The gut-lung axis and microbiome dysbiosis in non-tuberculous mycobacterial infections: immune mechanisms, clinical implications, and therapeutic frontiers
- Open Access
- 01.12.2025
- Review
Erschienen in:
Abstract
Introduction
Tuberculosis (TB) remains the leading infectious cause of death globally, but in low-incidence countries such as Denmark, Scotland, the United States, and Canada, infections caused by non-tuberculous mycobacteria (NTM) now outnumber TB cases [1‐5]. NTM are environmental organisms increasingly responsible for opportunistic infections, presenting a growing public health concern [6, 7]. In response, several international guidelines—such as those by the American Thoracic Society (ATS), British Thoracic Society, and a 2020 consensus involving ATS, the European Respiratory Society (ERS), Infectious Diseases Society of America (IDSA), and European Society of Clinical Microbiology and Infectious Diseases (ESCMID)—have been developed to guide diagnosis and management [8‐10].
The rising incidence of NTM is attributed to improved diagnostic methods, a growing immunocompromised population (which increased from 2.7 to 6.6% in the U.S. from 2013 to 2021) [11], and increased survival in patients with cystic fibrosis [12].
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Environmental factors, particularly climate change, also play a role. Higher NTM prevalence is seen in tropical climates and regions with greater precipitation, evapotranspiration, and temperature [13‐15]. For instance, Hawaii shows a fourfold higher per capita NTM infection rate than the mainland U.S [14]. Activities like swimming and hiking in tropical regions further increase exposure risk [16]. Additionally, contaminated medical equipment such as heater-cooler devices and dialysis machines has been implicated in outbreaks involving M. chimaera and M. saskatchewanense [17‐20].
Over 200 NTM species exist, though only a subset are pathogenic. Rapidly growing mycobacteria (RGM) include the M. fortuitum, M. chelonae–abscessus, M. mucogenicum, and M. smegmatis groups, as well as early pigmenting and non-pigmented RGM species. Slow-growing mycobacteria (SGM) include clinically important species like the M. avium complex (MAC), M. kansasii, M. xenopi, and others [8, 21, 22].
NTM infections manifest in four main clinical forms [8]. Pulmonary disease, comprising over 90% of cases [23], presents with chronic cough, fatigue, and characteristic imaging patterns—fibrocavitary (typically in older men with COPD) and nodular/bronchiectatic (more common in women). MAC is the leading cause, followed by M. kansasii and M. abscessus [24], with notable contributions from M. chimaera and M. saskatchewanense [25, 26]. Pediatric cervical lymphadenitis is usually due to MAC and responds well to surgical excision [27]. Skin and soft tissue infections arise from trauma or contaminated water, with species like M. marinum, M. ulcerans, and rapidly growing NTM responsible such as M. abscessus, M. chelonae, and M. fortuitum [28‐31]. Disseminated infections, mainly affecting severely immunocompromised individuals (e.g., AIDS patients with CD4 < 50), are most often caused by MAC and characterized by systemic symptoms such as fever, weight loss, and hepatosplenomegaly [32]. Other causes include anti–interferon-γ autoantibodies or IL-12/IFN-γ pathway defects. RGM can also infect bones, joints, eyes, and indwelling devices [33].
Treatment is prolonged and often suboptimal. MAC therapy typically requires three antibiotics over 18 months, with culture conversion rates between 45 and 70% and relapse rates up to 60% [17]. Clinical trials listed on ClinicalTrials.gov show a focus on optimizing existing regimens and repurposing drugs like bedaquiline and linezolid [17, 34]. Treatment development is hindered by NTM’s biofilm formation, complex cell walls, and antimicrobial resistance, emphasizing the need for more targeted research and novel therapies [17, 35, 36].
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Literature search strategy
To support this narrative review, we conducted a structured literature search using PubMed, Scopus, and Web of Science databases. The search covered publications from January 2000 to March 2025. Search terms included combinations of (“non-tuberculous mycobacteria” OR “nontuberculous mycobacteria” OR “NTM”) AND (“microbiome” OR “microbiota” OR “gut-lung axis” OR “dysbiosis”). We included peer-reviewed original research articles and review written in English that focused on: NTM pathogenesis or clinical outcomes in relation to microbiome alterations, Gut-lung axis interactions relevant to NTM disease, or immunological mechanisms influenced by microbiota in NTM infection. We excluded case reports, non-English publications, editorials, and studies unrelated to microbiome-host interaction in the context of NTM. Articles were selected based on relevance, scientific quality, and their contribution to understanding the role of the microbiome in NTM disease (Fig. 1).
Fig. 1
Summary of the literature selection process for the relationship between NTM and Microbiome
Immune systems and NTM
Figure 2 illustrates the role of the immune system in NTM pathogenesis. T helper 1 (Th1) immunity was initially believed to protect against non-tuberculous mycobacterial lung disease (NTM-LD) [37]. Upon inhalation of aerosolized NTM, alveolar macrophages recognize mycobacterial components via pattern recognition receptors (PRRs), particularly toll-like receptors (TLRs), which activate downstream inflammatory pathways such as mitogen-activated protein kinases (MAPKs) and nuclear factor-κB (NF-κB). TLR2 is especially important, recognizing 19-kDa lipoproteins and glycolipids, while TLR4 and TLR9 detect heat shock proteins and CpG DNA [33, 38, 39].
Fig. 2
The red downward arrow indicates decreased expressiveness or activity, while the green or yellow upward arrow suggests an increase. The blue circle represents TH1 cells, which are involved in pro-inflammatory responses, including the production of IFN-γ and IL-12—both of which are reduced in NTM-LD. In NTM-LD, TH2 cells (depicted by a purple circle) drive antibody production and allergic responses, and are associated with elevated levels of TGF-β, IgE, and eosinophils. TH17 cells, marked by an orange circle, play a role in mucosal defense and inflammation, but are diminished in NTM-LD patients. IFN-γ and IL-12, key Th1 cytokines, are downregulated in NTM-LD. TGF-β, associated with Th2 responses, is upregulated. Th2-related biomarkers such as IgE and eosinophils are also elevated in NTM-LD. Additionally, NTM-LD and immune stimulation increase levels of the immunosuppressive cytokine IL-10. The disease also upregulates immune checkpoint molecules PD-1 and PD-L1, contributing to T-cell dysfunction and immune evasion
TLR2 has been most extensively studied, with genetic variations linked to MAC-LD susceptibility [40]. Polymorphisms and reduced transcription levels of the TLR2 gene have been strongly associated with susceptibility to MAC-LD [41]. Additionally, trehalose dimycolate activates inflammatory responses via both C-type lectin receptor and TLR2 [42].
Macrophages initiate the IL-12–IFN-γ axis, a critical pathway connecting innate and adaptive immunity, and its impairment increases vulnerability to NTM [43, 44]. Tumor necrosis factor-alpha (TNF-α), produced by natural killer (NK) cells and macrophages, is essential for granuloma formation and mycobacterial control [45]. Effective Th1 responses involving IL-12, IFN-γ, and TNF-α are central to containment, while IL-17 from Th17 cells enhances Th1 immunity and has antimycobacterial effects. T-bet-deficient mice show heightened inflammation and reduced MAC resistance due to impaired Th1 responses [39, 46].
Systemic immune responses in NTM-LD are less well understood, particularly in non-AIDS patients, with inconsistent findings [39]. Some studies report decreased Th1 cytokines (IFN-γ, IL-12) alongside increased Th2 cytokines like transforming growth factor-beta (TGF-β) and elevated IgE and eosinophils in MAC-LD, indicating a Th2-skewed response [40, 47, 48]. Immunosuppressive factors such as IL-10 and checkpoint markers PD-1/PD-L1 are also elevated, contributing to weakened Th1 and Th17 responses [47, 49]. MAC exposure induces PD-1 expression and reduces protective immunity. Winthrop et al. found IL-10 production by non-CD4 + T cells and diminished Th17 responses in NTM-LD patients with nodular bronchiectasis [50]. This suggests dysregulated T-cell responses may underlie systemic susceptibility [39].
However, most findings come from cross-sectional studies, limiting conclusions about causality. Longitudinal and mechanistic research is needed to clarify the role of host immunity in NTM-LD pathogenesis [39].
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Gut-lung axis
Immune system interactions
The gut microbiome is better understood than the lung microbiome, which was long considered sterile until recent advances disproved this notion [51]. These two systems are interconnected, influencing microbial composition and immune responses. While the gut hosts about 100 trillion microbes—especially in the colon—the lung microbiome is far less dense, with approximately 10–100 bacteria per 1,000 human cells [52].
Microbiome composition is shaped by early-life factors (e.g., delivery mode, gestational age, feeding type) and later influenced by lifestyle, diet, body mass index (BMI), exercise, and medications [53]. Despite relative stability, intra- and inter-individual variability impacts gut–lung axis (GLA) communication in health and disease [52].
Gastrointestinal diseases like inflammatory bowel disease (IBD), ulcerative colitis, and Crohn’s disease have been associated with respiratory illnesses such as asthma, COPD, and CF [54, 55]. Dysbiosis—a key feature of GLA interaction—is influenced by antibiotics, stress, diet, and metabolic disorders [56].
Immune balance across the gut and lungs depends on epithelial barrier integrity, microbiota diversity, macrophage activity, and T-cell regulation in the gut lamina propria. Dysbiosis can cause intestinal inflammation, epithelial apoptosis, tight junction disruption, and increased permeability. This allows inflammatory mediators and microbial metabolites (e.g., TNF-α, TGF-β, IL-1β, IL-5, IL-6, IL-8, IL-13, IL-17, IL-18, IL-33, and chemokines like CCL2, CCL3, CCL4, CCL7, CCL20, CXCL5, CXCL8, CXCL10, and RANTES) to enter circulation [52, 56]. These mediators travel via blood and lymphatic systems to affect distant organs like the lungs. Immune cell (neutrophils and T lymphocytes) migration is facilitated by molecules like CCR9 and integrin α4β7 on T lymphocytes [52, 56].
Gut-lung crosstalk modulates inflammation and immunity. Gut-derived lipopolysaccharide (LPS) can reach the lungs and cause acute lung injury via TLR4/NF-κB activation in alveolar macrophages [57]. Faecal microbiota transplantation (FMT) may reverse these effects by restoring beneficial short-chain fatty acid (SCFA)-producing bacteria like butyrate-producing bacteria such as Clostridiaceae, Erysipelotrichaeceae, Lachnospiraceaes, and Ruminococcaceae, which modulate immune responses and reduce asthma symptoms [58]. Gut-resident segmented filamentous bacteria (SFB) can promote lung inflammation via Th17 cells and are implicated in autoimmunity [58, 59]. In this context, several studies have investigated the impact of FMT on TB treatment. In a comprehensive review, fecal microbiota transplantation (FMT) is explored as a potential adjunctive treatment for TB, particularly intestinal TB (ITB), due to its effectiveness in modulating gut dysbiosis and immune responses in IBD. TB, especially ITB, alters the gut microbiota by reducing beneficial bacteria such as Firmicutes and Faecalibacterium (responsible for producing SCFAs), while increasing pro-inflammatory taxa like Proteobacteria, which exacerbates systemic inflammation and impairs immune function. Animal studies have shown that antibiotic-induced dysbiosis worsens TB outcomes, whereas FMT helps restore microbial balance, enhances Th1 immune responses (e.g., IFN-γ, TNF-α), and reduces pathogen load. Dysregulation of the IL-22/IL-17 axis and granuloma formation are immunological features common to both TB and IBD, further supporting the potential of FMT to restore immune homeostasis in TB. However, most evidence for FMT’s efficacy comes from murine models, highlighting the urgent need for clinical studies to establish its safety, effectiveness, and optimal application in humans. FMT may improve TB outcomes by correcting dysbiosis and modulating host immunity, potentially reducing drug resistance and treatment-related side effects [60].
A recent study proposes FMT as a potential adjunct therapy for extensively drug-resistant TB (XDR-TB), aiming to address the limitations of current treatments, which are often prolonged, toxic, and ineffective. FMT may enhance immune responses to M. tuberculosis through the gut-lung axis—a bidirectional link between gut and lung microbiota mediated by metabolites, cytokines, and immune cells. Anti-TB drugs can cause long-term gut dysbiosis, increasing the risk of reinfection and treatment failure. TB patients often show reduced microbial diversity and imbalances in beneficial (e.g., Bifidobacterium) and harmful (e.g., Proteobacteria) bacteria in the gut, lungs, and oral cavity, potentially impairing immune function. FMT, already proven safe and effective for recurrent Clostridium difficile infections, may help restore microbial balance and improve outcomes. Though generally safe (with < 2% severe adverse events reported in other conditions), its safety and efficacy in XDR-TB remain untested. Pilot studies are needed to assess feasibility, followed by randomized controlled trials to evaluate clinical outcomes such as sputum conversion, survival, and microbiome restoration. While promising, FMT requires rigorous clinical validation before being integrated into TB treatment protocols [61].
Microbiological crosstalk
The gut microbiota plays essential roles in digestion, nutrient absorption, intestinal barrier integrity, vitamin synthesis, and immune system modulation by promoting tolerance to antigens and supporting systemic immunity [65, 66]. It also influences lung microbiota composition, respiratory immune responses, and homeostasis [67‐69].
Anatomically and embryologically connected through the mouth, pharynx, and mucosal surfaces, the gastrointestinal and respiratory tracts communicate via the bloodstream and lymphatic system, allowing microbial and immunological crosstalk [70].
This gut–lung interaction is mediated by microbial cross-feeding, SCFA production, and immune cell activation. Key gut microbes such as Ruminococcus bromii and Eubacterium rectale ferment starch into SCFAs—acetate, propionate, and butyrate—that support metabolic regulation, oxidative stress control, epithelial barrier integrity, and immune modulation in both gut and lung [66, 67, 69, 71, 72].
Though SCFA levels in the lungs are relatively low, they exert systemic effects by acting on immune cells in the gut-associated lymphoid tissue, which can migrate to the lungs [70]. SCFAs also influence bone marrow hematopoiesis, enhancing immune cell production. In mice, dietary propionate boosted phagocytic activity of macrophages and dendritic cell precursors, protecting against allergic airway inflammation without promoting Th2 responses [73, 74].
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Experimental data—mostly from animal studies—supports gut–lung immunomodulation. For instance, oral Lactobacillus plantarum administration in dysbiotic mice upregulated C-type lectin and MHC II expression in lung dendritic cells, activating memory CD4 + T cells and reducing Mycobacterium tuberculosis burden [75].
The COVID-19 pandemic further highlighted gut–lung interactions. SARS-CoV-2 infection disrupted gut microbiota, depleting beneficial taxa (e.g., Faecalibacterium, Eubacterium, Roseburia, Lactobacillus) while increasing opportunistic pathogens (Enterobacterales, Enterococcus). These imbalances correlated with worse clinical outcomes and heightened inflammation [76, 77].
Thus, gut microbiome disruptions—via antibiotics, diet, or disease—can impair gastrointestinal and respiratory health, exacerbating chronic lung conditions and acute infections [52].
Lung and gut microbiota composition
Figure 3 depicts the bidirectional interactions within the gut-lung axis. The gastrointestinal (GI) and respiratory tracts both host complex microbial ecosystems, but the gut microbiota is significantly more abundant and diverse. Comprising trillions of bacteria, archaea, fungi, viruses, and protozoa, it is the most densely populated microbial habitat in the human body [78, 79]. Although once thought to begin colonization only after birth, recent evidence suggests that microbial exposure may start in utero—via the placenta, amniotic fluid, and fetal membranes [80].
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Fig. 3
This figure illustrates the interconnected roles of the respiratory microbiota, local lung immune responses, and gut-derived signals in pulmonary health. Microbial metabolites, immune cells, and signaling molecules—particularly those produced in the stomach and intestines—contribute to lung immunity and inflammation regulation. Red arrows in the image indicate the circulatory system as the main pathway through which cytokines, short-chain fatty acids (SCFAs), lipopolysaccharides (LPS), and immune cells travel from the gut to the lungs. Blue arrows represent the lymphatic system, which facilitates immune communication and cellular trafficking across organs. The yellow arrow marks the vagus nerve, a critical neuroimmune conduit that transmits neurotransmitters and neuropeptides between the gastrointestinal tract and the lungs. The figure highlights several beneficial gut microbes. SCFA-producing bacteria such as Ruminococcus bromii, Eubacterium rectale, and members of the Ruminococcaceae family support immune homeostasis between the gut and lungs. Additional bacteria, including Lachnospiraceae and Erysipelotrichaceae, contribute to butyrate production and help modulate inflammation. Ruminospiraceae supports both SCFA production and microbial cross-feeding interactions. Molecular mediators like SCFAs—including acetate, propionate, and butyrate—help reduce inflammation and regulate immune function in both the gut and lungs. Cytokines such as IL-1β, IL-6, and TNF-α serve as key signaling molecules initiating immune responses. T cells activated in the gut express CCR9 and integrin α4β7 receptors, allowing their migration to the lungs. Lung epithelial cells, in turn, express pattern recognition receptors (PRRs) such as Toll-like receptors (TLRs), which detect gut-derived microbial components known as pathogen-associated molecular patterns (PAMPs). Finally, the figure depicts a feedback loop in which lung inflammation or infection negatively influences gut health. This bidirectional communication includes lung-derived interferons and systemic inflammatory responses that alter the gut microbiota and compromise intestinal barrier integrity, emphasizing the integrated nature of the gut-lung axis
Meconium studies confirm the presence of Firmicutes, including Bacillus, Staphylococcus, Streptococcus mitis, Escherichia fergusonii, and Lactobacillus [81, 82], with microbial composition varying by maternal health conditions such as diabetes [80].
Feeding method heavily influences postnatal gut microbiota development. Breastfeeding enriches beneficial genera like Bifidobacterium and Lactobacillus, Staphylococcus, and Streptococcus, while formula feeding is associated with higher levels of Bacteroides, Clostridium, and Proteobacteria [83]. In adults, the gut microbiota is relatively stable, with Firmicutes and Bacteroidetes making up 90% of the population. Firmicutes include Streptococcus, Eubacterium, Ruminococcus, Lactobacillus, Enterococcus, Veillonella, and Clostridium, while Bacteroides and Prevotella dominate the Bacteroidetes phylum, along with Bifidobacterium from Actinobacteria [70, 79].
Diet is a key determinant of microbial composition. A Western diet high in animal protein, refined carbs, and fats reduces SCFA-producing bacteria like Bifidobacterium, Eubacterium, and Roseburia, while increasing Bacteroides and Clostridium [78, 84]. In contrast, a Mediterranean diet promotes microbial diversity and the abundance of beneficial taxa such as Lactobacillus, Bifidobacterium, and Prevotella, and reduces Bacteroides fragilis and Clostridium spp [78, 79]. Microbes like Ruminococcus bromii and Eubacterium rectale contribute to SCFA production—e.g., acetate, formate, and butyrate—through dietary substrate fermentation and microbial cross-feeding [71].
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In contrast, the lung microbiota is much less dense and largely shaped by environmental exposure and microbial migration from the upper airways or the gut [85, 86]. Despite its lower abundance, it plays an essential role in respiratory health and immune regulation [52].
The respiratory tract microbiota is dominated by four phyla: Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes. In the upper respiratory tract (nasal cavity, oropharynx), common genera include Streptococcus, Staphylococcus, Haemophilus, Fusobacterium, Moraxella, Neisseria, Corynebacterium, Alloprevotella, and Dolosigranulum [87, 88]. The lower respiratory tract is dominated by Prevotella (up to 50%), along with Streptococcus, Veillonella, Haemophilus, Fusobacterium, and Neisseria [68, 87, 89].
Neural and metabolic pathways
Metabolic and neural signaling pathways are central to gut–lung communication, allowing the bidirectional exchange of metabolites, hormones, and immune mediators. SCFAs—notably acetate, propionate, and butyrate—are key gut-derived metabolites produced via the microbial fermentation of dietary fibers [90]. These SCFAs serve as energy sources for colonocytes and modulate local immune responses. Unused SCFAs can enter systemic circulation via the liver, reaching peripheral organs such as the lungs [91].
SCFAs contribute to respiratory health by exerting anti-inflammatory and immunomodulatory effects. They regulate bone marrow hematopoiesis, suppress dendritic cell activation, promote regulatory T cell (Treg) differentiation, and limit neutrophil recruitment to inflamed tissues [52, 92].
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In addition to metabolites, neural communication between the gut and lungs is facilitated by the enteric nervous system and the vagus nerve. Neurotransmitters and neuropeptides produced in the gut influence systemic immune responses and modulate inflammation across both organ systems [93].
Other gut-derived metabolites—including bile acids, lipids, and amino acids—also influence lung immunity. Bile acids, for example, possess antimicrobial properties and modulate pulmonary immune cell function [94]. Soluble microbial products and pathogen-associated molecular patterns (PAMPs) from the gut microbiota may enter circulation and affect immune signaling in the lungs [95].
PAMPs are recognized by PRRs such as TLRs and NOD-like receptors (NLRs), which are expressed on immune and epithelial cells [96]. These receptors initiate host defense responses and tissue repair processes but may also promote chronic inflammation if overstimulated, disrupting immune homeostasis and increasing the risk of inflammatory and autoimmune diseases [52].
The role of the airway Microbiome in NTM
Although few studies are currently available, understanding the role of the microbiome in NTM disease may offer insights into its pathophysiology and potential treatments. This task is complicated by ongoing debate over whether NTM represents mere colonization or a true infection, the latter involving direct tissue invasion. The distinction between intermittent and persistent NTM presence as infection-related sequelae has been contentious since the initial recognition of NTM disease. Historical evidence suggests that certain NTM species—such as M. gordonae, M. fortuitum, and M. chelonae—may often reflect chronic colonization of the lungs without a clear association with clinical disease [97, 98]. Table 1 summarizes the impact of the airway microbiome on NTM infection.
Table 1
Summary table of studies from the role of the airway Microbiome in NTM
Study Authors & Year | Study Population | Methods (Sample & Analysis) | Key Findings | Limitations |
|---|---|---|---|---|
Macovei et al., 2015 | 10 healthy individuals | Oral/upper airway samples; 16 S rRNA sequencing | NTM DNA detected in all nostril samples; suggests “mycobacteriome” exists in healthy people | Small sample size; did not confirm viability or disease association |
Yamasaki et al., 2015 | 29 non-CF bronchiectasis patients | BAL fluid; DNA sequencing | NTM patients had less Haemophilus, Pseudomonas, and more Streptococcus; enriched anaerobes | Small size; included mostly mild disease |
Sulaiman et al., 2018 | 106 non-CF bronchiectasis patients | Sputum, oral wash, BAL; 16 S rRNA sequencing | Oxalobacteraceae enriched in NTM + BAL; sputum differences minimal | Sputum may not reflect lower airway microbiome; sequencing missed Mycobacterium |
Philley et al., 2019 | NTM patients with and without breast cancer | Sputum samples; 16 S sequencing | Fusobacterium enriched in NTM; lower alpha-diversity; hormonal links hypothesized | Small, pilot study; unclear cancer influence |
Caverly et al., 2021 | 24 CF patients with NTM | 188 sputum samples; sequencing, co-occurrence networks | Certain genera linked to persistent NTM; tighter microbial networks in worse outcomes | CF-specific; small sample |
Iwasaki et al., 2021 | 25 MAC-suspected patients | BAL; culture and sequencing | Pseudomonas absent in MAC + patients; distinct profiles in early MAC | Small sample; 16 S cannot distinguish NTM species |
Kang et al., 2021 | 14 NTM-PD vs. 10 healthy | Protected brush/bronchial washing; 16 S sequencing | Less diversity in NTM-PD; more Pseudomonas and Rhodococcus | Small size; upper airway not sampled |
Kim et al., 2022 (L-arginine) | NTM-PD patients and mice | Metabolomics, sequencing, flow cytometry | L-arginine boosts immunity and alters gut microbiota; B. pseudolongum reduces NTM | Primarily in mice; needs clinical trials |
Kim et al., 2023 | 14 patients over 12 months of treatment | Serial sputum; 16 S rRNA sequencing | Responders showed taxonomic shifts and Mycobacterium reduction; refractory patients had stable dysbiosis | Small; functional shifts not deeply analyzed |
Kim et al., 2023 (tissue) | 23 resected lung tissues | Diseased vs. non-diseased regions; 16 S sequencing | Diseased regions more diverse; different genus-level composition | Small; sequencing misses species-level info |
Qin et al., 2023 | NTM-PD patients by immune status | Sputum, BAL; metagenomics | Immunosuppressed patients had more dysbiosis, more Aspergillus/Escherichia, higher NTM abundance | Single center; causality unclear |
Choi et al., 2023 | 10 NTM-PD vs. 10 healthy | Sputum and stool; 16 S rRNA sequencing | Lower diversity in NTM; more Prevotella and Veillonella in sputum; more Bacteroidetes in gut | Small cohort; cross-sectional |
Belheouane et al., 2024 | TB, NTM-LD, inflammatory disease patients | BAL; 16 S and whole metagenome sequencing | Serratia dominant; TB had more diversity and distinct Serratia variants | Retrospective; low-biomass issues |
Lin et al., 2024 | Humans and mice | Gut samples, TLR2 activation, infection models | Loss of Prevotella copri impairs immunity; restoring it reduces NTM susceptibility | Animal models; human data correlative |
Fujita et al., 2024 | 19 BAL samples from NTM and bronchiectasis | BAL; culture and 16 S sequencing | NTM-LD had greater diversity; dominated by oral taxa vs. pathogen-dominated bronchiectasis | Small size; possible antibiotic effect |
Huang et al., 2024 | 126 NTM-LD patients (2-yr follow-up) | Sputum; 16 S rRNA; progression prediction | Progressors had lower diversity, enriched virulence/biofilm taxa; model predicted progression (AUC = 0.87) | Lacks external validation; uses 16 S inference |
Won et al., 2024 | 47 patients with/without reflux | BAL; 16 S sequencing, pepsin ELISA | GERD linked to less diversity; Pseudomonas/Staph enriched; reflux may drive dysbiosis | Small; no healthy controls |
Song et al., 2024 | 35 NTM-PD (stable vs. treatment) | Sputum; 16 S rRNA sequencing | Certain genera linked to stability or refractory disease; histidine metabolism enriched in treated group | Small; sputum may reflect oral flora |
Shu et al., 2024 | Humans and mice | Gut-lung axis, TLR2 activity, infection model | Prevotella loss increases NTM risk; restoring improves immunity | Experimental; human-mouse translation needed |
Emerging research suggests that NTM may colonize the airways of healthy individuals without causing active infection. Macovei et al.. detected NTM DNA in samples from the nostrils, buccal mucosa, oropharynx, and dental plaque of healthy subjects, supporting the concept of a “nontuberculous mycobacteriome” that may exist independently of disease [99].
Microbiome composition differs significantly between NTM-positive and NTM-negative individuals. Yamasaki et al.. analyzed bronchoalveolar lavage (BAL) samples from non-cystic fibrosis (non-CF) bronchiectasis patients and found that those with NTM disease had decreased relative abundances of Haemophilus, Pseudomonas, and Staphylococcus, and increased levels of Streptococcus, Prevotella, Fusobacterium, Propionibacterium, and Veillonella (47% vs. 18%). This microbial shift may be associated with hypoxic microenvironments induced by mycobacterial presence [100].
Sulaiman et al.. examined sputum, oral wash, and BAL samples from non-CF bronchiectasis patients. The majority of sputum samples were dominated by Prevotella, Veillonella, and Corynebacterium, while oral wash samples predominantly contained Streptococcus, Rothia, and Actinomyces. While no significant differences in microbial diversity were observed in induced sputum between NTM-positive and -negative groups, oral wash samples from NTM-positive individuals had increased β-diversity. BAL samples showed enrichment of Oxalobacteraceae in NTM-positive patients and Porphyromonas in NTM-negative individuals. The study also revealed limitations in detecting NTM via sequencing in culture-positive samples and noted that sputum may not accurately reflect lower airway microbiota [101].
In another study, the sputum microbiomes of women with NTM disease, with or without breast cancer, were compared to healthy controls. While the NTM disease and breast cancer (NTM-BC) and without breast cancer (NTM-0) groups shared dominant genera such as Haemophilus, Streptococcus, Neisseria, Veillonella, Rothia, Fusobacterium, Leptotrichia, and Prevotella, they exhibited reduced α-diversity compared to controls [102]. Fusobacterium, which has been associated with both respiratory and colorectal pathology, was enriched in both NTM groups. The authors proposed that estrogen-like compounds metabolized by specific microbes such as Bacteroides, Faecalibacterium, Alistipes, Fusobacterium, Prevotella, Staphylococcus, and Streptococcus might influence both microbial composition and cancer risk, although further functional studies are needed to confirm this [51, 102].
Caverly et al.. analyzed 188 sputum samples from 24 CF patients and found that genera such as Pseudomonas, Streptococcus, Veillonella, Prevotella, and Rothia were positively associated with NTM pulmonary disease (NTM-PD), whereas Staphylococcus, Gemella, and Stenotrophomonas were negatively associated. Patients with poorer outcomes exhibited tightly interconnected microbial communities and more frequent use of inhaled corticosteroids, suggesting both microbial composition and therapeutic exposures influence disease progression [103].
Belheouane et al.. examined BAL samples using 16 S rRNA and metagenomic sequencing and identified a dominant presence of Serratia and unclassified Yersiniaceae across TB and NTM-LD cohorts. Exploratory metagenomic analyses identified species such as Serratia liquefaciens, S. grimesii, S. myotis, and S. quinivorans in both TB and NTM-LD patients. TB patients exhibited higher diversity and unique Serratia subspecies signatures, indicating strain-specific microbiome patterns with potential clinical implications [104].
Qin et al.. investigated immunological influences on NTM-PD using metagenomic analysis of BAL and sputum samples. Immunosuppressed patients had reduced microbial diversity and enriched taxa such as Aspergillus and Escherichia, with more complex microbial co-occurrence networks—highlighting immune status as a driver of microbial dysbiosis and disease severity [105].
Lin et al.. demonstrated a link between gut dysbiosis and NTM-LD susceptibility. Reduced abundance of Prevotella copri impaired TLR2-mediated immunity in both patients and antibiotic-treated mice. Oral administration of P. copri or its capsular polysaccharides restored immune competence and reduced infection, emphasizing the gut-lung axis as a key factor in disease modulation [106].
In a study by Choi et al.. compared lung and gut microbiota of 10 NTM-PD patients and 10 controls. NTM-PD patients, particularly those underweight, showed decreased alpha and beta diversity. Sputum samples were enriched in Prevotella and Veillonella, while fecal samples had more Bacteroidetes and fewer Firmicutes, linking nutritional status, dysbiosis, and disease vulnerability. The study highlighted a strong association between microbiome dysbiosis and susceptibility to NTM-PD, suggesting that disruptions in the gut-lung axis may contribute to disease development. These findings support the potential for microbiota-targeted therapies in the management of NTM-PD [107].
In another study, Kim et al. investigated L-arginine supplementation as a strategy to enhance pulmonary immunity. Patients with NTM-PD exhibited reduced L-arginine levels and elevated urea, suggesting impaired macrophage function. In a murine model, L-arginine supplementation promoted the growth of Bifidobacterium pseudolongum, enhanced activation of M1 macrophages, and increased IFN-γ-producing T cells. Notably, even in cases involving drug-resistant NTM strains, microbiota from L-arginine-treated mice—or direct administration of B. pseudolongum or inosine—enhanced resistance to infection. These findings highlight the role of L-arginine in regulating metabolic pathways and pulmonary immune responses against NTM-PD. Its functions include nitrogenous waste clearance, nitric oxide production, protein kinase signaling, and modulation of immune activity. Compared to healthy controls, NTM-PD patients had significantly lower blood L-arginine levels, which may impair immune responses by preventing the metabolic polarization of macrophages toward an M1-like microbicidal phenotype. L-arginine not only enriches beneficial Bifidobacterium species but also activates antimicrobial M1 macrophages and Th1 effector responses in the lungs. These alterations in the gut microbiota enhance host defense mechanisms against NTM-PD. In NTM-infected mice, L-arginine supplementation significantly reduced lung bacterial burden, increased IFN-γ-producing effector T cells, and promoted macrophage polarization toward a microbicidal M1 phenotype. Thus, L-arginine may represent a promising host-directed therapy for NTM-PD, particularly in cases of chronic or drug-resistant infections [108].
Recent studies have illuminated key features of the lung and gut microbiome in NTM-LD. Iwasaki et al.. analyzed bronchoalveolar lavage fluid from asymptomatic patients suspected of MAC-LD and found Mycobacterium detection via 16 S rRNA sequencing to be less sensitive than culture. Notably, Pseudomonas was absent in MAC-positive cases, suggesting possible microbial exclusion patterns, though overall diversity was similar across groups [109].
In another investigation, Fujita et al. compared NTM-LD and bronchiectasis patients, reporting higher microbial diversity and oral commensal dominance (e.g., Streptococcus, Prevotella, Rothia) in NTM-LD, contrasting with pathogen-rich microbiota (e.g., Pseudomonas, Haemophilus) in bronchiectasis. These findings suggest that NTM-LD may arise in a more balanced and diverse microbial environment influenced by oral commensals, while bronchiectasis appears to be associated with a dysbiotic, pathogen-dominated microbiome [110].
In 2024, Hung-Ling Huang et al.. followed 126 NTM-LD patients and found that disease progression correlated with reduced alpha-diversity, distinct beta-diversity, and enrichment of genera such as Burkholderia, Pseudomonas, Sphingomonas, Candidatus Saccharibacteria, Phocaeicola, Pelomonas, and Phaseolactobacterium. Functional profiles in progressors revealed increased biofilm and virulence pathways, supporting the microbiome’s prognostic potential. This study highlights the promise of sputum microbiome profiling as a non-invasive tool for predicting NTM-LD progression and enabling early, personalized intervention strategies [111].
In 2023, Kim et al.. analyzed paired lung tissue from NTM-PD patients and found greater microbial richness in disease-involved regions, with taxa like Limnohabitans, Rahnella, Lachnospira, and Phaseolarctobacterium enriched in affected tissue and Acinetobacter in non-involved tissues, suggesting dysbiosis may influence disease persistence. The reduced abundance of Acinetobacter in diseased tissues may reflect microbial competition or shifts in immune responses. Enrichment of Lachnospira and Phaseolarctobacterium in involved areas mirrors observations in TB and other pulmonary diseases, though their functional roles in NTM-PD pathogenesis remain unclear [112].
In 2024, Eun Jeong Won et al.. studied the impact of gastroesophageal reflux disease (GERD) on NTM-PD, finding that reflux was associated with reduced microbial diversity and increased Pseudomonas aeruginosa and Staphylococcus aureus in NTM-PD patients. In contrast, reflux patients without NTM-PD had greater levels of Haemophilus influenzae and Klebsiella pneumoniae. Linear discriminant analysis identified reflux-associated taxa, including Selenomonas artemidis and Dolosigranulum pigrum in NTM-PD patients, and P. aeruginosa in non-NTM-PD patients. Reflux also altered microbial functional pathways, suggesting its role in respiratory dysbiosis [113].
In another study, Kang et al.. reported lower airway microbial diversity and enrichment of the phylum level (e.g., Ignavibacteriae, Deinococcus-Thermus) and the genus level (e.g., Pseudomonas, Rhodococcus) in NTM-PD patients compared to healthy controls, pointing to a unique microbial signature potentially contributing to disease pathogenesis [114].
Song et al.. found that stable NTM-PD patients harbored more oral commensals, while treated patients showed altered microbiota, with certain taxa (e.g., Porphyromonas pasteri, Haemophilus parahaemolyticus, Prevotella nanceiensis, Gemella haemolysans) associated with treatment success and others (e.g., Atopobium, Parvimonas) linked to refractoriness. These findings suggest that specific microbial communities may influence NTM-PD disease stability and treatment outcomes [115].
Shu et al.. demonstrated that gut microbiota dysbiosis—specifically Prevotella depletion—impaired TLR2 signaling and increased susceptibility to NTM-LD in mice. Restoration of Prevotella improved immune responses, implicating the gut–lung axis in disease vulnerability [116].
Kim et al.. monitored changes in the sputum microbiome during antibiotic treatment in patients with NTM-PD, observing a decline in alpha-diversity over time across all individuals. Patients who achieved culture conversion showed marked shifts in microbial composition (beta-diversity), whereas refractory patients maintained stable, treatment-resistant communities dominated by Veillonella dispar, Fusobacterium periodonticum, and Pseudomonas aeruginosa. These findings suggest that persistent microbial populations may contribute to treatment resistance in NTM-PD [115].
It has been suggested that patients with NTM-PD and respiratory microbiome dysbiosis may experience poorer treatment outcomes. In refractory NTM-PD patients undergoing antibiotic therapy, increases in Veillonella dispar, Fusobacterium periodonticum, and Pseudomonas aeruginosa have been observed. This may result from dysbiosis caused by prolonged antibiotic use without successful NTM eradication, or from the establishment of a microbial environment by these species that supports NTM persistence. In patients who respond to treatment (conversion group), antibiotic therapy generally reduces most microbial taxa, with no significant expansion of others. Antibiotics that effectively target NTM or inhibit the proliferation of pathogenic bacteria in the lower airways may help maintain a stable respiratory microbial ecosystem.
Gut microbiome dysbiosis in NTM-PD patients may also influence disease progression and treatment response. Alterations in gut microbiota, particularly those affecting SCFA production, can impact the expression of tight junction proteins in the colonic epithelium, compromise intestinal barrier integrity, increase mucosal permeability, and modulate inflammatory cytokine levels. Diet-based interventions aimed at restoring gut microbiota balance—as indicated by improvements in body mass index (BMI)—may provide therapeutic benefits for patients with NTM-PD.
Collectively, studies on the microbiome in NTM-PD reveal distinct microbial profiles linked to disease status and progression, though most findings remain correlational. The heterogeneity of the NTM population—spanning immune status, radiographic patterns, and environmental exposures—limits generalizability. Limitations of sequencing methods, including inability to confirm microbial viability and low resolution in low-biomass samples, further complicate interpretation. Comparisons are often restricted to healthy controls or bronchiectasis patients, underscoring the need for broader disease controls to identify NTM-specific signatures. Future research should prioritize longitudinal, multi-omics studies with improved techniques to isolate viable, clinically relevant microbes, enabling better insight into pathogenesis and facilitating targeted diagnostics and therapies [98].
In conclusion, NTM infections represent a growing public health challenge, shaped by complex interactions between host immunity, environmental exposure, and microbiome dynamics. The gut-lung axis emerges as a critical regulator of disease susceptibility, where dysbiosis disrupts immune homeostasis and facilitates NTM persistence. Key findings include the role of SCFAs in enhancing pulmonary defenses, the antagonistic relationship between Pseudomonas and MAC in airway niches, and the potential of L-arginine or Prevotella-based interventions to restore microbial balance. Current therapies, reliant on prolonged antibiotic regimens, are limited by resistance and inefficacy, necessitating innovative approaches such as microbiota transplantation, dietary modulation, and immune checkpoint modulation. Future research must prioritize mechanistic studies to elucidate microbiome-immune crosstalk, validate predictive microbial biomarkers, and develop targeted strategies for high-risk populations. By integrating multi-omics data and clinical cohorts, precision medicine could transform NTM management, mitigating the burden of this recalcitrant pathogen.
Acknowledgements
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The authors declare no competing interests.
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