Accurate prediction of which patient will progress from a sub-clinical Mycobacterium tuberculosis infection to active tuberculosis represents an elusive, yet critical, clinical research objective. From the individual perspective, progression can be considered to be the product of a series of unfortunate events or even a run of bad luck. Here, we identify the subtle physiological relationships that can influence the odds of progression to active TB and how this progression may reflect directed dysbiosis in a number of interrelated systems. Most infected individuals who progress to disease have apparently good immune responses, but these responses are, at times, compromised by either local or systemic environmental factors. Obvious disease promoting processes, such as tissue-damaging granulomata, usually manifest in the lung, but illness is systemic. This apparent dichotomy between local and systemic reflects a clear need to define the factors that promote progression to active disease within the context of the body as a physiological whole. We discuss aspects of the host environment that can impact expression of immunity, including the microbiome, glucocorticoid-mediated regulation, catecholamines and interaction between the gut, liver and lung. We suggest the importance of integrating precision medicine into our analyses of experimental outcomes such that apparently conflicting results are not contentious, but rather reflect the impact of these subtle relationships with our environment and microbiota.
The gambler’s burden
Immunity to Mycobacterium tuberculosis (Mtb) could be considered to be something akin to immunological roulette, whereby infection, establishment of latent pulmonary infection and, for an unlucky few, progression to disease are determined by both well-understood immunological mechanisms and more subtle influences, whose inconsistent impacts can vary between studies and laboratory experiments. From the single patient’s perspective, however, progression from latent infection to active disease in the absence of primary immunological dysfunction could be seen to be a run of bad luck. Individuals infected latently have entered into a contest with Mtb akin to that of a casual gambler playing roulette. As is often the case for the casual gambler, they may never win big (i.e., clear their infection) nor 'lose their shirt’ (i.e., suffer progression to active disease), but without knowing the house rules, they cannot improve their odds of a favorable outcome.
A lucky few are able to eliminate bacteria during primary infection, analogous to the luckiest spin of the wheel combined with a high-pay-out wager. Others may, however, succumb to a run of bad luck, or imprudent wagers, resulting in increasingly severe losses, i.e., an individual pulmonary lesion may become active after a period of quiescence, and this may signal that the odds are now tipped in favor of the disease. The risk of progression occurring in a given patient is likely impacted by intrinsic and extrinsic factors that contribute to the environment within which the protective response is acting. It is these small but multiple factors that promote progression from ‘casual gambling’ (Latent TB Infection, LTBI) to ‘losing one’s shirt’ (active TB) that are increasingly being identified. There is support for—and significant interest in—integrating these risk factors within a comprehensive model of TB progression. The refinement of our understanding of how these specific risk factors interact to promote progression is made more critical as the incidence of extensively and completely drug resistant TB increases.1
How the wheel spins and bets are made: primary insights on progression
For the seasoned gambler playing roulette, a successful long-term outcome requires a nuanced understanding of the system. The system consists of a spinning disk with numbered pockets on which a ball is counter-spun to generate randomness; the gambler wagers on several outcomes whose pay-out is commensurate with the chance of success. Similarly in TB, Mtb enters a host and the outcome (pay-out) depends upon a variety of more or less likely interactions with the host’s defense mechanisms. When one aspect of the response becomes compromised, the odds are changed and the casual gambler may be on the path to losing their shirt. The role of the TB researcher is to ensure that the house rules are clear and gamblers can place their bets where they are most likely to win.
While we know several major factors that will tip the game towards disease, we need to pursue a more nuanced understanding of the less well-known or minor factors that can combine to impact expression of immunity following Mtb infection. Among the major factors, we know that HIV infection predisposes the co-infected individual to active TB2 and while this is ameliorated by antiretroviral therapy (ART), those on ART are still more likely to progress to active TB.3, 4, 5 There is also an increased risk of active TB in individuals with type 2 diabetes mellitus (DM), and the spread of DM into areas of high LTBI incidence has generated new urgency to define the factors underlying this increased risk.6, 7 HIV and DM can be clinically managed as discrete chronic diseases, each with its own cause, systemic effects and treatment modality; however, they share the ability to compromise the patient’s expression of TB immunity. As a consequence, increasing proportions of DM-afflicted populations suffer from dysregulated metabolic pathways, and the impact of this disruption on the inflammatory outcomes of Mtb infection cannot be ignored.8 Similarly, anti-TNF therapy for arthritis, organ transplantation, cancer chemotherapy, smoking and pulmonary comorbidities, such as COPD, are also important risk factors for TB and act directly or indirectly to limit immunity.9, 10, 11, 12 The ability of these diverse factors to tip the balance from LTBI to active TB suggests that while immune responses may be intact in many individuals, they are unable to mediate immunity if they are compromised by environmental factors. Immunity is likely, therefore, the product of a broadly well-balanced immune response working within a healthy and well-balanced host.
In the absence of a vaccine that rapidly eliminates Mtb at the time of infection, it remains critical that we define the factors that limit immunity in exposed individuals regardless of their risk factors. In the past, we have discussed the importance of the rapid recruitment of protective T cells into the lung prior to the infected site from becoming inflamed and acting as an immunosuppressive environment.13, 14 We are beginning to imagine a dual-component host-directed therapeutic model in which appropriately activated T-cells, i.e., the strength of antigen-specific signaling directs T cells to provide optimal macrophage killing, are mobilized to traffic into pulmonary lesions quickly while the tissue environment is modulated to favor minimal inflammation, i.e., lacking a strong neutrophilic infiltrate, where effective antimycobacterial immunity can be optimally expressed.15, 16 While much of this model remains aspirational, the mechanistic underpinnings of this work are progressing. With such progress in understanding how host metabolic, physiological and immunological pathways are integrated, new avenues in managing TB via host directed therapy or immunotherapy are opening up.17, 18, 19, 20, 21 Here, we identify some of the many factors that have the potential to impact immunity but remain unexplored and should be considered as we move forward in management of progression from latent to active disease using vaccination and immunotherapeutic interventions.
Changing the odds: how environment skews progression
Returning to our allegorical gambler, we realize that simply understanding how the game is played does not allow the gambler to win big every time. Looking further, we discover that in roulette, the odds of winning a bet are reduced by the presence of one or two green slots on the wheel, which represent outcomes where the house wins. Importantly, the presence of these ‘house-advantage’ slots reduces the gambler’s overall odds of winning. In an analogous manner, the host’s own physiology may skew the odds and reduce the capacity to control Mtb infection. In addition to appropriate T cell responses, a specific cellular environment is required for expression of antimycobacterial immunity by the infected phagocyte. If this cellular environment is compromised by an inability to recruit activatable phagocytes or by disruption of signaling between T cells and phagocytes, then despite the intact protective T cell function, the conflict between Mtb and its host is skewed toward the bacteria and development of active disease. While the preceding is a key example of how the environment acts locally to influence the development of disease, it is also the case that systemic influences are important in disease progression.
One emerging example of an environmental influence that may skew the immune response is the impact of the microbiota on our homeostatic and immune surveillance mechanisms.22 Humans are superorganisms,23 and as a consequence, we devote considerable energy into the selective cultivation of our microbiomes.24 These microbiomes link the host with its environment. In the case of the intestinal flora, gut-associated lymphoid tissue and related structures (including the mucosa) provide essential feedback to maintain a balance between immunological tolerance and inflammation while supporting the diverse microbial ecosystem that is critical for digestion and the production of essential nutrients;25 this same microbiome interacts with the host’s external environment, which is sampled through oral ingestion. In this context, the gut microbiome is capable of resisting colonization by exogenous enteric pathogens while alerting the host to the presence of potential pathogens via specific interactions with toll-like receptors or through direct competition for bacterial nutrients and secretion of catabolites, such as short-chain fatty acids.26 In a similar manner, the lung microbiota provides an immunological interface with our airborne environment to distinguish between organisms and antigens that are largely innocuous versus those with the capacity to cause infection and disease.27 Further, the skin microbiota is part of an immunological interface and can provide surveillance within our larger physical biomes. Each of these instances are characterized by highly responsive and adaptive relationships between the host, biome, and larger environment. The host has separate but related specialized systems whose sole purposes are to sample their local interaction space (i.e., lung or gut mucosa or skin) to establish tolerance. Other related systems specialize in the surveillance of signals that are indicative of microbial infection or colonization to temporarily suppress local tolerance to drive an immune response. Thus, these interactions with the local environment are variously classified as symbiotic, commensal or neutral, where the classifications themselves are fluid; these interactions change over time and are related to microbial diversity, physical location of the microorganism and their inherent genetic capabilities.28, 29, 30
While this dynamic commensal relationship is maintained long-term, our specifically cultivated microbiomes can and do change.23 They develop and mature from infancy, through adolescence and into adulthood31 and reflect the microbes in our environment, change of season, geographic location and stress on the host. Layering complexity on top of complexity, we know that drug treatment also alters the microbiome, leading to qualitative changes in markers of health.22 Thus, one rule change that is capable of altering the odds in favor of progression from LTBI to active TB may be altering the microbiome. Further, the use of anti-Mtb drugs (as in preventive therapy) has the capacity to alter the nature of the superorganism32 and thereby impact disease progression.
It is well known that the gut microbiome plays an important immune and metabolic function and that alterations of gut microbe communities can modulate systemic immune changes.22, 33 The means by which the microbiota impact Mtb infection are being investigated in small animal models, and it appears that pulmonary Mtb infection alters gut microbe diversity as there is a rapid loss of microbial diversity in the gut following aerosol infection in mouse models.34 Further, manipulating the gut microbiome in mice can impact the control of the bacterial burden in the lung and alter the immune correlates in these mice.35 Helminth infections, while not universal, are prevalent in areas where Mtb infections are high, and again, mouse models suggest that they impact the immune response by compromising the naïve T cell pool, thereby limiting T cell mediated immunity.36 Helminthiasis or Helicobacter pylori infection37 are risk factors for more aggressive Mtb progression and infestation, as these parasitic or bacterial pathogens promote Mtb pathogenesis.
Gut microbes are known to influence the systemic metabolism of the host. Many of the metabolites present in the gastrointestinal tract of mammals are either directly produced by microbial flora or their concentration is modulated in a given disease state by specific microbial species;38 of the many important examples present in the literature, one of the most directly applicable is butyrate, a short-chain fatty acid that arises from bacterial degradation of dietary fiber, whose effect on PBMCs from healthy volunteers has been associated with anti-inflammatory induction of IL-10.39
Analysis of host biofluids, such as urine, can provide insights into gut microbe diversity. Indeed, urine metabolome analysis of TB patients suggests a change in co-metabolite abundance produced by the gut microbiome during TB disease.40, 41, 42 It is unclear whether these changes in co-metabolites are a cause or effect of alterations of the gut metabolome during infection. However, since these co-metabolites are important correlates of microbial diversity, they offers a potentially new direction in the development of host directed therapy by targeting the gut microbiome to restore its diversity and establish beneficial immune-metabolic conditions.43, 44 Further defining the impact of anti-TB drugs on the gut microbiome should be considered before developing such strategies.
Importantly, the gut microbiome is neither independent nor autonomous; it influences, communicates with and is impacted by the lung microbiota, a key participant in a range of pulmonary diseases,45, 46 including TB.34, 45, 46, 47 While the lung microbiome supports a relatively low-density community of organisms,45 it shares a number of similarities with the gut, in that the diversity and composition of its inhabitants vary with the physiological location, i.e., the composition of upper airway microbial communities is different than that of microbial communities of the lower airways.48 In addition, it is known that these microbial populations develop and evolve as the host matures.49 Further, the gut and lung microbiomes are in constant communication through micro aspiration of gut flora into the lungs and through swallowing mucocilliary secretions.50 The role of this cross-communication between the lung and gut microbiota in disease is a topic of much research in patients with cystic fibrosis, where dysbiosis of the lung and gut are clearly seen.51 In TB, the lung microbiome could be considered to be the gatekeeper to respiratory health,52 and manipulation of this microbiome could be seen as a tool to modulate lung immune responses and thereby alter the risk of infection, severity of disease and/or odds of progression.53 This concept of the lung microbiota as a treatment modality is exciting and is based on a number of observations, including descriptions of how the diversity of the lung microbiome decreases during pulmonary TB and chemotherapy 32 as well as how susceptibility in murine models of tuberculosis can be modulated by alteration of their gut microbiomes.35 Human studies are based on sputum cultures and/or genomic methods to compare TB with other pulmonary conditions;54, 55, 56 however, the outcomes are inconsistent.47 There are relatively few studies that investigate the pathways by which host physiology, lung (and other) microbiomes and colonizing mycobacterium interact to change the odds of progression.32, 37, 53, 56 These ambitious studies highlight the inherent complexity of these relationships and suggest that we need new tools to identify the factors that are necessary to create a less TB-permissive environment, i.e., how do we best stack the odds in favor of our notional gambler? To address the multifactorial complexity embodied in these relationships, there are several promising analytic approaches, including multivariate linear mixed models,57 COnstraint-Based Reconstruction and Analysis Toolbox (COBRA)58, 59 models of tissue- or context-specific metabolism 60, 61, 62, 63 or mixed microbial populations,64, 65 and software, such as Pathway Tools (SRI, International), that can be used as frameworks for modeling community metabolism.
Does the gambler always lose?
We know that the odds are largely on the side of our allegorical gambler, in that most people who are infected do not develop disease. However, for those at risk of progression, what is the best policy? The enduring hope of many gamblers is the big win, where long odds against winning intersect with a high-pay-out wager—but the odds are strongly against this outcome. Some gamblers, however, adopt a different strategy, whereby they attempt to win small using small, low risk wagers, thereby incrementally building their stake over the course of the game or, at the very least, not losing everything based on a single unlucky spin. Completing the analogy, our notional gambler has reframed his measure of success from the long-odds-against lucky spin combined with a high-pay-out wager to staying in the game as long as his stake holds out. By focusing neither on their bets nor spins, but instead on his funds (i.e., general health resources), the gambler can seemingly beat the odds. In a similar manner, perhaps we should reframe our question from ‘how does progression from LTBI to active disease occur’ to ‘how do we delay progression as long as possible’? In this regard, the general health of the TB patient has been the subject of many studies since the start of the sanatorium movement at the turn of the last century.66 Resting the body, feeding the patient, reducing stress and measuring the impact of weather on disease progression have all been part of our historic battle against TB. In pre-antibiotic studies at TB sanatoria, the combined contribution of the former was shown to extend patient life and enhance patient welfare. Acknowledging the evidence from pre-chemotherapeutic treatment of disease clearly informs our questions about how our personal environments influence our various microbial interactions and how this, in turn, impacts expression of immunity.
One area that is beginning to illuminate how the immune system supports overall physiological homeostasis is unbiased metabolic analyses of the host. Excitingly, unbiased studies, which have profiled both the cellular and humoral metabolomic aspects of the blood following vaccination, indicate metabolic that there are patterns associated with successful immune responses.67 How then do the local and systemic metabolic activities of the host impact the immune response? In local terms, metabolic activity can influence cellular function; for instance, an activated T cell requires arginine to function; however, activated macrophages sequester this amino acid for their own functions and may therefore limit the local functionality of T cells.68 In fact, metabolic variation within granuloma has been recently shown, where the eicosanoid pathway is bifurcated into proinflammatory metabolites in the caseous center and an anti-inflammatory signature towards the cellular periphery.69 In systemic terms, the hypothalamic/pituitary axis (HPA) regulates production of glucocorticoids, potent modulators of the proinflammatory response that are required to maintain LTBI and avoid progression to TB. While transcriptomic and metabolomic analyses of TB-affected lung tissue have provided mechanistic insights into local alteration of metabolic activity, it is critical that we develop a full understanding of the systemic impact of the specific physiological changes associated with TB on immunity.
The ability of glucocorticoids to limit proinflammatory immune responses has led to the use of synthetic glucocorticoids in the treatment of various inflammatory and autoimmune diseases.70 This artificial use has highlighted the ability of glucocorticoids to impact progression to active TB disease as corticosteroid treatment is a key risk factor for the development of pulmonary TB71, 72 and, in experimental animal models, dexamethasone is used to reactivate persistent Mtb bacteria.73 By measuring natural glucocorticoid levels in TB patients, studies have shown that higher levels of cortisol or cortisol/cortisone and cortisol/DHEA ratios are associated with active disease.74, 75, 76 While these measurements suggest a role for glucocorticoids in disease, the relative levels of biologically active cortisol relative to inactive cortisone is determined within the affected tissue by 11β-hydroxysteroid dehydrogenase enzymes. Thus, measurements occurring systemically may not reflect causal connections or local actions. Epidemiological findings from different parts of the world also demonstrate that glucose intolerance in TB patients is increased relative to controls, while TB patients and Mtb-infected mice have elevated glucose levels42, 77 and exhibit altered cholesterol, HDL, TG and free fatty acid levels relative to controls.78, 79, 80, 81 These observations make glucose and lipid metabolism potential targets for the development of host directed therapy. Currently, biochemical pathways that link metabolism and pathology have been suggested by work at the cellular level,82 but there is an ongoing need to holistically define the dysregulation of homeostatic pathways during TB.
The classical symptoms of TB reflect both a systemic immune response and metabolic disorder, where the role of the liver in mediating these symptoms is not fully defined. The liver and adipose tissue together control nutrient mobilization as well as carbohydrate and lipid metabolism in mammals. They respond to injury and infection at distant organs and modulate whole body homeostasis through various acute phase proteins and hormones, such as adiponectin and leptin. Many of these molecules are also observed at elevated or altered levels in the serum of TB patients.83, 84, 85 In the mouse model, the absence of leptin or leptin receptor makes animals more susceptible to Mtb infection, which is associated with altered immune and metabolic parameters.86, 87 At the cellular level, Mtb exploits the lipid metabolism of host macrophages for its own survival via regulators of cellular metabolism, such as peroxisome proliferator activated nuclear receptors (PPARs).88, 89 TB patients and Mtb-infected animals have elevated levels of the natural ligands of the PPAR family, and Mtb-infected mice with ablated leptin signaling show increased expression of hepatic PPAR-γ,86 further implicating the PPAR family in TB pathogenesis. Expression of PPAR-γ in the liver is important in glucose metabolism, as hepatic deletion of PPAR-γ results in better glucose clearance and lower serum level glucose.90 While PPAR-γ is clearly implicated in TB, the specific role of individual PPAR-γ-expressing tissues in whole body homeostasis during TB remains undefined. The more defined these pathways are, the better we will be able to pharmacologically target specific effects without creating unwanted side effects.91
Another class of molecules that are synthesized by the adrenal medulla via the HPA axis is catecholamines. Importantly, catecholamines are found at elevated levels in the biofluids of TB patients 42, 92, 93 and increase with the severity of disease;93, 94 catecholamine levels in the lung are also associated with the microbiome of this organ.95 The catecholamines dopamine, epinephrine and norepinephrine act as hormones, neurotransmitters and immunomodulators and their action is mediated through adrenergic receptors.96 Macrophages, which are the critical niche for Mtb and form the major constituent of TB lesions in the lung and produce catecholamines, which regulate inflammation in this organ.97 Catecholamines also directly act on T cells,98, 99 macrophages 100 and neutrophils,101 and all of these cells are critical for the control of TB.13, 102 We propose that catecholamine production among other metabolic markers can increase the risk for Mtb-infected individuals to develop TB disease. Dysregulation of catecholamines probably also plays a role in the metabolically related risk factors for TB, such as DM, malnutrition or stress. Therefore, activation of the HPA axis following the initial stress of Mtb infection, as well as subsequent stress-related events, may promote both establishment of bacterial colonization and subsequent development of active disease.
Winning the game by betting small
In this brief overview, we highlight the need for a systematic approach to understand the subtle relationships that increase the odds of progression from latent to active TB on an individual basis. By integrating our understanding of immunity and the effects of chemotherapeutics on an infected patient with our growing appreciation of their complex relationships with their personal microbiomes, we hope to better predict which individuals are more likely to progress to active and transmissible disease. Such an appreciation may explain differences in experimental outcomes—both in animal and human studies—that are associated with the location where the experiments are performed as well as the physiological state of the host. While it is easy to suggest integration of immunological, physiological, microbiological and environmental data into our analysis of experimental outcomes, it is more difficult to state how best to do this. At a minimum, we must be aware that different experimental outcomes from apparently identical experiments in different laboratories may be a result of our inability to measure and control for unknown variables between locations. As we move forward in the development of more sophisticated interventions, such as host-directed therapy or post-exposure vaccination, we need to be sure to bring an element of precision medicine to our studies and treatment plans.103 We must design our experiments such that they target the correct right pathway within the appropriate environmental context, be it the intracellular environment of the mycobacterial lesion, combined personal microbiomic environment or physical environment in which the patient lives. In our opinion, we currently lack sufficient understanding of the diversity and relative influence of signals or products derived from our various microbiomes that exert systemic effects. Further, we must gain an understanding of how these signals are integrated and the mechanisms by which they influence immunity. Finally, we must be able to link an individual’s personal microbiota and larger environmental experience with their immune function. Once understood, we may be able to reinterpret some of these perplexing differences in experimental outcomes to appreciate that each reflects an accurate result within the context of better-defined differences in some critical environmental factor(s). We should work to change the metaphor from a gambler whose fortune depends on the unlikely combination of a lucky spin and high pay-out wager to one of who plays the game with the complete understanding of the odds and whose objective is to preserve his stake as long as possible.
Dheda K, Gumbo T, Maartens G, Dooley KE, McNerney R, Murray M et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir Med 2017; 5: 291–360.
Bruchfeld J, Correia-Neves M, Källenius G . Tuberculosis and HIV Coinfection. Cold Spring Harbor Perspectives in Medicine 2015; 5: a017871.
Du Bruyn E, Wilkinson RJ . The Immune Interaction between HIV-1 Infection and Mycobacterium tuberculosis. Microbiol Spectr 2016; 4.
Gupta A, Wood R, Kaplan R, Bekker LG, Lawn SD . Prevalent and incident tuberculosis are independent risk factors for mortality among patients accessing antiretroviral therapy in South Africa. PLoS One 2013; 8: e55824.
Gupta A, Wood R, Kaplan R, Bekker LG, Lawn SD . Tuberculosis incidence rates during 8 years of follow-up of an antiretroviral treatment cohort in South Africa: comparison with rates in the community. PLoS One 2012; 7: e34156.
Critchley JA, Restrepo BI, Ronacher K, Kapur A, Bremer AA, Schlesinger LS et al. Defining a research agenda to address the converging epidemics of tuberculosis and diabetes. Part 1: Epidemiology and clinical management. Chest 2017; 152: 165–173.
Ronacher K, van Crevel R, Critchley J, Bremer AA, Schlesinger LS, Kapur A et al. Defining a research agenda to address the converging epidemics of tuberculosis and diabetes. Part 2: Underlying biological mechanisms. Chest 2017; 152: 174–180.
Prada-Medina CA, Fukutani KF, Pavan Kumar N, Gil-Santana L, Babu S, Lichtenstein F et al. Systems Immunology of Diabetes-Tuberculosis Comorbidity Reveals Signatures of Disease Complications. Sci Rep 2017; 7: 1999.
Bruns H, Meinken C, Schauenberg P, Harter G, Kern P, Modlin RL et al. Anti-TNF immunotherapy reduces CD8+ T cell-mediated antimicrobial activity against Mycobacterium tuberculosis in humans. J Clin Invest 2009; 119: 1167–1177.
Jacobs RE, Gu P, Chachoua A . Reactivation of pulmonary tuberculosis during cancer treatment. Int J Mycobacteriol 2015; 4: 337–340.
Meije Y, Piersimoni C, Torre-Cisneros J, Dilektasli AG, Aguado JM, Hosts ESGoIiC. Mycobacterial infections in solid organ transplant recipients. Clin Microbiol Infect 2014; 20 (Suppl 7): 89–101.
Inghammar M, Ekbom A, Engstrom G, Ljungberg B, Romanus V, Lofdahl CG et al. COPD and the risk of tuberculosis—a population-based cohort study. PLoS One 2010; 5: e10138.
Orme I, Robinson R, Cooper A . The balance between protective and pathogenic immune responses in the TB infected lung. Nat Immunol 2015; 16: 57–63.
Robinson R, Orme I, Cooper A . The onset of adaptive immunity in the mouse model of tuberculosis and the factors which compromise its expression. Immunol Rev 2015; 264: 46–59.
Santosuosso M, McCormick S, Roediger E, Zhang X, Zganiacz A, Lichty BD et al. Mucosal luminal manipulation of T cell geography switches on protective efficacy by otherwise ineffective parenteral genetic immunization. J Immunol 2007; 178: 2387–2395.
Griffiths KL, Ahmed M, Das S, Gopal R, Horne W, Connell TD et al. Targeting dendritic cells to accelerate T-cell activation overcomes a bottleneck in tuberculosis vaccine efficacy. Nat Commun 2016; 7: 13894.
Zumla A, Rao M, Wallis RS, Kaufmann SH, Rustomjee R, Mwaba P et al. Host-directed therapies for infectious diseases: current status, recent progress, and future prospects. Lancet Infect Dis 2016; 16: e47–e63.
Kiran D, Podell BK, Chambers M, Basaraba RJ . Host-directed therapy targeting the Mycobacterium tuberculosis granuloma: a review. Semin Immunopathol 2016; 38: 167–183.
Wallis RS, Hafner R . Advancing host-directed therapy for tuberculosis. Nat Rev Immunol 2015; 15: 255–263.
Tobin DM . Host-Directed Therapies for Tuberculosis. Cold Spring Harb Perspect Med 2015; 5.
Wallis RS, Maeurer M, Mwaba P, Chakaya J, Rustomjee R, Migliori GB et al. Tuberculosis—advances in development of new drugs, treatment regimens, host-directed therapies, and biomarkers. Lancet Infect Dis 2016; 16: e34–e46.
Sekirov I, Russell SL, Antunes LCM, Finlay BB . Gut Microbiota in Health and Disease. Physiol Rev 2010; 90: 859–904.
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 2012; 486: 207–214.
Ley RE, Peterson DA, Gordon JI . Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell 2006; 124: 837–848.
O'Hara AM, Shanahan F . The gut flora as a forgotten organ. EMBO Rep 2006; 7: 688–693.
Yurist-Doutsch S, Arrieta MC, Vogt SL, Finlay BB . Gastrointestinal microbiota-mediated control of enteric pathogens. Annu Rev Genet 2014; 48: 361–382.
Cui L, Morris A, Huang L, Beck JM, Twigg HL 3rd, von Mutius E et al. The microbiome and the lung. Annals of the American Thoracic Society 2014; 11 (Suppl 4): S227–S232.
Levy M, Kolodziejczyk AA, Thaiss CA, Elinav E . Dysbiosis and the immune system. Nat Rev Immunol 2017; 17: 219–232.
Messer JS, Liechty ER, Vogel OA, Chang EB . Evolutionary and ecological forces that shape the bacterial communities of the human gut. Mucosal Immunol 2017; 10: 567–579.
Schroeder BO, Backhed F . Signals from the gut microbiota to distant organs in physiology and disease. Nat Med 2016; 22: 1079–1089.
Wopereis H, Oozeer R, Knipping K, Belzer C, Knol J . The first thousand days—intestinal microbiology of early life: establishing a symbiosis. Pediatr Allergy Immunol 2014; 25: 428–438.
Hong BY, Maulen NP, Adami AJ, Granados H, Balcells ME, Cervantes J . Microbiome Changes during Tuberculosis and Antituberculous Therapy. Clin Microbiol Rev 2016; 29: 915–926.
Sanchez B, Gueimonde M, Pena AS, Bernardo D . Intestinal microbiota as modulators of the immune system. J Immunol Res 2015; 2015: 159094.
Winglee K, Eloe-Fadrosh E, Gupta S, Guo H, Fraser C, Bishai W . Aerosol Mycobacterium tuberculosis infection causes rapid loss of diversity in gut microbiota. PLoS One 2014; 9: e97048.
Khan N, Vidyarthi A, Nadeem S, Negi S, Nair G, Agrewala JN . Alteration in the Gut Microbiota Provokes Susceptibility to Tuberculosis. Front Immunol 2016; 7: 529.
King IL, Mohrs K, Meli AP, Downey J, Lanthier P, Tzelepis F et al. Intestinal helminth infection impacts the systemic distribution and function of the naive lymphocyte pool. Mucosal Immunol 2017; 10: 1160–1168.
Majlessi L, Sayes F, Bureau JF, Pawlik A, Michel V, Jouvion G et al. Colonization with Helicobacter is concomitant with modified gut microbiota and drastic failure of the immune control of Mycobacterium tuberculosis. Mucosal Immunol 2017; 10: 1178–1189.
Joyce SA, Gahan CG . The gut microbiota and the metabolic health of the host. Curr Opin Gastroenterol 2014; 30: 120–127.
Lachmandas E, van den Heuvel CN, Damen MS, Cleophas MC, Netea MG, van Crevel R . Diabetes Mellitus and Increased Tuberculosis Susceptibility: The Role of Short-Chain Fatty Acids. J Diabetes Res 2016; 2016: 6014631.
Mason S, van Furth AMT, Solomons R, Wevers RA, van Reenen M, Reinecke CJ . A putative urinary biosignature for diagnosis and follow-up of tuberculous meningitis in children: outcome of a metabolomics study disclosing host-pathogen responses. Metabolomics 2016; 12: 110.
Armstrong CW, McGregor NR, Lewis DP, Butt HL, Gooley PR . The association of fecal microbiota and fecal, blood serum and urine metabolites in myalgic encephalomyelitis/chronic fatigue syndrome. Metabolomics 2016; 13: 8.
Das MK, Bishwal SC, Das A, Dabral D, Badireddy VK, Pandit B et al. Deregulated tyrosine-phenylalanine metabolism in pulmonary tuberculosis patients. J Proteome Res 2015; 14: 1947–1956.
Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 2016; 535: 376–381.
Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W et al. Host-gut microbiota metabolic interactions. Science 2012; 336: 1262–1267.
Sze MA, Dimitriu PA, Hayashi S, Elliott WM, McDonough JE, Gosselink JV et al. The lung tissue microbiome in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012; 185: 1073–1080.
Hilty M, Burke C, Pedro H, Cardenas P, Bush A, Bossley C et al. Disordered microbial communities in asthmatic airways. PLoS One 2010; 5: e8578.
Adami AJ, Cervantes JL . The microbiome at the pulmonary alveolar niche and its role in Mycobacterium tuberculosis infection. Tuberculosis (Edinb) 2015; 95: 651–658.
Charlson ES, Bittinger K, Haas AR, Fitzgerald AS, Frank I, Yadav A et al. Topographical continuity of bacterial populations in the healthy human respiratory tract. Am J Respir Crit Care Med 2011; 184: 957–963.
Warner BB, Hamvas A . Lungs, microbes and the developing neonate. Neonatology 2015; 107: 337–343.
He Y, Wen Q, Yao F, Xu D, Huang Y, Wang J . Gut-lung axis: The microbial contributions and clinical implications. Crit Rev Microbiol 2017; 43: 81–95.
Rogers GB, Carroll MP, Hoffman LR, Walker AW, Fine DA, Bruce KD . Comparing the microbiota of the cystic fibrosis lung and human gut. Gut Microbes 2010; 1: 85–93.
Man WH, de Steenhuijsen Piters WA, Bogaert D . The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat Rev Microbiol 2017; 15: 259–270.
Wood MR, Yu EA, Mehta S . The Human Microbiome in the Fight Against Tuberculosis. Am J Trop Med Hyg 2017; 96: 1274–1284.
Cheung MK, Lam WY, Fung WY, Law PT, Au CH, Nong W et al. Sputum microbiota in tuberculosis as revealed by 16S rRNA pyrosequencing. PLoS One 2013; 8: e54574.
Cui Z, Zhou Y, Li H, Zhang Y, Zhang S, Tang S et al. Complex sputum microbial composition in patients with pulmonary tuberculosis. BMC Microbiol 2012; 12: 276.
Wu J, Liu W, He L, Huang F, Chen J, Cui P et al. Sputum microbiota associated with new, recurrent and treatment failure tuberculosis. PLoS One 2013; 8: e83445.
Zhao J, Murray S, Lipuma JJ . Modeling the impact of antibiotic exposure on human microbiota. Sci Rep 2014; 4: 4345.
Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ . Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2007; 2: 727–738.
Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 2011; 6: 1290–1307.
Becker SA, Palsson BO . Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 2008; 4: e1000082.
Jerby L, Shlomi T, Ruppin E . Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 2010; 6: 401.
Wang Y, Eddy JA, Price ND . Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol 2012; 6: 153.
Vlassis N, Pacheco MP, Sauter T . Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol 2014; 10: e1003424.
Gomez JA, Hoffner K, Barton PI . DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis. BMC bioinformatics 2014; 15: 409.
Chen J, Gomez JA, Hoffner K, Phalak P, Barton PI, Henson MA . Spatiotemporal modeling of microbial metabolism. BMC Syst Biol 2016; 10: 21.
Hotaling M . A rare romance in medicine:The life and leagcy of Edward Livingstone TrudeauFirst edn.North Country Books: New York. 2016.
Li S, Sullivan NL, Rouphael N, Yu T, Banton S, Maddur MS et al. Metabolic Phenotypes of Response to Vaccination in Humans. Cell 2017; 169: 862–877 e817.
Van de Velde LA, Subramanian C, Smith AM, Barron L, Qualls JE, Neale G et al. T Cells Encountering Myeloid Cells Programmed for Amino Acid-dependent Immunosuppression Use Rictor/mTORC2 Protein for Proliferative Checkpoint Decisions. J Biol Chem 2017; 292: 15–30.
Marakalala MJ, Raju RM, Sharma K, Zhang YJ, Eugenin EA, Prideaux B et al. Inflammatory signaling in human tuberculosis granulomas is spatially organized. Nat Med 2016; 22: 531–538.
Cain DW, Cidlowski JA . Immune regulation by glucocorticoids. Nat Rev Immunol 2017; 17: 233–247.
Dong YH, Chang CH, Wu FL, Shen LJ, Calverley PMA, Lofdahl CG et al. Use of inhaled corticosteroids in patients with COPD and the risk of TB and influenza: A systematic review and meta-analysis of randomized controlled trials. a systematic review and meta-analysis of randomized controlled trials. Chest 2014; 145: 1286–1297.
Winthrop KL, Iseman M . Bedfellows: mycobacteria and rheumatoid arthritis in the era of biologic therapy. Nat Rev Rheumatol 2013; 9: 524–531.
Manabe YC, Kesavan AK, Lopez-Molina J, Hatem CL, Brooks M, Fujiwara R et al. The aerosol rabbit model of TB latency, reactivation and immune reconstitution inflammatory syndrome. Tuberculosis (Edinb) 2008; 88: 187–196.
Baker RW, Walker BR, Shaw RJ, Honour JW, Jessop DS, Lightman SL et al. Increased cortisol: cortisone ratio in acute pulmonary tuberculosis. Am J Respir Crit Care Med 2000; 162: 1641–1647.
Rook G, Baker R, Walker B, Honour J, Jessop D, Hernandez-Pando R et al. Local regulation of glucocorticoid activity in sites of inflammation. Insights from the study of tuberculosis. Ann N Y Acad Sci 2000; 917: 913–922.
Rey AD, Mahuad CV, Bozza VV, Bogue C, Farroni MA, Bay ML et al. Endocrine and cytokine responses in humans with pulmonary tuberculosis. Brain Behav Immun 2007; 21: 171–179.
Shin JH, Yang JY, Jeon BY, Yoon YJ, Cho SN, Kang YH et al. (1)H NMR-based metabolomic profiling in mice infected with Mycobacterium tuberculosis. J Proteome Res 2011; 10: 2238–2247.
Martens GW, Vallerskog T, Kornfeld H, Hypercholesterolemic LDL . receptor-deficient mice mount a neutrophilic response to tuberculosis despite the timely expression of protective immunity. J Leukoc Biol 2012; 91: 849–857.
Deniz O, Gumus S, Yaman H, Ciftci F, Ors F, Cakir E et al. Serum total cholesterol, HDL-C and LDL-C concentrations significantly correlate with the radiological extent of disease and the degree of smear positivity in patients with pulmonary tuberculosis. Clin Biochem 2007; 40: 162–166.
Sahin F, Yildiz P . Distinctive biochemical changes in pulmonary tuberculosis and pneumonia. Arch Med Sci 2013; 9: 656–661.
Singh V, Goyal RK, Mathur MN . Serum cholesterol in patients with pulmonary tuberculosis. J Indian Med Assoc 1977; 69: 220–222.
Mayer-Barber KD, Sher A . Cytokine and lipid mediator networks in tuberculosis. Immunol Rev 2015; 264: 264–275.
Keicho N, Matsushita I, Tanaka T, Shimbo T, Hang NT, Sakurada S et al. Circulating levels of adiponectin, leptin, fetuin-A and retinol-binding protein in patients with tuberculosis: markers of metabolism and inflammation. PLoS One 2012; 7: e38703.
Zheng Y, Ma A, Wang Q, Han X, Cai J, Schouten EG et al. Relation of leptin, ghrelin and inflammatory cytokines with body mass index in pulmonary tuberculosis patients with and without type 2 diabetes mellitus. PLoS One 2013; 8: e80122.
Perna V, Perez-Perez A, Fernandez-Riejos P, Polo-Padillo J, Batista N, Dominguez-Castellano A et al. Effective treatment of pulmonary tuberculosis restores plasma leptin levels. Eur Cytokine Netw 2013; 24: 157–161.
Wieland CW, Florquin S, Chan ED, Leemans JC, Weijer S, Verbon A et al. Pulmonary Mycobacterium tuberculosis infection in leptin-deficient ob/ob mice. Int Immunol 2005; 17: 1399–1408.
Lemos MP, Rhee KY, McKinney JD . Expression of the leptin receptor outside of bone marrow-derived cells regulates tuberculosis control and lung macrophage MHC expression. J Immunol 2011; 187: 3776–3784.
Rajaram MV, Brooks MN, Morris JD, Torrelles JB, Azad AK, Schlesinger LS . Mycobacterium tuberculosis activates human macrophage peroxisome proliferator-activated receptor gamma linking mannose receptor recognition to regulation of immune responses. J Immunol 2010; 185: 929–942.
Mahajan S, Dkhar HK, Chandra V, Dave S, Nanduri R, Janmeja AK et al. Mycobacterium tuberculosis modulates macrophage lipid-sensing nuclear receptors PPARgamma and TR4 for survival. J Immunol 2012; 188: 5593–5603.
Moran-Salvador E, Lopez-Parra M, Garcia-Alonso V, Titos E, Martinez-Clemente M, Gonzalez-Periz A et al. Role for PPARgamma in obesity-induced hepatic steatosis as determined by hepatocyte- and macrophage-specific conditional knockouts. FASEB J 2011; 25: 2538–2550.
Ahmadian M, Suh JM, Hah N, Liddle C, Atkins AR, Downes M et al. PPARgamma signaling and metabolism: the good, the bad and the future. Nat Med 2013; 19: 557–566.
Opolot JO, Theron AJ, Anderson R, Feldman C . Acute phase proteins and stress hormone responses in patients with newly diagnosed active pulmonary tuberculosis. Lung 2015; 193: 13–18.
Hafeiz AA, Issa HA, el-Kammah B, Abdel-Hafez MA, Abdou MA, Abdel-Khalek M et al. Plasma catecholamines in pulmonary tuberculosis. Kekkaku 1992; 67: 647–652.
Vaccarezza JR, Ruiz DC . The Excretion of a Urinary Metabolite of Epinephrine and Norepinephrine in Tuberculosis. Am Rev Respir Dis 1969; 100: 398–400.
Dickson RP, Erb-Downward JR, Prescott HC, Martinez FJ, Curtis JL, Lama VN et al. Intraalveolar Catecholamines and the Human Lung Microbiome. Am J Respir Crit Care Med 2015; 192: 257–259.
Rittirsch D, Flierl MA, Ward PA . Harmful molecular mechanisms in sepsis. Nat Rev Immunol 2008; 8: 776–787.
Flierl MA, Rittirsch D, Nadeau BA, Chen AJ, Sarma JV, Zetoune FS et al. Phagocyte-derived catecholamines enhance acute inflammatory injury. Nature 2007; 449: 721–725.
Nakagome K, Imamura M, Okada H, Kawahata K, Inoue T, Hashimoto K et al. Dopamine D1-like receptor antagonist attenuates Th17-mediated immune response and ovalbumin antigen-induced neutrophilic airway inflammation. J Immunol 2011; 186: 5975–5982.
Nakano K, Higashi T, Takagi R, Hashimoto K, Tanaka Y, Matsushita S . Dopamine released by dendritic cells polarizes Th2 differentiation. Int Immunol 2009; 21: 645–654.
Grailer JJ, Haggadone MD, Sarma JV, Zetoune FS, Ward PA . Induction of M2 regulatory macrophages through the beta2-adrenergic receptor with protection during endotoxemia and acute lung injury. J Innate Immun 2014; 6: 607–618.
Kim MH, Gorouhi F, Ramirez S, Granick JL, Byrne BA, Soulika AM et al. Catecholamine stress alters neutrophil trafficking and impairs wound healing by beta2-adrenergic receptor-mediated upregulation of IL-6. J Invest Dermatol 2014; 134: 809–817.
Dorhoi A, Iannaccone M, Maertzdorf J, Nouailles G, Weiner J 3rd, Kaufmann SH . Reverse translation in tuberculosis: neutrophils provide clues for understanding development of active disease. Front Immunol 2014; 5: 36.
Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. National Academy of Sciences.: Washington DC, USA. 2011.
The authors declare no conflict of interest.
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Pearl, J., Das, M. & Cooper, A. Immunological roulette: Luck or something more? Considering the connections between host and environment in TB. Cell Mol Immunol 15, 226–232 (2018). https://doi.org/10.1038/cmi.2017.145
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