A co-evolutionary perspective on humans and Mycobacterium tuberculosis in the era of systems biology

The study of tuberculosis (TB) pioneered infectious disease research in the modern scientific era, contributing to the formulation of Koch’s postulates demonstrating that an illness can have an infectious origin (Kaufmann, 2003). Mycobacterium tuberculosis (Mtb) infects and survives within macrophages, subverting the host immune response by multiple mechanisms including inhibition of phagosome maturation and downregulation of antigen presenting molecules, leading to the formation of complex immune aggregates, known as granulomas (O’Garra et al., 2013). Even though TB was the first definitively identified infection, it remains the world’s deadliest infectious disease despite well over 100 years of research (Trajman et al., 2025). This contrast raises a critical question: why is Mtb proving so resistant to human efforts to control it?

Mtb has eluded attempts to develop a fully protective vaccine, despite a partially effective vaccine being available since the 1920s. Mycobacterium bovis BCG was developed by sequential culture of M. bovis and protects children against disseminated TB but has limited protection against adult disease (Mangtani et al., 2014; Trunz et al., 2006). Although exciting progress has been made with a vaccine that reduces progression to overt TB disease by 50% when given to those with immunological evidence of latent infection in a phase II study (Tait et al., 2019), currently the immune mechanisms underpinning protection have not been identified. Subsequently, a major trial of an alternative vaccine showed no efficacy in preventing recurrence. Indeed, despite being highly immunogenic, the relapse rate tended to be higher in the vaccine group (Borges et al., 2025). Similarly, repeat BCG vaccination does not increase protection, despite inducing a strong Mtb-specific CD4 T cell response (Schmidt et al., 2025).

These difficulties highlight the priority of understanding the host–pathogen interaction more fully. We have insufficient knowledge of key steps in disease progression to develop transformative interventions. Heterogeneity across the spectrum of human TB is well described (Barry et al., 2009; Cadena et al., 2017), but the majority of fundamental investigations into disease mechanisms are based on the premise of a consistent underlying process, and that this can be understood through reductionist scientific approaches. However, clinical observations demonstrate that there are multiple paths to TB, and so seeking to define a single mechanism is likely to be flawed.

Mtb has co-evolved with humans for millennia, with some estimates suggesting up to 70,000 years (Brites and Gagneux, 2015), though other analyses suggest the most recent common ancestor was ~6000 years before present (Bos et al., 2014; Kay et al., 2015). The field of paleoarchaeology provides extensive evidence of TB from the early Neolithic period in the Middle East, with approximately 5% of skeletons from a 10,000-year-old village showing signs of TB (Dutour, 2023). This was just before animal domestication and pottery, in hunter-gatherers who built stone houses, and so Mtb was already successfully transmitting in humans before the subsequent population growth that occurred with farming (Dutour, 2023). Potentially, to survive in relatively small hunter-gatherer communities, Mtb may have needed to have reduced virulence to avoid excessive deaths and a latent period to permit sustainable transmission in low population numbers (Gagneux, 2012). With the expansion and increased density of human populations, more rapid progression to TB disease can be sustained, consistent with analysis that most TB progression in high-incidence settings occurs within 1–2 years of exposure (Behr et al., 2018).

Mtb successfully persisted over the ages and then flourished in the crowded populations that occurred with the industrial revolution (Dubos and Dubos, 1987), giving rise to the modern TB era approximately 250 years ago. The fundamental cause of TB remained unknown until Koch’s seminal work (Kaufmann, 2003). Early investigations demonstrated that the first Mtb infection point was the lung base, while Mtb exits from the apices of the lungs (Ghon, 1916). This life cycle must involve several distinct host/pathogen interactions, as initially immune evasion is required for Mtb to survive, but then later immune engagement is necessary to cause the inflammation and lung destruction needed to optimize transmission (Elkington and Friedland, 2015). Cavitary lung disease leads to proliferation of extracellular bacteria and increased transmission (Yoder et al., 2004). Notably, most people (approximately 90%) initially infected with Mtb never progress to active, clinical disease (Trajman et al., 2025). In addition, in the pre-antibiotic era, the progression and regression of different lesions in the same individual were observed on chest radiographs, and one third of patients with active TB disease self-healed, showing that the host–pathogen interaction is finely balanced at all stages of infection (Dubos and Dubos, 1987).

Modern immunological techniques and the development of biologic therapies that target specific immune processes have provided extensive insight into TB disease mechanisms. The greatly increased occurrence of TB in the context of HIV co-infection, for example, highlighted immunodeficiency as a major driver of disease (Kwan and Ernst, 2011). Similarly, the occurrence of TB after anti-TNF-α therapy for autoimmune disease confirmed the importance of TNF-α in control of latent infection (Keane et al., 2001). Furthermore, genetic investigations have identified numerous immunodeficiencies via studies of Mendelian Susceptibility to Mycobacterial Diseases (MSMD), with mutations typically along the IL-12/IFN-γ/STAT signaling pathway (Jouanguy et al., 1996; Dupuis et al., 2001; Arias et al., 2024). With less clearly defined immunologic mechanisms, malnutrition is a significant risk factor for TB (Dheda et al., 2016), and food supplementation reduces TB incidence in contacts (Bhargava et al., 2023). Therefore, diverse immune deficiencies can lead to active TB.

The vast majority of patients who develop TB, however, have no clear identifiable immunodeficiency. Indeed, Comstock’s seminal study from the 1970s showed that children from Haiti with the strongest recall responses to Mtb antigens actually had the greatest subsequent risk of developing TB (Comstock et al., 1974). These observations have been replicated in more recent studies using IFN-γ release assays (IGRAs) in response to TB antigens, where higher IFN-γ production associates with increased risk of progressing to disease in both children and adults (Andrews et al., 2017; Ledesma et al., 2021). TB is most common in young adults in their immunological prime and more frequent in males than females, characterized by an excessive inflammatory response (Horton et al., 2016). The implication that TB can also be caused by immune excess is now supported by recently introduced cancer immunotherapies (Tezera et al., 2020b). Anti-PD-1 treatment, which activates the immune response and represents the immunological opposite to anti-TNF therapy, should control TB if immunodeficiency were the critical component. However, anti-PD-1 treatment can lead to rapid reactivation of latent TB infection, first identified in case reports (Elkington et al., 2018). This finding is supported by studies in mice (Lázár-Molnár et al., 2010; Barber et al., 2011), the non-human primate (Kauffman et al., 2021) and 3D cellular models (Tezera et al., 2020a), and ultimately has been validated by patient registry studies (Liu et al., 2022; Zhu et al., 2022). Similarly, type II diabetes is associated with an increased risk of TB (Dheda et al., 2016), characterized by a hyper-inflammatory immune response (Eckold et al., 2021). Therefore, diverse clinical evidence demonstrates that there are multiple immunological disturbances that can lead to TB disease (Behr et al., 2024; Figure 1).

Figure 1

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Even when TB develops in the face of a ‘normal’ immune system, there are likely to be many different subgroups that have not yet been identified due to limitations in standard immunological profiling. A quarter of the world’s population is thought to be exposed to Mtb (Houben and Dodd, 2016), and so the vast majority of those infected with Mtb remain healthy lifelong (Trajman et al., 2025), while those who progress to active TB likely represent distinct outlier populations. The diverse causes of active TB, such as anti-TNF, MSMD, HIV, diabetes, and anti-PD1, demonstrate that patients do not reach TB by a single pathway, but instead lose the immunological balance that controls Mtb by multiple paths. These observations may explain why genetic studies have generally failed to find consistent predispositions. Evidence of heritability can be demonstrated but is often hard to validate in different populations (Schurz et al., 2024). Mutations in the macrophage endosomal protein NRAMP1, for example, were shown to associate with TB in an early seminal study (Bellamy et al., 1998), but since then, few consistent traits of genetic susceptibility to TB have been identified (Abel et al., 2014).

Recent genomic studies demonstrate the complex co-evolution of host and Mtb. The International Tuberculosis Host Genetics Consortium’s first analysis found one significant host genetic variant, human leukocyte antigen-II region (rs28383206), which conferred TB susceptibility across nine genome-wide association studies across three continents (Schurz et al., 2024). However, other variants previously associated with TB susceptibility were not replicated. Another approach using genome-to-genome analysis of paired human and Mtb samples in Peru identified another determinant on chromosome 6, rs3130660, in the flotillin-1 (FLOT1) gene (Luo et al., 2024). Together with our understanding of the co-adaptation of different Mtb lineages with human migrations (Comas et al., 2013), the host immune response to Mtb is likely to be dependent on ancestral-related genetic factors and complex host–pathogen dynamics which remain incompletely understood.

Studies of individuals who resist Mtb infection despite recurrent exposure generate a diverse list of potentially protective features, including T cell subsets or activation (Cross et al., 2024; Sun et al., 2024b; Dallmann-Sauer et al., 2025), TNF-α responses (Simmons et al., 2022), antibody activity (Lu et al., 2019), and innate immune training (Verrall et al., 2020). These human observations are supported by murine genetic experiments, which show that increased susceptibility to Mtb can result from a wide range of immunological alterations (Smith et al., 2022). Similarly, infant vaccine studies show that there are distinct patterns of response that may determine vaccine efficacy (Fletcher et al., 2016). However, despite this evidence of diversity, the majority of fundamental studies continue to seek a single underlying mechanism that leads to TB disease progression, which is incompatible with clinical observations.

Ultimately, to transmit efficiently, Mtb needs to cause pulmonary disease to then spread by airborne droplets (Yoder et al., 2004), and for Mtb, it does not matter the route taken, as long as it ends at pulmonary TB. Increasing evidence suggests that asymptomatic transmission may be important in high-incidence settings, potentially in the absence of overt pulmonary disease (Ryckman et al., 2022; Dinkele et al., 2024; Patterson et al., 2024). Experimentally, TB is a fundamentally challenging disease to model, as the interaction is prolonged and Mtb is an obligate human pathogen (Elkington et al., 2019). The primary driver of advances in immunology in the last decades has been transgenic mice (Gros and Casanova, 2023), but the mouse model of TB does not accurately reflect human disease (Young, 2009). Disease heterogeneity has been highlighted in describing clinical TB endotypes observed during active disease (DiNardo et al., 2021), with different endotypes exhibiting diverse immunological characteristics and association with outcome (DiNardo et al., 2022). However, we propose that insufficient attention has been given to the different immunological pathways that may converge on the same disease phenotype.

Just as new tools are providing insights into the complexity of human TB progression, advances in mycobacterial genomics are highlighting the unique nature of the human-Mtb relationship (Koleske et al., 2023). Mtb is a near-clonal organism, with evidence suggesting that there has been only one successful and sustained penetration into the human population (Comas et al., 2013; Koleske et al., 2023; Goig et al., 2025). The entire spectrum of Mtb strains globally only differs by a total of approximately 2000 SNPs (Koleske et al., 2023). This genetic conservation has persisted from the most recent common ancestor (Chiner-Oms et al., 2019) during the expansion of Mtb in human populations since the industrial revolution, which created much denser human aggregations suitable for transmission (Dubos and Dubos, 1987). This suggests that Mtb was already close to being optimized for human hosts. Further evolution may have been to increase transmission within specific populations as humans diverged genetically (Goig et al., 2025), with a recent study suggesting that Mtb may in fact be becoming attenuated to increase spread in populations (Culviner et al., 2025). Hence, Mtb may be evolving over time to optimize its transmission within humans depending on population density.

A very similar organism, M. canetti, can cause disease but cannot transmit from human to human (Yenew et al., 2023). Similarly, M. bovis is 99.9% identical to Mtb (Garnier et al., 2003), but has never achieved sustained human-to-human transmission, despite many millions of human exposures during the pre-pasteurization era and common lymph node infections (Goig et al., 2025). Therefore, human TB is caused exclusively by Mtb, unlike other very closely related mycobacteria, which maintain infection cycles in other higher mammals but not humans (Goig et al., 2025). Clues to the key pathogenic mechanisms may lie in the differences between mycobacterial species (Danchuk et al., 2025), but ultimately, similarities between Mtb strains must be critical to their ongoing success. For example, the hyper-conservation of T cell epitopes may be evidence that Mtb manipulates the host immune response to favor disease and transmission (Comas et al., 2010). Given the size of the Mtb genome, identifying the critical conserved features will be challenging, especially as half of its genes still lack a known function 25 years after it was first sequenced (Nathan, 2023).

One highly intriguing proposition is that latent TB may itself give humans an evolutionary advantage (Nathan, 2023). Exposure to Mtb modifies innate immune training via reprogramming hematopoietic stem cells (Khan et al., 2020), and so a mechanism whereby Mtb could protect from other fatal infections is plausible. Similarly, M. bovis BCG, a live-attenuated strain of M. bovis, causes innate immune training (Kaufmann et al., 2018), suggesting this effect is common to Mtb and BCG. BCG vaccination reduces mortality much more significantly than can be explained by the effect on TB incidence alone (Jurczak and Druszczynska, 2025), with a protective effect confirmed in diverse studies (Moorlag et al., 2019). For example, BCG reduces viral infections in infants in Africa (Stensballe et al., 2005) and experimentally challenged adults (Arts et al., 2018), and associates with improved survival in Europe (Rieckmann et al., 2017). Mtb and BCG can protect against SARS-CoV2 infection (Rosas Mejia et al., 2022; Hilligan et al., 2022), although this did not translate to efficacy in a clinical trial (Pittet et al., 2023). Together, these observations strongly imply that mycobacterial infection protects from other infectious causes of death.

Mtb almost certainly first became established in humans in East Africa (Comas et al., 2013; Goig et al., 2025), potentially about 70,000 years ago (Brites and Gagneux, 2015), although this timeline is debated. Several human migrations out of Africa are thought to have preceded this date, but all ultimately became extinct, with the first sustained human dispersal 60–70,000 years ago (Vallini et al., 2024). Mtb diversity mirrors human mitochondrial genome diversity, further implying that Mtb disseminated with human populations from East Africa (Comas et al., 2013). The primary selective pressure in these early communities would have been infectious disease (Dobson and Carper, 1996). This raises a novel hypothesis that a survival advantage for the first successful human migrants out of Africa was Mtb circulating in the community, reducing mortality from other infectious diseases and thereby enabling sustainable population growth.

Mtb transmission may have benefitted humans by increasing innate immune resistance to infection at the cost of 10% disease penetrance that permits Mtb propagation. This selective pressure over many millennia would progressively remove genotypes that lead to high susceptibility to TB, but equally would select against individuals with complete resistance to initial Mtb infection (Figure 2). This could explain why consistent genetic traits for susceptibility or resistance to TB have been hard to identify (Abel et al., 2014). More recent mass infection events, such as the smallpox epidemics that killed approximately 25% of the population (Dobson and Carper, 1996), may have further favored individuals immunologically trained by Mtb. Likewise, successive waves of plague killed approximately 25% of the population, at the end of the Roman empire and then in the European middle ages, adding to selective pressure from endemic infections (Little, 2007; Benedictow, 2004). If humans have been selected to be permissive to Mtb infection but resistant to TB disease, which could be regarded as colonization, it suggests the development of active disease must be a relatively unusual event in a subset of outlier individuals. In some sense, we could be regarded as having a symbiotic relationship with Mtb, with disease representing a necessary evil, caused by an imbalance in the predominantly stable host–pathogen interaction (Divangahi et al., 2018; Olive and Sassetti, 2018).

Figure 2

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The early histological era described the wide range of human TB lesions and granuloma types and identified TB as a disease characterized by spatial organization (Hunter, 2016). Classically, the granuloma has been proposed to be where the outcome of the host–pathogen interaction is determined (Pagán and Ramakrishnan, 2018). Recent methodological advances are permitting much greater dissection of events and further highlight the importance of spatial organization within the granuloma (Sawyer et al., 2023; McCaffrey et al., 2022; Marakalala et al., 2016). However, just as the early X-ray era showed some lesions progressing and some regressing, these studies demonstrate the great heterogeneity between granuloma types. Studies in the non-human primate have allowed investigation into features of progressing and controlling granulomas, identifying potential correlates of immune control (Gideon et al., 2022), but even this model only partially recapitulates human disease.

Furthermore, the recent spatial studies highlight the complexity of cellular players, including the established fulcrum of macrophages and T cells, but additionally the importance of B cells, neutrophils, and fibroblasts. For example, fibroblasts are emerging as important immune regulators in other lung diseases (Ghonim et al., 2023), and so it seems highly likely that they play an active role in TB-related inflammation. Fibroblast zonation can lead to feed-forward inflammatory loops and so may propagate disease (Davidson et al., 2021). Consequently, the full spectrum of cell types in Mtb-infected lesions needs to be considered. Given that multiple underlying immunological pathways can lead to active TB, it seems unlikely that a single cellular component will fully explain the balance between Mtb containment and progression to active disease. However, the majority of studies continue to look for a single consistent immune mechanism; discussions rarely state ‘this is one of several potential routes to TB disease’ (Sun et al., 2024b; Gideon et al., 2022; Winchell et al., 2023; Swanson et al., 2023; Proulx et al., 2025).

Considering the clinical and experimental evidence, immunological failure is likely to be a multistep process, whereby either one large deficit or numerous small imbalances can lead to progression and disease (Figure 3). Mtb and humans interact over many years, as the pathogen is difficult to eradicate due to its highly evolved survival mechanisms (Russell, 2011), and therefore in those individuals in whom it survives, there is a long period where it can escape host control. Potentially, these different paths may ultimately cross or converge; if so, understanding the key nodes will allow more broadly effective treatments to emerge. For example, lung extracellular matrix degradation could be regarded as a final common pathway (Elkington et al., 2022), but, again, this may result from different collagenases including macrophage-derived MMP-1 or neutrophil-derived MMP-8 (Elkington et al., 2011; Ong et al., 2015).

Figure 3

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The recent adoption of ‘omic’ methodologies, including single-cell transcriptomics, spatial transcriptomics, and proteomics, offers unprecedented opportunities to dissect the mechanisms of human TB pathogenesis and accelerate the development of effective interventions. These approaches generate large-scale, complex datasets that capture the heterogeneity of TB lesions. However, this complexity also presents significant analytical challenges. Standard computational pipelines, if not adapted to account for the biological and technical variability in these data, are unlikely to deliver robust or reproducible insights. A major obstacle is the integration of data from multiple studies and platforms, which can differ both within a single omic layer (horizontal integration) and across multiple omic layers (vertical integration) (Zheng et al., 2024). Such integration risks data loss and inconsistent results, especially when data are not harmonized.

The prolonged co-evolution between host and pathogen has resulted in multiple immunological pathways to active TB, significantly adding to the biological heterogeneity that complicates the analysis and interpretation of multi-omic data. Addressing this complexity requires well-annotated clinical cohorts that capture the full spectrum of TB heterogeneity, ideally with longitudinal outcome data rather than single time-point snapshots. Comprehensive clinical descriptors and associated data such as laboratory analyses and chest X-rays will permit definition of each clinical phenotype. To accommodate the diversity of disease pathways, novel bioinformatic approaches are needed that move beyond the assumption of a single sequence of events that results in active TB.

The power of multi-omic approaches to reveal complex molecular relationships depends on both the quality of the omic data and the fit between experimental design and data integration strategies. For example, correlation-based integration requires matched samples across omics, sufficient sample numbers, and comparable variance structures. These requirements are often overlooked, leading to insufficient power, noisy data, and unrealistic integration (Tarazona et al., 2020). Each omic technology brings its own challenges. Signal-to-noise ratios differ across modalities, and appropriate algorithms are needed to estimate sample size and power for each. Notably, using the same sample numbers across modalities does not ensure comparable statistical power; achieving equal power can require unbalanced sample sizes (Tarazona et al., 2021). Other recognized challenges include the interpretation and validation of multi-omic models, standardized annotation, and data sharing (Tarazona et al., 2021). Recent developments include web-based, user-friendly tools that enable both knowledge- and data-driven multi-omic integration (Ewald et al., 2024). Importantly, a wider use of artificial intelligence (AI) is transforming omics data analysis by providing robust methods to interpret complex biological datasets (Ahmed et al., 2024). Machine learning and deep learning techniques are now routinely applied to DNA, transcriptomic, proteomic, and metabolomic data, enabling more integrated and comprehensive analyses (Ahmed et al., 2024). In proteomics, for example, AI-driven approaches have enhanced peptide measurement predictions and accelerated biomarker discovery, often outperforming conventional assays (Mann et al., 2021). To improve interpretability, explainable AI (XAI) methods are increasingly employed, with feature relevance mapping and visual explanations emerging as preferred post hoc strategies (Toussaint et al., 2023). However, despite these developments, significant challenges remain in implementing XAI. Further research is needed to overcome these barriers and unlock the full translational potential of AI in omics analysis (Mann et al., 2021; Toussaint et al., 2023). Ultimately, the utility of these innovations will depend on the functional validation of the resulting models.

Given the complexity of human TB, careful study design and unbiased integration methods that accommodate data limitations are essential. Spatial context is particularly important, as TB pathology involves three-dimensional immune responses. Extracellular matrix remodeling is a hallmark of TB granulomas (Elkington et al., 2022), and the matrix regulates host cell biology (Bansaccal et al., 2023). Consequently, multiple data inputs such as matrix composition and organization may be needed alongside host and Mtb transcriptomic data. Ultimately, local cellular events must be modeled at the tissue level, providing a second layer of computational complexity (Palla et al., 2022).

Addressing these challenges will require substantial investment in analytical capacity. Multi-omics is already transforming our understanding of disease heterogeneity, facilitating the identification of previously unrecognized subgroups, refining prognostic and therapeutic approaches, and providing deeper mechanistic insights. This strategy has successfully stratified rare tumors (Sun et al., 2024a), profiled healthy populations (Halama et al., 2024), and enabled health screening to reveal previously hidden disease and risk subgroups (Garg et al., 2024), supporting the shift toward precision medicine. As human disease processes are rarely uniform, advances in multi-omic study design and analysis for TB will likely benefit a broad range of conditions. Ultimately, this comprehensive understanding of disease pathophysiology can then lead to more targeted and stratified treatment, although implementation will require developments in companion diagnostics to accurately stratify patients.

Ultimately, the complexity of human TB underlies our inability to deploy a transformative intervention, and so global mortality remains depressingly high. The human–Mtb interaction is so closely co-evolved that experimental findings need to be interpreted in light of human disease phenomena. Multiomic studies in TB are currently being undertaken on small numbers due to cost and challenges in obtaining appropriate clinical samples, in specific regions, and so are unlikely to reflect the global heterogeneity. Therefore, results need to be interpreted with caution and wider studies will be needed to confirm the generalizability of findings. A critical aspect will be carefully curated and fully accessible metadata, so that as the body of omic datasets on human TB increases, they can be accurately integrated into wider analyses. Recurrent mining of these datasets is likely to be fundamental to understanding the breadth of TB pathogenesis. Missing metadata makes interpretation difficult, and in worst cases, misleading. In addition, innovative computational approaches will be required whereby the analysis specifically accommodates multiple immune pathways to disease.

Given the toll that TB takes in the poorest parts of the world, we have a moral imperative to end the epidemic (Reid et al., 2023). To achieve this, we must acknowledge the complexity of human TB that has resulted from our prolonged co-evolution with the pathogen and the selective pressure of persistent Mtb exposure over millennia. Success depends on integrating the full spectrum of TB disease into our bioinformatic analyses, and ultimately understanding TB’s complexity can then inform logical interventions. If we seek a single mechanistic explanation of TB disease, this seems to be unlikely to be successful.

1. 1. Abel L
   2. El-Baghdadi J
   3. Bousfiha AA
   4. Casanova JL
   5. Schurr E

   (2014) Human genetics of tuberculosis: a long and winding road

   Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369:20130428.

   https://doi.org/10.1098/rstb.2013.0428

   • PubMed
   • Google Scholar
2. 1. Ahmed Z
   2. Wan S
   3. Zhang F
   4. Zhong W

   (2024) Artificial intelligence for omics data analysis

   BMC Methods 1:4.

   https://doi.org/10.1186/s44330-024-00004-5

   • Google Scholar
3. 1. Andrews JR
   2. Nemes E
   3. Tameris M
   4. Landry BS
   5. Mahomed H
   6. McClain JB
   7. Fletcher HA
   8. Hanekom WA
   9. Wood R
   10. McShane H
   11. Scriba TJ
   12. Hatherill M

   (2017) Serial QuantiFERON testing and tuberculosis disease risk among young children: an observational cohort study

   The Lancet. Respiratory Medicine 5:282–290.

   https://doi.org/10.1016/S2213-2600(17)30060-7

   • PubMed
   • Google Scholar
4. 1. Arias AA
   2. Neehus A-L
   3. Ogishi M
   4. Meynier V
   5. Krebs A
   6. Lazarov T
   7. Lee AM
   8. Arango-Franco CA
   9. Yang R
   10. Orrego J
   11. Corcini Berndt M
   12. Rojas J
   13. Li H
   14. Rinchai D
   15. Erazo-Borrás L
   16. Han JE
   17. Pillay B
   18. Ponsin K
   19. Chaldebas M
   20. Philippot Q
   21. Bohlen J
   22. Rosain J
   23. Le Voyer T
   24. Janotte T
   25. Amarajeeva K
   26. Soudée C
   27. Brollo M
   28. Wiegmann K
   29. Marquant Q
   30. Seeleuthner Y
   31. Lee D
   32. Lainé C
   33. Kloos D
   34. Bailey R
   35. Bastard P
   36. Keating N
   37. Rapaport F
   38. Khan T
   39. Moncada-Vélez M
   40. Carmona MC
   41. Obando C
   42. Alvarez J
   43. Cataño JC
   44. Martínez-Rosado LL
   45. Sanchez JP
   46. Tejada-Giraldo M
   47. L’Honneur A-S
   48. Agudelo ML
   49. Perez-Zapata LJ
   50. Arboleda DM
   51. Alzate JF
   52. Cabarcas F
   53. Zuluaga A
   54. Pelham SJ
   55. Ensser A
   56. Schmidt M
   57. Velásquez-Lopera MM
   58. Jouanguy E
   59. Puel A
   60. Krönke M
   61. Ghirardello S
   62. Borghesi A
   63. Pahari S
   64. Boisson B
   65. Pittaluga S
   66. Ma CS
   67. Emile J-F
   68. Notarangelo LD
   69. Tangye SG
   70. Marr N
   71. Lachmann N
   72. Salvator H
   73. Schlesinger LS
   74. Zhang P
   75. Glickman MS
   76. Nathan CF
   77. Geissmann F
   78. Abel L
   79. Franco JL
   80. Bustamante J
   81. Casanova J-L
   82. Boisson-Dupuis S

   (2024) Tuberculosis in otherwise healthy adults with inherited TNF deficiency

   Nature 633:417–425.

   https://doi.org/10.1038/s41586-024-07866-3

   • PubMed
   • Google Scholar
5. 1. Arts RJW
   2. Moorlag S
   3. Novakovic B
   4. Li Y
   5. Wang SY
   6. Oosting M
   7. Kumar V
   8. Xavier RJ
   9. Wijmenga C
   10. Joosten LAB
   11. Reusken C
   12. Benn CS
   13. Aaby P
   14. Koopmans MP
   15. Stunnenberg HG
   16. van Crevel R
   17. Netea MG

   (2018) BCG vaccination protects against experimental viral infection in humans through the induction of cytokines associated with trained immunity

   Cell Host & Microbe 23:89–100.

   https://doi.org/10.1016/j.chom.2017.12.010

   • PubMed
   • Google Scholar
6. 1. Bansaccal N
   2. Vieugue P
   3. Sarate R
   4. Song Y
   5. Minguijon E
   6. Miroshnikova YA
   7. Zeuschner D
   8. Collin A
   9. Allard J
   10. Engelman D
   11. Delaunois AL
   12. Liagre M
   13. de Groote L
   14. Timmerman E
   15. Van Haver D
   16. Impens F
   17. Salmon I
   18. Wickström SA
   19. Sifrim A
   20. Blanpain C

   (2023) The extracellular matrix dictates regional competence for tumour initiation

   Nature 623:828–835.

   https://doi.org/10.1038/s41586-023-06740-y

   • PubMed
   • Google Scholar
7. 1. Barber DL
   2. Mayer-Barber KD
   3. Feng CG
   4. Sharpe AH
   5. Sher A

   (2011) CD4 T cells promote rather than control tuberculosis in the absence of PD-1-mediated inhibition

   Journal of Immunology 186:1598–1607.

   https://doi.org/10.4049/jimmunol.1003304

   • PubMed
   • Google Scholar
8. 1. Barry CE
   2. Boshoff HI
   3. Dartois V
   4. Dick T
   5. Ehrt S
   6. Flynn J
   7. Schnappinger D
   8. Wilkinson RJ
   9. Young D

   (2009) The spectrum of latent tuberculosis: rethinking the biology and intervention strategies

   Nature Reviews. Microbiology 7:845–855.

   https://doi.org/10.1038/nrmicro2236

   • PubMed
   • Google Scholar
9. 1. Behr MA
   2. Edelstein PH
   3. Ramakrishnan L

   (2018) Revisiting the timetable of tuberculosis

   BMJ 362:k2738.

   https://doi.org/10.1136/bmj.k2738

   • PubMed
   • Google Scholar
10. 1. Behr MA
    2. Edelstein PH
    3. Ramakrishnan L

    (2024) Rethinking the burden of latent tuberculosis to reprioritize research

    Nature Microbiology 9:1157–1158.

    https://doi.org/10.1038/s41564-024-01683-0

    • PubMed
    • Google Scholar
11. 1. Bellamy R
    2. Ruwende C
    3. Corrah T
    4. McAdam KP
    5. Whittle HC
    6. Hill AV

    (1998) Variations in the NRAMP1 gene and susceptibility to tuberculosis in West Africans

    The New England Journal of Medicine 338:640–644.

    https://doi.org/10.1056/NEJM199803053381002

    • PubMed
    • Google Scholar
12. Book

    1. Benedictow OJ

    (2004)

    The Black Death, 1346-1353: The Complete History

    Boydell Press.

    • Google Scholar
13. 1. Bhargava A
    2. Bhargava M
    3. Meher A
    4. Benedetti A
    5. Velayutham B
    6. Sai Teja G
    7. Watson B
    8. Barik G
    9. Pathak RR
    10. Prasad R
    11. Dayal R
    12. Madhukeshwar AK
    13. Chadha V
    14. Pai M
    15. Joshi R
    16. Menzies D
    17. Swaminathan S

    (2023) Nutritional supplementation to prevent tuberculosis incidence in household contacts of patients with pulmonary tuberculosis in India (RATIONS): a field-based, open-label, cluster-randomised, controlled trial

    Lancet 402:627–640.

    https://doi.org/10.1016/S0140-6736(23)01231-X

    • PubMed
    • Google Scholar
14. 1. Borges ÁH
    2. Russell M
    3. Tait D
    4. Scriba TJ
    5. Nemes E
    6. Skallerup P
    7. van Brakel E
    8. Cabibbe AM
    9. Cirillo DM
    10. Leuvennink-Steyn M
    11. Rutkowski KT
    12. Wood GK
    13. Thierry-Carstensen B
    14. Tingskov PN
    15. Meldgaard EC
    16. Kristiansen MP
    17. Søndergaard RE
    18. Hansen CH
    19. Follmann F
    20. Jensen CG
    21. Gela A
    22. Ntinginya NE
    23. Ruhwald M
    24. Shenje J
    25. White L
    26. Innes C
    27. Selepe P
    28. Ngaraguza B
    29. Holmgren C
    30. Collings T
    31. Andersen P
    32. Dawson R
    33. Churchyard G
    34. Sabi I
    35. Diacon AH
    36. Mortensen R
    37. Hatherill M
    38. POR TB study group

    (2025) Immunogenicity, safety, and efficacy of the vaccine H56:IC31 in reducing the rate of tuberculosis disease recurrence in HIV-negative adults successfully treated for drug-susceptible pulmonary tuberculosis: a double-blind, randomised, placebo-controlled, phase 2b trial

    The Lancet. Infectious Diseases 25:751–763.

    https://doi.org/10.1016/S1473-3099(24)00814-4

    • PubMed
    • Google Scholar
15. 1. Bos KI
    2. Harkins KM
    3. Herbig A
    4. Coscolla M
    5. Weber N
    6. Comas I
    7. Forrest SA
    8. Bryant JM
    9. Harris SR
    10. Schuenemann VJ
    11. Campbell TJ
    12. Majander K
    13. Wilbur AK
    14. Guichon RA
    15. Wolfe Steadman DL
    16. Cook DC
    17. Niemann S
    18. Behr MA
    19. Zumarraga M
    20. Bastida R
    21. Huson D
    22. Nieselt K
    23. Young D
    24. Parkhill J
    25. Buikstra JE
    26. Gagneux S
    27. Stone AC
    28. Krause J

    (2014) Pre-Columbian mycobacterial genomes reveal seals as a source of New World human tuberculosis

    Nature 514:494–497.

    https://doi.org/10.1038/nature13591

    • PubMed
    • Google Scholar
16. 1. Brites D
    2. Gagneux S

    (2015) Co-evolution of Mycobacterium tuberculosis and Homo sapiens

    Immunological Reviews 264:6–24.

    https://doi.org/10.1111/imr.12264

    • PubMed
    • Google Scholar
17. 1. Cadena AM
    2. Fortune SM
    3. Flynn JL

    (2017) Heterogeneity in tuberculosis

    Nature Reviews. Immunology 17:691–702.

    https://doi.org/10.1038/nri.2017.69

    • PubMed
    • Google Scholar
18. 1. Chiner-Oms Á
    2. Sánchez-Busó L
    3. Corander J
    4. Gagneux S
    5. Harris SR
    6. Young D
    7. González-Candelas F
    8. Comas I

    (2019) Genomic determinants of speciation and spread of the Mycobacterium tuberculosis complex

    Science Advances 5:eaaw3307.

    https://doi.org/10.1126/sciadv.aaw3307

    • Google Scholar
19. 1. Comas I
    2. Chakravartti J
    3. Small PM
    4. Galagan J
    5. Niemann S
    6. Kremer K
    7. Ernst JD
    8. Gagneux S

    (2010) Human T cell epitopes of Mycobacterium tuberculosis are evolutionarily hyperconserved

    Nature Genetics 42:498–503.

    https://doi.org/10.1038/ng.590

    • PubMed
    • Google Scholar
20. 1. Comas I
    2. Coscolla M
    3. Luo T
    4. Borrell S
    5. Holt KE
    6. Kato-Maeda M
    7. Parkhill J
    8. Malla B
    9. Berg S
    10. Thwaites G
    11. Yeboah-Manu D
    12. Bothamley G
    13. Mei J
    14. Wei L
    15. Bentley S
    16. Harris SR
    17. Niemann S
    18. Diel R
    19. Aseffa A
    20. Gao Q
    21. Young D
    22. Gagneux S

    (2013) Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans

    Nature Genetics 45:1176–1182.

    https://doi.org/10.1038/ng.2744

    • PubMed
    • Google Scholar
21. 1. Comstock GW
    2. Livesay VT
    3. Woolpert SF

    (1974) The prognosis of a positive tuberculin reaction in childhood and adolescence

    American Journal of Epidemiology 99:131–138.

    https://doi.org/10.1093/oxfordjournals.aje.a121593

    • PubMed
    • Google Scholar
22. 1. Cross DL
    2. Layton ED
    3. Yu KK
    4. Smith MT
    5. Aguilar MS
    6. Li S
    7. Wilcox EC
    8. Chapuis AG
    9. Mayanja-Kizza H
    10. Stein CM
    11. Boom WH
    12. Hawn TR
    13. Bradley P
    14. Newell EW
    15. Seshadri C

    (2024) MR1-restricted T cell clonotypes are associated with “resistance” to Mycobacterium tuberculosis infection

    JCI Insight 9:e166505.

    https://doi.org/10.1172/jci.insight.166505

    • PubMed
    • Google Scholar
23. 1. Culviner PH
    2. Frey AM
    3. Liu Q
    4. Ha DTM
    5. Thai PVK
    6. Thu DDA
    7. Quang NL
    8. Calderon R
    9. Lecca L
    10. Caws M
    11. Dunstan SJ
    12. Murray MB
    13. Thuong NTT
    14. Fortune SM

    (2025) Evolution of Mycobacterium tuberculosis transcription regulation is associated with increased transmission and drug resistance

    Cell 188:6620–6635.

    https://doi.org/10.1016/j.cell.2025.09.005

    • PubMed
    • Google Scholar
24. 1. Dallmann-Sauer M
    2. Fava VM
    3. Malherbe ST
    4. MacDonald CE
    5. Orlova M
    6. Kroon EE
    7. Cobat A
    8. Boisson-Dupuis S
    9. Hoal EG
    10. Abel L
    11. Möller M
    12. Casanova JL
    13. Walzl G
    14. Du Plessis N
    15. Schurr E

    (2025) Mycobacterium tuberculosis resisters despite HIV exhibit activated T cells and macrophages in their pulmonary alveoli

    The Journal of Clinical Investigation 135:e188016.

    https://doi.org/10.1172/JCI188016

    • PubMed
    • Google Scholar
25. 1. Danchuk SN
    2. Duffy SC
    3. Sullivan J
    4. Beenish Rufai S
    5. McIntosh FA
    6. Lupien A
    7. Harrison LB
    8. Ghasemi Goojani H
    9. Taylor L
    10. Wei Y
    11. Joubert P
    12. Mortensen R
    13. Chen JM
    14. Niroula N
    15. Stevens R
    16. Norleen C
    17. Kapur V
    18. Behr MA

    (2025) Virulence hierarchies within the Mycobacterium tuberculosis complex

    PNAS 122:e2507104122.

    https://doi.org/10.1073/pnas.2507104122

    • PubMed
    • Google Scholar
26. 1. Davidson S
    2. Coles M
    3. Thomas T
    4. Kollias G
    5. Ludewig B
    6. Turley S
    7. Brenner M
    8. Buckley CD

    (2021) Fibroblasts as immune regulators in infection, inflammation and cancer

    Nature Reviews. Immunology 21:704–717.

    https://doi.org/10.1038/s41577-021-00540-z

    • PubMed
    • Google Scholar
27. 1. Dheda K
    2. Barry CE
    3. Maartens G

    (2016) Tuberculosis

    Lancet 387:1211–1226.

    https://doi.org/10.1016/S0140-6736(15)00151-8

    • PubMed
    • Google Scholar
28. 1. DiNardo AR
    2. Nishiguchi T
    3. Grimm SL
    4. Schlesinger LS
    5. Graviss EA
    6. Cirillo JD
    7. Coarfa C
    8. Mandalakas AM
    9. Heyckendorf J
    10. Kaufmann SHE
    11. Lange C
    12. Netea MG
    13. Van Crevel R

    (2021) Tuberculosis endotypes to guide stratified host-directed therapy

    Med 2:217–232.

    https://doi.org/10.1016/j.medj.2020.11.003

    • PubMed
    • Google Scholar
29. 1. DiNardo AR
    2. Gandhi T
    3. Heyckendorf J
    4. Grimm SL
    5. Rajapakshe K
    6. Nishiguchi T
    7. Reimann M
    8. Kirchner HL
    9. Kahari J
    10. Dlamini Q
    11. Lange C
    12. Goldmann T
    13. Marwitz S
    14. Cirillo JD
    15. Kaufmann SHE
    16. Netea MG
    17. van Crevel R
    18. Mandalakas AM
    19. Coarfa C
    20. DZIF-TB cohort study group

    (2022) Gene expression signatures identify biologically and clinically distinct tuberculosis endotypes

    The European Respiratory Journal 60:2102263.

    https://doi.org/10.1183/13993003.02263-2021

    • PubMed
    • Google Scholar
30. 1. Dinkele R
    2. Gessner S
    3. Patterson B
    4. McKerry A
    5. Hoosen Z
    6. Vazi A
    7. Seldon R
    8. Koch A
    9. Warner DF
    10. Wood R

    (2024) Persistent Mycobacterium tuberculosis bioaerosol release in a tuberculosis-endemic setting

    iScience 27:110731.

    https://doi.org/10.1016/j.isci.2024.110731

    • PubMed
    • Google Scholar
31. 1. Divangahi M
    2. Khan N
    3. Kaufmann E

    (2018) Beyond killing Mycobacterium tuberculosis: disease tolerance

    Frontiers in Immunology 9:2976.

    https://doi.org/10.3389/fimmu.2018.02976

    • PubMed
    • Google Scholar
32. 1. Dobson AP
    2. Carper ER

    (1996) Infectious diseases and human population history

    BioScience 46:115–126.

    https://doi.org/10.2307/1312814

    • Google Scholar
33. Book

    1. Dubos R
    2. Dubos J

    (1987)

    The White Plague: Tuberculosis, Man, and Society

    Rutgers University Press.

    • Google Scholar
34. 1. Dupuis S
    2. Dargemont C
    3. Fieschi C
    4. Thomassin N
    5. Rosenzweig S
    6. Harris J
    7. Holland SM
    8. Schreiber RD
    9. Casanova JL

    (2001) Impairment of mycobacterial but not viral immunity by a germline human STAT1 mutation

    Science 293:300–303.

    https://doi.org/10.1126/science.1061154

    • PubMed
    • Google Scholar
35. 1. Dutour O

    (2023) The paleopathology and paleoepidemiology of Upper paleolithic tuberculosis: review of evidence and hypotheses

    Tuberculosis 143S:102348.

    https://doi.org/10.1016/j.tube.2023.102348

    • PubMed
    • Google Scholar
36. 1. Eckold C
    2. Kumar V
    3. Weiner J
    4. Alisjahbana B
    5. Riza A-L
    6. Ronacher K
    7. Coronel J
    8. Kerry-Barnard S
    9. Malherbe ST
    10. Kleynhans L
    11. Stanley K
    12. Ruslami R
    13. Ioana M
    14. Ugarte-Gil C
    15. Walzl G
    16. van Crevel R
    17. Wijmenga C
    18. Critchley JA
    19. Dockrell HM
    20. Cliff JM
    21. TANDEM consortium

    (2021) Impact of intermediate hyperglycemia and diabetes on immune dysfunction in tuberculosis

    Clinical Infectious Diseases 72:69–78.

    https://doi.org/10.1093/cid/ciaa751

    • PubMed
    • Google Scholar
37. 1. Elkington Paul
    2. Shiomi T
    3. Breen R
    4. Nuttall RK
    5. Ugarte-Gil CA
    6. Walker NF
    7. Saraiva L
    8. Pedersen B
    9. Mauri F
    10. Lipman M
    11. Edwards DR
    12. Robertson BD
    13. D’Armiento J
    14. Friedland JS

    (2011) MMP-1 drives immunopathology in human tuberculosis and transgenic mice

    The Journal of Clinical Investigation 121:1827–1833.

    https://doi.org/10.1172/JCI45666

    • PubMed
    • Google Scholar
38. 1. Elkington PT
    2. Friedland JS

    (2015) Permutations of time and place in tuberculosis

    The Lancet. Infectious Diseases 15:1357–1360.

    https://doi.org/10.1016/S1473-3099(15)00135-8

    • PubMed
    • Google Scholar
39. 1. Elkington PT
    2. Bateman AC
    3. Thomas GJ
    4. Ottensmeier CH

    (2018) Implications of tuberculosis reactivation after immune checkpoint inhibition

    American Journal of Respiratory and Critical Care Medicine 198:1451–1453.

    https://doi.org/10.1164/rccm.201807-1250LE

    • PubMed
    • Google Scholar
40. 1. Elkington P
    2. Lerm M
    3. Kapoor N
    4. Mahon R
    5. Pienaar E
    6. Huh D
    7. Kaushal D
    8. Schlesinger LS

    (2019) In vitro granuloma models of tuberculosis: potential and challenges

    The Journal of Infectious Diseases 219:1858–1866.

    https://doi.org/10.1093/infdis/jiz020

    • PubMed
    • Google Scholar
41. 1. Elkington P
    2. Polak ME
    3. Reichmann MT
    4. Leslie A

    (2022) Understanding the tuberculosis granuloma: the matrix revolutions

    Trends in Molecular Medicine 28:143–154.

    https://doi.org/10.1016/j.molmed.2021.11.004

    • PubMed
    • Google Scholar
42. 1. Ewald JD
    2. Zhou G
    3. Lu Y
    4. Kolic J
    5. Ellis C
    6. Johnson JD
    7. Macdonald PE
    8. Xia J

    (2024) Web-based multi-omics integration using the Analyst software suite

    Nature Protocols 19:1467–1497.

    https://doi.org/10.1038/s41596-023-00950-4

    • PubMed
    • Google Scholar
43. 1. Fletcher HA
    2. Filali-Mouhim A
    3. Nemes E
    4. Hawkridge A
    5. Keyser A
    6. Njikan S
    7. Hatherill M
    8. Scriba TJ
    9. Abel B
    10. Kagina BM
    11. Veldsman A
    12. Agudelo NM
    13. Kaplan G
    14. Hussey GD
    15. Sekaly R-P
    16. Hanekom WA
    17. BCG study team

    (2016) Human newborn bacille Calmette-Guérin vaccination and risk of tuberculosis disease: a case-control study

    BMC Medicine 14:76.

    https://doi.org/10.1186/s12916-016-0617-3

    • PubMed
    • Google Scholar
44. 1. Gagneux S

    (2012) Host-pathogen coevolution in human tuberculosis

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 367:850–859.

    https://doi.org/10.1098/rstb.2011.0316

    • PubMed
    • Google Scholar
45. 1. Garg M
    2. Karpinski M
    3. Matelska D
    4. Middleton L
    5. Burren OS
    6. Hu F
    7. Wheeler E
    8. Smith KR
    9. Fabre MA
    10. Mitchell J
    11. O’Neill A
    12. Ashley EA
    13. Harper AR
    14. Wang Q
    15. Dhindsa RS
    16. Petrovski S
    17. Vitsios D

    (2024) Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank

    Nature Genetics 56:1821–1831.

    https://doi.org/10.1038/s41588-024-01898-1

    • PubMed
    • Google Scholar
46. 1. Garnier T
    2. Eiglmeier K
    3. Camus J-C
    4. Medina N
    5. Mansoor H
    6. Pryor M
    7. Duthoy S
    8. Grondin S
    9. Lacroix C
    10. Monsempe C
    11. Simon S
    12. Harris B
    13. Atkin R
    14. Doggett J
    15. Mayes R
    16. Keating L
    17. Wheeler PR
    18. Parkhill J
    19. Barrell BG
    20. Cole ST
    21. Gordon SV
    22. Hewinson RG

    (2003) The complete genome sequence of Mycobacterium bovis

    PNAS 100:7877–7882.

    https://doi.org/10.1073/pnas.1130426100

    • PubMed
    • Google Scholar
47. Book

    1. Ghon A

    (1916)

    The Primary Lung Focus of Tuberculosis in Children

    J. & A. Churchill.

    • Google Scholar
48. 1. Ghonim MA
    2. Boyd DF
    3. Flerlage T
    4. Thomas PG

    (2023) Pulmonary inflammation and fibroblast immunoregulation: from bench to bedside

    The Journal of Clinical Investigation 133:e170499.

    https://doi.org/10.1172/JCI170499

    • PubMed
    • Google Scholar
49. 1. Gideon HP
    2. Hughes TK
    3. Tzouanas CN
    4. Wadsworth MH II
    5. Tu AA
    6. Gierahn TM
    7. Peters JM
    8. Hopkins FF
    9. Wei JR
    10. Kummerlowe C
    11. Grant NL
    12. Nargan K
    13. Phuah JY
    14. Borish HJ
    15. Maiello P
    16. White AG
    17. Winchell CG
    18. Nyquist SK
    19. Ganchua SKC
    20. Myers A
    21. Patel KV
    22. Ameel CL
    23. Cochran CT
    24. Ibrahim S
    25. Tomko JA
    26. Frye LJ
    27. Rosenberg JM
    28. Shih A
    29. Chao M
    30. Klein E
    31. Scanga CA
    32. Ordovas-Montanes J
    33. Berger B
    34. Mattila JT
    35. Madansein R
    36. Love JC
    37. Lin PL
    38. Leslie A
    39. Behar SM
    40. Bryson B
    41. Flynn JL
    42. Fortune SM
    43. Shalek AK

    (2022) Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control

    Immunity 55:827–846.

    https://doi.org/10.1016/j.immuni.2022.04.004

    • PubMed
    • Google Scholar
50. 1. Goig GA
    2. Windels EM
    3. Loiseau C
    4. Stritt C
    5. Biru L
    6. Borrell S
    7. Brites D
    8. Gagneux S

    (2025) Ecology, global diversity and evolutionary mechanisms in the Mycobacterium tuberculosis complex

    Nature Reviews. Microbiology 23:602–614.

    https://doi.org/10.1038/s41579-025-01159-w

    • PubMed
    • Google Scholar
51. 1. Gros P
    2. Casanova JL

    (2023) Reconciling mouse and human immunology at the altar of genetics

    Annual Review of Immunology 41:39–71.

    https://doi.org/10.1146/annurev-immunol-101721-065201

    • PubMed
    • Google Scholar
52. 1. Halama A
    2. Zaghlool S
    3. Thareja G
    4. Kader S
    5. Al Muftah W
    6. Mook-Kanamori M
    7. Sarwath H
    8. Mohamoud YA
    9. Stephan N
    10. Ameling S
    11. Pucic Baković M
    12. Krumsiek J
    13. Prehn C
    14. Adamski J
    15. Schwenk JM
    16. Friedrich N
    17. Völker U
    18. Wuhrer M
    19. Lauc G
    20. Najafi-Shoushtari SH
    21. Malek JA
    22. Graumann J
    23. Mook-Kanamori D
    24. Schmidt F
    25. Suhre K

    (2024) A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

    Nature Communications 15:7111.

    https://doi.org/10.1038/s41467-024-51134-x

    • PubMed
    • Google Scholar
53. 1. Hilligan KL
    2. Namasivayam S
    3. Clancy CS
    4. O’Mard D
    5. Oland SD
    6. Robertson SJ
    7. Baker PJ
    8. Castro E
    9. Garza NL
    10. Lafont BAP
    11. Johnson R
    12. Ronchese F
    13. Mayer-Barber KD
    14. Best SM
    15. Sher A

    (2022) Intravenous administration of BCG protects mice against lethal SARS-CoV-2 challenge

    The Journal of Experimental Medicine 219:e20211862.

    https://doi.org/10.1084/jem.20211862

    • PubMed
    • Google Scholar
54. 1. Horton KC
    2. MacPherson P
    3. Houben RMGJ
    4. White RG
    5. Corbett EL

    (2016) Sex differences in tuberculosis burden and notifications in low- and middle-income countries: a systematic review and meta-analysis

    PLOS Medicine 13:e1002119.

    https://doi.org/10.1371/journal.pmed.1002119

    • PubMed
    • Google Scholar
55. 1. Houben R
    2. Dodd PJ

    (2016) The global burden of latent tuberculosis infection: a re-estimation using mathematical modelling

    PLOS Medicine 13:e1002152.

    https://doi.org/10.1371/journal.pmed.1002152

    • PubMed
    • Google Scholar
56. 1. Hunter RL

    (2016) Tuberculosis as a three-act play: a new paradigm for the pathogenesis of pulmonary tuberculosis

    Tuberculosis 97:8–17.

    https://doi.org/10.1016/j.tube.2015.11.010

    • PubMed
    • Google Scholar
57. 1. Jouanguy E
    2. Altare F
    3. Lamhamedi S
    4. Revy P
    5. Emile JF
    6. Newport M
    7. Levin M
    8. Blanche S
    9. Seboun E
    10. Fischer A
    11. Casanova JL

    (1996) Interferon-gamma-receptor deficiency in an infant with fatal bacille Calmette-Guérin infection

    The New England Journal of Medicine 335:1956–1961.

    https://doi.org/10.1056/NEJM199612263352604

    • PubMed
    • Google Scholar
58. 1. Jurczak M
    2. Druszczynska M

    (2025) Beyond tuberculosis: the surprising immunological benefits of the Bacillus Calmette-Guérin (BCG) vaccine in infectious, auto-immune, and inflammatory diseases

    Pathogens 14:196.

    https://doi.org/10.3390/pathogens14020196

    • PubMed
    • Google Scholar
59. 1. Kauffman KD
    2. Sakai S
    3. Lora NE
    4. Namasivayam S
    5. Baker PJ
    6. Kamenyeva O
    7. Foreman TW
    8. Nelson CE
    9. Oliveira-de-Souza D
    10. Vinhaes CL
    11. Yaniv Z
    12. Lindestam Arleham CS
    13. Sette A
    14. Freeman GJ
    15. Moore R
    16. Sher A
    17. Mayer-Barber KD
    18. Andrade BB
    19. Kabat J
    20. Via LE
    21. Barber DL
    22. NIAID/DIR Tuberculosis Imaging Program

    (2021) PD-1 blockade exacerbates Mycobacterium tuberculosis infection in rhesus macaques

    Science Immunology 6:eabf3861.

    https://doi.org/10.1126/sciimmunol.abf3861

    • PubMed
    • Google Scholar
60. 1. Kaufmann SHE

    (2003) A short history of Robert Koch’s fight against tuberculosis: those who do not remember the past are condemned to repeat it

    Tuberculosis 83:86–90.

    https://doi.org/10.1016/s1472-9792(02)00064-1

    • PubMed
    • Google Scholar
61. 1. Kaufmann E
    2. Sanz J
    3. Dunn JL
    4. Khan N
    5. Mendonça LE
    6. Pacis A
    7. Tzelepis F
    8. Pernet E
    9. Dumaine A
    10. Grenier JC
    11. Mailhot-Léonard F
    12. Ahmed E
    13. Belle J
    14. Besla R
    15. Mazer B
    16. King IL
    17. Nijnik A
    18. Robbins CS
    19. Barreiro LB
    20. Divangahi M

    (2018) BCG educates hematopoietic stem cells to generate protective innate immunity against tuberculosis

    Cell 172:176–190.

    https://doi.org/10.1016/j.cell.2017.12.031

    • PubMed
    • Google Scholar
62. 1. Kay GL
    2. Sergeant MJ
    3. Zhou Z
    4. Chan JZ-M
    5. Millard A
    6. Quick J
    7. Szikossy I
    8. Pap I
    9. Spigelman M
    10. Loman NJ
    11. Achtman M
    12. Donoghue HD
    13. Pallen MJ

    (2015) Eighteenth-century genomes show that mixed infections were common at time of peak tuberculosis in Europe

    Nature Communications 6:6717.

    https://doi.org/10.1038/ncomms7717

    • PubMed
    • Google Scholar
63. 1. Keane J
    2. Gershon S
    3. Wise RP
    4. Mirabile-Levens E
    5. Kasznica J
    6. Schwieterman WD
    7. Siegel JN
    8. Braun MM

    (2001) Tuberculosis associated with infliximab, a tumor necrosis factor alpha-neutralizing agent

    The New England Journal of Medicine 345:1098–1104.

    https://doi.org/10.1056/NEJMoa011110

    • PubMed
    • Google Scholar
64. 1. Khan N
    2. Downey J
    3. Sanz J
    4. Kaufmann E
    5. Blankenhaus B
    6. Pacis A
    7. Pernet E
    8. Ahmed E
    9. Cardoso S
    10. Nijnik A
    11. Mazer B
    12. Sassetti C
    13. Behr MA
    14. Soares MP
    15. Barreiro LB
    16. Divangahi M

    (2020) M. tuberculosis reprograms hematopoietic stem cells to limit myelopoiesis and impair trained immunity

    Cell 183:752–770.

    https://doi.org/10.1016/j.cell.2020.09.062

    • PubMed
    • Google Scholar
65. 1. Koleske BN
    2. Jacobs WR Jr
    3. Bishai WR

    (2023) The Mycobacterium tuberculosis genome at 25 years: lessons and lingering questions

    The Journal of Clinical Investigation 133:e173156.

    https://doi.org/10.1172/JCI173156

    • PubMed
    • Google Scholar
66. 1. Kwan CK
    2. Ernst JD

    (2011) HIV and tuberculosis: a deadly human syndemic

    Clinical Microbiology Reviews 24:351–376.

    https://doi.org/10.1128/CMR.00042-10

    • PubMed
    • Google Scholar
67. 1. Lázár-Molnár E
    2. Chen B
    3. Sweeney KA
    4. Wang EJ
    5. Liu W
    6. Lin J
    7. Porcelli SA
    8. Almo SC
    9. Nathenson SG
    10. Jacobs WR Jr

    (2010) Programmed death-1 (PD-1)-deficient mice are extraordinarily sensitive to tuberculosis

    PNAS 107:13402–13407.

    https://doi.org/10.1073/pnas.1007394107

    • PubMed
    • Google Scholar
68. 1. Ledesma JR
    2. Ma J
    3. Zheng P
    4. Ross JM
    5. Vos T
    6. Kyu HH

    (2021) Interferon-gamma release assay levels and risk of progression to active tuberculosis: a systematic review and dose-response meta-regression analysis

    BMC Infectious Diseases 21:467.

    https://doi.org/10.1186/s12879-021-06141-4

    • PubMed
    • Google Scholar
69. Book

    1. Little LK

    (2007) Plague and the End of Antiquity: The Pandemic of 541-750

    Cambridge University Press.

    https://doi.org/10.1017/CBO9780511812934

    • Google Scholar
70. 1. Liu K
    2. Wang D
    3. Yao C
    4. Qiao M
    5. Li Q
    6. Ren W
    7. Li S
    8. Gao M
    9. Pang Y

    (2022) Increased tuberculosis incidence due to immunotherapy based on PD-1 and PD-L1 blockade: a systematic review and meta-analysis

    Frontiers in Immunology 13:727220.

    https://doi.org/10.3389/fimmu.2022.727220

    • PubMed
    • Google Scholar
71. 1. Lu LL
    2. Smith MT
    3. Yu KKQ
    4. Luedemann C
    5. Suscovich TJ
    6. Grace PS
    7. Cain A
    8. Yu WH
    9. McKitrick TR
    10. Lauffenburger D
    11. Cummings RD
    12. Mayanja-Kizza H
    13. Hawn TR
    14. Boom WH
    15. Stein CM
    16. Fortune SM
    17. Seshadri C
    18. Alter G

    (2019) IFN-γ-independent immune markers of Mycobacterium tuberculosis exposure

    Nature Medicine 25:977–987.

    https://doi.org/10.1038/s41591-019-0441-3

    • PubMed
    • Google Scholar
72. 1. Luo Y
    2. Huang CC
    3. Howard NC
    4. Wang X
    5. Liu Q
    6. Li X
    7. Zhu J
    8. Amariuta T
    9. Asgari S
    10. Ishigaki K
    11. Calderon R
    12. Raman S
    13. Ramnarine AK
    14. Mayfield JA
    15. Moody DB
    16. Lecca L
    17. Fortune SM
    18. Murray MB
    19. Raychaudhuri S

    (2024) Paired analysis of host and pathogen genomes identifies determinants of human tuberculosis

    Nature Communications 15:10393.

    https://doi.org/10.1038/s41467-024-54741-w

    • Google Scholar
73. 1. Mangtani P
    2. Abubakar I
    3. Ariti C
    4. Beynon R
    5. Pimpin L
    6. Fine PEM
    7. Rodrigues LC
    8. Smith PG
    9. Lipman M
    10. Whiting PF
    11. Sterne JA

    (2014) Protection by BCG vaccine against tuberculosis: a systematic review of randomized controlled trials

    Clinical Infectious Diseases 58:470–480.

    https://doi.org/10.1093/cid/cit790

    • PubMed
    • Google Scholar
74. 1. Mann M
    2. Kumar C
    3. Zeng WF
    4. Strauss MT

    (2021) Artificial intelligence for proteomics and biomarker discovery

    Cell Systems 12:759–770.

    https://doi.org/10.1016/j.cels.2021.06.006

    • PubMed
    • Google Scholar
75. 1. Marakalala MJ
    2. Raju RM
    3. Sharma K
    4. Zhang YJ
    5. Eugenin EA
    6. Prideaux B
    7. Daudelin IB
    8. Chen PY
    9. Booty MG
    10. Kim JH
    11. Eum SY
    12. Via LE
    13. Behar SM
    14. Barry CE
    15. Mann M
    16. Dartois V
    17. Rubin EJ

    (2016) Inflammatory signaling in human tuberculosis granulomas is spatially organized

    Nature Medicine 22:531–538.

    https://doi.org/10.1038/nm.4073

    • PubMed
    • Google Scholar
76. 1. McCaffrey EF
    2. Donato M
    3. Keren L
    4. Chen Z
    5. Delmastro A
    6. Fitzpatrick MB
    7. Gupta S
    8. Greenwald NF
    9. Baranski A
    10. Graf W
    11. Kumar R
    12. Bosse M
    13. Fullaway CC
    14. Ramdial PK
    15. Forgó E
    16. Jojic V
    17. Van Valen D
    18. Mehra S
    19. Khader SA
    20. Bendall SC
    21. van de Rijn M
    22. Kalman D
    23. Kaushal D
    24. Hunter RL
    25. Banaei N
    26. Steyn AJC
    27. Khatri P
    28. Angelo M

    (2022) The immunoregulatory landscape of human tuberculosis granulomas

    Nature Immunology 23:318–329.

    https://doi.org/10.1038/s41590-021-01121-x

    • PubMed
    • Google Scholar
77. 1. Moorlag SJCFM
    2. Arts RJW
    3. van Crevel R
    4. Netea MG

    (2019) Non-specific effects of BCG vaccine on viral infections

    Clinical Microbiology and Infection 25:1473–1478.

    https://doi.org/10.1016/j.cmi.2019.04.020

    • PubMed
    • Google Scholar
78. 1. Nathan C

    (2023) Mycobacterium tuberculosis as teacher

    Nature Microbiology 8:1606–1608.

    https://doi.org/10.1038/s41564-023-01454-3

    • PubMed
    • Google Scholar
79. 1. O’Garra A
    2. Redford PS
    3. McNab FW
    4. Bloom CI
    5. Wilkinson RJ
    6. Berry MPR

    (2013) The immune response in tuberculosis

    Annual Review of Immunology 31:475–527.

    https://doi.org/10.1146/annurev-immunol-032712-095939

    • PubMed
    • Google Scholar
80. 1. Olive AJ
    2. Sassetti CM

    (2018) Tolerating the unwelcome guest; how the host withstands persistent Mycobacterium tuberculosis

    Frontiers in Immunology 9:2094.

    https://doi.org/10.3389/fimmu.2018.02094

    • PubMed
    • Google Scholar
81. 1. Ong CWM
    2. Elkington PT
    3. Brilha S
    4. Ugarte-Gil C
    5. Tome-Esteban MT
    6. Tezera LB
    7. Pabisiak PJ
    8. Moores RC
    9. Sathyamoorthy T
    10. Patel V
    11. Gilman RH
    12. Porter JC
    13. Friedland JS

    (2015) Neutrophil-Derived MMP-8 Drives AMPK-dependent matrix destruction in human pulmonary tuberculosis

    PLOS Pathogens 11:e1004917.

    https://doi.org/10.1371/journal.ppat.1004917

    • PubMed
    • Google Scholar
82. 1. Pagán AJ
    2. Ramakrishnan L

    (2018) The formation and function of granulomas

    Annual Review of Immunology 36:639–665.

    https://doi.org/10.1146/annurev-immunol-032712-100022

    • PubMed
    • Google Scholar
83. 1. Palla G
    2. Fischer DS
    3. Regev A
    4. Theis FJ

    (2022) Spatial components of molecular tissue biology

    Nature Biotechnology 40:308–318.

    https://doi.org/10.1038/s41587-021-01182-1

    • PubMed
    • Google Scholar
84. 1. Patterson B
    2. Dinkele R
    3. Gessner S
    4. Koch A
    5. Hoosen Z
    6. January V
    7. Leonard B
    8. McKerry A
    9. Seldon R
    10. Vazi A
    11. Hermans S
    12. Cobelens F
    13. Warner DF
    14. Wood R

    (2024) Aerosolization of viable Mycobacterium tuberculosis bacilli by tuberculosis clinic attendees independent of sputum-Xpert Ultra status

    PNAS 121:e2314813121.

    https://doi.org/10.1073/pnas.2314813121

    • PubMed
    • Google Scholar
85. 1. Pittet LF
    2. Messina NL
    3. Orsini F
    4. Moore CL
    5. Abruzzo V
    6. Barry S
    7. Bonnici R
    8. Bonten M
    9. Campbell J
    10. Croda J
    11. Dalcolmo M
    12. Gardiner K
    13. Gell G
    14. Germano S
    15. Gomes-Silva A
    16. Goodall C
    17. Gwee A
    18. Jamieson T
    19. Jardim B
    20. Kollmann TR
    21. Lacerda MVG
    22. Lee KJ
    23. Lucas M
    24. Lynn DJ
    25. Manning L
    26. Marshall HS
    27. McDonald E
    28. Munns CF
    29. Nicholson S
    30. O’Connell A
    31. de Oliveira RD
    32. Perlen S
    33. Perrett KP
    34. Prat-Aymerich C
    35. Richmond PC
    36. Rodriguez-Baño J
    37. Dos Santos G
    38. da Silva PV
    39. Teo JW
    40. Villanueva P
    41. Warris A
    42. Wood NJ
    43. Davidson A
    44. Curtis N
    45. BRACE Trial Consortium Group

    (2023) Randomized trial of BCG vaccine to protect against Covid-19 in health care workers

    The New England Journal of Medicine 388:1582–1596.

    https://doi.org/10.1056/NEJMoa2212616

    • PubMed
    • Google Scholar
86. 1. Proulx MK
    2. Wiggins CD
    3. Reames CJ
    4. Wu C
    5. Kiritsy MC
    6. Xu P
    7. Gallant JC
    8. Grace PS
    9. Fenderson BA
    10. Smith CM
    11. Lindestam Arlehamn CS
    12. Alter G
    13. Lauffenburger DA
    14. Sassetti CM

    (2025) Noncanonical T cell responses are associated with protection from tuberculosis in mice and humans

    The Journal of Experimental Medicine 222:e20241760.

    https://doi.org/10.1084/jem.20241760

    • PubMed
    • Google Scholar
87. 1. Reid M
    2. Agbassi YJP
    3. Arinaminpathy N
    4. Bercasio A
    5. Bhargava A
    6. Bhargava M
    7. Bloom A
    8. Cattamanchi A
    9. Chaisson R
    10. Chin D
    11. Churchyard G
    12. Cox H
    13. Denkinger CM
    14. Ditiu L
    15. Dowdy D
    16. Dybul M
    17. Fauci A
    18. Fedaku E
    19. Gidado M
    20. Harrington M
    21. Hauser J
    22. Heitkamp P
    23. Herbert N
    24. Herna Sari A
    25. Hopewell P
    26. Kendall E
    27. Khan A
    28. Kim A
    29. Koek I
    30. Kondratyuk S
    31. Krishnan N
    32. Ku C-C
    33. Lessem E
    34. McConnell EV
    35. Nahid P
    36. Oliver M
    37. Pai M
    38. Raviglione M
    39. Ryckman T
    40. Schäferhoff M
    41. Silva S
    42. Small P
    43. Stallworthy G
    44. Temesgen Z
    45. van Weezenbeek K
    46. Vassall A
    47. Velásquez GE
    48. Venkatesan N
    49. Yamey G
    50. Zimmerman A
    51. Jamison D
    52. Swaminathan S
    53. Goosby E

    (2023) Scientific advances and the end of tuberculosis: a report from the Lancet Commission on Tuberculosis

    Lancet 402:1473–1498.

    https://doi.org/10.1016/S0140-6736(23)01379-X

    • PubMed
    • Google Scholar
88. 1. Rieckmann A
    2. Villumsen M
    3. Sørup S
    4. Haugaard LK
    5. Ravn H
    6. Roth A
    7. Baker JL
    8. Benn CS
    9. Aaby P

    (2017) Vaccinations against smallpox and tuberculosis are associated with better long-term survival: a Danish case-cohort study 1971-2010

    International Journal of Epidemiology 46:695–705.

    https://doi.org/10.1093/ije/dyw120

    • PubMed
    • Google Scholar
89. 1. Rosas Mejia O
    2. Gloag ES
    3. Li J
    4. Ruane-Foster M
    5. Claeys TA
    6. Farkas D
    7. Wang S-H
    8. Farkas L
    9. Xin G
    10. Robinson RT

    (2022) Mice infected with Mycobacterium tuberculosis are resistant to acute disease caused by secondary infection with SARS-CoV-2

    PLOS Pathogens 18:e1010093.

    https://doi.org/10.1371/journal.ppat.1010093

    • PubMed
    • Google Scholar
90. 1. Russell DG

    (2011) Mycobacterium tuberculosis and the intimate discourse of a chronic infection

    Immunological Reviews 240:252–268.

    https://doi.org/10.1111/j.1600-065X.2010.00984.x

    • PubMed
    • Google Scholar
91. 1. Ryckman TS
    2. Dowdy DW
    3. Kendall EA

    (2022) Infectious and clinical tuberculosis trajectories: Bayesian modeling with case finding implications

    PNAS 119:e2211045119.

    https://doi.org/10.1073/pnas.2211045119

    • PubMed
    • Google Scholar
92. 1. Sawyer AJ
    2. Patrick E
    3. Edwards J
    4. Wilmott JS
    5. Fielder T
    6. Yang Q
    7. Barber DL
    8. Ernst JD
    9. Britton WJ
    10. Palendira U
    11. Chen X
    12. Feng CG

    (2023) Spatial mapping reveals granuloma diversity and histopathological superstructure in human tuberculosis

    The Journal of Experimental Medicine 220:e20221392.

    https://doi.org/10.1084/jem.20221392

    • PubMed
    • Google Scholar
93. 1. Schmidt AC
    2. Fairlie L
    3. Hellström E
    4. Luabeya Kany Kany A
    5. Middelkoop K
    6. Naidoo K
    7. Nair G
    8. Gela A
    9. Nemes E
    10. Scriba TJ
    11. Cinar A
    12. Frahm N
    13. Mogg R
    14. Kaufman D
    15. Dunne MW
    16. Hatherill M
    17. BCG REVAX Study Team

    (2025) BCG revaccination for the prevention of Mycobacterium tuberculosis infection

    The New England Journal of Medicine 392:1789–1800.

    https://doi.org/10.1056/NEJMoa2412381

    • PubMed
    • Google Scholar
94. 1. Schurz H
    2. Naranbhai V
    3. Yates TA
    4. Gilchrist JJ
    5. Parks T
    6. Dodd PJ
    7. Möller M
    8. Hoal EG
    9. Morris AP
    10. Hill AVS
    11. International Tuberculosis Host Genetics Consortium

    (2024) Multi-ancestry meta-analysis of host genetic susceptibility to tuberculosis identifies shared genetic architecture

    eLife 13:e84394.

    https://doi.org/10.7554/eLife.84394

    • PubMed
    • Google Scholar
95. 1. Simmons JD
    2. Dill-McFarland KA
    3. Stein CM
    4. Van PT
    5. Chihota V
    6. Ntshiqa T
    7. Maenetje P
    8. Peterson GJ
    9. Benchek P
    10. Nsereko M
    11. Velen K
    12. Fielding KL
    13. Grant AD
    14. Gottardo R
    15. Mayanja-Kizza H
    16. Wallis RS
    17. Churchyard G
    18. Boom WH
    19. Hawn TR

    (2022) Monocyte transcriptional responses to Mycobacterium tuberculosis associate with resistance to tuberculin skin test and interferon gamma release assay conversion

    mSphere 7:e0015922.

    https://doi.org/10.1128/msphere.00159-22

    • PubMed
    • Google Scholar
96. 1. Smith CM
    2. Baker RE
    3. Proulx MK
    4. Mishra BB
    5. Long JE
    6. Park SW
    7. Lee H-N
    8. Kiritsy MC
    9. Bellerose MM
    10. Olive AJ
    11. Murphy KC
    12. Papavinasasundaram K
    13. Boehm FJ
    14. Reames CJ
    15. Meade RK
    16. Hampton BK
    17. Linnertz CL
    18. Shaw GD
    19. Hock P
    20. Bell TA
    21. Ehrt S
    22. Schnappinger D
    23. Pardo-Manuel de Villena F
    24. Ferris MT
    25. Ioerger TR
    26. Sassetti CM

    (2022) Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice

    eLife 11:e74419.

    https://doi.org/10.7554/eLife.74419

    • PubMed
    • Google Scholar
97. 1. Stensballe LG
    2. Nante E
    3. Jensen IP
    4. Kofoed P-E
    5. Poulsen A
    6. Jensen H
    7. Newport M
    8. Marchant A
    9. Aaby P

    (2005) Acute lower respiratory tract infections and respiratory syncytial virus in infants in Guinea-Bissau: a beneficial effect of BCG vaccination for girls community based case-control study

    Vaccine 23:1251–1257.

    https://doi.org/10.1016/j.vaccine.2004.09.006

    • PubMed
    • Google Scholar
98. 1. Sun L
    2. Guo W
    3. Guo L
    4. Chen X
    5. Zhou H
    6. Yan S
    7. Zhao G
    8. Bao H
    9. Wu X
    10. Shao Y
    11. Ying J
    12. Lin L

    (2024a) Molecular landscape and multi-omic measurements of heterogeneity in fetal adenocarcinoma of the lung

    NPJ Precision Oncology 8:99.

    https://doi.org/10.1038/s41698-024-00569-y

    • PubMed
    • Google Scholar
99. 1. Sun M
    2. Phan JM
    3. Kieswetter NS
    4. Huang H
    5. Yu KKQ
    6. Smith MT
    7. Liu YE
    8. Wang C
    9. Gupta S
    10. Obermoser G
    11. Maecker HT
    12. Krishnan A
    13. Suresh S
    14. Gupta N
    15. Rieck M
    16. Acs P
    17. Ghanizada M
    18. Chiou S-H
    19. Khatri P
    20. Boom WH
    21. Hawn TR
    22. Stein CM
    23. Mayanja-Kizza H
    24. Davis MM
    25. Seshadri C

    (2024b) Specific CD4⁺ T cell phenotypes associate with bacterial control in people who “resist” infection with Mycobacterium tuberculosis

    Nature Immunology 25:1411–1421.

    https://doi.org/10.1038/s41590-024-01897-8

    • PubMed
    • Google Scholar
100. 1. Swanson RV
     2. Gupta A
     3. Foreman TW
     4. Lu L
     5. Choreno-Parra JA
     6. Mbandi SK
     7. Rosa BA
     8. Akter S
     9. Das S
     10. Ahmed M
     11. Garcia-Hernandez MDLL
     12. Singh DK
     13. Esaulova E
     14. Artyomov MN
     15. Gommerman J
     16. Mehra S
     17. Zuniga J
     18. Mitreva M
     19. Scriba TJ
     20. Rangel-Moreno J
     21. Kaushal D
     22. Khader SA

     (2023) Antigen-specific B cells direct T follicular-like helper cells into lymphoid follicles to mediate Mycobacterium tuberculosis control

     Nature Immunology 24:855–868.

     https://doi.org/10.1038/s41590-023-01476-3

     • PubMed
     • Google Scholar
101. 1. Tait DR
     2. Hatherill M
     3. Van Der Meeren O
     4. Ginsberg AM
     5. Van Brakel E
     6. Salaun B
     7. Scriba TJ
     8. Akite EJ
     9. Ayles HM
     10. Bollaerts A
     11. Demoitié M-A
     12. Diacon A
     13. Evans TG
     14. Gillard P
     15. Hellström E
     16. Innes JC
     17. Lempicki M
     18. Malahleha M
     19. Martinson N
     20. Mesia Vela D
     21. Muyoyeta M
     22. Nduba V
     23. Pascal TG
     24. Tameris M
     25. Thienemann F
     26. Wilkinson RJ
     27. Roman F

     (2019) Final analysis of a trial of M72/AS01_E vaccine to prevent tuberculosis

     The New England Journal of Medicine 381:2429–2439.

     https://doi.org/10.1056/NEJMoa1909953

     • PubMed
     • Google Scholar
102. 1. Tarazona S
     2. Balzano-Nogueira L
     3. Gómez-Cabrero D
     4. Schmidt A
     5. Imhof A
     6. Hankemeier T
     7. Tegnér J
     8. Westerhuis JA
     9. Conesa A

     (2020) Harmonization of quality metrics and power calculation in multi-omic studies

     Nature Communications 11:3092.

     https://doi.org/10.1038/s41467-020-16937-8

     • PubMed
     • Google Scholar
103. 1. Tarazona S
     2. Arzalluz-Luque A
     3. Conesa A

     (2021) Undisclosed, unmet and neglected challenges in multi-omics studies

     Nature Computational Science 1:395–402.

     https://doi.org/10.1038/s43588-021-00086-z

     • PubMed
     • Google Scholar
104. 1. Tezera LB
     2. Bielecka MK
     3. Ogongo P
     4. Walker NF
     5. Ellis M
     6. Garay-Baquero DJ
     7. Thomas K
     8. Reichmann MT
     9. Johnston DA
     10. Wilkinson KA
     11. Ahmed M
     12. Jogai S
     13. Jayasinghe SN
     14. Wilkinson RJ
     15. Mansour S
     16. Thomas GJ
     17. Ottensmeier CH
     18. Leslie A
     19. Elkington PT

     (2020a) Anti-PD-1 immunotherapy leads to tuberculosis reactivation via dysregulation of TNF-α

     eLife 9:e52668.

     https://doi.org/10.7554/eLife.52668

     • PubMed
     • Google Scholar
105. 1. Tezera LB
     2. Mansour S
     3. Elkington P

     (2020b) Reconsidering the optimal immune response to Mycobacterium tuberculosis

     American Journal of Respiratory and Critical Care Medicine 201:407–413.

     https://doi.org/10.1164/rccm.201908-1506PP

     • PubMed
     • Google Scholar
106. 1. Toussaint PA
     2. Leiser F
     3. Thiebes S
     4. Schlesner M
     5. Brors B
     6. Sunyaev A

     (2023) Explainable artificial intelligence for omics data: a systematic mapping study

     Briefings in Bioinformatics 25:bbad453.

     https://doi.org/10.1093/bib/bbad453

     • PubMed
     • Google Scholar
107. 1. Trajman A
     2. Campbell JR
     3. Kunor T
     4. Ruslami R
     5. Amanullah F
     6. Behr MA
     7. Menzies D

     (2025) Tuberculosis

     The Lancet 405:850–866.

     https://doi.org/10.1016/S0140-6736(24)02479-6

     • Google Scholar
108. 1. Trunz BB
     2. Fine P
     3. Dye C

     (2006) Effect of BCG vaccination on childhood tuberculous meningitis and miliary tuberculosis worldwide: a meta-analysis and assessment of cost-effectiveness

     Lancet 367:1173–1180.

     https://doi.org/10.1016/S0140-6736(06)68507-3

     • PubMed
     • Google Scholar
109. 1. Vallini L
     2. Zampieri C
     3. Shoaee MJ
     4. Bortolini E
     5. Marciani G
     6. Aneli S
     7. Pievani T
     8. Benazzi S
     9. Barausse A
     10. Mezzavilla M
     11. Petraglia MD
     12. Pagani L

     (2024) The Persian plateau served as hub for Homo sapiens after the main out of Africa dispersal

     Nature Communications 15:1882.

     https://doi.org/10.1038/s41467-024-46161-7

     • PubMed
     • Google Scholar
110. 1. Verrall AJ
     2. Schneider M
     3. Alisjahbana B
     4. Apriani L
     5. van Laarhoven A
     6. Koeken VACM
     7. van Dorp S
     8. Diadani E
     9. Utama F
     10. Hannaway RF
     11. Indrati A
     12. Netea MG
     13. Sharples K
     14. Hill PC
     15. Ussher JE
     16. van Crevel R

     (2020) Early clearance of Mycobacterium tuberculosis is associated with increased innate immune responses

     The Journal of Infectious Diseases 221:1342–1350.

     https://doi.org/10.1093/infdis/jiz147

     • PubMed
     • Google Scholar
111. 1. Winchell CG
     2. Nyquist SK
     3. Chao MC
     4. Maiello P
     5. Myers AJ
     6. Hopkins F
     7. Chase M
     8. Gideon HP
     9. Patel KV
     10. Bromley JD
     11. Simonson AW
     12. Floyd-O’Sullivan R
     13. Wadsworth M
     14. Rosenberg JM
     15. Uddin R
     16. Hughes T
     17. Kelly RJ
     18. Griffo J
     19. Tomko J
     20. Klein E
     21. Berger B
     22. Scanga CA
     23. Mattila J
     24. Fortune SM
     25. Shalek AK
     26. Lin PL
     27. Flynn JL

     (2023) CD8+ lymphocytes are critical for early control of tuberculosis in macaques

     The Journal of Experimental Medicine 220:e20230707.

     https://doi.org/10.1084/jem.20230707

     • PubMed
     • Google Scholar
112. 1. Yenew B
     2. Ghodousi A
     3. Diriba G
     4. Tesfaye E
     5. Cabibbe AM
     6. Amare M
     7. Moga S
     8. Alemu A
     9. Dagne B
     10. Sinshaw W
     11. Mollalign H
     12. Meaza A
     13. Tadesse M
     14. Gamtesa DF
     15. Abebaw Y
     16. Seid G
     17. Zerihun B
     18. Getu M
     19. Chiacchiaretta M
     20. Gaudin C
     21. Marceau M
     22. Didelot X
     23. Tolera G
     24. Abdella S
     25. Kebede A
     26. Getahun M
     27. Mehammed Z
     28. Supply P
     29. Cirillo DM

     (2023) A smooth tubercle bacillus from Ethiopia phylogenetically close to the Mycobacterium tuberculosis complex

     Nature Communications 14:7519.

     https://doi.org/10.1038/s41467-023-42755-9

     • PubMed
     • Google Scholar
113. 1. Yoder MA
     2. Lamichhane G
     3. Bishai WR

     (2004)

     Cavitary pulmonary tuberculosis: The Holey Grail of disease transmission

     Current Science 86:74–81.

     • Google Scholar
114. 1. Young D

     (2009) Animal models of tuberculosis

     European Journal of Immunology 39:2011–2014.

     https://doi.org/10.1002/eji.200939542

     • PubMed
     • Google Scholar
115. 1. Zheng Y
     2. Liu Y
     3. Yang J
     4. Dong L
     5. Zhang R
     6. Tian S
     7. Yu Y
     8. Ren L
     9. Hou W
     10. Zhu F
     11. Mai Y
     12. Han J
     13. Zhang L
     14. Jiang H
     15. Lin L
     16. Lou J
     17. Li R
     18. Lin J
     19. Liu H
     20. Kong Z
     21. Wang D
     22. Dai F
     23. Bao D
     24. Cao Z
     25. Chen Q
     26. Chen Q
     27. Chen X
     28. Gao Y
     29. Jiang H
     30. Li B
     31. Li B
     32. Li J
     33. Liu R
     34. Qing T
     35. Shang E
     36. Shang J
     37. Sun S
     38. Wang H
     39. Wang X
     40. Zhang N
     41. Zhang P
     42. Zhang R
     43. Zhu S
     44. Scherer A
     45. Wang J
     46. Wang J
     47. Huo Y
     48. Liu G
     49. Cao C
     50. Shao L
     51. Xu J
     52. Hong H
     53. Xiao W
     54. Liang X
     55. Lu D
     56. Jin L
     57. Tong W
     58. Ding C
     59. Li J
     60. Fang X
     61. Shi L

     (2024) Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

     Nature Biotechnology 42:1133–1149.

     https://doi.org/10.1038/s41587-023-01934-1

     • PubMed
     • Google Scholar
116. 1. Zhu J
     2. He Z
     3. Liang D
     4. Yu X
     5. Qiu K
     6. Wu J

     (2022) Pulmonary tuberculosis associated with immune checkpoint inhibitors: a pharmacovigilance study

     Thorax 77:721–723.

     https://doi.org/10.1136/thoraxjnl-2021-217575

     • PubMed
     • Google Scholar

Author details

1. Michaela T Reichmann

   NIHR Biomedical Research Centre, Clinical and Experimental Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6714-8400

2. Liku B Tezera

   1. NIHR Biomedical Research Centre, Clinical and Experimental Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
   2. Institute for Life Sciences, Southampton, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7898-6709

3. Laura Denney

   NIHR Biomedical Research Centre, Clinical and Experimental Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

4. Hannah Schiff

   NIHR Biomedical Research Centre, Clinical and Experimental Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

5. Andres Vallejo

   1. NIHR Biomedical Research Centre, Clinical and Experimental Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
   2. Institute for Life Sciences, Southampton, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

6. Salah Mansour

   1. NIHR Biomedical Research Centre, Clinical and Experimental Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
   2. Institute for Life Sciences, Southampton, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5982-734X

7. Alasdair Leslie

   1. Africa Health Research Institute, Durban, South Africa
   2. College of Health Sciences, School of Laboratory Medicine and Medical Sciences, University of KwaZulu Natal, Durban, South Africa
   3. Department of Infection and Immunity, University College London, London, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

8. Diana J Garay-Baquero

   1. NIHR Biomedical Research Centre, Clinical and Experimental Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
   2. Institute for Life Sciences, Southampton, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9450-8504

9. Paul T Elkington

   1. NIHR Biomedical Research Centre, Clinical and Experimental Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
   2. Institute for Life Sciences, Southampton, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Writing – original draft

   For correspondence

   p.elkington@soton.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0390-0613

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