ABTEILUNG INNERE MEDIZIN II Schwerpunkt Endokrinologie und Diabetologie

A COMPARISON OF GENE EXPRESSION IN TOLERATED AND REJECTED ISLET GRAFTS BY GENE MICROARRAY ANALYSIS

VERGLEICH DER GENEXPRESSION IN TOLERIERTEN UND ABGESTOSSENEN INSELTRANSPLANTATEN MITTELS MICROARRAY-ANALYSE

aus der MEDIZINISCHEN UNIVERSITÄTSKLINIK ABTEILUNG INNERE MEDIZIN II Schwerpunkt Endokrinologie und Diabetologie der ALBERT-LUDWIGS-UNIVERSITÄT FREIBURG I. BR. in Kooperation mit dem DIABETES INSTITUTE FOR IMMUNOLOGY AND TRANSPLANTATION DER UNIVERSITY OF MINNESOTA

INAUGURAL-DISSERTATION ZUR ERLANGUNG DES MEDIZINISCHEN DOKTORGRADES DER MEDIZINISCHEN FAKULTÄT DER ALBERT-LUDWIGS-UNIVERSITÄT FREIBURG I. BR.

2003

TOBIAS BERG BERNKASTEL-KUES DEKAN: Prof. Dr. med. Josef Zentner

1. GUTACHTER: Prof. Dr. med. Martin Reincke

2. GUTACHTER: Prof. Dr. med. Lars Christian Rump

JAHR DER PROMOTION: 2005

FOR

BRITTA A comparison of gene expression in tolerated and rejected islet grafts by gene microarray analysis

INTRODUCTION ...... 6

MATERIAL AND METHODS ...... 11

Animals...... 11

Transplant protocol...... 11 Treatment ...... 11 Bone marrow transplantation ...... 11 Islet isolation and transplantation...... 12

Islet graft function and harvest...... 13

Microarray hybridization...... 13 RNA isolation...... 13 Reverse transcription...... 14 In-vitro transcription ...... 15 Array hybridization, scanning & analysis...... 15

Real-time RT-PCR to monitor gene expression ...... 18

Gene expression analysis by RNase Protection Assay ...... 20

RESULTS...... 21

Course of transplantation and histology ...... 21

General overview of microarray analysis result...... 22

T cell genes ...... 28

Costimulation and adhesion molecules ...... 29

Complement system and innate immunity...... 29

Cytokines and cytokine receptors...... 30

Chemokines and chemokine receptors...... 32

Confirmation of microarray results ...... 34

DISCUSSION ...... 37

SUMMARY...... 41

ZUSAMMENFASSUNG ...... 42 REFERENCE LIST...... 43

ACKNOWLEDGEMENTS ...... 52

PUBLICATIONS...... 54

LEBENSLAUF ...... 55

Introduction Seite -6-

Introduction

Type I diabetes is a serious condition that affects approximately one million individuals in the United States (55) and about 174,000 people (2000) in Germany (43). Other estimates are even higher especially if latent autoimmune diabetes in adults is considered as well. It has long been known that most cases of type I diabetes are caused by a T cell-regulated autoimmune disease with immune-mediated destruction of the insulin-producing β-cells in the islets of Langerhans in the pancreas. Signs for this are the development of islet-antigen specific antibodies in the serum of type I diabetes patients (9) and the incidental transfer of diabetes from one individual to another by bone marrow transplantation (31). The survival of a patient with type I diabetes depends on a lifelong replacement therapy with insulin, so that until insulin became available, type I diabetes was a lethal disease (7). Modern intensified insulin replacement therapy can prolong the life-span of patients with type I diabetes, but their life expectancy is still limited by the earlier or later occurring onset of diabetes complications. Even with intensified insulin therapy glucose control cannot be completely normalized and glucose control is one of the most important factors for the long-term prognosis of a type I diabetic patient (4; 61). An additional approach to the treatment of type I diabetes is prancreas transplantation which has first been performed by Richard Lillehei and William D. Kelly at the University of Minnesota in 1966 (28) and has developed into a successful method for the treatment of diabetic patients under certain circumstances such as hypoglycemia unawareness or end-stage renal disease, where a combined pancreas-kidney transplant has advantages in comparison to kidney transplantation alone (20; 59). A different approach for restoring islet function in a diabetic patient is the transplantation of isolated islets, which is a promising perspective for the cure of type I diabetes without invasive surgical procedures.

An islet transplant is basically performed by harvesting a donor pancreas and extracting islets from it. The islet extraction for the purpose of human transplantation is today done using the automated method for pancreatic islet isolation (30; 47). A special blend of collagenase enzyme is pumped down the pancreatic duct in a controlled perfusion system designed to cleave islets from their acinar restraining matrix. The pancreas is then transferred to a recirculating Ricordi digestion chamber. After extensive washing, islets are purified on a continuous density gradient in a cell apheresis centrifuge system. The transfer of the islets into the patient can be carried out without surgery under local anesthetic using radiological guidance. Islets are implanted into the portal vein where they embolize to the liver, nest and Introduction Seite -7- develop a new blood supply. The procedure can be completed as a day case, with patients discharged from hospital in less than 24 hours (54).

Donor Recipient

Figure 1: Systematic overview of islet transplantation procedure. Steps of the process are the procurement of the pancreas (usually from a brain-dead cadaver donor), the islet isolation procedure with enzymatic and mechanic disruption of the pancreas followed by a density gradient separation and finally the transplantation by portal vein infusion with ultrasonographic or radiological guidance or in a surgical procedure. Figure from the homepage of the Diabetes Institute for Immunology and Transplantation. (www.diabetesinstitute.org)

Although it has been successfully used as autotransplantation procedure for the treatment of patients undergoing total pancreatectomy (48) and despite occasional successes (3), islet transplantation for type I diabetes had not been a very successful procedure for quite a long time. In the transplants performed between 1990 and 1997 only 37 % of all transplanted islet grafts survived one year with insulin independence in 11 % of all transplanted patients. In the transplants from the period from 1998 to 2000 there was an improvement in graft survival to 55 % at one year, but insulin independence was only achieved in 14 % of all transplanted patients (10).

But recently the field of islet transplantation has made significant progress. A.M. James Shapiro and Jonathan R.T. Lakey in Edmonton achieved insulin independence and established normoglycemia in a very high percentage of patients with type 1 diabetes (53). Introduction Seite -8-

82% of them have maintained independence from insulin at the 1-year mark (49). The success can be explained by the use of a sufficient number of isolated human islets, which in this protocol requires the isolation of islets from between two and four donors, and the use of an improved immunosuppression protocol that contains no corticosteroids and only low doses of tacrolimus, thereby limiting the use of diabetogenic drugs in the islet transplant recipients. The initial success could be replicated in additional patients and with the same and other very similar protocols by other centers. Improvements in the isolation protocols such as the two- layer shipping protocol for the transport of a procured human pancreas, careful patient selection and the use of the humanized anti-T cell antibody hOKT3γ1 (Ala-Ala) for induction therapy made it possible to achieve insulin independence with the islets from one pancreas in more than half of all treated patients (26). With these results islet transplantation became the first example of a successful cell replacement therapy for a cell type outside the hematopoetic system.

Increasing the feasibility and applicability of islet transplantation as a viable clinical therapy for type 1 diabetes depends among other issues on developing approaches to limit or even discontinue immunosuppressive drug treatment. Limiting immunosuppressive treatment could be facilitated by the induction of donor-specific tolerance to islet grafts. Mixed chimerism achieved by engraftment of hematopoetic stem cells before, during or after organ transplantation could be one approach for donor-specific tolerance induction (56). This principle is known since the 1950s from groundbreaking experiments performed in mice by Peter Medawar and others (8). It has just recently made its way into clinical application in the successful treatment of myeloma patients with chronic kidney failure by successive bone marrow- and kidney-transplantation from the same donor without the need for permanent immunosuppression. (12)

To demonstrate that a similar model could also be applied in islet transplantation, Wu et. al. have established a model of donor-specific tolerance induction to MHC-matched allogeneic islet grafts in spontaneously diabetic NOD mice. The NOD mouse provides an interesting model for human type 1 diabetes because, as in the human disease, spontaneous diabetes development in this animal model is dependent on autoimmune mechanisms. (37) Tolerance in this model was achieved by simultaneous islet and bone marrow transplantation under nonmyeloablative and irradiation-free conditioning therapy (64). The presence of donor Introduction Seite -9- chimerism could be demonstrated by PCR and the transplanted mice remained normoglycemic with intact grafts for more than 100 days posttransplantation. Interestingly, mononuclear cell infiltration surrounding the islets was found not only in rejected grafts, but also in tolerated grafts. Similar observations have previously been made by other groups in other transplant settings (6; 14). The role of these infiltrated mononuclear cells surrounding islet grafts in tolerated recipients is of great interest and is also addressed in this work.

Another approach to a limitation of immunosuppression in organ transplantation could be the successful establishment of immune monitoring assays. If there was an assay that reliably predicted rejection, patients could be weaned off immunosuppression without endangering the graft. It has just recently been demonstrated that assays based on the monitoring of T cell effector functions could provide tools to achieve this aim. Monitoring of T cell effector functions in transplant models has been done based on standardized versions of immunological assays such as the ELISPOT assay (17) and also methods based on the monitoring of gene expression of certain T cell effector molecules by quantitative PCR (58).

The use of microarrays is an excellent approach for analyzing gene expression over large numbers of genes in parallel in a single experiment (5). Microarrays can generate a large amount of genetic information about biological mechanisms occurring at the same time, thereby helping to identify genes and pathways related to specific biologic responses and disease pathogenesis. The development of commercially available high density oligonucleotide arrays (35) in conjunction with the progress that has been achieved through the sequencing of both the human (39; 63) and the murine (19; 40) genome allows a better and better coverage of the whole genome. The use of microarrays provides a great improvement in comparison to traditional methods of global expression analysis such as differential display (34) or subtractive hybridization. It provides quantitative information on a large number of different genes in a much less labor-intensive way than traditional methods. Since using microarrays allows to analyze gene expression in an unbiased way, it potentially enables the detection of the really important changes in gene expression profiles including genes not previously thought of. In the context of human health and treatment, the knowledge gained from these types of measurements can help determine the causes and consequences of disease, how drugs and drug candidates work in cells and organisms, and what gene products Introduction Seite -10- might have therapeutic uses themselves or may be appropriate targets for therapeutic intervention. (36)

The microarray approach has already been used to screen gene expression in transplanted tissues undergoing acute rejection in human patients with kidney transplantation (2) and has been used to define subgroups of acute rejection with a different pathology and prognosis (52). In rodent models of heart transplantation, gene expression profiles in the acutely rejected grafts were studied to identify the genes specifically related to acute rejection (50; 51; 57). Those studies indicated that gene microarray analysis is useful for screening gene expression in transplanted grafts during acute rejection. However, gene microarrary analysis has not been used in an islet transplant model or a tolerance induction model so far.

In this study in the spontaneously diabetic NOD mouse model, gene expression was compared between rejected and tolerated islet grafts by using gene microarray analysis based on Affymetrix oligonucleotide arrays. Changes were confirmed by using more conventional methods of transcription analysis. The first aim in this study is to elucidate the gene expression properties of observed mononuclear infiltrates in the setting of tolerance and in the setting of rejection to help with the explanation of mechanisms of tolerance. A second aim is to define fundamental gene expression changes during the process of rejection to better explain the mechanisms of rejection in this model to detect potential targets for immune monitoring or interventions to prevent rejection.

Material and methods Seite -11-

Material and methods

Animals

Female NOD mice and male NOR mice were purchased from Jackson Laboratories (Bar Harbor, ME). Balb/c mice were purchased from Taconic Farms (Germantown, NY). These mice were housed in microisolated cages in pathogen-free animal facilities at the University of Minnesota. Male NOR mice, that are H-2 identical and differ from NOD mice in minor antigens, were used as islet and bone marrow donors. Female NOD mice were monitored for diabetes weekly after 12 weeks of age. Diabetes was defined when the non-fasting blood glucose level was greater than 400 mg/dl on 2 consecutive measurements. The blood glucose level was measured by a Glucometer Elite blood glucose monitoring system (Bayer Co, Elkhart, IN). These diabetic NOD mice received daily insulin treatment for at least 2 weeks before undergoing islet and bone marrow transplantation. All experiments were performed in according to the guidelines of the Institutional Animal Care and Use Committee.

Transplant protocol

Treatment

Fludarabine phosphate (Fludara, Berlex Laboratories, NJ) at 200 mg/kg and cyclophosphamide (Cytoxan, Bristol-Myers Squibb Co, Princeton, NJ) at 100 mg/kg were given intraperitoneally on days -2 and -1 pretransplantation. Rabbit anti-mouse lymphocyte serum (ALS, Accurate Chemical & Scientific, NY) at a dose of 0.3 ml was given i. p. on days -1 and 0. Rapamycin (Rapamune, Wyeth Laboratories, PA) at 1 mg/kg/day was administered by gavage on days -1 and 0, then once every 2 days until 14 days posttransplantation (Figure 2).

Bone marrow transplantation

After anesthetization, the femoral and tibial bones from male NOR mice were removed, and bone marrow cells were flushed out with PBS using a 10-ml syringe with a 26-gauge needle. Cell suspensions were washed twice with PBS. Viable cells were counted and adjusted to 1x108 cells/ml. With a 30-gauge needle, 1 ml of bone marrow cells was injected into each mouse through the tail vein.

Material and methods Seite -12-

Islet isolation and transplantation

The islets were isolated according to a protocol that is similar to a previously published isolation protocol (21). Briefly, we injected 2.5 ml of Hanks’ balanced salt solution (HBSS, Life Technology, MD) containing 2 mg/ml collagenase from Cl. Histolyticum (catalog #17449, Serva, Heidelberg, Germany) into the pancreatic duct. The distended pancreas was removed and incubated at 37°C for 16 minutes. The islets were purified by centrifugation on gradients comprising 3 different densities (1.130, 1.110, and 1.070). After centrifugation, the distinct layer of islets was collected and washed. Islets free of acinar cells, vessels, lymph nodes, and ducts were used for transplantation. Islet transplantation was performed as described previously (21). Briefly, the left kidney was exposed through a lumbar incision under sterile conditions. PE-50 polyethylene tubing (Becton Dickinson, Parsippany, NJ) containing 400 islets was inserted beneath the kidney capsule and gently pushed from the lower pole to the upper pole, where the islets were deposited. To test donor-specific tolerance, second donor NOR-specific islet grafts or third-party Balb/c islet grafts were transplanted under the right kidney capsule of NOD mice that had returned to hyperglycemia after their first islet grafts were removed.

Figure 2: Protocol of simultanous islet and bone marrow transplantation

The simultaneous bone marrow– and islet transplantation protocol shown above developed by Wu et al. consists of a conditioning treatment with Fludarabine and Cyclophosphamide with both myelosuppressive and immunosuppressive effect and an immunosuppressive protocol consisting of ALS (Anti-Lymphocyte Serum) and Rapamycin facilitating the engraftment of the transplanted hematopoetic stem cells and islets. A simultaneous transfer of the islets and the bone marrow allowed a rapid correction of insulin-dependent diabetes.

Material and methods Seite -13-

Islet graft function and harvest

Nonfasting blood glucose levels of each recipient were measured to monitor islet graft survival daily during the first week, then twice a week. Graft rejection was defined by the day the blood glucose level exceeded 200 mg/dl and remained high for 2 successive days.

The left kidney in recipients of first islet graft, or the right kidney in those with a second islet graft was removed after either rejection or survival >100 days. Islet grafts were harvested under microscope (Figure 3). Three rejected first islet grafts and 3 tolerated second islet grafts were frozen in liquid nitrogen and later subjected to RNA isolation. For histological testing, other rejected or tolerated first or second islet grafts were fixed in 10% formalin solution and embedded in paraffin; sections were stained with hematoxylin and eosin for histological examination.

Figure 3: NOR islet graft under the kidney capsule NOD mouse After 100 days of good graft function, graft nephrectomy was performed for the demonstration of the functional role of the graft for blood sugar correction and for the histological analysis of the graft. Picture taken during the graft nephrectomy. The arrow is pointing to the localization of the islet graft (from (64))

Microarray hybridization

RNA isolation

RNA was isolated from harvested islet grafts using the Qiagen RNeasy Total RNA isolation kit (Qiagen, Chatsworth, CA) according to manufacturer’s instructions for RNA isolation from eukaryotic tissue samples (46). Frozen tissue samples were disrupted and homogenized in 600 µl of buffer RLT using a Polytron PT10-35 tissue homogenizer (Kinematica AG, Material and methods Seite -14-

Switzerland) for 1 min. The tissue lysate was cleared by centrifugation in an Eppendorf 5415C microcentrifuge (Eppendorf AG, Germany) at maximum speed for three minutes. The cleared lysate was transferred to a new microcentrifuge tube and precipitated by the addition of 600 µl of 70 % Ethanol (Aaper alcohol and chemical company, Shelbyville KY). After precipitation the RNA was bound to an RNeasy mini column by centrifugation for 15 s at 8000 g. After this step 700 µl of buffer RW1 were added to the columns, incubated at room temperature for 1 min and spun through the column for 15 s at 8000 g. After this step the column was washed twice by adding 500 µl of buffer RPE and centrifugation for 15 s at 8000 g. After the washing steps the RNA was eluted from the column by centrifugation for 2 min at 8000 g with 50 µl of Rnase-free water after incubation for 1 min at room temperature.

After RNA isolation the obtained total RNA was quantified by spectrophotometry at 260 nm using a Beckman DU-70 spectrophotometer (Beckman/Coulter, Fullerton CA).

Reverse transcription

Double-stranded cDNA was synthesized from between 5 and 10 µg of total RNA using an oligo-dT primer containing a T7 RNA polymerase promoter (Operon, Alameda, CA). The first-strand reaction was carried out in 20 µl final reaction mix containing the template RNA, 100 pmol of the primer, 10 mM DTT (InVitrogen, Carlsbad, CA), 500 µM of each dNTP (InVitrogen, Carlsbad, CA), ~ 6 µg of T4gp32 (USB) and 200 U Superscript II reverse transcriptase (InVitrogen, Carlsbad, CA) in 1 x first-strand buffer (InVitrogen, Carlsbad, CA). Before the completion of the reaction mix template and primer were heated to 65°C for 5 min After completion the reaction mix was incubated for 1 hour at 42°C.

Second-strand synthesis was carried out in a 150 µl reaction for a total of 2 hours. The final reaction composition consisted of the first-strand reaction, 200 µM of each dNTP (InVitrogen, Carlsbad, CA), 40 U E. coli DNA polymerase I (New England Biolabs, Beverly, MA), 10 U E. coli DNA ligase (New England Biolabs, Beverly, MA) and 2 U E. coli RNase H (InVitrogen, Carlsbad, CA) in 1x second-strand buffer (InVitrogen, Carlsbad, CA). After 2 hours the double-stranded cDNA was polished by adding 20 U T4 DNA polymerase (New England Biolabs, Beverly, MA) and incubating for 5 minutes at 16°C. After second-strand synthesis, the reactions were extracted with phenol/chloroform/isoamyl in Phase Lock Gel- tube (Eppendorf, Hamburg, Germany). After the extraction the double-stranded cDNA was purified and concentrated with 3 washes with 500 µl of DEPC-treated water on Microcon 100 columns (Millipore, Billerica, MA). Material and methods Seite -15-

In-vitro transcription

Because of the included T7 promoter the double-stranded cDNA could be used as template for in-vitro transcription. In-vitro transcription incorporating biotinylated CTP and UTP was performed using the BioArray HighYield RNA Transcript Labeling Kit (Enzo Diagnostics, Farmingdale, NY) following manufacturer’s instructions. The in-vitro transcriptions yielded between 25 and 42 µg biotin-labeled cRNA. The labeled cRNA was purified by Qiagen RNeasy affinity columns (Qiagen, Chatsworth, CA) following manufacturer’s instructions for RNA cleanup. The amount of labeled cRNA was quantified by spectrophotometry at 260 nm. Labeled cRNA (15 µg) was then fragmented at 94°C for 35 min in fragmentation buffer (40 mM Tris-acetate (pH 8.1), 100 mM KOAc, 30 mM MgOAc) and then used to prepare a hybridization mixture, which included probe array controls and blocking reagents.

Array hybridization, scanning & analysis

The mixture was initially hybridized to Affymetrix test arrays to evaluate the quality of the cRNA and then to Affymetrix Murine U74A oligonucleotide arrays (Affymetrix, Santa Clara, CA) representing about 6,000 known genes and about 6,000 EST (expressed sequence tag)- sequences. All hybridizations were performed at the Biomedical Genomics Center (BMGC) of the University of Minnesota. After the hybridization, arrays were washed and stained on an Affymetrix fluidics station. Hybridization, washing and staining was done according to the appropriate protocols provided by Affymetrix. The arrays were then scanned on a Hewlett- Packard GeneArray Scanner and the resultant image captured as a data image file. From the data image files, gene transcript levels were determined using algorithms in the Affymetrix Microarray Suite software 4.0.1. The high quality of the array hybridizations was ensured by previous hybridization to test arrays as well as by ensuring that the reported quality measure are in the expected range.

To account for differences in the probe preparation, array hybridization and staining all data were scaled to an average intensity of 1,000 to be able to compare different samples. Each sample from the tolerant group was compared with every each from the rejecting group yielding a total of 9 different comparisons. Data were exported to Microsoft Excel 2000 for sorting and handling.

Material and methods Seite -16-

Genes that were increased or decreased by more than 2-fold in all 9 comparisons were regarded as consistently associated with tolerance or rejection. These genes were assigned to functional clusters using PubMed, Ovid, and data provided from Affymetrix at www.netaffx.com. They were analyzed further by calculating the mean of the fold changes of all 9 different comparisons and a 95% confidence interval.

Material and methods Seite -17-

Figure 4: Overview of eukaryotic target labeling for GeneChip® expression array Starting from total RNA double-stranded cDNA is synthesized and a T7-RNA polymerase promotor introduced. Biotin-labeled antisense cRNA is produced from cDNA in an in-vitro transcription reaction and prepared for hybridization to the GeneChip. After a washing and staining procedure arrays are scanned and derived data is analyzed. Figuremodified from a picture provided by Affymetrix. Material and methods Seite -18-

Real-time RT-PCR to monitor gene expression Real-time RT-PCR was carried out as two-step RT-PCR. First, a reverse transcription of 2 µg of total RNA was done in a 20 µl-reaction using 2.5 µM of random hexamers (Applied Biosystems, Foster City, CA), 10 mM DTT (InVitrogen, Carlsbad, CA), 500 µM of each dNTP (InVitrogen, Carlsbad, CA), RNase inhibitor (Applied Biosystems, Foster City, CA) and 200 U Superscript II reverse transcriptase (InVitrogen, Carlsbad, CA) in 1 x first-strand buffer (InVitrogen, Carlsbad, CA). Real-time PCR reactions were run on an ABI Prism 7700 Sequence Detection System (Applied Biosystems, Foster City, CA) at the AGAC (University of Minnesota). PCR was set up in 25 µl reactions. Each reaction contained approximately 90 ng template cDNA, 100 nM forward primer, 100 nM reverse primer in 1x SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) containing SYBR Green 1 dye, AmpliTaq Gold DNA Polymerase, dNTPs (with dUTP), Passive Reference 1 and buffer components. Real-time PCR was done as recommended by Applied Biosystems. After 2 minutes at 50°C and 10 minutes at 95°C, 40 PCR cycles were run with a denaturing step at 95°C for 15 seconds and a combined annealing and elongation step at 60°C for 1 minute. The formation of clear products that showed the expected product size was confirmed once by standard agarose gel electrophoresis. Melting temperature analysis was performed using an additional function of the ABI Prism 7700 Sequence Detection System and used to confirm the formation of the specific PCR product and was used as control for all further PCR runs. The melting point is determined by the temperature at which a maximum level of fluorescence is measured during a gradual heating process from 60 to 93°C. Only reactions with specific product formation were considered for analysis.

Primers for quantitative real-time PCR were designed using Primer 3 software (Whitehead Institute, http://www-genome.wi.mit.edu/cgi-bin/primer/primer3_www.cgi) and optimized for an annealing temperature of 60 °C. Where possible, primers were designed to cross an intron.

Material and methods Seite -19-

Table 1: Primers used for quantitative realtime PCR. Primers are listed 5’ to 3’ in all cases (* intron-crossing) Target Sense Primer Antisense Primer FasL* GGCTCTGGTTGGAATGGGATTAG CCTGTTAAATGGGCCACACTCCT IFNγ TACACACTGCATCTTGGCTTTGC TTGCCAGTTCCTCCAGATATCCA L-Selectin-Ligand* AGAGACCACAAGACCCACCACCT TCGTGATACGACTGGCACCAGAG Granzyme B* ATCAGCAGCCTGAGGCGATATGT AGGGATGACTTGCTGGGTCTTCT Rantes GCTCCAATCTTGCAGTCGTGTTT GGGAAGCGTATACAGGGTCAGAA TGFβ* ATACGTCAGACATTCGGGAAGCA AGACAGCCACTCAGGCGTATCAG IL1β* GCTCTCCACCTCAATGGACAGAA GGGTATTGCTTGGGATCCACACT CD28 CCCGTCTACCTGCTCATCATCAC GTTTGGGTCAAGCCACACAGACT CTLA4* TTTGGATCCTTGTCGCAGTTAGC CGGCCTTTCAGTTGATGGGAATA β-actin* GCCGCTCTAGGCACCAG GCCAGATCTTCTCCATGTCG

Data were analyzed using the comparative CT analysis (delta delta CT analysis) as described in the ABI Prism 7700 Sequence Detection System User Bulletin #2 (5). In this method, the amount of target in different samples is normalized to an endogenous reference, in this case β- actin. The normalized data were used to calculate relative fold changes of a given mRNA between different samples. The fold change calculation in this method is based on the determination of the threshold cycle CT for both, the analyzed gene and the endogenous reference β-actin . The threshold cycle is the cycle of the PCR, in which the fluorescence level in the PCR reaction measured by the ABI Prism 7700 Sequence Detection System crosses a defined threshold that must be well above the background level and in the log-linear phase of the amplification.

Using the CT values a fold change can be calculated according to the following formulas:

−∆∆C T ∆∆C ∆C ∆C Fold change = 2 with T = T (Sample rejection) – T (Sample tolerance)

C C = ( T (analyzed gene) - T (β-actin)) (Sample

C C rejection) - ( T (analyzed gene) - T (β-actin)) (Sample tolerance)

∆C ∆C All PCRs were done in triplicate. T was calculated for all replicates. All the calculated T s

∆∆C were used to calculate T . Fold change calculations were done using the calculated mean and the 95% confidence intervals. Means and 95 % confidence intervals were calculated for Material and methods Seite -20-

∆∆C the T values, before they were used for the fold change calculation which resulted in average fold changes and a 95 % confidence interval range.

Gene expression analysis by RNase Protection Assay

Multi-probe RNase protection assay was performed according to manufacturer’s instructions (BD PharMingen, San Diego, CA) with some modifications. Radioactively labeled probes were synthesized from the mCK-1 Multi-Probe Template Set (BD PharMingen, San Diego, CA). The final reaction (20 µl) contained 100 µCi [α-32]UTP (Amersham, Piscataway, NJ), ATP, GTP, CTP (5 nmol each); DTT (100 nmol); RNase inhibitor (10 U); transcription buffer (1x) and T7 RNA polymerase (Promega, Madison, WI). After 1 hour at 37°C the mixture was treated with DNase (Promega, Madison, WI) for 15 minutes, purified on spin columns (home- made from Insulin syringe and Sephadex G25 (Amersham, Piscataway, NJ)) and radiation was quantified on a Packard TriCarb 2500 TR. Probe RNA equivalent to 2.9 x 105 cpm / µl was hybridized with 3.5 µg of total RNA that has previously been precipitated with 6.5 µg of yeast tRNA (Roche Molecular Biochemicals, Indianapolis, IN). Hybridization was performed in 10 µl of hybridization buffer (80 % formamide (Fisher Scientific, Hanover Park, IL); 0.4 M NaCl; 1 mM EDTA; 200 mM PIPES (Sigma, St. Louis, MO) pH 6.4) at 55°C overnight after a 5 min denaturing step at 85°C. Nonhybridized probe was then digested (45 minutes, 30°C) by adding a solution (100 µl) of 200 ng/ml RNase A (Sigma, St. Louis, MO) and 600 U/ml RNase T1 (Roche Molecular Biochemicals, Indianapolis, IN) in 10 mM Tris, 300 mM NaCL, 5mM EDTA, pH 7.5 (Sigma, St. Louis, MO). After adding RNase, the samples were digested (15 minutes, 37°C) with 20 µl of a mixture containing proteinase K (Roche Molecular Biochemicals, Indianapolis, IN) (0,833 mg / ml) in 3.33 % Na-lauroylsarcosine (Sigma, St. Louis, MO). RNA duplexes were extracted using 250 µl of 4 M guanidinoisothiocyanate (InVitrogen, Carlsbad, CA) (with 10 µl/ml of β-mercaptoethanol (Sigma, St. Louis, MO)), precipitated with 25 µg of yeast tRNA using 400 µl of isopropanol (Sigma, St. Louis, MO) and dissolved in 5 µl of loading buffer (80 % formamide (Fisher Scientific, Hanover Park, IL), 1 mM EDTA (Sigma, St. Louis, MO) pH 8, dyes (bromophenol blue, xylene blue (both Sigma, St. Louis, MO)). Samples were electrophoresed in a standard 6 % polyacrylamide / 8 M urea (both Sigma, St. Louis, MO) sequencing gel (Sequi-Gen GT from BioRad, Hercules, CA). The gel was dried and placed on X-OMAT film (Kodak, Rochester, NY) with intensifying screen and exposed at –80°C. Results Seite -21-

Results

Course of transplantation and histology

Spontaneously diabetic female NOD mice were transplanted with islets from male NOR donor mice. All untreated animals (n=12) quickly rejected their transplanted graft. Three rejected first islet grafts for gene expression studies were derived from untreated diabetic NOD mice that rejected their grafts between 10 and 13 days posttransplant. In the animals that were treated with the combined islet- and bone marrow-transplant protocol, all grafts survived for more than 100 days (Figure 5). After removal of the graft by nephrectomy all animals returned to hyperglycemia. A second islet graft from the same donor strain was tolerated without additional treatment. Three of these tolerated second donor-specific islet grafts were obtained after 100 days posttransplant.

Figure 5: Course of transplantation for primary graft with and without tolerance-inducing protocol Without tolerance-inducing protocol there is an accelerated rejection of the transplanted islets (blue line with B). With the tolerance-inducing protocol including bone marrow transfer and a conditioning protocol there is no graft failure until day 100 after transplantation in all transplanted animals (purple line with F). Tolerance cannot be achieved with conditioning protocol alone (red line with H) or with bone marrow transfer alone (green line with J).

Results Seite -22-

Histologic examination of additional grafts derived in a similar way showed a strong mononuclear infiltrate with complete islet destruction in the rejecting mice and a faint mononuclear infiltrate surrounding intact islets in the tolerant animals (30) (Figure 6).

Figure 6: Histological analysis of tolerated and rejected islet grafts. Histological analysis by HE staining of tolerated and rejected islet grafts derived after 100 days of graft function or at the time of rejection.

General overview of microarray analysis result

High-density oligonucleotide arrays were used to analyze gene expression differences in tolerated and rejected islet grafts. These arrays allowed the parallel study of about 12,000 different mouse genes. All assessed quality control measures were in the expected range ensuring a good comparability between the different hybridizations Results Seite -23-

Table 2: Quality control measures for microarray hybridizations. Percentage of genes on the Average abundance level of chip genes called Present Marginal Absent Present Marginal Absent Tolerance 1 55,80% 1,30% 42,90% 279,80 36,30 9,30 Tolerance 2 54,50% 1,60% 44,00% 247,30 27,00 9,40 Tolerance 3 56,40% 1,40% 42,20% 307,30 21,90 10,30 Rejection 1 57,50% 1,30% 41,20% 265,20 35,80 9,30 Rejection 2 57,10% 1,30% 41,70% 261,00 36,50 10,60 Rejection 3 58,90% 1,30% 39,80% 271,00 34,50 9,60

Background Housekeeping genes b-Actin 3’/5’ ratio GAPDH 3’/5’ ratio Tolerance 1 43,50 1,73 1,12 Tolerance 2 51,18 2,91 1,32 Tolerance 3 49,09 2,07 1,20 Rejection 1 52,09 1,56 1,05 Rejection 2 58,31 1,69 0,87 Rejection 3 52,48 1,67 1,02 Background: background signal detected on the array expected range: 20 – 100 3’/5’ ratio: ratio between probe sets at 3’ and 5’-end of defined housekeeping genes expected range: < 3

Comparing 3 tolerated grafts and 3 rejected grafts made it possible to do a total of 9 different comparison analyses. A high expression of insulin and somatostatin confirmed the presence of the islet grafts in all processed samples. The expression of glucagon was consistently higher in all tolerated grafts. Transcript abundance represented by the average difference levels for the different hormones is shown in the following table 3. Levels represent the average of the three chip experiments. Results Seite -24-

Table 3: Hormone gene expression during tolerance and rejection Abundance in rejected samples Abundance in tolerated samples Gene Probe set ID (+/- 95% confidence interval) (+/- 95% confidence interval)

Insulin I 97658_f_at 70452,2 (+/- 4518,8) 89301 (+/- 12365,2)

Insulin I 97659_r_at 74921,4 (+/- 7019,6) 92890,6 (+/- 12631,4)

Insulin II 100150_f_at 63197,2 (+/- 3257,2) 79908,9 (+/- 10439,8)

Glucagon 94633_at 2488,1 (+/- 948,5) 14912,5 (+/- 6224,0)

Somatostatin 95436_at 9622,4 (+/- 433,9) 20360,4 (+/- 5299,6) Of over 12,000 genes studied between 1,667 and 2,252 different genes were found to be expressed more than 2-fold higher in the rejected grafts. Between 1,055 and 1,648 different genes were found to be expressed more than 2-fold higher in the tolerated grafts (Table 4 and 5).

Table 4: Number of genes found to be higher expressed in rejected grafts

Rejection 1 Rejection 2 Rejection 3

Tolerance 1 1698 1798 2252

Tolerance 2 1758 1761 2098

Tolerance 3 1667 1893 2101

Table 5: Number of genes found to be higher expressed in tolerated grafts

Rejection 1 Rejection 2 Rejection 3

Tolerance 1 1115 1326 1158

Tolerance 2 1253 1285 1055

Tolerance 3 1453 1648 1363

In all of the 9 different comparisons, 524 genes were consistently higher in the rejected grafts, but only 57 genes were found to be expressed in higher levels in the tolerated grafts. These genes were analyzed further by classifying them in different functional groups (Table 6). Apart from genes related to immune function during the setting of rejection there is also a high number of genes related to proliferation such as Ki 67 (X82786) and cytoskeleton genes such as beta-Tubulin (X04663) expressed at higher levels during rejection. Genes that did not fit into any of the given groups were summarized as “others”. This group also contained the most profoundly changed gene, Serum amyloid A3 (X03505), an acute-phase protein. Results Seite -25-

Table 6: Classification of the consistently increased genes to different functional groups

Genes Rejected Islet Grafts Tolerated Islet Grafts T-cell genes 35 0 Costimulation and adhesion 25 2 Signaling molecules and receptors 46 5 Cytoskeleton genes 13 4 Proliferation genes 43 0 Intracellular signaling 50 7 Transcription factors 21 4 Apoptosis 7 0 Innate immunity and complement system 22 0 Antigen presentation 33 0 Intermediary metabolism 18 6 Others 60 11 ESTs 151 18

The following pages show the 20 most profoundly upregulated genes in the setting of tolerance and the 20 most profoundly upregulated genes in the setting of rejection. Results Seite -26-

Table 7: The 20 most profoundly increased genes in the setting of tolerance Accession Functional Fold change Standard Gene title Probe set ID Number Category Average deviation Albumin 1 (Alb1) 94777_at X13060 Other 60,83 91,92 Fatty acid binding protein 1, liver Intermediary (Fabp1) 94075_at Y14660 metabolism 55,04 73,09 Intermediary Cytochrome P450, 2c39 (Cyp2c39) 98295_at AF047726 metabolism 21,29 25,27 cDNA clone UI-M-BH2.2-aoo-f-05-0- UI (identical with protein phosphatase 1, regulatory (inhibitor) subunit 1A (Ppp1r1a)) 96114_at AW122076 EST 17,56 8,25 cDNA clone IMAGE:1245547 (identical with hypothetical protein MGC27690) 93448_at AA798076EST 15,33 3,96 cDNA clone IMAGE:1396720 (identical with leptin (Lep)) 98444_g_at AI882416 EST 15,17 5,72 Signaling Insulin-like growth factor binding molecules and protein, acid labile subunit (Igfals) 97987_at U66900 receptors 14,9 7,54 Kidney androgen regulated protein (Kap) 94199_at M22810 Other 11,37 8,71 Signaling Proprotein convertase subtilisin/kexin molecules and type 6 (Pcsk6) 101196_at D50060 receptors 10,84 3,22 Pre-B lymphocyte gene 1 (Vpreb1) 92972_at X05557 Other 10,62 4,65 Keratin complex 1, acidic, gene 24 Cytoskeleton (Krt1-24) 98998_r_at AF020790 genes 8,88 4,78 Glycosylation dependent cell adhesion molecule 1 (Glycam1) Costimulation (=endothelial ligand for L-selectin ) 98063_at M93428 & adhesion 8,36 1,02 cDNA clone IMAGE:522043 (identical with glutathione S-transferase, mu 6 (Gstm6)) 104637_at AI326397 EST 8,29 7,06 Cytochrome c oxidase, subunit VI a, Intermediary polypeptide 2 (Cox6a2 ) 99667_at U08439 metabolism 8,04 3,45 RIKEN cDNA clone 4732428J02 (identical with laminin, beta 3 (Lamb3)) 161188_f_at AV238648EST 7,02 3,66 cDNA clone IMAGE:2645062 (identical with expressed sequence AA409500) 96218_at AW214322 EST 6,71 2,25 Fanconi anemia, complementation group C (Fancc) 160895_at L08266 Other 6,67 2,8 Signaling molecules and Glucagon (Gcg) 94633_at Z46845 receptors 6 2,91 Sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3B (Sema3b) 99460_at X85990 Other 5,54 1 Potassium voltage-gated channel, subfamily H (eag-related), member 2 (Kcnh2) 100961_at AF012871 Other 5,3 1,4 Results Seite -27-

Table 8: The 20 most profoundly increased genes in the setting of rejection Accession Functional Fold change Standard Gene title Probe set ID Number Category Average deviation Serum amyloid A 3 (Saa3) 102712_at X03505 Other 117,48 85,31 Cytoskeleton Tubulin, beta 5 (Tubb5) 94789_r_at X04663 genes 59,77 27,67 CD3 antigen, epsilon polypeptide (CD3e) 102971_at M23376 T cell genes 48,06 17,33

Chitinase 3-like 1 (Chi3l1 ) 99952_at X93035 Other 37,66 17,58

Granzyme B (Gzmb) 102877_at M12302 T cell genes 35,04 10,02 Interferon gamma inducible protein, 47 kDa (Ifi47) 104750_at M63630 Other 34,26 16,43

T-cell receptor alpha chain (Tcra) 97945_at M16118 T cell genes 34,11 39,83 Interferon gamma inducing factor Signaling binding protein (Igifbp) = interleukin- molecules and 18 binding protein 92689_at AB019505 receptors 31,67 12,05 Signaling Chemokine (C-C motif) ligand 5 molecules and (CCL5) = Rantes 98406_at AF065947 receptors 30,52 9,38 Antigen Lymphocyte antigen 86 (Ly86) = MD1 94425_at AB007599 presentation 28,57 13,64 Proliferation & DNA Schlafen 2 (Slfn2) 92471_i_at AF099973 modification 26,64 18,38 RIKEN clone 9030206H06 (identical with chemokine (C-C) receptor 5 (CCR5)) 161968_f_at AV370035 EST 26,13 15,55 Hematopoietic cell specific Lyn Intracellular substrate 1 (Hcls1 ) 99461_at X84797 signaling 25,1 15,19

RIKEN cDNA 5730414A08 gene 104276_at AA983101 EST 24,98 2,75 Innate immunity & complement Fc receptor, IgG, high affinity I (Fcgr1) 101793_at X70980 factors 23,08 3,16

CD8 antigen, alpha chain (CD8a) 102975_at U34881 T cell genes 22,8 9,51 Signaling Interleukin 1 receptor antagonist molecules and (Il1rn) 93871_at L32838 receptors 22,68 3,72 Mus musculus cDNA clone 2810003K04 (identical with aldo-keto reductase family 1, member B3 (aldose reductase) (Akr1b3)) 93637_at AV133992 EST 22,2 11,78 G protein-coupled receptor kinase 6 Intracellular (Gprk6) 99457_at Y15798 signaling 21,3 17,78

CD5 antigen (Cd5) 92715_at M15177 T cell genes 21,28 4,16

Results Seite -28- T cell genes

Genes that are typically expressed by T cells were strongly represented in the group of genes that most profoundly increased during rejection. Of the 10 most profoundly increased genes, 3 are typically expressed by T cells. We noted a 48.06-fold higher expression of CD3ε (M23376) in these rejected grafts. Also much higher expressed in the rejected grafts were genes belonging to the CD3 signaling complex, such as CD3γ (M18228) or CD3 δ (X02339); TCR (T cell receptor) components like TCRα (M16118) or TCR β (M20878); and typical T cell-signaling molecules like LCK (M12056) and ZAP 70 (U04379) (data not shown). The high expression of CD8α (22.8-fold higher than in the tolerated grafts) suggested a high presence of cytotoxic T cells, which are probably responsible for the high production of cytotoxic T cell activation markers in the graft. Among those Granzyme B (M12302) is the most profoundly increased, with a 35.04-fold increase during rejection. Fas ligand (U06948) increased by 9.46-fold and Perforin (X12760) increased by 5.51-fold (Figure 7).

Figure 7: Differential gene expression patterns in tolerated and rejected islet grafts in T-cell genes. Genes were considered differentially expressed if a change of at least 2-fold was observed in all of the 9 different comparisons between 3 tolerated and 3 rejected grafts. The x-axis represents the average fold-change during rejection relative to tolerance. Positive values indicate a higher expression in the rejected islet grafts; negative values indicate that the transcript is more abundant in tolerated grafts. A selection of genes from the functional groups is shown. Error bars represent the 95% confidence interval.

Results Seite -29- Costimulation and adhesion molecules

A higher expression level of a number of costimulatory molecules was found in the rejected grafts, including molecules that are constitutively expressed on T cells, such as CD28 (M34563), and also molecules that are induced on activation, such as OX40 (Z21674) and ICOS (AB023132). Interestingly, two adhesion molecules L-selectin ligand (M93428) and vascular cadherin 2 (Y08715) had higher expression in tolerated grafts (Figure 8).

Figure 8: Differential gene expression patterns in tolerated and rejected islet grafts in costimulation and adhesion genes. Genes were considered differentially expressed if a change of at least 2-fold was observed in all of the 9 different comparisons between 3 tolerated and 3 rejected grafts. The x-axis represents the average fold-change during rejection compared to tolerance. Positive values indicate a higher expression in the rejected islet grafts; negative values indicate that the transcript is more abundant in tolerated grafts. A selection of genes the functional group is shown. Error bars represent the 95% confidence interval.

Complement system and innate immunity

The gene chip experiments also identified molecules belonging to the complement and innate immune system that are increased in rejected grafts. Among them were genes expressed especially in macrophages FcγRI (X70980), lysozyme P (X51547), lysozyme M (M21050) or complement receptor CR3 (Mac-1) (M31039), all recruited during graft rejection. Notably, there was also an increased local production of the complement components C1q alpha polypeptide (X58861), C1q B chain (M22531), C1q c polypeptide (X66295), and properdin (X12905) (Figure 9). Results Seite -30-

Figure 9: Differential gene expression patterns in tolerated and rejected islet grafts in genes related to innate immunity. Genes were considered differentially expressed if a change of at least 2-fold was observed in all of the 9 different comparisons between 3 tolerated and 3 rejected grafts. The x-axis represents the average fold-change during rejection relative to tolerance. Positive values indicate a higher expression in the rejected islet grafts. A selection of genes from the functional group is shown. Error bars represent the 95% confidence interval.

Cytokines and cytokine receptors

We found a number of different cytokines and cytokine receptors differentially expressed between tolerated and rejected islet grafts (Figure 10). IL-1β (M15131) was highly upregulated in the rejected islet grafts with an average fold change of 10. IL-1β converting enzyme (L28095) was elevated by 4.21-fold. In order to further analyze the functional aspects of tolerance in our model of tolerance induction, we used all cytokine expression data found in our gene chip results to determine the cytokine profile in the setting of tolerance and in the setting of rejection. This cytokine profile showed an association of Th1 response cytokines with rejection, especially IFN-γ (K00083), which increased with 4.84 fold. In concordance with the higher levels of Th1 cytokines in the rejected grafts, we also found a high expression of transcription factors associated with the Th1 phenotype in the rejected grafts. These included B-ATF (AF017021) with an increase of 8.57 fold, STAT4 (U06923) with 5.7 fold, and IRF1 (M21065) with 3.83 fold. Interestingly, we also found that TGF-β (AJ009862) and IFN-β (V00755) increased during rejection. Another observation Results Seite -31- was the very high increase of the supposedly regulatory molecules, such as IL-1 receptor antagonist (IL-1rn) (L32838) and IL-18 binding protein (IL-18bp) (AB019505). Among the cytokine receptors that increased during rejection, different receptor components of the IL-3 receptor were found: β2 chain (M29855) with 9.2 fold; α chain (X64534) with 7.87 fold; and β1 chain (M34397) with 4.44 fold. The IL-18 receptor (U43673) and the IL-10 receptor (L12120) also increased (Figure 11).

Figure 10: Differential gene expression profile in tolerated and rejected islet grafts in cytokines Genes were considered differentially expressed if a change of at least 2-fold was observed in all of the 9 different comparisons between 3 tolerated and 3 rejected grafts (shaded bars). Cytokines that did not fit into this criterion are represented with nonshaded bars. The x-axis represents the average fold-change during rejection relative to tolerance. Positive values indicate a higher expression in the rejected islet grafts; negative values indicate that the transcript is more abundant in tolerated grafts. Error bars represent the 95% confidence interval. Results Seite -32-

Figure 11: Differential gene expression profile in tolerated and rejected islet grafts in cytokines Genes were considered differentially expressed if a change of at least 2-fold was observed in all of the 9 different comparisons between 3 tolerated and 3 rejected grafts. The x-axis represents the average fold change during rejection relative to tolerance. Positive values indicate a higher expression in the rejected islet grafts. Error bars represent the 95% confidence interval.

Chemokines and chemokine receptors

As with the cytokines, we also detected differential expression of a number of different chemokines and their receptors (Figure 12). The 3 most highly upregulated chemokines during rejection in our model were RANTES (AF065947), with an increase of 30.52 fold; MCP-1 (M19681), with an increase of 17.26 fold; and MIG (M34815), with an increase of 16.16 fold. Their respective chemokine receptors had similar patterns as the chemokines themselves. In the rejected grafts, CCR5 was 9.78-fold higher, CCR2, 9.11-fold higher; and CXCR3, 2.92-fold higher (Figure 13).

Results Seite -33-

Figure 12: Differential gene expression profile in tolerated and rejected islet grafts in chemokines Genes were considered differentially expressed if a change of at least 2-fold was observed in all of the 9 different comparisons between 3 tolerated and 3 rejected grafts. The x-axis represents the average fold change during rejection relative to tolerance. Positive values indicate a higher expression in the rejected islet grafts. Error bars represent the 95% confidence interval.

Figure 13: Differential gene expression profile in tolerated and rejected islet grafts in chemokine receptors Genes were considered differentially expressed if a change of at least 2-fold was observed in all of the 9 different comparisons between 3 tolerated and 3 rejected grafts. The x-axis represents the average fold change during rejection relative to tolerance. Positive values indicate a higher expression in the rejected islet grafts. Error bars represent the 95% confidence interval. Results Seite -34- Confirmation of microarray results

To confirm the differential expression indicated by the gene chip experiments, we performed quantitative real-time PCRs for Fas ligand, IFN-γ, granzyme B, IL-1β, RANTES, L-selectin ligand CD28 and CTLA4. Control gel electrophoresis demonstrated the formation of one clear PCR product for all PCRs apart from the one for CD28 which was excluded from further analysis (Figure 14).

Figure 14: Gel electrophoresis and melting temperature for specific PCR product formation in quantitative realtime PCR

Specific product formation was controlled by agarose gelelectrophoresis in a 1.5% agarose gel. The figure represents the result of the primary analysis of the designed primers. + represents a positive control, NTC a no-template control of primer dimer formation. The PCR for CD28 was excluded from analysis because of non-specific product formation. For subsequent reactions with multiple replicates specific product formation was controlled by melting point analysis. Specific melting points of PCR products are given in a table.

Although the exact numbers differed between the different methods, our data demonstrated that the general trends from the gene chip analysis could also be found in the quantitative realtime PCR (Figure 15). Thereby, quantitative realtime PCR demonstrates the validity of the Genechip® approach.

Results Seite -35-

Figure 15: Comparison of real-time PCR results with gene chip results for selected genes identified as differentially expressed by gene chip analysis.

The y-axis represents the average fold-change during rejection relative to tolerance. Values above 1 indicate a higher expression in the rejected islet grafts; values below 1 indicate that the transcript is more abundant in tolerated grafts. Average fold change was calculated as the average of the 9 different comparisons between 3 tolerated and 3 rejected grafts for both genechip analysis and quantitative real-time PCR. Fold change data for quantitative real-time PCR was derived using the comparative Ct method.

Trends detected in the cytokine profile were confirmed and further evaluated by the RNase protection assay (Figure 16). We found an upregulation of IFN-γ, and interestingly also IL-10 in the rejected grafts. Expression of IL-15 was similar in the rejected and tolerated grafts.

Results Seite -36-

Figure 16: Cytokine profile of tolerated and rejected grafts derived by RNase protection assay. The results shown here represent the results for cytokines expressed at high abundance levels. L32 is a housekeeping gene presented as a balancing control. Figures below the autoradiography represent the abundance levels detected by Genechip® analysis (average difference levels detected by the microarray).

Discussion Seite -37-

Discussion

The results of this study demonstrate the feasibility of the use of oligonucleotide microarrays in a rodent model of islet transplantation, in which islet graft rejection was based on both allograft rejection and recurrence of autoimmunity. A concentrated localization of the grafts under the kidney capsule made it possible to harvest them without too much contamination of surrounding tissue and to extract enough RNA for the performance of gene expression studies.

The experimental design of this pilot study was limited by the availability of long-term surviving islet grafts and the number of microarrays that could be used for this purpose. In order to keep the information contained by the sample-to-sample variation and in order to be able to potentially exclude a sample that does not achieve the expected hybridization quality, it was decided not to pool samples, but to hybridize each sample to a separate microarray. As suggested by investigators analyzing this subject on a theoretical basis (32) and according to recommendations by the manufacturer, the number of replicates was set to three for rejection and three for tolerance, resulting in a total of six microarrays to be hybridized. To limit the number of detected genes and to reduce the number of false positive genes detected, an algorithm was applied that only considered those genes that were consistently increased or decreased more than 2-fold in all pairwise comparisons. One potential drawback of this approach is that potentially important changes are missed, because they happen below this arbitrary level or because they happen close to the saturation of the probe sets on the microarray. But still this approach was justified given the fact that only six data sets are available to evaluate thousands of variables and given the fact that the purpose of this study was to make valid positive statements. This makes it necessary to exclude the risk for false positive results which can easily happen at the lower limit of detection of the chip, where the background noise starts to play an important role.

Using this approach, a high number of genes including many genes with known or at least suspected function in immunity were found consistently increased during rejection. This might be due to the recruitment of different cell populations to the rejecting grafts, their differential activity and an actively ongoing immune response in the rejected grafts, which is known to impact the regulation of a high number of different genes. Discussion Seite -38-

Among the expression changes that were associated with rejection, a high number of genes were associated with the function of cytotoxic T cells, such as Granzyme B, Fas ligand and Perforin. Intragraft expression of these cytotoxic T cell-related immune activation markers was previously shown to be associated with acute kidney graft rejection in humans (58). Their local and systemic expression was also correlated with islet allograft rejection in a nonhuman primate model (22) and in a rodent model (41). Here we confirmed that their local expression increased in islet graft rejection in diabetic NOD mice. We also confirmed it was easily possible to detect them in a parallel analysis of thousands of genes using gene chip technology.

We observed a strong dominance of a Th1 T cell response in the rejected islet grafts from genes found in different functional clusters, including cytokines classically produced by Th1 T cells, most notably IFN-γ. Other investigators previously demonstrated that IFN-γ is associated with cytotoxic function in the process of islet graft rejection (15; 24; 42).

Chemokines and chemokine receptors regulate cell traffic and positioning in inflammatory conditions. They are responsible for recruiting infiltrated molecular cells in the graft (13). We found that RANTES was the most highly expressed chemokine, followed by MCP-1 and MIG in the rejected islet grafts. It had been shown that RANTES gene expression is associated with acute kidney allograft rejection in humans (44) and acute heart allograft rejection in mice (51). MCP-1 and MIG are also known to play a critical role in acute graft rejection (18; 29). MIG in this context seems to be of particular interest, because its expression has been detected as elevated in most previous microarray studies performed in the context of transplantation (2; 50; 51). Consistent with that observation, we also found an increased expression of chemokine receptors, such as CCR5, CCR1, and CXCR3 during rejection. These chemokine receptors play key roles in T cell activation, recruitment, and allograft destruction during organ graft rejection (13; 16; 23). CCR5 also plays an important role in orchestrating the Th1 immune response leading to islet allograft rejection (1). Upregulation of chemokine receptor expression explains the complex migratory pathways taken by infiltrated monocular cells. Our results provide new insights into the mechanisms that control priming, effector function, and memory responses during islet graft rejection.

Surprisingly, among the molecules that increased in our rejected islet grafts, there were also molecules that are classically more associated with downregulation or control of an immune Discussion Seite -39- response and could also be expected in the tolerated grafts. Among them were costimulatory molecules such as CTLA-4 and GITR, cytokines such as TGFβ and IL-10, and molecules that regulate the function of inflammatory molecules such as IL-1 receptor antagonist and IL-18 binding protein. This finding could be due to the fact that they also represent markers of a general immune activation. It has been shown that the production of IL-18 binding protein increased in response to IFN-γ (45), which was present in high amounts in our transplant rejection model. IL-1 receptor antagonist has also been detected at extremely high levels in patients undergoing heart allograft rejection (62). It is known that both CTLA-4 and GITR increase during T cell activation (38) and that TGFβ (27) and IL-10 (65) also have a role in models of acute rejection. It is also possible that our model only represented a late stage of rejection in which there was downregulation of the already-started rejection response. A detailed time course analysis could provide further insight. Our results do not formally exclude the possibility that these molecules contribute to the maintenance of tolerance in our model, but it is less likely that they are the key factors.

Only a very low number of genes seemed to be expressed at a higher level in the tolerated grafts. The limited difference in insulin expression detected by microarray analysis inconsistent with the histological observation of severe insulitis is most likely due to the fact that the probes for the highly expressed gene insulin in islet grafts are saturated shifting the levels out of the linear region of detection of the microarray system. The increased expression of the adhesion molecule L-selectin ligand (GlyCAM-1) in our model is intriguing. It could potentially help mediate the immigration of a population of diabetes-preventing or islet rejection-preventing cells into the grafts. This population has been described by several authors and expresses L-selectin (CD62L) at high levels. In cotransfer experiments Herbelin et. al. discovered that a population of TCRαß+CD4+CD62L+ thymocytes in prediabetic NOD mice, emerging from the mainstream differentiation pathway, have a high potency to regulate autoreactive effectors derived from diabetic NOD, when cotransferred with them into NOD- SCID recipients (25). In a similar transfer model Lepault et. al. showed that regulatory CD4+ splenocytes in prediabetic (NOD) mice, that prevent diabetes induced by the transfer of autoreactive effectors into NOD-SCID recipients, belong to the CD4+ CD62Lhigh T cell subset (33). Furthermore Szanya et. al. demonstrated that among CD4+CD25+ T cells, a novel population of regulatory immune cells, only CD4+CD25+CD62L+, and not CD4+CD25+CD62L- splenocytes inhibit diabetes transfer into immune-compromised NOD mice, whereas both subsets are equally effective in suppression assays in vitro (60). Discussion Seite -40-

These results demonstrate that tolerated and rejected islet grafts differentially express a number of genes belonging to different functional clusters. Some of the differentially expressed genes we identified are well-established, known markers of islet graft rejection or of organ graft rejection in general. The parallels found between this study and previous microarray studies performed in the context of transplantation and acute rejection such as the increased expression of MIG as well as the detection of known markers of acute rejection (e. g. the cytotoxic T cell activation markers Granzyme B, Fas Ligand and Perforin) not detected in some of these studies demonstrates the high validity of the reported results here. (2; 50; 51). Our study thus confirms that gene microarray analysis of islet grafts gives meaningful results and can be used to detect the specific gene expression profile representative of the biologic mechanisms of tolerance and rejection. This approach can be helpful in identifying markers associated with rejection that are useful for immune monitoring, as well as molecules that represent targets to improve immunosuppression or tolerance strategies.

A better understanding of the molecular mechanism of islet graft rejection and of tolerance in the setting of autoimmune diabetes will improve the treatment of type 1 diabetes by islet transplantation. The results suggest that infiltrating mononuclear cells in rejected and tolerated islet grafts show differential expression in many genes from different functional clusters. Yet, we could not address, with this approach, what kind of specific infiltrated cells express these genes; whether they are also expressed in the early stage of islet graft destruction; or whether they are related to allogeneic rejection or to recurrence of autoimmune diabetes. So, future studies are needed to systematically explore gene expression profiling of specific cell types from islet grafts during rejection focusing on both alloimmune and autoimmune response and during the induction of tolerance. These questions could potentially be addressed by integrating the novel technology of laser capture microdissection with gene microarray analysis (36). Summary Seite -41-

Summary

Background. It has recently been demonstrated that donor-specific tolerance to MHC-matched minor-antigen-mismatched islet allografts in diabetic NOD mice could be induced by simultaneous islet and bone marrow transplantation preventing both graft rejection and recurrence of autoimmunity. Despite the presence of tolerance and good graft function mononuclear cell infiltration surrounding the islet was found in tolerated grafts. To elucidate the differences between these mononuclear infiltrates in the presence of tolerance and destructive mononuclear infiltrates during graft rejection as well as their influence on intragraft gene expression, tolerated islet grafts from the described model and rejected islet grafts from untreated animals were subjected to gene expression studies.

Methods. Gene expression analysis in tolerated and rejected islet grafts was performed by using Affymetrix Murine U74A oligonucleotide arrays. Real-time PCR and RNase protection assay on selected genes were performed to confirm the results of microarray analysis.

Results. Of over 12,000 genes studied, 57 genes were expressed at consistently higher levels in tolerated islet grafts, and 524 genes in rejected islet grafts. Genes from a variety of different functional clusters were found to be different between rejected and tolerated grafts. In the rejected islet grafts, a number of T cell surface markers (e.g., CD3, CD8) and of cytotoxicity- related genes (e.g., granzyme B and Fas ligand) were highly expressed. Also in the rejected grafts, a number of cytokines and chemokines and their receptors (e.g., IL-1β, IFN-γ, MIG, RANTES) were found at high levels. The differential expression of selected genes found by microarray analysis was also confirmed by real-time PCR and RNase protection assay.

Conclusion. In our studies, infiltrating mononuclear cells in rejected and tolerated islet grafts showed differential expression in a number of genes belonging to different functional clusters. Gene microarray analysis can be used to detect gene expression differences representative of the biologic mechanisms of tolerance and rejection and thereby provide us a new tool for the discovery of genes that can be used in immune monitoring and as target for new medical therapies for the treatment of rejection. Zusammenfassung Seite -42-

Zusammenfassung

In jüngster Zeit konnte gezeigt werden, dass es durch gleichzeitige Knochenmark- und Inselzelltransplantation möglich ist, in diabetischen NOD-Mäusen Toleranz gegenüber MHC- identischen Inseltransplantaten zu induzieren und so sowohl eine Abstoßung als auch ein Wiederauftreten der Autoimmunreaktion zu verhindern. Trotz vorhandener Toleranz und guter Transplantatfunktion zeigte sich in diesen Transplantaten eine Infiltration durch mononukleäre Zellen, die die Inseln umgaben, ohne sie in ihrer Struktur oder Funktion zu schädigen. Um die Unterschiede zwischen diesen toleranzassoziierten Infiltraten und den destruktiven Infiltraten während Abstoßung sowie ihren Einfluß auf die Genexpression in den Inseltransplantaten zu ermitteln, wurden vergleichende Genexpressionsstudien mit tolerierten Transplantaten aus dem beschriebenen Toleranzinduktionsmodell und abgestoßenen Transplantaten von unbehandelten Tieren durchgeführt.

Das Genexpressionsmuster in tolerierten und abgestoßenen Inseltransplantaten wurde mittels Affymetrix U74A-Oligonukleotidarrays verglichen. Die Ergebnisse dieser Versuche wurde mittels konventionellen Methoden der Genexpressionsanalyse (quantitative realtime PCR, RNase-Protektions-Assay) bestätigt.

Von mehr als 12.000 untersuchten Genen wurden 57 Gene vermehrt in tolerierten Inseltransplantaten exprimiert und 524 in abgestoßenen Inseltransplantaten. Tolerierte und abgestoßene Inseltransplantate unterscheiden sich dabei in der Expression von Genen unterschiedlicher funktioneller Gruppen. In abgestoßenen Inseltransplantaten fand sich eine höhere Expression einer Anzahl von Oberflächenmarkern von T-Lymphozyten (z. B. CD3, CD8) und von Genen, die bei ihren zytotoxischen Eigenschaften eine Rolle spielen (z. B. Granzyme B, Fas Ligand). Ebenso fand sich in den abgestoßenen Transplantaten eine gesteigerte Expression einer Anzahl an Zytokinen und Chemokinen sowie ihrer Rezeptoren (z. B. IL-1β, IFN-γ, MIG, RANTES). Die unterschiedliche Expression von einigen ausgewählten Genen, die mittels Microarray-Analyse ermittelt wurde, konnte mittels quantitativer realtime-PCR und RNase-Protektions-Assay bestätigt werden. Die Ergebnisse dieser Versuche legen nahe, dass sich die mononukleären Zellen, die im Kontext einer ablaufenden Abstoßung und die bei Toleranz ein Transplantat infiltieren, im Expressionsmuster der Gene unterschiedlicher funktioneller Gruppen unterscheiden. Die Microarray-Analyse mit Oligonukleotidarrays kann genutzt werden, um die Genexpressionsunterschiede zu ermitteln, die repräsentativ für die biologischen Mechanismen von Toleranz und Abstoßung sind. Auf diese Weise kann sie somit Zielgene für die Entwicklung von Methoden zur Überwachung der Immunantwort und Ansatzpunkte für neue Medikamente liefern. References Seite -43-

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Acknowledgements

At this point I want to thank all the people who made this work possible with help and support. First of all, I would like to thank Prof. Dr. Martin Reincke who with his offer to accept this work as my doctoral thesis has been a consistently supportive background to me and thereby made it possible that I could do this work in the USA. I also want to acknowledge Bernhard J. Hering MD, Director of the Islet Transplantation Program at the Diabetes Institute for Immunology and Transplantation (DIIT), who made my research year in the USA possible by asking me to come to Minneapolis as research scholar. He has given me ideas for different interesting projects and his generosity allowed to realize them in the most technically advanced way. I would also like to express my gratitude to David E. R. Sutherland MD PhD, Director of the DIIT, whose friendliness has always been contagious and whose scientific and medical achievements I deeply admire. My particular thanks go to Zhiguang Guo MD PhD, Director of Basic Research at the DIIT, who together with me developed the project that resulted in this thesis and who helped me to attend two impressive international meetings. My thanks also go to the whole small animal team: Tao Wu, Yisheng Pan, Neal Heuss and Hannes Kalscheuer. Brett Levay-Young PhD was a great help and teacher for me. He introduced me into the secrets of molecular biology and we together conquered the world of genechips. I also want to thank Gretchen Unger PhD, who shared her benchspace with me and who had to stand all the mess I’ve made there. I also received great support from Aaron Becker and Jill Plumb-Smith in the Affymetrix Core Facility at the Biomedical Genomics Center of the University of Minnesota who helped me with the hybridization of the chips and the shipment of data around the world. Nicole Kirchhoff DVM was a great help for me as well. She taught me, how to do histology on islets and she helped me with the nice pictures of tolerated and rejected islets. I want to thank Mike Murtaugh and the whole Veterinary Pathobiology group, especially Kendra Hyland, Cheryl Dvorak and Colleen Finnigan, whose quantitative PCR machine I was allowed to use and who shared their experience in this technique with me. Jeff Ansite greatly supported me with the ordering of all the necessary supplies for this project and for all the other things I worked at. Martha Kasper was of great help with all organizational question surrounding both of my stays in the USA. Through personal invitations she also let me get to know, how life works in an extended US American family. Acknowledgments Seite -53-

I also give my thanks to Sue Clemmings and Maria Hardstedt, who helped me with their interest in this project and whenever there was trouble ahead during my time in the USA. My thanks also go to Marc Jenkins, whose Immunology Class in the Microbiology, Immunology and Cancer Biology PhD program helped me so much in writing this thesis. I also want to thank Catherine Verfaillie and the Stem Cell Institute at the University of Minnesota who offered me the great experience to work in her lab and thereby gave me great insights into the developing field of Stem Cell Biology. I am of course greatly indebted to all the other colleges at the DIIT. They gave me a warm welcome and made my time in the USA fun.

Particular thanks go to the German National Merit Foundation (Studienstiftung des deutschen Volkes) who supported me financially during my time in the USA and throughout my studies and encouraged me to give my studies an international orientation.

Finally my thanks go to my parents. They greatly managed my stuff here in Germany while I was abroad and they always had an open ear when there were problems.

Last, but not least, I want to thank my girlfriend Britta Gebauer. She helped me with the difficult decision to take up the adventure of pursuing my doctoral thesis abroad. She had to stand all the complaints that I had when there were frustrations and she supported me when I had to leave her last summer to go back to the DIIT to finish what I had begun. Thanks for everything! Publications Seite -54-

Publications

This work resulted in the following original publications and contributions for international meetings:

Tobias Berg, Tao Wu, Brett Levay-Young, Neal Heuss, Yisheng Pan, Nicole Kirchhof, David

ER Sutherland, Bernhard J Hering, and Zhiguang Guo. COMPARISON OF TOLERATED

AND REJECTED ISLET GRAFTS: A GENE-EXPRESSION STUDY manuscript submitted for publication in CELL TRANSPLANTATION

Zhiguang Guo, Tobias Berg, Tao Wu, Brett Levay-Young, Neal Heuss, Yisheng Pan, Nicole

Kirchhof, David ER Sutherland, Bernhard J Hering. (2002) A COMPARISON OF GENE

EXPRESSION IN TOLERATED AND REJECTED ISLET GRAFTS BY GENE

MICROARRAY ANALYSIS. Transplantation 74(Supplement): 93 (Abstract No. 243) presented as oral presentation by Tobias Berg at the XIX. International Congress of The Transplantation Society in Miami (August 25 – August 30, 2002)

Tobias Berg, Tao Wu, Brett Levay-Young, Neal Heuss, Yisheng Pan, Nicole Kirchhof, David

ER Sutherland, Bernhard J Hering, and Zhiguang Guo. A COMPARISON OF GENE

EXPRESSION IN TOLERATED AND REJECTED ISLET GRAFTS BY GENE

MICROARRAY ANALYSIS. American Journal of Transplantation 2 (Supplement 3):

331 (Abstract No. 771) accepted for poster presentation at the American Transplant Congress in Washington DC (April 26 – May 1, 2003).

Lebenslauf Seite -55-

Tobias Berg

Merzhauserstr. 86 D-79100 Freiburg i. Br. E-mail: [email protected]

LEBENSLAUF

Geburtsdatum: * 19. März 1977 in Bernkastel-Kues Eltern Dr. Walter Berg & Margarete Berg Geschwister: Andreas Berg

BILDUNGSGANG

1983-1987 Grundschule in Hermeskeil und Losheim

1987-1996 Peter-Wust-Gymnasium, Merzig

1996 Abitur

1996-1997 Wehrdienst im Sanitätsbataillon 10, Horb und der

Luftlandesanitätskompanie 260, Lebach

1997-1999 Medizinstudium an der Universität Rostock seit 1998 Förderung durch die Studienstiftung des deutschen Volkes

1999 Ärztliche Vorprüfung (Physikum) (Note: sehr gut)

1999-2000 Medizinstudium an der Universität des Saarlandes, Homburg/Saar

2000 Ärztliche Prüfung Teil I (Note: sehr gut)

2000-2001 Forschungsaufenthalt am Diabetes Institute for Immunology and

Transplantation der University of Minnesota, Minneapolis, USA seit 2001 Medizinstudium an der Albert-Ludwigs-Universität, Freiburg

2003 Ärztliche Prüfung Teil II (Note: sehr gut)

Lebenslauf Seite -56-

AKTIVITÄTEN UND QUALIFIKATIONEN

1992 ‘Jugend forscht’ (Physik) Thema: ‘Stereobilder’ (1. Preis im Saarland) 1994 ’41. Europäischer Wettbewerb’ Thema: ‘Europäische Integration’ 1. Preis und Sonderpreis der Europa-Union, Deutschland, der Jungen Europäischen Föderalisten und des Wirtschaftsministers 06/1996 Krankenpflegepraktikum Caritas-Krankenhaus Dillingen 02/1998 – 03/1998 Krankenpflegepraktikum St.-Josef-Krankenhaus, Losheim am See 05/1998 ‘Hanseatic Students´ Days of Science 1998’ (internationaler Kurs über molekularbiologische Methoden) 08/1998 – 10/1998 Krankenpflegepraktikum St.-Josef-Krankenhaus, Losheim am See 09/1998 Sommerakademie der Studienstiftung in Alpbach, Österreich Thema: ‘Regulatory mechanisms in normal and malignant cells’ 05/1999 ‘Hanseatic Students´ Days of Science 1999’ (internationaler Kurs über molekularbiologische Methoden) 09/1998 Sommerakademie der Studienstiftung in St. Johann, Italien Thema: ‘Evolution – in vivo, in vitro et in machina’ 02/2002 Famulatur an der Medizinischen Klinik I der Universität Freiburg Hämatologie und Onkologie 03/2002 Famulatur am Elisabeth-Krankenhaus in Wadern Innere Medizin seit 05/2002 Tätigkeit als wissenschaftliche Hilfskraft an der Medizinischen Klinik I der Universität Freiburg 03/2003 – 04/2003 Famulatur am Royal-Perth-Hospital in Perth, Australien

SPRACHEN UND HOBBIES Sprachen: Englisch, Französisch, Italienisch (Grundkenntnisse) Hobbies: Skifahren (Übungsleiter seit 1994)

Freiburg am 26. November 2003