Immunomodulation by bacterial products promotes innate signatures favorable to macrophage responses in tuberculosis infection

Background: Tuberculosis remains a leading cause of death from infectious diseases globally, underscoring the need to boost innate responses in monocytes and macrophages to enhance early control of Mycobacterium tuberculosis infection. Trained immunity, a form of innate immune memory, enhances macrophage responsiveness through epigenet ic and metabolic reprogramming, offering a promising approach to strengthen host defenses against M. tuberculosis.

Methods: This study evaluated the immunomodulatory potential of pharmaceutical-grade bacterial suspension (BS) and bacterial lysates (BL) in human monocyte-derived macrophages (MDM) and their role in innate response to M. tuberculosis infection. MDMs were stimulated with M. bovis BCG, BS, and BL following a training protocol described for BCG-dependent trained immunity.

Results: We observed that BS and BL induced sustained cytokine responses and a metabolic transcriptional profile upon secondary stimulation with M. tuberculosis. BS and BL promoted increased IL-1b production in M. tuberculosis-infected MDMs. Additionally, the expression of surface markers shifted to high levels of CD80, CD86, HLA-DR, TLR2, and CD16 and low expression of CD163, TLR9, CCR2, and TLR4, consistent with an M1 phenotype. Moreover, BS and BL upregulated antimicrobial transcriptional signatures, including autophagy-related MAP1LC3 and ATG16L1.

Discussion: These findings indicate that BS and BL engage training-associated transcriptional and phenotypic changes, providing new adjunctive strategies to boost innate responses in tuberculosis and other chronic infections.

1 Introduction

Despite widespread use of antibiotics and the BCG vaccine, tuberculosis (TB) remains a leading cause of death from infectious diseases globally (1), underscoring the need to boost innate responses in monocytes and macrophages to enhance early control of Mycobacterium tuberculosis (Mtb) infection. Historically, immune responses have been categorized into two arms: innate immunity, which acts rapidly and non-specifically, and adaptive immunity, which is slower but highly specific and long-lasting. However, recent discoveries indicate that innate immune cells can also acquire a nonspecific form of memory, known as trained immunity (2). This process involves recognition of pathogens through pattern recognition receptors, triggering changes in gene expression, metabolism, and chromatin structure that can last for weeks or months after the initial infection. These changes train the cells to respond more effectively to future infections, with the same or unrelated pathogens (3, 4). In the context of TB, such reprogramming may enhance macrophage antimicrobial functions, providing a novel avenue for strengthening early immune defenses against M. tuberculosis.

Monocytes and macrophages are highly susceptible to trained immunity after stimulation with M. bovis BCG or β-glucan. Following BCG vaccination, human monocytes exhibit a substantial and long-lasting ability to elicit higher responses to secondary stimulation with different pathogens associated with increases in proinflammatory cytokine levels, changes in histone modifications, and gene activation (5). This reprogramming likely occurs at the level of hematopoietic stem cells as β-glucan induces a myeloid differentiation bias, resulting in increased circulating monocytes and their capacity to secrete cytokines in mice (6). Additionally, β-glucan- and BCG-induced trained immunity initiates the functional reprogramming of monocyte metabolism toward aerobic glycolysis, involving AKT activity, mammalian target of rapamycin (mTOR), and hypoxia-inducible factor 1-alpha (HIF-1α), to develop an enhanced response capacity to subsequent stimulation (7, 8).

The increased metabolic activity associated with trained immunity generates a significant number of immunomodulatory metabolites, such as fumarate, succinate, or itaconate, which play a central role in inducing long-term reprogramming of innate immune cells (9). The accumulation of acetyl-CoA induces histone acetylation, fumarate inhibits histone demethylases, thus inducing epigenetic changes in human monocytes, and itaconate inhibits succinate oxidation, causing translocation of the anti-inflammatory transcription factor NRF2 (10–12). Itaconate can induce transcriptional, epigenomic, and metabolic changes characteristic of trained immunity, while producing short-term anti-inflammatory characteristics (13). The metabolic rewiring of innate immune cells regulates their plasticity and epigenetic reprogramming (14). Evidence from human and murine models has demonstrated that trained macrophages exhibit heightened proinflammatory responses, improved pathogen clearance, and epigenetic marks (e.g., H3K27ac, H3K4me3) that prime genes related to immune activation (15–17).

Macrophages are central to controlling M. tuberculosis infection by modulating effector mechanisms to limit bacterial replication and expansion, promote the differentiation of antigen-specific T cells, and drive the production of proinflammatory cytokines that promote the migration of other immune cells, with possible involvement of trained immunity (18, 19). This project investigated the potential of harnessing trained immunity to augment macrophage-mediated responses against M. tuberculosis. Typical trained immunity inducers, e.g., M. bovis BCG, Candida albicans, and β-glucan, are still unsuitable for immunotherapy. Because trained immunity is initiated when macrophages recognize conserved pathogen-associated molecular patterns (PAMPs) through their pattern-recognition receptors, we hypothesize that bacterial lysates and whole-bacteria immunomodulators, which contain multiple Gram-negative and Gram-positive microorganisms and are a rich source of PAMPs, such as peptidoglycan, lipoteichoic and teichoic acids, diacyl- and triacyl-lipopeptides, muramyldipeptide, and flagellin (20), may serve as potent inducers of trained immunity in macrophages. Both immunomodulators are already approved for the prevention of recurrent respiratory and urinary tract infections (21–23).

In this study, we first determined that commercially available bacteria-derived immunomodulators induced training-associated transcriptional and phenotypic changes in human macrophages. To assess the influence of macrophage polarization on antimicrobial function, we characterized the trained macrophage phenotype using spectral flow cytometry. Finally, we examined the effect of such training on infection with M. tuberculosis. We found that bacteria-derived immunomodulators offer a safe and accessible immunotherapeutic for inducing trained-like signatures in human macrophages.

2 Materials and methods

2.1 Samples

This study was performed using buffy coats from healthy donors to the blood banks of the Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas (INER) and Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán. Overall, we included 16 healthy subjects. This study was approved by the institutional review boards, which waived informed consent on grounds of donor anonymity and assigned the number C43-22.

2.2 Monocyte-derived macrophages

Peripheral blood mononuclear cells were obtained using a Lymphoprep gradient (Axis Shield, San Jose, CA, USA). Monocytes were then isolated by positive selection using CD14+ magnetic bead technology (Miltenyi Biotec, CA, USA) conjugated to anti-CD14 antibodies. Monocytes in suspension were adjusted to 1×10⁶ cells/ml in RPMI-1640 (Lonza, Walkersville, MD), supplemented with 200 nM L-glutamine (Lonza, Walkersville, MD), 5 µg/L gentamicin (Lonza, Walkersville, MD), and 10% human serum (Valley Biomedical, Winchester, VA 22602, USA), herein referred to as complete medium. Human monocytes were cultured for 7 days to generate monocyte-derived macrophages (MDMs).

2.3 Bacteria and bacteria-derived immunomodulators

M. bovis BCG (ATCC 35743) and M. tuberculosis H37Ra (ATCC 25177) were grown at 37°C in 7H9 broth (Difco, Detroit, MI) supplemented with ADC Enrichment (Beckton Dickinson, BD, San Jose, CA), 0.2% glycerol, and 0.05% Tween-80 (Sigma-Aldrich, St. Louis, MO USA), and quantitated by the colony forming units method using solid Middlebrook 7H10 medium (Difco) supplemented with 0.5% glycerol, OADC Enrichment (BD) and 0.05% Tween-80. Aliquots were stored at -80°C until use.

Bacterial lysate Pulmonarom PML (Sanofi-Aventis, México) and whole-bacteria immunotherapeutic IPI Bacterial Suspension (ASAC Pharmaceutical Immunology, ASAC S.A., México) were used in their commercial oral formulations, and the CFU counts were used as reported by the manufacturers in their technical sheets.

2.4 Trained immunity model

Monocytes were seeded at 1×10⁶ cells/mL in complete medium, and cultured in the presence of Bacillus Calmette-Guérin (BCG) at an MOI 1, 100 ng/mL lipopolysaccharide (LPS, Sigma-Aldrich, St. Louis, MO USA), bacterial lysate (BL) at 1×10⁸ CFU/ml, and bacterial suspension (BS) at 1×10⁶ CFU/ml, incubated for 24 hours at 37°C in 5% CO₂. After 24 hours, supernatants were removed and stored at –20°C. The plate was washed twice and replenished with complete medium to differentiate monocyte-derived macrophages (MDMs) for an additional 6 days. On day 6, culture supernatants were collected and stored at –20°C, and the medium was replenished. Secondary stimulation with M. tuberculosis H37Ra at an MOI of 5, LPS, or both was carried out for an additional 24 hours, after which the culture supernatants were collected (day 7) and stored at –20 °C until use.

Untrained control macrophages were incubated in complete medium for 6 days and subsequently infected with M. tuberculosis H37Ra at an MOI of 5, with LPS, or with both for an additional 24 hours.

In selected experiments, we used 1mM of methyltransferase inhibitor 5'deoxy-5'-(methylthio) adenosine (MTA, brand, country), which can prevent the induction of a trained phenotype (24), for 1 hour before exposing the monocytes to the training stimuli to prevent epigenetic changes. MTA was replenished after the training stimulus was washed. The cells were cultured and infected with M. tuberculosis H37Ra as described above.

2.5 Cytokine measurements

The levels of pro-inflammatory cytokines IL-12p70, TNF-α, IL-10, IL-6, IL-1β, and IL-8 were measured in the supernatants at 24 hours, day 6, and day 7 using the Cytometric Bead Array (CBA) kit from BD (San José, CA, USA), according to the manufacturer’s instructions. Samples were analyzed using a FACS CANTO II flow cytometer (BD, San José, CA, USA), and results were processed with FCAP software (BD, San José, CA, USA). Preliminary pilot measurements of IL-1β were determined by ELISA following the manufacturer’s instructions (Mabtech, Cincinnati, OH, USA), and TNFα was measured as previously reported (25).

2.6 Gene expression

MDMs trained for 7 days as described above were lysed with RLT buffer (Qiagen, Germantown, Maryland, USA) for total RNA extraction using the RNeasy Microkit (Qiagen) according to the manufacturer’s instructions. RNA concentration was subsequently measured using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Millersburg, Pennsylvania, USA), and samples were stored at -80°C until use. Complementary DNA (cDNA) was then synthesized using the SuperScript First-Strand Synthesis System kit (Invitrogen, Millersburg, Pennsylvania, USA), according to the manufacturer’s protocol. Gene expression levels of metabolic enzymes PFKP, HK2, GLUD1, and GLS, as well as genes associated with antimicrobial activity such as CAMP, NPC2, MAP1LC3, and ATG16L1, were analyzed by real-time PCR using TaqMan technology and a QuantStudio 12 Flex system (Applied Biosystems, Waltham, USA). Expression differences were calculated using the 2^−ΔΔCt method, with B2M as the housekeeping gene. Details of the TaqMan assays appear in Supplementary Table 1.

2.7 Macrophage phenotype

Trained MDMs were detached using ice-cold PBS (Corning™Mediatech™ Glendale, Arizona, USA), transferred to polypropylene tubes, washed twice with cold PBS, and centrifuged at 1500 rpm for 5 minutes. The supernatant was discarded, and 1 µL of viability dye AquaDye (Invitrogen) was added and incubated at room temperature in the dark for 20 minutes. After incubation, cells were washed, centrifuged, and the supernatant was discarded. The cell pellet was then resuspended in 2 µL of SBF (Invitrogen) plus 50 µL of Brilliant Stain Buffer, followed by the addition of the antibody cocktail: CXCR4-PE-Cy5 (Biolegend, San Diego, CA, USA) CD3-BV510, CD14 PE-CF594, CD16-APC-H7, CD163-BUV395, TLR2-BUV615, TLR4-PE, TLR9-APC, CD64-PE-Cy7, HLA-DR R718, CD36-BV605, CD80-BV421, CD86-BV650, CCR2-BV711, CD209-FITC, and CD206-BV786 (Becton Dickinson, BD, San José, CA, USA), additional details in Supplementary Table 1). Cells were incubated for 30 minutes at 4°C in the dark. After staining, cells were washed twice with cold PBS and resuspended in a final volume of 200 µL PBS.

Single-stained controls for spectral unmixing were prepared using BD CompBeads Plus (BD) following the manufacturer’s instructions. All samples were acquired using the FACS Discover S8 spectral flow cytometer with integrated CellView and FACS Chorus software (BD). Spectral unmixing was carried out using the single-stained controls and unstained samples, allowing for autofluorescence subtraction and deconvolution of overlapping emission spectra. Data analysis was performed using FlowJo v10.10 (BD). Dimensionality reduction analysis was conducted using t-distributed stochastic neighbor embedding (t-SNE) and FlowSOM for unsupervised quantitative clustering.

2.8 Statistical analyses

The macrophage’s phenotype was only described, not compared. Multiple comparisons were performed using one-way ANOVA followed by Dunnett’s post hoc test. Relative gene expression was assessed with a one-sample Wilcoxon test. Analyses were conducted using GraphPad Prism 8.0, considering p-values < 0.05 statistically significant.

3 Results

3.1 Immunomodulatory properties of BS and BL

Given that BS and BL were liquid pharmaceutical formulations sold for oral administration, we first evaluated their ability to stimulate human monocytes in vitro. A pilot study was conducted to identify concentrations that elicited immunostimulatory activity without cytotoxicity (Supplementary Figure 1). To further characterize their immunomodulatory potential, we quantified the production of IL-12p70, TNFα, IL-10, IL-6, IL-8, and IL-1β in monocytes stimulated for 24 hours with BS (1 × 10⁶ CFU/mL) or BL (1 × 10⁸ CFU/mL). Lipopolysaccharide (LPS) and M. bovis BCG were included as positive controls, while unstimulated cells cultured in medium served as the negative control.

BS stimulation significantly increased the monocytes’ production of TNF-α and IL-6. In contrast, BL stimulation induced significant levels of IL-6, IL-1β, and IL-10, suggesting the activation of distinct immunological pathways. Surprisingly, all stimulators reduced the expression of IL-8. As expected, LPS elicited robust production of all measured cytokines, whereas BCG infection selectively induced proinflammatory cytokines (Figure 1).

Figure 1

Figure 1. BS and BL formulations induced innate responses in human monocytes. Monocytes were cultured in the presence of BS (1x10⁶ UFC/ml) and BL (1x10⁸ UFC/ml) for 24 hours. We included 100 ng/mL of LPS and infection with BCG at an MOI of 1 as a positive control, and medium as a negative control. Cytokines IL-12p70, TNFα, IL-10, IL-6, IL-8, and IL-1β were measured in the supernatants by flow cytometry using a CBA assay (n=6). Means ± SD are depicted. *p<0.05 compared to Medium. One-way ANOVA, followed by Dunnett post hoc for multiple comparisons.

3.2 BS and BL induce trained immunity-like signatures in macrophages

Next, we investigated whether BS and BL induce trained immunity in monocytes, enhancing responses to M. tuberculosis infection. We used a training protocol previously reported for BCG training (24), which involved applying a primary stimulus to monocytes, followed by a washout and a 6-day resting phase (Figure 2A). On day 6, the cells, now differentiated into MDMs, received a secondary stimulus, followed by a 24-hour incubation period. TNF-α production of trained MDMs was measured at day 7 and reported as fold change relative to untrained MDMs’ production (Figure 2B). Untrained MDMs infected with M. tuberculosis were used as the reference henceforth. MDMs trained with BCG exhibited enhanced TNF-α production in response to M. tuberculosis infection, consistent with trained-like signatures. MDMs trained with BS or BCG and infected with M. tuberculosis had the highest response, although it did not reach significance. Training with BL did not affect the TNF-α response (Figure 2B).

Figure 2

Figure 2. Induction of trained immunity. (A) Schematic representation of the training protocol in primary human monocytes. Cells were stimulated with a primary stimulus for 24 hours. The first stimulus was washed out, followed by a 6-day resting period. On day 6, the secondary stimulus was incorporated, and cells were cultured for an additional 24 hours. Supernatants were collected at 24h, day 6, and 24h post-infection (day 7). Primary stimulus included medium, BCG (MOI 1), LPS (100ng/ml), bacterial suspension (BS, 1x10⁶ UFC/ml), and bacterial lysates (BL, 1x10⁸ UFC/ml). The secondary stimuli were LPS (100ng/ml) and M. tuberculosis H37Ra (MOI 5). (B) Untrained macrophages were infected with M. tuberculosis H37Ra (MOI 5), and their TNF-α production was set at 1. Responses of trained macrophages are reported as the fold change in TNF-α production relative to untrained macrophages. Depicted are the Means ± SEM (n=6). (C) Monocytes stimulated with LPS following the training protocol. TNF-α production was measured at 24 hours, on day 6 (D6), and after M. tuberculosis infection on day 7 (D7). Means ± SD are depicted; *p<0.05, one-way ANOVA followed by Dunnett’s post hoc (n=6). (D) Schematic of the tricarboxylic acid cycle (TCA) depicting the intervention points for the enzymes associated with trained immunity, glycolysis (HK2 and PFKP), and glutamine (GLS and GLUD) metabolism. (E) Trained macrophages infected with M. tuberculosis for 24 h were lysed and retrotranscribed for gene expression assessment by qPCR. Log₂ Fold changes relative to untrained macrophages were calculated. Depicted are individual results with means and SD; *p<0.05, one-way ANOVA followed by Dunnett’s post hoc (n=8).

Because we did not know whether the large amounts of PAMPs present in BS and BL, many of which originated from gram-negative bacteria, would result in tolerance rather than activation, we included lipopolysaccharide (LPS), a known inducer of tolerance, for comparative purposes. At 24 hours, LPS induced robust TNF-α production, which was lost after the 6-day rest. The LPS-prestimulated MDMs were unresponsive to a secondary stimulation with M. tuberculosis, consistent with tolerance. Regular MDMs produced the expected levels of TNFα when stimulated with LPS for 24 h or infected with M. tuberculosis and then stimulated with LPS. This indicates that LPS produced tolerized macrophages during the 6-day training protocol rather than inducing trained immunity (Figure 2C, Supplementary Figure 2).

To confirm trained immunity and its metabolic regulation, we assessed expression of enzymes involved in glycolysis (Hexokinase 2, HK2, and Phosphofructokinase, PFKP) and glutamine metabolism (glutaminase, GLS, and glutamate dehydrogenase 1, GLUD1) (Figure 2D). Significant increases in PFKP and GLS gene expression were observed with BS and BL treatment compared to untrained MDMs. BL also significantly upregulated GLUD1. No significant differences in HK2 expression were observed. These findings suggest that BS and BL likely induce trained immunity, primarily by enhancing PFKP and glutamine metabolism. BCG tended to increase expression of all four genes, but this did not reach significance; however, BS and BL responses were higher than those during training with BCG, suggesting their role in the metabolic reprogramming underlying training-associated transcriptional and phenotypic changes (Figure 2E).

3.3 Characterization of trained macrophage phenotype

The morphological and functional diversity of macrophages plays a key role in either controlling or facilitating the dissemination of M. tuberculosis. In simplified terms, M1 macrophages are pro-inflammatory and can eliminate or contain intracellular pathogens, whereas M2 macrophages tend to support intracellular persistence and are often associated with tissue repair and immunoregulation (26). Recent evidence suggests that the phenotype of trained macrophages is strongly influenced by the nature of the training stimulus (27, 28). To explore this, we used spectral flow cytometry combined with dimensionality reduction techniques to characterize macrophage phenotypes in our model.

We defined M1 macrophages by surface expression of CD80, CD86, TLR2, TLR4, HLA-DR, CD64, CCR2, CD14, and CD16, and M2 macrophages by expression of CD206, CD209, CD163, CD36, CXCR4, TLR9, and CD16 (Figure 3A). Cells from trained and untrained macrophages infected with M. tuberculosis were collected from six individuals and concatenated for analysis (see gating strategy in Supplementary Figure 3). We then performed t-SNE on day 7, 24 h post-infection, to visualize the patterns of the 15 surface markers’ expression (Figure 3B). The individual expression profiles for each marker are shown in Supplementary Figure 4.

Figure 3

Figure 3. Immunophenotype of trained macrophages. (A) Schematics of M1 and M2, typical membrane markers, and functional phenotypes. (B) Macrophages were infected with M. tuberculosis (MOI 5) for 24 (h) Cells were stained with antibodies anti CD80, CD86, TLR2, TLR4, HLA-DR, CD64, CCR2, CD14, CD16, CD206, CD209, CD163, CD36, CXCR4, and TLR9 to determine their expression by spectral flow cytometry. All live single macrophages from 6 individuals were concatenated (2,405,332 total events) and analyzed using dimensionality reduction via t-SNE. The untrained (C), trained (D), and tolerized (E) macrophage phenotype profile is depicted (See the gating strategy in Supplementary Figure 3 and individual markers expression in Supplementary Figure 4) (F) Spectral population viewer of trained macrophages in comparison with untrained macrophages. Violin plots depict the range of expressions for each marker.

The t-SNE analysis revealed distinct clustering profiles among untrained MDMs depending on the condition: uninfected, stimulated with LPS, or infected with M. tuberculosis (Figure 3C). In contrast, trained MDMs, whether primed with BCG, BS, or BL, displayed more homogeneous clustering patterns after M. tuberculosis infection, suggesting convergence toward a typical trained phenotype, although each condition retained unique characteristics (Figure 3D). Interestingly, macrophages tolerized with LPS showed an expression pattern that partially overlapped with that of BL-trained MDMs, suggesting that differences between immune tolerance and certain trained states may be in the functionality of signaling pathways (Figure 3E). The violins in the spectral viewer showed that the relative expression of the markers of interest differ between trained and untrained MDMs infected with M. tuberculosis, suggesting that the differences in the expression profile may be attributed to the training (Figure 3F). For instance, in untrained MDMs, M. tuberculosis infection shifted the MDMs general phenotype toward a mixed phenotype with M1 (CD86^hiHLA-DR^hiCD14^hiCD64^hiCXCR4^hi) and M2 (CD163^hiCD36^hi) markers highly expressed, consistent with a flexible state. However, BCG-, BS-, and BL-trained MDMs exhibited the highest expression of M1 markers.

3.4 Macrophages trained with BCG, BS, and BL exhibit an M1 phenotype

Because MDMs are expected to be a mixture of phenotypically different subpopulations, we performed unsupervised clustering using FlowSOM, followed by metacluster annotation with Cluster Explorer to further characterize phenotypic diversity among macrophages (Figures 4A, B). We identified eight distinct macrophage clusters across all experimental conditions using expression data from 15 surface markers associated with M1 and M2 polarization (Figure 4C). Figure 4D shows the expression level for each marker in each cluster. In general, TLR9 was the lowest expressed marker, followed by CD163. CD86 and HLA-DR were highly expressed, whereas CD14, CD36, CD64, CD80, CCR2, and CXCR4 were differentially expressed. The most abundant macrophage subpopulation (cluster #7, Figure 4C) exhibited high levels of CD86, CD64, and HLA-DR, as well as medium levels of CD80, CD36, CD14, and TLR2, all of which indicate an M1 phenotype.

Figure 4

Figure 4. Cluster analysis of the trained macrophages’ phenotype. After dimensionality reduction (n=6), clusters of macrophage subpopulations were determined using FlowSOM. (A) The FlowSOM node map shows a marker-based clustering structure. Eight metaclusters were identified; (B) t-SNE projection of metaclusters illustrating phenotypic distribution across conditions. Clusters were further compared between experimental conditions using ClusterExplorer. (C) Frequency of each metacluster. (D) Heatmap showing relative expression of 15 surface markers across metaclusters with blue-red expression level range. The color bar, with maximum intensity in red, indicates the expression level, and the absence of expression is represented in blue. (E) Metacluster distribution across experimental conditions. The Cluster Heatmap shows, in blue to yellow, the frequency of the clusters, with yellow at the maximum intensity (most abundant).

Next, we investigated which macrophage subpopulation was present across experimental conditions (Figure 4E). The color bar in the heatmap indicates the frequency of each cluster across conditions. Untrained MDMs infected with M. tuberculosis (Med+Mtb) had a higher abundance of clusters 6, 7, and 8, which exhibit a mixed phenotype with high expression of both M1 and M2 markers. Those clusters were less abundant in trained MDMs. BS-trained macrophages showed selective enrichment in M1 phenotype clusters with high expression of CD80, CD86, HLA-DR, TLR2, and CD16, and low expression of CD163, TLR9, CCR2, and TLR4 (clusters 1, 2, 3, and 5). In fact, BS-trained macrophages exhibited a greater diversity of macrophage subpopulations. BL-trained macrophages were mainly enriched in clusters 2 and 5.

To further characterize the trained macrophages’ phenotype, we evaluated the production of IL-1β, TNF-α, IL-6, IL-12p70, IL-8, and IL-10 cytokines in the culture supernatants 24 h after M. tuberculosis infection. Here, macrophages were incubated in the absence or presence of MTA, an inhibitor of histone 3 lysine 4 trimethylation (24), thereby inhibiting epigenetic reprogramming before the training stimulus was administered. BS- and BL-trained MDMs produced higher IL-1β and TNF-α than BCG-trained macrophages, supporting their capacity to induce a trained proinflammatory state, consistent with the M1 phenotype. This phenotype was significantly inhibited by preincubation with MTA before training (Figure 5), highlighting the dependence of epigenetic reprogramming for training. Interestingly, training did not affect IL-6, IL-8, and IL-10 production, suggesting other responses associated with M. tuberculosis infection were not triggered. Levels of IL-12p70 were negligible, likely due to the absence of lymphocytes in the culture (data not shown).

Figure 5

Figure 5. Cytokine production of trained macrophages. Monocytes were trained with BCG, BS, and BL in the presence or absence of 1mM of 5'-deoxy-5'-(methylthio) adenosine (MTA) for 24 h. The training stimuli were washed, and the macrophages were allowed to differentiate for 6 days. Trained macrophages were infected with M. tuberculosis for 24 h, and cytokine production was measured in the supernatants by flow cytometry using CBA technology. Depicted are individual results with means and SD. *p<0.05 compared with untrained macrophages infected with Mtb. One-way ANOVA followed by Tukey’s post hoc (n=6).

3.5 Trained macrophages express enhanced antimicrobial mechanisms

Because M1 macrophages possess antimicrobial properties, we measured the expression of genes involved in macrophage antimicrobial responses (NPC2, CAMP, MAP1LC3, and ATG16L1) in trained MDMs. In untrained or LPS-tolerized MDMs, the infection alone downregulated the expression of all four genes relative to uninfected MDMs (Figures 6A–C). We determined gene expression relative to uninfected, untrained MDMs to assess the effect of M. tuberculosis infection in trained MDMs. BS and BL training restored macrophage ability to upregulate NPC2 and the autophagy-related genes MAP1LC3 and ATG16L1 upon Mtb infection (Figures 6D, F). BCG-trained MDMs only upregulated the expression of the autophagy-related genes upon M. tuberculosis infection (Figure 6F). CAMP expression was downregulated upon M. tuberculosis infection, regardless of the immunomodulation status of the MDMs.

Figure 6

Figure 6. Gene expression of antimicrobial effectors of trained macrophages. Untrained macrophages were infected with M. tuberculosis for 24 h, lysed, and retrotranscribed to determine the gene expression of NPC2 (A), CAMP (B), and autophagy-related MAP1LC3 and ATG16L1 (C) by qPCR. Log₂ Fold changes relative to uninfected macrophages were calculated. Depicted are medians and IQR; *p<0.05, Wilcoxon’s one-sample test (n=8). Gene expression of NPC2 (D), CAMP (E), and MAP1LC3 and ATG16L1 (F) of trained macrophages infected with M. tuberculosis was measured. Log₂ Fold changes relative to untrained macrophages were calculated. Depicted are medians and IQR; *p<0.05, Wilcoxon’s one-sample test (n=8).

4 Discussion

BCG-induced trained immunity, a memory-like response in innate immune cells that enhances protection against various pathogens beyond Mycobacterium tuberculosis, is characterized by epigenetic and metabolic reprogramming of monocytes and macrophages, leading to increased cytokine production upon secondary stimulation (29–31). In addition to the BCG vaccine, other trained immunity inducers, such as β-glucan, have proven helpful for M. tuberculosis control, but are not ready for human therapy (32, 33). This study explored the potential of inducing trained immunity-associated signatures in macrophages as a novel therapeutic strategy to improve control of TB infection using bacteria-derived immunomodulators. Orally administered bacterial suspensions (BS) and lysates (BL) are immunotherapeutics approved for the prevention of recurrent respiratory tract infections and asthma and allergy episodes in children and older adults (34–36). However, their capacity to induce trained immunity and macrophage reprogramming relevant to M. tuberculosis infection remains underexplored. Here, we stimulated human macrophages with pharmaceutical preparations of bacterial suspension (BS) and bacterial lysates (BL). We assessed their ability to induce trained immunity and modulate macrophage responses to M. tuberculosis infection.

Because we used pharmaceutical oral formulations, we first investigated whether BS and BL were suitable for in vitro research. We found they were indeed immunostimulatory, because they differentially induced TNF-α, IL-1β, IL-6, and IL-10 production and had no toxic effect on cell viability. Specifically, BL induced IL-6 and IL-1β production, whereas BS augmented TNF-α and IL-6 levels. Interestingly, BL also increased IL-10 levels, consistent with IL-10 being overexpressed during S. aureus infection to prevent bacterial dissemination (35). Surprisingly, IL-8 was downregulated by all immunomodulators. Although there is evidence that some bacteria, such as S. aureus, can inhibit IL-8 production, thereby increasing susceptibility to infection (36), activating innate immunity without increasing neutrophil influx by these immunomodulators would indeed be a benefit worth further investigation. Further, we determined the ability of BS and BL to induce trained immunity. We observed the typical signatures of trained immunity, such as enhanced TNF-α production upon a secondary stimulus applied 6 days after priming, and upregulation of glycolysis and glutamine metabolism enzymes. Primary human monocytes trained with β-glucan, Candida albicans, and BCG rapidly shift monocyte metabolism toward glycolysis and upregulate genes involved in lipid metabolism and biosynthesis (37). Cytokines TNF-α, IL-1β, and IL-6 are hallmarks of trained immunity. Thus, BS and BL can induce a trained-like immunity in human MDMs. The differences in cytokine production patterns between BS and BL may mainly reflect the fact that BS contains whole bacteria, whereas BL contains lysed bacteria.

Specifically, we observed that BS and BL upregulated the expression of enzymes relevant to glycolysis and glutamine metabolism. Glutamine is a primary energy substrate for lymphocytes and macrophages, as well as a precursor to nucleotides. It is necessary for phagocytic activity (38). Glutamine and glucose are poorly oxidized by macrophages, but they may produce important precursors for DNA, RNA, protein, and lipid synthesis. Moreover, other effects of glutamine metabolism may influence cellular activation during trained immunity. Glutamate can be utilized for ATP and NADPH production, as well as for the production of acetyl-CoA, which serves as a substrate for acetyltransferase enzymes. Notably, H3K27Ac is an important histone mark associated with active promoters in trained monocytes (39).

Macrophage polarization influences antimicrobial function. Macrophages can adopt different activation states or phenotypes, classically activated (M1) and alternatively activated (M2), which have distinct functional profiles. M1 macrophages demonstrate a host‐protective profile during early M. tuberculosis infection, with enhanced autophagy marked by increased MAP1LC3 lipidation, ATG expression, and efficient lysosomal trafficking, heightened antigen presentation, and a pro‐inflammatory transcriptional program producing high levels of IL‐12, TNF‐α, IL‐1β, and nitric oxide that restrict bacterial growth (40, 41). In contrast, M2 macrophages consistently exhibit reduced autophagy and antigen presentation, along with increased levels of IL‐10, IL‐4, and specific chemokines (e.g., CCL18, CCL22), thereby establishing an anti-inflammatory milieu that permits bacterial persistence (42).

When we investigated the phenotype of BS- and BL-trained MDMs, we observed a heterogeneous mixture of macrophage subpopulations in the culture. Still, we found that training elicited the generation of unique subpopulations compatible with an M1 phenotype, with high expression of HLA-DR, CD80, CD86, highly relevant for antigen presentation, and TLR4 for innate responses readiness.

These phenotypic differences have functional implications. The subpopulations enriched in trained macrophages likely reflect a shared reprogramming toward enhanced responsiveness and activation thresholds. This may prime them for more effective early containment of intracellular pathogens such as M. tuberculosis. In contrast, the mixed clustering of untrained macrophages highlights their limited and context-dependent responses to infection. The resemblance between LPS tolerance and BL training could point to alternative pathways of immune adaptation, potentially balancing pro-inflammatory and regulatory functions during chronic or repeated microbial exposure. These findings suggest that training with BS and BL reshape macrophage phenotypes and influence the host response to M. tuberculosis. This offers insight into how microbial-derived agents might be leveraged to improve innate immune preparedness.

The macrophage phenotype is relevant and central to the mechanisms and therapeutic potential of trained immunity in TB. The success of trained immunity-based interventions likely depends on the induction of macrophage states that are functionally optimized for early and effective control of M. tuberculosis. For instance, BCG induces a pro-inflammatory, bactericidal type 1 (M1) polarization phenotype in infected macrophages, involving the thrombospondin-TLR pathway (43). Trained immunity induced by damage-associated molecular patterns (DAMPs) produces anti-inflammatory macrophages (44). Metabolic reprogramming may generate atypical pro-inflammatory M2 macrophages (45). Here, we found that BS and BL generated a more diverse and heterogeneous macrophage population than BCG, which shifted towards the antimicrobial M1 phenotype, whereas untrained macrophages comprised mixed M1 and M2 populations. Moreover, cytokine production was consistent with the M1 phenotype and was dependent on epigenetic reprogramming, because it was reversed when MTA, an H3K4me3 inhibitor, was used before training; at a minimum, our observations were partially attributable to histone methylation.

To further confirm the M1 phenotype, we determined gene expression of antimicrobial effectors. We selected NPC2, critical for macrophage antimicrobial activity against M. tuberculosis by regulating cholesterol levels and lysosomal acidification (46), cathelicidin antimicrobial peptide (CAMP), also known as LL-37, and autophagy-related MAP1LC3 and ATG16L1, because their expression has been previously reported to participate in macrophage antimicrobial activity against M. tuberculosis, especially in the context of immunomodulation with bacteria-derived molecules (47–50). In untrained MDMs, the infection with M. tuberculosis downregulated the expression of all four genes, likely related to immune evasion. MDMs trained with BS and BL restored their ability to upregulate NPC2, MAP1LC3, and ATG16L1 after infection with M. tuberculosis. Unexpectedly, CAMP remained downregulated even in trained macrophages, which suggests selective activation of antimicrobial programs associated with trained immunity. In this regard, CAMP expression is not consistently induced in human macrophages by many common bacterial stimuli, including Salmonella (51) and Mycobacterium tuberculosis (49), and its induction may require a different type of inducer, such as vitamin D. Further evaluations on bacterial killing will define the full extent of the antimicrobial programs activated in BS- and BL-trained macrophages.

BS and BL induced trained immunity-like features that influenced their responses to M. tuberculosis infection in human MDMs, with distinct differences between them and BCG. Compared with BCG, both BS and BL increased the expression of genes involved in glucose and glutamate metabolism, NPC2, and autophagy. BCG broadly altered macrophage phenotype by downregulating more surface markers, whereas BS and BL favored M1-like subsets, with BS also showing greater phenotypic diversity. Cytokine profiles diverged: BS responses resembled those of BCG, while BL induced higher IL-1β expression. These differences likely reflect the nature of the stimuli: BCG as a single microorganism, BS as a bacterial mixture, and BL as a mixture of PAMPs.

The most striking observation was the absence of TNF-α enhancement with any training stimulus. While this regulated response may be clinically beneficial, it contrasts with prior studies. BCG vaccination enhances trained immunity and boosts innate and adaptive responses to M. tuberculosis (52, 53). Similarly, BCG training enhances TNF-α and IL-6 production in response to LPS stimulation (54), whereas β-glucan, a single PAMP, promotes IL-1β–dependent trained immunity against M. tuberculosis (32). In the setting of TB, a macrophage phenotype that favors IL-1β production, upregulation of antimicrobial effectors, and antigen presentation is more likely to control bacterial replication. Therefore, the ability to influence or stabilize a macrophage phenotype with enhanced antimicrobial activity is central to the rationale for trained immunity-based interventions. Identification of well-defined compounds that induce trained immunity and are already approved for human therapy can improve our understanding of innate immune memory and broaden the scope of its clinical applications.

This study has limitations. The study focuses primarily on indicative signatures and therefore does not yet encompass all dimensions of microbial control, cellular reprogramming, or long-term immune modulation within the framework of trained immunity. While our results highlight relevant trends, broader mechanistic and comparative approaches remain to be explored. These aspects offer opportunities for future refinement and expansion, thereby strengthening and contextualizing the emerging insights presented here.

5 Conclusions

Our results demonstrate that oral formulations of bacterial suspension and bacterial lysates can induce training-associated transcriptional and phenotypic changes in human macrophages, including increased cytokine responses, phenotypic activation, and transcriptional reprogramming. Given the central role of trained macrophages in the early containment of Mycobacterium tuberculosis, these immunological signatures point towards new adjunctive strategies to boost innate responses in tuberculosis and other chronic infections. Further in vivo validation is warranted to define their therapeutic potential.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by Comité de ética en Investigación. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because we ensured the anonymity of volunteers’ personal information.

Author contributions

DPR-R: Formal Analysis, Investigation, Methodology, Resources, Visualization, Writing – original draft, Writing – review & editing. EM: Methodology, Visualization, Writing – review & editing. HR-G: Investigation, Writing – review & editing. MT: Resources, Writing – review & editing. JZ: Resources, Writing – review & editing. EJ: Conceptualization, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was funded by INER, code C43-22.

Acknowledgments

We thank Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI) for the scholarship to DPRR and Becton Dickinson for lending the FACS Discover S8 spectral cytometer and technical support. We thank Luz María Vera for administrative support and the Crispín-Rosetti Laboratory of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán for supplying us with buffy coats.

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2025.1664444/full#supplementary-material

References

1. World Health Organization. Global tuberculosis report. Geneva: World Health Organization (2024).

Google Scholar

2. Ter Steeg L, Domínguez-Andrés J, Netea MG, B Joosten LA, and Van Crevel R. Trained immunity as a preventive measure for surgical site infections. Clin Microbiol Rev. (2021) 34:e0004921. doi: 10.1128/cmr.00049-21

PubMed Abstract | Crossref Full Text | Google Scholar

3. Netea MG, Joosten LAB, Latz E, Mills KHG, Natoli G, Stunnenberg HG, et al. Trained immunity: A program of innate immune memory in health and disease. Science. (2016) 352:427. doi: 10.1126/science.aaf1098

PubMed Abstract | Crossref Full Text | Google Scholar

4. Netea MG, Quintin J, and Van Der Meer JWM. Trained immunity: A memory for innate host defense. Cell Host Microbe. (2011) 9:355–61. doi: 10.1016/j.chom.2011.04.006

PubMed Abstract | Crossref Full Text | Google Scholar

5. Magadán S, Mikelez-Alonso I, Borrego F, and González-Fernández Á. Nanoparticles and trained immunity: Glimpse into the future. Adv Drug Deliv Rev. (2021) 175:113821. doi: 10.1016/j.addr.2021.05.031

PubMed Abstract | Crossref Full Text | Google Scholar

6. Kaufmann E, Sanz J, Dunn JL, Khan N, Mendonça LE, Pacis A, et al. BCG educates hematopoietic stem cells to generate protective innate immunity against tuberculosis. Cell. (2018) 172:176–190.e19. doi: 10.1016/j.cell.2017.12.031

PubMed Abstract | Crossref Full Text | Google Scholar

7. Arts RJW, Carvalho A, La Rocca C, Palma C, Rodrigues F, Silvestre R, et al. Immunometabolic pathways in BCG-induced trained immunity. Cell Rep. (2016) 17:2562–71. doi: 10.1016/j.celrep.2016.11.011

PubMed Abstract | Crossref Full Text | Google Scholar

8. Netea MG, Domínguez-Andrés J, Barreiro LB, Chavakis T, Divangahi M, Fuchs E, et al. Defining trained immunity and its role in health and disease. Nat Rev Immunol. (2020) 20:375–88. doi: 10.1038/s41577-020-0285-6

PubMed Abstract | Crossref Full Text | Google Scholar

9. Dominguez-Andres J and Netea MG. Long-term reprogramming of the innate immune system. J Leuk Biol. (2019) 105:329–38. doi: 10.1002/JLB.MR0318-104R

PubMed Abstract | Crossref Full Text | Google Scholar

10. Arts RJW, Joosten LAB, and Netea MG. The potential role of trained immunity in autoimmune and autoinflammatory disorders. Front Immunol. (2018) 9. doi: 10.3389/fimmu.2018.00298

PubMed Abstract | Crossref Full Text | Google Scholar

11. Lampropoulou V, Sergushichev A, Bambouskova M, Nair S, Vincent EE, Loginicheva E, et al. Itaconate links inhibition of succinate dehydrogenase with macrophage metabolic remodeling and regulation of inflammation. Cell Metab. (2016) 24:158–66. doi: 10.1016/j.cmet.2016.06.004

PubMed Abstract | Crossref Full Text | Google Scholar

12. Bambouskova M, Gorvel L, Lampropoulou V, Sergushichev A, Loginicheva E, Johnson K, et al. Electrophilic properties of itaconate and derivatives regulate the IκBζ-ATF3 inflammatory axis. Nature. (2018) 556:501–4. doi: 10.1038/s41586-018-0052-z

PubMed Abstract | Crossref Full Text | Google Scholar

13. Ferreira AV, Kostidis S, Groh LA, Koeken VACM, Bruno M, Baydemir I, et al. Dimethyl itaconate induces long-term innate immune responses and confers protection against infection. Cell Rep. (2023) 42:112658. doi: 10.1016/j.celrep.2023.112658

PubMed Abstract | Crossref Full Text | Google Scholar

14. Arts RJW, Novakovic B, Ter Horst R, Carvalho A, Bekkering S, Lachmandas E, et al. Glutaminolysis and fumarate accumulation integrate immunometabolic and epigenetic programs in trained immunity. Cell Metab. (2016) 24:807–19. doi: 10.1016/j.cmet.2016.10.008

PubMed Abstract | Crossref Full Text | Google Scholar

15. Zhou H, Lu X, Huang J, Jordan P, Ma S, Xu L, et al. Induction of trained immunity protects neonatal mice against microbial sepsis by boosting both the inflammatory response and antimicrobial activity. J Inflammation Res. (2022) 15:3829–45. doi: 10.2147/JIR.S363995

PubMed Abstract | Crossref Full Text | Google Scholar

16. Stothers CL, Burelbach KR, Owen AM, Patil NK, McBride MA, Bohannon JK, et al. β-glucan induces distinct and protective innate immune memory in differentiated macrophages. J Immunol. (2021) 207:2785–98. doi: 10.4049/jimmunol.2100107

PubMed Abstract | Crossref Full Text | Google Scholar

17. Arts RJW, Moorlag SJCFM, Novakovic B, Li Y, Wang SY, Oosting M, et al. BCG Vaccination Protects against Experimental Viral Infection in Humans through the Induction of Cytokines Associated with Trained Immunity. Cell Host Microbe. (2018) 23:89–100.e5. doi: 10.1016/j.chom.2017.12.010

PubMed Abstract | Crossref Full Text | Google Scholar

18. Zhou J, Lv J, Carlson C, Liu H, Wang H, Xu T, et al. Trained immunity contributes to the prevention of Mycobacterium tuberculosis infection, a novel role of autophagy. Emerg Microbes Infect. (2021) 10:578–88. doi: 10.1080/22221751.2021.1899771

PubMed Abstract | Crossref Full Text | Google Scholar

19. Herrera BMT, Torres RM, Juárez CE, et al. Mecanismos moleculares de la respuesta inmune en la tuberculosis pulmonar humana. Rev Inst Nal Enf Resp Mex. 18:327–36.

Google Scholar

20. Kumar S, Ingle H, Prasad DVR, and Kumar H. Recognition of bacterial infection by innate immune sensors. Crit Rev Microbiol. (2013) 39:229–46. doi: 10.3109/1040841X.2012.706249

PubMed Abstract | Crossref Full Text | Google Scholar

21. Ahumada-Cota RE, Hernandez-Chiñas U, Milián-Suazo F, Chávez-berrocal ME, Navarro-Ocaña A, Martínez-Gómez D, et al. Effect and analysis of bacterial lysates for the treatment of recurrent urinary tract infections in adults. Pathogens. (2020) 9:102. doi: 10.3390/pathogens9020102

PubMed Abstract | Crossref Full Text | Google Scholar

22. Rebolledo L, Rodríguez-Vigil C, Carmen L, Llorente E, Guallar M, Villoria J, et al. Bacterial immunotherapy is highly effective in reducing recurrent upper respiratory tract infections in children: a prospective observational study. Eur Arch Otolaryngol. (2023) 280:4519–30. doi: 10.1007/s00405-023-08035-4

PubMed Abstract | Crossref Full Text | Google Scholar

23. Aarestrup PF, Aarestrup MF JV, Aarestrup B, Cheloni EP, and Aarestrup FM. Long-term sublingual bacterial immunotherapy prevents ear, nose and throat infections: A real-life study. SAGE Open Med. (2025) 13. doi: 10.1177/20503121241309514

PubMed Abstract | Crossref Full Text | Google Scholar

24. Carlile SR, Cahill SC, O’Brien EC, Neto NGB, Monaghan MG, and McLoughlin RM. Staphylococcus aureus induced trained immunity in macrophages confers heterologous protection against gram-negative bacterial infection. iScience. (2024) 27:111284. doi: 10.1016/j.isci.2024.111284

PubMed Abstract | Crossref Full Text | Google Scholar

25. Carranza C, Juárez E, Torres M, Ellner JJ, Sada E, and Schwander SK. Mycobacterium tuberculosis growth control by lung macrophages and CD8 cells from patient contacts. Am J Respir Crit Care Med. (2006) 173:238–45. doi: 10.1164/rccm.200503-411OC

PubMed Abstract | Crossref Full Text | Google Scholar

26. Ahmad F, Rani A, Alam A, Zarin S, Pandey S, Singh H, et al. Macrophage: A cell with many faces and functions in tuberculosis. Front Immunol. (2022) 13. doi: 10.3389/fimmu.2022.747799

PubMed Abstract | Crossref Full Text | Google Scholar

27. Knight HR, Ketter E, Ung T, Weiss A, Ajit J, Chen Q, et al. High-throughput screen identifies non inflammatory small molecule inducers of trained immunity. Proc Natl Acad Sci U.S.A. (2024) 121:e2400413121. doi: 10.1073/pnas.2400413121

PubMed Abstract | Crossref Full Text | Google Scholar

28. Ferluga J, Yasmin H, Al-Ahdal MN, Bhakta S, and Kishore U. Natural and trained innate immunity against Mycobacterium tuberculosis. Immunobiology. (2020) 225:151951. doi: 10.1016/j.imbio.2020.151951

PubMed Abstract | Crossref Full Text | Google Scholar

29. Pasco ST and Anguita J. Lessons from bacillus calmette-guérin: harnessing trained immunity for vaccine development. Cells. (2020) 9:2109. doi: 10.3390/cells9092109

PubMed Abstract | Crossref Full Text | Google Scholar

30. Koeken VACM, Verrall AJ, Netea MG, Hill PC, and Van Crevel R. Trained innate immunity and resistance to Mycobacterium tuberculosis infection. Clin Microbiol Infect. (2019) 25:1468–72. doi: 10.1016/j.cmi.2019.02.015

PubMed Abstract | Crossref Full Text | Google Scholar

31. Debisarun PA, Kilic G, De Bree LCJ, Pennings LJ, Van Ingen J, Benn CS, et al. The impact of BCG dose and revaccination on trained immunity. Clin Immunol. (2023) 246:109208. doi: 10.1016/j.clim.2022.109208

PubMed Abstract | Crossref Full Text | Google Scholar

32. Moorlag SJCFM, Khan N, Novakovic B, Kaufmann E, Jansen T, Van Crevel R, et al. β-Glucan Induces Protective Trained Immunity against Mycobacterium tuberculosis Infection: A Key Role for IL-1. Cell Rep. (2020) 31:107634. doi: 10.1016/j.celrep.2020.107634

PubMed Abstract | Crossref Full Text | Google Scholar

33. Zhou J, Lv J, Carlson C, Liu H, Wang H, Xu T, et al. Trained immunity contributes to the prevention of Mycobacterium tuberculosis infection, a novel role of autophagy. Emerg Microbes Infect. (2021) 10:578–88. doi: 10.1080/22221751.2021.1899771

PubMed Abstract | Crossref Full Text | Google Scholar

34. Cervantes-Trujano E, Estrada-García I, Wong Baeza I, Castañeda Casimiro J, and Andaluz Garza MDC. Efectividad de la suspensión bacteriana sublingual en la prevención de infecciones de vías aéreas de repetición en pediatría. Rev allerg Mex. (2023) 70:206. doi: 10.29262/ram.v70i3.1292

PubMed Abstract | Crossref Full Text | Google Scholar

35. Ma J, Liu G, Yao Y, Xu F, and Lv N. The effect of bacterial lysates on patients with recurrent respiratory tract infections: A meta-analysis. Val Health. (2014) 17:A780. doi: 10.1016/j.jval.2014.08.373

PubMed Abstract | Crossref Full Text | Google Scholar

36. Braido F, Tarantini F, Ghiglione V, Melioli G, and Canonica GW. Bacterial lysate in the prevention of acute exacerbation of COPD and in respiratory recurrent infections. Int J COPD. (2007) 2:335–45.

PubMed Abstract | Google Scholar

37. Adams K, Weber KS, and Johnson SM. Exposome and immunity training: How pathogen exposure order influences innate immune cell lineage commitment and function. Int J Mol Sci. (2020) 21:1–26. doi: 10.3390/ijms21228462

PubMed Abstract | Crossref Full Text | Google Scholar

38. Curi R, Newsholme P, Pithon-Curi TC, Pires-De-Melo M, Garcia C, Homem-De-Bittencourt PI, et al. Metabolic fate of glutamine in immune cells. Braz J Med Biol Res. (1999) 32:15–21. doi: 10.1590/s0100-879x1999000100002

PubMed Abstract | Crossref Full Text | Google Scholar

39. Arts RJW, Joosten LAB, and Netea MG. Immunometabolic circuits in trained immunity. Semin Immunol. (2016) 28:425–30. doi: 10.1016/j.smim.2016.09.002

PubMed Abstract | Crossref Full Text | Google Scholar

40. Khan A, Mann L, Papanna R, Won JH, Mani A, Canaday D, et al. Macrophage heterogeneity in human macrophages correlates with autophagy-dependent anti-mycobacterial activity and ability to present antigen to CD4 T cells. J Immunol. (2017) 198:208.8–8. doi: 10.4049/jimmunol.198.Supp.208.8

Crossref Full Text | Google Scholar

41. Khan A, Zhang K, Singh VK, Mishra A, Kachroo P, Bing T, et al. Human M1 macrophages express unique innate immune response genes after mycobacterial infection to defend against tuberculosis. Commun Biol. (2022) 5:480. doi: 10.1038/s42003-022-03387-9

PubMed Abstract | Crossref Full Text | Google Scholar

42. Yang Z, Wang J, Pi J, Hu D, Xu J, Zhao Y, et al. Identification and validation of genes related to macrophage polarization and cell death modes under mycobacterium tuberculosis infection. J Inflammation Res. (2024) 17:1397–411. doi: 10.2147/JIR.S448372

PubMed Abstract | Crossref Full Text | Google Scholar

43. Ferluga J, Yasmin H, Al-Ahdal MN, Bhakta S, and Kishore U. Natural and trained innate immunity against Mycobacterium tuberculosis. Immunobiology. (2020) 225:151951. doi: 10.1016/j.imbio.2020.151951

PubMed Abstract | Crossref Full Text | Google Scholar

44. Zhang H, Chen T, Ren J, Xia Y, Onuma A, Wang Y, et al. Pre-operative exercise therapy triggers anti-inflammatory trained immunity of Kupffer cells through metabolic reprogramming. Nat Metab. (2021) 3:843–58. doi: 10.1038/s42255-021-00402-x

PubMed Abstract | Crossref Full Text | Google Scholar

45. Dang B, Gao Q, Zhang L, Zhang J, Cai H, Zhu Y, et al. The glycolysis/HIF-1α axis defines the inflammatory role of IL-4-primed macrophages. Cell Rep. (2023) 42:112471. doi: 10.1016/j.celrep.2023.112471

PubMed Abstract | Crossref Full Text | Google Scholar

46. Wheelwright M, Kim EW, Inkeles MS, De Leon A, Pellegrini M, Krutzik SR, et al. All- Trans Retinoic Acid–Triggered Antimicrobial Activity against Mycobacterium tuberculosis Is Dependent on NPC2. J Immunol. (2014) 192:2280–90. doi: 10.4049/jimmunol.1301686

PubMed Abstract | Crossref Full Text | Google Scholar

47. Juárez E, Carranza C, Hernández-Sánchez F, León-Contreras JC, Hernández-Pando R, Escobedo D, et al. NOD2 enhances the innate response of alveolar macrophages to Mycobacterium tuberculosis in humans. Eur J Immunol. (2012) 42:880–9. doi: 10.1002/eji.201142105

PubMed Abstract | Crossref Full Text | Google Scholar

48. Rivas-Santiago B, Hernandez-Pando R, Carranza C, Juarez E, Contreras JL, Aguilar-Leon D, et al. Expression of cathelicidin LL-37 during Mycobacterium tuberculosis infection in human alveolar macrophages, monocytes, neutrophils, and epithelial cells. Infect Immun. (2008) 76:935–41. doi: 10.1128/IAI.01218-07

PubMed Abstract | Crossref Full Text | Google Scholar

49. Herrera MT, Juárez E, Guzmán-Beltrán S, Torres M, Luna-Morales VA, Villalana-Alvarez LD, et al. High vitamin D concentrations restore the ability to express LL37 by M. tuberculosis-infected human macrophages. Biomolecules. (2022) 12:268. doi: 10.3390/biom12020268

PubMed Abstract | Crossref Full Text | Google Scholar

50. Juárez E, Carranza C, Hernández-Sánchez F, Loyola E, Escobedo D, León-Contreras JC, et al. Nucleotide-oligomerizing domain-1 (NOD1) receptor activation induces pro-inflammatory responses and autophagy in human alveolar macrophages. BMC Pulm Med. (2014) 14:152. doi: 10.1186/1471-2466-14-152

PubMed Abstract | Crossref Full Text | Google Scholar

51. Strandberg KL, Richards SM, and Gunn JS. Cathelicidin antimicrobial peptide expression is not induced or required for bacterial clearance during Salmonella enterica infection of human monocyte-derived macrophages. Infect Immun. (2012) 80:3930–8. doi: 10.1128/IAI.00672-12

PubMed Abstract | Crossref Full Text | Google Scholar

52. Ahmed A, Tripathi H, Van Meijgaarden KE, Kumar NC, Adiga V, Rakshit S, et al. BCG revaccination in adults enhances pro-inflammatory markers of trained immunity along with anti-inflammatory pathways. iScience. (2023) 26:107889. doi: 10.1016/j.isci.2023.107889

PubMed Abstract | Crossref Full Text | Google Scholar

53. Kleinnijenhuis J, Quintin J, Preijers F, Joosten LAB, Ifrim DC, Saeed S, et al. Bacille Calmette-Guérin induces NOD2-dependent nonspecific protection from reinfection via epigenetic reprogramming of monocytes. Proc Natl Acad Sci U.S.A. (2012) 109:17537–42. doi: 10.1073/pnas.1202870109

PubMed Abstract | Crossref Full Text | Google Scholar

54. Arts RJW, Carvalho A, La Rocca C, Palma C, Rodrigues F, Silvestre R, et al. Immunometabolic pathways in BCG-induced trained immunity. Cell Rep. (2016) 17:2562–71. doi: 10.1016/j.celrep.2016.11.011

PubMed Abstract | Crossref Full Text | Google Scholar