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Navigating T-Cell Immunometabolism in Transplantation

Tanimine, Naoki, MD, PhD1; Turka, Laurence, A., MD1; Priyadharshini, Bhavana, PhD1

doi: 10.1097/TP.0000000000001951
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Recently, a new discipline termed “immunometabolism” has transformed the field of immunology. It encompasses the study of the intrinsic metabolic pathways of different immune subsets and their impact on cellular fate and function. For instance, broadly speaking, proinflammatory cells depend on glycolysis and glutamine oxidation, whereas cells involved in anti-inflammatory response, such as Foxp3+ regulatory T (Treg) cells, use predominantly fatty acid oxidation. However, although a useful paradigm, this actually is a reductionist view, and the engagement of these metabolic pathways is not mutually exclusive between these subsets. Over the past several years, new insights and new methods to better dissect, define, and harness the metabolic properties of immune cells for immunotherapeutic purposes have come to the forefront. In this review, we will discuss the metabolic heterogeneity of different T-cell subsets as well as basic principles of integrative technologies, such as metabolomics, which can be used to better understand the metabolic signatures of immune responses. Given the interest of exploiting this information in the context of transplantation, we will highlight the scope of immunometabolism in unraveling novel mechanisms of immune regulation that can be manipulated to promote Treg cell stability and function while inhibiting T effectors to establish long-term transplantation tolerance.

Proinflammatory/effector cells and anti-inflammatory/Treg have different energetic metabolism each depending on particular signaling pathways. Different techniques to analyze metabolism in immune cells are described as well as emerging tolerogenic strategies to favor the metabolism of tolerogenic cells.

1 Center for Transplantation Sciences, Department of Surgery, Massachusetts General Hospital, Boston, MA.

Received 28 June 2017. Revision received 15 August 2017.

Accepted 1 September 2017.

This work was supported by NIH grant P01HL018646, Naito Foundation Grant of Studying Oversea, and Uehara Memorial Foundation Research Fellow Grant.

The authors declare no conflicts of interest.

All authors participated in drafting and revising the paper.

Correspondence: Bhavana Priyadharshini, PhD, Center for Transplantation Sciences, MGH East, 149 13th Street, Room 5101, Boston, MA 02129. (bpriyadharshini@mgh.harvard.edu).

In recent years, a new field called “immunometabolism that is at the intersection of immunity and metabolism has emerged as the next big frontier in immunology.”1 This field encompasses the study of the distinct metabolic programs in various immune cell subsets that influence their functionality and differentiation.2 Resting immune cells generate and use only minimal energy, that is, the engine is on idle and their needs are modest. However, upon activation, immune cells become metabolically active and shift their metabolism from a catabolic state to an anabolic state, via aerobic glycolysis, to meet their bioenergetic demands. This is termed as “Warburg Effect,” a feature first identified by Dr. Otto Warburg in 1924 in cancer cells, where glucose-derived pyruvate is shunted to lactate instead of being metabolized in the mitochondria despite the presence of abundant oxygen, hence the term “aerobic glycolysis”3 (Figure 1). Warburg metabolism is usually associated with a process called “anaplerosis” which is the oxidation of energy substrates, such as glutamine to replenish tricarboxylic acid (TCA) cycle, intermediates in the mitochondria (Figure 1). Despite being less energy efficient, the shift toward aerobic glycolysis, away from mitochondrial metabolism, is beneficial for cells as it generates major glycolytic intermediates that are precursors to anabolic processes, such as the synthesis of nucleotides, amino acids, and lipids (through the pentose phosphate pathway, serine biosynthetic pathway, and the de novo fatty acid (FA) synthesis pathway, respectively), which are all required for cell growth and proliferation2 (Figure 1). In contrast to activated T effector cells and immunogenic/proinflammatory antigen-presenting cells, such as M1 macrophages and dendritic cells, which rely on the abovementioned metabolic programming, anti-inflammatory cells, such as M2 macrophages, CD8 memory cells and differentiated regulatory T (Treg) cells rely instead on FA oxidation (FAO) in the mitochondria2 (Figure 1). These metabolic programs occur generally due to alterations in PI3K/Akt/mammalian target of rapamycin (mTOR) signaling axis. For instance, proinflammatory cells are typically associated with elevated signaling of this axis, whereas anti-inflammatory cells are less dependent on it. Consequently, these signaling differences in turn give rise to specific metabolites that influence the functional differentiation of these cells. Together, these studies not only suggest how cellular processes are intimately linked to metabolism but also indicate that different types of immune cells may be differentially sensitive to various metabolic manipulations, owing to their specific metabolic signatures.4-6

FIGURE 1

FIGURE 1

Among the several immune cell types that promote tolerance, Foxp3+ Treg cells have particularly been under the spotlight, as they constitute one of the main mechanisms of immune regulation. Given the critical role of Treg cells in immune regulation, in this review, we will focus on comparing their metabolism to T effector cells while providing an overview of various new strategies that one can use to study this phenomenon in the context of experimental and preclinical transplantation studies.

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Immunometabolism: The New Kid on the Block

Immune cells engage in a bidirectional interaction between extrinsic cues and intrinsic metabolic signaling. The role of how metabolic pathways control different aspects of lymphocyte biology has been summarized previously.2 Here, we highlight some of the most recent developments that show how metabolic programming ultimately affects an immune cell's activation status and its functional differentiation.

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T Effector Cells

Because activated T cells transition from a quiescent state to an activated state, they increase their biomass required for cell growth and proliferation. During this process, T cells have been shown to undergo asymmetric cell division when each of the daughter cells differentiates either toward an effector or memory phenotype cell. These fate decision processes are mediated by the metabolic programming driven by the asymmetric distribution of the activity of mTOR complex 1 (mTORC1) that correlates with c-Myc distribution.7,8 For example, c-Mychi daughter cells show increased mTORC1 activity and upregulate Glut1, a key surface membrane transporter of glucose into the cell, and increase their glycolytic activity, which promotes effector cell function. Conversely, the cells with lower mTORC1 activity use lipid metabolism, which promotes the generation of long-lived memory cells.6 The increase in glycolysis in cells of effector lineage is not only required to meet the metabolic demands of cell growth but is also required for optimal production of proinflammatory cytokines, such as IL-2 and IFN-g.9 This was shown to be dependent on the sequestration of bifunctional glycolytic enzymes, such as glyceraldehyde 3-phosphate dehydrogenase, that normally binds to the 3′ untranslated region of IFNg and inhibits its translation.9 Interestingly, another bifunctional glycolytic enzyme, lactate dehydrogenase A (LDHA), has also been shown to play a role in promoting IFN-g expression via epigenetic modifications (via maintenance of high level of acetyl CoA and augmenting histone acetylation).10 These studies suggest that metabolic control of T effector function can be multifactorial. Consistent with these findings, genetic deletion or pharmacological inhibition of Glut1 disrupts optimal function of alloreactive effector T cells and ameliorates graft-versus-host disease (GVHD) mortality in mice undergoing bone marrow transplantation.6,11,12 In a stringent skin transplantation model, inhibition of both glucose metabolism and glutamine metabolism (considered as an anaplerotic source of nucleotides and polyamines biosynthesis during T-cell proliferation) by drug combination therapy prevented acute rejection.13 These studies therefore suggest that metabolism is intimately linked to the immune cellular processes and their effector function.

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Treg Cells

The importance of glycolytic metabolism for T effector cells is further highlighted in studies that show that inhibition of the glycolytic-lipogenic pathway during cell culture prevents Th17 cell differentiation and instead promotes the generation of in vitro induced Treg (iTreg) cells.14,15 Glutamine deprivation of naive CD4+ T cells, another critical fuel source of T effector cells, also leads to differentiation into iTreg cells even under Th1 effector cell type skewing conditions.16 These cell fate decisions of T cells are dictated by differential mTORC1 signaling where higher mTOR activity is critical for effector cell differentiation, whereas lower mTOR activity favors iTreg cell generation. These cells instead depend on AMP-activated protein kinase (AMPK) signaling, a reciprocal sensor to mTOR, that facilitates mitochondrial FAO which is required for the induction of Foxp3. Thus, blockade of mTOR with rapamycin or activation of AMPK via metformin, an antidiabetic drug, increases iTreg cell lineage development and their suppressive function.4

The in vivo counterpart of iTreg cells is termed peripherally derived Treg (pTreg) cells. In accordance with published guidelines, we use “iTreg” cell only for in vitro generated Treg cells and “pTreg” cell for the in vivo generated population.17 Both these cells originate from naive T cells and acquire Foxp3 after peripheral stimulation by antigen under noninflammatory conditions, for example, in the presence of TGF-β (Figure 2). The maintenance of Foxp3 expression postactivation in both iTreg cells and pTreg cells is associated with the partial demethylation of CpG motifs in the Treg cell-specific demethylation region (TSDR) within the Foxp3 gene. However, both these cell types are believed to have inherent lineage instability, because they fail to fully demethylate these CpG motifs and thus may lose Foxp3 expression and acquire effector function especially in conditions of inflammation.18,19 In contrast to iTreg cells, thymically derived Treg (tTreg) cells maintain full demethylation of the Foxp3 TSDR and are considered more functionally stable.20

FIGURE 2

FIGURE 2

It is becoming increasingly evident that the metabolic requirements of tTreg cells may be different than iTreg cells. For instance, mTORC1-dependent glycolytic-lipogenesis that promotes cholesterol biosynthesis is indispensable for functional fitness of tTreg cells. These metabolic processes are associated with the upregulation of Treg cell signature markers, such as cytotoxic T lymphocyte-associated protein 4 and inducible T cell costimulator, and their suppressive function.21 Furthermore, it was reported recently that freshly isolated human ex vivo Treg cells are highly glycolytic and engage in both glycolysis and FAO when cultured in vitro.22 These findings highlight the importance of mTORC1 signaling and glycolytic metabolism in tTreg cell stability and function in contrast to AMPK signaling and FAO for iTreg cells. However, there have been reports that suggest otherwise. For instance, a recent study showed that although toll-like receptor-induced mTORC1 signaling promotes tTreg cell proliferation through enhancing glycolysis and Glut1 expression, it simultaneously impairs tTreg cells suppressive capacity.23 Autophagy deficiency can also enhance c-Myc function and mTORC1 signaling resulting in increased glycolytic metabolism that is associated with defective tTreg cell stability and function.24 Furthermore, Treg cell-specific deletion of phosphatase and tensin homolog, a negative regulator of PI(3)K, enhances a glycolytic program that is associated with compromised function and lineage stability of tTreg cells.25 Finally, studies in human iTreg cells (induced by suboptimal stimulation without IL-2) have recently shown that they can in fact depend on glycolysis to sequester enolase1, a bifunctional glycolytic enzyme that represses the E2 variant of Foxp3 required for their suppressive function. These studies show that although gaining the ability to undergo enhanced glycolysis may be detrimental to tTreg cell stability and function, it does not necessarily equate toward loss of suppressive function in iTreg cells.26 Together these contradictory findings suggest that tTreg cells and iTreg cells (pTreg cells) have complex but clearly different metabolic requirements in different contexts.

The discrepancies in these studies may be attributed in part to species (mice vs human) differences or variations in experimental systems. However, another possibility should be considered as well. The population of freshly isolated Foxp3+ Treg cells from secondary lymphoid tissues is likely composed of both tTreg cells and pTreg cells. Hence, the metabolic differences seen in these studies may just be a reflection of heterogeneity within Treg cell population. At present, there is yet no reliable way to distinguish the 2 subsets of Treg cells. Recently, 2 markers, neuropilin 1, a semaphorin III receptor, as well as the transcription factor Helios have been reported to be expressed by exclusively by tTreg cells.27,28 However, unlike mouse Treg cells, human Foxp3+ T cells do not express neuropilin 1.29-31 In addition, Helios can be induced upon T-cell activation and hence cannot be used definitively to distinguish tTreg cells and pTreg cells.32 Therefore, a lack of good distinguishing markers makes it difficult to study these 2 populations. Despite these limitations, studies showing biological differences between tTreg cells and pTreg cells in terms of their T cell receptor repertoire owing to their origin, demethylation of TSDR region of the Foxp3 promoter (indicated above), have been reported.33Figure 2 summarizes the key distinct features between these 2 subsets that may in turn be linked to their differing metabolic signatures.

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Strategies to Evaluate Immunometabolism

Understanding the key mechanisms and metabolic functions of immune cells, like that of other cell types, can be broadly classified into 2 levels (1) basic metabolic characterization and (2) advanced metabolite profiling. Several new analytical technologies have recently emerged that can provide a holistic picture of the metabolic signatures of immune cells under physiological and pathological conditions. Here, we will summarize the basic aspects of these technologies that serve as complementary tools along with traditional metabolic assessment strategies for the study of immunometabolism.

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Basic Metabolic Characterization

The rapid and a reliable assessment of cellular bioenergetics in real time using the seahorse XF Bioanalyzer has increasingly become a useful technique to profile the overall picture of the metabolic function of cells. The measurement of oxygen consumption rate (OCR) and the extracellular acidification rate (ECAR) serve as surrogate markers for mitochondrial metabolism and glycolysis, respectively. The measurement of OCR after the sequential addition of inhibitors such as oligomycin (mitochondrial adenosine triphosphate synthase inhibitor), trifluoromethoxy carbonylcyanide phenylhydrazone (proton ion hole uncoupler) and complex 1 and 3 inhibitors, such as rotenone and antimycin, help gauge one of key parameters called the spare respiratory capacity. This is a unique feature shared between memory and Treg cells that rely heavily on FAO.4 In contrast to this, measurement of ECAR after addition of glucose, oligomycin, and 2 deoxy glucose (2DG) helps in the assessment of the cell's glycolytic capacity, another key feature that is synonymous with T effector cell metabolism and activated bone marrow cells.14,34 Along with parallel analyses of metabolic signaling pathways, such as mTOR and AMPK signaling, the expression nutrient transporters involved in glucose, glutamine, and FA uptake and measuring the expression of key rate limiting enzymes and their activities via traditional methods of qPCR, immunoblotting, and flow-based methods can help to lay the foundation for understanding the basic metabolic phenotype of immune cells.

Along these lines, assessing mitochondrial physiology is yet another powerful tool for understanding the metabolic phenotype of immune cells. In this regard, mitochondria distinctly play a fundamental role as bioenergetic hubs for mitochondrial oxidative phosphorylation via TCA cycle and electron transport chain to generate adenosine triphosphate. Mitochondrial dynamics have been shown to be crucial in dictating T-cell fate between naive, effector, and memory T-cell states. Recently, the morphological adaptation of mitochondria in memory cells toward a fused state, as opposed to a fissioned state (effector cells), was shown to be critical for T-cell longevity, a key attractive feature required for cells to survive in vivo.35 Furthermore, assessing mitochondrial function via flow-based assessments for mitochondrial mass, membrane potential, and reactive oxygen species (ROS) production aids in the understanding of the cellular bioenergetics that is linked to stress response. iTreg cells engaging in FAO have been shown to be equipped with higher levels of ROS, and treatment with ROS scavengers decreases Foxp3 expression, suggesting the critical role for ROS in serving as a metabolic signaling molecule for Treg cell function.14

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Advanced Metabolomic Profiling

Although seahorse XF technology is a good tool to obtain a quick snapshot of immune cell metabolic activity (ECAR and OCR changes), it offers only a limited picture as it does not indicate which specific pathways are being used in cells and to what extent. Metabolomics offers a solution as it delves deeper into the comprehensive measurement of metabolites and their usage in various pathways. Specifically, this technology comprises the identification and quantification of thousands of metabolites usually under the molecular weight of 1200 kDa. These small molecule metabolites include sugars, amino acids, FAs, polyamine, antioxidants, and the many other classes of compounds. Metabolomics is similar to other -omic approaches, that is, genomics for genes, transcriptomics for RNA and proteomics for proteins, and serves as integrative complementary tool that allows not only for the identification of the new biomarkers but also mechanistic insights into key metabolic pathways and their checkpoints (Figure 3A). Two analytical platforms, namely, nuclear magnetic resonance spectroscopy and mass spectrometry coupled usually with liquid chromatography to separate individual metabolites are the most commonly used in metabolomic analyses.36 Despite the potential impact of several fluctuating environmental and intrinsic factors on cellular metabolism, integrative metabolomic data analyses (with other -omic studies or biostatistical models) can discriminate stable metabolic changes from individual variations and help construct meaningful metabolic networks.37 To obtain a comprehensive picture of the metabolic profile, 2 major metabolomic approaches have been currently employed. They compose of (A) determining the abundance of the metabolites at steady state levels and (B) understanding the directionality and the rate of the metabolic flow38 (Figure 3B).

FIGURE 3

FIGURE 3

  • (A). Abundance: Untargeted metabolomic approaches that profile the levels of several metabolites are currently being integrated to an already established genomic/transcriptomic analyses platforms using biofluids, such as blood, plasma, and urine samples in the aim to identify new biomarkers in kidney, liver, and heart transplantation and has been reviewed elsewhere.39 Similarly, such approaches have been increasingly adapted to better assess the abundance of metabolites in key metabolic pathways in immune cells under various conditions. One of the first examples in lymphocytes, integrating metabolomics with traditional fuel flux measurements that use radiolabeled carbon substrates, used an integrative approach to assess the steady-state levels/concentrations of key metabolites in glycolysis, glutamine, and FAO postactivation. Consistent with their metabolic flux findings, this study validated that T-cell activation caused a dramatic increase in the accumulation of metabolites involved in glycolysis and glutamine metabolism while causing a concomitant decrease in FAO, all of which was shown to be driven by transcription factor c-Myc.40 Since then, many studies adapting this strategy have helped gain insight into the metabolic programming during T-cell activation. For instance, metabolomic profiling assays assessing donor T cells that induce GVHD also revealed the increase in metabolites involved in glycolysis and glutamine metabolism and decrease in FAO that have been shown to be unexpectedly dependent on the expression of PDL1 on T cells.11,41 Although this form of profiling approach has many advantages in terms of its ability to map and quantify metabolic pathways, these steady-state analyses cannot determine whether the change in the quantity of certain metabolites is due to the changed activity of a synthesizing enzyme or a consuming enzyme. Follow up of abundance studies with stable isotope tracer analysis is therefore the next step in fully evaluating the metabolic signature of cells.
  • (B). Directionality and Metabolic flux: stable isotope tracer analysis helps in assessing the metabolic directionality and the flux and hence results in a better understanding of the mechanistic dynamics of the metabolic pathways that are involved. Traditionally, stable isotopes used to study metabolic process constitute labeled carbon (13C), hydrogen (2H), and nitrogen (15N). These are used both in vitro and in vivo to determine the enrichment of carbon labeling in metabolites over time. Each of the labeled species of a metabolite is term as an “isotopomer.” Techniques of liquid chromatography/mass spectrometry or nuclear magnetic resonance spectroscopy obtain isotope fraction labeling and mass isotopomer distribution information. This determines the relative abundance of labeled carbon species and the specific positions of carbon labeling in metabolite pools. Together, these data can help reveal the metabolic fate of substrates. Figure 3C shows a simplified example of a roadmap to decipher the 13C labeling patterns in citrate using uniformly labeled 13C glucose. The incorporation of labeled carbon into various isotopmers of a metabolite [in this case citrate] gives rise to “mass isotopomer distribution” for citrate. By evaluating the total 13C labeled enrichment fraction as well as examining the mass isotopomer distribution, one can interpret relative directionality and flux of metabolic pathways used under steady state and over time. Using such studies, the importance of glutamine metabolism in activated T cells has been recently highlighted using 13C labeling of 3 primary substrates under different culture conditions. Studies using uniformly labeled U-13C–labeled glucose during glutamine starvation of activated T cells indicated a depletion of TCA cycle intermediates, such as citrate and fumarate (via the assessment of reduction of relative abundance of 13C labeled citrate or fumarate) that was not compensated by glucose, indicating the absolute requirement of glutamine in serving as an anaplerotic source for TCA cycle intermediates during T cell activation.42 In addition, under glucose limiting conditions, glutamine can compensate as an energy source to generate glycolytic intermediates, such as pyruvate (increased in the relative abundance of 13C label in pyruvate), for its use in TCA cycle via the process of glutamine decarboxylation, a process shown to be dependent on AMPK signaling.42 Interestingly, other 13C-labeled glutamine studies also showed higher 13C incorporation from labeled glutamine in ribose rather than from the traditional source of labeled glucose in activated donor T cells during GVHD, suggesting the role of glutamine metabolism in feeding biosynthetic pathways such nucleotide biosynthesis.34 Furthermore, studies showing the distribution of labeled palmitate, another fuel source in different glutamate isotopomers, suggested that palmitate can also serve as another source for glutamine intermediates required for donor T-cell activation during GVHD. Together, these stable isotope-labeling studies underscore the precise importance of glutamine and palmitate metabolism in serving as anaplerotic energy sources for biosynthetic processes such as nucleotide synthesis and mitochondrial metabolism during T-cell activation.

Interestingly, 13C incorporation from labeled glucose to palmitate was minimal in donor T cells during GVHD, indicating that contribution to FA synthesis from the glycolytic lipogenesis is very little in these cells.34 In contrast to these studies, 13C incorporation from U-13C6 labeled glucose to palmitate has been shown to be substantial in T cells that have been differentiated toward the Th17 cells but not iTreg cells in conditions of autoimmunity.15 Together, these studies indicate the advantage of labeling studies in dissecting the precise metabolic signatures of immune cells in different scenarios. These studies bring in another layer of understanding that suggest that metabolic wiring of immune cells is plastic owing to the context of immune cell activation.

In addition to determining the directionality, stable isotope labeling analyses can be combined with computational models to provide the velocities of biochemical reactions in key metabolic nodes under physiological and pathological states. This aspect is beyond the scope of this review and is covered elsewhere.36,37 Together, these studies highlight the power of these analyses to decipher the unique metabolic dependencies and flexibilities in immune cells that might be affected either due to intrinsic factors (specific fuel choices made during cell differentiation) or extrinsic factors such as substrate limitations and can be used to determine these differences perhaps during metabolic manipulation.

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Potential Applications of Immunometabolism in Transplantation

Because metabolic alterations represent a state of immediate cellular responses to stresses, metabolomic measurements have been suggested as a strategy for rapid assessment of graft function and rejection. For instance, ROS and metabolites, such as trimethylamine N-oxide, are typically produced during ischemia/reperfusion injury.39 Further, metabolic profiling studies of serum after kidney transplantation revealed that the intermediate of tryptophan metabolism, kynurenine, is altered during acute rejection.39 Now, with the emergence of the field of immunometabolism and the advancement of metabolomic techniques, increasing attention is being paid to using these approaches not only to identify novel drug targets but also to define key metabolic nodes that can serve as prognostic biomarkers to monitor recipient’s immune status during transplantation (Figure 4). Given the emergence of Treg cell cellular therapy for tolerance induction and maintenance, metabolic strategies to generate functionally stable and long-lived efficacious Treg cells either during ex vivo Treg cell expansion protocols (described below) are an attractive option and could enable the use of drugs which are not suitable for in vivo administration.

FIGURE 4

FIGURE 4

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Immunometabolic Intervention

The evidence of the feasibility of metabolic manipulation for immune-mediated conditions began emerging first in models of allergy and autoimmune diseases. For instance, administration of the AMPK activator metformin, which blocks lipogenesis and activates FAO, increased the frequency and number of Treg cells in a mouse model of asthma.4 Additionally, experimental use of another AMPK activator, 5-aminoimidazole-4-carboxamide ribonucleotide, has been shown to suppress IFN-γ production by effector T cells.42 Blocking the glycolytic-lipogenic pathway in experimental autoimmune encephalitis using inhibitors, such as 2DG (which blocks glycolysis by inhibiting hexokinase) and soraphen (which inhibits glycolytic lipogenesis by blocking acetyl-coA carboxylase) (Figure 1), decreased Th17 cell while reciprocally promoting Treg cells.15 Furthermore, the dual blockade of glycolysis by 2DG and mitochondrial metabolism by metformin normalized the overall metabolism of CD4+ T cells and decreased symptoms in murine model of SLE.43

Interestingly, similar strategies in transplantation models have yielded promising results. For instance, a combination therapy of 2DG+ inhibition of glutamine metabolism via DON+ metformin delayed or prevented rejection in fully mismatched skin and heart transplant experiments in mice.13 This was accompanied by the increase in the proportion of Treg cells, suggesting that balance between Treg cells and T effectors had been modified by metabolic intervention. Furthermore, in a model of GVHD, the small molecules BZ423, which blocks mitochondrial respiration, or etomoxir, which blocks FAO (Figure 1), ameliorate the symptoms of GVHD by blocking donor-reactive T cells while sparing bone marrow cells that are predominantly glycolytic. Interestingly, glycolysis blockade using PKF15 (which blocks the key glycolytic enzyme 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 also blocked donor-reactive T cells and ameliorated the symptoms in a different model of GVHD (Figure 1).11 These studies suggest that activated T cells may be capable of preferentially engaging in 1 metabolic program over the other in a context-dependent manner. Alternatively, the metabolic demands of a rapidly proliferating population of cells, such as in GVHD, requires optimal usage of all metabolic pathways. Interestingly, freshly isolated human Treg cells can engage in both glycolysis and FAO.22 As described earlier, glycolysis is required for optimal Foxp3 induction in human iTreg cells, suggesting that strategies that can enhance glycolysis in these cells may be useful.26 In this context, UK5099, an inhibitor of mitochondrial pyruvate carrier (Figures 1 and 5), which was recently shown to impair pyruvate metabolism and enhance aerobic glycolysis resulting in metabolic reprogramming of cancerous cells toward stem-like cells, could serve as a potential approach to boost glycolysis and enhance Foxp3 expression in human iTreg cells.44,45

FIGURE 5

FIGURE 5

Given the exponential rise of recent immunometabolic studies that are beginning to reveal the impact of different metabolic pathways on immune function, there is tremendous opportunity for the development of potentially new metabolic modulators.2,46 These modulators can be broadly classified into 4 categories, that is, (1) targeting signaling pathways, (2) targeting metabolic enzymes, (3) targeting mitochondrial reactions, and (4) targeting epigenetic alterations. They have been summarized in Figure 5. Given the distinct metabolic requirements of different subsets of immune cells, the idea of cellular selectivity with minimal toxicity is truly highlighted by the promise of metabolic manipulation in organ transplantation.

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Integration of Metabolomics With Other Omics

In addition to providing novel mechanistic insights into immune cell function that can be modulated, immunometabolism is starting to usher in a new wave of mechanistic studies and biomarker discovery that can be used during immune monitoring posttransplantation. The use of metabolomics as a strategy to determine biomarkers to monitor graft function has already been shown in models of kidney ischemia injury, liver, and heart transplantation.47 Despite posing complex challenges, recent work showing the integration of metabolomics with T-cell proteomics and phosphoproteomic approaches indicates a potential for establishing a multi-marker monitoring strategy for immune cells during transplantation. For instance, a recent study adapting this multilevel omics approach revealed the critical role for mitochondrial enzyme cox10 in facilitating T-cell quiescence exit and T-cell differentiation, indicating the importance of remodeling of mitochondrial bioenergetics during T-cell activation.48 Analysis of the proteomic landscape of activated human Treg cells also revealed enrichment of proteins and enzymes involved in mitochondrial metabolism, suggesting that proteins in mitochondrial pathway represents a common functional module linked to T-cell activation in both T effectors and Treg cells.22

At the same time, the next realm that is beginning to emerge is the influence of metabolism on events, such as transcriptional regulation, chromatin modeling, and epigenetics that are linked to cell fate and functionality. For instance, proteomic analyses have revealed the upregulation key transcriptional regulators that regulate T-cell survival in response to amino acid arginine levels.49 As described earlier, increased glycolysis due to LDHA activity can affect histone acetylation of IFNG loci and regulate its expression in T effector cells.10 Likewise, gene regulatory processes have been shown to dictate their stability and function in Treg cells. Recently, metabolites, such as vitamin A, have been shown to increase histone acetylation of Foxp3 gene promoter while also facilitating CpG demethylation of Foxp3 locus, both of which are required for optimal expression of Foxp3.50 Similarly, metabolites, such as vitamin C, have recently been shown to increase the activity of ten-eleven translocation enzyme (TET) enzymes that are crucial for DNA demethylation of Foxp3 locus and stability of Foxp3 expression similar to glutamine metabolism intermediate alpha-ketoglutarate that also activates TET activity and histone modifications in hematopoietic stem cells51 (Figure 5). Furthermore, newer studies indicate the importance of chromatin modifiers EZH2 (a protein shown to be repressed in response to glucose restriction) in regulating the expression of several Treg cell lineage genes required for the establishment of their Treg cell identity.52 Hence, integration of metabolomics studies with other approaches, such as epigenomics, can help obtain a comprehensive picture of the various metabolic functional modules and the differentially regulated molecular circuits in immune cells especially in the context of metabolic manipulation.

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Metabolic Manipulation in Tolerogenic Cell Therapy: A Brief Perspective

The safety and potential therapeutic efficacy of Treg cell therapy has been reported in phase I/II trials of bone marrow transplantation and type I diabetes, and currently being conducting for solid organ transplantation. Efficient ex vivo expansion of a stable and functional Treg cell population is a prerequisite for their successful implementation in immunotherapy and novel ways to promote high-quality Treg cells are currently being explored. Given the intrinsic role of immunometabolism in regulating various aspects of Treg cell biology and function, broadening the application of metabolic manipulation to Treg cell expansion protocols hold tremendous promise. Recent reports using a culturing strategy with rapamycin show encouraging signs of enhanced cell expansion and Treg cell stability.53 In addition to mTOR inhibitors, existing and emerging metabolic modulating targets that may have selective desired effects for promoting Treg cell proliferation, stability, and function are being currently considered for this purpose.

In addition to polyclonal or donor antigen specific Treg cell therapy, recent advancements in chimeric antigen receptors (CARs) technology and its effects on metabolic properties of cells are beginning to provide new insight for metabolic manipulation for Treg cell therapy. In the field of cancer, CAR T cells with intracellular 4-1BB signaling domain has been recently reported to maximize antitumor activity by favoring oxidative metabolism and promoting central memory differentiation.54 The modular nature of the CAR, extracellular antigen-binding domain fused to intracellular cell signaling domains, allows for optimization by replacement of various components. Given the role of PD1 and CTLA-4 in initiating metabolic pathways in blocking glycolysis while enhancing FAO, both serve as attractive candidates for Treg cell therapy including CAR Treg cell therapies to induce FAO and reduce glycolysis in Treg cells, a process essential for the suppressive function. This indicates an indirect strategy to modulate metabolism and possibility to selectively manipulate metabolic signaling pathways in cell type-specific manner.

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CONCLUSIONS

The field of immunometabolism until recently has been primarily focused in models of infectious disease or in cancer immunology. The objective of this review was to provide an overview of this field and an insight into various strategies that can be used to navigate it from a tolerance standpoint. Given the rise of CAR and CRISPR engineering technologies and the wide ranging effects of metabolic changes on epigenetic regulation and cellular fate and function, the goal to shape a personalized immune response with favorable balance of T effectors/Treg cells in conditions of autoimmunity and transplantation may no longer seem that far-fetched.

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REFERENCES

1. Mathis D, Shoelson SE. Immunometabolism: an emerging frontier. Nat Rev Immunol. 2011;11:81.
2. O'Neill LA, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nat Rev Immunol. 2016;16:553–565.
3. Warburg O. On the origin of cancer cells. Science. 1956;123:309–314.
4. Michalek RD, Gerriets VA, Jacobs SR, et al. Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. J Immunol. 2011;186:3299–3303.
5. Shi LZ, Wang R, Huang G, et al. HIF1alpha-dependent glycolytic pathway orchestrates a metabolic checkpoint for the differentiation of TH17 and Treg cells. J Exp Med. 2011;208:1367–1376.
6. Macintyre AN, Gerriets VA, Nichols AG, et al. The glucose transporter Glut1 is selectively essential for CD4 T cell activation and effector function. Cell Metab. 2014;20:61–72.
7. Pollizzi KN, Sun IH, Patel CH, et al. Asymmetric inheritance of mTORC1 kinase activity during division dictates CD8(+) T cell differentiation. Nat Immunol. 2016;17:704–711.
8. Verbist KC, Guy CS, Milasta S, et al. Metabolic maintenance of cell asymmetry following division in activated T lymphocytes. Nature. 2016;532:389–393.
9. Chang CH, Curtis JD, Maggi LB Jr, et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell. 2013;153:1239–1251.
10. Peng M, Yin N, Chhangawala S, et al. Aerobic glycolysis promotes T helper 1 cell differentiation through an epigenetic mechanism. Science. 2016;354:481–484.
11. Nguyen HD, Chatterjee S, Haarberg KM, et al. Metabolic reprogramming of alloantigen-activated T cells after hematopoietic cell transplantation. J Clin Invest. 2016;126:1337–1352.
12. Raha S, Raud B, Oberdorfer L, et al. Disruption of de novo fatty acid synthesis via acetyl-CoA carboxylase 1 inhibition prevents acute graft-versus-host disease. Eur J Immunol. 2016;46:2233–2238.
13. Lee CF, Lo YC, Cheng CH, et al. Preventing allograft rejection by targeting immune metabolism. Cell Rep. 2015;13:760–770.
14. Gerriets VA, Kishton RJ, Nichols AG, et al. Metabolic programming and PDHK1 control CD4+ T cell subsets and inflammation. J Clin Invest. 2015;125:194–207.
15. Berod L, Friedrich C, Nandan A, et al. De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells. Nat Med. 2014;20:1327–1333.
16. Klysz D, Tai X, Robert PA, et al. Glutamine-dependent α-ketoglutarate production regulates the balance between T helper 1 cell and regulatory T cell generation. Sci Signal. 2015;8:ra97.
17. Abbas AK, Benoist C, Bluestone JA, et al. Regulatory T cells: recommendations to simplify the nomenclature. Nat Immunol. 2013;14:307–308.
18. Zhou X, Bailey-Bucktrout SL, Jeker LT, et al. Instability of the transcription factor Foxp3 leads to the generation of pathogenic memory T cells in vivo. Nat Immunol. 2009;10:1000–1007.
19. Bailey-Bucktrout SL, Martinez-Llordella M, Zhou X, et al. Self-antigen-driven activation induces instability of regulatory T cells during an inflammatory autoimmune response. Immunity. 2013;39:949–962.
20. Huehn J, Polansky JK, Hamann A. Epigenetic control of FOXP3 expression: the key to a stable regulatory T-cell lineage? Nat Rev Immunol. 2009;9:83–89.
21. Zeng H, Yang K, Cloer C, et al. mTORC1 couples immune signals and metabolic programming to establish T(reg)-cell function. Nature. 2013;499:485–490.
22. Procaccini C, Carbone F, Di Silvestre D, et al. The proteomic landscape of human ex vivo regulatory and conventional T cells reveals specific metabolic requirements. Immunity. 2016;44:406–421.
23. Gerriets VA, Kishton RJ, Johnson MO, et al. Foxp3 and Toll-like receptor signaling balance Treg cell anabolic metabolism for suppression. Nat Immunol. 2016;17:1459–1466.
24. Wei J, Long L, Yang K, et al. Autophagy enforces functional integrity of regulatory T cells by coupling environmental cues and metabolic homeostasis. Nat Immunol. 2016;17:277–285.
25. Huynh A, DuPage M, Priyadharshini B, et al. Control of PI(3) kinase in Treg cells maintains homeostasis and lineage stability. Nat Immunol. 2015;16:188–196.
26. De Rosa V, Galgani M, Porcellini A, et al. Glycolysis controls the induction of human regulatory T cells by modulating the expression of FOXP3 exon 2 splicing variants. Nat Immunol. 2015;16:1174–1184.
27. Delgoffe GM, Woo SR, Turnis ME, et al. Stability and function of regulatory T cells is maintained by a neuropilin-1-semaphorin-4a axis. Nature. 2013;501:252–256.
28. Thornton AM, Korty PE, Tran DQ, et al. Expression of Helios, an Ikaros transcription factor family member, differentiates thymic-derived from peripherally induced Foxp3+ T regulatory cells. J Immunol. 2010;184:3433–3441.
29. Milpied P, Renand A, Bruneau J, et al. Neuropilin-1 is not a marker of human Foxp3+ Treg. Eur J Immunol. 2009;39:1466–1471.
30. Weiss JM, Bilate AM, Gobert M, et al. Neuropilin 1 is expressed on thymus-derived natural regulatory T cells, but not mucosa-generated induced Foxp3+ T reg cells. J Exp Med. 2012;209:1723–1742, S1721.
31. Yadav M, Louvet C, Davini D, et al. Neuropilin-1 distinguishes natural and inducible regulatory T cells among regulatory T cell subsets in vivo. J Exp Med. 2012;209:1713–1722, S1711-S1719.
32. Akimova T, Beier UH, Wang L, et al. Helios expression is a marker of T cell activation and proliferation. PLoS One. 2011;6:e24226.
33. Newton R, Priyadharshini B, Turka LA. Immunometabolism of regulatory T cells. Nat Immunol. 2016;17:618–625.
34. Glick GD, Rossignol R, Lyssiotis CA, et al. Anaplerotic metabolism of alloreactive T cells provides a metabolic approach to treat graft-versus-host disease. J Pharmacol Exp Ther. 2014;351:298–307.
35. Buck MD, O'Sullivan D, Klein Geltink RI, et al. Mitochondrial Dynamics Controls T Cell Fate through Metabolic Programming. Cell. 2016;166:63–76.
36. Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17:451–459.
37. Hocher B, Adamski J. Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol. 2017;13:269–284.
38. Deberardinis RJ. Analyzing Metabolism in Biological System. Navigating Metabolism Appendix.
39. Bonneau E, Tétreault N, Robitaille R, et al. Metabolomics: perspectives on potential biomarkers in organ transplantation and immunosuppressant toxicity. Clin Biochem. 2016;49:377–384.
40. Wang R, Dillon CP, Shi LZ, et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity. 2011;35:871–882.
41. Blagih J, Coulombe F, Vincent EE, et al. Programmed death ligand-1 expression on donor T cells drives graft-versus-host disease lethality. J Clin Invest. 2016;126:2642–2660.
42. Blagih J, Coulombe F, Vincent EE, et al. The energy sensor AMPK regulates T cell metabolic adaptation and effector responses in vivo. Immunity. 2015;42:41–54.
43. Yin Y, Choi SC, Xu Z, et al. Normalization of CD4+ T cell metabolism reverses lupus. Sci Transl Med. 2015;7:274ra218.
44. Schell JC, Olson KA, Jiang L, et al. A role for the mitochondrial pyruvate carrier as a repressor of the Warburg effect and colon cancer cell growth. Mol Cell. 2014;56:400–413.
45. Zhong Y, Li X, Yu D, et al. Application of mitochondrial pyruvate carrier blocker UK5099 creates metabolic reprogram and greater stem-like properties in LnCap prostate cancer cells in vitro. Oncotarget. 2015;6:37758–37769.
46. Buck MD, O'Sullivan D, Pearce EL. T cell metabolism drives immunity. J Exp Med. 2015;212:1345–1360.
47. Wishart DS. Metabolomics: the principles and potential applications to transplantation. Am J Transplant. 2005;5:2814–2820.
48. Tan H, Yang K, Li Y, et al. Integrative proteomics and phosphoproteomics profiling reveals dynamic signaling networks and bioenergetics pathways underlying T cell activation. Immunity. 2017;46:488–503.
49. Geiger R, Rieckmann JC, Wolf T, et al. L-Arginine modulates t cell metabolism and enhances survival and anti-tumor activity. Cell. 2016;167:829–842. e813.
50. Lu L, Lan Q, Li Z, et al. Critical role of all-trans retinoic acid in stabilizing human natural regulatory T cells under inflammatory conditions. Proc Natl Acad Sci U S A. 2014;111:E3432–E3440.
51. Yue X, Trifari S, Äijö T, et al. Control of Foxp3 stability through modulation of TET activity. J Exp Med. 2016;213:377–397.
52. DuPage M, Chopra G, Quiros J, et al. The chromatin-modifying enzyme Ezh2 is critical for the maintenance of regulatory T cell identity after activation. Immunity. 2015;42:227–238.
53. Safinia N, Vaikunthanathan T, Fraser H, et al. Successful expansion of functional and stable regulatory T cells for immunotherapy in liver transplantation. Oncotarget. 2016;7:7563–7577.
54. Kawalekar OU, O'Connor RS, Fraietta JA, et al. Distinct signaling of coreceptors regulates specific metabolism pathways and impacts memory development in CAR T cells. Immunity. 2016;44:380–390.
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