A significant feature of macrophages is dynamic plasticity, expressed by their ability to polarize towards distinct activation states (Lundahl et al., 2017; Sica et al., 2015). Activation with interferon gamma (IFNγ) together with lipopolysaccharides (LPS) yields the bactericidal and pro-inflammatory ‘classically activated’ or M1 macrophages (Ferrante and Leibovich, 2012; Sica et al., 2015), whereas activation with the type 2 and regulatory cytokines, interleukin (IL)–4, IL-13, IL-10 and transforming growth factor (TGF)β, results in ‘alternatively activated’ M2 macrophages, which enhance allergic responses, resolve inflammation and induce tissue remodeling (Bystrom et al., 2008; Mantovani et al., 2004). Another key difference between these activation states are their metabolic profiles: whereas classically activated macrophages switch to near exclusive use of glycolysis to drive their ATP production, alternatively activated macrophages instead upregulate their oxidative phosphorylation (OXPHOS) machinery (Van den Bossche et al., 2016; Van den Bossche et al., 2017).

Macrophages are key host innate immune cells for controlling the causative agent of tuberculosis (TB), Mycobacterium tuberculosis (Cohen et al., 2018). M. tuberculosis has arguably caused the most deaths of any pathogen in human history and continues to be the cause of more than a million deaths annually: in 2020, 10 million people fell ill with active TB and 1.5 million died from the disease (World Health Organization team, 2021). Whilst classifying macrophages into classical and alternative activation states is a simplification – the reality is a broad spectrum of various differentiation states that is continuously regulated by a myriad of signals (Sica et al., 2015) – it is nevertheless considered that for a host to control TB infection, classical macrophage activation is vital (Jouanguy et al., 1999; Philips and Ernst, 2012). Furthermore, a macrophage metabolic shift to glycolysis has been postulated to be a key for effective killing and thus overall control of TB infection (Gleeson et al., 2016; Huang et al., 2018). By contrast, alternatively activated macrophages directly oppose bactericidal responses, which has been demonstrated to lead to enhanced bacterial burden and TB pathology (Moreira-Teixeira et al., 2016; Shi et al., 2019). Due in part to the established ability of classically activated macrophages to kill M. tuberculosis, inducing Th1 immunity has been a key aim for TB vaccine development (Abebe, 2012; Andersen and Kaufmann, 2014; Ottenhoff et al., 2010). Apart from targeting adaptive immune memory, another promising approach has emerged in recent years: bolstering innate immune killing capacity by the induction of innate training (Khader et al., 2019; Moorlag et al., 2020).

Innate training is regarded as a form of immunological memory. It is a phenomenon where a primary challenge, such as a vaccination or an infection, induces epigenetic changes in innate immune cells, which alters their responses following a secondary challenge (Arts et al., 2018; Saeed et al., 2014; van der Meer et al., 2015). Unlike adaptive immune memory, the secondary challenge does not need to be related to the primary challenge. For instance, Bacille Calmette-Guérin (BCG) vaccination has been demonstrated to protect severe combined immunodeficiency (SCID) mice from lethal Candida albicans infection; reducing fungal burden and significantly improving survival (Kleinnijenhuis et al., 2012). This ability of the BCG vaccine to train innate immunity is now believed to be a mechanism by which it induces its protection against M. tuberculosis. Apart from the BCG vaccine, it has been demonstrated that other organisms and compounds can induce innate training, such as β-glucan (van der Meer et al., 2015) which induced protective innate training against virulent M. tuberculosis, as shown by enhanced mouse survival following in vivo infection, and enhanced human monocyte pro-inflammatory cytokine secretion following ex vivo infection (Moorlag et al., 2020).

Conversely, there is a risk that certain immune challenges could lead to innate immune re-programming that hinders protective immune responses. For instance, recent reports have demonstrated how virulent M. tuberculosis (Khan et al., 2020) and mycobacterial phenolic glycans (Lundahl et al., 2020) can program macrophages to attenuate bactericidal responses to subsequent mycobacterial challenge. In the current study, macrophage activation caused by former or concurrent parasitic infections is considered regarding the possibility of innate immune re-programming that hinders protective immune responses against this disease. Geographically, there is extensive overlap between tuberculosis endemic areas and the presence of helminths (Salgame et al., 2013). With regard to macrophage activation and combatting tuberculosis, this is an issue as parasites induce type 2 responses, leading to alternative macrophage activation instead of classical activation and associated bactericidal responses (Chatterjee et al., 2017; Li and Zhou, 2013). Indeed, it was recently demonstrated how products of the helminth Fasciola hepatica can train murine macrophages for enhanced secretion of the anti-inflammatory cytokine IL-10 (Quinn et al., 2019).

Due to the relevance of macrophage plasticity for the control of mycobacterial infection, we primarily focused upon macrophage activation and training in this context. Unexpectedly, our data indicates that macrophage activation with IL-4 and IL-13 induces innate training that enhances pro-inflammatory and bactericidal responses against mycobacteria. Although murine macrophages trained with IL-4 and IL-13 resemble classically activated macrophages, we identify that they do not adopt their typical metabolic profile, instead retaining heightened OXPHOS activity and notably lacking a dependency on glucose and glycolysis. Lastly, we identify IL-10 as a negative regulator of this innate training response, which may have obscured previous identification of this macrophage phenotype.

Although ‘classical’ and ‘alternative’ activation are used to describe the two extremes of macrophage polarization, the reality is that a broad spectrum of activation states exist, affected by a multitude of signals. Macrophages are key immune cells for determining the outcome of TB disease, and growing evidence indicates that the macrophage population during M. tuberculosis infection is highly heterogeneous. Thus, elucidating which subtypes best impede the bacterium is critical for understanding disease control (Khan et al., 2019). Alternative macrophage activation is particularly relevant to TB pathology, both because alveolar macrophages – the initial hosts of M. tuberculosis upon infection – are biased towards alternative activation (Huang et al., 2019), and also considering the influence of concurrent parasitic infection. Given its ultimate relevance in TB pathology, macrophage activation was used as the framework for the work presented herein. The results demonstrate that stimuli of alternative macrophage activation impact innate immune memory, which provides an additional dimension to the noted diversity among macrophage populations.

The current work demonstrates that prior activation with IL-4 and IL-13 enhances anti-mycobacterial responses towards a subsequent challenge, including enhanced inflammatory cytokine secretion (Figure 2A) and BCG killing capacity (Figure 1D). Regarding how this phenomenon is driven, inhibition of DNA methylation prior to activation with IL-4 and IL-13 impeded the enhanced inflammatory cytokine secretion induced by a secondary challenge, suggesting that this effect is mediated, at least in part, via epigenetic modification (Figure 2—figure supplement 3A). However, the role of epigenetic changes – a hallmark for innate training – for the induction of these enhanced responses remains to be addressed in future studies. A role for DNA methylation is in part supported by prior work showing that epigenetic reprogramming is a key component of alternative macrophage activation – crucially de-methylation of H3K27me3 at promoters of key genes such as Arg1, Retnla and Chitl3 is required for their expression (Ishii et al., 2009; Liu et al., 2017). However, whether certain epigenetic changes are sustained, or perhaps altered, between the acute activation stage on Day 0 and the secondary stimulation on Day 6 remains to be investigated. Furthermore, given that epigenetic modification is important for alternative macrophage activation, impeding DNA methylation may have also had a downstream effect upon IL-4 and IL-13 activation, which in turn could have resulted in the reduced inflammatory cytokine secretion to subsequent challenge.

Although further research is required to comprehensively map which mechanism is driving this innate training phenomenon, the current study identifies a macrophage phenotype that is counterintuitively induced by cytokines previously associated with suboptimal mycobacterial containment. Whilst alternatively activated macrophages have been demonstrated to provide an intracellular environment that is favorable for TB growth (Kahnert et al., 2006), resulting in greater bacterial burden (Moreira-Teixeira et al., 2016) in an acute setting, there have been some conflicting data concerning the interplay between parasites and mycobacterial infection. Parasitic infection has been shown to enhance mycobacterial bacterial burden in vivo (Monin et al., 2015; Potian et al., 2011) and enhance human TB pathology (Amelio et al., 2017; Mabbott, 2018), but has in some cases also been shown to enhance protection against mycobacterial infection (Aira et al., 2017; O’Shea et al., 2018). Furthermore, discrepancies have been highlighted specifically regarding macrophage activation. In a model of M. tuberculosis macrophage infection in vitro, prior incubation (48 hr) with antigens from Hymenolepis diminuta, Trichuris muris and Schistosoma mansoni resulted in alternative macrophage activation, but only incubation with the H. diminuta and T. muris antigens resulted in enhanced mycobacterial growth (Aira et al., 2017). Moreover, the increased growth was accompanied by enhanced secretion of IL-10. This however was not surprising, given that IL-10 has been demonstrated to prevent phagolysosome maturation in human macrophages (O’Leary et al., 2011) and promote TB disease progression in mice (Beamer et al., 2008). Herein, an additional mechanism is proposed, where IL-10 reduces IL-4 and IL-13 induced macrophage training and subsequent control of mycobacterial challenge.

BMDM activation with IL-4 and IL-13, with or without the addition of IL-10, resulted in similar activation states: comparable expression of Arg1, Retnla and Chil3 on Day 0 (Figure 6B) and 7 (Figure 6—figure supplement 1C7), reduced expression of CD80 and upregulation of CD206 on Day 0 (Figure 6—figure supplement 1B), as well as reduced secretion of cytokines TNFα, IL-6 and IL-10 following stimulation with Mtb on Day 0 (Figure 6—figure supplement 1AA). Whilst this hyporesponsive profile was maintained in the M(4/13/10), the trained M(4/13) on the other hand demonstrated enhanced pro-inflammatory and bactericidal mechanisms in response to mycobacterial challenge a week after initial activation (Figure 6). This intimated that IL-10 was a negative regulator of innate training induced by IL-4 and IL-13 and indicates that distinct cytokine-mediated modes of alternative macrophage activation differ regarding their innate memory programming. The ability of IL-10 to modulate innate training was herein further demonstrated by investigating innate memory induced by β-glucan (Figure 6G), where the addition of IL-10 during the training stage resulted in attenuated cytokine secretion following secondary LPS stimulation. This discovery has thereby revealed an exciting avenue for future research in this field.

In response to mycobacterial challenge the IL-4 and IL-13 trained cells adopted a phenotype similar to classically activated BMDMs: a skewing towards pro-inflammatory cytokine secretion with concomitant increased expression of Nos2 and NO production (Figure 2), as well as enhanced mycobacterial killing capacity (Figure 1). Classical macrophage activation is intrinsically linked with a glycolytic shift in metabolism; it is thus not surprising that prior work has identified an enhancement of glycolysis as critical for efficient M. tuberculosis killing (Gleeson et al., 2016; Huang et al., 2018), and that impeding glycolysis attenuates the ability of macrophages to control TB infection (Hackett et al., 2020). Moreover, upon M. tuberculosis infection, macrophages appear to undergo a biphasic metabolic profile, where they switch from an initial increase in glycolytic metabolism to an enhancement of the TCA cycle and OXPHOS, a switch which allows mycobacterial survival and disease progression (Shi et al., 2019). As such, it was expected that the trained M(4/13) would shift towards glycolytic metabolism. However, this was not the case, as Mtb stimulation resulted in enhanced OCR and SRC, indicating the use and upregulation of OXPHOS (Figure 3). Furthermore, LC-MS analysis of metabolites indicated increased use of FAO, as well as enhanced creatine, further supporting the continued use of M2-typical metabolism. This was moreover confirmed with the use of the ATP synthase inhibitor oligomycin, where it was demonstrated that inhibition of OXPHOS reduced trained M(4/13) cytokine secretion; by contrast, TNFα upregulation in M(IFNγ/LPS) was unaffected. Furthermore, when trained M(4/13) were incubated in a glucose depleted environment during the week preceding secondary stimulation, the pro-inflammatory shift was enhanced as reflected in increased TNFα and IL-6 secretion combined with reduced IL-10 production (Figure 4B). This was in stark contrast to the effect of glucose depletion on untrained M(-) and trained M(IFNγ/LPS), where the secretion of all measured cytokines was attenuated. This contrast between M(4/13) and M(IFNγ/LPS) cemented their differences regarding metabolic dependency on glucose. It should be noted that prior work has identified differences in macrophage metabolic profiles in response to infection with live M. tuberculosis compared with the killed bacterium or infection with attenuated BCG (Cumming et al., 2018). However, the consensus is that classic macrophage activation, and an accompanying shift to glycolytic metabolism, is paramount for effective mycobacterial killing and propagation of inflammatory responses. It is surprising therefore that these results summarily intimated that trained M(4/13) do not rely on glycolysis to fuel anti-mycobacterial responses.

The addition of a glycolysis inhibitor reduced cytokine secretion, from M(-), M(IFNγ/LPS), as well as M(4/13) following secondary stimulation (Figure 3—figure supplement 2A-B). That inhibition of glycolysis impeded pro-inflammatory cytokine secretion, but glucose depletion did not, indicated that glycolysis in trained M(4/13) was fueling the TCA cycle and downstream OXPHOS, and upon glucose depletion, other pathways, such as glutaminolysis, were compensating for this loss. Glutaminolysis has been shown to be upregulated following macrophage activation with IL-4 and has been demonstrated to compensate for glycolysis inhibition in driving the TCA cycle and OXPHOS (Wang et al., 2018). In the present study, it was moreover observed that the use of a glutaminolysis inhibitor reduced TNFα and IL-6 secretion following secondary stimulation in trained M(4/13) specifically, and not in untrained M(-) or trained M(IFNγ/LPS) (Figure 4A). This work suggests that OXPHOS can be important for pro-inflammatory responses, an observation that is supported by previous work demonstrating that mitochondrial stress is a mechanism of macrophage tolerance (Timblin et al., 2021) and that the ETC is important for nod-like receptor family pyrin domain containing 3 (NLRP3) inflammasome activity (Billingham et al., 2022). In the latter study, however, LPS induced TNFα secretion was not impeded by inhibition of ETC complexes I and II, demonstrating once more that the metabolic phenotype of the trained M(4/13), explored in the current study, is highly unexpected and warrants further study.

Regarding classical macrophage activation, in the current study concurrent LPS and IFNγ stimulation did not appear to lead to tolerance, as secondary stimulation with Mtb on Day 0 (Figure 2—figure supplement 1D) or Day 6 (Figure 2A) resulted in enhanced TNFα and dampened IL-10 secretion. This is somewhat contrary to prior work demonstrating how LPS induces tolerance in macrophages (Domínguez-Andrés et al., 2019; Timblin et al., 2021). This discrepancy may be due to the relatively low concentration of LPS employed in the present study, and/or the concurrent stimulation with IFNγ. However, although tolerance was not induced, for the classically activated BMDMs and MoDMs there was a reduction in viability over time; as a result there were too few cells to reach the detection limit to measure extracellular flux in the trained M(IFNγ/LPS) BMDM. It could be that the metabolic switch to aerobic glycolysis results in a macrophage phenotype that cannot be sustained over such a long period in culture. Overall, this presumed loss of viability greatly limited the number of experiments where trained M(IL-4/13) could be directly compared with trained M(IFNγ/LPS). Although further investigation of long-term activation states of M(IFNγ/LPS) are warranted, the accumulated results for M(IFNγ/LPS) in the current study demonstrate the distinctive metabolic phenotype driving the inflammatory responses in the trained M(IL-4/13).

The metabolic profile in trained M(4/13), as summarized in Figure 5, is not only distinct from classically activated macrophages, but also from that seen with other training stimuli, such as β-glucan. Innate training is being tested as a means to bolster innate immunity against M. tuberculosis (Khader et al., 2019) and it was recently reported that training with β-glucan was protective against subsequent M tuberculosis infection, as seen by enhanced human monocyte pro-inflammatory cytokine secretion and increased mouse survival in vivo (Moorlag et al., 2020). Similar to classical macrophage activation, β-glucan training results in a glycolytic shift, as demonstrated in human monocytes (Cheng et al., 2014). As such, the phenotype of the trained M(4/13) is distinct from other macrophage phenotypes previously demonstrated to protect against TB, and thus offers an additional avenue for future research regarding strategies for combatting this disease.

The majority of the presented experiments were carried out in a murine in vitro system, using live BCG or irradiated M. tuberculosis H37Rv. Therefore, future work should address the translation of our findings to TB in vivo models, as well as in the context of human TB disease pathology. Regarding metabolic profiles, prior work has identified differences in human macrophage metabolic phenotypes in response to infection with live virulent M. tuberculosis compared with the killed bacterium or infection with BCG; BCG induced greater glycolytic metabolism, whilst infection with virulent M. tuberculosis H37Rv resulted in a quiescent phenotype where both extracellular acidification and oxygen consumption were dampened with increasing MOI (Cumming et al., 2018). Although preliminary work herein has demonstrated that human monocyte derived macrophages trained with IL-4 and IL-13 show a markedly enhanced secretion of TNFα in response to secondary stimulation with either irradiated Mtb (Figure 7) or PAM3CSK4 (Figure 7—figure supplement 1), the metabolic profile driving these responses remains to be investigated, as well as how this macrophage activation state would impact on the outcome of infection with virulent M. tuberculosis.

Van den Bossche et al., 2016 highlighted a key adaptive distinction between classical and alternative macrophage activation: that alternatively activated macrophages can be ‘re-polarized’ due to their metabolic versatility, whereas the mitochondrial dysfunction which occurs during classical macrophage activation prevents such reprogramming. The current study identifies an additional adaptive capacity where activation of macrophages with IL-4 and IL-13 programs BMDMs to better respond to a subsequent mycobacterial challenge, whilst crucially retaining their metabolic diversity. In conclusion, our work presents a new insight into how innate training with IL-4 and IL-13 can enhance macrophage pro-inflammatory responses and mycobacterial killing. This unexpected finding shows how macrophage plasticity belies the usual M1-M2 dichotomy and provides a new framework to explore the impact of comorbidities, such as parasitic infections, on TB disease burden.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data were submitted to Mendeley (https://doi.org/10.17632/ncbph43m85.3).

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Author details

1. Mimmi LE Lundahl

   1. School of Biochemistry and Immunology, Adjuvant Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
   2. School of Chemistry, Scanlan Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   lundahlm@tcd.ie

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3924-4072

2. Morgane Mitermite

   School of Veterinary Medicine, UCD Veterinary Sciences Centre, University College Dublin, Dublin, Ireland

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Dylan Gerard Ryan

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9169-2134

3. Dylan Gerard Ryan

   1. School of Biochemistry and Immunology, Inflammation Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
   2. Hutchison/MRC Research centre, MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Morgane Mitermite

   Competing interests

   No competing interests declared

4. Sarah Case

   School of Biochemistry and Immunology, Macrophage Homeostasis Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

   Contribution

   Methodology

   Competing interests

   No competing interests declared

5. Niamh C Williams

   School of Biochemistry and Immunology, Inflammation Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Ming Yang

   Hutchison/MRC Research centre, MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Roisin I Lynch

   School of Biochemistry and Immunology, Adjuvant Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

   Contribution

   Methodology

   Competing interests

   No competing interests declared

8. Eimear Lagan

   School of Genetics and Microbiology, Department of Genetics, Trinity College Dublin, Dublin, Ireland

   Contribution

   Methodology

   Competing interests

   No competing interests declared

9. Filipa M Lebre

   School of Biochemistry and Immunology, Adjuvant Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Aoife L Gorman

    School of Biochemistry and Immunology, Adjuvant Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

    Contribution

    Resources, Formal analysis, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Bojan Stojkovic

    School of Veterinary Medicine, UCD Veterinary Sciences Centre, University College Dublin, Dublin, Ireland

    Contribution

    Formal analysis, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Adrian P Bracken

    School of Genetics and Microbiology, Department of Genetics, Trinity College Dublin, Dublin, Ireland

    Contribution

    Investigation

    Competing interests

    No competing interests declared

13. Christian Frezza

    Hutchison/MRC Research centre, MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. Frederick J Sheedy

    School of Biochemistry and Immunology, Macrophage Homeostasis Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

    Contribution

    Investigation

    Competing interests

    No competing interests declared

15. Eoin M Scanlan

    School of Chemistry, Scanlan Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

    Contribution

    Formal analysis, Supervision, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

16. Luke AJ O'Neill

    School of Biochemistry and Immunology, Inflammation Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

    Contribution

    Formal analysis, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

17. Stephen V Gordon

    School of Veterinary Medicine, UCD Veterinary Sciences Centre, University College Dublin, Dublin, Ireland

    Contribution

    Conceptualization, Resources, Formal analysis, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

18. Ed C Lavelle

    School of Biochemistry and Immunology, Adjuvant Research Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

    Contribution

    Conceptualization, Supervision, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    lavellee@tcd.ie

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3167-1080

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