extfieldsofstudy: Biology Medicine

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In vivo reprogramming of pancreatic acinar cells to three islet endocrine subtypes

Direct lineage conversion of adult cells is a promising approach for regenerative medicine. A major challenge of lineage conversion is to generate specific cell subtypes. The pancreatic islets contain three major hormone-secreting endocrine subtypes: insulin+ β-cells, glucagon+ α-cells, and somatostatin+ δ-cells. We previously reported that a combination of three transcription factors, Ngn3, Mafa, and Pdx1, directly reprograms pancreatic acinar cells to β-cells. We now show that acinar cells can be converted to δ-like and α-like cells by Ngn3 and Ngn3+Mafa respectively. Thus, three major islet endocrine subtypes can be derived by acinar reprogramming. Ngn3 promotes establishment of a generic endocrine state in acinar cells, and also promotes δ-specification in the absence of other factors. δ-specification is in turn suppressed by Mafa and Pdx1 during α- and β-cell induction. These studies identify a set of defined factors whose combinatorial actions reprogram acinar cells to distinct islet endocrine subtypes in vivo. DOI: http://dx.doi.org/10.7554/eLife.01846.001

Introduction
Cellular reprogramming is a rapidly expanding area of regenerative medicine. With suitable reprogramming factors, adult cells can be instructively converted to induced pluripotent stem cells (pluripotent reprogramming) or other types of adult cells (lineage reprogramming) (Gurdon and Melton, 2008;Graf and Enver, 2009). Induced pluripotent stem cells (iPS) can be differentiated into many cell types in the body. However, the generation of iPS cells and their subsequent differentiation is a lengthy and technically demanding process. Lineage conversion between adult cell types offers a promising alternative, directly producing defined cell types in vitro or even in vivo that may be used for disease modeling and cellular therapies Vierbuchen and Wernig, 2011). Recent examples of lineage reprogramming include the conversion of pre-B cells to macrophages, pancreatic acinar, α-cells, and gut cells to insulin-secreting β-cells, cardiac fibroblasts to cardiomyocyte-like cells, amniotic cells to endothelial cells, and skin fibroblasts to neurons, oligodendrocytes, neural precursors, or blood progenitors (Xie et al., 2004;Ieda et al., 2010;Szabo et al., 2010;Thorel et al., 2010;Vierbuchen et al., 2010;Caiazzo et al., 2011;Yang et al., 2011;Ginsberg et al., 2012;Han et al., 2012;Song et al., 2012;Talchai et al., 2012;Thier et al., 2012;Najm et al., 2013;Yang et al., 2013).
Despite increasing success of lineage conversion, a major challenge of this approach is to direct the formation of specific cell types: there is a great diversity of cell types in the adult body, and many of them are further differentiated into closely related subtypes. The most extensive subtype diversification can be found in the mammalian central nervous system, where hundreds of neuronal subtypes exist. A few mammalian neuronal subtypes, including dopaminergic-like and motoneuron-like cells, have been produced from fibroblast conversion (Caiazzo et al., 2011;Son et al., 2011); methods to generate many others remain to be defined. To study subtype specification in lineage reprogramming, it is necessary to first establish models, where a defined set of factors promote formation of distinct subtypes. A recent study in Caenorhabditis elegans provided such an example, where removal of a chromatin factor confers neurogenic competence to germ cells, which can be subsequently converted to different neuronal subtypes by neuron selector genes (Tursun et al., 2011).
To establish models of mammalian subtype specification in lineage reprogramming, we focused our studies in a relatively simple system, the adult pancreas, where the endocrine islets are surrounded by acinar cells, a type of exocrine cells that secret digestive enzymes. The islets contain three major endocrine subtypes: insulin + β-cells, glucagon + α-cells, and somatostatin + δ-cells. β-cells secret insulin and play a key role in blood glucose regulation, whereas αand δ-cells secrete glucagon and somatostatin to support β-cell function (Edlund, 2001;Jensen, 2004).
We reported previously that pancreatic acinar cells can be directly converted to insulin + β-cells in adult mouse pancreas by combined actions of three transcription factors, Ngn3, Pdx1, and Mafa (referred to as M3 factors) . We now report that acinar cells can also be converted to the other endocrine subtypes, namely, somatostatin + δ-like cells and glucagon + α-like cells, by Ngn3 and Ngn3+Mafa respectively. A defined set of factors can therefore reprogram acinar cells to the three major islet endocrine subtypes. Further studies indicate that Ngn3, but not Mafa and Pdx1, promotes establishment of a generic endocrine state in acinar cells at the onset of reprogramming by suppressing acinar fate-regulators and activating pan-endocrine genes. Ngn3 also promotes δ-subtype specification in the absence of other factors. Mafa and Ngn3 in turn suppress δ-specification in αand β-cell formation, thus ensuring creation of singular endocrine subtypes. Our studies establish a series of models where combinatorial functions of defined factors convert pancreatic acinar cells to three distinct endocrine subtypes in vivo. These models provide a powerful system to gain mechanistic understanding of the lineage reprogramming process.

Results
Reprogramming acinar to δ-, α-, and β-like endocrine cells We have previously reported that pancreatic acinar cells can be converted to insulin + β-like cells by the combined activity of three reprogramming factors: Ngn3, Mafa, and Pdx1, referred to as M3 factors . Employing the same experimental system of adenoviral expression in adult mouse eLife digest In mammals, the pancreas is responsible for controlling blood sugar by secreting insulin from specialized β-cells. Other cells in the pancreas, called δ-cells and α-cells, secrete other hormones to assist the β-cells. Diabetes is caused when this system breaks down: either the body attacks its own β-cells (type I diabetes), or the body stops responding properly to insulin (type II).
Type I diabetes is usually treated with insulin injections, but there is increasing interest in the possibility of replacing the defective β-cells instead. Building on previous work in which a fourth type of pancreatic cell, called an acinar cell, was reprogrammed to become a β-cell, Li et al. have now shown that the same technique can be used to produce αand δ-cells as well. Just as the reprogrammed β-cells secreted insulin, like real β-cells, the reprogrammed αand δ-cells also behaved like real αand δ-cells.
The reprogramming technique relies on using a combination of three transcription factorswhich are called Ngn3, Pdx1 and Mafa-to treat the acinar cells from mice. Previously, it was shown that using a combination of all three transcription factors reprogrammed the acinar cells to become β-cells. Now, Li et al. show that the Ngn3 transcription factor on its own appears to suppress certain genes that are usually expressed in acinar cells, and goes on to cause the acinar cells to become δ-cells. However, a combination of Ngn3 and Mafa produces a mixture of αand δ-cells. The next challenge is to adapt this reprogramming technique to generate different types of hormone secreting cells from human tissue sources in order to explore its therapeutic potential. DOI: 10.7554/eLife.01846.002 pancreas, which specifically targets acinar cells ( Figure 1A, Figure 1-figure supplement 1), we examined the role of individual M3 factors in endocrine reprogramming. Surprisingly, Ngn3 alone induced formation of somatostatin + (Sst) cells in approximately 40% of infected cells ( Figure 1B-D), whereas Mafa or Pdx1 alone did not induce any hormone positive cells (Figure 1-figure supplement 2). In addition, co-infection of Ngn3-and Mafa-induced formation of both glucagon + (Gcg) and somatostatin + cells, which are distinct from each other ( Figures 1E,F). The other two-factor combinations, Ngn3 with Pdx1 and Pdx1 with Mafa, did not yield hormone positive cells (Figure 1-figure supplement 2). Somatostatin and glucagon are the principle hormones of endocrine δand α-cells. These data suggest that different combinations of three reprogramming factors could convert pancreatic acinar cells in vivo to the three major islet endocrine cell types: δ-, αand β-cells. The expression of reprogramming factors in δand α-cell induction is transient (Figure 1-figure supplement 3), similar to β-cell induction using the same experimental approach . To confirm the identity of the induced Sst + and Gcg + cells, we examined whether the induced cells have key features of endogenous δand α-cells.
Ngn3 converts acinar to δ-like cells Among the major islet endocrine cell types, relatively little is known about δ-cell biology and genes important for δ-cell development and function. Among the few δ-cell-specific genes identified are somatostatin and cholecystokinin receptor B (Cckbr) (Morisset et al., 2000). Our analysis revealed that the majority of induced δ-like cells co-express Sst and Cckbr 30 days after induction (87 ±7% by immunohistochemistry, Figure 2A). The Sst + cells also express the endocrine factors Pax6 and synaptophysin ( Figure 2B,C). The Sst + induced δ-cells were present in adult pancreas 2 months after induction ( Figure 2-figure supplement 1).
A major method to recognize and distinguish the different islet endocrine subtypes is by ultrastructural analysis. In particular, the secretory granules of each islet subtype have characteristic morphology (Larsson et al., 1976;Leiter et al., 1979). Electron microscopy analysis revealed that the secretory granules of induced δ-cells are spherical or ellipsoidal with matrix filling the entire granule space ( Figure 2E,E′). This morphology is typical of endogenous δ-cells ( Figure 2D,D′) and distinct from that of α-or β-granules ( Figure 2-figure supplement 2). In addition, we observed that the induced δ-cells were embedded among acinar cells ( Figure 2E, arrow points to dense assembly of endoplasmic reticulum in a neighboring acinar cell), consistent with their origins from acinar cells. In contrast, endogenous δ-cells reside exclusively within islets ( Figure 2D).
To further characterize the induced δ-cells, we generated gene expression data from FACS purified induced δ-cells (day 10 after infection) using mCherry, a fluorescent marker coexpressed with Ngn3 ( Figure 1A). Cherry + cells contain approximately 40% induced δ-cells. Gene profiling with illumina arrays yielded 1283 genes enriched in the induced δ-cells (30 days after induction) relative to acinar cells (GEO: GSE52522). Because there is currently no method available that allows purification of endogenous δ-cells, we compared their expression profile with that of whole islets, which are comprised largely of β-cells. 632 of the induced genes (49%) overlapped with the islet-enriched gene set that we previously reported ( Figure 2F)  (GEO: GSE12025). Given that δ-cells represent a minor fraction of total mouse islet cells (5.5% as determined by FACS, see Figure 2-figure supplement 4), the non-overlapping genes (651) may contain δ-cell-enriched factors that are underrepresented in the whole-islet samples. Indeed, among the top 30 most highly induced genes in the δ-like cells, many show low expression in whole islet samples ( Figure 2G). We detected up-regulation of Hhex ( Figure 2G), a gene recently implicated in δ-cell biology (72nd ADA abstract, Klaus Kaestner lab). In contrast, glucagon, insulin, and the β-cell marker Nkx6.1 are abundantly expressed in islets but absent from induced δ-cells ( Figure 2G).
We analyzed the DNA methylation status of several gene promoters to assess epigenetic changes in the conversion of acinar to δ-like cells. These genes included Somatostatin (δ-cells), Amylase2 (acinar cells), and Insulin2 (β-cells). Studies have shown that insulin2 gene expression is regulated by DNA sequences located within approximately 400 bp upstream of the transcription start site (TSS) (Hay and Docherty, 2006). Similarly, approximately 200 bp of the Amylase 2 promoter is sufficient to direct acinar-specific expression (Minami et al., 2005). We therefore assayed CpGs located in these critical promoter regions (Figure 2-figure supplement 3). There are very limited studies on the Somatostatin promoter so we assayed seven CpGs that fall within a highly conversed promoter region ( Figure 2-figure supplement 5). We adapted an intracellular FACS protocol to purify endogenous alone induced 40 ± 3% of the infected mCherry + cells to become somatostatin + (Sst). (E-G) Co-infection of two separate viruses carrying Ngn3 and Mafa resulted in the formation of both glucagon + (Gcg) and somatostatin + cells in 11 ± 6% and 9 ± 5% of infected cells, respectively. (H-I) Co-expression of Ngn3, Mafa, Pdx1, and mCherry from a single polycistronic construct led to exclusive formation of insulin + cells in 47 ± 8% of the mCherry + cells.  (Pechhold et al., 2009). We note that this protocol allows isolation of genomic DNA but not intact mRNA from the pancreatic tissues. We have not been successful at generating gene-profiling data from the induced δ-cells using the intracellular FACS method.
None of the CpG sites assayed in the somatostatin promoters was methylated in all samples tested ( Figure 2-figure supplement 5), indicating that this promoter is not subject to regulation by DNA methylation. Amylase2, a gene exclusively expressed in pancreatic acinar cells, was largely unmethylated in acinar cells ( Figure 2H). In contrast, the induced δ-cells showed strong methylation in this promoter similar to islet δ-cells ( Figure 2H and Figure 2-figure supplement 6). The Insulin2 promoter was fully methylated in acinar cells but partially demethylated in both endogenous and induced δ-cells ( Figure 2H). For both amylase and Insulin promoters, the methylation differences of acinar/islet δ-cell and acinar/ induced δ-cell are statistically highly significant (p<0.001) whereas there is no significant difference between islet δ-cells and induced δ-cells (p=0.23 and 0.30 for amylase and insulin promoter respectively). These studies suggest that substantial DNA methylation changes occurred during acinar to δ-cell conversion in the promoters we studied. It is notable that not all cell fate conversion events are associated with DNA methylation changes. For example, no significant DNA methylation was observed in the conversion of pre-B cells to macrophages (Rodriguez-Ubreva et al., 2012).
We evaluated the ability of induced δ-cells to secret hormones in an in vitro secretion assay. The acinar fraction that contains induced δ-cells was isolated 30 days after induction and stimulated with the secretagogue Arginine. The induced δ-cells responded to Arginine and released somatostatin, in a manner similar to isolated islets which contain endogenous δ-cells ( Figure 2I). In contrast, control acinar cells did not respond to Arginine stimulation ( Figure 2I), consistent with the fact that they do not express endocrine hormones. These data suggest that induced δ-cells possess cellular machineries necessary for hormone production, storage, and release.
The data described above collectively indicate that δ-like cells induced by Ngn3 expression in adult pancreas possess key features of endogenous δ-cells.

Ngn3 and Mafa converts acinar to α-like cells
Co-infection of adult mouse pancreas with two different adenoviruses carrying Ngn3-and Mafainduced formation of glucagon + cells ( Figure 3A). This co-infection also induced somatostatin + cells, which are distinct from the glucagon + cells ( Figure 3A′). We tested different ratios of Ngn3/Mafa viruses and observed that a 1:1 ratio yielded the most number of Gcg + cells (Figures 1F and 9 ± 5% at 1:1 ratio, and data not shown). Due to the random nature of co-infection, cells that received predominately Ngn3 infection likely become the Sst + cells. In contrast to co-infection by two separate viruses, polycistronic co-expression of Ngn3 and Mafa from a single construct yielded substantially reduced number of glucagon + cells (less than 1%, data not shown). We therefore used co-infection to induce glucagon + cells in all subsequent experiments.
The most important gene that controls endogenous α-cell development and identity is the transcription factor Arx. Genetic deletion of Arx during embryonic development results in the absence of α-cells, whereas ectopic expression of Arx in β-cells causes their phenotypic drift towards α-cells (Collombat et al., 2003;Dhawan et al., 2011). We observed strong Arx expression in all induced Gcg + cells, although some Gcg − cells also expressed Arx ( Figures 3B,B′). In addition, all Gcg + cells expressed the endocrine genes Pax6 ( Figure 3C,C′) and synaptophysin ( Figure 3D,D′). Due to the intermingling of induced Gcg + cells and Sst + cells, we were unable to purify these cells for detailed transcriptome analysis. The induced α-cells are readily observable 2 month after induction   Figure 3-figure supplement 1). Electron microscopy analysis of the induced α-cells indicates that they share a similar ultrastructure as the endogenous α-cells ( Figure 3E,F). The secretory granules have a narrow halo between the core and membrane ( Figure 3E′,F′) that is typical of α-granules and distinct from δand β-granules (Figure 2-figure supplement 2). The induced α-like cells were intermingled among acinar cells ( Figure 3F, arrow points to zymogene granules of a neighboring acinar cells), whereas endogenous α-cells were found exclusively within islets ( Figure 3E). In an in vitro hormone secretion assay, the acinar fraction that contains induced α-cells isolated from the adult pancreas 30 days after induction responded to the secretagogue Arginine and released glucagon ( Figure 3H), indicating their ability to produce and secrete hormone.
We used intracellular FACS to isolate endogenous and induced α-cells ( . Statistical analysis showed no significant difference between endogenous and induced cells at glucagon and insulin promoters (p=0.26, 0.50, respectively). However, a difference was detected at the amylase promoter (p=0.02). Amylase protein expression was absent in the majority of Gcg + cells (over 95%). However, a small fraction (<5% of all Gcg + cells) were Amylase + (Figure 3-figure supplement 4), indicating incomplete reprogramming in a small subset of glucagon + cells.
Taken together, these results indicate that induced α-cells possess key features of endogenous α-cells.
Induced δand α-cells are converted from adult acinar cells in the absence of cell proliferation We previously reported that the acinar cell is the cell-of-origin for most of the induced β-cells in M3 factor-mediated reprogramming of adult pancreas . This is in part due to preferential infection of adenovirus for acinar cells but not the other pancreatic cell types . To test whether the induced δ-and α-cells also derive from acinar cells, we used a genetic lineage tracing strategy similar to the previous study. A Ptf1aCreER mouse line, which drives CreER expression exclusively in acinar cells of the adult pancreas, was crossed with a Rosa-floxed-Stop-YFP (RosaYFP) reporter line to create bigenic Ptf1aCreER::RosaYFP animals. Tamoxifen induction resulted in the labeling of 20-30% of mature acinar cells ( Figure 4A′,B′), consistent with published report of this line (Pan et al., 2013). After adenoviral delivery of Ngn3 or Ngn3+Mafa, which targets acinar cells, we confirmed that acinar cells can give rise to both δ-like and α-like cells ( Figure 4A,B). The induced endocrine cells are also smaller than acinar cells ( Figure 4A,B, arrows), consistent with previous report .
Continuous BrdU labeling showed that the conversion of acinar cells to induced δand α-cells occurred largely in the absence of cell proliferation ( Figure 4C,D). Only about 1% of the induced δand α-cells incorporated BrdU in a 10-day reprogramming period ( Figure 4E).
These studies confirmed that induced δand α-cells derive from adult acinar cells and that the acinar conversion occurred largely in the absence of cell proliferation.
Ngn3 promotes an endocrine state in acinar cells by suppressing acinar fate regulators and activating pan-endocrine genes In principle, the conversion of one cell fate to another involves two major components: suppression of the original cell fate and activation of a new one within the same cell. We examined the ability of the Ptf1a, Nr5a2, and Mist1 are key acinar cell-fate regulators. They are expressed in adult acinar; their genetic deletion results in abnormalities of acinar development and function (Pin et al., 2001;Kawaguchi et al., 2002;Lin et al., 2004;Beres et al., 2006;Holmstrom et al., 2011). We observed that Ngn3 and Mafa, but not Pdx1, strongly suppressed Ptf1a, Mist1, and Nr5a2 expression 4 days after gene delivery in pancreas ( Figure 5A,B,E,F,I,J,M).   (Ahlgren et al., 1997;Sander et al., 1997;Ashery-Padan et al., 2004). Ngn3, but not Mafa or Pdx1, activated the expression of these pan-endocrine genes at day 4 after pancreas infection (Figure 5C,D,G,H,K,L,M).
The data discussed above together indicate that among the three reprogramming factors, Ngn3 alone possesses the ability to initiate two key events in endocrine reprogramming, namely, acinar suppression and pan-endocrine activation, thereby establishing a generic endocrine state in acinar cells.

Acinar factors are molecular barriers of endocrine reprogramming
Given the important role played by key acinar factors in maintaining acinar cell fate, we hypothesized that these factors could act as molecular barriers in endocrine conversion. We tested this hypothesis by co-infecting pancreas with two viruses carrying Nr5a2 and Ngn3. 10 days after the infection, analyses revealed that persistent expression of Nr5a2 strongly blocked induction of Pax6 and Sst (Figure 6B,E,G), compared with Ngn3 alone controls (Figure 6A,D,G). Another acinar factor Ptf1a similarly suppressed Pax6 and Sst induction (Figure 6C,F,G). These results demonstrate that key acinar factors are potent molecular barriers; their down-regulation is a prerequisite for endocrine reprogramming.

Establishment of endocrine state precedes activation of endocrine subtype-specific genes in acinar conversion
To further understand the temporal sequence of acinar to endocrine subtype conversion, we examined three key events: acinar suppression, pan-endocrine activation, and subtype-specific gene activation. In Ngn3-induced acinar to δ-cell conversion, we observed strong suppression of the acinar factor Mist1 at day 2 after Ngn3 delivery ( Figure 7A). The pan-endocrine gene Pax6 was also robustly activated at day 2 ( Figure 7B). In contrast, expression of δ-specific genes somatostatin and CCKbr was not detected until day 4 ( Figure 7G,H) and became strongly expressed at day 10 ( Figure 7K,L). A similar temporal sequence of acinar suppression (Mist1) and pan-endocrine activation (Pax6) followed by β-specific gene activation (Nkx6.1 and insulin) was also observed in acinar to β-cell conversion (Figure 8). These data collectively suggest that a generic endocrine state was established in acinar cells at the onset of reprogramming, temporally preceding endocrine subtype specification.

Pdx1 and Mafa can suppress δ-subtype specification
The studies discussed above indicate that Ngn3 has two main functions in acinar to endocrine conversion: establishment of an endocrine state, and δ-subtype specification in the absence of other factors. Ngn3 is also part of the reprogramming factor combination in the induction of α-cells (Ngn3+Mafa) and β-cells (Ngn3+Mafa + Pdx1), which raises the question of how a singular αor β-cell fate is established. We tested the possibility that δ-specification may be suppressed by Pdx1 and/or Mafa. Polycistronic co-expression of Mafa with Ngn3 reduced the efficiency of somatostatin induction from 40 ± 3% in control Ngn3 animals ( Figure 9A) to 24 ± 7% in Ngn3.Mafa animals ( Figure 9B,D). Further increasing the amount of Mafa in the mixture led to a stronger suppression of δ-cell induction (data not shown). Polycistronic co-expression of Pdx1 with Ngn3 led to near complete suppression of somatostatin + cells (2 ± 1%, Figure 9C,D). Immunostaining with pan-endocrine factor synaptophysin revealed that Cherry + somatostatin − cells were synaptophysin + ( Figure 9C, inset), suggesting that Ngn3 converted acinar cells to an endocrine state, but that δ-subtype specification was blocked. These data indicate that Pdx1 and Mafa can suppress δ-subtype specification, which is likely part of the mechanism to ensure formation of distinct αand β-subtypes upon coexpression of multiple reprogramming factors.

Discussion
Our studies indicate that pancreatic acinar cells can be directly converted to endocrine δand α-like cells by in vivo expression of Ngn3 or Ngn3+Mafa respectively. Together with our previous report of β-cell reprogramming with Ngn3+Mafa+Pdx1, these studies provide a set of reprogramming models Figure 7. Acinar suppression and pan-endocrine activation precedes subtype-specific gene activation in acinar to δ-cell conversion. In acinar to δ-cell conversion induced by Ngn3, strong suppression of the acinar factor Mist1 was observed in the Cherry + -infected cells at day 2 (A). The pan-endocrine factor Pax6 was also induced at day 2 (B). The Mist1 -Pax6 + state was maintained in the majority of Cherry + cells at later time points (E, F, I, J). In contrast, δ-subtype specific factors Sst and CCkbr were not induced until day 4 (G, H) and became robustly expressed at day 10 (K, L). In control samples expressing cherry alone, the majority of cherry + acinar cells had Mist1 expression (M), and none had induced endocrine gene expression (N, O, P). Quantifications are shown as mean ± s.d (Q). At least 1000 cherry + cells counted from three different animals. DOI: 10.7554/eLife.01846.022 Research article Figure 8. Acinar suppression and pan-endocrine activation precedes subtype-specific gene activation in acinar to β-cell conversion. In acinar to β-cell conversion induced by M3 (Ngn3+Mafa+Pdx1), near complete suppression of the acinar factor Mist1 was observed in the Cherry + -infected cells at day 2 (A). The pan-endocrine factor Pax6 was also robustly induced at day 2 (B). The Mist1 − Pax6 + state was maintained in the majority of Cherry + cells at later time points (E, F, I, J). In contrast, β-subtype specific factors insulin and Nkx6.1 were not induced until day 5 (G, H) and became more robustly expressed at day 10 (K, L). In control samples expressing cherry alone, the majority of cherry + acinar cells had Mist1 expression (M), and none had induced endocrine gene expression (N, O, P). Quantifications are shown as mean ± s.d (Q). At least 1000 cherry + cells counted from three different animals. DOI: 10.7554/eLife.01846.023 where combinatorial actions of three factors lead to conversion of acinar cells to the three major islet endocrine subtypes in vivo ( Figure 10A).
Our studies indicate that acinar to endocrine reprogramming includes two main processes: establishment of a generic endocrine state and endocrine subtype specification ( Figure 10B). Ngn3 plays a central role in promoting the endocrine state at the onset of reprogramming by suppressing acinar fate regulators and activating pan-endocrine genes ( Figure 10B, upper panel). This early step is critical as continued expression of key acinar factors will block endocrine conversion.
Compared with Ngn3, Mafa suppresses acinar regulators but does not activate pan-endocrine genes, whereas Pdx1 lacks the ability to initiate either of these two events. It is likely that the main function of Mafa and Pdx1 is to collaborate with Ngn3 to activate subtype-specific genes such as Arx in α-induction (this study) and Nkx6.1 in β-induction . Another important function of Mafa and Pdx1 is to suppress δ-specification, thus ensuring formation of singular αand β-subtypes, and preventing hybrid cells ( Figure 10B, lower panel). Our data together suggest that each of the Research article Figure 9. Pdx1 and Mafa can suppress δ-subtype specification. Compared with robust induction of Sst + cells by Ngn3 alone (A), polycistronic co-expression of Mafa and Ngn3 led to strong reduction of Sst + cells (B). Polycistronic co-expression of Pdx1 and Ngn3 nearly completely suppressed Sst + cell induction (C). The Sstcherry + cells expressed synaptophysin (Syn) (C, inset), suggesting that these cells acquired an endocrine identity but δ-specification was blocked. Quantifications are shown as mean ± s.d (D). At least 1000 cherry + cells counted from three different animals. **p<0.01, ***p<0.001. Mann-Whitney test. DOI: 10.7554/eLife.01846.024 three factors possesses both activator and suppressor functions ( Figure 10C). It will be important to fully elucidate the molecular details of these functions in future studies.
During pancreatic development, numerous studies have highlighted the importance of Ngn3 in controlling endocrine fate specification (Jensen, 2004). A recent study showed that ectopic expression of Ngn3 in embryonic pancreas suppressed exocrine fate specification (Qu et al., 2013). Thus, Ngn3 can suppress exocrine fate and promote endocrine fate during both embryogenesis in progenitor cells and during adulthood in differentiated acinar cells. The function of Pdx1 has also been extensively studied and demonstrated to be critical in early pancreatic fate determination, as well as a later role in beta cell biology (Murtaugh, 2007). It is not clear, however, exactly what role Pdx1 plays in δand α-cell specification in embryogenesis. Mafa is expressed in pancreatic β-cells during development and plays a role in controlling β-cell gene transcription (Hang and Stein, 2011). The ability of Mafa to induce α-cell formation in collaboration with Ngn3 from acinar is nevertheless not entirely surprising. Its close homologue Mafb is expressed during pancreas development in a subset of endocrine progenitor cells (Nishimura et al., 2006). Mafb deletion leads to decreased αand β-cell numbers, suggesting a role for Maf factor in α-cell formation (Artner et al., 2007;Nishimura et al., 2008). Taken together, there are clear similarities in the function of Ngn3, Mafa, and Pdx1 in normal endocrine development and endocrine reprogramming from acinar cells. Nevertheless, the epigenetic landscape of pancreatic progenitors and adult acinar cells in which these factors operate is presumably very different. Further, the 'generic endocrine state' established by Ngn3 in acinar cells is not equivalent to an endocrine progenitor in pancreas development. For example, expression of pancreatic progenitor genes such as Sox9 and Hnf6 was not detected during the reprogramming process (data not shown).
One surprising finding from our study is that Ngn3 alone promotes δ-cell formation from acinar cells. Among the pancreatic endocrine subtypes, relatively little is known about the δ-cells. No genetic factors have been discovered that specify δ-cell fate in pancreas development. The molecular details of how Ngn3 functions to specify δ-subtype remains to be determined.
Conversion of acinar cells to α-cells requires both Ngn3 and Mafa. The optimal method to induce Gcg + α-like cells is by co-expression of Ngn3 and Mafa from two separate viruses at a 1:1 ratio. Varying the ratio of the two viruses or polycistronic co-expression of both factors led to reduced α-cell formation. These results are reminiscent of induced pluripotent reprogramming from fibroblasts where polycistronic co-expression of OSKM factors led to substantially reduced efficiency of iPS formation compared with random infection by OSKM factors (Carey et al., 2009;Chang et al., 2009). This difference has been suggested to result from a need for appropriate reprogramming factor stoichiometry (Carey et al., 2011). We speculate that the induction of α-like cells may similarly require optimized Ngn3/Mafa stoichiometry along with other conditions such as specific expression level and dynamics of the factors, which can be met at the more 'flexible' co-injection system with each cell expressing variable levels of Ngn3/Mafa, compared with the more 'fixed' polycistronic expression system. Future experiments will be required to elucidate mechanisms of α-cell induction.
Our studies suggest that a suitable strategy to produce specific subtypes of cells by lineage conversion is to combine factors that confer broad competence with factors that confer subtype specificity. An elegant example was demonstrated in C. elegans, where removal of a chromatin factor and employment of neuron selector genes allows conversion of germ cells to different neuronal subtypes (Tursun et al., 2011). The mammalian system is far more complex, but similar principles may well apply. It is hoped that insight from lineage reprogramming studies will lead to informed design and improved technology, thereby helping to unlock the tremendous therapeutic potential of this approach.

Construction and purification of adenovirus
Genes of interest were first cloned into a shuttle vector containing a 2A-Cherry, then into the pAd/ CMV/V5-DEST adenoviral vector (Invitrogen, Grand Island, NY). High titer virus (2-10 × 10 10 pfu/ml) was obtained by purification with the Vivapure Adenopack (Sartorius, Bohemia, NY). Viral tittering was performed with direct immuno-staining of inserted genes (Pdx1, Mafa, Ngn3) 2 days after infection in HEK293A cells. Viral preparations that did not reach at least 2 × 10 10 pfu/ml in one round of purification had poor induction efficiency in vivo, and were not used.

Animals, surgery
Rag1 −/− animals were obtained from Jackson Labs (Bar Harbor, ME). Adult animals (2-3 month) were injected with 100 µl of purified adenovirus (typically 1-2 × 10 9 pfu, dilution with saline of high titer stocks) directly into the splenic lobe of the dorsal pancreas with a 3/10 cc Insulin Syringe (Becton Dickinson, East Rutherford, NJ). All experiments were performed under approved institutional regulations.

Islet and acinar preparations, FACS of cherry + cells, gene profiling
Standard procedures were used to separate adult mouse pancreas into islet fraction and exocrine fraction after intra-ductal perfusion and digestion with liberase (Roche, Indianapolis, IN). After Dithizone staining, the acinar fraction is manually picked to eliminate all visible islets. Islets and acinar clusters are further dissociated into single cells by EGTA treatment. Cherry + cells were subsequently isolated by fluorescent activated sorting (FACS) with FACSaria (BD Bioscience, San Jose, CA). RNA was extracted (Qiagene RNeasy kit, Germantown, MD), cRNA synthesized (Ambion Amplification kit, Grand Island, NY), and genome-wide gene profiling performed with Illumina arrays.

Intracellular FACS sorting of somatostatin + and glucagon + cells
Intracytoplasmic staining of pancreatic cells was performed as previously described (Pechhold et al., 2009) with minor modifications. Cells were fixed with 4% paraformaldehyde in PBS for 5 min on ice, diluted in wash buffer (WB) (1:10), centrifuged at 250×g for 5 min, and permeabilized with detergent wash buffer (WB(d)) for 30 min on ice. Primary antibodies and final concentration used for the intracytoplasmic staining are mouse monoclonal anti-Glucagon (K79bB10, Sigma St. Louis, MO; 1/1000) and Goat anti-Somatostatin (Santa Cruz; sc-7819; 1/500). All primary antibodies were pre-labeled with Alexa Fluor 594, Alexa Fluor 488, or Alexa Fluor 647, using Zenon antibody labeling kits according to the manufacture's protocol. Intracellular FACS was carried out with FACSaria (BD Bioscience).

DNA methylation analysis, bisulphite sequencing, COBRA
Genomic DNA was purified using RecoverAll Total Nucleic Acid Isolation Kit (Invitrogen) and treated with EpiTect Bisulfite Kit (QIAGEN) according to the manufacture's protocols. The bisulfited genomic DNA was amplified using a touch-down PCR protocol (Amylase2a2 and Ins2 promoters, detailed PCR parameters available upon request) or using a nested PCR protocol (Sst and Glucagon promoters). All PCR reactions were performed using HotStart Taq DNA polymerase (QIAGEN). For nested PCR: 95°C for 15 min followed by 45 cycles of 95°C/30 s, 52°C/30 s, 72°C/1 min, and last elongation at 72°C for 10 min. The final PCR product was purified using MinElute PCR Purification Kit (Qiagen), cloned, and sequenced. The sequences were analyzed using BiQ Analyzer software (Bock et al. 2005). For COBRA analysis (Combined bisulfite restriction analysis) of Amylase 2a promoter, the PCR product was generated and cut with Taqα1 enzyme. All primers used are listed in Table 1.

Ptf1aCreER labeling of exocrine cells
Ptf1aCreER;RosaYFP double heterozygous animals were generated by mating homozygous Cpa1CreER males with RosaYFP homozygous females (Jackson lab). 2-month-old Cpa1CreER;RosaYFP adults were injected with Tamoxifen at 6 mg per animal every third day for four times to label acinar cells.

Radioimmunoassay (RIA)
Exocrine tissues that contain the induced endocrine cells were harvested after pancreas dissociation and islets removal. After washing with Preculture Medium (1 and 5 mM glucose in PBS for Sst and Gcg RIA, respectively), exocrine tissue (approximately 20 mg per sample) and islets (20-30 islets per sample) were incubated in 1 ml pre-warmed Preculture Medium at 37° C for 1 hr. After removing Preculture Medium, the exocrine tissue and islet samples were re-suspended in 500 ml PBS in the absence or presence of 20 mM Arginine and incubated at 37° C for 1 hr. Supernatants were harvested. Somatostatin EURIA kit (Euro Diagnostica, Malmö, Sweden) and Glucagon RIA kit (Millipore, Darmstadt, Germany) were used to assess Somatostatin and Glucagon content of the incubation medium. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Author contributions WL, MN, QZ, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article; AZ, MS, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article; CW, Conception and design, Analysis and interpretation of data, Contributed unpublished essential data or reagents; DAM, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

Ethics
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (29-13) of Harvard University Faculty of Arts and Sciences. The Harvard University (HU)/Faculty of Arts and Science (FAS) animal-care and use program maintains full AAALAC accreditation, is assured with OLAW (A3593-01), and is currently registered with the USDA. All surgery was performed under approved and appropriate anesthesia, and every effort was made to minimize suffering.

Major datasets
The following dataset was generated: Author (


text:
Targeting T Cell Immunometabolism for Cancer Immunotherapy; Understanding the Impact of the Tumor Microenvironment

The immune system has a key role to play in controlling cancer initiation and progression. T cell activation, which is central to anti-tumor immune responses, coincides with changes in cellular metabolism. Naïve T cells predominantly require an ATP generating metabolic profile, whereas proliferating effector T cells require anabolic metabolic profiles that promote rapid growth and proliferation. Furthermore, specific T cell subsets require distinct energetic and biosynthetic pathways to match their functional requirements. The often hostile tumor microenvironment can affect T cell immune responses by altering the resulting cellular metabolism. Tailoring immune responses by manipulating cellular metabolic pathways may provide an exciting new option for cancer immunotherapy. T cell responses might also be skewed via metabolic manipulation to treat the complications of obesity-associated inflammation, which is a rapidly growing global health problem and a major risk factor for many malignancies. In this review, the diverse metabolic requirements of T cells in anti-tumor immunity are discussed, as well as the profound influence of the tumor microenvironment and the possible avenues for manipulation to enhance anti-tumor immunity.

INTRODUCTION
A unique bioenergetic challenge is faced by cells of the immune system when they recognize mutated or over expressed tumor antigens presented by antigen presenting cells. They must rapidly proliferate with a specific phenotype and function to eradicate tumor cells (1). The ability of a T cell to transition from a naïve, to effector, to a memory phenotype is determined by metabolism and the metabolic program varies to match the T cell subset in order to enable cell survival and function (2). Activated T cells switch from naïve T cells, mainly requiring generation of ATP and replacement biosynthesis (3) to effector T cells (T EFF ), which must rapidly increase cell mass and elicit effector functions through cytokine production and cytotoxicity (4). This switch from primarily oxidative phosphorylation (OxPhos) to glycolysis supports the demands of rapid cell growth. Primary fuels and amino acids, such as glucose and glutamine must be available to support these distinct metabolic pathways (5,6). Unavailability of these fuels occurs during malnutrition and can lead to immunosuppression (7), while excess fuel in diet-induced obesity is associated with a constant low level state of inflammation (8). We have previously identified T cells as key players in adipose tissue inflammation in obesity-associated cancer and here, we suggest that their metabolism might be targeted to control chronic inflammation and lessen the clinical manifestations of obesity (9). In comparison, the long lifespan of memory T cells (T MEM ) poses a different metabolic demand and to facilitate this need, T MEM cells utilize fatty acid oxidation (FAO) as their main energy source (10). The balance between different T cell subsets can be altered by multiple environmental factors in the tumor resulting in the activation of metabolic pathways and the establishment of inappropriate T cell responses. Direct manipulation of T cell metabolism has the potential to provide a new avenue for cancer immunotherapy and obesity-associated inflammation and this will be discussed.

HOW T CELL METABOLISM AFFECTS FUNCTION
At each stage of a T cell's development, its metabolism must support the function of the cell through the provision of energy and biosynthetic precursors (11) (Figure 1). It is vital that T cells undergo the appropriate activation and differentiation to ensure that homeostasis is maintained (12).

NAÏVE T CELLS
The primary function of naïve T cells is antigenic surveillance, which requires relatively small amounts of ATP to support processes such as ion homeostasis, membrane integrity, and rearrangement of the actin cytoskeleton for movement (13). Thus naïve T cells have a catabolic metabolic profile that favors energy production over biosynthesis (14). It is estimated that 96% of ATP in naïve T cells is generated through OxPhos, with the remaining 4% being generated by glycolysis (3). It is an oxygen dependent process and produces 36 molecules of ATP per glucose molecule (1). Naïve T cells require extrinsic signals to maintain this basal metabolism. Interleukin-7 (IL-7) regulates glucose uptake via the cell surface trafficking of Glut1, which is modulated by the PI3K/Akt/mTOR pathway (15). This regulation of glucose uptake www.frontiersin.org FIGURE 1 | Changes in T cell metabolism are related to function: Over the course of an immune response the metabolism of a T cell changes. Naïve T cells mainly used a mixed fuel OxPhos to produce ATP. Once activated the T cell adopts a metabolic profile that is similar to many cancer cells (Warburg effect). Consumption of glucose and glutamine increases to support increased cell growth and proliferation. A subset of activated T cells survives to become T MEM reverting back to lipid oxidation with increased capacity for efficient energy generation and survival. Activation of T cells results in changes in migration patterns to ensure they can successfully migrate to areas of inflammation and cancer.
by IL-7 is critical to maintain homeostasis and to prevent apoptosis by the pro-apoptotic protein Bim (16).

ACTIVATED T CELLS
In contrast, T EFF cells adopt an anabolic metabolic profile to meet the increased energy requirements associated with cell growth, proliferation, differentiation, and effector functions (4). T EFF cells utilize aerobic glycolysis even though it is less efficient than OxPhos yielding only four moles of ATP per glucose molecule (17). Aerobic glycolysis provides advantages over OxPhos in rapidly dividing cells; firstly, metabolic intermediates needed for cell growth and proliferation are generated by aerobic glycolysis (11). Secondly, increased OxPhos results in increased production of reactive oxygen species (ROS), which can lead to apoptosis of T cells (18). Thirdly, aerobic glycolysis produces ATP faster than OxPhos. Therefore, once glucose is not a limiting factor, the pace of glycolytic flux will produce enough ATP to meet the energy demand of the T EFF cells (19). Finally glycolysis is not oxygen dependent and can accommodate T cell survival in a hypoxic environment, a common feature of many solid malignancies (20). OxPhos is still utilized in T EFF cells however aerobic glycolysis is the main pathway for glucose metabolism (21). Activated T cells must engage distinct transcriptional programs to both establish their differentiation status and elicit the effector functions required (22). These changes are similar to those observed in rapidly dividing tumor cells with many of the same metabolic regulators involved, including mTOR (23). The main transcription factors up-regulated by mTOR are c-Myc and hypoxia-inducible factor-1α (HIF-1α) and it appears that only c-Myc is necessary for the glycolytic switch that occurs during early T cell growth and proliferation. mTOR dependent up-regulation of c-Myc in the first 24 h following T cell activation is crucial in the transition from a naïve T cell to a T EFF cell (21). Effector CD4 + T cells can be distinguished from effector CD8 + T cells due to their ability to increase OxPhos by up to two-fold after activation, whereas activated CD8 + T cells do not increase OxPhos above that of resting cells (24). This results in activated CD4 + T cells having a survival advantage over CD8 + T cells when pro-glycolytic signals are limited (25). CD8 + T cells execute the majority of immune-mediated tumor killing through the release of cytolytic factors, such as perforin and granzymes (26). Depending on the polarizing signals in the tumor microenvironment the different CD4 + T cell subsets can promote or suppress a defensive host response to cancer (27). Each CD4 + T cell subset has a unique metabolic profile, which is adapted to its function with Th1, Th2, and Th17 cells displaying a glycolytic metabolic phenotype (28). In contrast to Th1 and Th17 cells, Th2 cells readily develop in the absence of mTORC1 but fail to develop in the absence of mTORC2 (29). T REG cells unlike other T EFF cells mainly use lipid metabolism and have elevated levels of AMPK activation (28). The utilization of lipid oxidation by T REG cells might play a central role in their survival advantage over T EFF cells and in the maintenance of a stable pool of pro-tumor immune cells (30).
One area that currently warrants further investigation is how metabolism affects, and is affected by migration of activated T cells. Migration from nutrient and oxygen rich regions of the peripheral blood and lymphatics to the often hypoxic and acidic tumor microenvironment can have a major impact on T cell metabolism and their resulting effector function (31). Naïve and T MEM cells circulate continuously among the blood vessels, lymph vessels, and lymphatic tissue, whereas activated T cells must suspend migration in the lymphoid tissue to proliferate and then regain their motility to migrate to the tumor (32). Naïve and T MEM cells express CD62L, CCR7, and sphingosine-1-phosphate (S1P), which allows homing to and retention in secondary lymphoid organs (33) (Figure 1). Activation of the PI3K-Akt-mTOR pathway down regulates CD62L, CCR7, and S1P (34). Activation of this pathway also leads to up-regulated expression of pro-inflammatory adhesion molecules such as very late antigen-4 (VLA-4) and chemokine receptors such as CXCR3 (32). These changes allow T EFF cells to migrate to areas of inflammation and might influence their prevalence in visceral adipose tissue (VAT), where they play a pathological role in driving obesity-associated inflammation (9,33).

MEMORY T CELLS
Following resolution of an immune response most T EFF cells undergo apoptosis while some remain as T MEM cells, responsible for enhanced immunity against future re-exposure to invading pathogens or tumors (35). While T EFF cells proliferate rapidly and are short-lived, T MEM cells have lower turnover rates. This long life span of T MEM cells poses a unique metabolic demand on these cells. The production of a stable pool of T MEM cells is reliant on a corresponding decrease in mTOR signaling and a shift to fatty acid oxidation (FAO) (36). T MEM cells unlike naïve and T EFF cells have a greater spare respiratory capacity (SRC) in their mitochondria (37) (Figure 1). This allows for rapid ATP production, which gives a bioenergetic advantage upon secondary exposure to its antigen (38). IL-15 increases SRC by enhancing mitochondrial biogenesis and the expression of carnitine palmitoyltransferase I (CPTI). CPTI is a key enzyme involved in FAO. Since FAO takes place in the mitochondria, T MEM cells can increase their use of fatty acids for energy via OxPhos, with a consequential decrease in dependence on glycolysis. This results in T MEM cells having a survival advantage over T EFF cells in the absence of pro-glycolysis signals, when the initial priming antigen has been eliminated and cytokines such as IL-2 dissipate (37).

HYPOXIA
Tumor growth tends to outgrow the developing blood supply resulting in a hypoxic microenvironment (39). This hypoxic environment can have multiple effects on T cell responses. Roman et al. demonstrated that under hypoxic conditions the secretion of effector CD4 + cytokines such as IFN-α was increased resulting in an increased immune response (40). In contrast, other studies have found that hypoxia results in an impaired immune response as a result of reduced IL-2 (41). Hypoxia can affect T cell metabolism through the increased production of lactate by tumor cells within the tumor microenvironment. An increase in lactate in the tumor microenvironment results in decreased T cell proliferation and cytokine production, due to decreased glycolysis (42). HIF-1α is a master transcriptional regulator in the response to low O 2 levels and is also involved in neoplastic pathways, including angiogenesis, cell survival, and treatment resistance (43). Levels of HIF-1α increase in activated T cells and can also be induced by other stimuli under normoxic conditions such as mTOR signaling (44,45). HIF-1α is responsible for the glycolytic response that occurs downstream of mTORc1 activation (46). With the exception of Th17 cells, the role of HIF-1α in T cell subset differentiation within the tumor microenvironment has not been clearly defined in the literature and focused studies examining the role of HIF-1α on specific T cell subsets are warranted. Some studies examining T cells in general have shown that it can decrease activated T cell numbers through increased apoptosis (47). Other studies have demonstrated opposing results, showing that HIF-1α prevents apoptosis by up-regulating adrenomedullin (48). A few studies have shown that HIF-1α plays an important role in the differentiation of Th17 cells, which are highly glycolytic and are believed to rely more on glycolysis than other T cell subsets (49). Induction of both Th17 and T REG responses are closely related as they share a common requirement for transforming growth factor (TGF)-β. The expression of HIF-1α plays an important role in regulating the balance of Th17/T REG responses as it inhibits differentiation of T REG cells by targeting Foxp3 for proteasomal degradation (50). It also directly regulates Th17 differentiation by activating transcription of RORγt and collaborates with RORγt to activate Th17 signature genes, such as the IL-17 gene during development (50). The up-regulation of HIF-1α results in high levels of glycolysis in Th17 cells by increasing the expression of Glut1 and enzymes such as pyruvate kinase muscle (PKM) (49). Together with the downregulation of mitochondrial oxygen consumption, www.frontiersin.org this prevents the entry of pyruvate into the TCA cycle (51). It is therefore likely that the constant state of low level inflammation present in many tumors may be due, at least in part, to HIF-1α directed Th17 cell differentiation and decreased T REG cell differentiation (50). A Th17 driven immune response has been linked to infection induced colon cancer in mouse models (52) and Helicobacter Pylori infection which can induce gastric cancer (53). Research has recently discovered that not all Th17 cells are pathogenic and drive autoimmune tissue injury (54). Development of pathogenic Th17 cells is dependent on exposure to IL 23, which diminishes the production of the anti-inflammatory cytokine IL-10 (55).

NUTRIENT DEPRIVATION
Reduction of nutrients present in the microenvironment is associated with an impaired anti-tumor immune response (56). Nutrient deprivation inhibits mTOR activity which is vital for T cell metabolism (57). Glucose is essential for T EFF cell survival and proliferation (5), IFN-γ production (58), and cytolytic activity via production of granzyme and perforin (59). T cell proliferation is inhibited in the absence of glucose even when other metabolic substrates such as fatty acids and glutamine are present (58). T cell activation is also dependent on extracellular glutamine (6). Glutamine is converted to glutamate and subsequently to α-ketoglutarate, which enters into the TCA cycle to generate citrate and pyruvate. This process is known as anaplerosis. It replaces the metabolites that are removed from the TCA cycle for the biosynthesis of fatty acids, nucleotides, and proteins allowing the T EFF cells to maintain the integrity of the TCA cycle function (60). Chang et al. demonstrated that lymphoma cells can impose nutrient deprivation on T cells by depleting glucose and glutamine resources. This can lead to decreased release of cytokines, such as IFN-γ, from T EFF cells (61). Arginine is an example of another amino acid which is vital for many T cell functions such as proliferation (62). Research carried out by Rodriguez et al. demonstrated that myeloid derived suppressor cells in the tumor microenvironment express high levels of arginase-1. The resulting lower levels of arginine led to inhibition of T cell receptor expression and antigen specific T cell responses (63). Sequestration of cysteine by myeloid derived suppressor cells is another way in which amino acid deprivation occurs and subsequently results in the inhibition of T cell activation (64). Tumor cells and non-malignant stromal cells can elicit immunosuppressive effects through the expression of amino acid catabolic enzymes, such as indoleamine 2,3-dioxygenase (IDO) which catalyzes the degradation of tryptophan (65). In fact, IDO expression by tumor cells has been shown to correlate with a poor clinical prognosis in several cancers including ovarian (66) and endometrial cancer (67). Elevated IDO expression causes both the depletion of tryptophan and the production of immunosuppressive tryptophan metabolites (68). Such metabolites can impair T cell function (69) and lead to T cell apoptosis (70), thus resulting in less effective anti-tumor T cell responses. Nutrient limitation can also induce autophagy in T EFF cells, as a survival mechanism to generate an intracellular source of nutrients (71). Reduced levels of amino acid or decreased ATP/AMP ratios result in AMPK activation, which phosphorylates the protein kinase unc-51-like kinase 1/2 (Ulk1/2). Activation of Ulk1/2 then initiates autophagy (72). In addition to autophagy, increased metabolic stress due to nutrient deprivation can ultimately lead to T cell apoptosis (73).

CHRONIC T CELL ACTIVATION
Chronic T cell activation occurs due to constant antigen exposure and can induce a state of T cell non-responsiveness termed exhaustion. T cell exhaustion is defined by poor effector function, continued expression of inhibitory receptors, and a gene expression profile distinct from T EFF or T MEM cells (74). The tumor microenvironment establishes an immunosuppressive environment in which T cells respond in a similar manner to exhausted T cells in chronic viral infections (75). This may partially explain why tumors continue to grow despite the presence of tumor specific T cells (76). Baitsch et al. examined T cells from metastases in patients with advanced stage III-IV melanoma and found that these T cells exhibited an exhaustion profile and produced insufficient levels of IFN-γ. The T cell exhaustion at the metastatic tumor sites was induced by continuous antigen exposure coupled with inhibitory signals from tumor cells and non-malignant stromal cells (77). The programed death receptor 1 (PD-1) is an immune-inhibitory receptor, which is expressed on chronically activated T cells. Ahmadzadeh et al. found increased expression of this receptor on tumor infiltrating T cells compared with T cells in peripheral tissue and the blood of patients with metastatic melanoma. The augmented PD-1 expression was associated with impaired effector function in these T cells, decreased proliferation, and reduced ability to differentiate into T MEM cells (76). Cytotoxic T-lymphocyte antigen-4 (CTLA-4) is another negative regulator of T cells, which is upregulated following T cell activation and during T cell exhaustion (78). Higher levels of CTLA-4 expression on T cells out competes CD28 for binding to CD80 and CD86 on the antigen presenting cell, thus preventing co-stimulation of the T cell (79). CTLA-4 and PD-1 also participate in the regulation of cellular metabolism by blocking CD28-mediated metabolic changes following T cell activation. Ligations of CTLA-4 and PD-1 have been shown to inhibit PI3K/Akt/mTOR signaling resulting in diminished glucose uptake and glycolytic rate (80). Binding of CTLA-4 expressed on T REG cells to CD80 and CD86 on dendritic cells is believed to suppress T cell responses through the up-regulation of IDO expression, thus revealing another mechanism through which CTLA-4 can contribute to tumor antigen tolerance.

TARGETING T CELL METABOLISM; A NEW AVENUE FOR CANCER IMMUNOTHERAPY
Dysregulation of T cell subsets can result in many immunemediated disorders, due to a disruption in the balance between protective immunity and suppression of pathological inflammation (1). Modulation of immune cell metabolism has been investigated in conditions where there is a chronic overreaction of the immune system, such as asthma. Chronically activated CD4 + T cells isolated from asthma patients have been shown to overexpress pyruvate dehydrogenase kinase-1 (PDK-1), which results in the production of high levels of lactate which promotes aerobic glycolysis. Treatment with the PDK-1 inhibitor dichloroacetate (DCA) inhibited the proliferation of T cells and reduced inflammatory cytokine production by switching the metabolism back to OxPhos. In mouse models this resulted in reduced airway inflammation (81). This study, together with others, has attempted to dampen excessive inflammatory responses through attenuation of T EFF cell responses and enhancement of T REG cell responses by targeting metabolic pathways. Treatment of T cells isolated from a mouse model of asthma with metformin induced the generation of T REG cells through the activation of AMPK and therefore, conferred protective immunity without inappropriate inflammation (28). Metformin is an oral anti-diabetic drug, which reduces hepatic glucose production and increases insulin sensitivity and glucose uptake by muscles and adipocytes, resulting in decreased insulinemia and insulin sensitivity (82). Zhou et al. demonstrated that following inhibition of complex I of the mitochondrial respiratory chain, metformin induces a decrease in ATP synthesis and increases the AMP/ATP ratio, thus stimulating AMPK (83). Studies targeting T cell metabolism have already been conducted in mouse models of arthritis, lupus, uveitis, and asthma and similar studies in obesity-associated cancer might elucidate a mechanism by which the T cell inflammatory responses can be abrogated in adipose tissue to attenuate tumor-promoting chronic inflammation (84)(85)(86)(87). Targeting T cell metabolism for cancer immunotherapy must take a different approach by augmenting the beneficial anti-tumor responses of T EFF cells initially, leading to T MEM cell generation and by attenuating the responses of T REG cells. A detailed understanding of the exact metabolic needs of different T cell subsets will allow therapies to be designed in order to achieve this (27).

DECREASING T REG CELL PROLIFERATION
Increased numbers of T REG cells within the tumor microenvironment have been reported in patients with cancer (88) and are associated with a poorer outcome (89). One theory proposed is that tumor cells both secrete and induce the secretion of TGF-β/IL-10 from immature dendritic cells and results in the generation of T REG cells from CD4 + CD25 − naïve T cells (90). It has been demonstrated in mouse models of cancer that depletion of T REG cells improves endogenous immune-mediated tumor rejection (91). Clinical trials have shown the feasibility and relative safety of managing T REG cells in human cancer, although treatment effects have been modest. Ontak is a monovalent human IL-2 fusion toxin licensed to treat human CD25 + cutaneous T cell lymphoma (92). Due to the phenotypic similarity of CD4 + CD25 + cutaneous T cell lymphoma cells and CD4 + CD25 + T REG cells, Ontak was tested in many cancers. Some studies demonstrated depletion in T REG cell numbers and improved immunity in patients with metastatic ovarian carcinoma (93), while other studies showed no benefit in melanoma patients (94). Modulation of T REG cell metabolism to suppress their differentiation is a novel approach being investigated in vivo. T REG cells require lipid oxidation for differentiation and treatment of these cells with etomoxir, a CPT1A blocker, has been successful in suppressing T REG cell generation (Figure 2). This study is very promising for cancer therapy due to the fact that T REG cell generation was decreased but the development of IFN-γ secreting Th1 cells was not affected (28).

INCREASING T EFF CELL PROLIFERATION
Perhaps the most beneficial approach to boost anti-tumor immunity is to increase activated tumor specific T EFF cell numbers (Figure 2). The development of IDO inhibitors is one avenue currently being explored for reactivation of T cells which have become exhausted or tolerant to tumor antigens. One of the most widely studied IDO inhibitors is 1-methyl-tryptophan. In combination with other chemotherapeutic agents such as cyclophosphamide, it led to the regression of established tumors in a mouse model of HER-2 breast cancer (95). Another novel IDO inhibitor: INCB024360 inhibited tumor growth in cell and mouse models due to increased T cell proliferation and IFN-γ production (96). Reactivating T EFF cells by increasing tryptophan levels is advantageous over reactivation by increasing glycolysis, due to the similar glycolytic metabolic pathways used by both T cells and tumor cells for cell survival and growth (97). Doedens et al. have demonstrated that genetically activating the HIF-1α pathway in a B16 mouse melanoma tumor cell line can prevent T EFF cell exhaustion by sustaining the effector functions of T cells despite persistent antigen exposure (98). As previously discussed, the HIF-1α pathway can serve as both a positive and negative regulator of T EFF cells. Further research is required to elucidate the net effect of using HIF-1α activators to boost T EFF cell-mediated responses to persistent tumor antigen challenge. Blockade of CTLA-4 by antibodies prevents the inhibitory signal to the T cell resulting in an enhanced T cell response against tumor cells and improved anti-tumor activity (79). Ipilimumab is a fully humanized IgG1 monoclonal antibody against the extracellular domain of CTLA-4 and has been approved for the treatment of metastatic melanoma (99). Ipilimumab binding to CTLA-4 allows CD28 on the T cell to bind to the co-stimulatory molecules CD80 and CD86 on the antigen presenting cell. This is cited as the mechanism of action of ipilimumab (99). CTLA-4 also inhibits increased glucose metabolism following T cell activation which is highly relevant as increased glucose metabolism is vital to sustain the increased metabolic demands that occur in the transition of a naïve T cell to a T EFF cell (80,100). Therefore, it can be concluded from this evidence that the success of ipilimumab in the treatment of malignant melanoma (101) might be in part due to its negative effects on glucose metabolism. Some drug therapies currently available affect T cell metabolism as well as their intended therapeutic targets. Imatinib is a tyrosine kinase inhibitor originally designed to target Bcr-Abl in patients with chronic myeloid leukemia (102). However, it has been shown to have opposing effects on T cell metabolism. Balachandran et al. found that imatinib can activate CD8 + T cells and induce T REG cell apoptosis in gastrointestinal tumors through downregulation of IDO, thus resulting in augmented anti-tumor activity (103). Others found that imatinib inhibits both normal and leukemic T cell response through the inhibition of Lck-mediated T cell receptor signaling (104,105). T cell receptor signaling is necessary for maximum glucose uptake following TCR stimulation, through elevated expression and cell surface trafficking of the glucose transporter Glut1. Without correct T cell receptor signaling, T cells cannot correctly adjust their metabolic profile to become T EFF cells and in this manner, imatinib might be detrimental to T cell mediated anti-tumor immune responses (58).

www.frontiersin.org FIGURE 2 | Activation of T cells results in metabolic reprograming, which influences their proliferation, differentiation, and effector functions.
Aerobic glycolysis becomes the predominant metabolic pathway used by CD8 + effector cells, CD4 + Th1, Th2, and Th17 cells, by contrast T REG and T MEM rely on lipid oxidation. T cells that differentiate into Th17 cells primarily rely on glycolysis controlled by HIF-1α. T MEM and T REG rely on lipid oxidation under the control of AMPK. Tumor specific T MEM cell prevalence can be enhanced through the inhibition of glycolysis following treatment with the hexokinase-2 inhibitor 2DG. In addition, inhibition of mTOR by rapamycin and activation of AMPK by the mitochondrial complex I inhibitor metformin can also led to increased survival of T MEM cells. Etomoxir, a specific inhibitor of CPT1A results in the reduction of T REG prevalence and has also shown promising results in delaying tumor development.

INCREASING T MEM CELL PREVALENCE
Increasing the number of long-lived tumor specific T MEM cells can lead to sustained anti-tumor activity due to their increased survival and proliferative capacities. In a study by Sukumar et al., the glucose analog 2-deoxyglucose (2DG), an inhibitor of hexokinase-2 (Hk2), was used to block glycolysis (Figure 2). This resulted in the increased activity of signaling pathways and transcription factors that enhance T MEM cell development, such as AMPK which negatively regulated mTOR and Foxo1, and resulted in enhanced anti-tumor activity of CD8 + T cells (106). Other methods to increase the generation of T MEM cells by the modulation of fatty acid metabolism have also been investigated. Treatment with metformin to activate AMPK promoted T MEM cell generation and was able to significantly improve the efficacy of an experimental anti-cancer vaccine in vivo (10) (Figure 2). Metformin has already been shown to inhibit tumor formation due to its direct action on the tumor, but its use as a potentiator of T MEM cell activity still requires further investigation (107). Many mechanisms of action have been proposed for the anti-cancer properties of metformin, including its actions on mTOR (108) and novel actions such as up-regulation of miR33a resulting in down regulated c-Myc expression (109). Rapamycin, through the inhibition of mTOR (specifically complex 1) reduces glycolysis and increases lipid oxidation and can also enhance the formation of T MEM cells (36) (Figure 2). Other modes of action investigated for rapamycininduced development of T MEM cells include the inhibition of the Th1 promoting transcription factor T-bet and the stabilization of the expression of the related transcription factor eomesodermin (110). Treatment of mice with rapamycin following viral infection not only enhanced frequencies but also accelerated the transition from T EFF cells to virus specific T MEM cells without inhibiting the ability of the infected animal to clear the antigen (111). Due to the multiple actions of mTOR, rapamycin can potentially exert multiple effects on T cell metabolism, particularly its rapid induction of autophagy, which can be both a pro-and anti-survival process in T cells (112). Like metformin, rapamycin can directly target and inhibit tumor cell growth in many ways such as induction of apoptosis (113) and inhibiting angiogenesis (114). Unlike metformin however, rapamycin has immunosuppressive properties and is used as anti-rejection therapy due to its anti-proliferative actions (115). Although to date, the anti-cancer effects of rapamycin and its derivatives are more prominent than the immunosuppressant effects, further research is warranted to ensure this is also the case in the long term (116). Another important role for any drugs developed to enhance the formation of T MEM cells is to enhance the efficacy of cancer vaccines. The main goal of any vaccination Frontiers in Oncology | Molecular and Cellular Oncology is to induce an effective T MEM cell response (117), many of the experiments targeting T MEM cell generation have a better recall response and are more protective following re-challenge (111).

TARGETING T CELL METABOLISM TO PREVENT AND TREAT OBESITY-ASSOCIATED CANCER
Obesity induces a state of tumor-promoting chronic inflammation, marked by elevated levels of free fatty acids, abnormal cytokine production, and activation of inflammatory signaling pathways (118,119). The expansion of VAT in obesity, results in increased blood supply, inflammatory immune cell infiltration, and enhanced pro-inflammatory cytokine release (9,(120)(121)(122)126). Our group have previously shown that activated IFN-γ producing CD4 + and CD8 + T cells are significantly enriched in obese VAT, while others have found that frequencies of T REG cells are decreased (9,123,124). Therefore, obese VAT is characterized by an increased prevalence of pro-inflammatory T cells and reduced numbers of anti-inflammatory T cells, which is likely to contribute to adipose tissue inflammation and the clinical manifestations of obesity, including cancer. Developing effective immunotherapies for obesity-associated malignancies represents a unique challenge; to attenuate chronic systemic inflammation while simultaneously augmenting specific immunity at tumor sites. As proinflammatory Th1 cells appear to play a crucial role in obesityassociated inflammation, the alteration of the metabolic profile of T cells in the VAT may present a favorable therapeutic option. There is currently a paucity of studies investigating the efficacy of targeting immunometabolism as a mechanism to control tumorpromoting systemic inflammation associated with obesity. However, there is some precedence for targeting metabolism to control systemic inflammation in autoimmune diseases. For instance, inhibition of both the mTOR and MAPK (ERK) pathways using rapamycin and the MEK1/2 inhibitor PD325901 was identified as a means of robustly blocking effector CD4 + T cell proliferation in a mouse model of arthritis (84). This study found that inhibition of both pathways was more effective than inhibiting either mTOR or ERK alone and resulted in greatly decreased disease severity in a mouse model of arthritis. Since our group has previously identified T EFF cells as key players in adipose tissue inflammation, ERK and mTOR inhibitors might elicit a similar anti-inflammatory effect in obesity (9). Others have found that beauvericin-mediated inhibition of T cell activation in colitis was achieved by targeting P13K and AKT (86). Treatment with beauvericin reduced pro-inflammatory cytokine production, weight loss, diarrhea, and mortality in this mouse model of colitis (86). In addition, pro-inflammatory cytokine production and clinical symptoms in experimental autoimmune uveitis (EAU) were reduced through the inhibition of PI3K/AKT by a novel anti-angiogenic peptide H-RN, thus presenting another potential therapeutic option for attenuating obesity-associated inflammation (87). The small molecule inhibitor IPI-145 has also shown promising therapeutic value in the blockade of PI3K in collagen-induced arthritis, ovalbumininduced asthma, and systemic lupus suggesting that this molecule might be applicable and effective in autoimmune and multiple inflammatory disorders, including obesity (85). The inhibitor elicited its effects through blockade of both PI3K-δ and PI3K-γ and subsequent suppression of both innate and adaptive immune responses. This resulted in reduced pro-inflammatory cytokine production and disease severity in all mouse models tested (85). From these studies, it appears that anti-inflammatory agents targeting PI3K, AKT, and mTOR could be a favorable option in the treatment of obesity-associated inflammation and cancer. However, metabolic targets for the treatment of obesity-associated cancer and disease should first be identified through definitive studies that will elucidate the metabolic profiles of T cells in VAT and the corresponding tumor. A therapy targeting specific metabolic pathways of VAT T cells could be used alone as a prophylactic in obese individuals who are at risk of developing malignancy or in combination with standard therapies in patients who have already developed an obesity-associated malignancy. Focused studies are currently lacking but clearly warranted in this area.

CONCLUSION
It is clear that T cell survival, function, and differentiation are intimately linked to T cell metabolism. The increase in glycolysis and glutaminolysis facilitate the rapid proliferation of T cells required during activation to allow fast clearance of pathogens or tumor cells. T REG and T MEM cells favor FAO, which allows these cells to survive long term, when glucose and growth factors are limited. This ensures the control of inappropriate immune responses and long term immunity. Extensive metabolic reprograming not only fulfils the extra biosynthetic demands of activated T cells, but it is also integrated with signaling pathways that determine T cell fate, thus enhancing our understanding of distinct T cell subset function and regulation. The different metabolic pathways may also provide a new opportunity for modulating the immune response during tumor development and progression. One of the hallmarks of cancer is evasion of the immune system (125) and in the past few years our understanding of how this occurs has greatly improved. One area which holds promise for cancer immunotherapy is the manipulation of immune responses, such that T EFF and T MEM cell-mediated responses are enhanced, while those of T REG cells are decreased, ultimately providing a therapy that might "switch" back on the immune system to target the tumor cell. Reactivating exhausted T cells by adjusting their metabolism also holds potential as a treatment for cancer, however more research needs to be carried out to fully elucidate the role of cellular metabolism in T cell exhaustion. Due to the similarities between tumor cells and activated T cell's metabolic programing, any therapies designed to modulate tumor cell metabolism must take into account their effects on T cell metabolism in the immune response to cancer.
Since the WHO now estimates that obesity is directly attributable for as many as 41% of malignancies and with the rates of obesity continuing to increase dramatically, it is clear that novel therapeutic options are urgently required. Since activated inflammatory T EFF cells are found to be enriched in the VAT of cancer patients, it is likely that they play pathological roles in driving obesity-associated inflammation. Attenuation of this inflammation might be achieved via metabolic manipulation of inflammatory T EFF cell differentiation and activation. Suppression of excessive pro-inflammatory T cell responses through inhibition of metabolic pathways has already been demonstrated across a spectrum of chronic inflammatory disorders in mice and could represent a viable therapeutic target for obesity-associated cancers.

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The possibilities of modulating anti-tumor immune responses through targeting T cell metabolic pathways hold huge promise and clearly warrant further investigation.