1 Laboratory of Experimental Medicine, Université Libre de Bruxelles, Brussels, Belgium
2 Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Skejby, Denmark
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ABSTRACT |
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The ß-cell dysfunction and death in type 1 diabetes and following islet transplantation is the result of direct contact with activated macrophages and T-cells, and/or exposure to soluble mediators secreted by these cells, such as cytokines, oxygen free radicals, and nitric oxide (NO) (1). In vitro, ß-cell exposure to interleukin (IL)-1ß alone induces functional impairment, whereas exposure to IL-1ß in combination with interferon (IFN)- and/or tumor necrosis factor-
induces ß-cell death by apoptosis in rodent and human islets of Langerhans after a period of 49 days (1).
Cytokines modify the expression of several genes in the ß-cell (1). An indirect proapoptotic effect of cytokines is the upregulation of the Fas receptor in rodent and human ß-cells, increasing the susceptibility of these cells to apoptosis mediated by the Fas ligand expressed on islet-infiltrating macrophages and T-cells (2). IL-1ß and IFN- also play a role in the inflammatory destruction of islet grafts immediately after transplantation (1,3,4), a process that hampers the success of islet transplantation in patients with type 1 diabetes. This inflammatory environment induces expression of Fas in the transplanted ß-cells (5) and chemokines, such as the macrophage chemoattractant protein-1 (MCP-1) (6,7), fractalkine, interferon inducible protein-10 (IP-10), and macrophage inflammatory protein-3
(MIP-3
) (8,9), contributing to mononuclear cell homing (9). There is increasing evidence that apoptosis is the main mode of ß-cell death in the development of type 1 diabetes and after islet transplantation (1). Commitment to apoptosis, a highly regulated process, is affected by extracellular signals, intracellular ATP levels, phosphorylation cascades, and expression of diverse pro- and antiapoptotic genes (1), which remain to be identified.
We have described by microarray analysis more than 200 genes and expressed sequence tags (ESTs) that are up- or downregulated by a 6- or 24-h exposure of rat ß-cells to IL-1ß and/or IFN- (8,10). Cytokines induce stress-response genes that are either protective or deleterious for ß-cell survival, whereas several genes related to differentiated ß-cell functions are downregulated. Several cytokine-induced genes are potentially regulated by the transcription factor nuclear factor (NF)-
B (10). Inhibition of cytokine-induced NF-
B activation by adenovirus-mediated expression of a NF-
B super-repressor (I
B(SA)2) significantly improves ß-cell survival, mainly through inhibition of apoptosis (1012). The microarray experiments described above were performed at two time points (6 and 24 h) after cytokine exposure and did not allow discrimination between early and late effects of cytokines on gene expression. Moreover, they did not provide a detailed information on the pattern of NO-dependent genes. Therefore, we performed a detailed time-course microarray analysis to detect transient regulation of gene expression by cytokines in the presence or absence of the inducible NO synthase (iNOS) blocker NG-methyl-L-arginine (NMA). These data allowed classification of cytokine-induced genes in clusters based on their function and on their temporal profile of expression.
Cluster analysis of microarray data allows an integrated understanding of biological processes and provides indication of the function of novel genes coexpressed with genes of known function (13). It is empirically stated that clustering analysis of microarray studies requires at least five time point measurements. The large number of cells required for a detailed time-course microarray analysis (around 6 x 107 cells) makes it impossible to use purified primary ß-cells in these experiments. Therefore, we chose to perform the present experiments using the well-differentiated insulin-producing cell line INS-1E (14). Cells were treated in parallel over a period of 124 h (six time points) with the cytokines IL-1ß + IFN- and/or NMA. The time points and combination of cytokines were chosen based on detailed time-course analysis for induction of apoptosis. Microarray analysis was performed using the Affymetrix system, as described in previous publications by our group (8,10). With this approach, we detected 698 differentially regulated mRNAs in response to cytokines, of which >60% are novel. Forty-six percent of the cytokine-regulated genes were NO dependent, highlighting the importance of NO in late regulation of gene expression. By using k-means cluster analysis (15), the detected genes were assigned to 15 distinct temporal profiles. The present set of microarray data opens several new lines of research, which are discussed below, and provides a detailed and comprehensive resource for scientists interested in the mechanisms of ß-cell dysfunction and death in type 1 diabetes.
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RESEARCH DESIGN AND METHODS |
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Microarray and clustering analysis.
Total RNA isolated from INS-1E cells (at least 10 µg/sample) was used to prepare biotinylated cRNA. The marked cRNA was hybridized to the rat U34-A oligonucleotide array (Affymetrix, Santa Clara, CA) as previously described (8,10). A total number of 48 U34-A arrays were utilized in the present series of experiments. Analysis of differential expression was performed by the GeneChip Suite software (version 4.0.1). Arrays were normalized by global scaling, with the arrays scaled to an average intensity of 150. Samples from duplicate experiments (INS-1E cells from different passage numbers) were hybridized to increase the robustness of the results. Gene expression was considered as modified according to previously described criteria (8,10). Briefly, genes were considered as modified by cytokines when they fulfilled the following criteria for at least one of the six time points studied: 1) the mRNA was present in either control or cytokine-treated cells in both experiments, 2) the mean average fold change (experimental group versus control) was 2.5, and 3) the fold change in each individual duplicate was
2.0. We have used our own curated "ß-Cell Gene Bank" to assign the filtered genes into their respective functional clusters. The ESTs that had homology to a known sequence were annotated using the Resourcerer 6.0 database (22). A cytokine-modified gene, according to the criteria described above (mean average fold change
2.5 and the fold change in each individual
2.0) was considered NO-dependent if its expression in the presence of cytokines was increased/decreased >50% by NMA (mean of two experiments) in at least one time point and increased/decreased >30% at a second time point (mean of two experiments). These criteria of considering genes as NO dependent is arbitrary and may result in the inclusion of genes that are only partially regulated by the radical. Clustering analysis was performed to classify the genes according to their temporal variation in response to cytokines. For this purpose, the log-transformed expression values of the cytokine-induced mRNAs (genes + ESTs) were mean and variance normalized. The preprocessed data were used as input data in the J-Express clustering software (23), and a distance matrix was created with the Pearson correlation distance measure. K-means clustering method analysis was performed to create 15 clusters after the initial observation of 15 patterns with the hierarchical clustering method (13). The k-means algorithm is explained in detail elsewhere (15,24).
RT-PCR analysis.
RT-PCR was performed on poly(A)+ RNA as described (10). Primers used for PCR amplification were for notch-1 F-CTCACGCTGATGTCAATGCT, R-GTGTGGGAGACAGAGTGGGT (366 bp); delta-1 F-AAGGCCCGAGTCTGTCTACT, R-TGCTAACTCCGAGATGAACC (256 bp); jagged-1 F-AGCCTGTGAGCCTTCCTTAT, R-AAGCCACTGTTAAGACAGAGC (241 bp). The primers for glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were described previously (8). The ethidium bromide-stained agarose gels were photographed under ultraviolet transillumination using a Kodak Digital Science EDAS 290 camera (Eastman Kodak, Brussels, Belgium). The abundance of the PCR products were assessed by Biomax one-dimensional image analysis software (Kodak), and mRNA contents were expressed as optical densities corrected for GAPDH.
Promoter studies.
Plasmid constructs containing the iNOS and MCP-1 gene promoters were prepared and studied as described previously (25,26). Transfected INS-1E cells were exposed to cytokines with or without NMA for 1224 h (same concentrations as above). Luciferase activities were assayed with the Dual-Luciferase Reporter Assay System (Promega). Test values were corrected for the luciferase activity value of the internal control plasmid, pRL-CMV. The results for cytokine-exposed cells were expressed as a fold induction of the luciferase activity in control condition, taking control (no cytokine added) value as 1. NMA alone did not affect the promoter activity in any of the conditions studied (data not shown).
Statistical analysis.
Results are given as means ± SE. Comparisons versus the respective control groups were performed using the Students paired t test or Wilcoxons signed-rank test, as indicated.
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RESULTS |
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After filtering the expression values following the stringent criteria outlined in RESEARCH DESIGN AND METHODS, 936 mRNA probes encoding for 698 known genes and ESTs were detected as cytokine regulated (in the Affymetrix array, there are sometimes 24 distinct probes for the same gene or EST). We confirmed by RT-PCR eight selected genes detected as cytokine-modified by the microarray analysis, namely MCP-1, HO, insulin, iNOS, and isl-1 (data not shown), jagged-1, delta-1, and notch-1 (see below). Moreover, we confirmed that, as previously described for primary ß-cells (8,10), cytokines induce a nearly 50% decrease in pancreatic duodenal homeobox factor (Pdx)-1 mRNA expression in INS-1E cells after 24 h, an effect prevented by NMA (data not shown).
The known genes were further assigned into functional clusters, as shown in Tables 1 and 2 (the total list of genes is provided in an online appendix available at http://diabetes.diabetesjournals.org). Of these genes, nearly half were NO dependent. The distribution of NO-dependent/independent genes was 50% in most functional groups of genes, with the following exceptions. 1) In the metabolism group, 60% of the genes are NO dependent. The most NO-dependent metabolism subgroups are amino acids (67%) and ATP (87%). 2) In the cell cycle group, 71% of the genes are NO dependent. 3) In the cytokine/chemokine and major histocompatibility complex (MHC)-related gene groups, 87 and 100%, respectively, of the genes are NO independent.
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A general analysis of cytokine-modified genes (Table 1) revealed that 19.6% of the filtered genes are metabolism related, making it the most prevalent group (Table 1; section 1.0). Surprisingly, the largest group of metabolism genes were those involved in lipid metabolism (5.4% of the total number of genes; Table 1; section 1.4), with nearly 60% of these genes classified as NO dependent. The transcription factor peroxisome proliferatoractivated receptor-, which may enhance lipid uptake (30), was upregulated by IL-1ß + IFN-
after 4 h of exposure, and the increased expression was maintained up to 24 h (Table 2; section 1.4). In line with this observation, CD-36 (scavenger receptor class B) and adipophilin, both downstream targets of peroxisome proliferatoractivated receptor-
(30), were upregulated by IL-1ß + IFN-
(Table 2; section 1.4). Cytokines caused a parallel and late decrease in the expression of genes involved in free fatty acid ß-oxidation (acetylcoenzyme A dehydrogenase, 1-3-oxacyl-CoA thiolase, peroxisomal enzymes, and carnithine palmitoyltransferase II) and cholesterol biosynthesis (cytosolic 3-hydroxy 3-methylglutaryl-CoA synthase and reductase and steroid 5
-reductase) (31) (Table 2; section 1.4). Moreover, cytokines induced genes that may lead to increasing exogenous free fatty acid apport (lipoprotein lipase and LDL receptor) and genes involved in lipid storage (adipophilin) (Table 2; section 1.4).
Cytokines decreased the expression of several genes related to differentiated ß-cell functions and preservation of ß-cell mass, including Pdx-1, Isl-1, insulin, GLUT2, glucokinase, and diverse receptors for incretins and growth hormones (10) (Table 2). An intriguing finding was the upregulation of jagged-1 and delta-1, with peak of expression after 812 h in an NO-dependent manner, followed by a progressive decrease at later time points (Table 2; 6.0). These findings were confirmed by RT-PCR in INS-1E cells (Fig. 3A and B) and primary ß-cells (M.I.D. and D.L.E., unpublished data). Jagged-1 and delta-1 are two ligands of the notch receptors (32,33). Notch-1 is expressed in INS-1E cells but remains unchanged following exposure to cytokines as indicated by RT-PCR analysis (Fig. 3C). In good agreement with the RT-PCR data, notch-1 mRNA was detected as "present" but not changed by cytokines in the microarray analysis (data not shown).
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Two genes that may participate in ß-cell defense against cytokines are manganese superoxide dismutase (MnSOD) and IB, both with the property of decreasing NF-
B activation (37,38). MnSOD and I
B mRNAs were already induced by cytokines after 1 h (Fig. 4I) in an NO-independent manner and maintained an increased expression during the 24-h follow-up. Despite the upregulation of I
B and MnSOD, and of other putative defense/repair genes such as HO, heat shock protein (hsp)70, Gas-6, and glutathione-S-transferase (Table 2; section 12.0), prolonged exposure of ß-cells to IL-1ß and IFN-
culminates in apoptosis, as shown in Fig. 1.
To further examine the general pattern of cytokine-modified genes, all mRNAs described in online supplement S1 (available at http://diabetes.diabetesjournals.org) were reclassified based on their temporal pattern of gene expression, using the k-means clustering method (Fig. 5). (The complete list of genes in the different clusters is outlined in online supplement S2, available at http://diabetes.diabetesjournals.org.) To decide on the number of clusters to be used, we initially performed hierarchical clustering and observed 15 major profiles of gene expression. To further validate this selection, we performed k-means clustering using 14 or 16 clusters and obtained similarly shaped profiles (data not shown). Genes related to signal transduction and transcription factors were present in all clusters at similar proportions (mostly around 1020% of the total number of genes). Examination of the clusters reinforces the notion that NO, which starts to be synthesized in large amounts after 68 h, is crucial for late-expressed genes. Thus, the two most "NO-related clusters" were clusters 6 and 12 with 75 and 60%, respectively, of the genes listed as NO dependent (Fig. 5) (online supplement S2). In both cases, the major variation in gene expression, either stimulation (cluster 6) or inhibition (cluster 12), was observed between 824 h. Cluster 6 contains a large proportion of MHC-related genes, whose expression peaks at around 812 h. These genes are mostly IFN-induced and NO independent (10). The other cluster containing a high proportion of MHC-related genes is cluster 11, with a transitory peak of gene expression at 812 h and with only 32% of the genes as NO dependent. If we remove the HLA-related genes from the calculation in cluster 6, the number of NO-dependent genes climbs to 86%. Clusters 6 and 12 also contain a high proportion of "defense/repair" (around 9% of the total) genes but not of apoptosis-related genes. The highest number of apoptosis/endoplasmic reticulum stress-related genes (8.2%) is located in cluster 8, a cluster characterized by early (2 h) and transitory increase in gene expression. This cluster also contains the highest number of genes related to cell cycle (six genes). As an example of apoptosis-related genes, both caspase 3 and voltage-dependent anion channel (VDAC) are present in cluster 8 (online supplement S2; section 13.0). They have an NO independent peak of expression after 2 h (3.7- to 8.1-fold increase) and return to basal levels after 48 h. Most cytokines, chemokines, and adhesion molecules are classified in the same clusters (clusters 5 and 10).
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DISCUSSION |
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Microarray analysis is a precise and reproducible technique to measure the dynamics of a genetic network at the mRNA level (46,47). The rate of confirmation by semiquantitative RT-PCR for 43 cytokine-modified genes detected in our previous (8,10) (J. Rasschaert, D. Liu, A.K.C., B.K., M.K., T.Ø., and D.L.E., unpublished observations) and present microarray studies is >90%. This rate of confirmation increases to >95% with the use of real-time PCR (B.K., A.K.C., M.I.D., M.K., N.M., T.Ø., and D.L.E., unpublished observations). An important issue is whether the observed modifications in mRNA expression correspond to changes in protein expression. This is often the case, as suggested by the following observations. 1) Studies on the expression of mRNA and protein/enzyme activity for specific cytokine-modified genes in ß-cells, such as insulin, MnSOD, AS, ornitine decarboxylase, Pdx-1, hsp70, iNOS, A20, MCP-1, IL-15, MIP-3, and IP-10 showed a good correlation between these parameters (810,40,4855). 2) Total protein biosynthesis is not inhibited in rat ß-cells or mouse islets exposed to cytokines for 24 h (50,56). If mRNA and protein expression often correlates well, as mentioned above, how can we explain that proteomic studies on IL-1exposed neonatal rat pancreatic islets (57,58) failed to identify most of the genes described as modified in our present and previous microarray analysis (8,10)? Notably, these proteomic analyses (57,58) also failed to detect many cytokine-induced proteins previously identified as modified by Western blot and enzyme-linked immunosorbent assay, including abundantly expressed proteins, such as insulin (50), iNOS (51), MnSOD (48), GADD153/CHOP (A.K.C., F. Ortis, and D.L.E., unpublished observations), and the chemokines MCP-1 (52), IL-15 (9), MIP-3
(9), and IP-10 (9). This suggests that the proteomic approach utilized in these previous studies (57,58), although clearly of interest, is not yet sensitive enough to allow adequate comparison with the sensitive and reproducible microarray approach.
We have previously observed by microarray analysis and RT-PCR that cytokines decrease the expression of several genes related to differentiated ß-cell functions and preservation of ß-cell mass, including Pdx-1, Isl-1, insulin, GLUT2, glucokinase, and diverse receptors for incretins and growth hormones (8,10). Our present data confirm and extend these findings, providing a broader picture of the genes potentially involved in ß-cell dedifferentiation during an immune-mediated assault. An intriguing and novel finding was the upregulation of jagged-1 and delta-1. Jagged-1 and delta-1 are two ligands of the notch receptors (32,33), and we observed that notch-1 is expressed in INS-1E cells but remains mostly unchanged following exposure to cytokines. Notch signaling plays an important role in the control of embryonic endodermal endocrine development (59). This pathway activates the expression of Hes genes, encoding transcription repressors, which in turn inhibit expression of genes promoting endocrine differentiation, such as neurogenins (33). Differentiation is repressed in the endocrine precursor cells expressing the activated notch receptors, whereas the signaling cells are free to express neurogenins and differentiate into endocrine cells (33). In line with this model, expression of NeuroD1/BETA2 and NeuroD2, transcription factors that induce and maintain the differentiated state of insulin-producing ß-cells, was downregulated in response to cytokines (Table 2; section 9.0). The targets of NeuroD1/BETA2, namely glucokinase (60), insulin (61), secretin (62), and glucagon (63), were also decreased in the present experiments (Table 2; sections 1.1 and 4.0). The cytokine-induced reexpression of jagged-1 and delta-1, and of other genes potentially involved in the notch signaling pathway, raises the question of the putative effects of this pathway in differentiated ß-cells exposed to an autoimmune attack. Two possibilities are a contributory role for the loss of the differentiated ß-cell phenotype and prevention, in a paracrine fashion, of the differentiation of newly generated ß-cells formed as a compensatory response to progressive ß-cell destruction (64). Of note, in another model of autoimmune disease, multiple sclerosis in mice, cytokine-induced reexpression of the notch pathway prevents maturation of oligodendrocytes and hence efficient remyelination of axons in the multiple sclerosis lesions (65).
The relative concentration of pro- and antiapoptotic proteins from the Bcl-2 family may decide cellular outcome following some proapoptotic stimuli (35,36). The two Bcl-2-related genes most consistently induced by cytokines in the present experiments were Bid and Bak. Bid, a proapoptotic protein, is cleaved by caspase 8 and increases mitochondrial permeability by releasing Bax-like factors from Bcl-2 and stimulating the oligomerization and membrane insertion of Bax and/or Bak (36). Bax forms tetrameric channels in the outer membrane of the mitochondria, allowing the release of apoptogenic factors such as cytochrome c from the mitochondrial intermembrane space (35,36). Many forms of cell death require either Bax or Bak (66). The proapoptotic activity of Bak is mostly neutralized by binding to Bcl-xL, whereas Bax is inhibited to a major extent by Bcl-2 (35,36). Taking this into account, we examined the ratios of Bak/Bcl-xL and Bax/Bcl-2 expression in cytokine-treated cells, utilizing the absolute values of expression obtained in the arrays (data not shown). The ratios of Bak/Bcl-xL showed a sharp increase after 4 h (13.2 and 15.1, respectively, as compared with 2.7 and 4.1, respectively, in control; n = 2 similar experiments), returning to basal levels at later time points. On the other hand, cytokines did not change the ratios Bax/Bcl-2 at any of the different time points studied (data not shown) and also failed to decrease Bcl-2 expression. Two other genes found upregulated in the initial hours of cytokine exposure and potentially related to apoptosis are caspase 3 and VDAC. Caspase activation is partially regulated at the transcriptional level, and upregulation of caspases contributes to cell death in chronic neurological diseases (67). VDAC is located in the outer mitochondrial membrane, and participates in the formation of the mitochondrial permeability transition pore, allowing the release of proapoptotic molecules and the sequential activation of caspases 9 and 3 (35,36). The observed early (24 h) upregulation of caspase 3, VDAC, and the ratio of the expression of Bak/Bcl-xL may predispose the ß-cells to enter the apoptosis program, pending additional stimuli.
To further examine the general pattern of cytokine-modified genes, all mRNAs described in online supplement S1 were reclassified based on their temporal pattern of gene expression, using the k-means clustering method. K-means is an efficient clustering method to process large datasets (24), and it has been successfully used to determine time-course gene expression data in synchronized yeast (15) and in mouse adipocytes (68). Genes related to signal transduction and transcription factors were present in all clusters at similar proportions (mostly around 1020% of the total number of genes). This homogeneous dispersion is puzzling; we expected transcription factors to be mostly present in "early induced" clusters (12 h). If we bear in mind, however, that rodent ß-cells continuously exposed to cytokines go through different functional phases, including stimulation in the first 13 h, functional inhibition after 68 h (69) and progressive cell damage after 812 h (1), it becomes evident that ß-cells will need to continuously activate diverse transcription factors and signal transduction mechanisms to adapt to the continuous changes in internal homeostasis induced by cytokines. We also observed that two of the clusters, namely clusters 5 and 10, contain many genes previously described as NF-B dependent (10). For instance, we have shown by detailed promoter studies that NF-
B regulates transcription of iNOS (present in cluster 5) (25), MnSOD (70) (present in cluster 10), and MCP-1 (26) (present in cluster 10). Moreover, the chemokine cytokine-induced neutrophil chemoattractant-1 and the cytokine IL-15, whose expression is at least partially prevented by an NF-
B blocker in ß-cells (10), are also present in these clusters, as is also the case for IL-1
, tumor necrosis factor-ß, and cyclooxygenase-2, three genes shown to be NF-
B dependent in other tissues (71). Temporal coexpression studies may provide useful information on the nature of the transcription factors regulating groups of genes relevant for ß-cell fate, and we intend to perform "in silico" analysis (72) to search for binding sites for key transcription factors present in genes from the different clusters.
We have presently conducted the first systematic time course microarray and cluster analysis of cytokine-exposed insulin-producing cells in the presence or absence of the iNOS blocker NMA. The results obtained increased by more than twofold the number of known cytokine-modified genes, and also provide the first comprehensive analysis of cytokine-induced and NO-dependent mRNAs in insulin producing cells. This collection of genes and gene clusters is an exciting resource for researchers interested in understanding the functional inhibitory and proapoptotic effects of cytokines in ß-cells. Moreover, these data provide novel and often surprising insights into the molecular patterns triggered in ß-cells faced with a protracted immune assault.
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ACKNOWLEDGMENTS |
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We gratefully acknowledge Dr. Y.M. Feng for help with cell culture and Drs. M. Cnop, H. Heimberg, and C. Van Huffel for helpful discussions. We thank the personnel from the Laboratory of Experimental Medicine, ULB: M.A. Neef, J. Schoonheydt, M. Urbain, and G. Vandenbroeck for technical assistance and C. Demesmaeker for secreterial help.
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FOOTNOTES |
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Address correspondence reprint requests to Décio L. Eizirik, Laboratory of Experimental Medicine CP 618, Université Libre de Bruxelles, Route de Lennik, 808, B-1070, Brussels, Belgium. E-mail: deizirik{at}ulb.ac.be
Received for publication May 15, 2003 and accepted in revised form August 19, 2003
AS, argininosuccinate synthetase; EST, expressed sequence tag; GADD, growth arrest and DNA damage; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GTP, guanosine triphosphate; GTPCH, GTP cyclohydrolase I; HMG, 3-hydroxy 3-methylglutaryl; hsp, heat shock protein; IFN, interferon; IL, interleukin; iNOS, inducible nitric oxide synthase; IP-10, interferon inducible protein-10; MCP, macrophage chemoattractant protein, MHC, major histocompatibility complex; MIP-3, macrophage inflammatory protein-3
; MnSOD, manganese superoxide dismutase; NF, nuclear factor; NMA, NG-monomethyl-L-arginine; Pdx-1, pancreatic duodenal homeobox factor-1; PI, propidium iodide; SERCA2b, sarco(endo)plasmic reticulum Ca+2 ATPase type 2 b; VDAC, voltage-dependent anion channel
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REFERENCES |
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