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Tuesday, May 10, 2011

Engagement of S1P1-degradative mechanisms leads to vascular leak in mice

Tuesday, May 10, 2011
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J Clin Invest. doi:10.1172/JCI45403.
Copyright © 2011, The American Society for Clinical Investigation. Myat Lin Oo1, Sung-Hee Chang1, Shobha Thangada2, Ming-Tao Wu2, Karim Rezaul2, Victoria Blaho1, Sun-Il Hwang3, David K. Han2 and Timothy Hla1

1Center for Vascular Biology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, Cornell University, New York, New York, USA.
2Center for Vascular Biology, University of Connecticut Health Center, Farmington, Connecticut, USA.
3Mass Spectrometry Core Facility, Cannon Research Center, Carolinas Medical Center, Charlotte, North Carolina, USA.

Address correspondence to: Timothy Hla, Department of Pathology and Laboratory Medicine, Center for Vascular Biology, Box 69, Weill Cornell Medical College, Cornell University, 1300 York Avenue, New York, New York 10065, USA. Phone: 212.746.9953; Fax: 212.746.2830; E-mail: tih2002@med.cornell.edu.

Published May 9, 2011
Received for publication October 12, 2010, and accepted in revised form March 30, 2011.

GPCR inhibitors are highly prevalent in modern therapeutics. However, interference with complex GPCR regulatory mechanisms leads to both therapeutic efficacy and adverse effects. Recently, the sphingosine-1-phosphate (S1P) receptor inhibitor FTY720 (also known as Fingolimod), which induces lymphopenia and prevents neuroinflammation, was adopted as a disease-modifying therapeutic in multiple sclerosis. Although highly efficacious, dose-dependent increases in adverse events have tempered its utility. We show here that FTY720P induces phosphorylation of the C-terminal domain of S1P receptor 1 (S1P1) at multiple sites, resulting in GPCR internalization, polyubiquitinylation, and degradation. We also identified the ubiquitin E3 ligase WWP2 in the GPCR complex and demonstrated its requirement in FTY720-induced receptor degradation. GPCR degradation was not essential for the induction of lymphopenia, but was critical for pulmonary vascular leak in vivo. Prevention of receptor phosphorylation, internalization, and degradation inhibited vascular leak, which suggests that discrete mechanisms of S1P receptor regulation are responsible for the efficacy and adverse events associated with this class of therapeutics.

Sphingosine-1-phosphate (S1P) is a lipid mediator that interacts with GPCRs to induce cellular responses (1, 2). In mammals, S1P is enriched in plasma and is low in interstitial fluids, thus forming a gradient between vascular and nonvascular compartments. Hematopoietic cells use this S1P gradient as a spatial cue to egress from a low-S1P environment (i.e., lymph nodes, thymus, and peripheral tissues) to a high-S1P environment (i.e., lymph and plasma) (3, 4). The prototypic S1P receptor 1 (S1P1), originally isolated from vascular ECs (5), is essential for the process of lymphocyte egress (6). Recent work suggests that plasma membrane–localized S1P1 on immune cells is essential for efficient egress (7). In addition, complex regulatory mechanisms, including receptor phosphorylation (8), endocytosis, recycling (9), and direct binding with regulatory proteins such as CD69 (10, 11), are involved in the control of plasma membrane residency of S1P1 and lymphocyte egress.

The S1P receptor modulator FTY720, which is phosphorylated by sphingosine kinase–2 (12, 13), interacts with S1P1 at high affinity and activates the receptor (14, 15). However, FTY720P (a phosphorylated derivative of FTY720) potently induces receptor internalization and blocks recycling, resulting in irreversible internalization of S1P1 in vitro (16) and in vivo (7, 17). Thus, FTY720P makes immune cells refractory to the S1P concentration gradient and thereby inhibits egress. However, S1P1 is expressed by other cells, for example ECs, and FTY720P is known to influence the function of these cells (13). Recently, we showed that FTY720P induces polyubiquitinylation of S1P1 and proteasomal degradation in ECs and HEK293 cells in vitro (8). Whether this occurs in vivo is not known. Indeed, plasma S1P is essential for the maintenance of normal barrier function of the vascular endothelium and to resist inflammation-induced vascular leak (18). Additionally, chronic FTY720 treatment in mice inhibits tumor angiogenesis (19).

Clinical studies have demonstrated that FTY720 is highly efficacious in the treatment of multiple sclerosis (20, 21). In phase III studies, oral administration of 0.5–1.25 mg/d FTY720 reduced disease progression better than placebo or ß-interferon. However, there was a dose-dependent increase in adverse events, including reduced pulmonary function and increased macula edema. The mechanisms involved in such adverse effects are not known. Since the primary target of FTY720 is thought to be functional antagonism of S1P1 in autoreactive immune cells that induce CNS inflammation and destroy myelinated axons (22, 23), it is important to further define the molecular mechanisms involved in the interaction of FTY720P with S1P1 in various cell types.

In this report, we describe the detailed mechanism of regulation of S1P1 upon binding to FTY720P. We showed that phosphorylation led to multisite polyubiquitinylation of the C-terminal domain that required the E3 ubiquitin ligase WWP2. Moreover, we showed that S1P1 degradation contributed to pulmonary vascular leak in vivo, which suggests that interference with S1P1 levels may lead to vascular pathologies.

FTY720P induces posttranslational modifications in the C-terminal tail of S1P1. To define the posttranslational modifications that occur on S1P1 after FTY720P binding, we developed a robust system to isolate preparative amounts of S1P1. We isolated a stable HEK293 clone expressing the tandem-affinity purification–tagged (TAP-tagged) construct (S1P1-Tap-Tag). Figure 1A illustrates the fusion protein; this construct allowed enrichment of S1P1 by TAP using chitin- and calmodulin-affinity matrices (24). As shown in Figure 1B, efficient purification and elution of S1P1-Tap-Tag fusion protein were obtained, as determined by IB analysis. Preparative amounts of S1P1-Tap-Tag fusion protein were isolated from HEK293 cells treated or not with FTY720P for 30 minutes. The resulting eluates were separated by SDS-PAGE (Figure 1C) and stained with Coomassie blue, the gel region between 39 and 85 kDa was cut and trypsin-digested in the gel, and the resultant peptides were analyzed by liquid chromatography–tandem mass spectrometry (LC/MS/MS). The full-length S1P1-Tap-Tag protein was reduced in the FTY720P-treated lane, consistent with the fact that this reagent induces receptor degradation. We identified numerous C-terminal peptides with phosphorylated and ubiquitinylated residues (Table 1). For example, phosphorylation of S336, S351, and S353 and ubiquitinylation at K341, K354, and K363 were observed. Spectral count analysis indicated a general increase in phosphorylation and ubiquitinylation of C-terminal tail after FTY720P treatment (Figure 1D). Although our LC/MS/MS analysis may not be comprehensive, given the intrinsic difficulty in proteomic analysis of hydrophobic GPCRs, these data nevertheless suggest that FTY720P induces posttranslational modifications at multiple sites in the C-terminal tail of S1P1.

Figure 1 Posttranslational modification of S1P1 after FTY720P treatment. (A) Schematic of S1P1 fused with the tandem-affinity construct. CBD, calmodulin-binding domain; Chi-BD, chitin-binding domain. (B) Purification and IB analysis of the S1P1 tandem-affinity construct. (C) Coomassie blue staining of purified S1P1 from HEK293 cells after treatment with vehicle or FTY720P (100 nM for 30 minutes). (D) Schematic diagram of seventh transmembrane domain (TM) and C-terminal tail of S1P1. Shown are the S-palmitoylation sites on cysteine residues (C) as well as phosphorylated serine and ubiquitinylated lysine residues identified by LC/MS/MS. Spectral counts of modified peptides are also shown.

Table 1 Phosphorylation and ubiquitination sites in the C terminus of S1P1

Phosphorylation leads to multisite polyubiquitinylation of the C-terminal tail of S1P1 in the intracellular vesicles. To further analyze the importance of posttranslational modifications in the C-terminal tail of S1P1, we mutated the serine residues to nonphosphorylatable alanine residues. The resulting S1P1-GFP mutants were transfected into HEK293 cells, and stable clones were isolated. We then tested the ability of FTY720P to induce S1P1-GFP degradation, which is completely dependent on receptor endocytosis, ubiquitinylation, and degradation (8). The S5A phosphorylation mutant (i.e., S351A, S353A, S355A, S358A, and S359A) was completely resistant to FTY720P-induced degradation (Figure 2A). However, mutation of S336 or of 2 serines (S358A and S359A) did not appreciably affect FTY720P-induced receptor degradation. Mutation of 3 serines (S351A, S353A, and S355A) partially reversed FTY720P-induced receptor degradation. These data suggest that phosphorylation of multiple serine residues is needed for FTY720P-induced S1P1 degradation.

Figure 2 Phosphorylation-dependent multisite polyubiquitinylation of S1P1. (A and B) S1P1 WT and mutants were stably transfected into HEK293 cells and treated with 100 nM FTY720P for the indicated times, and S1P1 expression was examined by IB analysis. (C) HEK293 cells expressing WT or degradation-resistant mutants of S1P1 were treated with vehicle, S1P, or FTY720P for 1 hour and imaged with a confocal fluorescence microscope (oil immersion objective). Original magnification, ×63. (D) Cells in C were treated with 100 nM FTY720P for 1 hour, after which S1P1 was subjected to IP with anti-GFP antibody and IB with anti-ubiquitin antibody.

To determine the importance of ubiquitinylation of the C-terminal domain, we mutated the 4 lysine residues to nonubiquitinatable arginines, either individually or together. As shown in Figure 2B, K339R, K341R, and K354R mutants did not show an appreciable difference in the FTY720P-induced degradation rate. Even mutation of 2 or 3 lysines (K339R and K341R, or K339R, K341R, and K354R) did not prevent FTY720P-induced degradation. In contrast, the ubiquitin acceptor mutant K4R (all 4 mutations; K330R, K339R, K341R, and K354R) completely resisted FTY720P-induced receptor degradation, which suggests that multiple ubiquitinylation events in these acceptor lysines of the C-terminal domain are required for S1P1 degradation. Alternatively, ubiquitinylation at even a single lysine is sufficient to target the receptor for degradation.

To determine whether ubiquitinylation is needed for receptor endocytosis, we imaged HEK293 cells stably expressing WT (S1P1-GFP), S1P1-S5A-GFP, or S1P1-K4R-GFP constructs by confocal fluorescence microscopy. As shown in Figure 2C, the S1P1-K4R-GFP mutant exhibited receptor internalization similar to that of WT in response to S1P or FTY720P, which suggests that C-terminal ubiquitinylation is not essential for ligand-induced endocytosis. In contrast, the S1P1-S5A-GFP mutant was internalization resistant, consistent with the knowledge that phosphorylation is essential for ß-arrestin recruitment and receptor endocytosis (8).

We next determined the relationship between phosphorylation and ubiquitinylation in the C-terminal domain using S1P1 mutants. HEK293 cells stably expressing S1P1-GFP, S1P1-S5A-GFP, or S1P1-K4R-GFP were treated with FTY720P, and the receptor molecules were subjected to IP and IB with anti-ubiquitin antibodies. As shown in Figure 2D, the WT S1P1-GFP molecule was robustly polyubiquitinylated, whereas S1P1-S5A-GFP and S1P1-K4R-GFP were not. These data strongly suggest that phosphorylation is required for ubiquitinylation of the C-terminal domain of S1P1 after FTY720P treatment.

Identification of WWP2 as a E3 ubiquitin ligase critical for FTY720P-induced receptor degradation. We sequenced the S1P1-associated polypeptides from FTY720P-treated HEK293 cells by LC/MS/MS analysis. The NEDD4 family member E3 ubiquitin ligase WWP2 (25) was identified with high statistical confidence in the proteomic analysis (Figure 3A and Table 2). To confirm the proteomic identification of WWP2, we used co-IP analysis to confirm that these 2 proteins are found in a complex. We expressed Flag-tagged S1P1 and WWP2 in HEK293 cells and performed S1P1 IP, followed by IB analysis with anti-WWP2 antibody. As shown in Figure 3, B and C, S1P1 IPs contained WWP2, and WWP2 IPs contained S1P1, which suggests that these proteins were found in a complex. The association of S1P1 with WWP2 was not modulated by FTY720P treatment (Supplemental Figure 1; supplemental material available online with this article; doi: 10.1172/JCI45403DS1). Interestingly, WWP2 was also found in IPs of the S5A and K4R mutants (Supplemental Figure 1), which indicates that association of this E3 ubiquitin ligase to the GPCR complex is constitutive.

Figure 3 The E3 ubiquitin ligase WWP2 binds to S1P1. (A) TAP of S1P1 and associated polypeptides from HEK293 cells was analyzed by LC/MS/MS, which identified the NEDD4-like E3 ubiquitin-protein ligase WWP2 (accession no. O00308; locus WWP2_HUMAN; GI no. 32171765; peptide count, 4). The location of the peptides (red text) and proteomic data are shown in Table 2. (B) HEK293 cells expressing Flag-tagged S1P1 were transiently transfected with WWP2, lysed, and subjected to IP with anti-S1P1 IgG and IB with WWP2 antibody. (C) Cell lysates from B were subjected to IP by anti-WWP2 IgG and IB with S1P1 antibody.

Table 2 WWP2 peptides in S1P1-associated proteins after FTY720P treatment of HEK293 cells

Next, we determined whether WWP2 is capable of influencing the ubiquitinylation of S1P1. Membrane proteins from HEK293 cells expressing WWP2 with S1P1-GFP, S1P1-?1-GFP (C-terminal deletion; ref. 9), or S1P1-S5A-GFP (8) were subjected to IP with anti-GFP antibody and IB with anti-ubiquitin IgG to detect receptor ubiquitinylation. As shown in Figure 4A, polyubiquitinylation of S1P1 was seen only in the WT S1P1-GFP lane, which suggests that WWP2 ubiquitinylates the C-terminal domain of S1P1. Additionally, since S1P1-S5A-GFP was not ubiquitinylated, we conclude that phosphorylation at the C-terminal domain was required to activate the E3 ligase activity of WWP2.

Figure 4 WWP2-dependent C-terminal polyubiquitinylation and degradation of S1P1 after FTY720P treatment. (A) S1P1-GFP, S1P1-?1-GFP, or S1P1-S5A-GFP cells were transfected with pcDNA3 or pcDNA3-WWP2, lysed, and subjected to IP with anti-GFP IgG and IB with ubiquitin antibody. The same membrane was stripped and reprobed with GFP antibody to examine receptor levels, and expression of WWP2 was determined. (B) Cells were treated with 100 nM FTY720P for the indicated times, and expression of S1P1, WWP2, and ß-actin were examined by IB analysis. (C) HEK293 cells expressing S1P1-GFP were stably transduced with lentiviral shRNA for WWP2 (sh-WWP2) and treated with 100 nM FTY720P for the indicated times. Expression of S1P1, WWP2, and ß-actin were examined by IB analysis. sh-Cont, control shRNA. (D) HUVECs were stably transduced with lentiviral particles of pCDH-WWP2 or pCDH-copGFP and treated with 100 nM FTY720P for the indicated times, after which IB analysis was done. (E) Endogenous WWP2 in HUVECs was silenced by transduction of GIPZ lentiviral shRNAmir for WWP2 or control, and expression of S1P1, WWP2, and ß-actin was determined by IB analysis. The experiment was repeated at least 3 times with similar results.

We next tested the effects of WWP2 on S1P1 levels in HEK293 cells in which S1P1 was ectopically expressed as well as in HUVECs, which express this receptor endogenously. Overexpression of WWP2 in HEK293 cells and HUVECs resulted in enhanced degradation of S1P1-GFP protein after FTY720P treatment (Figure 4, B and D). In contrast, S1P treatment did not induce degradation of the S1P1-GFP protein (Supplemental Figure 2). Overexpression of related E3 ubiquitin ligases NEDD4-1, NEDD4-2, AIP4, and Cbl did not alter FTY720P-induced S1P1 degradation (Supplemental Figure 3), which is suggestive of specificity. Moreover, WWP2 over expression did not induce the degradation of S1P1-S5A-GFP or S1P1-K4R-GFP mutants after FTY720P treatment (Supplemental Figure 4). To determine whether endogenous WWP2 is involved, we used shRNA targeted against WWP2 and prepared stable clones of HEK293 cells and HUVECs by lentivirus-mediated transduction. As shown in Figure 4, C and E, substantial suppression of WWP2 polypeptide was observed concomitant with substantial attenuation of FTY720P-induced S1P1 degradation in shRNA-expressing cells compared with control shRNA–expressing counterparts. These data strongly suggest that FTY720P-induced degradation of S1P1 requires WWP2-dependent C-terminal ubiquitinylation of S1P1.

FTY720 treatment at a supralymphopenic dose results in degradation of S1P1 in vivo and induction of vascular leak. Oral administration of FTY720 to mice results in rapid and profound lymphopenia at a 0.3–0.5 mg/kg dose (7, 15, 26). We showed recently that 0.5 mg/kg FTY720 treatment induced maximal lymphopenia by 2 hours, which was sustained for more than 72 hours (7). To determine whether FTY720 induces receptor degradation in vivo, we analyzed S1P1 levels in lung tissue, which abundantly expresses this receptor (27). It is also known that S1P1 action is essential for adherens junction assembly (28) and inhibition of pathologic vascular permeability both in vitro (29) and in vivo (13, 30). In WT mice, a single lymphopenic dose of FTY720 (0.5 mg/kg) induced partial S1P1 degradation and a measurable increase in vascular permeability. In contrast, a supratherapeutic dose (5 mg/kg) induced quantitative receptor degradation that was accompanied by a massive increase (~6-fold above basal) in vascular permeability (Figure 5, A and B).

Figure 5 Degradation of S1P1 in vivo induces vascular permeability. (A) WT and S1P1-S5A knockin mice were administered FTY720 or vehicle. After 24 hours, lung membrane extracts were prepared, and S1P1 antigen levels were determined. Quantification of S1P1 degradation is also shown. Results are from a representative experiment repeated 3 times. n = 3. (B) FTY720 was administered to mice as in A. After 23 hours, EBD was intravenously injected, and lungs were photographed. To quantify vascular leak, lungs were solubilized, and EBD extravasation was determined (see Methods). n = 17 (WT); 7 (S1P1-S5A). (C) WT and S1P1-S5A knockin mice were administered AUY954 (10 mg/kg for 24 hours), W146 (10 mg/kg for 3 hours), or vehicle. Lung membrane extracts were prepared, and S1P1 antigen levels were determined by IB analysis. Quantification of S1P1 degradation is also shown. n = 3. (D) AUY954 and W146 were administered to mice as in C. After treatment, EBD was intravenously injected, and lungs were photographed. Lungs were then solubilized, and EBD extravasation was quantified. n = 3–5 (WT); 3 (S1P1-S5A). All values are mean ± SD. *P < 0.05; **P < 0.01; ***P < 0.001.

The S1P1-specific receptor modulator AUY954, a potent functional antagonist that induces receptor downregulation (31), also caused receptor degradation in the lung tissue and induced potent vascular permeability (Figure 5, C and D). In addition, W146, a competitive antagonist of S1P1 (32), induced vascular permeability at early time points, even though it did not induce substantial receptor degradation. These results suggest that S1P1 antagonism or degradation is responsible for increased vascular permeability in vivo.

S1P1-S5A knockin mice (7) were tested to see whether defective receptor internalization affects receptor levels and vascular leak after FTY720, AUY954, or W146 treatment. As shown in Figure 5, A–D, the internalization-deficient mutant resisted receptor degradation and vascular leak markedly in response to both FTY720 and AUY954 treatment. Even though the S1P1-S5A mice resisted W146-induced vascular leak, the difference was not as significant as those of the other 2 agents that induced receptor degradation. These data indicate that S1P1 levels are critical for maintaining vascular integrity and resistance to vascular leak–inducing agents.

Our work has revealed the detailed mechanism of S1P1 fate after interaction with FTY720, which has entered the clinic as a disease-modifying therapy in multiple sclerosis (20, 21, 33). We showed that FTY720P potently induced a complex pattern of phosphorylation and ubiquitinylation in the C-terminal domain of S1P1. Upon FTY720P treatment, C-terminal phosphorylated S1P1 is endocytosed via the ß-arrestin–dependent pathway (8). In this report, we documented using LC/MS/MS, multisite phosphorylation, and polyubiquitinylation of S1P1. We also showed that phosphorylation was required for ubiquitinylation and that ubiquitinylation was not required for receptor endocytosis. FTY720P-induced phosphorylation and ubiquitinylation led to the proteasomal degradation of S1P1. In sharp contrast, the natural ligand S1P induced internalization of S1P1, but did not stimulate ubiquitinylation and degradation. The unique ability of FTY720P to induce polyubiqui­tinylation of S1P1 may be due to its resistance to degradation by S1P phosphatases (34) and the lyase (35). Alternatively, FTY720P-bound receptor may be locked in a conformation that prevents phosphatase action on the receptor, which may lead to blockage in receptor recycling and targeting to the degradative pathway.

Second, we identified the E3 ubiquitin ligase WWP2 by LC/MS/MS-based sequencing of S1P1-associated polypeptides. Since alterations in WWP2 levels profoundly regulated the rate of FTY720P-induced S1P1 degradation in HEK293 cells and HUVECs, we argue that WWP2 is a critical factor in S1P1 degradation. Related E3 ubiquitin ligases NEDD4-1, NEDD4-2, AIP4, and Cbl did not alter FTY720P-induced S1P1 degradation, suggestive of specificity. Interestingly, the association of S1P1 with WWP2 was not modulated by FTY720P treatment. In addition, WWP2 was capable of associating with S1P1-S5A and S1P1-K4R mutants, which suggests that phosphorylation and ubiquitinylation of the receptor are not required. Indeed, WWP2 protein contains discrete protein interaction domains such as the WW domain and a C2 domain, which could be involved in targeting the E3 ligase to membrane domains containing S1P1 (25). A similar NEDD4 family ubiquitin E3 ligase called AIP4 targets the chemokine receptor CXCR4 for degradation (36). Together, these findings suggest a model in which S1P1 is found in a regulatory protein complex containing WWP2. Further activation of WWP2 likely occurs in late endosomes, where the E3 ligase activity is stimulated to catalyze polyubiquitinylation of the GPCR, leading to its proteasomal degradation. Indeed, recent findings implicate that arrestin domain–containing proteins recruit the NEDD4 E3 ligase to the endocytosed ß2-adrenergic receptor (37). Since physiological levels of S1P did not induce WWP2-dependent receptor degradation, we favor the notion that S1P1 degradation by WWP2 may be a regulatory mechanism in inflammatory and immune responses when high S1P levels are produced, leading to exaggerated receptor stimulation (38), and when the pharmacological agent FTY720 is introduced. Thus, sustained internalization of S1P1 would lead to WWP2-dependent S1P1 degradation as a feedback regulatory mechanism.

Having elucidated the mechanism of S1P1 degradation, we set out to understand whether this process is relevant in the efficacy or adverse events associated with FTY720 usage. Based on preclinical and clinical data, inhibition of autoreactive immune cell trafficking is thought to be the primary mode of action of FTY720 (26, 39, 40). In addition, receptor antagonism in the neural cells may also be involved in therapeutic efficacy (41). Indeed, data from mouse models demonstrate that plasma membrane–localized S1P1 on immune cells determines egress rates from lymphoid organs (7, 42). Since therapeutic doses of FTY720 achieve maximal lymphopenia but only result in partial degradation of S1P1, receptor degradation per se does not appear to be required for FTY720P-induced lymphopenia. Indeed, our previous data suggest that SEW2871, which induces S1P1-dependent lymphopenia (43), does not induce receptor degradation (8). Thus, lymphopenia correlates highly with receptor residency on the plasma membrane (7). In contrast, receptor degradation tracks with vascular leak.

Our present studies showed that S1P1 degradation and vascular leak in the lungs of WT mice were significantly attenuated in the S1P1-S5A knockin mice. We suggest that lack of C-terminal phosphorylation of vascular endothelial S1P1 in the mutant mice prevents efficient internalization after FTY720 treatment and allows for sustained signaling in the plasma membrane compartment, resulting in better preservation of barrier function of the pulmonary vasculature. Although we cannot completely rule out signaling of mutant receptors in nonvascular cells or alterations in arrestin-dependent mechanisms, we favor the model of sustained plasma membrane signaling of S1P1 in the preservation of EC barrier function. Moreover, our data also support the concept that internalized receptors undergo ubiquitinylation and proteasomal degradation after FTY720 treatment. AUY954, which targets S1P1 selectively (31), also resulted in a similar phenomenon. Thus, reduced expression of S1P1 in the vascular ECs may loosen adherens junctions (28) and induce increased vascular permeability. However, acute suppression of S1P1 function by W146, a competitive S1P1 selective antagonist, resulted in rapid vascular leak (30) without inducing receptor degradation. These data strongly suggest that endothelial S1P1 is critical for the control of vascular permeability. This work further complements the recent finding that plasma S1P levels are required for lung endothelial barrier function (18). In addition, a recent publication demonstrated induction of vascular leak, pulmonary inflammation, and fibrosis by prolonged treatment with FTY720 and AUY954 in a bleomycin-induced mouse model (44).

Although efficacious, clinical use of FTY720 also showed dose-dependent increases in adverse events (20, 45). For example, approximately 8% of patients experienced respiratory symptoms (cough, dyspnea, bronchitis) associated with depression of lung function. In addition, approximately 5% of patients experienced macular edema. Such events are likely to be caused by increased permeability of respective vascular beds. Our data suggest that degradation of S1P1 receptors induced by FTY720 may lead to increased vascular permeability. We speculate that heterogeneity in S1P1 expression and/or degradation machinery may predispose some individuals to adverse effects of S1P1 modulators. Since S1P1 levels are under dynamic control, these findings may lead to novel strategies to upregulate vascular S1P1 and obviate such side effects.

In conclusion, these studies highlight that interference with GPCR regulation can lead to mechanistically discrete steps of therapeutic modulation that can explain both efficacy and adverse events of drugs. Thus, a pharmacological agent that spares EC S1P1 while targeting lymphocyte S1P1 may lead to a better therapeutic index in the control of various autoimmune diseases.

Chemicals and reagents. S1P was purchased from Biomol Research Laboratories Inc. FTY720 and W146 were purchased from Cayman Chemical. AUY954 was a gift from Novartis Pharmaceuticals. Fatty acid–free BSA, ß-glycerophosphate, CHAPS, and ß-actin antibody were from Sigma-Aldrich. n-Octyl-ß-D-glucopyranoside (OG) was from A.G. Scientific Inc. Ubiquitin monoclonal antibody (P4D1) and WWP2 polyclonal antibody were from Santa Cruz Biotechnology Inc. GFP antibodies, monoclonal and polyclonal, were from Abcam.

Cell culture and transfection. HEK293 cells stably expressing S1P1 WT and various mutants tagged with GFP were grown in DMEM supplemented with 10% heat-inactivated FBS (Invitrogen), 50 U/ml penicillin, and 50 µg/ml streptomycin (Invitrogen). HUVECs (p4-10; Clonetics) were cultured in M199 medium supplemented with 10% FBS and heparin-stabilized endothelial growth factor, as previously described (46). HEK293 cells were transfected with the indicated expression plasmids using calcium phosphate–mediated transfection or LipofectAMINE 2000 transfection reagent or oligofectamine (Invitrogen) according to the manufacturer’s instructions.

DNA and shRNA lentiviral constructs. GFP-tagged S1P1 (WT and mutants) or S1P1 WT were generated by PCR using pEGFP-N1 (EDG1) S1P1 (9) as a template and inserted into pcDNA3 vector or into TAP-tagged (chitin-binding domain/calmodulin-binding domain; provided by M. Wright, University of Iowa, Carver School of Medicine, Iowa City, Iowa, USA) pcDNA3.1 vector. Human GIPZ lentiviral shRNAmir for WWP2 (clone ID, V2LHS_13525, V2LHS_196442, V2LHS_261811) and pGIPZ lentiviral shRNAmir control vector were from Thermo Scientific/Open Biosystems Inc. Production of viral particles from HEK293T was performed according to the manufacturer’s instructions. The lentiviral particles were incubated with HEK293 cells stably expressing S1P1-GFP and HUVECs in their growth medium. 48 hours later, all cells were selected in Puromycin (1–2 µg/ml) for 1 week. Cells were lysed and examined for WWP2 expression by Western blot and real-time quantitative RT-PCR.

For WWP2 overexpression, full-length WWP2 was transferred from pcDNA3 vector (expression plasmid provided by M. Ikeda and R. Longnecker, Northwestern University, Chicago, Illinois, USA) into lentiviral vector pCDH from System Biosciences. HEK293T cells were transfected by pCDH-WWP2 together with pPACK packaging plasmid mix. 48 hours later, lentiviral particles were collected and incubated in HUVECs. WWP2-transduced HUVECs were selected with puromycin (1–2 µg/ml) in growth media for 1 week. As a control, HUVECs were stably transduced with lentiviral particles of cop-GFP collected from cotransfection of pCDH-copGFP and pPACK packaging plasmid mix into HEK293T cells.

pRK5-AIP4, pRc/CMV-Nedd4-1, pRc/CMV-Nedd4-2, and KIAA0439 were provided by the laboratories of T. Pawson (Mount Sinai Hospital, Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada) and D. Rotin (Hospital for Sick Children, Toronto, Ontario, Canada). The expression plasmid of c-Cbl was a gift from B. Mayer (University of Connecticut Health Center, Farmington, Connecticut, USA). pcDNA3.1-flag-AIP4 was provided by M. Ikeda and R. Longnecker (Northwestern University, Chicago, Illinois, USA).

Analysis of S1P1 degradation by IB. HEK 293 stable clones of S1P1 WT and its mutant variants were grown to 50%–65% confluence, incubated in 2% charcoal-stripped serum for 2 days and starved additional 2 hours in serum-free DMEM. Cells were then treated with FTY720P (10 nM–100 nM) for indicated times. Cells were washed with PBS and lysed by addition of SDS sample buffer, sonication briefly, denatured at 95°C for 5 minutes. Protein concentrations were determined by Pierce bicinchoninic acid (BCA) protein assay kit. Equal amounts of proteins were separated into 10% polyacrylamide gel and transblotted on nitrocellulose membrane. Blots were incubated with anti-S1P1 polyclonal antibody (H60; Santa Cruz Biotechnology Inc.) and visualized by chemiluminescence (Millipore). Blots were then stripped and probed by anti–ß-actin antibodies (Sigma-Aldrich). Films were scanned and normalized for total protein using the ß-actin blots.

Immunofluorescence analysis. 2 × 105 cells were plated in fibronectin-coated 35-mm glass-bottom Petri dishes. 1 day later, cells were incubated in 2% charcoal-treated FBS for 2 days, washed, and serum starved 2 hours before experiment. Cells were washed with ice-cold PBS, fixed, and examined on a Zeiss LSM710 confocal microscope. For immunofluorescence analysis, S1P1 (H60; Santa Cruz Biotechnology Inc.) polyclonal antibody was conducted and antibody staining was visualized with Alexa Fluor 488 goat anti-rabbit for polyclonal (1:2,000 dilution) IgG (Invitrogen). Fluorescence was excited using a 488-nm argon laser, and emitted fluorescence was detected with 505-nm long-pass filter.

Detection of ubiquitinated receptors. S1P1-expressing HEK293 cells in 100 mm dish (~90% confluent) were cultured with 2% charcoal-stripped serum for 2 days. Then medium was replaced by serum-free DMEM for 2 hours and incubated with FTY720P for 1 hour and lysed (50 mM Tris, pH 7.4; 150 mM NaCl; 1% Triton X-100; 10 mM ß-glycerophosphate; 1 mM Na3VO4; 1 mM NaF; 20 mM CHAPS). Cell lysates were precleared by protein A/G agarose beads and subjected to IP with anti-GFP polyclonal antibody overnight, and S1P1-bound proteins were separated in 10 % SDS-PAGE gel and probed with anti-ubiquitin antibody. For protein binding between S1P1 and WWP2, cells were extracted with the lysis buffer containing 0.5% Triton X-100 and centrifuged as described above.

Analysis of S1P1 expression in the mice. All animal protocols were approved by the IACUC of University of Connecticut Health Center and Weill Cornell Medical College. C57BL/6 mice were purchased from The Jackson Laboratory. Generation of the internalization-deficient S1p1S5A/S5A mice was described previously (7). Briefly, a mutant S1P1 (SRSKSDNSS to ARAKADNAA) was knocked into the mouse genome by homologous recombination.

FTY720 treatment. WT and S5A-S1P1 mice (6–8 weeks old) were treated by oral gavage with 0.5 or 5 mg/kg FTY720 in 2% 2-hydoxypropyl-ß-cyclodextran (Sigma-Aldrich). Control animals received 2% 2-hydoxypropyl-ß-cyclodextran (vehicle) in water. The mice were anesthetized by a cocktail of 100 mg/kg ketamine plus 100 mg/kg xylazine by i.p. injection. Then, the chest wall was opened to expose the heart. The left ventricle of the heart was cut, and 5 ml filtered PBS was perfused into the right ventricle through the needle of the in vivo perfusion system (AutoMate Scientific Inc.). Then heart, thymus, and lung were removed and immediately transferred into liquid nitrogen. For protein extraction from the lungs, fresh tissue was chopped, resuspended in hypotonic buffer (10 mM Tris-HCl, pH 7.8–8.0; 1 mM EDTA), blended by polytron, and placed on ice for 10–15 minutes. Lung tissue in hypotonic buffer was then homogenized by Dounce Tissue Grinder, 7 ml, 13 × 82 mm, and transferred into 1.5 ml eppendorf tubes. Tissues were further subjected to 3 freeze/thaw cycles. The homogenate was then centrifuged for 15 minutes at 32,869 g at 4°C. The supernatant was discarded, and the membrane pellet was washed with PBS 3 times and resuspended in 300 µl extraction buffer by repeated pipetting. Membrane proteins were then extracted by rotation in cold room for 16 hours. The lysates were centrifuged for 5 minutes at 10,000 g at 4°C. The supernatant was collected, and protein concentration was determined by Pierce BCA protein assay kit. S1P1 protein level was then examined by Western blot.

TAP of S1P1 protein for MS/MS. HEK293 cells were stably transfected by pCDNA3.1-S1P1-TAP plasmid using G418 selection. S1P1 protein was purified according to the TAP-tagged protein purification procedure (47, 48), with some modifications. Briefly, HEK293 cells stably expressing neomycin G418–resistant S1P1 were washed twice with cold PBS and lysed in chilled buffer including 50 mM Tris-HCl (pH 8), 1 mM Na3VO4, 10 mM glycero-2-phosphate, 10% glycerin, 1% Triton X-100, 20 mM CHAPS, 60 mM OG, 5 mM sodium pyrophosphate, and 150 mM NaCl. Cell lysates were cleared by centrifugation at 10,000 g for 15 minutes and 100,000 g for 60 minutes again. The clear supernatants were incubated with chitin beads by rocking for overnight at 4°C. The chitin column was washed with TEV (enzyme digestion site; ExxYxQS/G) buffer (10 mM Tris-HCl, pH 8; 0.5 mM EDTA, pH 8; 1% Triton X-100; 20 mM CHAPS; 60 mM OG; 150 mM NaCl; 1 mM DTT). TEV cleavage was performed using AcTEV protease (Invitrogen) according to the manufacturer’s instructions. TEV-cleaved proteins were incubated with calmodulin beads in calmodulin-binding buffer (10 mM Tris-Cl, pH 8.0; 10 mM 2-mercaptoethanol; 150 mM NaCl; 1 mM magnesium acetate; 1 mM imidazole; 2 mM CaCl2; 1% Triton X-100) overnight at 4°C. After the beads were washed with calmodulin-binding buffer, bound proteins were eluted with EGTA buffer, containing 25 mM EGTA in PBS or boiled in low SDS-PAGE sample buffer.

Sample preparation for mass spectrometry analysis of S1P1 and its associated polypeptides. Affinity-purified receptor and receptor-associated proteins were separated on a 4%–12% linear gradient NuPage gel (Invitrogen). Gels were lightly stained with Coomassie blue R-250 (50% methanol, 10% acetic acid, 0.1% R-250) for 10 minutes and destained overnight in destaining solution (5% methanol, 7% acetic acid). After imaging, the area from the top to the bottom of each lane of the Coomassie-stained gel was cut essentially at 2-mm intervals (some slices were wider because of the absence of any prominent band at those positions). Each gel slice was cut into small pieces (approximately 1-mm cubes) and transferred to 500-µl microcentrifuge tubes. The gel pieces were washed with 200 µl of 50 mM NH4HCO3 for 30 minutes at room temperature. The supernatant was removed, and 200 µl destaining solution (50 mM NH4HCO3 in 50% CH3CN) was added for 20 minutes at room temperature. The gel pieces were completely destained by repeated washes with NH4HCO3 and NH4HCO3/CH3CN if necessary. The destained gel pieces were dehydrated with 100% CH3CN and dried briefly in a vacuum concentrator (CentriVap). Gel pieces were rehydrated with 20–30 µl trypsin solution (12.5 ng/µl in 100 mM NH4HCO3) on ice for 45 minutes. In-gel digestion was performed at 37°C for 18–20 hours. The resulting peptides were extracted according to the protocol of Shevchenko et al. (49). Extracted peptides were dried in Centrivap, redissolved in solvent A (5% acetonitrile, 0.4% acetic acid, and 0.005% heptaflourobutyric acid), and stored at –20°C until mass spectrometric analysis was performed.

LC/MS/MS. To identify unmodified peptides from S1P1, S1P1 phosphopeptides, S1P1-ubiquitinated peptides, and peptides from S1P1-associated proteins, MS/MS was performed on a linear ion trap (LTQ; Thermo Finnigan) platform. A microcapillary fused silica column (12 cm long, 200 µm inside diameter) was packed with Magic C18 beads (5 µm particle size, 200-Å pore size; Michrom Bioresources). Peptides were separated at a flow rate of 200 nl/min by flow splitting. The solvent gradient of HPLC was linear from 100% solvent A (5% acetonitrile, 0.4% acetic acid, 0.005% heptafluorobutyric acid) to 80% solvent B (100% acetonitrile, 0.4% acetic acid, 0.005% heptafluoro­butyric acid) for 85 minutes. The eluent was introduced directly into an LTQ mass spectrometer via electrospray ionization. Each full mass spectrometry scan was followed by 5 MS/MS scans of the most intense ions with data-dependent selection using the dynamic exclusion option (Top 5 method).

Identification of S1P1-interacting proteins and posttranscriptional modification of S1P1. The acquired data files were converted to .dat file format prior to searching the sequence database. MS/MS datasets were searched against a composite database of human protein sequences (56,709 entries, release November 30, 2004, downloaded from Advanced Biomedical Computation Center, NCI-Frederick; database UniProtKB/Swiss-Prot was used for searching, release 56.9 on March 3, 2009, http://www.ebi.ac.uk/uniprot/) and its reverse complement with the SEQUEST algorithm (SEQUEST-PVM, version 27, revision 0) on a multinode Linux cluster. SEQUEST parameters were as follows: all pre-search filtering thresholds disregarded; mass tolerance of 3.0 Da for precursor ions; full tryptic-end constraint with allowing for 1 potential missed cleavage; variable modification (+80.0 Da) to account for potential phosphorylations on serine, threonine, and tyrosine residues; variable modification of ubiquitination (+114.1 Da) for potential ubiquitination on lysine residues; variable modification of Met (+16.0 Da). SEQUEST summary files were submitted to INTERACT (50) for subsequent data filtering. For identification of S1P1-associated proteins, the dataset was filtered with following stringent X-correlation score (Xcorr) criteria: 1.8, 2.2, and 3.5 for 1+, 2+, and 3+ peptides, respectively. Under this criterion, WWP2 (NEDD4-like E3 ubiquitin-protein ligase) was identified with 4 unique peptides with high confidence. In the case of identification of S1P1 phosphorylation and ubiquitination sites, Xcorr was less stringent: 1.6, 1.8, and 3.0 for 1+, 2+, and 3+ peptides, respectively. In all cases, delta correlation score (dCn) was 0.1 or greater. To measure the probability of correct phosphorylation site localization (Ascore calculation, –10logP; ref. 51), the phosphopeptide dataset was submitted to http://ascore.med.harvard.edu/ascore.php. Finally, all potential MS/MS spectral matches were subjected to manual inspection as a final validation step prior to acceptance as valid peptide identification. The spectral count of identified phosphopeptides and ubiquitinated peptides from FTY720P-treated or untreated samples was used for qualitative measurement of S1P1-mediated events.

Lung permeability assay. Pulmonary vascular leakage after administration of FTY720, AUY954, or W146 was measured by Evan blue dye (EBD) accumulation assay. EBD (0.5% in saline) was injected into the tail vein for 90 minutes before tissue harvest. FTY720 (oral gavage) and AUY954 (i.p.) were treated for 24 hours, whereas W146 was treated for 3 hours. Pulmonary vasculature was perfused with 5 ml normal saline through the right ventricle, while allowing the perfusate to drain from the incision in the left ventricle. Lungs were removed, photographed, weighed, and dried at 56°C overnight. Dry lung weight was measured again, and EBD was dissolved in formamide (Sigma-Aldrich) at 37°C for 24 hours and quantitated spectrophotometrically at 620 and 740 nm.

Statistics. Statistical analysis for all experiments was performed using 2-way ANOVA followed by Bonferroni post-tests for group comparison using Prism software. A P value less than 0.05 was considered significant.

View Supplemental data

This work was supported by NIH grants HL89934 and HL70694 to T. Hla and by an AHA Founders affiliate postdoctoral fellowship award to M.L. Oo. We thank Michael Wright, Masato Ikeda, Richard Longnecker, Tony Pawson, Bruce Mayer, and Daniela Rotin for the gift of reagents and Ralph Nachman, Domenic Falcone, Andrew J. Dannenberg, and Hideru Obinata for critical comments.


Conflict of interest: T. Hla served as a consultant for Connecticut Children’s Medical Center, Sidley Austin LLP, and Wilmer Hale LLP.


Citation for this article: J Clin Invest doi:10.1172/JCI45403.

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Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients

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J Clin Invest. doi:10.1172/JCI46102.
Copyright © 2011, The American Society for Clinical Investigation. Lukas Baitsch1, Petra Baumgaertner1, Estelle Devêvre1, Sunil K. Raghav2, Amandine Legat1, Leticia Barba1, Sébastien Wieckowski3, Hanifa Bouzourene3, Bart Deplancke2, Pedro Romero4, Nathalie Rufer1,3 and Daniel E. Speiser1

1Clinical Tumor Immune-Biology Unit, Ludwig Institute for Cancer Research, Lausanne, Switzerland.
2Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
3University Hospital Center and University of Lausanne, Lausanne, Switzerland.
4Translational Tumor Immunology Group, Ludwig Institute for Cancer Research, Lausanne, Switzerland.

Address correspondence to: Daniel Speiser, Ludwig Institute for Cancer Research, Hôpital Orthopédique, 05/1552, Av. P.-Decker 4, CH-1011 Lausanne, Switzerland. Phone: 41.21.314.0182; Fax: 41.21.314.7477; E-mail: d.e.speiser@gmail.com.

Published May 9, 2011
Received for publication December 14, 2010, and accepted in revised form March 16, 2011.

In chronic viral infections, CD8+ T cells become functionally deficient and display multiple molecular alterations. In contrast, only little is known of self- and tumor-specific CD8+ T cells from mice and humans. Here we determined molecular profiles of tumor-specific CD8+ T cells from melanoma patients. In peripheral blood from patients vaccinated with CpG and the melanoma antigen Melan-A/MART-1 peptide, we found functional effector T cell populations, with only small but nevertheless significant differences in T cells specific for persistent herpesviruses (EBV and CMV). In contrast, Melan-A/MART-1–specific T cells isolated from metastases from patients with melanoma expressed a large variety of genes associated with T cell exhaustion. The identified exhaustion profile revealed extended molecular alterations. Our data demonstrate a remarkable coexistence of effector cells in circulation and exhausted cells in the tumor environment. Functional T cell impairment is mediated by inhibitory receptors and further molecular pathways, which represent potential targets for cancer therapy.

CD8+ T cell responses in acute viral diseases have been extensively characterized in mice and humans (1–6). While viruses multiply rapidly during the first week of infection, CD8+ T cells become activated and expand vigorously, reaching a peak of T cell effector function. In parallel with the consequent decline of viral antigen, the majority of CD8+ T cells undergo apoptosis (contraction phase). After pathogen clearance, memory T cells persist for years at low frequencies, ready for accelerated protective immune responses in case of reinfection.

When pathogens are not eliminated, T cells may persist in much larger numbers. They are composed of large numbers of effector cells and low percentages of memory cells. Essentially, there are 2 scenarios of long-term CD8+ T cell activity in viral infection: the first scenario is observed, e.g., in persistent herpesvirus infection (e.g., EBV, CMV) in healthy individuals, where T cells successfully contain the viruses and thus are protective even though they do not eliminate the viruses entirely. The second scenario is associated with viral spread and progressive tissue damage in the presence of large numbers of CD8+ T cells, e.g., in HIV-1, HBV, or HCV infection, and in the murine model of lymphocytic choriomeningitis virus clone 13 (LCMV clone 13) infection. These 2 scenarios are distinguished by a fundamentally different functional competence of CD8+ T cells. In the first scenario, such as in healthy donors infected with EBV or CMV, viral antigen-specific T cells are functionally competent and thus ready for immediate cytokine production and cytotoxicity (7). These cells contribute to rapid reduction of viral load and restoration of health by viral containment to small anatomical compartments (8–10). In contrast, CD8+ T cells in the second scenario (i.e., the failure of viral containment) are functionally impaired (11, 12). The murine infection with LCMV clone 13 is a prototype model of functional T cell impairment, called T cell exhaustion, with progressively reduced production of IL-2, TNF-a, and IFN-?, followed by incapacity to lyse (infected) target cells (13, 14). Analysis of such cells has led to significant discoveries, such as the identification of PD-1, a major inhibitory receptor involved in T cell function (15). Gene expression profiling of murine T cells allowed a global assessment, revealing that T cell exhaustion is associated with numerous molecular alterations, affecting genes regulating chemotaxis, adhesion, coreceptors, migration, metabolism, and energy (2). Hereafter, we call these multiple changes exhaustion profile.

In humans, functional deficits were found in HIV-1–, HCV-, and HBV-specific CD8+ T cells (11, 16, 17), and a recent gene expression study described an exhaustion profile in HIV-1 patients (18). In contrast to virus-specific T cells, only little is known of self- and tumor-specific CD8+ T cells. In humans and mice, it remains to be determined whether functional impairments of bona fide self-antigen–specific T cells represent exhaustion, anergy, or other functional states. In melanoma patients, there are substantial numbers of long-term persisting effector-memory CD8+ T cells, despite failures of immune protection from disease. Circulating human tumor-specific CD8+ T cells may be cytotoxic and produce cytokines in vivo (19–21), indicating that self- and tumor-specific human CD8+ T cells can reach functional competence after potent immunotherapy such as vaccination with peptide, incomplete Freund’s adjuvant (IFA), and CpG (19) or after adoptive transfer (22). In contrast to peripheral blood, T cells from metastasis are functionally deficient, with abnormally low cytokine production and upregulation of the inhibitory receptors PD-1, CTLA-4, and TIM-3 (20, 23–25). Functional deficiency is reversible, since T cells isolated from melanoma tissue can restore IFN-? production after short-term in vitro culture (20). However, it remains to be determined whether this functional impairment involves further molecular pathways, possibly resembling T cell exhaustion or anergy as defined in animal models (2, 26).

The identification of mechanisms responsible for functional impairment of self- and tumor-specific T cells may reveal targets for novel cancer therapies. Human CD8+ T cell responses specific for the melanoma antigen Melan-A/MART-1 represent a model wherein self-specific T cells can be studied in great detail. Furthermore, we took advantage of the strong immunogenicity of vaccination with peptide plus CpG (19). By direct ex vivo analysis, we compared Melan-A/MART-1–specific T cells (hereafter called tumor-specific T cells) with virus-specific T cells by microarray analysis, quantitative PCR (qPCR), and flow cytometry. Recent studies focused on circulating T cells (27), whereas T cells residing in tumor tissue remain poorly characterized. Therefore, we isolated T cells from both peripheral blood and metastases. We found that the former show molecular and functional features of effector cells, similar to circulating CMV-specific T cells, demonstrating that human self- and tumor-specific T cells have the potential to become competent effector cells. In marked contrast, the tumor-specific T cells isolated from metastatic tissue displayed an exhaustion profile, consisting of large numbers of molecular alterations.

Naive and virus-specific T cells show no significant differences between melanoma patients and healthy donors. Recently we demonstrated reproducibility of gene expression profiling of small numbers of (1,000) T cells (28). Applying this technique (Supplemental Figure 1, A–D; supplemental material available online with this article; doi: 10.1172/JCI46102DS1), we analyzed naive and antigen-specific T cells upon sorting of PBMC subsets by flow cytometry. We compared gene expression profiles of naive CD8+ T cells from melanoma patients and healthy donors and found no significant differences (Figure 1A), confirming previous studies (29). For the isolation of antigen-specific cells, we used tetramers and sorted T cells specific for the tumor antigen Melan-A/MART-1, the EBV antigen BMLF1, and the CMV antigen pp65. We compared EBV-specific T cells between healthy donors and patients and did not observe significant differences in gene expression (Figure 1B). In parallel, we found similar phenotypes and similar IFN-? production (Figure 1, C and D). Thus, many CD8+ T cells appeared relatively normal in our patients.

Figure 1 Naive and virus-specific T cells show no significant differences between melanoma patients and healthy donors. (A and B) Volcano plots for all gene probes on the microarray, showing expression differences and P values of naive T cells (healthy donors [HDs] versus patients; A) or EBV-specific T cells (healthy donors versus patients; B). Each point represents 1 gene probe. (C) Lymphocytes were stained with an A2/EBV BMLF1280–288 tetramer together with anti-CD8, anti-CD45RA, and anti-CCR7. The inset shows a dot plot distinguishing the phenotypes among total CD8+ T cells analyzed as controls: naive (N) (CD45RA+CCR7+), central memory (CM) (CD45RA–CCR7+), effector memory (EM) (CD45RA–CCR7–) and effector memory RA+ cells (EMRA) (CD45RA+CCR7–). Bar graph depicts the percentage (mean ± SD) of each phenotype of total CD8+ cells or total EBV tetramer-positive populations from healthy donors or patients. (D) IFN-? production by EBV-specific T cells upon 4-hour stimulation. Whiskers in box plots indicate maximum and minimum values measured. Cross indicates the mean, while line indicates the median.

Gene expression profiling of naive versus nonnaive T cells. Before analyzing tumor-specific T cells, we validated our approach using only 1,000 cells, by searching for the known molecular differences between naive and nonnaive CD8+ T cells (28, 30). We selected genes showing a 3-fold or greater change between naive and nonnaive CD8+ T cells, plus a P value adjusted for the false discovery rate (FDR) of less than 0.05 (Supplemental Figure 2A). With this strategy, we identified 409 upregulated and 364 downregulated genes in naive relative to nonnaive CD8+ T cells (Supplemental Table 1) and found that all naive T cell populations clustered together and apart from all nonnaive T cells (Figure 2A). We selected 8 genes for verification by qPCR. Without exception, they confirmed the microarray results, whereby qPCR detected quantitatively larger differences, owing to the higher sensitivity of qPCR (Supplemental Figure 2B). Additionally, the data for many of the differentially expressed genes (e.g., CCR7, LEF1, SELL, IFNG, GZMB, and HLADR; Supplemental Figure 2C) confirmed well-known differences between naive and nonnaive T cells.

Figure 2 Gene expression of naive and effector T cells from peripheral blood. (A) Two-way hierarchical clustering based on the identified 773 genes, separating all naive from nonnaive T cells. Red indicates overexpression and blue underexpression relative to the mean. Each row represents 1 gene and each column 1 1,000-cell sample from 1 patient or healthy donor. (B) Relative overexpression of GO terms associated with the identified genes, calculated as described in Methods. (C and D) GSEA of publicly available gene sets describing naive and effector T cells. Positions of selected example genes are indicated. Gene sets comprise genes enriched in naive T cells (C) or in effector cells (D). Genes to the left and right of the rank-ordered list are enriched in naive T cells and nonnaive T cells, respectively.

We assessed biological classification of the 773 differentially expressed genes by assigning them to 9 Gene Ontology (GO) terms and then determined whether any of these GO terms were overrepresented in our list compared with the predicted frequency in a random gene list. Not surprisingly, we found about twice as many immune response genes as the number predicted from a random gene test (Figure 2B). Additionally, the GO terms for translation, cell death, and apoptosis were overrepresented in nonnaive cells, whereas genes involved in DNA repair were underrepresented.

In 2005, Willinger et al. made a thorough gene expression analysis of human CD8+ T cells from healthy donors without distinction of antigen specificity (31). They determined large differences between naive and total effector cells, providing gene sets characteristic for the distinction of the 2 populations. From these data, we used 2 gene sets, one which is up- and one which is downregulated in effector CD8+ T cells. Furthermore, in 2007, Wherry et al. defined gene sets that were up- or downregulated in antigen-specific memory, effector, and exhausted CD8+ T cells from LCMV-infected mice (2). While the gene sets from Willinger et al. described long-term effects of effector differentiation (analysis of total human CD8+ T cell subsets in steady state), the gene sets from Wherry et al. described shorter-term changes of gene expression (model of acute and chronic infection). With Gene Set Enrichment Analysis (GSEA), we determined whether gene sets were enriched in our rank-ordered list of differentially expressed genes. Our naive T cells showed upregulation of the 2 gene sets downregulated in effector cells as identified by Wherry et al. (ref. 2 and Figure 2C) and by Willinger et al. (ref. 31 and Figure 2C). Conversely, the gene sets enriched in our nonnaive T cells were those upregulated in effector cells as defined by Wherry et al. (Figure 2D) and by Willinger et al. (Figure 2D). Together, these data confirm the reproducibility of microarray analysis of highly purified cells, validating our approach of ex vivo analysis of antigen-specific T cells with small cell numbers.

Different gene expression profiles between circulating tumor- and virus-specific T cells. A major aim of our study was to determine whether tumor-specific CD8+ T cells were similar to or different from virus-specific T cells. By applying the same selection criteria as above (i.e., fold change = 3, adjusted P < 0.05), we found 390 genes that were differentially expressed between tumor- and EBV-specific T cells (259 upregulated and 131 downregulated) (Figure 3A and Supplemental Table 2), while only 184 genes (72 upregulated and 112 downregulated) were differentially expressed when compared with CMV-specific T cells (Figure 3B and Supplemental Table 3). Therefore, the differences between CMV- and tumor-specific T cells were smaller than between EBV- and tumor-specific T cells. A 2-way hierarchical clustering with these probes showed clear distinction between tumor- and EBV-specific (Figure 3C) and tumor- and CMV-specific T cell populations (Figure 3D) from the individual patients and healthy donors despite the high genetic heterogeneity between individuals and the similarity of surface markers of these T cell populations (Supplemental Figure 3A). Microarray data were confirmed through the analysis of a series of genes by qPCR, among them several inhibitory receptors (Figure 3, E and G). As compared with both EBV- and CMV-specific cells, TIM3 and CTLA4 were upregulated in tumor-specific T cells, while CD160 was upregulated in virus-specific T cells (Figure 3, E and G). 2B4 was upregulated in CMV-specific T cells. Interestingly, as compared with EBV-specific T cells, tumor-specific T cells expressed more mRNA encoding granzyme B (GZMB) and granulysin (GNLY), but less XCL1 (lymphotactin). XCL1 was also upregulated in CMV-specific T cells. Finally, we performed a GO term analysis and found that the differences between tumor- and the 2 virus-specific T cell populations were smaller (Figure 3, F and H) than the differences of naive versus nonnaive T cells (Figure 2B). Remarkably, immune response genes were not specifically overrepresented relative to a random gene list, suggesting overall similar expression of immune genes in effector T cells specific for EBV, CMV, and Melan-A/MART-1, despite the differences found for inhibitory receptors.

Figure 3 Gene expression of circulating CD8+ T cells depending on antigen specificity. (A and B) Volcano plots for all gene probes, showing differential expression and P values of the comparison of tumor- versus EBV-specific T cells (A) or tumor- versus CMV-specific T cells (B); diagramming is similar to that in Figure 1. (C and D) Two-way hierarchical clustering based on the identified gene probes separating all tumor-specific T cells from EBV- (C, 405 gene probes corresponding to 390 genes) and from CMV-specific T cells (D, 187 gene probes corresponding to 184 genes). Red indicates overexpression and blue underexpression relative to the mean. Each row represents 1 gene and each column 1 1,000-cell sample from 1 patient (tumor-, EBV- and CMV-specific cells) or healthy donor (EBV-specific cells, n = 4). (E and G) Log fold changes between tumor- and EBV- (E) or tumor- and CMV-specific T cells (G) of data from microarrays (blue bars) and qPCR (red bars). Positive and negative values indicate overexpression in tumor- and in virus-specific T cells, respectively. Data are represented as mean ± SEM. (F and H) Relative overexpression of GO terms associated with the identified 390 genes (Melan-A/MART-1 versus EBV; F) or with the identified 184 genes (Melan-A/MART-1 versus CMV; H), calculated as described in Methods.

The gene expression profile of circulating tumor-specific CD8+ T cells corresponds to late-differentiated effector cells. EBV- and CMV-specific T cells are recognized as prototypes of early- and late-differentiated effector cells, respectively (7). This distinction fits with the phenotypes of these 2 populations (Supplemental Figure 3A). We created rank-ordered gene lists to compare tumor-specific with the 2 virus-specific CD8+ T cell populations. The gene sets defined as upregulated in effector cells by Wherry et al. (ref. 2 and Figure 4A) and Willinger et al. (ref. 31 and Figure 4A) were enriched in tumor-specific cells, as compared with their EBV-specific counterparts. In contrast, the only gene set enriched in EBV-specific T cells compared with tumor-specific T cells was the small gene set containing genes specifically upregulated in memory cells when compared with naive CD8+ T cells as defined by Wherry et al. (Figure 4B). This is likely due to the lower degree of effector differentiation of EBV-specific T cells (which are nevertheless predominantly effector rather than memory cells; Supplemental Figure 3A). When we compared tumor- with CMV-specific CD8+ T cells, we could not find enrichment for any gene set (Figure 4C), confirming the late differentiation stage of tumor-specific T cells. To verify the differential expression of granzyme B and perforin ex vivo on the protein level, we performed intracellular staining. As expected, the tumor- and CMV-specific CD8+ T cells expressed more granzyme B and perforin than the EBV-specific CD8+ T cells (Figure 4D). However, all 3 antigen-experienced T cells produced high levels of IFN-? after 4 hours triggering with peptide-loaded T2 cells (Supplemental Figure 3B). Together, our results demonstrate that tumor- and CMV-specific CD8+ T cells resembled each other closely, while EBV-specific CD8+ T cells were in earlier stages of effector differentiation.

Figure 4 Circulating tumor-specific T cells are late-differentiated effector cells, resembling CMV-specific T cells. (A) Gene set enrichment of genes describing effector cells (see Figure 2D). Genes to the left and right of the rank-ordered list are enriched in tumor- and EBV-specific T cells, respectively. (B) Gene set enrichment of genes describing memory cells (2). Genes to the left and right of the rank-ordered list are enriched in tumor- and EBV-specific T cells, respectively. (C) No differences were found between Melan-A/MART-1– and CMV-specific T cells, demonstrated by a gene set defining effector cell–related genes (31). (D) Intracellular staining of naive and antigen-specific T cells. Top panels show 1 representative example; below are the combined results of all samples (EBV and CMV, n = 5; Melan-A, n = 15; naive, n = 25). Data of EBV- and CMV-specific T cells are from healthy donors, while data of tumor-specific T cells are from patients. ***P < 0.001. Whiskers in box plots indicate maximum and minimum values measured. Line indicates the median.

In contrast to circulating T cells, tumor-specific T cells from tumor-infiltrated lymph nodes show an exhaustion profile. Previous studies indicated that functional impairment of tumor-specific T cells may occur primarily in situ (20, 24), which was also the case after strong systemic T cell activation by CpG-based vaccination (25). Therefore, we established a clinical investigation protocol to recover large numbers of live cells from tumor-infiltrated lymph nodes (TILN). This enabled us to perform functional studies and gene expression analysis ex vivo from tumor-specific T cells from TILN, in comparison with circulating T cells. Tumor-specific T cells from metastases showed highly insufficient IFN-? production upon 4-hour peptide triggering (Figure 5A), as published previously (20, 24). Microarray analysis allowed the identification of 332 genes (201 up- and 131 downregulated in TILN; Supplemental Table 4) that were differentially expressed between tumor-specific CD8+ T cells from PBMC versus TILN, using the same criteria as before (Figure 5B). Hierarchical clustering using these genes divided the 13 samples into 2 groups only, one for blood and the other for TILN-derived tumor-specific T cells (Figure 5C). qPCR performed for a selection of genes allowed proper validation (Figure 5D). Among the genes upregulated in tumor-specific cells from TILN were the lymph node retention receptor CRTAM, the chemokines XCL1 and XCL2, the activation marker TNFRSF9, and the inhibitory receptor CTLA4. CXCL13, a B cell chemoattractant usually found in the B cell compartment of lymph nodes, was one of the most highly overexpressed genes. Among the genes downregulated in TILN cells were the cell-growth–regulating protein LYAR and the inhibitory receptor KLRG1. When classifying the differentially expressed genes into broad GO terms, we found that genes involved in cell death and apoptosis and in the immune response were overrepresented compared with a randomly selected gene list (Figure 5E). To obtain a more general overview of the differences of tumor-specific CD8+ T cells from blood versus TILN, we studied gene sets specific for effector cells, naive cells, memory cells, and exhausted cells, as described above. Remarkably, the gene set described for exhausted T cells (2) was significantly enriched in tumor-specific cells from TILN, in contrast with the gene sets characteristic for naive, memory, and effector T cells (Figure 5F). These large-scale data demonstrate an impressive exhaustion profile, with extended molecular alterations of multiple pathways in tumor-specific CD8+ T cells from metastases.

Figure 5 Exhaustion profile of tumor-specific T cells in situ. (A) IFN-? production by tumor-specific T cells from the circulation (blood; n = 6) or TILN (n = 8) after 4-hour antigen stimulation. Whiskers in box plots indicate maximum and minimum values measured. Cross indicates the mean, while line indicates the median. **P < 0.01. (B) Differential gene expression by tumor-specific T cells isolated from blood versus TILN, as illustrated by a volcano plot for all gene probes. (C) Two-way hierarchical clustering based on the identified 346 genes separating all blood-derived tumor-specific T cells from their TILN counterparts. Red indicates overexpression and blue underexpression relative to the mean. Each row represents 1 gene and each column 1 1,000-cell sample from 1 patient. (D) Log fold change between tumor-specific T cells from blood versus TILN; data from microarrays (blue bars) and qPCR (red bars). Positive and negative values indicate overexpression in tumor-specific T cells from TILN and from blood, respectively. Mean ± SEM. (E) Relative overexpression of GO terms associated with the identified genes, calculated as described in Methods. (F) Enrichment of the gene set described for exhausted T cells (2) in TILN-derived tumor-specific T cells, relative to their blood-derived counterparts. The positions of inhibitory receptors found in this gene set on the rank-ordered gene list are indicated. A position to the left indicates enrichment in TILN-derived cells, a position to the right enrichment in blood-derived cells.

Enhanced expression of inhibitory receptors, such as CTLA4 and LAG3, was observed in T cell exhaustion (2, 23, 32–34). Interestingly, their expression was enriched in TILN cells, with the notable exceptions of PTGER2 and KLRG1 (Figure 5F). However, KLRG1 was described as more strongly expressed in functionally competent effector cells than in exhausted T cells (2), compatible with our data. The absolute expression values of selected inhibitory receptors are detailed in Table 1. Although it seems likely that the tumor microenvironment plays a role, the reasons for the observed enhanced expression of inhibitory receptors remain to be elucidated.

Table 1 Expression of selected inhibitory receptors by tumor-specific T cells

Differential protein expression of multiple inhibitory receptors by tumor- and virus-specific CD8+ T cells. To determine expression of inhibitory receptors at the protein level, we produced tetramers labeled with (multiple) different fluorochromes and used them in combination with several monoclonal antibodies (multi-tetramer staining; Figure 6A). Compatible with mRNA data, CD160 and 2B4 were more frequently expressed by both EBV- and CMV-specific T cells than by tumor-specific T cells from peripheral blood (Figure 6B), in agreement with a study reporting that most CD160+ cells coexpressed 2B4 (35). In contrast, circulating tumor-specific T cells expressed more TIM-3 and more PD-1 than the 2 virus-specific T cell populations (Figure 6B), in line with 2 recent reports of TIM-3+PD-1+ cells among tumor-specific T cells (23, 36). Large percentages of PD-1+ tumor-specific T cells coexpressed TIM-3 and/or KLRG-1. Similar results were obtained when we analyzed the mean fluorescence intensity (Supplemental Figure 4). Our technique allowed analyzing simultaneous coexpression of multiple inhibitory receptors, for CD160, KLRG-1, PD-1, and TIM-3, or for 2B4, LAG-3, and CTLA-4. We found a pronounced increase in inhibitory receptor coexpression from naive to central memory, effector memory, and effector memory RA+ cells (data not shown). On antigen-specific T cells, there were various combinations of inhibitory receptors. Melan-A–specific T cells from TILN expressed more CTLA-4, LAG-3, and TIM-3, but less KRLG-1 than their counterparts from peripheral blood (Figure 6C), confirming the results obtained by the microarray analysis. These data reveal a high level of heterogeneity, with multiple antigen-specific T cell subpopulations expressing different combinations of inhibitory receptors. It is likely that many of these subpopulations are effector memory cells and effector memory RA+, as they make up the vast majority of Melan-A–specific T cells (Supplemental Figure 3A). Naive and central memory cells were infrequent, but may nevertheless contribute to this heterogeneity. Furthermore, extended studies are necessary to determine the functional impact of coexpressed inhibitory receptors. Finally, the marked differences between tumor-, CMV-, and EBV-specific T cells suggest different roles of inhibitory receptors in viral infection versus cancer.

Figure 6 Multi-tetramer staining assessing coexpression of inhibitory receptors. (A) Staining with tetramers binding to EBV- (PE–Texas Red), Melan-A/MART-1– (APC–eFluor 780), or CMV- (PE–Texas Red and APC–eFluor 780) specific T cells (labeling tetramers with 2 instead of 1 fluorochrome identifies larger numbers of epitope-specific T cell populations than the number of fluorescence channels used). T cells were analyzed for coexpression of 7 inhibitory receptors: KLRG-1 (Alexa Fluor 488), TIM-3 (PE), PD-1 (PerCP-eFluor710), and CD160 (Alexa Fluor 647), or LAG-3 (FITC), 2B4 (PE-Cy5.5), and CTLA-4 (APC). (B) Expression of 7 different inhibitory receptors. Histograms of a representative sample are gated on CD8+ tetramer+ cells. Box plots summarize the data of all patients analyzed (EBV, n = 16; CMV, n = 6; Melan-A blood, n = 10, except for CTLA-4, n = 3; Melan-A TILN, n = 8–9). Whiskers in box plots indicate the maximum and minimum values measured. Cross indicates the mean, while line indicates the median. *P < 0.05; #P < 0.01; §P < 0.001. (C) Coexpression of 0 to 4 and 0 to 3 inhibitory receptors was analyzed with SPICE (48).

In peripheral blood, tumor-specific T cells induced by vaccination showed an effector cell profile (Supplemental Figure 5), similar to CMV-specific T cells and similar to the murine counterpart of CD8+ T cells in acute LCMV Armstrong infection (2). Differentiation of EBV-specific CD8+ T cells was less pronounced, but they nevertheless resembled effector cells. In contrast to these 4 effector cell populations, tumor-specific T cells in situ displayed an exhaustion profile, with significant similarity to murine T cells in chronic infection with LCMV clone 13 (2).

Tumor-infiltrating T cells are functionally deficient (20, 23, 24, 34), which is likely coresponsible for the limited efficacy of immunotherapy. However, the underlying mechanisms remain poorly characterized, in contrast with chronic infectious diseases (1, 2). Our finding of T cell exhaustion in melanoma metastases results from what we believe is the first comprehensive molecular characterization of self- and tumor-specific T cells, providing explanations for their functional impairment. Tumor-specific T cells from metastases showed considerable molecular alterations, with surprisingly strong overexpression of many genes regulating various cell functions. This included genes involved in immune responses, cell death and apoptosis, and cell cycle and DNA repair. Thus, the data point to enhanced immune activation and apoptosis, and problems in maintaining DNA integrity and sustaining cell cycling in T cells of metastases.

We did not find significant correlations between our T cell data and clinical results (e.g., patient survival). However, phase I studies such as the present trial of immunotherapy are not suited for clinical outcome analysis. Rather, they are designed for providing enhanced biological insight. Indeed, we identified specific molecular alterations potentially representing molecular targets for improved therapy. Nevertheless, further studies are required to determine which of these targets are most promising for evaluation in large-scale phase III clinical trials.

Based on the available evidence for functional T cell impairment in HIV-1, HBV, and HCV infections (11, 16–18, 37), it will be useful to perform comparative molecular profiling of T cells in different infections and malignancies in order to identify similarities and differences, providing the rational basis for therapy optimization. Very recently, HIV-1–specific T cells have been profiled, with identification of T cell exhaustion and BATF upregulation by PD-1 in patients failing to control HIV infection (18). Even though we did not find enhanced BATF expression in tumor-specific T cells from TILN, we observed similarities in gene expression signatures and upregulation of multiple inhibitory receptors on tumor-specific T cells also at the protein level.

Besides analysis of tumor-specific T cells after vaccination, it would be interesting to profile spontaneously arising T cell responses and naive tumor-specific T cells from patients and healthy donors, with the aim of identifying disease mechanisms responsible for altered T cell function. We expect that tumor-specific T cells from healthy donors would show an expression profile similar to total naive CD8+ T cells, while spontaneously responding T cells may show some degree of effector cell differentiation. However, such studies are technically challenging, since tumor-specific T cells in healthy donors and early stages of cancer are rare and difficult to isolate for ex vivo analysis. Therefore, laboratory techniques must be optimized for comprehensive characterization of even smaller cell numbers, ultimately down to the single cell level.

T cell tolerance to self and tumor antigens is assured by negative selection in the thymus and through anergy induction and T cell deletion in the periphery. Anergy has been characterized in at least 9 different experimental settings, most of them in vitro models and/or CD4+ T cell models (26). Unfortunately, no comprehensive gene expression data are available. Therefore we could not systematically evaluate anergy in our study. Nevertheless, we made an attempt by evaluating 29 anergy-related genes described in a model of ionomycin-induced anergy and a model of deletional tolerance (38, 39). We found that some of these genes (e.g., CBLB and CTLA4) were enriched in tumor-specific T cells from metastases (Supplemental Table 4), but most of the described genes (e.g., ITCH, EGR2, and DGKZ) were not enriched (not shown).

Our study was performed in patients with advanced stage III–IV melanoma. It has been hypothesized that late cancer stages may be associated with T cell exhaustion (1), whereas anergy and tolerance would be induced already at early stages of tumorigenesis (40, 41). Possibly, self- and tumor-specific T cells may show discrete alterations already at the naive stage and/or after spontaneous activation. Perhaps anergy mechanisms are functional even at later disease stages. The elucidation of these points requires further methodological progress. For the time being, our data support the conclusion that exhaustion likely contributes to the functional deficiencies, but does not rule out the involvement of further mechanisms such as anergy or self tolerance.

In circulating tumor-specific T cells, we found effector cell signatures compatible with their ample production of granzyme B and perforin (Figure 4) and efficient expression of IFN-? upon 4-hour triggering with antigen (Supplemental Figure 3B and refs. 20, 21). Due to the high efficacy of CpG 7909 as adjuvant, the circulating tumor-specific T cells studied here were more strongly activated (19) than in most other cancer vaccine studies with their lower frequencies and less pronounced effector cell differentiation. Thus, our data of circulating cells are not representative for the latter, but nevertheless demonstrate that self- and tumor-specific T cells have the potential to become effector cells. Despite the high efficacy of the adjuvant used, we could not observe significant bystander effects on circulating T cells with specificities other than for the vaccine (Figure 1).

One could argue that vaccination should have activated the tumor-specific T cells to an even higher degree than CMV-specific T cells in healthy donors (which was actually the case in some of our melanoma patients; our unpublished observations). Protection from latent CMV is likely less demanding for T cells than protection from acute viral disease. Possibly, even more strongly activated T cells may be required for protection from cancer progression. Indeed, adoptive transfer therapy has shown that tumor-specific T cells at much higher frequency and strong activation can eliminate large melanoma metastases (22). Molecular profiling of these cells in comparison with T cells during acute viral infections may reveal eventual differences from our data. Alternatively, therapeutic success and protection from disease may be primarily achieved due to high numbers of T cells with molecular properties similar to those described here. However, patients with acute viral infections are rarely accessible for clinicians and researchers. Moreover, antitumor vaccines rarely induce T cell responses comparable to acute viral infections. In contrast to vaccines consisting of synthetic molecules and inactivated pathogens, live vaccines (essentially vaccinia virus and yellow fever vaccine) can induce high T cell frequencies (42, 43) and efficient protection. Comprehensive profiling of these T cells is feasible and may likely contribute to identifying protective mechanisms of human T cells.

Differentiation from naive to effector T cells introduces large changes in expression of not only immune response genes, but also of genes involved in translation, in cell death and apoptosis, and in cell migration. These changes result in increased production of effector molecules, migration to pathologic tissue, and cell survival. Based on GO terms, we compared EBV- with circulating tumor-specific T cells and found that the latter overexpressed genes involved in translation, cell death, and apoptosis, likely reflecting the fact that the tumor-specific T cells were more advanced in effector cell differentiation. Compared with CMV-specific T cells, circulating tumor-specific T cells expressed slightly more genes related to transcription, but fewer genes involved in cell migration. Despite these distinctions, the 3 effector cell populations from peripheral blood were relatively similar.

Inhibitory receptors were prominent among the differentially expressed genes. This group of genes is attracting increasing attention, also because of its importance in T cell exhaustion and therapeutic potential (2). Nevertheless, the circulating tumor-specific T cells expressed granzyme B and perforin at high levels and were functionally competent (19). Apparently, effector cells can express inhibitory receptors but nevertheless maintain functional competence. Our study demonstrates coexistence of functional cells in circulation and exhausted cells in metastases. We have preliminary data indicating that this may occur even within individual T cell clonotypes (our unpublished observations). It appears that migration of T cells into the tumor tissue is associated with downregulation of cytokine production and exhaustion as a consequence of encountering inhibitory receptor ligands expressed in the tumor tissue, in conjunction with antigen recognition. Thus, exhaustion of T cells in metastases but not in peripheral blood may be linked to the frequent and strong expression of these ligands in the tumor microenvironment. This interpretation is compatible with our earlier findings that the functional deficiency of tumor-residing T cells is readily reversible, since T cells from metastases regain function after 1 to 2 days culture in vitro (20, 44).

HCV-specific T cells may coexpress up to 4 of the inhibitory receptors KLRG1, 2B4, CD160, and PD-1, correlating with CD127 downregulation and functional impairment (32). Even though many tumor-specific T cells expressed KLRG1, 2B4, PD-1, and TIM-3, they did not express CD160. This difference may be functionally relevant for HCV- versus tumor-specific T cells. Moreover, expression of inhibitory receptors was more abundant in T cells from TILN as opposed to blood. The differential coexpression of multiple inhibitory receptors in viral infection versus cancer, and depending on antigen specificity/differentiation status and anatomical localization, suggests that the functional regulation of antigen-specific T cells is more complex than previously thought.

In summary, our study provides comprehensive molecular profiles of human CD8+ T cells. Although tumor-specific T cells can acquire substantial effector cell properties, they display an exhaustion profile in metastases. With modern technologies applied to small cell numbers, it becomes increasingly possible to determine whether functional impairment and molecular exhaustion of tumor-specific T cells are due to their specificity for self antigen, and/or immune suppression in situ.

Healthy donors, melanoma patients, lymphocyte isolation, and flow cytometry. Blood from 4 A2+ healthy donors was obtained from the university blood transfusion center of Lausanne, Switzerland. Peripheral blood and surgery specimens were obtained from A*0201+ patients with stage III/IV metastatic melanoma. Patients had received multiple monthly low-dose vaccinations s.c. with 100 µg Melan-A/MART-1 peptide and CpG (500 µg PF-3512676/7909; provided by Pfizer/Coley Pharmaceutical Group), emulsified in IFA (300–600 µl Montanide ISA-51; provided by Seppic) as described previously (19). Analysis of circulating tumor-specific T cells was done after 11 ± 5 monthly vaccinations; the last was at a mean of 96 days before blood withdrawal. Tumor-specific T cells from TILN were prepared after finely mincing surgery specimens, which were obtained after 7 ± 2 monthly vaccinations, the last at a mean of 79 days before surgery. Vaccinations were done in the context of Ludwig Institute for Cancer Research trials (19, 45) and approved by the Ludwig Institute for Cancer Research protocol review committee as well as by the medical and ethical committees of the University Hospital (Lausanne). Blood and tissue were obtained upon informed patient consent, and the study was performed according to the relevant regulatory standards. Mononuclear cells were purified by density gradient using Lymphoprep (Axis-Shield) and immediately cryopreserved in RPMI 1640 supplemented with 40% FCS and 10% DMSO.

For microarray analysis, 1,000 cells from each sample were sorted using a Vantage SE directly into lysis and storage buffer provided by Miltenyi Biotec as shown in Supplemental Figure 1. CD8+ T cells were enriched using magnetic bead sorting (Miltenyi Biotec). Cells were stained on ice and diluted at one million cells/ml. Cells were stained with CD8-specific antibody, the dead cell marker DAPI, and either with lineage markers (CD4, CD14, CD16, CD19) together with A2/EBV BMLF1280–288 (GLCTLVAML), A2/CMV pp65495–503 (NLVPMVATV), or A2/Melan-A/MART-126–35A27L (ELAGIGILTV) tetramers binding to high- and low-affinity T cell receptors (46) or with CD45RA-, CCR7-, CD28-, and CD27-specific antibodies. Naive T cells were defined as CD8+CD45RA+CCR7+CD27+CD28+. The sorting strategy is shown in Supplemental Figure 1. Manipulations were done at 4°C, avoiding gene expression alteration due to staining and sorting. Sorting purity was high, as determined by analyzing aliquots before and after FACS sorting. Representative examples are shown in Supplemental Figure 1, B–D. Among CD8+ T cells, percentages for A2/EBV tetramer+ cells were 1.00 ± 0.89 (4 healthy donors and 12 patients); for A2/CMV tetramer+ cells, 1.53 ± 1.08 (7 patients); and for A2/Melan-A/MART-1 tetramer+ cells, 1.43 ± 1.31 in blood (11 patients) and 3.35 ± 3.35 in TILN (7 patients). After sorting, lysed cells were incubated for 10 minutes at 45°C and then directly frozen at –80°C.

Intracellular antibody staining was performed as previously described (27). In brief, cells from the CD8+ fraction were first stained with PE-labeled tetramers, followed by anti–CD8–Pacific Blue antibody. After washing in PBS, cells were incubated with LIVE/DEAD-Fixable-Aqua (Invitrogen) for dead cell exclusion, and fixed at room temperature (RT) during 30 minutes (1% formaldehyde buffer). Cells were washed and stained with mAbs anti–perforin-FITC or anti–granzyme B–FITC (BD) in FACS buffer with 0.1% saponin for 30 minutes at 4°C. For the staining of IFN-?, CD8+ cells were stimulated with peptide-loaded T2 cells for 4 hours in the presence of Brefeldin-A (Sigma-Aldrich) prior to antibody staining with anti–IFN-?–PE-Cy7 (BD Pharmingen). Data of IFN-?–production from tumor-specific T cells were previously published (20).

For antibody staining of multiple inhibitory receptors, samples were purified and enriched as described above and then stained using tetramers detecting the same EBV, CMV, or Melan-A/MART-1 epitopes as described above. Melan-A–specific tetramers were labeled with APC–eFluor 780 (eBioscience), EBV-specific tetramers were labeled with PE–Texas Red (BD Pharmingen), and CMV-specific tetramers were labeled with both APC–eFluor 780 and PE–Texas Red, allowing for individual analysis of T cells specific for the 3 epitopes in a single sample (multi-tetramer staining technique; ref. 47). After 45 minutes at 4°C, cells were washed and surface staining was performed for CD8, CCR7, CD45RA and (a) LAG-3 (Alexis Biochemicals) and 2B4 (BioLegend) or (b) KLRG-1 (gift from H.-P. Pircher, Department of Immunology, University of Freiburg, Freiburg, Germany), TIM-3 (R&D Systems), PD-1 (eBioscience), and CD160 (eBioscience). Samples (a) were fixed at room temperature for 30 minutes (1% formaldehyde buffer) and then stained for CTLA-4 (BD Biosciences — Pharmingen) in FACS buffer with 0.1% saponin for 30 minutes at 4°C. LIVE/DEAD-Fixable-Aqua (Invitrogen) was used as a dead cell exclusion marker, and appropriate isotype controls were used to define negative populations. Data were acquired on a Gallios Flow Cytometer (Beckman Coulter) and analyzed using FlowJo 9.1 (TreeStar). Analysis of coexpression of inhibitory receptors used SPICE version 5.1 (48).

Microarray and qPCR. Gene expression profiling was done in 2 experiments. The first experiment included samples from blood-derived naive, EBV-, and tumor-specific T cells. The second experiment included tumor-specific T cells from blood and metastasis, and CMV-specific T cells from blood. Frozen samples were sent to Miltenyi Biotec and processed according to the vendor-recommended protocol for gene expression analysis. Samples were hybridized to Agilent Whole Human Genome Oligo Microarrays 4x44K and scanned using the Agilent microarray scanner system (Agilent). The Agilent Feature Extraction Software was used for readout and processing of image files. Background correction, filtering of data, and quantile normalization were done using the Agi4x44PreProcess software package as described in the package manual. The Limma software package was used to identify the differentially expressed genes and creation of rank-ordered lists. We analyzed eventual contaminations from B cells, monocytes, and dendritic cells, and found that expression levels of IGHG1, CD19, TLRs, and CD1 were between 0.28% and 2.72% of the respective expression of CD3E, confirming the high purity of our samples. We also evaluated intra-group variability possibly leading to high background. For this, we randomly split the data from 13 naive CD8+ T cell samples into 3 pairs of 2 groups of 6 and 7 samples each and analyzed differences between the groups. We found that none of the gene probes were different in any of the pairings, demonstrating that the background was low (data not shown). For nonnaive cells (Figure 2), the data from EBV- and tumor-specific CD8+ T cells were pooled. Genes were assigned to broad GO terms using the GO Term Mapper ( http://go.princeton.edu/cgi-bin/GOTermMapper), yielding both the percentage of submitted genes attributed to a given GO term versus the percentage of all annotated genes attributed to that GO term. Relative overrepresentation was calculated by dividing the percentage of submitted genes attributed to a GO term by the percentage of all available genes annotated with this GO term. Rank-ordered gene lists (ranked according to the B value) were analyzed with GSEA ( www.broadinstitute.org/gsea; ref. 49). Enrichment was considered significant if P was less than 0.05 and FDR was less than 0.25 as suggested in the online tool.

qPCR was performed to validate the enriched genes observed in microarray experiments. Custom-ordered oligos (Microsynth) were designed using the online tool from Universal Roche Library Assay Design Centre (Supplemental Table 5). Reaction mix used was Power Sybr Green Master Mix (Applied Biosystems), and amplification was monitored with Applied Biosystems 7900HT Fast Real-Time PCR System (15-minute enzyme activation and 40 cycles of 15 seconds 95°C, 1 minute 60°C). A Hamilton Liquid Handling Robotic System was used to assemble the 384-well plates. Amplified cDNA samples used for microarray analysis were diluted (1:50) and used for qPCR after confirming the linear and single product amplification by the primers. Samples were measured in triplicate. GAPDH was used as a housekeeping gene to calculate relative expression values.

Statistics. For quantitative comparisons, Student’s t test (2-sample 2-tailed comparison) or 1-way ANOVA with Tukey post-test (multiple-sample comparison) was performed with Prism 5.0; P < 0.05 was considered as significant. P values and FDRs for GSEA were calculated with 1,000 permutations in the online tool. Microarray analysis was done with relatively restrictive criteria, i.e., by considering gene probes as significant if the P value, corrected for a FDR of 0.05, was P = 0.05 and the fold change was = 3.

Accession numbers. The gene-expression data described in this paper have been deposited in the NCBI Gene Expression Omnibus and are accessible through the GEO accession number GSE24536.

View Supplemental data

We are obliged to the patients for their dedicated collaboration. We gratefully acknowledge M. Delorenzi, F. Schütz, H.-P. Pircher, M. Etzrodt, M. Pittet, M. Matter, O. Michielin, L.J. Old, J. O’Donnell-Tormey, E.W. Hoffman, and A. Krieg for essential contributions; D. Zehn, P. Ohashi, H.R. MacDonald, J. Skipper, and H.F. Oettgen for support; and P. Schneider, L. Derre, M. Braun, C. Christiansen-Jucht, C. Jandus, J.-P. Rivals, T. Lövgren, and M. Iancu for collaboration and advice. We thank P. Guillaume and I. Luescher for tetramers, and Pfizer and Coley Pharmaceutical Group (USA) for providing CpG 7909 (PF-3512676). This work was supported by the Ludwig Institute for Cancer Research, the Cancer Research Institute (USA), the Cancer Vaccine Collaborative, Atlantic Philanthropies (USA), the Wilhelm Sander-Foundation (Germany), the Swiss Cancer League (grant 02279-08-2008), the Swiss National Science Foundation, and the Swiss National Center of Competence in Research (NCCR) Molecular Oncology.


Conflict of interest: The authors have declared that no conflict of interest exists.


Citation for this article: J Clin Invest doi:10.1172/JCI46102.

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Monday, May 9, 2011

New Tool for Turning Genes on and Off Could Help Uncover Protein Functions

Monday, May 9, 2011
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Sunday, May 08, 2011


ROCHESTER, Minn. — Mayo Clinic researchers have designed a new tool for identifying protein function from genetic code. A team led by Stephen Ekker, Ph.D., succeeded in switching individual genes off and on in zebrafish, then observing embryonic and juvenile development. The study appears in the journal Nature Methods.


The work could help shed light on health-related problems such as how cancerous cells spread, what makes some people more prone to heart attacks, or how genes factor in addiction. More complicated issues, like the genetics of behavior, plasticity and cellular memory, stress, learning and epigenetics, could also be studied with this method.


The research at Mayo Clinic's Zebrafish Core Facility could help further unify biology and genomics by describing the complex interrelations of DNA, gene function and gene-protein expression and migration. The study examines protein expression and function from 350 loci among the zebrafish's approximately 25,000 protein-encoding genes. Researchers plan to identify another 2,000 loci.


"I consider this particular system a toolbox for answering fundamental scientific questions," says Dr. Ekker, a Mayo Clinic molecular biologist and lead author of the article. "This opens up the door to a segment of biology that has been impossible or impractical with existing genomics research methods."


The study includes several technical firsts in genetic research. Those include a highly effective and reversible insertional transposon mutagen. In nearly all loci tested, endogenous expression knockdown topped 99 percent.


The research yielded the first collection of conditional mutant alleles outside the mouse; unlike popular mouse conditional alleles that are switched from "on" to "off," zebrafish mutants conditionally go from "off" to "on," offering new insight into localized gene requirements. The transposon system results in fluorescence-tagged mutant chromosomes, opening the door to an array of new genetic screens that are difficult or impossible to conduct using more traditional mutagenesis methods, such as chemical or retroviral insertion.


The project also marks the first in vivo mutant protein trap in a vertebrate. Leveraging the natural transparency of the zebrafish larvae lets researchers document gene function and protein dynamics and trafficking for each protein-trapped locus. The research also ties gene/protein expression to function in a single system, providing a direct link among sequence, expression and function for each genetic locus. Researchers plan to integrate information from this study into a gene codex that could serve as a reference for information stored on the vertebrate genome.


Researchers exposed translucent zebrafish to transposons, "jumping genes" that move around inside the genome of a cell. The transposons instructed zebrafish cells to mark mutated proteins with a fluorescent protein 'tag.'


"This makes investigation of a whole new set of issues possible," Dr. Ekker says. "It adds an additional level of complexity to the genome project."


Dr. Ekker's team maintains about 50,000 fish in the Zebrafish Core Facility. To observe, photograph and document mutations of that many minnow-sized fish, the team works with an international team of researchers and gets helps from Rochester public elementary school teachers. Under a program with Mayo Clinic and Winona State University called InSciEd Out (Integrated Science Education Outreach), teachers document mutations and learn about the scientific method.


Other members of the research team include Karl Clark, Ph.D.; Yonghe Ding, Ph.D.; Stephanie Westcot; Victoria Bedell; Tammy Greenwood; Mark Urban; Kimberly Skuster; Andrew Petzold, Ph.D.; Jun Ni, Ph.D.; and Xiaolei Xu, Ph.D., all of Mayo Clinic; Darius Balciunas, Ph.D.; Aubrey Nielsen; and Sridhar Sivasubbu, Ph.D., all of the University of Minnesota; Hans-Martin Pogoda, Ph.D., and Matthias Hammerschmidt, Ph.D., of the University of Cologne in Germany; and Ashok Patowary and Vinod Scaria, Ph.D., of the Institute of Genomic and Integrative Biology in New Delhi.


The National Institute on Drug Abuse, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, and Mayo Clinic funded the study.


###


Mayo Clinic is a nonprofit worldwide leader in medical care, research and education for people from all walks of life. For more information, visit MayoClinic.com or MayoClinic.org/news.


For more information, contact:


Robert Nellis
507-284-5005 (days)
507-284-2511 (evenings)
newsbureau@mayo.edu




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Chronic HgCl2 treatment increases vasoconstriction induced by electrical field stimulation. Role of adrenergic and nitrergic innervation

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Clinical Science (2011) Immediate Publication, doi:10.1042/CS20110072 Chronic HgCl2 treatment increases vasoconstriction induced by electrical field stimulation. Role of adrenergic and nitrergic innervationJavier Blanco-Rivero, Lorena B Furieri, Dalton V Vassallo, Mercedes Salaíces and Gloria BalfagónFisiología, Facultad de Medicina. Universidad Autónoma de Madrid, Madrid, Madrid 28029, Spain. gloria.balfagon@uam.es


Objectives: We investigated the possible changes in rat mesenteric artery vascular innervation function caused by chronic exposure to low doses of mercuric chloride (HgCl2), as well as the mechanisms involved.


Methods: Rats were divided into two groups: control and HgCl2-treated rats (30 days, 1st dose 4.6 µg/kg, subsequent dose 0.07 µg/kg·day im). Vasomotor response to electrical field stimulation (EFS), noradrenaline (NA) and nitric oxide (NO) donor DEA-NO were studied, neuronal NO synthase (nNOS) and phosphorylated nNOS (P-nNOS) protein expression were analysed and NO, superoxide anions (O2.-) and NA releases were also determined.


Results: EFS-induced contraction was higher in the HgCl2-treated group. 1 mmol/L phentolamine decreased the response to EFS to a greater extent in HgCl2-treated rats. HgCl2 treatment increased vasoconstrictor response to exogenous NA and NA release. 0.1 mmol/L L-NAME increased the response to EFS in both experimental groups but the increase was greater in segments from control animals. HgCl2 treatment decreased NO release and increased O2.- production. Vasodilator response to DEA-NO was lower in HgCl2 animals. Tempol increased DEA-NO-induced relaxation to a greater extent in HgCl2-treated animals. nNOS expression was similar in arteries from both experimental groups, while P-nNOS was decreased in segments from HgCl2-treated animals.


Conclusion: HgCl2 treatment increased vasoconstrictor response to EFS as a result, at the least, of reduced NO bioavailability and increased adrenergic function. These findings offer further evidence that mercury, even at low concentrations, is an environmental risk factor for cardiovascular disease.



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Wednesday, May 4, 2011

TERM IN GENETIC MEDICINE WE SHOULD RECOGNIZE

Wednesday, May 4, 2011
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Bioinformatician
A person that uses mathematics and statistics to interpret the large amount of computer-generated data from genetic/genomic research or clinical studies.
Bioinformatics
A modern field of science that combines biology, computer science, and information technology. Bioinformatics is essential to better understanding and analyzing the large amounts of genetic and genomics data that can result from research studies. Complex bioinformatics analysis is also needed to interpret an Individual Genome Sequence (IGS).
Chromosome
DNA is organized into structures known as chromosomes. These are like volumes in a genomic encyclopedia. The human genome is comprised of 23 pairs of chromosomes.
Copy Number Variation
Sometimes several copies of a gene, segment of a gene, or stretch of DNA are present in the genome sequence. When the number of copies is different for one individual compared to another, this is known as copy number variation (CNV).
dbSNP
The Single Nucleotide Polymorphism Database (dbSNP) is a free online resource that contains all identified genetic variations for a variety of species, including human.
Deletion
A change in the DNA sequence in which one or more DNA bases are removed.
Dominant Disorder
An inherited disorder that results when only one copy of the gene is altered. Individuals with a dominant disorder have one gene copy that makes a normal protein and one gene copy that makes an abnormal protein. There is a 50-50 chance that an affected individual will pass on a dominant disorder to each of their children.
DNA
Deoxyribonucleic acid. A DNA base, or nucleotide, is the primary structure that makes up a DNA molecule. There are four different types of DNA bases; each represented by one of four letters (A, C, G, T). These are the letters that make the words of your genetic story. The human genome is estimated to contain more than three billion DNA base pairs.
DNA Sequencing
A method for determining the precise order of DNA bases, or nucleotides, in the genome.
Duplication
A change in the DNA sequence in which one or more bases are copied.
Expressivity
Expressivity is a concept that describes how an individual with a particular genetic condition may be affected. In particular, it refers to which symptoms may develop and how severe or mild those symptoms may be.
Gene
A gene is a defined segment of DNA that contains important genetic information and is inherited as a unit. The information within genes provides instructions on how to make proteins and other important biologic substances for the body. Think of genes as chapters or entries within the volumes of your genome encyclopedia. The human genome contains 20,000–25,000 distinct genes.
Genetic Counselor
A professional specializing in the communication of genetic information to patients and families. He or she often works closely with medical geneticists or doctors. A genetic counselor is trained in genetic risk assessment and counseling and can help support you and your doctor when making decisions about your health. You can locate a genetic counselor through the National Society of Genetic Counselors.
Genetic Disorder
A disease caused by a particular DNA change that is passed from parent to child. Also known as an inherited disorder.
Genetic (or Genomic) Sequence
A series of DNA bases (represented by letters). Think of your entire genomic sequence as a set of encyclopedias. Your body uses the information coded in your genome encyclopedia to build and run your body. The human genome sequence is made up of more than three billion DNA base pairs contained within 23 chromosomes.
Genetic Variation
Differences that exist from one individual’s DNA sequence to another. These are, in effect, what make you uniquely you.
Genome
Your complete DNA sequence, including the proteins required to read and maintain it as well as the many particles that provide its structure. It contains genes and more, such as segments of DNA that switch genes on and off. Consider your genome to be a library that houses your genome encyclopedia. Everything in your genome library has a purpose and researchers are still learning about each role.
Genome Risk Profile
An analysis of some parts of your genome by looking at a specific set of defined locations representing known locations of DNA variations in the genome. This type of testing service can provide information about things such as your risk for common diseases, carrier status for common recessive DNA changes, and insights on your genetic ancestry. Companies scan pre-defined locations in the genome for a “snapshot” look that is based on the most current discoveries at that time. As new discoveries are made, new analysis, or updates need to be performed.
Genotype
A particular DNA change, DNA sequence, or pattern of DNA changes in your genome. Your genotype can be used to identify you. In some cases, a specific genotype causes a specific physical characteristic or trait (phenotype).
Genotyping
The process of determining the genotype of a sample.
GINA (Genetic Information Nondiscrimination Act)—U.S. only
On May 21, 2008 the United States Congress passed GINA (Genetic Information Nondiscrimination Act). This act provides protection from improper use of your genetic information by health insurance companies and employers.
Individual Genome Sequencing (IGS)
Illumina’s personal genome sequencing service. It provides a way for people to obtain their DNA sequence for personal use. Illumina Clinical Services Laboratory is proud to be the first CLIA-certified, CAP-accredited laboratory to offer IGS. IGS has been developed using the highest standards by a team of licensed professionals, an external ethics advisory board, and experts from a variety of areas. The comprehensive nature of this testing represents a new step in the practice of medicine, in which the results will continue to be meaningful and relevant throughout a person’s lifetime.
Inherited Disease
A disease caused by a particular DNA change that is passed from parent to child. Also known as a genetic disorder.
Insertion
A change in the DNA sequence in which one or more bases are added or inserted.
Medical Geneticist
A medical doctor who evaluates patients for genetic concerns. This consultation may include a medical history, family history, and a detailed physical examination. Geneticists frequently work with genetic counselors. You can locate a medical geneticist through the American Board of Medical Genetics.
Nucleotide
See DNA.
Penetrance
Penetrance is a concept that describes whether an individual who has a gene change associated with a genetic disorder will develop any symptoms associated with the disorder. For example, if a condition has 20% penetrance, then a person who inherits the particular gene change will have a 20% chance of developing symptoms of the disorder in their lifetime.
Phenotype
A physical, health, or behavioral characteristic or trait. It is essentially the physical and psychological self, including health conditions we may develop. In some cases, a specific phenotype can be linked to specific genetic information.
Recessive Disorder
An inherited genetic disorder that requires both copies of a gene to be altered in such a way that neither of them work properly. The result is that the individual with a recessive condition does not produce the needed gene product or protein.
SNP
Single nucleotide polymorphism. A site in the genome where one DNA base letter is often substituted for another. SNPs may be linked to characteristics and/or disease risks. They are frequently used in genome risk profiling services.
Substitution
A change in the DNA sequence in which one base is substituted for another.
Translocation
A change in the DNA sequence in which several DNA bases are cut (deleted) from one location and pasted (inserted) into another. A balanced translocation means that the total amount of DNA remains the same even though it is in a different location. An unbalanced translocation means that some DNA has been lost and/or duplicated during the cut-and-paste process.

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Federal Funding For Stem Cell Research

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The U.S. Federal Court of Appeals has overturned an August 2010 ban on federal funding of embryonic stem cell research, paving the way for broader exploration of how stem cells function and how they can be harnessed to treat a wide range of currently incurable diseases.

The ruling has been welcomed by the Obama Administration, which attempted to lift the ban in 2009, and by the nation's top researchers in the field, including Arnold Kriegstein, MD, PhD, director of the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research at UCSF.


"This is a victory not only for the scientists, but for the patients who are waiting for treatments and cures for terrible diseases," Kriegstein said. "This ruling allows critical research to move forward, enabling scientists to compare human embryonic stem cells to other forms of stem cells, such as the cell lines which are derived from skin cells, and to pursue potentially life-saving therapies based on that research."


Kriegstein said the ruling will make a significant difference for stem cell research in general, including at UCSF, where the majority of stem cell investigators receive some funding from the National Institutes of Health for their research, as well as from private sources and from the state. The ruling enables those scientists to integrate research from various funding sources, thereby more quickly addressing the causes and therapies for diseases.


Kriegstein was one of two University of California scientists to file a Declaration in September 2010 in support of the UC Board of Regents' motion to intervene in the August lawsuit, Sherley v. Sebelius.


Sherly v. Sebelius had argued that when the Obama Administration lifted a ban on federal funding for the research in March 2009, it had violated the 1996 Dickey-Wicker Amendment which barred using taxpayer funds in research that destroyed embryos.


In response, a U.S. District Court judge temporarily ordered a ban on the use of federal money for the research until the court battle could be resolved.


The Appeals Court decision put the Dickey-Wicker question to rest, ruling that the amendment was "ambiguous" and that the NIH "seems reasonably to have concluded that although Dickey-Wicker bars funding for the destructive act of deriving an ESC (embryonic stem cell) from an embryo, it does not prohibit funding a research project in which an ESC will be used," according to the 2-1 decision.


"I am very happy with this decision, although I am surprised that it wasn't a unanimous vote," Kriegstein said. "In my opinion, the evidence in favor of pursuing this research is overwhelming compared to the arguments submitted to stop the research."


UCSF is one of two universities, along with the University of Wisconsin, that pioneered human embryonic stem cell research in the United States, beginning in the late 1990s.


UCSF has developed one of the largest programs in the nation, primarily funded by the California Institute for Regenerative Medicine, a voter-supported initiative that provided $3 billion to fund statewide research in lieu of federal funding for it. Funding from the NIH, private philanthropy and other state sources also have been critical for the program.


UCSF also launched the nation's first stem cell PhD program in 2010, for which the first class already has been chosen and will begin in fall 2011.


Source:
Kristen Bole
University of California - San Francisco


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Tuesday, May 3, 2011

New Gene Therapy Technique On Induced Pluripotent Stem Cells Holds Promise In Treating Immune System Disease

Tuesday, May 3, 2011
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Researchers have developed an effective technique that uses gene therapy on stem cells to correct chronic granulomatous disease (CGD) in cell culture, which could eventually serve as a treatment for this rare, inherited immune disorder, according to a study published in Blood, the Journal of the American Society of Hematology.

CGD prevents neutrophils, a type of white blood cell of the immune system, from making hydrogen peroxide, an essential defense against life-threatening bacterial and fungal infections. Most cases of CGD are a result of a mutation on the X chromosome, a type of CGD that is called "X-linked" (X-CGD).


While antibiotics can treat infections caused by X-CGD, they do not cure the disease itself. Patients with X-CGD can be cured with a hematopoietic stem cell (HSC) transplant from healthy bone marrow; however, finding a compatible donor is difficult. Even with a suitable donor, patients are at risk of developing graft-versus-host disease (GVHD), a serious and often deadly post-transplant complication that occurs when newly transplanted donor cells recognize a recipient's own cells as foreign and attack the patient's body.


Another treatment option under development for X-CGD is gene therapy, a technique for correcting defective genes responsible for disease development that involves manipulation of genetic material within an individual's blood-forming stem cells using genetically engineered viruses. However, this gene therapy has so far proved to be inefficient at correcting X-CGD. In addition, these engineered viruses insert new genetic material at random locations in the blood-forming stem cell genome, putting patients at significantly higher risk for developing genetic mutations that may eventually lead to serious blood disorders, including blood cancer.


In order to develop a more effective and safer gene therapy for X-CGD, researchers from the National Institute of Allergy and Infectious Disease (NIAID) at the National Institutes of Health (NIH) and The Johns Hopkins University School of Medicine embarked on a study using a more precise method for performing gene therapy that did not use viruses for the gene correction. Researchers removed adult stem cells from the bone marrow of a patient with X-CGD and genetically reprogrammed them to become induced pluripotent stem cells (iPS cells). Like embryonic stem cells, these patient-specific iPS cells can be grown and manipulated indefinitely in culture while retaining their capacity to differentiate into any cell type of the body, including HSCs.


"HSCs that are derived from gene corrected iPS cells are tissue-compatible with the patient and may create a way for the patient's own cells to be used in a transplant to cure the disease, removing the risk of GVHD or the need to find a compatible donor," said Harry L. Malech, MD, senior study author, Chief of the Laboratory of Host Defenses and Head of the Genetic Immunotherapy Section of NIAID at the NIH. "However, turning iPS cells into a large number of HSCs that are efficently transplantable remains technically difficult; therefore, our study aimed at demonstrating that it is possible to differentiate gene corrected iPS cells into a large number of corrected neutrophils. These corrected neutrophils, grown in culture, are tissue-compatible with the patient and may be used to manage the life-threatening infections that are caused by the disease."

Typically, iPS cells from a patient with an inherited disorder do not express disease traits, despite the fact that the iPS cell genome contains the expected mutation. The researchers were able to prove, in culture, that iPS cells from a patient with X-CGD could be differentiated into mature neutrophils that failed to produce hydrogen peroxide, thus expressing the disease trait. This is the first study in which the disease phenotype has been reproduced in neutrophils differentiated from X-CGD patient-specific iPS cells.

After discovering that the disease could be reproduced in cell culture, the researchers then sought to correct the disease and produce healthy neutrophils in culture. They used synthetic proteins called zinc finger nucleases (ZFNs) to target a corrective gene at a specifically defined location in the genome of the X-CGD iPS cells. The iPS cells were then carefully screened to identify those containing a single copy of the corrective gene properly inserted only at the safe site. The researchers observed that some of the gene-corrected iPS cells could differentiate into neutrophils that produced normal levels of hydrogen peroxide, effectively "correcting" the disease.


"This is the first study that uses ZFNs in specific targeting gene transfer to correct X-CGD," said Dr. Malech. "Demonstrating that this approach to gene therapy works with a single-gene disease such as X-CGD means that the results from our study offer not only a potential treatment for this disease, but more importantly, a technique by which other single-gene diseases can be corrected using specifically targeted gene therapy on iPS cells."


Source:
American Society of Hematology


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