T, Kim BK, Crick DC, Cynamon M, McCammon JA, Gennis RB, Oldfield E. 2015. Antiinfectives targeting enzymes and the proton motive force. Proc Natl Acad Sci USA 112:E7073-82 10.1073/pnas.1521988112. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 243.Chen C, Gardete S, Jansen RS, Shetty A, Dick T, Rhee KY, Dartois V. 2018. Verapamil targets membrane energetics in Mycobacterium tuberculosis. Antimicrob Agents Chemother 62:e02107-17 10.1128/AAC.02107-17. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 244.Lee BS, Kalia NP, Jin XEF, Hasenoehrl EJ, Berney M, Pethe K. 2019. Inhibitors of energy metabolism interfere with antibiotic-induced death in mycobacteria. J Biol Chem 294:1936–1943 10.1074/jbc.RA118.005732. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 245.Singh P, Rameshwaram NR, Ghosh S, Mukhopadhyay S. 2018. Cell envelope lipids in the pathophysiology of Mycobacterium tuberculosis. Future Microbiol 13:689–710 10.2217/fmb-2017-0135. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 246.Gago G, Diacovich L, Gramajo H. 2018. Lipid metabolism and its implication in mycobacteria-host interaction. Curr Opin Microbiol 41:36–42 10.1016/j.mib.2017.11.020. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 247.Layre E, Lee HJ, Young DC, Martinot AJ, Buter J, Minnaard AJ, Annand JW, Fortune SM, Snider BB, Matsunaga I, Rubin EJ, Alber T, Moody DB. 2014. Molecular profiling of Mycobacterium tuberculosis identifies tuberculosinyl nucleoside products of the virulence-associated enzyme Rv3378c. Proc Natl Acad Sci USA 111:2978–2983 10.1073/pnas.1315883111. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 248.Catalão MJ, Filipe SR, Pimentel M. 2019. Revisiting anti-tuberculosis therapeutic strategies that target the 2 peptidoglycan structure and synthesis. Front Microbiol 10:190 10.3389/fmicb.2019.00190. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 249.Makarov V, Manina G, Mikusova K, Möllmann U, Ryabova O, Saint-Joanis B, Dhar N, Pasca MR, Buroni S, Lucarelli AP, Milano A, De Rossi E, Belanova M, Bobovska A, Dianiskova P, Kordulakova J, Sala C, Fullam E, Schneider P, McKinney JD, Brodin P, Christophe T, Waddell S, Butcher P, Albrethsen J, Rosenkrands I, Brosch R, Nandi V, Bharath S, Gaonkar S, Shandil RK, Balasubramanian V, Balganesh T, Tyagi S, Grosset J, Riccardi G, Cole ST. 2009. Benzothiazinones kill Mycobacterium tuberculosis by blocking arabinan synthesis. Science 324:801–804 10.1126/science.1171583. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 250.Goldman RC. 2013. Why are membrane targets discovered by phenotypic screens and genome sequencing in Mycobacterium tuberculosis? Tuberculosis (Edinb) 93:569–588 10.1016/j.tube.2013.09.003. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 251.Abrahams KA, Besra GS. 2018. Mycobacterial cell wall biosynthesis: a multifaceted antibiotic target. Parasitology 145:116–133 10.1017/S0031182016002377. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 252.Queiroz A, Riley LW. 2017. Bacterial immunostat: Mycobacterium tuberculosis lipids and their role in the host immune response. Rev Soc Bras Med Trop 50:9–18 10.1590/0037-8682-0230-2016. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 253.Jackson M. 2014. The mycobacterial cell envelope-lipids. Cold Spring Harb Perspect Med 4:a021105 10.1101/cshperspect.a021105. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 254.Jankute M, Cox JAG, Harrison J, Besra GS. 2015. Assembly of the mycobacterial cell wall. Annu Rev Microbiol 69:405–423 10.1146/annurev-micro-091014-104121. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 255.Barnes DD, Lundahl MLE, Lavelle EC, Scanlan EM. 2017. The emergence of phenolic glycans as virulence factors in Mycobacterium tuberculosis. ACS Chem Biol 12:1969–1979 10.1021/acschembio.7b00394. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 256.Donoghue HD. 2016. Paleomicrobiology of human tuberculosis. Microbiol Spectr 4:PoH-0003-2014. [DOI] [PubMed] [Google Scholar]
  • 257.MacLean E, Broger T, Yerliyaka S, Fernandez-Carballo BL, Pai M, Denkinger CM. 2019. A systematic review of biomarkers to detect active tuberculosis. Nat Microbiol 4:748 10.1038/s41564-019-0380-2. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 258.Peterson EJ, Bailo R, Rothchild AC, Arrieta-Ortiz ML, Kaur A, Pan M, Mai D, Abidi AA, Cooper C, Aderem A, Bhatt A, Baliga NS. 2019. Path-seq identifies an essential mycolate remodeling program for mycobacterial host adaptation. Mol Syst Biol 15:e8584 10.15252/msb.20188584. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 259.Squeglia F, Ruggiero A, Berisio R. 2018. Chemistry of peptidoglycan in Mycobacterium tuberculosis life cycle: an off-the-wall balance of synthesis and degradation. Chemistry 24:2533–2546 10.1002/chem.201702973. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 260.Kamariza M, Shieh P, Ealand CS, Peters JS, Chu B, Rodriguez-Rivera FP, Babu Sait MR, Treuren WV, Martinson N, Kalscheuer R, Kana BD, Bertozzi CR. 2018. Rapid detection of Mycobacterium tuberculosis in sputum with a solvatochromic trehalose probe. Sci Transl Med 10:eaam6310 10.1126/scitranslmed.aam6310. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 261.Kamariza M, Shieh P, Bertozzi CR. 2018. Imaging mycobacterial trehalose glycolipids. Methods Enzymol 598:355–369 10.1016/bs.mie.2017.09.002. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 262.Fiolek TJ, Banahene N, Kavunja HW, Holmes NJ, Rylski AK, Pohane AA, Siegrist MS, Swarts BM. 2018. Engineering the mycomembrane of live mycobacteria with an expanded set of trehalose monomycolate analogues. ChemBioChem 20. 10.1002/cbic.201800687. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 263.García-Heredia A, Pohane AA, Melzer ES, Carr CR, Fiolek TJ, Rundell SR, Lim HC, Wagner JC, Morita YS, Swarts BM, Siegrist MS. 2018. Peptidoglycan precursor synthesis along the sidewall of pole-growing mycobacteria. eLife 7:e37243 10.7554/eLife.37243. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 264.Baranowski C, Welsh MA, Sham L-T, Eskandarian HA, Lim HC, Kieser KJ, Wagner JC, McKinney JD, Fantner GE, Ioerger TR, Walker S, Bernhardt TG, Rubin EJ, Rego EH. 2018. Maturing Mycobacterium smegmatis peptidoglycan requires non-canonical crosslinks to maintain shape. eLife 7:e37516 10.7554/eLife.37516. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 265.Holmes NJ, Kavunja HW, Yang Y, Vannest BD, Ramsey CN, Gepford DM, Banahene N, Poston AW, Piligian BF, Ronning DR, Ojha AK, Swarts BM. 2019. A FRET-based fluorogenic trehalose dimycolate analogue for probing mycomembrane-remodeling enzymes of mycobacteria. ACS Omega 4:4348–4359 10.1021/acsomega.9b00130. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 266.Layre E, Al-Mubarak R, Belisle JT, Moody DB. 2014. Mycobacterial lipidomics, p 341–360, In Hatfull GF, Jacobs WR Jr (ed), Molecular Genetics of Mycobacteria, 2nd ed. ASM Press, Washington, DC. [PubMed] [Google Scholar]
  • 267.Raghunandanan S, Jose L, Gopinath V, Kumar RA. 2019. Comparative label-free lipidomic analysis of Mycobacterium tuberculosis during dormancy and reactivation. Sci Rep 9:3660 10.1038/s41598-019-40051-5. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 268.Hermann C, Giddey AD, Nel AJM, Soares NC, Blackburn JM. 2019. Cell wall enrichment unveils proteomic changes in the cell wall during treatment of Mycobacterium smegmatis with sub-lethal concentrations of rifampicin. J Proteomics 191:166–179 10.1016/j.jprot.2018.02.019. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 269.Lahiri N, Shah RR, Layre E, Young D, Ford C, Murray MB, Fortune SM, Moody DB. 2016. Rifampin resistance mutations are associated with broad chemical remodeling of Mycobacterium tuberculosis. J Biol Chem 291:14248–14256 10.1074/jbc.M116.716704. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 270.Aakre CD, Phung TN, Huang D, Laub MT. 2013. A bacterial toxin inhibits DNA replication elongation through a direct interaction with the β sliding clamp. Mol Cell 52:617–628 10.1016/j.molcel.2013.10.014. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 271.Donald PR, Diacon AH, Lange C, Demers AM, von Groote-Bidlingmaier F, Nardell E. 2018. Droplets, dust and guinea pigs: an historical review of tuberculosis transmission research, 1878-1940. Int J Tuberc Lung Dis 22:972–982 10.5588/ijtld.18.0173. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 272.Nazarova EV, Montague CR, La T, Wilburn KM, Sukumar N, Lee W, Caldwell S, Russell DG, VanderVen BC. 2017. Rv3723/LucA coordinates fatty acid and cholesterol uptake in Mycobacterium tuberculosis. eLife 6:e26969 10.7554/eLife.26969. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 273.Miner MD, Chang JC, Pandey AK, Sassetti CM, Sherman DR. 2009. Role of cholesterol in Mycobacterium tuberculosis infection. Indian J Exp Biol 47:407–411. [PubMed] [Google Scholar]
  • 274.Larrouy-Maumus G. 2015. Cholesterol acquisition by Mycobacterium tuberculosis. Virulence 6:412–413 10.1080/21505594.2015.1053688. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 275.Chao A, Sieminski PJ, Owens CP, Goulding CW. 2019. Iron acquisition in Mycobacterium tuberculosis. Chem Rev 119:1193–1220 10.1021/acs.chemrev.8b00285. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 276.Tiwari S, van Tonder AJ, Vilchèze C, Mendes V, Thomas SE, Malek A, Chen B, Chen M, Kim J, Blundell TL, Parkhill J, Weinrick B, Berney M, Jacobs WR Jr. 2018. Arginine-deprivation-induced oxidative damage sterilizes Mycobacterium tuberculosis. Proc Natl Acad Sci USA 115:9779–9784 10.1073/pnas.1808874115. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 277.Gouzy A, Poquet Y, Neyrolles O. 2014. Amino acid capture and utilization within the Mycobacterium tuberculosis phagosome. Future Microbiol 9:631–637 10.2217/fmb.14.28. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 278.Niederweis M, Danilchanka O, Huff J, Hoffmann C, Engelhardt H. 2010. Mycobacterial outer membranes: in search of proteins. Trends Microbiol 18:109–116 10.1016/j.tim.2009.12.005. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 279.Tullius MV, Nava S, Horwitz MA. 2019. PPE37 Is essential for Mycobacterium tuberculosis heme-iron acquisition (HIA), and a defective PPE37 in Mycobacterium bovis BCG prevents HIA. Infect Immun 87:e00540-18. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 280.Danilchanka O, Pires D, Anes E, Niederweis M. 2015. The Mycobacterium tuberculosis outer membrane channel protein CpnT confers susceptibility to toxic molecules. Antimicrob Agents Chemother 59:2328–2336 10.1128/AAC.04222-14. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 281.Pajuelo D, Gonzalez-Juarbe N, Tak U, Sun J, Orihuela CJ, Niederweis M. 2018. NAD+ depletion triggers macrophage necroptosis, a cell death pathway exploited by Mycobacterium tuberculosis. Cell Rep 24:429–440 10.1016/j.celrep.2018.06.042. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 282.Sun J, Siroy A, Lokareddy RK, Speer A, Doornbos KS, Cingolani G, Niederweis M. 2015. The tuberculosis necrotizing toxin kills macrophages by hydrolyzing NAD. Nat Struct Mol Biol 22:672–678 10.1038/nsmb.3064. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 283.Tak U, Vlach J, Garza-Garcia A, William D, Danilchanka O, de Carvalho LPS, Saad JS, Niederweis M. 2019. The tuberculosis necrotizing toxin is an NAD+ and NADP+ glycohydrolase with distinct enzymatic properties. J Biol Chem 294:3024–3036 10.1074/jbc.RA118.005832. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 284.Gröschel MI, Sayes F, Simeone R, Majlessi L, Brosch R. 2016. ESX secretion systems: mycobacterial evolution to counter host immunity. Nat Rev Microbiol 14:677–691 10.1038/nrmicro.2016.131. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 285.Maueröder C, Chaurio RA, Dumych T, Podolska M, Lootsik MD, Culemann S, Friedrich RP, Bilyy R, Alexiou C, Schett G, Berens C, Herrmann M, Munoz LE. 2016. A blast without power: cell death induced by the tuberculosis-necrotizing toxin fails to elicit adequate immune responses. Cell Death Differ 23:1016–1025 10.1038/cdd.2016.4. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 286.Warner DF, Mizrahi V. 2008. Physiology of Mycobacterium tuberculosis, p 53–59. In Kaufmann SHE, Rubin E, Britton WJ, van Helde P (ed), Handbook of Tuberculosis. Wiley-VCH Verlag, Weinheim, Germany. [PubMed] [Google Scholar]
  • 287.Evans JC, Mizrahi V. 2018. Priming the tuberculosis drug pipeline: new antimycobacterial targets and agents. Curr Opin Microbiol 45:39–46 10.1016/j.mib.2018.02.006. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 288.Vincent AT, Nyongesa S, Morneau I, Reed MB, Tocheva EI, Veyrier FJ. 2018. The mycobacterial cell envelope: a relict from the past or the result of recent evolution? Front Microbiol 9:2341 10.3389/fmicb.2018.02341. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 289.Awasthi D, Freundlich JS. 2017. Antimycobacterial metabolism: illuminating Mycobacterium tuberculosis biology and drug discovery. Trends Microbiol 25:756–767 10.1016/j.tim.2017.05.007. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 290.Lee RE, Hurdle JG, Liu J, Bruhn DF, Matt T, Scherman MS, Vaddady PK, Zheng Z, Qi J, Akbergenov R, Das S, Madhura DB, Rathi C, Trivedi A, Villellas C, Lee RB, Rakesh, Waidyarachchi SL, Sun D, McNeil MR, Ainsa JA, Boshoff HI, Gonzalez-Juarrero M, Meibohm B, Böttger EC, Lenaerts AJ. 2014. Spectinamides: a new class of semisynthetic antituberculosis agents that overcome native drug efflux. Nat Med 20:152–158 10.1038/nm.3458. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 291.Adams KN, Takaki K, Connolly LE, Wiedenhoft H, Winglee K, Humbert O, Edelstein PH, Cosma CL, Ramakrishnan L. 2011. Drug tolerance in replicating mycobacteria mediated by a macrophage-induced efflux mechanism. Cell 145:39–53 10.1016/j.cell.2011.02.022. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 292.Te Brake LHM, de Knegt GJ, de Steenwinkel JE, van Dam TJP, Burger DM, Russel FGM, van Crevel R, Koenderink JB, Aarnoutse RE. 2018. The role of efflux pumps in tuberculosis treatment and their promise as a target in drug development: unraveling the black box. Annu Rev Pharmacol Toxicol 58:271–291 10.1146/annurev-pharmtox-010617-052438. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 293.Sarathy JP, Dartois V, Lee EJ. 2012. The role of transport mechanisms in Mycobacterium tuberculosis drug resistance and tolerance. Pharmaceuticals (Basel) 5:1210–1235 10.3390/ph5111210. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 294.Fange D, Nilsson K, Tenson T, Ehrenberg M. 2009. Drug efflux pump deficiency and drug target resistance masking in growing bacteria. Proc Natl Acad Sci USA 106:8215–8220 10.1073/pnas.0811514106. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 295.Franzblau SG, DeGroote MA, Cho SH, Andries K, Nuermberger E, Orme IM, Mdluli K, Angulo-Barturen I, Dick T, Dartois V, Lenaerts AJ. 2012. Comprehensive analysis of methods used for the evaluation of compounds against Mycobacterium tuberculosis. Tuberculosis (Edinb) 92:453–488 10.1016/j.tube.2012.07.003. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 296.Burns AR, Wallace IM, Wildenhain J, Tyers M, Giaever G, Bader GD, Nislow C, Cutler SR, Roy PJ. 2010. A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans. Nat Chem Biol 6:549–557 10.1038/nchembio.380. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 297.Richter MF, Drown BS, Riley AP, Garcia A, Shirai T, Svec RL, Hergenrother PJ. 2017. Predictive compound accumulation rules yield a broad-spectrum antibiotic. Nature 545:299–304 10.1038/nature22308. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 298.Rodriguez-Rivera FP, Zhou X, Theriot JA, Bertozzi CR. 2017. Visualization of mycobacterial membrane dynamics in live cells. J Am Chem Soc 139:3488–3495 10.1021/jacs.6b12541. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 299.Rodriguez-Rivera FP, Zhou X, Theriot JA, Bertozzi CR. 2018. Acute modulation of mycobacterial cell envelope biogenesis by front-line TB drugs. Angew Chem Int Ed Engl 57:5267–5272 10.1002/anie.201712020. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 300.Li W, Stevens CM, Pandya AN, Darzynkiewicz Z, Bhattarai P, Tong W, Gonzalez-Juarrero M, North EJ, Zgurskaya HI, Jackson M. 2019. Direct inhibition of MmpL3 by novel antitubercular compounds. ACS Infect Dis 5:1001–1012 10.1021/acsinfecdis.9b00048. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 301.Zhang B, Li J, Yang X, Wu L, Zhang J, Yang Y, Zhao Y, Zhang L, Yang X, Yang X, Cheng X, Liu Z, Jiang B, Jiang H, Guddat LW, Yang H, Rao Z. 2019. Crystal structures of membrane transporter MmpL3, an anti-TB drug target. Cell 176:636–648.e13 10.1016/j.cell.2019.01.003. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 302.Sarathy J, Dartois V, Dick T, Gengenbacher M. 2013. Reduced drug uptake in phenotypically resistant nutrient-starved nonreplicating Mycobacterium tuberculosis. Antimicrob Agents Chemother 57:1648–1653 10.1128/AAC.02202-12. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 303.Louw GE, Warren RM, Gey van Pittius NC, McEvoy CR, Van Helden PD, Victor TC. 2009. A balancing act: efflux/influx in mycobacterial drug resistance. Antimicrob Agents Chemother 53:3181–3189 10.1128/AAC.01577-08. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 304.Pule CM, Sampson SL, Warren RM, Black PA, van Helden PD, Victor TC, Louw GE. 2016. Efflux pump inhibitors: targeting mycobacterial efflux systems to enhance TB therapy. J Antimicrob Chemother 71:17–26 10.1093/jac/dkv316. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 305.Brecik M, Centárová I, Mukherjee R, Kolly GS, Huszár S, Bobovská A, Kilacsková E, Mokošová V, Svetlíková Z, Šarkan M, Neres J, Korduláková J, Cole ST, Mikušová K. 2015. DprE1 is a vulnerable tuberculosis drug target due to its cell wall localization. ACS Chem Biol 10:1631–1636 10.1021/acschembio.5b00237. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 306.Xu Z, Meshcheryakov VA, Poce G, Chng SS. 2017. MmpL3 is the flippase for mycolic acids in mycobacteria. Proc Natl Acad Sci USA 114:7993–7998 10.1073/pnas.1700062114. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 307.McNeil MB, Dennison D, Parish T. 2017. Mutations in MmpL3 alter membrane potential, hydrophobicity and antibiotic susceptibility in Mycobacterium smegmatis. Microbiology 163:1065–1070 10.1099/mic.0.000498. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 308.Wellington S, Nag PP, Michalska K, Johnston SE, Jedrzejczak RP, Kaushik VK, Clatworthy AE, Siddiqi N, McCarren P, Bajrami B, Maltseva NI, Combs S, Fisher SL, Joachimiak A, Schreiber SL, Hung DT. 2017. A small-molecule allosteric inhibitor of Mycobacterium tuberculosis tryptophan synthase. Nat Chem Biol 13:943–950 10.1038/nchembio.2420. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 309.Eldholm V, Balloux F. 2016. Antimicrobial resistance in Mycobacterium tuberculosis: the odd one out. Trends Microbiol 24:637–648 10.1016/j.tim.2016.03.007. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 310.Fonseca JD, Knight GM, McHugh TD. 2015. The complex evolution of antibiotic resistance in Mycobacterium tuberculosis. Int J Infect Dis 32:94–100 10.1016/j.ijid.2015.01.014. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 311.Meylan S, Andrews IW, Collins JJ. 2018. Targeting antibiotic tolerance, pathogen by pathogen. Cell 172:1228–1238 10.1016/j.cell.2018.01.037. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 312.Chaturvedi S, Wolf M, Vardi N, Chan M, Weinberger L. 2018. Disrupting transcriptional feedback yields an escape-resistant antiviral. bioRxiv https://doi.org/10.1101/464495:464495. [Google Scholar]
  • 313.Hicks ND, Yang J, Zhang X, Zhao B, Grad YH, Liu L, Ou X, Chang Z, Xia H, Zhou Y, Wang S, Dong J, Sun L, Zhu Y, Zhao Y, Jin Q, Fortune SM. 2018. Clinically prevalent mutations in Mycobacterium tuberculosis alter propionate metabolism and mediate multidrug tolerance. Nat Microbiol 3:1032–1042 10.1038/s41564-018-0218-3. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 314.Dunphy LJ, Yen P, Papin JA. 2019. Integrated experimental and computational analyses reveal differential metabolic functionality in antibiotic-resistant Pseudomonas aeruginosa. Cell Syst 8:3–14.e3 10.1016/j.cels.2018.12.002. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 315.Yano T, Kassovska-Bratinova S, Teh JS, Winkler J, Sullivan K, Isaacs A, Schechter NM, Rubin H. 2011. Reduction of clofazimine by mycobacterial type 2 NADH:quinone oxidoreductase: a pathway for the generation of bactericidal levels of reactive oxygen species. J Biol Chem 286:10276–10287 10.1074/jbc.M110.200501. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 316.Kalscheuer R, Syson K, Veeraraghavan U, Weinrick B, Biermann KE, Liu Z, Sacchettini JC, Besra G, Bornemann S, Jacobs WR Jr. 2010. Self-poisoning of Mycobacterium tuberculosis by targeting GlgE in an alpha-glucan pathway. Nat Chem Biol 6:376–384 10.1038/nchembio.340. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 317.Dutta NK, Klinkenberg LG, Vazquez MJ, Segura-Carro D, Colmenarejo G, Ramon F, Rodriguez-Miquel B, Mata-Cantero L, Porras-De Francisco E, Chuang YM, Rubin H, Lee JJ, Eoh H, Bader JS, Perez-Herran E, Mendoza-Losana A, Karakousis PC. 2019. Inhibiting the stringent response blocks Mycobacterium tuberculosis entry into quiescence and reduces persistence. Sci Adv 5:eaav2104 10.1126/sciadv.aav2104. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 318.Zheng H, Colvin CJ, Johnson BK, Kirchhoff PD, Wilson M, Jorgensen-Muga K, Larsen SD, Abramovitch RB. 2017. Inhibitors of Mycobacterium tuberculosis DosRST signaling and persistence. Nat Chem Biol 13:218–225 10.1038/nchembio.2259. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 319.Liu Y, Tan S, Huang L, Abramovitch RB, Rohde KH, Zimmerman MD, Chen C, Dartois V, VanderVen BC, Russell DG. 2016. Immune activation of the host cell induces drug tolerance in Mycobacterium tuberculosis both in vitro and in vivo. J Exp Med 213:809–825 10.1084/jem.20151248. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 320.Nandakumar M, Nathan C, Rhee KY. 2014. Isocitrate lyase mediates broad antibiotic tolerance in Mycobacterium tuberculosis. Nat Commun 5:4306 10.1038/ncomms5306. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 321.Kim JH, O’Brien KM, Sharma R, Boshoff HI, Rehren G, Chakraborty S, Wallach JB, Monteleone M, Wilson DJ, Aldrich CC, Barry CE III, Rhee KY, Ehrt S, Schnappinger D. 2013. A genetic strategy to identify targets for the development of drugs that prevent bacterial persistence. Proc Natl Acad Sci USA 110:19095–19100 10.1073/pnas.1315860110. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 322.Gutierrez A, Jain S, Bhargava P, Hamblin M, Lobritz MA, Collins JJ. 2017. Understanding and sensitizing density-dependent persistence to quinolone antibiotics. Mol Cell 68:1147–1154. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 323.Avraham R, Haseley N, Fan A, Bloom-Ackermann Z, Livny J, Hung DT. 2016. A highly multiplexed and sensitive RNA-seq protocol for simultaneous analysis of host and pathogen transcriptomes. Nat Protoc 11:1477–1491 10.1038/nprot.2016.090. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 324.Penaranda C, Hung DT. 2019. Single-cell RNA sequencing to understand host-pathogen interactions. ACS Infect Dis 5:336–344 10.1021/acsinfecdis.8b00369. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 325.Neubauer C, Kasi AS, Grahl N, Sessions AL, Kopf SH, Kato R, Hogan DA, Newman DK. 2018. Refining the application of microbial lipids as tracers of Staphylococcus aureus growth rates in cystic fibrosis sputum. J Bacteriol 200:e00365-18 10.1128/JB.00365-18. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 326.Hausmann B, Pelikan C, Rattei T, Loy A, Pester M. 2019. Long-term transcriptional activity at zero growth of a cosmopolitan rare biosphere member. MBio 10:e02189-18 10.1128/mBio.02189-18. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 327.Gallagher T, Phan J, Whiteson K. 2018. Getting our fingers on the pulse of slow-growing bacteria in hard-to-reach places. J Bacteriol 200:e00540-18 10.1128/JB.00540-18. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 328.Dheda K, Lenders L, Srivastava S, Magombedze G, Wainwright H, Raj P, Bush SJ, Pollara G, Steyn R, Davids M, Pooran A, Pennel T, Linegar A, McNerney R, Moodley L, Pasipanodya JG, Turner CT, Noursadeghi M, Warren RM, Wakeland E, Gumbo T. 2019. Spatial network mapping of pulmonary multidrug-resistant tuberculosis cavities using RNA sequencing. Am J Respir Crit Care Med 10.1164/rccm.201807-1361OC 10.1164/rccm.201807-1361OC. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 329.Daffé M, Crick DC, Jackson M. 2014. Genetics of capsular polysaccharides and cell envelope (glyco)lipids. Microbiol Spectr 2:MGM2-0021-2013 10.1128/microbiolspec.MGM2-0021-2013. [DOI] [PubMed] [Google Scholar]
  • 330.Gouzy A, Poquet Y, Neyrolles O. 2014. Nitrogen metabolism in Mycobacterium tuberculosis physiology and virulence. Nat Rev Microbiol 12:729–737 10.1038/nrmicro3349. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 331.Gouzy A, Larrouy-Maumus G, Bottai D, Levillain F, Dumas A, Wallach JB, Caire-Brandli I, de Chastellier C, Wu TD, Poincloux R, Brosch R, Guerquin-Kern JL, Schnappinger D, Sório de Carvalho LP, Poquet Y, Neyrolles O. 2014. Mycobacterium tuberculosis exploits asparagine to assimilate nitrogen and resist acid stress during infection. PLoS Pathog 10:e1003928 10.1371/journal.ppat.1003928. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 332.Gouzy A, Larrouy-Maumus G, Wu TD, Peixoto A, Levillain F, Lugo-Villarino G, Guerquin-Kern JL, de Carvalho LP, Poquet Y, Neyrolles O. 2013. Mycobacterium tuberculosis nitrogen assimilation and host colonization require aspartate. Nat Chem Biol 9:674–676 10.1038/nchembio.1355. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 333.Gouzy A, Poquet Y, Neyrolles O. 2013. A central role for aspartate in Mycobacterium tuberculosis physiology and virulence. Front Cell Infect Microbiol 3:68 10.3389/fcimb.2013.00068. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 334.Zeng L, Shi T, Zhao Q, Xie J. 2013. Mycobacterium sulfur metabolism and implications for novel drug targets. Cell Biochem Biophys 65:77–83 10.1007/s12013-012-9410-x. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 335.Hatzios SK, Bertozzi CR. 2011. The regulation of sulfur metabolism in Mycobacterium tuberculosis. PLoS Pathog 7:e1002036 10.1371/journal.ppat.1002036. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 336.Marcela Rodriguez G, Neyrolles O. 2014. Metallobiology of tuberculosis. Microbiol Spectr 2:MGM2-0012-2013 10.1128/microbiolspec.MGM2-0012-2013. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 337.Selengut JD, Haft DH. 2010. Unexpected abundance of coenzyme F(420)-dependent enzymes in Mycobacterium tuberculosis and other actinobacteria. J Bacteriol 192:5788–5798 10.1128/JB.00425-10. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 338.Gopinath K, Moosa A, Mizrahi V, Warner DF. 2013. Vitamin B(12) metabolism in Mycobacterium tuberculosis. Future Microbiol 8:1405–1418 10.2217/fmb.13.113. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 339.Ortega Ugalde S, Boot M, Commandeur JNM, Jennings P, Bitter W, Vos JC. 2019. Function, essentiality, and expression of cytochrome P450 enzymes and their cognate redox partners in Mycobacterium tuberculosis: are they drug targets? Appl Microbiol Biotechnol 103:3597–3614 10.1007/s00253-019-09697-z. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 340.Macheroux P, Kappes B, Ealick SE. 2011. Flavogenomics: a genomic and structural view of flavin-dependent proteins. FEBS J 278:2625–2634 10.1111/j.1742-4658.2011.08202.x. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 341.Ditse Z, Lamers MH, Warner DF. 2017. DNA Replication in Mycobacterium tuberculosis. Microbiol Spectr 5:TBTB2-0027-2016 10.1128/microbiolspec.TBTB2-0027-2016. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 342.Warner DF, Evans JC, Mizrahi V. 2014. Nucleotide metabolism and DNA replication. Microbiol Spectr 2:MGM2-0001-2013 10.1128/microbiolspec.MGM2-0001-2013. [PubMed] [DOI] [PubMed] [Google Scholar]
  • 343.Davis EO, Forse LN. 2009. DNA repair: key to survival? p 79–119. In Parish T, Brown A (ed), Mycobacterium: Genomics and Molecular Biology. Caister Academic Press, Poole, United Kingdom. [Google Scholar]
  • 344.Minato Y, Gohl DM, Thiede JM, Chacón JM, Harcombe WR, Maruyama F, Baughn AD. 2019. Genomewide assessment of Mycobacterium tuberculosis conditionally essential metabolic pathways. mSystems 4:e00070–19 10.1128/mSystems.00070-19. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 345.Schutz C, Barr D, Andrade BB, Shey M, Ward A, Janssen S, Burton R, Wilkinson KA, Sossen B, Fukutani KF, Nicol M, Maartens G, Wilkinson RJ, Meintjes G. 2019. Clinical, microbiologic, and immunologic determinants of mortality in hospitalized patients with HIV-associated tuberculosis: A prospective cohort study. PLoS Med 16:e1002840 10.1371/journal.pmed.1002840. [PubMed] [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Microbiology Spectrum are provided here courtesy of American Society for Microbiology (ASM)

RESOURCES