Akt-mTORC1 signaling regulates Acly to integrate metabolic input to control of macrophage activation

1Macrophages are pleiotropic cells that assume a variety of functions depending on tissue of residence and tissue state. Their ability to acquire diverse, context-dependent activities requires activation (or polarization) to distinct functional states, triggered by various factors including microbial products, cytokines, and growth factors. M1 or classical activation is triggered during infection by microbial products including LPS, leading to the transcriptional upregulation of genes encoding antimicrobial activities and inflammatory cytokines. M2 or alternative activation is triggered by IL-4 and IL-13 produced during parasite infections, and activates the transcription factor Stat6 to induce a transcriptional program that coordinates fibrosis, tissue remodeling, and Type 2 inflammation. Therefore, the induction of multi-component transcriptional programs underpins macrophage activation.

2While macrophage activation is relatively well-understood at the level of signal transduction, transcriptional regulation, and acquisition of new effector activities, the metabolic underpinnings remain less clear. An emerging view is that macrophage activation to particular states is associated with distinct metabolic shifts. For example, M1 macrophages upregulate glucose and glutamine utilization, while M2 macrophages augment β-oxidation and glutamine consumption. Importantly, such metabolic shifts critically support macrophage activation. Increased glycolytic flux in M1 macrophages is coupled to de novo lipogenesis, which enables ER and Golgi expansion and production of high levels of inflammatory cytokines. Another consequence of enhanced glycolysis is accumulation of the TCA cycle metabolite succinate, leading to stabilization of the transcription factor HIF-1α and transcriptional induction of Il1b and other target genes in the M1 macrophage. How oxidative metabolism boosts M2 activation is not clear, but glutamine metabolism fuels production of UDP-GlcNAC, an important modification of multiple M2 markers.

3Consistent with the idea that macrophage activation is supported by metabolic shifts, recent studies indicate that macrophage polarizing signals impinge on metabolic signaling pathways. Polarizing signals like LPS and IL-4 regulate the activity of Akt, mTORC1, and AMPK, presumably to coordinate metabolic processes that critically underlie macrophage polarization. Limited studies indicate that perturbing the activity of these metabolic regulators impairs macrophage metabolism and activation. For example, Akt mediates enhanced glycolysis to support lipid synthesis and inflammatory cytokine secretion in M1 macrophages. Akt similarly stimulates glucose-fueled lipid synthesis in growing and proliferating cells, where lipids are used to build cellular membranes. Therefore, M1 macrophages co-opt a metabolic process (Akt-dependent lipogenesis) in order to coordinate a macrophage-specific function (inflammatory cytokine secretion). In general, however, how polarizing signals control metabolic shifts, and the full implications of this for control of macrophage activation, remains poorly understood.

4Here we show that integration of the Akt-mTORC1 pathway into IL-4 signaling allows for selective control of some M2 responses. Control is exerted at the level of Acly, a key enzyme in Ac-CoA production, thereby modulating histone acetylation and transcriptional induction of a subset of M2 genes. Consistent with its role as an important metabolic sensor, the Akt-mTORC1 pathway couples metabolic input to such gene-specific control. Our findings also reveal subsets of the M2 response, including chemokine production and cellular proliferation, that are linked to metabolic state by Akt-mTORC1 signaling.

5Akt is a major metabolic regulator implicated in M2 activation, but the underlying mechanisms remain poorly characterized. To begin to address this question, we employed unbiased metabolic profiling of M2 macrophages, using LC/MS-based metabolomics and a platform that measures ~290 small metabolites representative of all major pathways of intermediary metabolism. Top enriched pathways include urea cycle and arginine and proline metabolism, consistent with previous studies indicating upregulation of arginine metabolism in M2 macrophages, as well as amino acid utilization and metabolism and nucleotide metabolism (Figure 1A, Supplementary file 1). Other top enriched pathways include glycolysis, amino sugar metabolism, and glycine, serine, and threonine metabolism, suggesting altered flux through glycolysis and glycolytic shunts (Figure 1A, Supplementary file 1).

6As M2 activation is thought to be sustained by fatty acid rather than glucose utilization, we decided to re-examine the role of glycolysis in M2 macrophages. We found that BMDMs increased glucose uptake in a time-dependent manner in response to IL-4 treatment. Such increase was reduced by cotreatment with the Akt inhibitor MK2206 (Figure 1B), indicating control by Akt and consistent with a role for Akt in regulating glycolysis in many settings. Moreover, enhanced glucose consumption in M2 macrophages was associated with an Akt-dependent increase in both glycolysis and oxidative metabolism, as indicated by extracellular flux assays (Figure 1C). Importantly, glycolytic flux was needed for optimal implementation of the M2 program. Similar to the β-oxidation inhibitor etomoxir, the glycolysis inhibitor 2-DG reduced IL-4-mediated induction of some M2 genes (Figure 1D). Therefore, Akt mediates enhanced glucose consumption in M2 macrophages, and this contributes to induction of M2 gene expression. Such glucose consumption may also fuel production of UDP-Glc-NAc, the substrate for glycosylation of some M2 markers. In contrast, Akt does not control β-oxidation in M2 macrophages (Figure 1E).

7Because the increase in glucose utilization was relatively modest, we considered that Akt could play additional roles in control of M2 activation and turned to an analysis of M2 gene regulation. We examined induction of Retnla, Arg1, Mgl2, Chi3l3, Cd36, and Fabp4, “hallmark” M2 genes commonly used in studies of M2 activation. Consistent with the role of Stat6 as a transcriptional master regulator of M2 activation, induction of these M2 genes was ablated in Stat6 KO BMDMs (Figure 2—figure supplement 1A). Importantly and as reported, Akt activity controlled the induction of a subset of M2 genes. In the presence of the Akt inhibitor MK2206, induction of Arg1, Retnla, and Mgl2 was reduced ~40–80%, while Chi3l3, Cd36, and Fabp4 were not affected (or even super-inducible) (Figure 2A). Use of a structurally distinct Akt inhibitor, Aktviii, yielded similar results, suggesting specificity in inhibition (data not shown). Below, these two groups of genes will be referred to as Akt-dependent and Akt-independent M2 genes, respectively.

8The IL-4R activates Jak-Stat signaling as well as Akt-mTORC1 signaling in macrophages (Figure 2—figure supplement 1B). Receptor ligation activates the latent activity of Jak1 and Jak3 kinases, leading to phosphorylation and activation of Stat6, as well as engagement of the adaptor protein IRS2. IRS2 recruits PI3K, which generates PIP3 from PIP2 leading to phosphorylation and activation of Akt. Activated Akt phosphorylates and inactivates the TSC complex, a negative regulator of mTORC1, to activate mTORC1. While the precise relationship between Jak-Stat and Akt-mTORC1 signaling remains unclear, the data in Figure 2A and Figure 2—figure supplement 1A suggest that they may operate in parallel and independently downstream of the IL-4R. Indeed, IL-4-mediated increases in Stat6 activation, as indicated by phosphorylation on Y641, was not affected in the presence of an Akt inhibitor (Figure 2B). Stat6 activity as measured by a Stat6-dependent luciferase reporter was also not impaired by inhibition of Akt activity (Figure 2—figure supplement 1C). Conversely, WT and Stat6 KO BMDMs could similarly activate Akt, as indicated by phosphorylation on S473, as well as mTORC1, as indicated by phosphorylation of the mTORC1 target S6K, in response to IL-4 (Figure 2B). These findings support the idea that the Jak-Stat and Akt-mTORC1 pathways are independent signaling branches downstream of the IL-4R, and suggest a basis by which all M2 genes are controlled by Stat6 while a subset receives additional inputs from the Akt-mTORC1 pathway.

9How might Akt signaling regulate a subset of M2 genes? A seminal study from Wellen and colleagues indicated that in cancer cells and differentiating adipocytes, metabolic state is linked to gene expression via effects on histone acetylation, thus we hypothesized that Akt may control histone acetylation to regulate M2 gene expression. Indeed, IL-4-treatment of BMDMs enhanced global acetylation of H3 and H4 histones, as indicated by western blot of whole cell lysates (Figure 2C, Figure 2—figure supplement 2A,B). Importantly, IL-4-inducible increases in global H3 and H4 acetylation were reduced by cotreatment with an Akt inhibitor, indicating at least partial dependence on Akt (Figure 2—figure supplement 2A,B). In contrast, tubulin acetylation was not modulated by IL-4 treatment (Figure 2—figure supplement 2A,B). We next examined gene-specific patterns of H3 and H4 acetylation by chromatin immunoprecipitation (ChIP) experiments. IL-4 treatment increased H3 and H4 acetylation at promoters of M2 genes (Figure 2D,E, Figure 2—figure supplement 2C), with the degree of inducible acetylation correlating fairly well with the degree of gene induction (Figure 2A). Interestingly, such increases in H3 and H4 acetylation were reduced by an Akt inhibitor at M2 genes induced in an Akt-dependent manner (Arg1, Retnla, Mgl2), but not at M2 genes induced independently of Akt (Chi3l3, Cd36, Fabp4) (Figure 2D,E). Pol II recruitment to M2 gene promoters paralleled H3 and H4 acetylation, and was controlled by Akt at M2 genes induced in an Akt-dependent manner (Figure 2—figure supplement 2D). Together, these findings support the hypothesis that Akt regulates histone acetylation and Pol II recruitment at a subset of M2 genes.

10How might Akt regulate increased histone acetylation in M2 macrophages? We hypothesized that Akt may control production of Ac-CoA, the metabolic substrate for histone acetylation. Using quantitative stable isotope dilution-LC-MS, we found that IL-4 treatment led to a maximal increase in Ac-CoA levels of ~40–75% (Figure 3A,C). A key regulator of Ac-CoA production is the enzyme Acly, which cleaves cytosolic citrate to produce a nuclear-cytoplasmic pool of Ac-CoA. Akt has been shown to phosphorylate and activate Acly, and we found that in M2 macrophages, IL-4 treatment stimulated the activating phosphorylation of Acly in an Akt-dependent manner (Figure 3B, Figure 3—figure supplement 1A). Use of lysates from MEFs transfected with ACLY siRNA confirmed specificity in detection of phosphorylated and total Acly (Figure 3—figure supplement 1B). Importantly, cotreatment with Akt or Acly inhibitors blocked the IL-4-mediated increases in Ac-CoA levels (Figure 3C), indicating Akt- and Acly-mediated control of Ac-CoA production in M2 macrophages. Conversely, citrate, the substrate for the Acly reaction, accumulated in the presence of the inhibitors (Figure 3—figure supplement 1C).

11Next, we asked about the carbon source of the Ac-CoA that supports optimal M2 gene induction. Untreated or IL-4-treated BMDMs were incubated with 13C6-glucose, 13C16-palmitate, 13C5-glutamine, followed by carbon tracing into Ac-CoA as assessed by LC-MS (Figure 3D). IL-4 treatment enhanced 13C (M+2) Ac-CoA labeling regardless of the tracer, indicating that all three metabolic fuels contributed to the elevated Ac-CoA pool. The highest labeling was observed in BMDMs fed palmitate. While LC-MS does not specifically measure the nuclear-cytosolic pool of Ac-CoA, these data suggests that palmitate may be the major carbon source for histone acetylation in M2 macrophages (Figure 3D).

12These data prompted us to investigate a role for Acly in M2 activation. Indeed, the Acly inhibitor SB-204990 reduced IL-4-mediated induction of Akt-dependent M2 genes (Arg1, Retnla, Mgl2) but not Akt-independent M2 genes (Chi3l3, Fabp4, Cd36) (Figure 4A). The structurally distinct Acly inhibitor MEDICA 16 had similar effects, indicating specificity in inhibition (data not shown). Moreover, SB-204990 treatment attenuated IL-4-mediated increases in H3 and H4 acetylation at promoters of Akt-dependent M2 genes, but not Akt-independent M2 genes (Figure 4B, Figure 4—figure supplement 1A). Likewise, SB-204990 treatment diminished Pol II recruitment at Akt-dependent M2 genes (Figure 4—figure supplement 1B).

13Because Akt and Acly regulate a global increase in Ac-CoA levels (Figure 3C) but control inducible histone acetylation only at some M2 gene promoters (Figure 2D,E, 4B, and Figure 4—figure supplement 1A), Ac-CoA production is necessary but not sufficient for stimulating gene-specific increases in histone acetylation, which must be conferred by specific transcription factors and histone acetyltransferases (HATs). The activity of some HATs, including p300, is regulated by Ac-CoA levels and metabolic status. Interestingly, the p300 inhibitor C646 reduced induction of Akt-dependent but not Akt-independent M2 genes (Figure 4C). Therefore, p300 may link the Akt/Acly-dependent rise in Ac-CoA levels to increased histone acetylation and gene induction at some Akt-dependent M2 genes, while distinct HATs at Akt-independent genes are insensitive to such modulation of Ac-CoA levels.

14Arginase activity is a hallmark feature of M2 activation that supports collagen production and polyamine synthesis. Consistent with effects on induction of Arg1, arginase activity was regulated by Acly and Akt (Figure 4D). Additionally, Akt and Acly inhibitors reduced induction of Akt-dependent M2 genes in peritoneal-elicited macrophages, indicating that control of M2 activation by the Akt-Acly axis may be applicable to multiple macrophage populations (Figure 4—figure supplement 2). Finally, induction of M2 gene expression by IL-13, a cytokine closely related to IL-4 that also triggers M2 activation, was also dependent on Akt and Acly (data not shown).

15Our findings that Akt regulates Acly activity to control Ac-CoA production and M2 activation led us to consider a role for mTORC1 in this process. mTORC1 is a key downstream effector of Akt signaling and their activities are intricately linked in many settings ( and Figure 2—figure supplement 1B). Indeed, we found that induction of Akt-dependent M2 genes was deficient in BMDMs lacking Raptor, a defining subunit of the mTORC1 complex. In contrast, induction of Akt-independent M2 genes was not reduced (Figure 5A). mTORC1 is known to stimulate Acly expression, and we found that Raptor-deficient BMDMs expressed lower levels of Acly protein (Figure 5B). Conversely, BMDMs with constitutive mTORC1 activity resulting from deletion of Tsc1, a negative regulator of mTORC1, displayed elevated Acly levels that were reduced by treatment with the mTORC1 inhibitor rapamycin (Figure 5—figure supplement 1). Additionally, we noted that IL-4-inducible Acly phosphorylation was reduced in Raptor-deficient BMDMs (Figure 5B). This raises the possibility that mTORC1 could also regulate Acly activating phosphorylation, through mechanisms that remain to be clarified in future studies. Taken together, these data indicate that the Akt-mTORC1 axis controls Acly activating phosphorylation and protein levels, likely contributing to its control of M2 activation.

16The Akt-mTORC1 pathway is a major metabolic sensor, and mTORC1 activity in particular is controlled by amino acid levels, ADP/ATP levels, and other metabolic inputs. Therefore, we considered that incorporation of the Akt-mTORC1 pathway into IL-4 signaling, parallel to canonical Jak-Stat signaling, may allow particular subsets of the M2 transcriptional program to integrate signals reflecting the cellular metabolic state (Figure 6A). Amino acids directly and potently regulate mTORC1 activity independent of the TSC complex and can also activate Akt in some contexts, hence we varied amino acid concentrations as a way to modulate Akt-mTORC1 activity. As expected, mTORC1 activity, as assessed by phosphorylation of its downstream target S6K, was greatly reduced in amino acid deficient media and intermediate in media containing low levels of amino acids (Figure 6B). In line with, increasing amino acid levels also augmented Akt activation, as indicated by enhanced phosphorylation on two critical residues, T308 and S473 (Figure 6B). Titrating amino acids had no effect on Stat6 phosphorylation and activation (Figure 6B), validating the use of this experimental model to modulate the Akt-mTORC1 axis independent of canonical Stat6 signaling. Consistent with effects on mTORC1 and Akt activity, amino acid levels dose dependently increased Acly phosphorylation and protein levels (Figure 6B) as well as Ac-CoA production (Figure 6C). Importantly, amino acids potentiated induction of Akt-dependent but not Akt-independent M2 genes (Figure 6D). This effect of amino acids was at least partially Raptor-dependent, indicating a critical role for mTORC1 in this process (Figure 6—figure supplement 1).

17We also examined M2 activation using the complementary model of leucine deprivation, since leucine is particularly critical in regulation of mTORC1 activity. Here comparisons were made between culture conditions that differed only in the presence or absence of one amino acid, without significant effects on total levels of amino acids. Culture in leucine-deficient media attenuated IL-4-inducible mTORC1 and Akt activity and Acly phosphorylation, but not Stat6 phosphorylation (Figure 6E). Importantly, leucine deficiency selectively reduced expression of Akt-dependent M2 genes (Figure 6F). Taken together, these results indicate that amino acids and likely other metabolic inputs feed into the Akt-mTORC1 axis to calibrate M2 activation to the metabolic state (Figure 6A).

18Finally, we found that physiological changes to nutrient levels can modulate M2 activation in adipose tissue macrophages (ATMs). ATM M2 polarization is thought to critically maintain insulin sensitivity in white adipose tissue, so such feeding-induced increases in M2 activation may coordinate responses to nutrient influx to mediate metabolic homeostasis in the postprandial state. Specifically, we found that Akt activation was increased in the fed state compared to the fasted state in the ATM-containing stromal vascular (SVF) fraction of the white adipose tissue (Figure 6—figure supplement 2A). Although we were unable to reliably detect pAcly or Acly in the SVF for technical reasons, global H3 acetylation (Figure 6—figure supplement 2A–B) and M2 gene expression (Figure 6—figure supplement 2C) followed a similar pattern and were elevated in the fed state. Expression of all M2 genes was elevated in the fed state (Figure 6—figure supplement 2C), consistent with an important role for IL-13, a critical regulator of ATM M2 polarization that is increased in the fed state (Figure 6—figure supplement 2D), in feeding-induced ATM polarization, although postprandial elevations in nutrients like amino acids and glucose may also contribute. Therefore, feeding-inducible Akt activity correlated with increases in histone acetylation and M2 activation in ATMs.

19We employed genome wide transcriptional profiling to obtain a comprehensive view of regulation of M2 activation by the Akt-Acly pathway. BMDMs were treated for 16 hr with IL-4 with or without Akt or Acly inhibitors, followed by RNA seq (Figure 7) or microarray analysis (data not shown). In the RNA seq analysis, 758 genes were induced >2.0 fold by IL-4, of which 91were downregulated >30% by both Akt and Acly inhibitors (including Arg1, Retnla, and Mgl2), confirming critical roles for Akt and Acly in control of M2 activation as well as substantial overlap in the activities of the two proteins (Figure 7A,B). A subset of Akt inhibitor sensitive genes was sensitive to Acly inhibitor (91/327), in line with a broader role for Akt in control of cell physiology. In contrast, most genes sensitive to Acly inhibitor were sensitive to Akt inhibitor (91/118). This indicates that in the context of M2 activation, Acly is a major target of Akt and is critically controlled by Akt activity, likely in regulation of Ac-CoA production and histone acetylation at M2 genes (Figure 7A,B).

20Gene enrichment analysis of the 91 Akt- and Acly-coregulated genes identified preferential enrichment of several pathways, including cell cycle and DNA replication (Figure 7C,D). IL-4 triggered BrdU labeling of a subset of BMDMs in vitro (data not shown) and proliferation of macrophages in vivo, thus IL-4 may stimulate macrophage proliferation in an Akt- and Acly-dependent manner. Consistently, metabolic processes underlying cellular proliferation were among the top enriched pathways in our metabolomics analysis, including nucleotide metabolism and protein biosynthesis (Figure 1A). Interestingly, chemokines were also enriched in Akt- and Acly-coregulated genes (Figure 7C–D), including Ccl2, Ccl7, Ccl17, and Ccl24. Akt- and Acly-dependent induction of CCL17 and CCL24 was confirmed by qRT-PCR and ELISA (Figure 7E–F). Finally, genes in the eosinophil associated ribonucleases (Ear) family were found to be regulated by Akt and Acly. While barely missing the stringent cutoffs that we set for the RNA-seq analysis, qPCR analysis confirmed coregulation of Ear2, Ear11, and Ear12 genes by the Akt-Acly pathway (Figure 7E). Ear genes are of interest because Ear2 and Ear11 are thought to have chemoattractant activity for dendritic cells and macrophages and are known to be highly induced in settings of Type 2 inflammation. Therefore, the transcriptional profiling analysis indicated that the Akt-Acly pathway controls selective subsets of the M2 program to allow their modulation by metabolic input (Figure 7—figure supplement 1A). As M2 macrophages play a key role in metabolic homeostasis, parasite infection, allergic diseases, and wound healing and tissue repair, these findings are relevant for metabolic control of macrophage function in diverse contexts.

21The Akt-mTORC1 pathway has a well-established role in promoting anabolic metabolism in growing/proliferating cells, tumor cells, and metabolic tissues. In the context of cellular proliferation, for example, Akt-mTORC1 activity couples growth factor signaling and nutrient availability to the synthesis of proteins, lipids, and nucleotides. In contrast, the role of the Akt-mTORC1 pathway in macrophages is much less intuitive. What is the teleological rationale for control of macrophage activation by Akt-mTORC1 signaling (and metabolism more generally)?

22Here we propose that IL-4 signaling co-opted the Akt-mTORC1 pathway to couple metabolic input to regulation of certain components of the M2 response, including chemokines and cellular proliferation (Figure 7—figure supplement 1A). This is supported by our findings that IL-4 signaling leads to parallel and independent activation of the Akt-mTORC1 pathway and the canonical Jak-Stat pathway, allowing the Akt-mTORC1 axis to regulate a subset of M2 genes through control of Acly activity/expression, Ac-CoA production, and histone acetylation. Why should some but not other components of the M2 response be regulated in this way? Control of cellular proliferation is intuitive, since Akt-mTORC1 signaling acts as a metabolic checkpoint in the context of cellular division to allow growth and proliferation only when nutrients are abundant. What about chemokines? We propose that chemokines may be controlled by the Akt-mTORC1 pathway because of their key role in amplifying energetically costly immune responses. This allows metabolic status to calibrate immune responses such that inflammation is amplified and sustained only under metabolically favorable conditions. Interestingly, previous studies have shown that a critical role for Akt-mTORC1 signaling in activated CD8 T cells is to support their migration to sites of inflammation. Therefore, Akt-mTORC1 signaling regulates both facets of immune response amplification, i.e., the ability of tissue-resident sentinel cells to mobilize activated leukocytes and of activated T cells to be recruited. Together these findings add another dimension to our emerging understanding of how metabolism supports leukocyte activation and immune responses.

23As discussed above, the Akt/Acly-dependent rise in Ac-CoA production is necessary but not sufficient for stimulating gene-specific increases in histone acetylation. Such specificity is most likely conferred by HATs with distinct Km. Indeed, our analysis suggests that p300 may preferentially regulate at least a subset of the Akt-dependent M2 genes (Figure 4C). Its high Km may allow p300 to link metabolic status and Akt/mTORC1 activity, in the form of Ac-CoA levels, to histone acetylation and transcriptional induction at some M2 genes (Figure 7—figure supplement 1B). In contrast, HATs at Akt-independent M2 genes may have a low Km and are thus insensitive to such modulation of Ac-CoA levels. Presumably, differential HAT recruitment is mediated by distinct transcription factors at Akt-dependent and independent M2 genes, which would be important to address in future studies.

24Although Akt activity has been linked to M2 activation,, the role of mTORC1 remained unclear. Here, we use Raptor△/△ BMDMs to show that mTORC1 activity stimulates M2 activation (Figure 5A). Furthermore, amino acids modulate mTORC1 activity (Figure 6B) to potentiate M2 gene induction in a Raptor-dependent manner (Figure 6D, Figure 6—figure supplement 1). Together these findings indicate that the Akt-mTORC1 signaling module supports M2 activation. Acly appears to be a key target, with its expression levels and activating phosphorylation controlled by mTORC1 and Akt respectively. In seeming contrast to these data indicating that mTORC1 supports M2 activation, we and others have shown that aberrantly increased mTORC1 activity in Tsc1-deficient BMDMs attenuates M2 activation. We hypothesize that the difference between the two models reflects divergent control of M2 activation by physiological and pathophysiological mTORC1 activity respectively. Downstream of the insulin receptor, such context-dependent roles of mTORC1 are well-established. In lean/healthy animals, mTORC1 critically mediates insulin signaling in metabolic tissues (to coordinate postprandial nutrient storage), but in obesity, chronic nutrient excess leads to an aberrant increase in mTORC1 activity that contributes directly to insulin resistance and metabolic dysregulation. Similarly, while physiological mTORC1 activity couples metabolic input to M2 activation, pathophysiological mTORC1 activation during chronic nutrient excess may impair M2 activation. It would be interesting to see if the latter is true in adipose tissue macrophages in the context of diet-induced obesity, and if so, the consequences for tissue inflammation and metabolic homeostasis.

25Interestingly, while inducible Akt phosphorylation occurred within minutes of IL-4 stimulation (Figure 2B), inducible Acly phosphorylation was detected with slightly delayed kinetics (~2 h, Figure 5B and data not shown). Such delay may reflect a need for other inputs that facilitate Akt-mediated Acly phosphorylation, or the reduced sensitivity and dynamic range of the pAcly antibody compared to the pAkt antibodies. Once pAcly is detectable at ~2 h, Akt and Acly phosphorylation nicely parallel and steadily increase up to (and perhaps beyond) 8 hr (Figure 5B). As expected, inducible Akt and Acly phosphorylation precede increases in global histone acetylation, which is observed starting only at 4 hr (Figure 2C). However, Ac-CoA levels increase only 8 hr after IL-4 stimulation (Figure 3A). One possibility, supported by the increase in global histone acetylation at 4 h, is that diversion of Ac-CoA into acetylated histones diminishes the free Ac-CoA pool. Another possibility is that because the LC-MS analysis measures bulk Ac-CoA rather than the nuclear-cytoplasmic pool relevant for histone acetylation, changes in mitochondrial Ac-CoA levels could be confounding. Again, global histone acetylation, which may more accurately reflect nuclear-cytoplasmic pools of Ac-CoA, increases 4 hr after IL-4 treatment (Figure 2C), as does gene-specific increases in histone acetylation at Akt-dependent M2 genes (Figure 2—figure supplement 2C). Therefore, we believe that the preponderance of the data support our model that IL-4 triggers Ac-CoA production and histone acetylation as a consequence of Akt-mediated Acly activation.

26Metabolic status has long been proposed to modulate epigenetic control of gene expression, but only recently have a handful of studies linked physiological changes in metabolite levels to chromatin regulation of gene expression. Here we show how the Akt-mTORC1 axis couples metabolic input in the form of Ac-CoA levels to histone acetylation and gene regulation, and importantly, to control specific subsets of the M2 program. In addition to a recent study, this is only the second example of how Akt-Acly signaling controls gene regulation through histone acetylation. Other macrophage polarizing signals and common gamma chain cytokines (γc) (e.g. IL-2, IL-15) engage the Akt-mTORC1 axis, thus our findings may have implications for multiple programs of macrophage polarization and leukocyte activation. Canonical signaling downstream of the polarizing signal or γc specifies which genes are induced, while regulation of Ac-CoA levels and histone acetylation by the Akt-mTORC1-Acly pathway allows metabolic input to calibrate genes encoding energetically demanding processes; it would be informative in future studies to determine the nature of these processes. Alternatively, Ac-CoA can be synthesized independently of the Akt-mTORC1-Acly axis by AceCS1 or nuclear pyruvate dehydrogenase to mediate histone acetylation. AceCS1 activity is controlled by SIRT1, thus providing a means for Ac-CoA production and histone acetylation in conditions of low energy or nutrients. Therefore, future studies to determine how gene-specific histone acetylation is regulated during different macrophage activation programs are warranted. These studies could pave the way towards new therapeutic approaches of modulating macrophage function in diverse contexts, including Type 2 inflammation, metabolic homeostasis, and antimicrobial immunity.

27BMDM cultures were established as described. For stimulations, BMDMs were pretreated for 1 hr with inhibitors followed by addition of 10 ng/ml IL-4 for 16 hr unless otherwise indicated. Inhibitors were used as follows: AKT inhibitor MK-2206, 2–5 μM (Selleck, Houston, TX); ACLY inhibitor SB-204990, 40 μM (Tocris, United Kingdom); p300 inhibitor C646, 10 μM; etomoxir, 200 μM (Sigma, St. Louis, MO), and 2-deoxy-glucose, 1 mM (Sigma). For amino acid titration experiments, BMDMs were plated in DMEM containing low levels of amino acids for 6 hr (to deplete cellular amino acid pools) prior to changing the media to DMEM with varying levels of amino acids (no, low, or normal) +/- IL-4 for 16 hr. Normal is normal tissue culture media, while low indicates media containing 5% of the normal levels of amino acids (obtained by mixing normal media and media lacking amino acids). In experiments with leucine free media, BMDMs were stimulated in complete DMEM or –Leu complete DMEM (Crystalgen, Commack, NY) +/- IL-4 for 16 hr. Tsc1△/△ BMDMs were described previously. BMDMs from UbiquitinC-CreERT2 Raptorfl/fl mice were treated with tamoxifen to delete Raptor; parallel treatment of Raptorfl/fl BMDMs were used as controls.

28C57BL/6 mice were used for in vivo studies and as a source of BMDMs. Mice were maintained at Harvard Medical School and all procedures were performed in accordance with the guidelines set forth by the Institutional Animal Care and Use Committees at the institution. To generate UbiquitinC-CreERT2 Raptorfl/fl mice, previously described Raptorfl/fl mice were crossed with UbiquitinC-CreERT2 mice (The Jackson Laboratory, Bar Harbor, ME) in David Sabatini’s laboratory at the Whitehead Institute in Cambridge, Massachusetts, in accordance with the guidelines set forth by the Institutional Animal Care and Use Committee at the institution.

29Cells were lysed directly in 6X SDS loading buffer (histone western blots) or in 1% NP-40 buffer (all other western blots). Protein concentration was determined using the Bradford method. Primary antibodies were purchased from Cell Signaling except for α-Tubulin (Sigma), acetylated Tubulin (Sigma), acetylated H3 (Millipore, Germany), acetylated H4 (Millipore), and total H4 (Abcam, Cambridge, MA).

30Arginase assay was done as described.

31Oxygen consumption and extracellular acidification rates were measured with a XF96 extracellular flux analyzer (Seahorse Bioscience, North Billerica, MA). Seahorse assay media containing 11 mM glucose or plain assay media was used for the mitochondrial and glycolysis stress tests respectively. OCR measurements were taken before and after the sequential addition of 1 μM oligomycin, 1.5 μM FCCP and 2 μM antimycin/rotenone (Sigma). ECAR measurements were taken before and after the sequential addition of 11 mM glucose, 1 μM oligomycin and 0.5 M 2-DG (Sigma). Values were normalized with Hoechst 33342 staining (Life Technologies, Carlsbad, CA).

32BMDMs were washed with Krebs-Ringer bicarbonate HEPES (KRBH) buffer once, followed by addition of 400 μl KRBH buffer. 100 μl loading buffer (KRBH buffer with 0.5 mM 2-deoxy-D-glucose (Sigma) and 1 μCi/well 3H-deoxy-D-glucose (2-3H[G]) (PerkinElmer, Waltham, MA, 1 mCi/ml in EtOH:water [9:1]) was added and incubated at 37°C for exactly 15 min. 20 μl stop solution (1.5 mM cytochalasin B (Sigma) in DMSO) was added, and the cells were washed with KRBH buffer before lysis in 0.1 N NaOH. The glucose uptake rate was determined by normalizing cellular 3H-deoxy-D-glucose count to protein concentrations.

33Fatty acid oxidation was done as described.

34ChIP was done as described, using acetylated H3 (Millipore 06–599), acetylated H4 (Millipore 06–866), or IgG (Santa Cruz, Dallas, TX, SC-2027) antibodies. Fold enrichment was calculated as ChIP signals normalized to input. ChIP primer sequences as well as position relative to transcription start site (TSS) are provided in Supplementary file 2.

35RNA was isolated using RNA-Bee (Tel-Test, Friendswood, TX) per manufacturers protocol. cDNA synthesis was done using High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA). A Bio-Rad C1000 Thermocycler was used for qPCR, and data was analyzed by means of the CFX Manger Software (Bio-Rad, Hercules, CA) using the delta/delta CT method. BMDM samples were normalized to hypoxanthine phosphoribosyltransferase while ex vivo samples were normalized to the macrophage marker CD68.

36BMDMs were electroporated using mouse macrophage nucleofector kit (Lonza, Hopkinton, MA) and the Amaxa machine with STAT6-Firefly luciferase (Addgene, Cambridge, MA, plasmid #35554) along with Renilla–Luciferase plasmid as a transfection control. BMDMs were stimulated with or without 10 ng/ml IL-4 4 hr post electroporation for another 24 hr. Cell lysates were collected and analyzed using the Promega Dual-Luciferase Reporter Assay System.

37BMDMs were lysed in 800 μl ice cold 10% TCA (Tricholoracetic acid). Sc5-sulfosalicylic acid (SSA), ammonium formate, [13C6]-glucose, sodium [13C16]-palmitate, and analytical standards for acyl-CoAs were from Sigma-Aldrich (St. Louis, MO). Optima LC-MS grade methanol, ammonium acetate, acetonitrile (ACN) and water were purchased from Fisher Scientific (Pittsburgh, PA). Calcium [13C315N1]-pantothenate was purchased from Isosciences (King of Prussia, PA). [13C315N1]-acyl-CoA internal standards for quantitation were generated by pan6 deficient yeast culture as previously described, with 100 µL of extract spiked into samples before extraction. Standard curves were prepared using the same batch of internal standard, and all samples were extracted by solid phase extraction as previously described. Acyl-CoAs were analyzed as previously described for quantitation and for isotopolog analysis by liquid chromatography-tandem mass spectrometry on an Agilent 1200 coupled to an API4000 in the positive ion mode monitoring the acyl-CoA specific neutral loss of 507 amu from each acyl-CoA, internal standard and isotopolog. For carbon tracing experiments, BMDMs were treated with 10 ng/ml IL-4 for 12 hr before the addition of tracers (2g/L 13C6-glucose, 50 μM 13C16-palmitate, or 2 mM 13C5-glutamine) for another 2 hr.

38BMDMs were stimulated for 10 hr with IL-4 before media was refreshed by addition of complete RPMI with IL-4 for another 2 hr. Preparation of cellular extracts was done as described. Steady state metabolomics was done at Beth Israel Deaconess Medical Center Mass Spectrometry Facility. Data analysis was performed as described.

39Strand-specific libraries were generated using 500ng RNA input using TruSeq library preparation kit (Illumina, San Diego, CA). cDNA libraries were multiplexed using specific unique adaptors and sequenced using Illumina NextSeq 500 under single end 75bp read length parameters. Reads were aligned to the mouse mm10 reference genome using TopHat using default settings. Alignments were restricted to uniquely mapping reads, with up to 2 mismatches permitted. RPKM was calculated as described for mm10 Refseq genes by counting exonic reads and dividing by mRNA length. Coexpressed gene classes were generated with Cluster3 by applying k-means clustering to mean-centered log2 (FPKM) expression values. Differential analyses was performed using DEseq using default parameters for the indicated comparisons. Cohort of IL-4 inducible genes was defined by following: >2 RPKM, Log2fold>1.0, DESeq P-adj<0.05 yielding 758 IL-4 inducible genes. Inhibition by AKT or ACLY inhibitors defined as 30% reduction in RPKM and DESeq P-adj <0.05. Enrichment of KEGG pathways and Gene Ontology (GO) terms analysis performed using DAVID.

408–10 week old C57BL/6 mice were fasted overnight or allowed to feed ad-libitum. Mice were sacrificed the next morning and the perigonadal adipose tissue was excised. A small section of whole adipose tissue (WAT) was homogenized in RNA-Bee for analysis of gene expression in unfractionated WAT. The remaining adipose tissue was minced and digested in 5 ml Krebs ringer buffer (KRBH) containing 2% fatty acid free BSA and 2 mg/ml collagenase (Sigma, C2674) for 20 min at 37°C. The resulting cell suspension was filtered through a 250 mm nylon mesh and centrifuged at 1200 RPM to obtain a cell pellet corresponding to the stromal vascular fraction (SVF), which was lysed for RNA extraction or western blotting.

41Statistical analysis was carried out using Prism (GraphPad) software. The student’s t-test was used to determine statistical significance, defined as *P<0.05, **P<0.01, and ***P<0.001.