Analysis of Transcriptional Factors and Regulation Networks in

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Analysis of Transcriptional Factors and Regulation Networks in Patients with Acute Renal Allograft Rejection Duojiao Wu,†,‡,§ Dong Zhu,†,§,| Ming Xu,†,§,| Ruiming Rong,†,| Qunye Tang,†,| Xiangdong Wang,*,†,‡,⊥ and Tongyu Zhu*,†,| Fudan University Zhongshan Hospital, Shanghai, China Received May 15, 2010

Acute rejection (AR) remains a major clinical challenge, leading to the development of chronic renal allograft failure. The aim of the present study was to explore potential transcriptional factors and regulation networks in the disease to predict the occurrence and process of AR and understand potential strategies to prevent from the disease. Three-hundred fifty-two patients with renal failure had kidney transplantation during March 2006 and March 2010, of which 85 suffered from AR. Plasma from 13 patients with kidney transplantation was collected, of which 5 were from patients with AR and 8 from those without AR. Among the 179 proteins identified by using iTRAQ labeling and quantitative proteomic technology, 66 proteins were at least 2-fold different between patients with or without AR. The results demonstrated that the dominant processes and responses were associated with inflammation and complement activation in AR. A number of transcription factors were identified in AR patients, including nuclear factor-κB, signal transducer and activator of transcription 1, signal transducer and activator of transcription 3. The analysis of transcription regulation networks suggested that the cross-talks among these key transcription factors might contribute to the acute response and coagulation pathway. Thus, our study provides a new description and insight into the molecular events in AR and potential strategies for identifying diagnostic biomarkers. Keywords: acute rejection • proteome regulation network • transcriptional factor • kidney transplantation

Introduction With a progression in new immunosuppressive agents, shortterm and long-term survivals of kidney allograft were improved over the last 15 years.1 However, the long-term survival rate after kidney transplantation remains low by 50%.2 Among the immunologic and nonimmunologic factors that contribute to a continuous deterioration of allograft function, acute rejection (AR) remains a major cause responsible for the development of chronic renal allograft failure and presents as the indicator of renal dysfunction and failure. The more successful prevention and treatment of patients with AR are still expected.3 Measurements, for example, mRNA in urinary lymphocytes, flow cytometry, and alloreactive peripheral blood lymphocytes, were considered to have their limitations to provide the description of potential regulation networks in AR.4 There were few studies to investigate transcriptional factors and regulation networks-related understanding of AR, although some studies compared proteomic profiles of clinical samples. * To whom correspondence should be addressed. Tongyu Zhu, e-mail, [email protected]; tel, 86-21-64038038; fax, 86-21-64038038. Xiangdong Wang, e-mail, [email protected]; tel, 86-21-64041990; fax, 86-2164041990-2295. † Shanghai Key Laboratory of Organ Transplantation. ‡ Biomedical Research Center. § These authors contributed equally to this manuscript. | Department of Urology. ⊥ Department of Respiratory Medicine. 10.1021/pr100473w

 2011 American Chemical Society

The present study aimed at exploring transcriptional factors and regulation networks involved in the disease, using iTRAQ labeling and quantitative proteomic technology and very rigid patient selection criteria, including allograft histology, allograft function, and clinical course. Clusters of up- and downregulated protein profiles were submitted to MetaCore for the construction of transcriptional factors and regulation networks. By identifying transcriptional factors and regulation networks, a new insight was proposed to the molecular pathogenesis of AR, to identify novel therapeutic targets or clinical biomarkers to intervene the pathological process.

Materials and Methods Plasma Samples. Three-hundred fifty-two patients with renal failure had kidney transplantation during March 2006 and March 2010, of which 85 suffered from AR. Plasma from 13 patients with kidney transplantation was collected, of which 5 were from patients with AR before the therapy for rejection and 8 were from those without AR. Potassium-EDTA plasma samples were collected intravenously and stored frozen at -80 °C. All samples were obtained with informed consent and ethics approval by the ethics board of Fudan University Zhongshan Hospital. Transplanted Patients. Patients were treated with a triple immunosuppressive regimen consisting of calcineurin-inhibitor (cyclosporine or tacrolimus), prednisone, and mycophenolatemofetil. Five patients with AR (Banff 2007’ IB to III5) before Journal of Proteome Research 2011, 10, 175–181 175 Published on Web 09/03/2010

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Table 1. Patient Characteristics

variable

Recipient gender, n (% female) Recipient age, mean ( SD Post-Tx time, median (month, range) Biopsy time Week posttransplant, median (range) Creatinine at biopsy [µmol/L], mean ( SD Hemoglobin at transplantation [g/L], mean ( SD PRA > 20% (%) HLA-mismatches, median (range) Deceased donor, n (%) Rejection type Acute cellular rejection, n Acute humoral rejection, n Banff IB (severe tubulitis), n (%) Banff IIA (moderate arteritis), n (%) Banff IIB (severe arteritis), n (%) Banff III, n (%) CCR2 (%) Immunosuppressive treatment FK506/Pred/MMF, n (%) CsA/Pred/MMF, n (%)

stable transplant (n ) 8)

acute clinical rejection (n ) 5)

1 (12.50) 38 ( 11 17 (7-32)

2 (40) 34 ( 11 18 (7-32)

24 (12-51)

10 (4-12)

97 ( 10

144 ( 23a

98 ( 36

100 ( 20

0 2 (0-3)

0 1 (1-2)

1 (12.50)

1 (20) 5 0 1 (20) 2 (40)

44.76 ( 4.99

1 (20) 1 (20) 58.50 ( 12.76

2 (25) 6 (75)

2 (40) 3 (60)

a P < 0.05 versus stable transplant group. Tx, Transplantation; HLA, Human leukocyte antigen; PRA, Panel reactive antibody; CCR2, Creatinine reduction ratio; FK506, Tacrolimus; Pred, Prednisone; MMF, Mycophenolate; CsA, Cyclosporine.

the treatment of the rejection and 8 patients without AR at Zhongshan Hospital (Shanghai, China) were recruited in the present study. AR was confirmed by biopsy specimens evaluated by an independent, blinded pathologist, while patients without AR were followed-up for more than 6-month with stable renal function. A biopsy specimen was judged adequate when g7 glomeruli and g1 vessel were present.6 The day of surgery was considered day 0. The following formulas7 were used for creatinine reduction ratio (CRR2) between day 1 and day 2: CRR2 (%) ) ([C1 - C2] × 100/C1 The allograft function, the clinical course, and the allograft biopsy result of two patient groups were extracted as Table 1. Immunodepletion of High-Abundance Proteins. High abundance proteins in plasma were depleted using Agilent Multiple Affinity Removal Column - Human 14 (MARS) kit (Agilent Technologies). The proteins from the immunodepletion column flow through were ultrafiltered (30kD) and then precipitated overnight in acetone at -20 °C. After acetone precipitation, the protein pellets were resuspended in dissolution buffer. The proteins in each depleted sample were measured by Bradford Protein Assay. Protein Digestion and Labeling with iTRAQ Reagents. The proteins of each sample were denatured, alkylated, and digested with sequencing-grade modified trypsin with a proteinto enzyme ratio of 20:1 at 37 °C overnight and then labeled with the iTRAQ tags as follows: patients with AR, iTRAQ 119 (IT119) and without AR iTRAQ 121 (IT121). The labeled digests were then mixed and dried. The analytic processes were 176

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repeated twice, including plasma depletion, protein digestion, iTRAQ labeling, SCX fractionation, and LC-MS/MS analysis. Off-line 2D LC-MS/MS. The combined peptide mixture was fractionated by strong cation exchange (SCX) chromatography on a 20AD high-performance liquid chromatography (HPLC) system (Shimadzu, Kyoto, Japan). The concentrated iTRAQ labeled sample was added to loading buffer (10 mM KH2PO4 in 25% acetonitrile, pH 2.6) and loaded onto the column. Buffer A was identical in composition to the loading buffer, and buffer B was buffer A containing 350 mM KCl. Separation was performed using a linear binary gradient of 0-80% buffer B in buffer A at a flow rate of 200 mL/min for 60 min. The absorbance at 214 and 280 nm was monitored and a total of 32 SCX fractions was collected along the gradient. These fractions were dried down by the rotary vacuum concentrator, dissolved in buffer C (5% acetonitrile, 0.1% FA) and analyzed on a QSTAR XL system (Applied Biosystems) interfaced with a 20AD HPLC system (Shimadzu). Peptides were separated on a Zorbax 300SB-C18 column (Agilent Technologies). The HPLC gradient was 5-35% buffer B (95% acetonitrile, 0.1% FA) in buffer A (5% ACN, 0.1% FA) at a flow rate of 0.3 mL/min for 90 min. Survey scans were acquired from m/z 400-1800 with up to four precursors selected for MS/MS from m/z 100-2000.Each SCX fraction was analyzed in duplicate. Data Analysis. The MS/MS spectra were extracted and searched against the International Protein Index (IPI) database (version 3.45, HUMAN) using ProteinPilot software (version 3.0, revision 114732, Applied Biosystems). The software reports two types of scores for each protein: unused ProtScore and total ProtScore. The unused ProtScore is a measurement of all the peptide evidence for a protein that is not better explained by a higher ranking protein. Using the following criteria to consider a protein for further statistical analysis: unused ProtScore >1.3 with at least one peptide with 95% confidence per repetition. The candidate proteins were examined in the Protein ID of the Protein Pilot software. Protein expression ratios were computed on basis of the peak area ratios of the peptides accounting for the same protein. The bias correction algorithm was applied to correct for unequal mixing during the combination of the different labeled samples, based on the assumption that most proteins do not change in expression. All quant ratios (both the average ratio for proteins and the individual peptide ratios) were corrected for the bias. Analysis of Regulation Networks. MetaCore (GeneGo, St. Joseph, MI) is an integrated software suite for functional analysis of experimental data.8 To functionally annotate the differentially expressed proteins identified in this study, the proteins (g2.0fold change) were entered into GeneGo’s MetaCore for analysis. The biological process enrichment was analyzed according to Gene Ontology processes. The genes encoding expressed overabundant proteins were used as the input list for generation of regulation networks using Transcription Regulation algorithm which generated subnetworks centered on transcription factors. Subnetworks are ranked by a P-value and interpreted in terms of Gene Ontology. For every transcription factor (TF) with direct target(s) in the root list, this algorithm generated a subnetwork consisting of all shortest paths to this TF from the closest receptor with direct ligand(s) in the root list.

Results The patients with AR had shorter post-transplantation biopsy time and higher mean serum creatinine level at the time of the renal allograft biopsy as compared with those without AR.

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Patients with Acute Renal Allograft Rejection

Figure 1. Top biologic processes of expressed proteins in AR are shown. Each enumerated annotation is assigned by the enrichment score represented as P value. The table lists enumerated annotations and fractions of identified proteins that belong to that particular biologic process category.

There was no significant difference in other measurements between patients with or without AR (Table 1). Data from the two MS repetitions identified 9268 distinct peptides corresponding to 179 unique proteins according to the parameter set as described above. The original data from the measurement is available in the Supporting Information. The two MS repetitions shared an overlap of 74% of the total unique protein of the combined data. And 155 proteins were identified with a global false discovery rates from fit values of 1%. A total of 66 proteins showed an abundance change of at least 2-fold in patients with AR as compared to those without AR, of which 41 were overexpressed and 25 were underexpressed. To further interpret the differentially expressed proteins in AR, we used MetaCore mapping tool to analyze and build the regulation networks involved in these differentially expressed proteins. Multiple sets of proteins were mapped into different process pathways, including acute inflammation response, complement activation, and regulation of response to stimulus consistent with the acute rejection hypothesis. The top five biological processes were associated with inflammatory processes, as shown in Figure 1. To further discover unknown pathways and associations involved in AR, clusters of up- and down-regulated proteins were submitted to MetaCore and subjected to the Analyze Networks of Transcription Regulation and Transcriptional Factor algorithm. The algorithms generated a set of regulation networks from the input list. The top five regulation networks were composed of 27-43 proteins altered in expression with p value ranging from 5 e-106 to 6 e-65. These top five networks included the regulation initiated through activation of nuclear factor-κB (NF-κB), signal transducer and activator of transcription 1(STAT1), STAT3, retinoid X receptor alpha (RARalpha). The proteins were connected through association with other objects, including transcription factors (e.g., NF-Y), binding proteins (e.g., Fibrinogen beta), receptors (e.g., insulin-like growth factor-2 receptor), enzymes (e.g., kinases, phosphatases, proteases, and GTPases), and coregulated proteins. All the interactions were based on MetaCore’s curated database. Therefore, any objects that contain the most connections to the root objects may represent key regulators in the clustered proteins. In Figure 2a, STAT3 an acute-phase response factor had positive effects on upregulating Fibrinogen alpha, complement 3, and receptor ligands Alpha-2-macroglobulin (A2M). STAT3, the key regulator of the network with the major role in

orchestrating observed changes of protein abundance. In addition, NF-κB and STAT1 were also noticed as key network objects, as shown in Figures 2 and 3. The differentially expressed proteins belonged to a diverse set of pathways and processes which may help to illustrate the mechanisms of AR. For example, a set of proteins related with coagulation process including fibrinogen alpha and beta chain precursors, prothrombin, coagulation factor V and X precursor were overexpressed as illustrated in Figure 4. These proteins in proteomics analysis were consistent with the findings from clinical tests of coagulation in patients (Table 2). A number of important cross-talks in AR were observed, for example, among SMAD3 and SP1 (Supplement Figure 1), among STAT3, CCR-alpha, and ETS1 (Supplement Figure 2), among ETS1, c-Myc, CREB1, and STAT5B (Supplement Figure 3), among ESR-related factors (Supplement Figure 4), among CREB1, c-Myc, STAT1, and thrombin (Supplement Figure 5), among STAT3-related factors (Supplement Figure 6), among AP1-related factors (Supplement Figure 7), and among RARbeta/RXR-alpha, c-Myc, and thrombin (Supplement Figure 8).The GO processes analysis described that the most of transcription factors were involved in response to external stimulus and inflammatory response (Supplement Table 1). The transcription factor subnetwork with most significant p value was composed by NF-Y, alpha-2/beta-1 integrin, thrombomodulin, A2M receptor and others (Figure 5). The GO process showed the responses to external stimulus were 56.9%, to wounding 45.1%, to stress 64.7%, and to stimulus 78.4%.

Discussion The present study explored a new orientation to investigate molecular mechanism and biomarkers of AR and initiated the understanding of transcriptional factors and regulation networks in the disease. The construction of complex networks provides a new framework for understanding the molecular basis of physiological or pathophysiological states.9 We found that identifying alterations in protein expression and mapping these to transcription networks provided a new insight into regulation mechanisms of AR. STAT3, STAT1 and NF-κB as key objects and their cross-talk and overlapping in the regulation network were found to play a significant role in regulation of immune coagulation process during AR. It seems that the molecular cross-talk may be responsible for the communication Journal of Proteome Research • Vol. 10, No. 1, 2011 177

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Figure 2. Representative transcription regulation networks based on differentially expressed data in patients with acute rejection using MetaCore network software and the Analyze Network algorithm. Two representative networks are shown: (a) the STAT3 network and (b) the STAT1 network. Red circles in the right corner of a gene indicate up-regulation and blue circles down-regulation.

Figure 3. Representative transcription factor NF-κB network based on expressed data in patients with acute rejection using MetaCore network software and the Analyze Network algorithm, as compared with those without acute rejection after kidney transplantation. Red circles in the right corner of a gene indicate up-regulation and blue circles down-regulation.

between hypoxia-responsive transcriptional pathways and inflammation and the importance in the etiology of inflammatory diseases.10 We found a set of proteins related with coagulation process that were overabundant in AR patients. Meanwhile, alpha-2178

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antiplasmin precursor was under-expressed which is responsible for inactivating plasmin, an important enzyme that participates in fibrinolysis and degradation of various other proteins.11 Our data demonstrated that a hypercoagulabale state existed in acute renal rejection patients, evidenced by high

Patients with Acute Renal Allograft Rejection

research articles results from the present study indicate that anticoagulation treatment should be considered after the transplantation, while the thrombotic situation of patients should be measured routinely prior to the transplantation.

Figure 4. Up-regulated proteins in patients with acute rejection, as compared with those without acute rejection after kidney transplantation, are color labeled (green) in the coagulation cascade. Table 2. Testing of Coagulation in Patients variable

stable transplant (n ) 8)

acute clinical rejection (n ) 5)

PT(second) APTT(second) Fibrinogen(mg/dl)

11.18 ( 0.70 26.63 ( 3.11 249.25 ( 39.69

11.20 ( 0.37 30.70 ( 5.41 335.25 ( 9.91a

a P < 0.05 versus stable transplant group. PT: Prothrombin time; APTT: Activated partial thromboplastin time.

circulating levels of fibrinogen in AR patients. Hypercoagulation in AR was also evidenced by the occurrence of fibrin aggregation or occlusive glomerulitis after kidney transplantation. The

STAT protein is a highly conserved STAT family of DNA binding transcription factors that were initially identified in the interferon signaling system.12,13And these proteins contribute to various biological processes, including the immune response, hematopoiesis, and oncogenesis. We found that the upstream of STAT3 signaling pathway was regulated by a family of tyrosine kinases (e.g., JAK1, JAK2) with both positive and negative effects on cell growth and proliferation.14,15 STAT3 an essential protein in the immune system has different functions dependent upon immune cell types, for example, stimulating proliferation of CD4+ T cells, or generating the T celldependent IgG response.16 Our results demonstrated that STAT3 was a key regulator involved in the pathologic process of AR through inducing the imbalance between coagulation and fibrinolysis. STAT3 activated the expression of Fibrinogen alpha, beta, and gamma in AR after kidney transplantation. Fibrinogen as a soluble plasma glycoprotein may be elevated in any form of inflammation, while hyperfibrinogenemia in patients with kidney transplantation could deteriorate renal function and predict high-risk cardiovascular diseases.17 We found that STAT3 could be activated by thrombin which induced further up-regulation of the fibrinogen expression and such positive feedback accelerated the production of fibrin, responsible for the development of acute renal failure.18 The similar effects of transcription regulation were found in STAT1. It is also possible that other transcriptional factors, for example, human fibrinogen-like protein 2, may be responsible

Figure 5. Representative cross-talk transcription factors networks based on expressed data in patients with acute rejection using MetaCore network software and the Analyze Network algorithm, as compared with those without acute rejection after kidney transplantation. The network showed transcription factors including NF-Y, NF-κB, and P53 that interplay to regulate inflammation and immune response. Thick cyan lines indicate the fragments of canonical pathways. Up-regulated genes are marked with red circles and down-regulated with blue circles. The “checkerboard” color indicates mixed expression for the gene between files or between multiple tags for the same proteins. Journal of Proteome Research • Vol. 10, No. 1, 2011 179

research articles for the immune injury in AR, since it was overexpressed mainly in renal tubule cells and involved in the infiltration of lymphoid cells.19 The down-regulation of A2M by NF-κB found in AR may be another process responsible for the overexpression of thrombin, since A2M has negative effect on thrombin expression. Upregulated thrombin inhibited NF-κB through activating protein C. Thippegowda PB, et al.20 reported that Ca(2+) influx signal prevents thrombin-induced apoptosis by inducing NF-κB-dependent A20 expression in endothelial cells. Our data showed that STAT3 could be activated by thrombin, leading to further upregulation of the fibrinogen expression. It was found that the expression of the gamma-fibrinogen gene was mainly controlled by STAT3 activation and then negatively regulated by the extent of interleukin-1betamediated NF-κB activity.21 There were further cross-talks among these key regulators during inflammation and immune response process of AR. NF-κB, STAT1, and STAT3 were suggested to regulate the coagulation system, which further testify that immune coagulation is a major contributor to the pathogenesis of graft rejection. The network analysis also demonstrated that nuclear transcription factor Y (NF-Y), NF-κB, c-Jun, and SMAD2 played specific regulation roles in AR by activating the A2M receptor, alpha-2/beta-1 integrin, and interleukin-1 receptor (type I). NF-Y plays a critical role in tissue-specific major histocompatibility complex class II gene transcription.22 Our data first provided the evidence that NY-Y was involved in acute reaction after kidney transplantation. Increased glomerular deposition of vWF was reported in renal allografts,23 indicating increased constitutive secretion of vWF from endothelial cells as a potential mechanism of chronic rejection. Our results showed that plasma level of vWF was 8-fold higher in AR, suggesting vWF may also play an important role in AR. NF-Y can function both as a repressor and activator of vWF transcription and its function may be modulated through its DNA binding sequences.24 The evidence that NF-Y controlled vWF transcription suggests NF-Y may play a critical role in acute rejection process by regulating vWF expression. The generation of representative subnetworks in AR will lead to identification of candidates of biomarkers and understanding of the AR pathogenesis. The transcription-level regulation information from the present study provided an evidence for intervention during AR, such as inhibition of STAT3. PIAS3, the main cellular inhibitor of STAT3, has been described as a modulator of DNA binding transcription factors.25,26 Regulation networks in AR should be further explored for potential targets of prevention and therapy.

Conclusions The present study described proteome profiling of the plasma from patients with AR after kidney transplantation, using the combination of proteomics and clinical findings based on allograft histology, allograft function, and measurements. Furthermore, the construction of transcription regulation networks identified the key transcription regulators in AR, including NF-κB, STAT1, and STAT3. The pathway analysis suggested that cross-talks among those key regulators manipulated the acute response including inflammation and immune coagulation in AR. Thus, the results from the present study provide a clear description of transcriptional factors and 180

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Wu et al. regulation networks in AR which may lead to new strategies for diagnosis and treatment.

Acknowledgment. This work was supported by Key Projects in the National Science & Technology Pillar Program in the Eleventh Five-year Plan Period (2008BAI60B04), grants from Science and Technology Commission of Shanghai Municipality (grant number 074119634, 08410701300, 09DZ2260300, 09411952000, 08PJ1402900, 08DZ2293104, 09540702600), Shanghai Leading Academic Discipline Project, NO B115, and Fudan University Distinguished Professor Grant. Note Added after ASAP Publication. This article was published ASAP on September 22, 2010. A file was added to the Supporting Information with original data as requested by a reader. This file is mentioned in the second paragraph of the Results section and in the Supporting Information paragraph. The correct version of the article was published on November 15, 2010. Supporting Information Available: Original data. This material is available free of charge via the Internet at http:// pubs.acs.org. References (1) Hariharan, S.; Johnson, C. P.; Bresnahan, B. A.; et al. Improved graft survival after renal transplantation in the United States, 1988 to 1996. N. Engl. J. Med. 2000, 342, 605–612. (2) Li, C.; Yang, C. W. The pathogenesis and treatment of chronic allograft nephropathy. Nat. Rev. Nephrol. 2009, 5, 513–519. (3) McLaren, A. J.; Fuggle, S. V. Welsh, K. I.Chronic allograft failure in human renal transplantation. Ann. Surg. 2000, 232, 98–103. (4) Kotsch, K.; Mashreghi, M. F.; Bold, G.; et al. Enhanced granulysin mRNA expression in urinary sediment in early and delayed acute renal allograft rejection. Transplantation 2004, 77, 1866–1875. (5) Solez, K.; Colvin, R. B.; Racusen, L. C.; et al. Banff 07 classification of renal allograft pathology: updates and future directions. Am. J. Transplant. 2008, 8, 753–760. (6) Schaub, S.; Rush, D.; Wilkins, J. Proteomic-Based Detection of Urine Proteins Associated with Acute Renal Allograft Rejection. J. Am. Soc. Nephrol. 2004, 15, 219–227. (7) Govani, M. V.; Kwon, O.; Batiuk, T. D.; et al. Creatinine Reduction Ratio and 24-h Creatinine Excretion on Posttransplant Day Two: Simple and Objective Tools to Define Graft Function. J. Am. Soc. Nephrol. 2002, 13, 1645–1649. (8) Nikolsky, Y.; Kirillov, E.; Zuev, R.; et al. Functional analysis of OMICs data and small molecule compounds in an integrated “knowledge-based” platform. Methods Mol. Biol. 2009, 563, 177– 196. (9) Schadt, E. E.; Friend, S. H.; Shaywitz, D. A. A network view of disease and compound screening. Nat. Rev. Drug Discov. 2009, 8, 286–295. (10) Safronova, O.; Morita, I. Transcriptome remodeling in hypoxic inflammation. J. Dent. Res. 2010, 89, 430–444. (11) Boffa, M. B.; Wang, W.; Bajzar, L.; et al. Plasma and Recombinant Thrombin-activable Fibrinolysis Inhibitor (TAFI) and Activated TAFI Compared with Respect to Glycosylation, Thrombin/Thrombomodulin-dependent Activation, Thermal Stability, and Enzymatic Properties. J. Biol. Chem. 1998, 273, 2127–2135. (12) Darnell, J. E., Jr.; Kerr, I. M.; Stark, G. R. Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. Science 1994, 264, 1415–1421. (13) Fu, X. Y.; Schindler, C.; Improta, T.; et al. The proteins of ISGF-3, the interferon alphainduced transcriptional activator, define a gene family involved in signal transduction. Proc. Natl. Acad. Sci. U.S.A. 1992, 89, 7840–7843. (14) Mu ¨ller, M.; Briscoe, J.; Laxton, C.; et al. The protein tyrosine kinase JAK1 complements defects in interferon-alpha/beta and -gamma signal transduction. Nature 1993, 366, 129–135. (15) Ihle, J. N.; Kerr, I. M. Jaks and Stats in signaling by the cytokine receptor superfamily. Trends Genet. 1995, 11, 69–74. (16) Fornek, J. L.; Tygrett, L. T.; Waldschmidt, T. J.; et al. Critical role for Stat3 in T-dependent terminal differentiation of IgG B cells. Blood 2006, 107, 1085–1091.

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(22) Currie, R. A. Biochemical Characterization of the NF-Y Transcription Factor Complex during B Lymphocyte Development. J. Biol. Chem. 1998, 273, 18220–18229. (23) Lagoo, A. S.; Buckley, P. J.; Burchell, L. J.; et al. Increased glomerular deposits of von Willebrand factor in chronic, but not acute, rejection of primate renal allografts. Transplantation 2000, 70, 877– 886. (24) Peng, Y.; Jahroudi, N. The NFY transcription factor functions as a repressor and activator of the von Willebrand factor promoter. Blood 2002, 99, 2408–2417. (25) Liu, B.; Yang, R.; Wong, K. A.; et al. Negative regulation of NF-kB signaling by PIAS1. Mol. Cell. Biol. 2005, 25, 1113–1123. (26) Shuai, K. Regulation of cytokine signaling pathways by PIAS proteins. Cell Res. 2006, 16, 196–202.

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