Two-stage metabolic remodelling in macrophages in response to lipopolysaccharide and interferon-γ stimulation

Abstract

In response to signals associated with infection or tissue damage, macrophages undergo a series of dynamic phenotypic changes. Here we show that during the response to LPS and interferon-γ stimulation, metabolic reprogramming in macrophages is also highly dynamic. Specifically, the TCA cycle undergoes a two-stage remodeling: the early stage is characterized by a transient accumulation of intermediates including succinate and itaconate, while the late stage is marked by the subsidence of these metabolites. The metabolic transition into the late stage is largely driven by the inhibition of pyruvate dehydrogenase complex (PDHC) and oxoglutarate dehydrogenase complex (OGDC), which is controlled by the dynamic changes in lipoylation state of both PDHC and OGDC E2 subunits and phosphorylation of PDHC E1 subunit. This dynamic metabolic reprogramming results in a transient metabolic state that strongly favors HIF-1α stabilization during the early stage, which subsides by the late stage; consistently, HIF-1α levels follow this trend. This study elucidates a dynamic and mechanistic picture of metabolic reprogramming in LPS and interferon-γ stimulated macrophages, and provides insights into how changing metabolism can regulate the functional transitions in macrophages over a course of immune response.

β-Hydroxybutyrate Oxidation Promotes the Accumulation of Immunometabolites in Activated Microglia Cells

“…The extracellular metabolic environment and the dynamic changes in the intracellular metabolic milieu orchestrated by metabolic reactions modulate the response to immune signals. In peripheral immune cells, the mechanisms by which inflammation affects energy metabolism are now well-established [ 9 , 10 ]. Similarly, in microglia, recent findings indicate that this immune cell type engages with different metabolic pathways depending on the pattern of stimulation [ 11 , 12 ].…”

Autofluorescence Imaging of 3D Tumor–Macrophage Microscale Cultures Resolves Spatial and Temporal Dynamics of Macrophage Metabolism

“…This study first confirmed that metabolic autofluorescence imaging can quantify changes in macrophage metabolism and distinguish macrophage subpopulations within intact, living samples. Differences in NAD(P)H and FAD autofluorescence were first observed in 2D cytokine-stimulated macrophages over 72 hours and reflected the expected metabolic shifts for M(IFN-γ) and M(IL4/IL13) macrophages (Fig 2) (43)(44)(45). This result is consistent with previous metabolic flux and metabolite accumulation studies showing increased glycolysis in LPS-or IFN-γ+TNF-α-stimulated (M1-like) macrophages and increased TCA cycle in IL-4-stimulated (M2-like) macrophages, as well as NAD(P)H lifetime studies distinguishing IFN-γ/LPS-treated and IL-4/IL-13 treated mouse bone marrow-derived macrophages (BMDMs) (16).…”

“…This analysis also demonstrates the sensitivity of autofluorescence measurements to temporal functional plasticity commonly reported during macrophage polarization. For example, the bimodal populations observed in redox ratio of polarized macrophages may reflect early-and late-phase metabolic reprogramming shown in recent studies to promote initial upregulation of glycolytic or oxidative processes prior to reestablishing a resting metabolic state (43,46). Notably, autofluorescence imaging provides a unique tool to monitor metabolically-distinct cell sub-populations, unlike standard metabolic flux or metabolite production measurements that monitor bulk changes within a pooled population of cells.…”

Discovery of protein modifications using high resolution differential mass spectrometry proteomics

“…To explore this idea, we sought to map protein modifications induced during mammalian cell differentiation. We chose to study the response of mouse RAW264.7 macrophage cells to lipopolysaccharide (LPS), a potent inducer of macrophage activation and differentiation that involves extensive protein and metabolic signaling (Alasoo et al 2015;Kamal et al 2018;Rattigan et al 2018;Seim et al 2019). We treated RAW264.7 cells with LPS using established methods and analyzed the resultant proteomes by two-dimensional nanoscale liquid chromatography and high-resolution mass spectrometry proteomics (Cifani et al 2018).…”