Immunometabolism at the interface between macrophages and pathogens

Abstract

It is generally regarded that the progression of an infection within host macrophages is the consequence of a failed immune response. However, recent appreciation of macrophage heterogeneity, with respect to both development and metabolism, indicates that the reality is more complex. Different lineages of tissue-resident macrophages respond divergently to microbial, environmental and immunological stimuli. The emerging picture that the developmental origin of macrophages determines their responses to immune stimulation and to infection stresses the importance of in vivo infection models. Recent investigations into the metabolism of infecting microorganisms and host macrophages indicate that their metabolic interface can be a major determinant of pathogen growth or containment. This Review focuses on the integration of data from existing studies, the identification of challenges in generating and interpreting data from ongoing studies and a discussion of the technologies and tools that are required to best address future questions in the field.

Figure 1 |. Phenotypic characteristics of the M1-like and M2-like macrophage polarization states.

The M1-like and M2-like macrophage polarization states are an in vitro paradigm that highlights the pro-inflammatory or anti-inflammatory phenotype of these cells but oversimplifies the highly specialized, transcriptomically dynamic and extremely heterogeneous nature of macrophages in vivo. In brief, M0 macrophages are activated to an M1-like macrophage status by exposure to IFNγ and/or tumour necrosis factor (TNF), or LPS, whereas M2-like macrophages are induced through treatment with IL-4 and/or IL-13. M1-like macrophages are more glycolytically active and markedly upregulate activities that are associated with microbicidal responses. By contrast, M2-like macrophages have increased mitochondrial respiration and enhanced fatty acid metabolism and upregulate activities that are associated with tissue remodeling or wound healing. ECM, extracellular matrix; NO, nitric oxide; OXPHOS, oxidative phosphorylation; TCA, tricarboxylic acid cycle.

Figure 2 |. Modulation of macrophage metabolism by microbial mediators.

Microorganisms modulate the metabolic status of macrophages through the activation of innate immune signalling pathways or through the use of specific effectors, known as ‘virulence’ factors. The figure summarizes the major pattern recognition receptors (PRRs) found at the cell surface of macrophages and inside the cells, together with key components of their signalling pathways. Microorganisms activate Toll-like receptor (TLR) signalling pathways in host cells through their nucleic acids and cell wall constituents — known as pathogen-associated molecular patterns (PAMPs) — which drive macrophages into a state of enhanced glycolysis and increased production of lactate. The TLRs sense their ligands within the endosomal network as well as at the cell surface. They use different adaptor proteins that can modulate the outcome of TLR ligation, although the dominant responses are linked to either the production of pro-inflammatory cytokines through the activation of nuclear factor-κB (NF-κB) or the induction of a type I interferon response through interferon regulatory factor 3 (IRF3) and IRF7. PAMPs that are delivered into the cell cytosol by bacterial secretion systems activate signalling pathways through NOD-like receptors (NLRs), retinoic acid inducible gene I (RIG-I) and melanoma differentiation-associated gene 5 (MDA5), with similar metabolic and cellular outcomes. The metabolic shift in the macrophages is accompanied by the upregulation of antimicrobial responses, including expression of indoleamine 2,3-dioxygenase (IDO) to limit tryptophan availability, production of itaconate through the activity of immunoresponsive gene 1 (IRG1) to intoxicate bacterial metabolism, and enhanced production of reactive oxygen and nitrogen species. ds, double-stranded; HIF1α, hypoxia inducible factor 1α; IKK, inhibitor of NF-κB kinase; IRAK, IL-1 receptor-associated kinase; LPS, lipopolysaccharide; MYD88, myeloid differentiation primary-response gene 88; NOS2, inducible nitric oxide synthase; TBK1, TANK-binding kinase 1; TIRAP, Toll/IL-1R (TIR)-domain-containing adaptor protein; TRAF, TNF receptor associated factor; TRAM, TRIF-related adaptor molecule; TRIF, TIR-domain-containing adaptor protein inducing IFNβ.

Figure 3 |. The links between macrophage phenotype and Salmonella Typhimurium physiology.

In mice infected with Salmonella enterica serovar Typhimurium, the presence of ‘persister’ bacteria was shown in splenic macrophages^,. These bacteria were non-replicating and had strong antibiotic tolerance. In vitro experiments established that the induction of a drug-tolerant phenotype was greatly enhanced in bacteria present in macrophages activated by IFNγ. Single-cell RNA-sequencing of in vitro-infected, bone marrow-derived macrophages indicated that rapidly growing bacteria were found mainly in macrophages expressing markers of M2-like polarization. These data suggested that the in vivo induction of drug tolerance in S. Typhimurium was most likely due to M1-like macrophages, and that support of bacterial growth was most likely due to macrophages with an M2-like phenotype.

Figure 4 |. The links between macrophage phenotype and the progression of Mycobacterium tuberculosis infection in non-human primates and mice.

a | In macaque monkeys infected with Mycobacterium tuberculosis, the bacterial burden in individual granulomas and progression to active disease were found to correlate directly with an increased ratio of macrophages expressing M2 markers over Ml markers^,. Progression was determined at the level of the individual granuloma without any consensus at the level of the host. b | In mice infected with fitness and replication reporter strains of M. tuberculosis, resident alveolar macrophages were more permissive of bacterial replication than were recruited interstitial macrophages. RNA-sequencing showed that the alveolar macrophages were more committed to fatty acid oxidation (FAO) and oxidative phosphorylation, whereas the interstitial macrophages had higher levels of glycolysis. Selective depletion of the alveolar macrophage population reduced the bacterial burden, whereas depletion of the interstitial macrophage population led to an increase in bacterial burden. Therefore, in agreement with the data from non-human primates^,, the ratio of alveolar (M2-like) macrophages to interstitial (M1-like) macrophages affects bacterial growth and disease progression in mice. In addition, drug-tolerant M. tuberculosis were more abundant in vivo in macrophages expressing M1-like activation markers.

Figure 5 |. Technologies and tools for investigating the metabolic interplay between host and pathogen.

The figure illustrates how fluorescent microbial fitness and replication reporter strains (BOX 2) can be used to infect appropriate animal models to generate in vivo heterogeneity of relevance to disease progression or control. Infected tissues are analyzed by the generation of single cell suspensions for flow cytometry to yield cell populations defined by immune markers and bacterial fitness profiles. These cell suspensions can be sorted according to the host or microbial readout of relevance and then assessed further by various RNA-sequencing protocols to define the transcriptomes of both host and pathogen, and by metabolic profiling through the analysis of intermediates or through metabolic flux studies. Small molecule inhibitors or drugs specific for host or microbial targets can be used to perturb the system and test hypotheses relevant to the metabolic interface between host cells and pathogens. Ultimately the data generated in the model systems need to be integrated with data from actual human disease to test its validity.