Inflammatory Bowel Disease

LINKÖPING UNIVERSITY MEDICAL DISSERTIONS No. 1231

Interindividual differences in thiopurine metabolism

- studies with focus on inflammatory bowel disease

Sofie Haglund

Division of Gastroenterology and Hepatology Department of Clinical and Experimental Medicine Faculty of Health Sciences, Linköping University SE-581 85 Linköping, Sweden

Linköping 2011

 Sofie Haglund, 2011

Published articles and figures have been reprinted with the permission of the copyright holders: Paper I. © American Association for Clinical Chemistry. Paper II. © John Wiley and Sons. Paper III. © Wolters Kluwer Health.

Figure 4. © Wolters Kluwer Health. Figure 6. © QIAGEN. All rights reserved. www.qiagen.com. Figure 7. © Copyright 2006 Life Technologies Corporation. All rights reserved. www.lifetechnologies.com. None of the entities identified support or endorse or accept liability in any way for the information that is used or the manner in which it is used. Figure 9. © Elsevier.

Printed in Sweden by LiU-Tryck, Linköping, Sweden, 2011 ISBN 978-91-7393-213-4 ISSN 0345-0082

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Contents

CONTENTS

ABSTRACT ...... 1

POPULÄRVETENSKAPLIG SAMMANFATTNING ...... 2

LIST OF PAPERS...... 5

ABBREVIATIONS...... 7

INTRODUCTION ...... 9 Inflammatory bowel disease...... 9 Treatment of inflammatory bowel disease ...... 11 Thiopurines...... 12 Metabolism of thiopurine drugs ...... 14 Therapeutic drug monitoring...... 18 Drugs interacting with thiopurine metabolism...... 20 Pharmacogenomics...... 21 Thiopurine S-methyltransferase ...... 22 Inosine 5´-monophosphate dehydrogenase ...... 25 Other enzymes in the thiopurine metabolism...... 27 AIMS...... 31

MATERIALS AND METHODS...... 33 Patients and healthy volunteers ...... 33 Methods ...... 35 Preparation techniques ...... 35 Pyrosequencing®...... 36 Determination of enzyme activities...... 38 Determination of metabolite concentrations ...... 39 Tissue culture and transfection...... 40 Determination of mRNA expression...... 41 Bioinformatic tools...... 43 Statistics ...... 43 RESULTS AND DISCUSSION...... 45 TPMT genotypes in Sweden ...... 45 Concordance between TPMT genotype and phenotype...... 46 IMPDH in thiopurine metabolism...... 52 Pharmacotranscriptomics in patients with different metabolite profiles...... 60 Contents

CONCLUSIONS...... 67

FUTURE ASPECTS...... 69

ACKNOWLEDGEMENTS...... 71

REFERENCES ...... 73

Abstract

ABSTRACT

The thiopurines, 6-mercaptopurine and its prodrug azathioprine, are used in the treatment of inflammatory bowel disease, ulcerative colitis and Crohn´s disease. The main active metabolites are the phosphorylated thioguanine nucleotides (6-TGNs) and methylated thioinosine monophosphate (meTIMP). Both groups contribute to the immunomodulatory effects. About 30-40% of patients fail to benefit from thiopurine treatment. A well-known cause of adverse reactions is decreased or absent thiopurine S-methyltransferase (TPMT) activity. Low TPMT activity is inherited as an autosomal codominant recessive trait and is present in approximately 10% of the population. Although several clinical issues can be solved from determination of TPMT activity, there are cases where it is not possible. In Sweden approximately 25% of IBD-patients display suboptimal 6-TGN concentrations and unexpectedly high concentrations of meTIMP despite a normal TPMT activity. A high meTIMP/6-TGN concentration ratio has been associated with both unresponsiveness to therapy and emergence of adverse reactions. Inosine 5’-monophosphate dehydrogenase (IMPDH) may constitute a candidate gene to explain this metabolite profile, as it is strategically positioned in the metabolic pathway of thiopurines where it competes with TPMT for their common substrate 6-TIMP. In paper I a pyrosequencing method was developed for genotyping of at that time all known genetic variants of TPMT. The concordance between genotype and phenotype in 30 individuals was 93%. The allele frequencies of TPMT*3A, *3B, *3C and *2 in a Swedish background population (n=800) were in agreement with those in other Caucasian or European populations. In Paper II-IV we explored the molecular basis of different metabolite profiles, i.e. low, normal and high meTIMP/6-TGN concentration ratios. The activity of IMPDH was measured in mononuclear cells (MNC). Patients with high metabolite ratios had lower IMPDH activity than patients with normal or low ratios, explained by an inverse correlation to red blood cells concentration of meTIMP. No correlation to 6-TGN was observed. Downregulation of IMPDH activity in HEK293 cells with genetically engineered TPMT activity was associated with an increase in meTIMP, but unexpectedly also of 6-TGN, irrespective of the TPMT status. These results suggest effects of pharmacogenes other than TPMT and IMPDH. A whole genome expression analysis was performed, (1) to identify new candidate genes that could explain differences in metabolite profiles, and (2) to study genes with known associations to the metabolic pathway of (thio)purines. The whole genome expression analysis did not identify any significant group differences. In analysis of the thiopurine related genes, three clusters of co-regulated genes were defined. A co-operation between expression levels of SLC29A1 and NT5E in explaining the meTIMP/6-TGN concentration ratio was observed, and individually SLC29A1 and NT5E correlated to 6-TGN and meTIMP, respectively. Pysosequencing is a convenient and flexible method which is now run in parallel to phenotyping in our laboratory. Our results also illustrate the complexity of the thiopurine metabolism and suggest that differences between metabolite profiles are explained either by interactions between several genes, each with a small contribution, or at the post-transcriptional level. Search for more precise tools to explain differences in metabolite profiles is needed. Furthermore, in order to investigate small effects it is necessary to analyse metabolite concentrations and gene expression levels, as well as enzyme activities in the target cells of therapy (MNC).

Populärvetenskaplig sammanfattning

POPULÄRVETENSKAPLIG SAMMANFATTNING

Det är sedan tidigare välkänt att genetiska variationer i enzymer (äggviteämnen) som omvandlar (metaboliserar) läkemedel i kroppen spelar stor roll för hur patienten tolererar dem. Vissa genetiska konstellationer medför en ökad risk för biverkningar, medan andra leder till utebliven effekt om patienten behandlas med standard-doser. Dessa patienter brukar kallas långsamma respektive hypersnabba metaboliserare och de behöver en reducerad respektive en förhöjd dos för att uppnå önskad läkemedelseffekt. En ökad kunskap om olika faktorer, såväl genetiska som andra, vilka påverkar omsättningen av läkemedel kan bidra till att individanpassa en potentiellt toxisk behandling för att undvika allvarliga biverkningar, men även utebliven effekt. Immunhämmande läkemedel som tiopuriner (Imurel® och Puri-Nethol®) används bland annat vid kronisk inflammatorisk tarmsjukdom (ulcerös colit och Crohns sjukdom). I kroppen omvandlas de via en omfattande metabolism till aktiva metaboliter; tioguaninnukleotider (6-TGN) och metyltioinosinmonofosfat (meTIMP). Användningen av tiopuriner begränsas av allvarliga biverkningar eller utebliven effekt hos upp till 40% av patienterna. Delvis förklaras den ökade risken för biverkningar av genetiska varianter som resulterar i låg aktivitet av enzymet tiopurin S-metyltransferas (TPMT). TPMT-aktiviteten korrelerar positivt till metaboliten meTIMP och negativt till de viktigaste immunhämmande metaboliterna 6-TGN. I en inledande studie utvecklades en metod för att bestämma alla då kända gen-varianter av TPMT. Förekomsten av de vanligaste varianterna (TPMT*2, TPMT*3A, TPMT*3B, TPMT*3C) undersöktes i en svensk bakgrundspopulation bestående av 800 individer från sydöstra Sverige. Vi observerade att 90% av befolkningen inte bar någon av de undersökta genvarianterna och de kan därför antas ha normal enzymaktivitet. Dessutom var frekvenserna av de olika genvarianterna ungefär desamma som i andra europeiska populationer. Alla oönskade läkemedelseffekter kan inte förklaras utifrån TPMT. Patienter med en oväntat hög meTIMP/6-TGN kvot och normal TPMT-aktivitet har i tidigare studier visats vara utsatta för såväl en större risk att svara sämre på behandlingen (otillräckligt 6-TGN), som en ökad risk för biverkningar (för mycket meTIMP). En reducerad aktivitet av enzymet inosinmonofosfat-dehydrogenas (IMPDH) skulle, utifrån dess placering i tiopurinmetabolismen, kunna förklara en sådan avvikande metabolitprofil. I två studier undersöktes mängden bildade metaboliter i relation till IMPDH-aktiviteten, dels i en oselekterad grupp patienter och dels i en grupp som valts ut utifrån specifika metabolitprofiler (hög, normal, eller låg meTIMP/6-TGN kvot). IMPDH-aktiviteten var lägre bland patienter med höga metabolitkvoter, vilket förklarades av ett negativt samband till mängden bildad meTIMP, men inget samband till koncentrationen 6-TGN. Med hjälp av en cellmodell (HEK293 celler) undersöktes IMPDH i ett kontrollerat system avseende tiopurinbehandlingen, för att kartlägga detta 2

Populärvetenskaplig sammanfattning enzyms roll i tiopurinmetabolismen ytterligare. Modellen tillät samtidigt modulering av TPMT-aktiviteten. Då IMPDH-aktiviteten nedreglerades, erhölls tvärtemot det förväntade, en stegring av 6-TGN oavsett TPMT-status, vilket antyder effekter av andra farmakogener än IMPDH och TPMT. Även om förhållandena i kroppen och i en cellmodell inte är helt jämförbara så pekar resultaten på att 6-TGN regleras av andra, ännu okända farmakogener. För att identifiera nya farmakogener av betydelse för metabolitprofilen utfördes en helgenoms-expressionsanalys där uttrycket av samtliga gener i det mänskliga genomet jämfördes grupperna emellan. Denna analys visade inga signifikanta gruppskillnader som kunde karaktärisera metabolitprofilerna. En separat analys av gener relaterade till tiopurinmetabolismen visade stora interindividuella skillnader i genuttryck, men endast små skillnader mellan metabolitprofilerna. Tre huvudkluster av samreglerade gener kunde definieras baserat på korrelationer mellan genexpressions-nivåer. Vidare korrelerade koncentrationen av meTIMP positivt till uttrycket av genen för 5´-ectonukleotidas (NT5E) och negativt till TPMT. Koncentrationen av 6-TGN korrelerade positivt till uttrycket av genen för transportproteinet equlibrative nucleoside transporter 1 (SLC29A1) och negativt till genen för enzymet hypoxantin-guanin- fosforibosyltransferas. En interaktion mellan genuttrycken av NT5E och SLC29A1 och kvoten meTIMP/6-TGN observerades. Sammantaget visar våra resultat att pyrosekvensering är en enkel och flexibel metod för genotypning. Den har införts i den kliniska verksamheten och används parallellt till aktivitetsmätningarna av TPMT. Vidare visar resultaten att regleringen av tiopurinmetabolismen är komplex och att en hög meTIMP/6-TGN kvot sannolikt involverar fler farmakogener än IMPDH and TPMT. Avsaknaden av signifikanta resultat i helgenoms-analysen kan bero på att en individs metabolitprofil orsakas av interaktioner mellan många små effekter. För att utreda sådana små effekter är det nödvändigt att såväl metabolitkoncentrationer som enzymaktiviteter och genuttryck analyseras i målcellerna för behandlingen, dvs de vita blodkropparna. Studierna ger en delvis förändrad bild av tiopurinmetabolismen, och belyser ytterligare dess komplexitet.

List of papers

LIST OF PAPERS

I. Pyrosequencing of TPMT alleles in a general Swedish population and in patients with inflammatory bowel disease. Sofie Haglund, Malin Lindqvist, Sven Almer, Curt Peterson, Jan Taipalensuu. Clinical Chemistry 2004: Feb;50(2): 288-295

II. IMPDH activity in thiopurine-treated patients with inflammatory bowel disease – relation to TPMT activity and metabolite concentrations. Sofie Haglund, Jan Taipalensuu, Curt Peterson, Sven Almer. British Journal of Clinical Pharmacology 2008: Jan;65(1): 69-77

III. The role of inosine-5´-monophosphate dehydrogenase in thiopurine metabolism in patients with inflammatory bowel disease. Sofie Haglund, Svante Vikingsson, Jan Söderman, Ulf Hindorf, Christer Grännö, Margareta Danelius, Sally Coulthard, Curt Peterson, Sven Almer. Therapeutic Drug Monitoring 2011: 33(2): 200-208

IV. Pharmacotranscriptomics in thiopurine treated patients with different metabolite profiles. Sofie Haglund, Sven Almer, Curt Peterson, Jan Söderman. Manuscript

Abbreviations

ABBREVIATIONS

AMTCI 4-amino-5-methylthiocarbonyl imidazole APS adenosine 5´-phosphosulfate 5-ASA 5-aminosalicylic acid AZA azathioprine CD Crohn´s disease CNT concentrative (Na+-dependent) nucleoside transporter (gene family SLC28) CRE cyclic AMP responsive element DNPS de novo purine biosynthesis ENT equilibrative nucleoside transporter (gene family SLC29) GMP reductase guanosine monophosphate reductase (genes GMPR1 and GMPR2) GMP synthetase guanosine monophosphate synthetase (gene GMPS) GST glutathione transferase HGPRT hypoxanthine guanine phosphoribosyltransferase (gene HPRT1) IBD inflammatory bowel disease IMPDH inosine 5´-monophosphate dehydrogenase (genes IMPDH1 and IMPDH2) IP-RP-HPLC ionpair reversed phase high performance liquid chromatography ITPase inosine triphosphatase (gene ITPA) MAP mitogen-activated protein 6-MP 6-mercaptopurine meTIMP methyl thioinosine monophosphate meTIDP methyl thioinosine diphosphate meTITP methyl thioinosine triphosphate MNC mononucelar cells MPA mycophenolic acid MRP multidrug resistant associated protein (genefamily ABCC) NAD nicotine adenosine dinucleotide NF-KB nuclear factor-ΚB qPCR quantitative polymerase chain reaction PRPP 5-phosphoribosyl-1-pyrophosphate RBC red blood cells SAM S-adenocyl methionine siRNA short interfering RNA 6-TG 6-thioguanine 6-TGMP 6-thioguanosine monophosphate 6-TGDP 6-thioguanosine diphosphate 6-TGTP 6-thioguanosine triphosphate 6-TGN 6-thioguanine nucleotides 6-TITP 6-thioinosine triphosphate TNF-α tumor necrosis factor alpha TPMT thiopurine S-methyltransferase (gene TPMT) UC ulcerative colitis XMP xanthosine monophosphate

Abbreviations

XO xantine oxidase (gene XDH)

Introduction

INTRODUCTION

Characteristics such as blood group, eye colour, and hair colour show interindividual variability. This is also the case for many drug metabolizing enzymes. Person-to-person differences in drug response may to a large extent be a consequence of differences at the genomic and/or transcriptomic level. Treatment with the immunomodulatory thiopurine drugs is associated with the risk of severe, sometimes fatal, adverse reactions. The metabolism of thiopurine drugs has therefore gained considerable interest in order to identify ways to individualize therapy to maximise clinical effects and to minimise the risk of adverse reactions. Thiopurine S-methyltransferase is an important pharmacogene in the context of thiopurine therapy and is known to influence clinical efficacy and adverse reactions, but previous studies have suggested that also other enzymes involved in the metabolism are of importance. The overall aim of this thesis was to advance the knowledge of factors that affect the metabolism of thiopurines, with a clinical perspective, to improve the care of patients with inflammatory bowel disease.

Inflammatory bowel disease Inflammatory bowel disease (IBD) includes ulcerative colitis (UC), Crohn’s disease (CD) and microscopic colitis. At present it is thought that a genetic predisposition in combination with exposure to unidentified environmental agents give rise to clinical disease through dysregulation of the intestinal mucosal immune system with subsequent destruction of bowel integrity. The discrepancy of IBD among monozygotic twins and the development of IBD among immigrants to high prevalence countries and in countries adopting the Western lifestyle highlight the importance of an interplay between genetic and environmental factors. Genetic factors appear to have greater importance in CD than in UC (Loftus 2004; Halfvarson et al. 2007). UC and CD are characterized by a chronic inflammation of the digestive tract and symptoms experienced include diarrhoea, abdominal pain and systemic symptoms of malaise, anorexia, fever and weight loss. The majority of patients have a relapsing course with periods of active disease, which are medically or surgically brought into clinical remissions of variable length. The diseases have common characteristics and in some patients no distinct diagnosis can be made. In these cases the term indeterminate colitis is used (Satsangi et al. 2006; Cho 2008).

Introduction

Incidence and environmental risk factors The highest incidence of IBD is in young adult life (20-35 years) and the highest incidence rates (new cases per 100 000 person-years) and prevalence for both UC and CD are found in northern Europe, the United Kingdom and North America. There seems to be a south to north and east to west gradient of IBD even though these boundaries gradually are diminishing. In Europe the incidence rate ranges from 1.5 to 20.3 cases per 100 000 person years for UC and from 0.7-9.8 cases per 100 000 person-years for CD. Around 50 000 to 68 000 new cases of UC and between 23 000 and 41 000 new cases of CD are diagnosed annually. Extrapolating these figures means that approximately 2.2 milj persons are affected by these diseases in Europe (Loftus 2004) and 1% of the population in Sweden (Lapidus 2009). UC is generally diagnosed 5-10 years later in life than CD and is slightly more common in men than women, whereas the opposite is true for CD. Men are diagnosed at higher age than women (Loftus 2004; Lapidus 2009). No association with social class has been described. The most significant environmental risk factors for IBD are smoking and appendectomy. Smoking is associated with a reduced risk of UC, and a history of recent cessation of smoking is common in patients presenting UC. Conversely, smoking increases the risk of relapse and surgery in CD. Appendectomy at low age generally appears to reduce the risk of UC and increase the risk of CD (Andersson et al. 2001; Andersson et al. 2003; Loftus 2004).

Genetic risk factors UC and CD are polygenic diseases. Genome wide scans have identified susceptibility regions on many chromosomes (Cho 2008; Kaser et al. 2010). Some loci are common with both UC and CD whereas others are found in only one of the two diseases. NOD2, which encodes an intracellular pattern recognition receptor, was the first IBD locus to be identified and is localised on chromosome 16. Three main mutations have been described (Hugot et al. 1996; Hugot et al. 2001). Variations in genes related to the signalling pathway of interleukin 23 (IL-23) including variation in IL23R, IL12B (encoding the p40 subunit of both IL-12 and IL-23) and STAT3 (encoding signal transducer and activator of transcription 3) are common to both forms of IBD as well as NK2 transcription factor related locus 3 (NKX2-3). Alterations in genes associated with the innate immune system such as NOD2 (also known as CARD15), autophagy related 16-like protein 1 (ATG16L1), immunity-related GTPase family M (IRGM), intelectins (ITLN1), and NALP3/cryopyrin (NLRP3) are specific to CD. Loci related to IL-10, intestinal epithelial cell function (ECM1) and E3 ubiquitin ligase (HERC2) appear to be specific to UC (Cho 2008; Kaser et al. 2010). The number of candidate loci is constantly growing. At least 71 CD and 47 UC susceptibility loci have been identified, most of them associated with moderate disease risk indicating interactions between several genetic loci and environmental factors (Franke et al. 2010; Anderson et al. 2011). Many candidate genes may have indirect

Introduction influence on therapeutic response due to common signalling pathways involved (Torok et al. 2008; Vermeire et al. 2010).

Treatment of inflammatory bowel disease

Treatment of UC and CD is symptomatic and currently based on aminosalicylates, corticosteroids, immunomodulatorys (thiopurines, methotrexate, and cyclosporine), biologicals such as anti-tumor necrosis factor alpha (TNF-α) antibodies, antibiotics, nutritional support and surgery (Travis et al. 2006; Timmer et al. 2007; Travis et al. 2008; D'Haens et al. 2010; Prefontaine et al. 2010).

5-aminosalicylic acid, mesalazine 5-aminosalicylic acid (5-ASA) is used to induce and maintain remission in mild to moderate IBD. The clinical effect is more evident in UC than in CD (van Bodegraven and Mulder 2006). Induction of peroxisome proliferator activated receptor gamma, which is a negative regulator of NF-ΚB, is one of many effects (Rousseaux et al. 2005). 5-ASA is metabolized via the polymorphic N-acetyltransferas 1 (NAT1) (Sim et al. 2008) predominantly expressed in the epithelial cells of the intestine and in liver (Windmill et al. 2000).

Corticosteroids Corticosteroids are mainly used for short-term induction of remission as they are associated with long-term side effects like Cushing´s syndrome, adrenal suppression, as well as growth retardation in children (Benchimol et al. 2008; Sherlock et al. 2010). After binding to one of the glucocorticoid receptors (GRα active, GRβ inactive) the entire complex is transported to the cell nucleus where it binds to specific motifs of DNA, regulating the expression of inflammatory genes (Farrell and Kelleher 2003; Beck et al. 2009). The relative expression of GRα and GRβ has been related to the response to corticosteroid therapy (Honda et al. 2000; Fujishima et al. 2009). Genetic variants of the receptors and of MDR1 (encoding the transportprotein P-glycoprotein) may cause hyper-responsiveness or resistance to therapy (Huizenga et al. 1998; De Iudicibus et al. 2007; Vermeire et al. 2010).

Immunomodulatory drugs A substantial part of patients with active IBD develops adverse reactions to corticosteroids, does not respond to corticosteroid treatment (steroid refractory) or experiences early relapses when the steroid dose is lowered or withdrawn (steroid dependent) (Munkholm et al. 1994). These patients are at great risk of future need of

Introduction extensive bowel resections. Patients who have not responded to 5-ASA and corticosteroids are qualified for more extensive immunomodulatory drugs (Travis et al. 2006; Travis et al. 2008) and the most successful treatment so far has been thiopurine treatment with azathioprine (AZA, Europe) or 6-mercaptopurine (6-MP, USA). Up to 40% of patients do not respond or are intolerant to thiopurine therapy which underscores the need to individualize therapy by identifying patients at risk for adverse reactions and patients who risk unresponsiveness (Ahmad et al. 2004; Hindorf et al. 2006; Jharap et al. 2010). A second line therapy, when thiopurines fail, is methotrexate (Preiss and Zeitz 2010).

Biologicals Anti-TNF-α antibodies are used in patients who are steroid dependent, not responding to thiopurines, or who experience complex fistulising CD (D'Haens et al. 2010). As a player in the innate immune system, TNF-α stimulates the production of other proinflammatory cytokines, enhances the expression of adhesion molecules, and activates cells of both the innate and adaptive immune system. Infliximab is a chimeric mouse/human monoclonal antibody to TNF-α. It neutralizes TNF-α, reduces TNF-α mediated apoptosis of epithelial cell (Zeissig et al. 2004) and induces apoptosis in activated T-cells (ten Hove et al. 2002). However, 20-40% of patients do not respond to therapy (Ahmad et al. 2004). Because of its high costs and rare but serious adverse infusion reactions the differential response to therapy has received great attention. The SONIC study (Study of biological and Immunomodulator Naïve patients In Crohn’s disease) showed that combination with thiopurine drugs improved long-term efficacy (Colombel et al. 2010; D'Haens et al. 2010). Natural candidate genes involved in the clinical response to anti TNF-α therapy include the TNF-α gene, the TNF-α receptor gene, and the metalloproteinases that cleave the membrane bound form of TNF-α to the soluble form, but also genes of apoptotic pathways (Arijs et al. 2009; Vermeire et al. 2010).

Thiopurines

History In the 1940:s the knowledge of nucleic acids was limited. The helical structure of DNA had not yet been proposed and even if the prevailing theory was that there were two types of purine bases and two types of pyrimidine bases in nucleic acids, the nature of the internucleotide linkage was unknown. George Hitchings theorized that, since all cells require nucleic acids it would be possible to stop the growth of rapidly dividing cells, such as bacteria and tumours, with antagonists to the nucleic acid bases (Elion 1989).

Introduction

Replacing oxygen with sulphur at the 6-position of guanine and hypoxanthine produced substances that could inhibit purine utilization. 6-MP and 6-thioguanine (6-TG, 2,6-diaminopurine) (Figure 1) were both effective inhibitors of the growth of L. casei, as well as of tumours in mice. Animal toxicology studies showed better tolerability for 6-MP. Clinical studies in children with acute leukaemia followed (Burchenal et al. 1953; Clarke et al. 1953). 6-MP produced complete remission although many patients relapsed after a median period of 12 months. This led the Food and Drug Administration to approve the drug for the use in leukaemia in 1953, just 2 years after the drug was synthesized (Elion 1989). To prolong the half-life of 6-MP, a 1-methyl-4-nitro-5-imidazole moiety was added to protect the reactive sulfurgroup from oxidation and hydrolysis. The product azathioprine, AZA, showed even better immunomodulatory effects than 6-MP in preventing rejection of kidney transplants in dogs (Calne et al. 1962) and in humans (Murray et al. 1963) which led to the use of AZA rather than 6-MP when immunosuppression was sought. In 1988, Gertrude Elion and George Hitchings received the Nobel Prize in Physiology or Medicine for their discoveries of principles for drug treatment.

Figure 1. Chemical structures of 6-mercaptopurine, azathioprine, and 6-thioguanine. 6-mercaptopurine is a thio-analogue of hypoxanthine. Azathioprine is the methylnitroimidazolyl derivative of 6-mercaptopurine. 6-thioguanine is a thio-analogue of guanine.

Thiopurines in IBD The thiopurine drugs have been used in IBD for decades. Currently they are mainly used as corticosteroid-sparing drugs for inducing and maintaining remission both in UC and CD. Studies have shown that up to 40% of patients do not respond or are intolerant to thiopurine therapy (Ahmad et al. 2004; Hindorf et al. 2006; Timmer et al. 2007; Prefontaine et al. 2009; Jharap et al. 2010). Therefore, it is urgent to identify factors that enable individualization of this important but potentially toxic therapy in order to avoid adverse reactions and circumvent refractoriness. It could also be asked which is the optimal duration of therapy, especially for patients in long-term clinical remission. Withdrawal studies have shown that long-term continuation may be favourable in the

Introduction majority of patients to avoid relapses. But a minority needlessly continue thiopurine therapy and are exposed to the risks associated with this therapy (Treton et al. 2009). Identification of patients at low risk of relapse would be as important as to identify those at risk for adverse reactions or unresponsiveness during standard treatment.

Thiopurines in other conditions Since the initial observations of the immunomodulatory effects in transplanted dogs, AZA is used to reduce the risk of graft versus host reactions (Murray et al. 1963; Chocair et al. 1993). 6-MP is used in combination with methotrexate to induce and maintain remission in childhood acute lymphoblastic leukaemia (Cheok et al. 2009), which affect approximately 100 individuals each year in Sweden. 6-TG is used as palliative therapy in acute myeloblastic leukaemia (Buchner et al. 2001). AZA has its role in systemic lupus erythematosus (Askanase et al. 2009) and in rheumatoid arthritis mainly as a corticosteroid-sparing drug (Black et al. 1998; Clunie and Lennard 2004).

Metabolism of thiopurine drugs Compared with many other drugs, the metabolism of thiopurines is complex. The peak plasma concentrations are reached after 1-2 hours in most patients following oral intake. The concentrations then rapidly decline with half-lives of less than 1 hour (Lafolie et al. 1986). AZA and 6-MP are both pro-drugs converted to active metabolites via extensive metabolism (Figure 2). Following oral administration the bioavailability of both AZA (27-83%) and 6-MP (5-37%) is poor (Zimm et al. 1983; Van Os et al. 1996). One reason is the extensive first pass metabolism to 8-OH-6-MP or thioxanthine (2-OH-6-MP) and then to thiouric acid via xanthine oxidase (XO) and/or aldehyde oxidase (Krenitsky et al. 1972; Keuzenkamp-Jansen et al. 1996; Rashidi et al. 2007). XO is expressed in intestinal epithelial cells and liver (Parks and Granger 1986), but is absent in circulating blood cells (Parks and Granger 1986). The inactive product thiouric acid is excreted in the urine. AZA is not a substrate of XO, but aldehyde oxidase has the potential to oxidize AZA to thiouric acid without generating 6-MP (Chalmers et al. 1969). AZA is converted to 6-MP in the presence of glutathione or other sulfhydryl- containing proteins (DeMiranda et al. 1973) that release the nitro-imidazole group in red blood cells (RBC). It is appreciated that 88% of AZA is converted to 6-MP and methyl-4-nitro-5-imidazol. The remaining 12% of thiolysis occurs on the other side of the sulphur atom of AZA, generating hypoxanthine and methyl-4-nitro-5-thioimidazole (Lennard 1992). Methyl-4-nitro-5-thioimidazole has been suggested to contribute to the immunomodulatory effect, as well as the development of adverse reactions (Sauer et al. 1988; McGovern et al. 2002). Recently it was shown that glutathione transferases (mainly GSTA1-1, A2-2 and M1-1), abundant in liver and intestine, are responsible for the major part of the conversion of AZA to 6-MP (Eklund et al. 2006).

Introduction

6-MP is converted by hypoxanthine guanine phosphoribosyltransferase (HGPRT) to 6-thioinosine monophosphate (6-TIMP), which is further metabolized via inosine 5´-monophosphate dehydrogenase (IMPDH), guanosine monophosphate synthetase (GMP synthase), and kinases and reductases to one of the two main groups of active metabolites; the 6-thioguanine nucleotides (6-TGNs) (De Abreu et al. 1995). 6-TGNs include thioguanosine 5´-monophosphate, -diphosphate and -triphosphate (6-TGMP, 6-TGDP, 6-TGTP), as well as deoxy-6-TGNs. 6-TGTP represents 85% of 6-TGN found in RBC (Neurath et al. 2005; Vikingsson et al. 2009; Karner et al. 2010). Thiopurine S-methyltransferase (TPMT) methylates 6-MP to form 6-methyl-MP, which is not a substrate of HGPRT and hence an inactive metabolite (Krynetski et al. 1995). TPMT competes with IMPDH for their common substrate 6-TIMP to form the other main metabolite; methyl thioinosine monophosphate (meTIMP), which also includes the -diphosphate and, -triphosphate (Zimmerman et al. 1974; Vikingsson et al. 2009). 6-TIMP may be phosphorylated by kinases to 6-thioinosine triphosphate (6-TITP). The resultant 6-TITP may be dephosphorylated by inosine triphosphatase (ITPase) to form 6-TIMP again (Figure 2). In comparison with 6-MP and AZA, the metabolism of 6-TG is less complicated. 6-TG is converted by HGPRT to 6-TGMP and further via kinases and reductases to 6-TGN and deoxy-6-TGNs. Thus, both 6-MP an 6-TG lead to the production of identical phosphorylated metabolites, the 6-TGNs (de Boer et al. 2007). 6-TG is also a substrate for TPMT, but not for XO (Krenitsky et al. 1972; Krynetski et al. 1995). Two pathways for thiouric acid formation from 6-TG exist. One is via guanase to thioxanthine before it can be metabolized by XO to thiouric acid, and the other is via aldehyde oxidase and guanase (Bronk et al. 1988; Kitchen et al. 1999).

Introduction

Figure 2. Schematic pathways of azathioprine (AZA) and 6-mercaptopurine (6-MP) metabolism, see text for details. GST, Glutathione transferase; GSH glutathione; XO, xanthine oxidase; AO, aldehyde oxidase; HGPRT, hypoxanthine guanine phosphoribosyltransferase; TPMT, thiopurine S-methyltransferase; SAM, S-adenosyl methionine; IMPDH, inosine 5´-monophosphate dehydrogenase; NAD, nicotine adenosine dinucleotide; ITPase, inosine triphosphatase; GMPS, guanosine monophosphate synthetase; GMP reductase, guanosine monophosphate reductase; GMP kinase, guanylate kinase; RNR, ribonucleotide reductase; NDPK, nucleotide diphosphate kinases; 6-TU, 6-thiouric acid; 6-TIMP, 6-thioinosine monophosphate; 6-TXMP, 6-thioxanthosine monophosphate; 6-TITP, 6-thioinosine triphosphate; meTIMP, methyl thioinosine 5´-monophosphate; 6-TGMP, 6-thioguanosine monophosphate; 6-TGDP, 6-thioguanosine diphosphate; 6-TGTP, 6-thioguanosine triphosphate; d, deoxy; 6-TGNs, 6-thioguanine nucleotides; PRPP, 5-phosphoribosyl-1-pyrophosphate; PRA, 5-phosphoribosylamine; AMP, adenosine monophosphate.

Mechanisms of action

Phosphorylated metabolites The most important immunomodulatory mechanism of AZA and 6-MP has traditionally been regarded as incorporation of deoxy-6-TGTP in DNA and 6-TGTP in RNA (Tidd and Paterson 1974). The chromatide and DNA structures are impaired when thioguanine nucleotides form base pairs with cytidines, or erroneously with thymidines (Krynetski et al. 2001). These base pairs are tolerated by both RNA and

Introduction

DNA-polymerases, but inhibit cell cycle progression through the S and G2 phases in the subsequent cell cycle. This provokes the postreplicative DNA mismatch repair system and results in a delayed cytotoxic effect (Maybaum and Mandel 1983; Fairchild et al. 1986; Swann et al. 1996). Glyceraldehyde 3-phosphate dehydrogenase also act as a sensor of the fraudulent base pairs in nucleic acids, triggering cell death (Krynetski et al. 2001). The presence of deoxy-6-TGTP in DNA impairs the activity of enzymes involved in DNA replication and repair, such as RNAse H, topoisomerase II and T4 DNA ligase (Krynetskaia et al. 1999; Somerville et al. 2003). In T-cells stimulated with IL-2 and via CD28 and CD3, apoptosis is prevented by the activation of bcl-XL via the small GTPase Rac1. The triphosphate, 6-TGTP, competes with GTP for the site in Rac1. Upon hydrolysis, 6-TGDP bound to Rac1 inhibits the activity of guanosine exchange factor Vav1, leading to accumulation of inactive Rac1 molecules and consequently blockage of downstream signalling of the T-cell receptor with reduced expression of Rac1 target genes, such as mitogen-activated protein (MAP) kinases, NF-ΚB, STAT3 and bcl-XL (Figure 3). In this way an antiapoptotic pathway turns to a mitochondrial pathway of apoptosis. This mechanism contributes to reduce the number of activated T-cells in the lamina propria of patients with IBD (Tiede et al. 2003; Poppe et al. 2006). Blockage of Rac1 or Rac2 also disturbs the interaction between cells of the immune system by abolished suppression of lamellipodia formation (Poppe et al. 2006). It alo reduces the production of Th1 cytokines such as interferon γ (Li et al. 2000; Poppe et al. 2006). Patients with high concentrations of 6-TGNs and with a high proportion of 6-TGTP have been shown to respond better to therapy than patients with a high proportion of 6-TGDP (Neurath et al. 2005).

MAP-kinases NF-ΚB 6-TGTP Rac1-6-TGTP Rac1-6-TGDP STAT-3 Bcl-XL Vav1

Figure 3. 6-TGTP binds Rac1. After hydrolysis, 6-TGDP inhibits the activity of guanosine exchange factor Vav1, leading to the accumulation of inactive Rac1, inhibition of GTP incorporation in Rac1 and blockage of downstream signalling.

Recently it was shown that thiopurines reduce the expression in activated lymphocytes of inflammatory genes such as tumour necrosis factor related apoptosis- inducing ligand, tumour necrosis factor receptor superfamily member 7, and α4-integrin (Thomas et al. 2005). In addition, 6-MP impairs in vitro differentiation of dendritic cells, and reduces their activation, which results in a more tolerogenic phenotype. The production of IL-23 and expression of CCR7 is reduced, and the synthesis of IL-10 induced, which has implications in the activation of Th1 and Th17 cells of the immune system (Aldinucci et al. 2010). It has been suggested that ihibition of proliferation of

Introduction activated T-cells and depletion of memory T-cells could contribute to the late onset of action of thiopurines (Ben-Horin et al. 2009).

Methylated metabolites Although methylation by TPMT is considered a competing pathway to that of 6-TGN formation some of the methylated metabolites have biological activity. Actively proliferating lymphocytes are dependent on de novo purine biosynthesis (DNPS). MeTIMP - also known as MMPR or MMP - produced via methylation of 6-TIMP by TPMT, is found in a concentration that outweighs the concentration of 6-TGNs. MeTIMP is an effective inhibitor of phosphoribosyl pyrophosphate (PRPP) amidotransferase, the rate-limiting enzyme of the DNPS (Tay et al. 1969; Bokkerink et al. 1993; Dervieux et al. 2001; Coulthard et al. 2002) (Figure 2). The DNA and RNA biosynthesis is reduced, which facilitates the incorporation of thioguanine nucleotides in DNA. The concentration of PRPP increases, which may contribute to increased salvage of (thio)purine precursors and increased activation of 6-MP to 6-TIMP via HGPRT, as PRPP is a cosubstrate of HGPRT. The imbalance between purine and pyrimidine nucleotides caused by the reduced DNPS and increased concentration of PRPP is proposed to contribute to cell death (Bokkerink et al. 1993; De Abreu et al. 1995). Methylated 6-TG is, in contrast to meTIMP, a weak inhibitor of the DNPS (Dervieux et al. 2001)

Therapeutic drug monitoring Therapeutic drug monitoring in patients treated with AZA or 6-MP is not widely applied although many, but not all, studies have found a correlation between clinical response, TPMT activity and 6-TGN, both in leukaemia and IBD (Lennard et al. 1987; Dubinsky et al. 2000; Cuffari et al. 2001; Hanai et al. 2010). Most studies have found an inverse correlation between TPMT activity and the concentration of 6-TGN and a positive correlation to the concentration of meTIMP (Lennard and Lilleyman 1987; Cuffari et al. 1996; Evans et al. 2001). Monitoring of thiopurine metabolites has been proposed to be most useful in patients not responding to standard doses of thiopurines, in order to explain refractoriness or adverse reactions (Stocco et al. 2010; van Asseldonk et al. 2010). A cut of at 6-TGN >230-260 pmol/8x108 RBC has been suggested as a lower limit for clinical efficacy (Osterman et al. 2006). 6-TGN above this threshold increases the likelihood of clinical response three-fold. However, this meta-analysis showed an important overlap between patients with active disease as compared with those in remission; 36% of patients were in remission despite lower 6-TGN concentrations and of patients with a 6-TGN level above the threshold, not more than 62% were in remission. Very high concentrations of 6-TGNs, often seen in patients with reduced TPMT activity treated with standard doses, may predispose to myelotoxicity (Lennard et al. 18

Introduction

1989; Evans et al. 2001; Ansari et al. 2008). Some centres have therefore adopted an upper reference limit at 450 pmol/8x108 RBC as clinically relevant (Gardiner et al. 2008). High concentrations of meTIMP are associated with an increased risk of hepatotoxicity (meTIMP >5700 pmol/8x108 RBC) (Dubinsky et al. 2000; Nygaard et al. 2004; Ansari et al. 2008; Gardiner et al. 2008), while other studies have shown an association with myelotoxicity (>11 450 pmol/8x108 RBC) (Hindorf et al. 2006; Peyrin- Biroulet et al. 2008). Patients homozygous for TPMT deficiency do not form meTIMP and they tolerate higher concentrations of 6-TGN than patients with normal TPMT activity (Lennard et al. 1990; Kaskas et al. 2003). Furthermore, patients treated with 6-TG also tolerate higher 6-TGN concentrations (Erb et al. 1998). This could be because methylated 6-TG, in comparison with meTIMP, is a less potent inhibitor of DNPS (Allan and Bennett 1971; Dervieux et al. 2001). The inhibition of DNPS by meTIMP may facilitate the incorporation of deoxy-6-TGTP in DNA making lower concentrations of 6-TGN cytotoxic (Bokkerink et al. 1993).

Metabolite profile with a high meTIMP/6-TGN concentration ratio Although clinical issues in many cases can be explained by TPMT, there are cases where it has not been possible (Colombel et al. 2000; Dubinsky et al. 2002; Palmieri et al. 2007), probably because of the large number of enzymes involved in the metabolism. Individualization of thiopurine dosing based on the concentration of 6-TGN may improve outcome. However, there are patients who preferentially metabolize AZA and 6-MP to meTIMP. The inability to produce adequate concentrations of 6-TGN has been associated with less therapeutic efficacy, but also with an increased risk of adverse events due to a high concentrations of meTIMP. Clinical studies have shown that responders to treatment are characterized by 6-TGN production and low meTIMP/6-TGN concentration ratios, whereas non-responders are characterized by unexpectedly high formation of methylated metabolites (meTIMP, also known as MMPR or MMP) and therefore ensuing high meTIMP/6-TGN concentration ratios, although similar TPMT activities exist in the two groups (Dubinsky et al. 2002). The underlying mechanism of this metabolite profile is unknown. We hypothesized that variation in activities of enzymes other than TPMT, such as IMPDH, could explain differences in metabolite profiles. This is because IMPDH is strategically positioned in the metabolic pathway of (thio)purines (Elion 1967; Weber 1983), it can use 6-TIMP as a substrate (Hampton 1963; Atkinson et al. 1965) and it competes with TPMT for their common substrate. However, it is also possible that other pharmacogenes such as transport proteins, other metabolizing enzymes, thiopurine target genes, apoptosis related genes, or yet unknown genes, are of importance for the metabolite profile and clinical effect.

Introduction

Drugs interacting with thiopurine metabolism

5-aminosalicylic acid In vitro studies have shown inhibition of TPMT by 5-ASA (Szumlanski and Weinshilboum 1995; Lewis et al. 1997; Xin et al. 2005). The concentration required for 50% inhibition varies between 129 µM (RBC ex vivo) and 1380 µM (recombinant TPMT) (Szumlanski and Weinshilboum 1995; Lewis et al. 1997; Xin et al. 2005). The effect in vivo is a matter of controversy based on the high concentration required to inhibit TPMT in vitro, compared with the plasma concentration received after oral administration of 5-ASA (25-50 µM), (Szumlanski and Weinshilboum 1995; Sparrow et al. 2005; Xin et al. 2005). Nevertheless, coadministration of 5-ASA has been associated with increased concentrations of 6-TGN (Lewis et al. 1997; Lowry et al. 2001; Gilissen et al. 2005; Hande et al. 2006; de Boer et al. 2007) that may cause leucopenia (Lowry et al. 2001; de Boer et al. 2007; Nguyen et al. 2010). No significant effects on meTIMP concentration or TPMT were observed (Gilissen et al. 2005; Hande et al. 2006; de Boer et al. 2007). However, analysis of TPMT activity is performed in washed RBC and does not necessarily reflect the putative inhibitory effect of 5-ASA in vivo (Shipkova et al. 2004). Urinary excretion of thiouric acid has been suggested as an indirect measure of the effect on TPMT in vivo (Ansari et al. 2008). Other studies have not been able to confirm the effects of 5-ASA on 6-TGN (Dubinsky et al. 2000; Hindorf et al. 2006; Ansari et al. 2008; Daperno et al. 2009).

Allopurinol A well-known drug-interaction is that between the XO inhibitor allopurinol and 6-MP (Rundles 1966; Zimm et al. 1983; Chocair et al. 1993). Allopurinol is converted by aldehyde oxidase to oxypurinol, which is an even more potent inhibitor of XO, and has considerable longer half-life (14-30 h) than allopurinol (1.2 h) (Day et al. 2007). Allopurinol inhibits the first pass metabolism of 6-MP to thiouric acid, increasing the bioavailability of 6-MP and also the risk of toxicity (Zimm et al. 1983; Elion 1989).

Methotrexate Methotrexate may affect thiopurine metabolism by inhibition of XO (Lewis et al. 1984), thereby increasing the bioavailability of 6-MP (Balis et al. 1987) and the concentration of active metabolites (Giverhaug et al. 1998; Pettersson et al. 2002). Inhibition of DNPS by methotrexate may facilitate the incorporation of 6-TGNs in DNA and RNA (Dervieux et al. 2002). In addition, methotrexate reduces the concentration of S-adenosyl-methionine (SAM), through its inhibitory effect on the metabolism of folic acid (Chabner et al. 1985).

Introduction

Other drugs Infliximab transiently increases the concentration of 6-TGN in AZA treated patients with CD (Roblin et al. 2003). AZA has been described to reduce the effects of warfarin, but the mechanism remains unknown (Vazquez et al. 2008). The diuretics furosemide, bendroflumethiazide, and trichlormethiazide (Lysaa et al. 1996) and the non steroid anti-inflammatory drugs naproxen, tolfenamic acid and mefenamic acid (Oselin and Anier 2007) inhibit TPMT in vitro. Coadministration of the IMPDH inhibitor Ribavirin restricts the formation of 6-TGNs and shunts the metabolism towards methylated metabolites, increasing the risk of myelotoxicity (Peyrin-Biroulet et al. 2008).

Pharmacogenomics Most drugs that enter the body are substrates of drug-metabolizing enzymes which results either in their activation or detoxification and elimination. Phase I reactions (oxidative) include transformation of parent compounds to more polar metabolites by unmasking or formation of functional groups (-OH, -NH2, -SH). These reactions include dealcylation, aliphatic and aromatic hydroxylation, oxidation and deamination (Daly 2010; Jancova et al. 2010). The most important phase I enzymes are the cytochrome P450 enzymes. Phase II enzymes (conjugative) add glutathione, methyl groups, sulphate or glucuronic acid to the sites created by phase I reactions. In general, the conjugates are more hydrophilic than the parent compounds, which allow excretion via bile or urine (Daly 2010; Jancova et al. 2010). The plasma concentration of drugs can vary more than 600 fold between individuals (Eichelbaum et al. 2006). Age, gender, concomitant therapy, disease severity, kidney and liver function are well-known factors that influence drug metabolism. Genetic variants of drug metabolizing enzymes, receptors and transport proteins as well as of disease susceptibility genes contribute (Daly 2010) and may explain up to 40% of the interindividual variability (for particular drugs as much as 95%) (Eichelbaum et al. 2006). However, in 35 - 40% of the cases other factors outweigh a genetic influence (Ingelman-Sundberg 2001; Eichelbaum et al. 2006). A genetic polymorphism is defined as the existence of two or more variants (alleles, sequence variants) in at least 1% of the population. A single nucleotide polymorphism (SNP) involves exchange, insertion or deletion at one nucleotide position in the DNA sequence (Strachan and Read 2000). SNPs of the coding region of the DNA sequence occur at approximately every 1/1250 nucleotide base (Ingelman-Sundberg 2001). SNPs that alter the amino acid sequence of a protein or functionality of regulatory motifs in DNA are expected to have the greatest influence on drug metabolism and response (Strachan and Read 2000; Daly 2010). Polymorphisms of drug metabolizing enzymes are often inherited as autosomal recessive or codominant traits. The normal population is characterized by extensive metabolism (wild-type). Poor metabolizers are characterized by a complete deficiency

Introduction

(homozygosity) or a reduced enzymatic activity or amount of protein (heterozygosity). Such patients are exposed to an increased risk of adverse events if treated with standard doses, whereas ultrafast metabolizers are at risk of refractoriness (Daly 2010). In 1959 Friederich Vogel coined the term pharmacogenetics, which is defined as the study of how variations in the DNA sequence affect drug response (Eichelbaum et al. 2006). With the human genome sequenced and new techniques, which make it possible to simultaneously analyse multiple genes, rather than one at a time, the term pharmacogenomics is more often used. A pharmacogenomic or transcriptomic profile may improve the possibility to individualize potentially toxic treatment, both in regard to dosage and choice of drug (Dahl and Sjoqvist 2000). It may also bring economical benefits both from a patient perspective with diminished loss of productivity, and for the economical impact on the health care system as concerns number of control visits, etc (Ahmad et al. 2004). It has been estimated that adverse drug reactions occur amongst 5% of all hospitalized patients in the United states, and may be responsible for as many as 100 0000 deaths annually (Lazarou et al. 1998). In Sweden ~13% of admissions to internal medicine units were caused by adverse drug reactions in 2002 (Mjorndal et al. 2002). Today genetic variants of many drug metabolizing enzymes have become more or less translated into clinical practise (Eichelbaum et al. 2006; Daly 2010); some examples among others are CYP2D6 (tricyclic antidepressive drugs/tamoxifen), CYP2C9 and VKORC1 (phenytoin/warfarin), CYP2C19 (omeprazol); NAT1, NAT2 (5-aminosalicylic acid, isoniazide), ERBB2, HER2 (trastuzumab), MDR1 (transport of xenobiotics), KRAS, EGFR (geftinib). Many, but inconsistent, results have been reported from pharmacogenomic studies of drugs used in the treatment of IBD. Even though attempts have been made to create pharmacogenomic guidelines the only test used in clinical practise today is genotyping of TPMT (Smith et al. 2010; Vermeire et al. 2010).

Thiopurine S-methyltransferase

Gene and pseudogene TPMT is a cytosolic SAM dependent enzyme, found in RBC, mononuclear cells (MNC), kidney and liver (Van Loon and Weinshilboum 1982; Woodson and Weinshilboum 1983; Szumlanski et al. 1992; McLeod et al. 1995; Coulthard et al. 1998). Substrates of TPMT include aromatic and heterocyclic sulphydryl compounds such as 6-TG and 6-MP (Deininger et al. 1994; Krynetski et al. 1995). The gene, located on chromosome 6p22.3, is approximately 34 kb with 10 exons, and encodes a protein of 245 aminoacids (Lee et al. 1995; Szumlanski et al. 1996; Krynetski et al. 1997). The second exon is not uniformly represented, indicating that TPMT is subject to alternative splicing (Szumlanski et al. 1996; Seki et al. 2000). A

Introduction processed pseudogene with 96% sequence similarity to the cDNA of TPMT, has been cloned and mapped to chromosome 18q21.1 (Lee et al. 1995). The physiological role and substrate of TPMT is unknown and deficiency is not associated with any pathophysiological condition. However, in patients treated with cisplatin reduced TPMT activity was associated with hearing loss (Ross et al. 2009).

Phenotyping and activity distribution In 1980 Weinshilboum et. al described a trimodal activity distribution of TPMT which conformed to a Hardy-Weinberg distribution for an autosomal codominant trait. This observation was confirmed in family-studies (Weinshilboum and Sladek 1980). Low or absent TPMT activity occurred in 0.3% (homozygotes) and intermediate activity in 11% (heterozygotes) of a Caucasian population, respectively (Weinshilboum and Sladek 1980). Higher frequencies of homozygous subjects (0.5% and 0.6%) have been reported by others (Schaeffeler et al. 2004; Cooper et al. 2008). The trimodal activity distribution has also been described in Sweden (Figure 4) (Pettersson et al. 2002). Most populations studied show a bimodal or trimodal activity distribution. The main indication for assessing TPMT status is to identify patients with very low TPMT activity. Studies have shown great risk of severe myelotoxicity if patients with complete TPMT deficiency are treated with standard doses of thiopurines and intermediate risk in those with partial TPMT deficiency (Lennard et al. 1989; Lennard et al. 1990; Black et al. 1998; Evans et al. 2001; Ansari et al. 2008). Phenotyping of TPMT is performed in vitro by exposing isolated RBC or whole blood to 6-MP or 6-TG (Weinshilboum et al. 1978; Ford et al. 2006). RBC are traditionally used as surrogate for the target cells, the MNC, mainly because of accessibility. The activity in RBC correlates to that in tissues such as liver, kidney, lymphocytes and leukaemic blasts (Van Loon and Weinshilboum 1982; Szumlanski et al. 1992; McLeod et al. 1995; Coulthard et al. 1998). The intraindividual variation in TPMT activity is ~7% (Giverhaug et al. 1996; Pettersson et al. 2002).

Introduction

females 25 males

Frequency 10

0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0 22.0 24.0 TPMT (U/ml pRBC)

Figure 4. Distribution of RBC TPMT activity in a Swedish population (n = 219). Reprinted with permission from Petterson et al. Therapeutic Drug Monitoring 2002; 24:351-8.

Genotypes in different populations The trimodal activity distribution of TPMT is the result of genetic variants. The wild-type TPMT allele, which encodes a highly active product, is designated TPMT*1 . TPMT*2 consists of one nucleotide substitution in exon V (G238C, Ala80Pro) (Krynetski et al. 1995). TPMT*3A (Szumlanski et al. 1996) consists of two nucleotide substitutions; G460A (exon VII; Ala154Thr) and A719G (exon X; Tyr240Cys). These genetic variants may exist separately as TPMT*3B (G460A) and TPMT*3C (A719G). The proteins encoded by these genetic variants are rapidly degraded, resulting in low enzymatic activity (Tai et al. 1997; Tai et al. 1999; Wang et al. 2003; Li et al. 2008). Many genetic variants of TPMT have been associated with reduced enzymatic activity. However, the most common ones ( TPMT*3A , *3C and *2 ) (Sahasranaman et al. 2008) explain the majority of cases (85-95%) with reduced activity in Caucasians, in Asians and African-Americans (Otterness et al. 1997; Yates et al. 1997; Hon et al. 1999; Loennechen et al. 2001; Sahasranaman et al. 2008). In the Caucasian population TPMT*3A accounts for 75-86% of variant alleles detected (Yates et al. 1997; Hon et al. 1999; Loennechen et al. 2001). The allele frequencies of TPMT*3A , *3C and *2 are approximately 4.5%, 0.4% and 0.17%, respectively (Spire-Vayron de la Moureyre et al. 1998; Ameyaw et al. 1999; Collie-Duguid et al. 1999; Hon et al. 1999; Loennechen et al. 2001; Rossi et al. 2001; Schaeffeler et al. 2008). The proportion of individuals who carry variant alleles is similar in Caucasians and in African-Americans and Africans (Ameyaw et al. 1999; Hon et al. 1999; McLeod et al. 1999). In the latter two populations TPMT*3C predominates even though both TPMT*3A and TPMT*2 are found among African-Americans in low frequency (17

Introduction and 9% of variant alleles, respectively) (Hon et al. 1999). In Asia 2-4.9% carry a variant allele, which is comparatively lower than in the rest of the world (Collie-Duguid et al. 1999; Hiratsuka et al. 2000; Kapoor et al. 2009; Ban et al. 2010). In western Asia TPMT*3A predominates, whereas TPMT*3C is more common in eastern Asia. TPMT*3B is rare and has only been detected in a few individuals (Otterness et al. 1997; Hiratsuka et al. 2000; Ban et al. 2010). In patients with wild-type TPMT a twofold difference in TPMT activity is found (Figure 4) indicating that other factors may influence TPMT activity. The promoter region of TPMT contains a variable number of tandem repeats. The number and intrinsic nucleotide pattern were inversely related to TPMT activity (Spire-Vayron de la Moureyre et al. 1998; Alves et al. 2001). However, other studies have failed to confirm these observations (Marinaki et al. 2003). Recently, a polymorphic trinucleotide repeat element was identified in the promoter of TPMT in two patients with ultrahigh TPMT activity (Roberts et al. 2008).

Inosine 5´-monophosphate dehydrogenase

Two isoforms IMPDH 1 and IMPDH 2 The concentration of purine nucleotides in the cell is regulated through de novo synthesis and the salvage pathway. By the salvage pathway, existing purines, nucleosides and nucleotides are recycled via HGPRT (encoded by HPRT1). In the de novo synthesis IMP is synthesized in ten steps from PRPP. IMPDH catalyzes the nicotine adenosine dinucleotide (NAD)-dependent oxidation of IMP to xanthosine monophosphate (XMP) and is a key enzyme in the pathway to guanine nucleotides (Jackson et al. 1975; Weber 1983). Total cellular IMPDH activity is accounted for by the expression of two isoforms; IMPDH 1 and IMPDH 2. The gene encoding IMPDH 1 is located on chromosome 7 (7q31.3-q32) and is 18 kb large (Gu et al. 1994). The gene encoding IMPDH 2 is located on chromosome 3 (3p21.2-24.2) and is 5.8 kb large (Zimmermann et al. 1995; Kost-Alimova et al. 1998). Both genes contain 14 highly conserved exons and introns that vary in size. The proteins comprise each 514 amino acids with 84% sequence identity (Natsumeda et al. 1990; Glesne and Huberman 1994). The substrate affinities, catalytic activities and Ki values of IMPDH 1 and IMPDH 2 are nearly identical (Km for IMP of 14 and 9 µM and Km for NAD of 42 and 32 µM, respectively). However, IMPDH 2 is 3.9 times more sensitive to inhibition by mycophenolic acid (Holmes et al. 1974; Carr et al. 1993). The two isoforms are not mutually redundant. Loss of both IMPDH2 alleles in mice results in early embryonic lethality despite presence of both HPRT1 and IMPDH1, while heterozygosity is associated with a normal phenotype. Conversely, loss of IMPDH1 does not induce the expression of IMPDH2 or HPRT1 (Gu et al. 2000; Gu et al. 2003). Inhibition of IMPDH by mycophenolate mofetil in human cardiac transplant

Introduction patients induces the activity of salvage enzymes in MNC (Devyatko et al. 2006), which may indicate species differences. IMPDH activity is similar between sexes (Glander et al. 2001) and display an intraindividual variability of ~14% (Glander et al. 2004; Devyatko et al. 2006).

IMPDH in proliferation and differentiation Both isoforms of IMPDH are expressed in most tissues. IMPDH1 is generally lower expressed than IMPDH2, except in peripheral MNC (Senda and Natsumeda 1994; Jain et al. 2004). The mRNA expression and activity increase upon neoplastic transformation and proliferation (Jackson et al. 1975) largely due to increased expression of IMPDH2 (Konno et al. 1991; Collart et al. 1992; Nagai et al. 1992). Both isoforms are induced in activated T-cells (Dayton et al. 1994; Gu et al. 1997; Jain et al. 2004). Cells stimulated to differentiate show reduced mRNA expression and IMPDH activity. The difference in activity between proliferating and differentiating cells (Kiguchi et al. 1990; Nagai et al. 1992) have led to the development and clinical use of IMPDH inhibitors, such as mycophenolate mofetil, as immunomodulatory drugs.

Regulation of IMPDH1 and IMPDH2 IMPDH is subject to post-transcriptional regulation by GMP. High concentrations of GMP reduce the expression of IMPDH whereas low concentrations induce the expression, as demonstrated by the addition of guanosine to cells in vitro (Glesne et al. 1991; Nagai et al. 1992). The expression of IMPDH1 is regulated in a tissue specific manner by three different promoters; P1, P2 and P3. The 4.0 kb transcript from the P1 promoter is mainly expressed in activated lymphocytes and monocytes (Dayton et al. 1994; Gu et al. 1997). The P1 promoter contains 9 AUG, two of which are in frame with the initiation codon in exon 1, indicating that two additional IMPDH 1 variants are possible with 8 or 85 additional amino acids in the N-terminal (Gu et al. 1997). The 2.7 kb transcript from the P2 promoter has only been detected in tumour cell lines. Reporter gene constructs have shown highest activity with the P3 promoter. The resulting 2.5 kb transcript is universally expressed (Gu et al. 1997). The expression of IMPDH2 is controlled by one single promoter that responds more specifically to growth stimuli (Gu et al. 1997; Zimmermann et al. 1997). This promoter contains binding sites for transcription factors such as tandem cyclic AMP responsive elements (CRE), Sp1 sites and a palindromic octamer sequence (Zimmermann et al. 1997), and generates a transcript of 2.3 kb in activated T-cells. A gene with unknown function is oriented head to head to IMPDH2. The intergenic promoter region is active in both directions, but with 7-fold less activity in the 3´ to 5´direction (Zimmermann et al. 1995; Zimmermann et al. 1997).

Introduction

Genetic variants of IMPDH1 and IMPDH2 Genetic variants of IMPDH1 are associated with the rare diseases autosomal dominant retinitis pigmentosa and Leber congenital amaurosis (Bowne et al. 2006). The mutations are predominantly located in the cystathionine-β-synthetase domain, which is involved in the capacity of IMPDH to bind single stranded RNA and DNA (McLean et al. 2004). At least nine mutations associated with visual disease are known today (Hedstrom 2008). Genetic variants of both IMPDH1 (Roberts et al. 2007; Wang et al. 2008; Wu et al. 2010) and IMPDH2 (Wang et al. 2007; Grinyo et al. 2008; Garat et al. 2009; Sombogaard et al. 2009; Winnicki et al. 2010; Wu et al. 2010) have been described in the transplant literature and in studies of healthy volunteers. Most of them have been characterized in vitro, but have not been investigated in the context of thiopurine metabolism.

Other enzymes in the thiopurine metabolism

Inosine triphosphatase, ITPase ITPase (OMIM accession number 147520, encoded by the gene ITPA, chromosome 20p) is involved in a metabolic loop where 6-TIMP is reconverted from 6-TITP (Figure 2). The ITPA 94C>A polymorphism present in approximately 5-7% of the Caucasian population (Marsh et al. 2004) is associated with reduced activity. Heterozygotes possess 23% of normal activity, whereas homozygotes lack enzymatic activity (Sumi et al. 2002). A second intronic polymorphism (IVS2 +21A>C) with a higher allele frequency (13%) presents a milder phenotype (Sumi et al. 2002; Shipkova et al. 2006). Haplotype analysis has shown that the 94C>A polymorphism is the most relevant in determining low ITPase activity (von Ahsen et al. 2008). In patients on thiopurine therapy and with the ITPA 94C>A polymorphism, it has been proposed that the metabolite 6-TITP would accumulate, leading to toxicity such as rash, flu-like symptoms and pancreatitis (Marinaki et al. 2004; Zelinkova et al. 2006; Ansari et al. 2008); others have failed to confirm these results (Gearry et al. 2004; van Dieren et al. 2005; Hindorf et al. 2006). In a large Italian study the frequency of ITPA 94C>A was higher in non-responders (22%) than in responders (11%) to therapy (Palmieri et al. 2007). The methylated product of 6-TITP, meTITP, is a poor substrate of ITPase (Bierau et al. 2007). ITPase deficiency may contribute to a high meTIMP/6-TGN concentration ratio by increasing the concentration of methylated metabolites (meTITP) or by reducing the formation of the phosphorylated ones (Lin et al. 2001; Bierau et al. 2007; Stocco et al. 2010). However, it was recently shown that ITPase deficiency in patients with acute lymphoblastic leukemia uniquely leads to increased concentration of methylated metabolites, with no effect on 6-TGN (Stocco et al. 2010).

Introduction

Hypoxanthine guanine phosphoribosyltransferase, HGPRT HGPRT (OMIM accession number 308000, encoded by the gene HPRT1, chromosome Xq26.1) catalyzes the initial step in the conversion of 6-MP and 6-TG to 6-TGN. Reduced HGPRT activity may at least partly explain resistance to thiopurine drugs (Rosman et al. 1974). However, no correlation between HGPRT activity and the concentration of 6-TGN in RBC has been described (Lennard et al. 1993). Complete deficiency of HGPRT causes Lesch-Nyhan syndrome (Nyhan et al. 1968). The literature over the consequences of genetic variants of HPRT1 on therapeutic efficacy and adverse reactions to thiopurines is scarce. However, no difference in allele frequencies of ITPA or HPRT1 variants was noted between patients with or without adverse reactions and a control population (Palmieri et al. 2007). Some studies have suggested that thiopurine therapy increases the mutation frequency in HPRT1 (de Boer et al. 2009).

Xanthine oxidase, XO XO (OMIM accession number 278300, encoded by the gene XDH, chromosome 2p23.1) (Figure 2), is present in intestinal mucosa and liver (Parks and Granger 1986), but not in circulating blood cells (Parks and Granger 1986). XO catabolises 6-MP to thiouric acid. There is a considerable interindividual variation in hepatic XO activity (Guerciolini et al. 1991). Between two and twenty percent of the population display low activity (Guerciolini et al. 1991; Kalow and Tang 1991). Men have higher hepatic activity than women (Guerciolini et al. 1991). Inhibition of XO by allopurinol in combination with a reduced thiopurine dose increases the concentration of 6-TGN and produces a marked decrease in meTIMP. This therapy has been used to reverse high meTIMP/6-TGN concentration ratios in non-responders and in patients with adverse reactions when conventional thiopurine doses were given (Chocair et al. 1993; Sparrow et al. 2007; Ansari et al. 2008). The underlying mechanism for this metabolic change is mainly unknown, but high XO activity per se does not explain a high meTIMP/6-TGN ratio (Wong et al. 2007). Inhibition of XO by allopurinol reduces the formation of reactive oxygen species, which may have beneficial effects on the cellular storage of glutathione, with reduced mitochondrial damage and decreased ATP-depletion (Lee and Farrell 2001; Tapner et al. 2004). In the context of thiopurine metabolism, genetic variants of XDH have mainly been associated with reduced activity with 6-thioxanthine (Kudo et al. 2010). One study suggested a weak protective effect against adverse events by genetic variants of XDH and molybdenum cofactor sulfurase (encoded by MOCOS), whereas a genetic variant of aldehyde oxidase predicted lack of response (Smith et al. 2009).

GMP synthetase GMP synthetase (OMIM accession number 600358, encoded by the gene GMPS, chromosome 3q24) is expressed in bone marrow, leukocytes, RBC, and liver (Page et al.

Introduction

1984) and catalyzes the amination of XMP to GMP in the presence of glutamine and ATP (Nakamura and Lou 1995). The expression level is high in proliferating and neoplastic transformed cells and reduced upon differentiation (Weber 1983; Hirst et al. 1994). 6-TXMP is a substrate and competitive inhibitor in relation to XMP. The triphosphates GTP, ITP, and XTP are inhibitors (Spector 1975). Downregulation of GMPS in MOLT-4 cells did not affect the sensitivity to 6-MP and 6-TG. However, in cells resistant to 6-MP or 6-TG the expression level of genes of DNPS enzymes, GMP synthetase and transport proteins were reduced (Albertioni et al. 2010). Preliminary clinical data indicate that genetic variants of GMPS at least partly may explain resistance to therapy (Kennedy 2010).

GMP reductase 1 and 2 GMP reductase (OMIM accession number 139265, 610781, encoded by the genes GMPR1 and GMPR2) catalyzes the NADPH dependent deamination of GMP to IMP (Nakamura et al. 1992). GMPR1 is located on chromosome 6p23 (Kondoh et al. 1991) and GMPR2 on chromosome 14q12 (Deng et al. 2002). GMP reductase is present in RBC and leukocytes (Deng et al. 2002). No studies have been conducted on the role of GMP reductase in the thiopurine metabolism. GMP reductase opposes the activity of IMPDH. The activity is not associated with proliferation, but in cells induced to differentiate (in which IMPDH activity decreases) the activity increases (Nakamura et al. 1992; Weber et al. 1992). Studies in Leishmania donovani suggest that allopurinol may have weak inhibitory properties on GMP reductase (Spector et al. 1984).

Glutathione transferases, GSTs The cytotoxic effects of AZA are at least in part due to glutathione depletion when AZA is converted to 6-MP. Cellular glutathione exhaustion may lead to mitochondrial damage and ATP depletion in hepatocytes (Lee and Farrell 2001; Tapner et al. 2004). Cytosolic glutathione transferases (GSTA1-1, A2-2 and M1-1), abundant in liver and intestine but absent in RBC, are responsible for over 90% of the conversion of AZA to 6-MP (Eklund et al. 2006). Some of the cytosolic isoenzymes (GSTM1-1, P1b and T1) are polymorphic (Cotton et al. 2000) and may affect clinical efficacy (Stocco et al. 2007). The prevalence of a GSTM1 (chromosome 1p13.3) null genotype that completely abolishes the enzymatic activity is 39-62% in European populations (Cotton et al. 2000). The activity distribution does not differ significantly between healthy individuals and patients with IBD (Hertervig et al. 1994), but the GSTM1 null genotype was under represented in patients who developed adverse reactions to AZA (Stocco et al. 2007). This could present an explanation to the tolerance of 6-MP in 47-60% of AZA intolerant patients (Bowen and Selby 2000; Hindorf et al. 2006; Lees et al. 2008; Hindorf et al. 2009).

Introduction

Cellular glutathione depletion is favoured by high GST activity. A gene duplication of GSTM1 (McLellan et al. 1997) and allelic variants of GSTA2 with high enzymatic activity with AZA (Zhang et al. 2010) have been described. Furthermore, GSTs are known inhibitors of the MAP kinase pathway and may affect the toxicity of AZA by interfering with this pathway (Townsend and Tew 2003).

Drug transport proteins Most nucleoside analogues enter and exit cells via equilibrative nucleoside transporters (ENT, encoded by the SLC29 gene family), concentrative Na+-dependent nucleoside transporters (CNT, encoded by the SLC28 gene family) and active transport via multidrug resistance associated proteins (MRP, encoded by the ABCC gene family) (Baldwin et al. 1999; Pastor-Anglada et al. 2005). Impaired function, or up or down regulation of different transport proteins with different specificities for thiopurine metabolites may affect the metabolite profile (Fotoohi et al. 2006; Mendoza et al. 2007; Peng et al. 2008; Ban et al. 2010; Li et al. 2010) as may variations in activities of intracellular nucleotidases and kinases (Rosman et al. 1974; Pieters et al. 1992).

Aims

AIMS

The overall aim of this thesis was to advance the knowledge of factors that affect the metabolism of thiopurine drugs, with a clinical perspective, to improve the care of patients with IBD.

Specific aims:

1. To develop a pyrosequencing method for genotyping of all known genetic variants of TPMT and investigate the concordance between genotype and phenotype in patients on thiopurine therapy. Further, to investigate the allele frequencies of the most common variant TPMT alleles in a Swedish background population.

2. To investigate the activity distribution of IMPDH in healthy volunteers and in patients with IBD.

3. To correlate IMPDH activity to the concentration of thiopurine metabolites.

4. To investigate the role of IMPDH in the thiopurine metabolism in a controlled in vitro system (human embryonic kidney cell line, HEK293) using RNA interference technique (siRNA) against IMPDH2.

5. To explore the molecular basis of differences in metabolite profiles in IBD-patients by examining the relationship between gene expression levels and metabolite profiles.

Materials and Methods

MATERIALS AND METHODS

Patients and healthy volunteers

The patients in this investigation were all diagnosed with IBD, except for a few cases (Table I).

Table I. Summary over patients and healthy volunteers included in study I-IV. Paper Ulcerative Crohn’s Other Healthy Total Reference colitis disease diagnosis individuals population I 16 7 1 6 30 800 II 25 25 - 100 150 III 22 34 4 - 60 IV 25 27 2 - 54

DNA samples from a regional biobank were used as reference population in the investigation of allele frequencies of TPMT in a Swedish general background population. The biobank consisted of genomic DNA samples from 800 individuals living in the Southeast region of Sweden; 400 females and 400 males. They had been randomly selected from the Swedish population register and anonymously included after informed consent. Their ethnicity had not been registered. The concordance between TPMT genotype and phenotype was investigated in 30 individuals (24 patients with IBD and 6 healthy volunteers) in Paper I. The patients in Paper II were unselected outpatients that came for regular follow-up visits. To investigate the distribution of IMPDH activity in healthy individuals, 100 blood donors were sampled (28 women aged 23-64, and 72 men aged 24-66). The patients in Paper III and IV were selected based on their metabolite profiles. Based on metabolite determinations over several years in our laboratory we defined a high metabolite ratio as meTIMP/6-TGN >20. This ratio corresponds to the 75th percentile of the meTIMP/6-TGN concentration ratios (median 12.0) amongst 1220 patients with IBD on thiopurine therapy and TPMT activity in the normal range (>8.9 U/mL pRBC). Secondly, a group of patients with metabolite ratios corresponding to the median ratio (range 10.0-14.0) were identified, and thirdly, patients who displayed a profile with metabolite ratios ≤4 and 6-TGN ≥100 pmol/8x108 RBC. A cut-off ≥100 pmol 6-TGN/8x108 RBC was used to ensure acceptable analytical precision. In Paper III, IMPDH activity and thiopurine metabolite concentrations were determined in patients with a normal TPMT phenotype and meTIMP/6-TGN concentration ratios >20 (n = 26); meTIMP/6-TGN ratios ≤ 20 (n = 21); a subgroup

Materials and Methods with a meTIMP/6-TGN ratio ≤4 (n = 6) and in ten patients with reduced TPMT activity. In paper IV, ten patients with meTIMP/6-TGN concentration ratio >20 (R20), four patients with metabolite ratios corresponding to the median metabolite ratio (Median), and seven patient who displayed a metabolite ratio ≤4 and 6-TGN ≥100 pmol/8x108 RBC (R4), were included in the microarray analysis. A separate validation cohort which comprised patients with meTIMP/6-TGN concentration ratios >20 (n = 9), patients with metabolite ratios ≤5.7 (n = 12) and a group with a metabolite ratio corresponding to the median metabolite ratio of the database (n = 12), was included to further evaluate microarray data. In all studies, patients who had received a blood transfusion within 4 months before sampling were not included. Altogether, 147 unique patients were investigated in Paper I-IV. The overlap between patient cohorts in Paper II-IV is illustrated in Figure 5.

Paper II n = 50

11 11 11

Paper III n = 60 Paper IV n = 54 12

Figure 5. Overlap between the patient cohorts in study II-IV. Three patients from paper I were included in Paper III.

Materials and Methods

Methods

The methods employed in the separates studies are outlined in Table II, and more detailed descriptions are given below.

Table II. Methods used in this thesis Technique Paper Preparation techniques Isolation of DNA I-IV Isolation of RNA from peripheral blood samples and HEK293 cells II-IV Isolation of mononuclear cells II-III

Pyrosequencinga I

Determination of enzyme activities TPMT activity in RBC I-IV TPMT activity in HEK293 cellsa III IMPDH activity in MNC and HEK293 cellsa II-III

Determination of metabolite concentrations 6-TGN and meTIMP concentrations in RBC II-IV Metabolite concentrations in HEK293 cellsa III

Tissue culture & transfection Tissue culture of HEK293 cellsa III Transient transfection with siRNA against IMPDH2 in HEK293 cells a III CellTiter Aqueous one solution cell proliferation assay III

Determination of mRNA expression Reverse transcription qPCRa III-IV Microarray Affymetrix GeneChip Human Genome U133 Plus 2.0 array IV aMethods established or developed during this thesis.

Preparation techniques

Isolation of DNA DNA was isolated from ethylenediaminetetraacetic acid anticoagulated peripheral blood with the EZ1 DNA blood 200 µL kit and Biorobot EZ1 (Qiagen, Hilden, Germany) (Paper I) and with the MagAttract DNA blood mini M48 kit and Biorobot M48 (Qiagen, Hilden Germany) (Paper II-IV), according to the manufacturers instructions.

Isolation of RNA from peripheral blood samples and HEK293 cells Blood samples were collected in PreAnalytiX PAXgene™ blood RNA tubes (Becton Dickinson, Franklin Lakes, NJ, USA). RNA was isolated on the day of

Materials and Methods sampling, using the PreAnalytiX PAXgene™ blood RNA kit (Qiagen, Hilden, Germany), according to the manufacturers instructions. RNA was isolated from HEK293 cells using the RNeasy® mini PLUS kit (Qiagen, Hilden, Germany), according to the manufacturers instructions. RNA concentration was assessed with Nanodrop® ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA) and RNA integrity with 2100 Bioanalyzer and the RNA 6000 Nano LabChip® kit (Agilent technologies, Santa Clara, CA, USA).

Isolation of mononuclear cells MNC were isolated using BD Vacutainer® CPT™ cell preparation tubes from Becton Dickinsson (Franklin Lakes, NJ, USA). The cells were washed twize in phosphate buffered saline before a lysate was prepared in water (10x106 cells/mL) by two cycles of freeze-thawing. The lysate was stored at -80°C until determination of IMPDH activity.

Pyrosequencing® Genotyping of TPMT and ITPA was performed using Pyrosequencing® (Figure 6). Sequence-specific polymerase chain reaction (PCR) products were amplified with a biotin lable on one primer. For each genotype reaction the PCR product was immobilized on streptavidin coated beads and denatured to single-stranded DNA by NaOH. A sequence specific primer was annealed to the single-stranded DNA template and sequencing reagents were added. The enzyme mixture contains DNA polymerase, ATP sulfurylase, luciferase, apyrase, and the substrates adenosine 5´-phosphosulfate (APS) and luciferin. Deoxynucleotide triphosphates were added in a user defined order based on the target sequence. Upon incorporation pyrophosphate (PPi) is released. ATP sulfurylas converts PPi to ATP in presence of APS. ATP is used in a reaction where luciferase converts luciferin to oxyluciferin. The light generated from this reaction is proportional to the number of incorporated deoxynucleotides and is detected by a charge-coupled device camera. The signal is converted to a peak in a pyrogram. Unincorporated deoxy-nucleotides and excessive ATP are degraded by apyrase between each cycle (Ronaghi et al. 1998). Deoxyadenosin-alfathio-triphosphate (dATPαS), which is efficiently used by the DNA polymerase but not recognized by luciferase, is used instead of dATP as building block in the reaction.

Materials and Methods

Figure 6. The Pyrosequencing® principle. DNA-polymerase incorporates dNTPs if they are complementary to the target sequence. Pyrophosphate (PPi) is released in equimolar amount. Sulfurylase converts PPi and adenosine 5´phosphosulfate (APS) to ATP, which is used by luciferase to converts luciferin to oxyluciferin. The light generated during this reaction is proportional to the number of dNTPs incorporated and is recorded by a charge-coupled device camera. The signal is converted to a peak in a pyrogram. Figure reprinted with permission from © QIAGEN, all rights reserved. www.qiagen.com.

Materials and Methods

Determination of enzyme activities

TPMT activity in RBC TPMT activity was measured in RBC as described previously (Weinshilboum et al. 1978; Klemetsdal et al. 1992). Briefly, the activity was determined by measuring the formation of 14C-labelled methylmercaptopurine from 6-MP with 14C-labelled SAM used as the methyl donor. 14C-labelled methylmercaptopurine was separated from 14C-labelled SAM by organic solvent extraction. The radioactivity of the organic phase was then measured by liquid scintillation counter. One unit of TPMT enzyme activity represents the formation of 1 nmol 14C-labelled methylmercaptopurine per mL packed RBC per hour of incubation (U/mL pRBC). A cut off at ≥9.0 U/mL pRBC was used to distinguish high from intermediate TPMT activity and 5.0 U/mL pRBC to distinguish intermediate from low activity (Pettersson et al. 2002). Intra- and interassay coefficients of variations were 3.3% and 4.7%, respectively.

TPMT activity in HEK293 cells Cell pellets were sonicated in a lysate buffer consisting of 50 mM TrisHCl (pH 7.6) supplemented with 2 mM dithiothreitol, 0.5 mM phenylmethanesulfonylfluoride, 20% glycerol and 0.5% Nonidet P-40. The lysates were centrifuged at 30 000 g for 10 min. The protein concentrations of the supernatants were determined using the RC DC protein assay kit (Bio-Rad Laboratories, Hercules, CA, USA). Induction of TPMT activity in muristerone A treated HEK293 cells was evidenced by the formation of 14C-labelled methylmercaptopurine from 6-MP with 14C-labelled SAM used as the methyl donor. The product (14C-labelled methylmercaptopurine) was analysed using reversed phase high performance liquid chromatography (RP-HPLC) with a Purospher STAR, RP18 column (3 µm, 55 x 4 mm) maintained at 30°C (Merck, Darmstadt, Germany). UV (290 nm) and radioactive signals were recorded. The intra- and interassay coefficients of variation were determined at 100 µg, 400 µg and 800 µg protein. The intra-assay (n = 5) and interassay (n = 4) coefficients of variation were 6.6, 8.4 and 4.5% and 14.9, 9.0 and 5.2%, respectively.

IMPDH activity in MNC and HEK293 cells IMPDH activity was measured in lysates of peripheral blood MNC (10x106 cells/mL) with the ion-pair (IP)-RP-HPLC method previously described by Glander et. al (Glander et al. 2001) with minor modifications: The MNC were isolated as described above instead of using Ficoll-Paque. The mobile phase had a higher concentration of tetrabutylammonium bisulfate than in the original method (14 mM instead of 7 mM), to improve separation in the chromatograms. IMPDH activity was expressed as nmol XMP formed from IMP per milligram protein and hour of incubation (nmol/mg protein/h). The standard curve comprised

Materials and Methods water solutions of XMP at concentrations ranging from 3.7 to 70.0 µM, but the linearity was evaluated up to 1225 µM. The intra-assay coefficient of variation (incubation condition) was 6% at 13 nmol/mg protein/h and the interassay coefficient of variation was approximately 10%. The lower limit of quantification was 73 pmol XMP, corresponding to 1.2 nmol/mg protein/h. These data were in agreement with the original observations (Glander et al. 2001). A Prontosil-120-5-C18-AQ 5 µm column was used in Paper II. In Paper III a shorter column with smaller particle size was used in order to reduce the separation time (120-3-C18-AQ 3 µm) (Bishoff Chromatography, Leonberg, Germany). Protein concentrations in cell lysates were determined using Pierce BCA protein assay kit (Biuret reaction).

Determination of metabolite concentrations

6-TGN and meTIMP concentrations in RBC 6-TGN and meTIMP were determined as previously described (Lennard and Singleton 1992; Pettersson et al. 2002). The nucleotides were converted to their corresponding base by acid hydrolysis, extracted by phenyl mercury adduct formation and then back to acid to release the base. The nucleotide bases were then analysed by RP-HPLC. The interassay coefficients of variation of 6-TGN and meTIMP were 13% and 26%, respectively. The lower limits of quantification were 20 and 300 pmol/8x108 RBC, respectively.

Definition of analysed metabolites There are some discrepancies in the literature regarding the definition of methylated metabolites. Terms commonly used are methylmercaptopurine (MMP or meMP), 6-methylmercaptopurine ribonucleoside or ribonucleotides (both abbreviated as MMPR), and meTIMP. Most laboratories use the methods developed by Lennard and Singleton (Lennard and Singleton 1992) or Dervieux and Boulieu (Dervieux and Boulieu 1998). In both these methods the methylated metabolites are hydrolyzed back to a common derivative, 4-amino-5-methylthiocarbonyl imidazole (AMTCI) under acidic conditions, making them indistinguishable. Consequently, both methods determine the sum of all methylated metabolites (Vikingsson et al. 2009). However, MMP and the riboside MMPR are not detected in RBC (Giverhaug et al. 1997), and in addition yet unknown substances may contribute to AMTCI as shown by a comparison between the method by Lennard and Singleton (AMTCI) and a method specific for meTIMP (Vikingsson et al. 2009). In routine analysis of 6-TGN the generated base 6-TG stems mainly from 6-TGMP, 6-TGDP and 6-TGTP, including a small amount of deoxy-6-TGNs (Vikingsson et al. 2009). Studies have shown that the riboside and the free base are not present in RBC and hence do not contribute to 6-TGN (Lennard and Singleton 1992; Vikingsson et al. 2009).

Materials and Methods

Metabolite concentrations in HEK293 cells To measure the concentrations of meTIMP, the sum of meTIDP and meTITP, 6-TGMP, the sum of 6-TGDP and 6-TGTP, 6-TIMP, 6-TXMP, GTP, ATP and XMP in HEK293 cells, a solution of acetonitrile/water with EDTA was added to cell pellets, followed by 6-ethyl-mercaptopurine, used as an internal standard. Cells were sonicated and proteins pelleted by centrifugation. The supernatant was concentrated, and KMnO4 was added to oxidize the thiolgroup. The reaction was stopped by the addition of H2O2 and the supernatant was analysed by IP-RP-HPLC. For detection a Waters 2487 Dual λ Absorbance Detector set at 256 and 289 nm and a Waters 474 Scanning Fluorescence Detector (excitation wavelength 330 nm, emission wavelength 410 nm) were used, all from Waters (Sollentuna, Sweden). The interassay coefficients of variation at the lowest and highest concentrations used in the calibration curves were below 20% (n = 5) except for GTP (28% at 28 pmol/1x106 cells and 27% at 2500 pmol/1x106 cells) and 6-TGMP (33% at 0.43 pmol/1x106 cells).

Tissue culture and transfection

Tissue culture of HEK293 cells

Cells were maintained at 37°C in a humidified atmosphere with 5% CO2 in Dulbecco’s modified Eagle’s medium supplemented with 10% heat-inactivated fetal calf serum and antibiotics (Geneticin G418 500 µg/mL and Zeocin 400 µg/mL). The cells had previously been transfected with the cDNA of TPMT under an inducible promoter system (Coulthard et al. 2002). Cells were subcultivated every third day.

Transient transfection with siRNA against IMPDH2 in HEK293 cells Short interfering RNAs (siRNAs) are 21-nucleotide RNA duplexes. When introduced into a cell they result in targeted post-transcriptional gene silencing. RNA duplexes are mixed with cationic lipids of the transfection reagent (Lipofectamin 2000, Invitrogen, Carlsbad, CA, USA). The positive charge interacts with the phosphate backbone of the nucleic acid forming a liposome/siRNA complex. It also facilitates the interaction between this complex and the negatively charged cell membrane allowing for fusion. Inside cells the double-stranded siRNA is assembled with nucleases in a complex that unwinds the double-stranded molecule to single-stranded siRNA. The single-stranded product guides the complex to the target mRNA sequence. There the complex degrades the siRNA/target mRNA duplex thereby silencing the expression of the target gene (Chesnoy and Huang 2000).

The IC50 value (50% inhibitory concentration on cell growth) of 6-MP in low and high TPMT expressing IMPDH2 down-regulated cells was evaluated using the CellTiter

Materials and Methods

96 AQueus one-solution cell proliferation assay (Promega, Madison, WI, USA). Briefly, a MTS tetrazolium compound (Owen´s reagent) is reduced by living cells to a coloured formazan product that is soluble in tissue culture medium. The absorbance at 490 nm is proportional to the amount of coloured product formed.

Determination of mRNA expression

Reverse transcription qPCR Hydrolysis probes (TaqMan® probes) are short oligonucleotides used in detection in real-time PCR. In gene expression analysis one probe per transcript is used in combination with sequence specific primers. The probe is constructed with a reporter dye (FAM; 6-carboxyfluorescein) bound to the 5´ end and an internal dark quencher. The probe binds to the template before and between primers. When the probe is intact the quencher reduces the fluorescence emitted by the reporter dye. During PCR the 5´–3´ exonuclease activity of the Taq polymerase cleaves the probe. This cleavage separates the reporter from the quencher increasing the reporter dyes signal and removes the probe from the template (Figure 7). The fluorescence produced is proportional to the amount of DNA template present in the reaction. The threshold value defines a fluorescence level that is significantly higher than the background and is in the exponential growth phase of PCR amplification. The threshold cycle (Ct) is proportional to the starting copy number in the reaction. The relative quantification of gene expression levels was based on the ∆∆Ct method (Livak and Schmittgen 2001). The expression of each target gene was normalized to the expression of three reference genes (∆Ct) in the sample and in a calibrator sample (sample with lowest expression level per transcript or a comparison group depending on purpose). The relative expression level of the target gene was calculated as the ratio between the sample of interest and the calibrator sample (∆∆Ct), in linear scale expressed as 2 –∆∆Ct.

Materials and Methods

Figure 7. The TaqMan® chemistry. A fluorescent reporter (R) dye and a quencher (Q) are attached to the TaqMan® probe. The probe anneals between the PCR primers.

When the probe is intact, the fluorescence from the reporter dye is quenched.

During each extension cycle, the 5´–3´ exonuclease activity of the Taq polymerase cleaves the reporter dye from the probe.

Once separated from the quencher, the reporter dye emits its characteristic fluorescence.

Microarray Globin mRNA in RBC accounts for ~70% of total mRNA in whole blood samples and can, in microarray analyses, interfere with the detection of other genes (Feezor et al. 2004). Globin reduction can improve detection sensitivity by increasing the number of present calls, and reduce the sample-to-sample variability (Field et al. 2007; Raghavachari et al. 2009). Locked nucleic acid probes are designed to hybridize close to the 3´-poly A tail of globin mRNAs. The RNA-DNA hybrids are recognized by RNAse H which cleaves the RNA, making these transcripts unavailable for the reverse transcription reagent. RNA samples were treated with the SnX™ globin depletion reagent (AROS_Applied_Biotechnology; Kruhoffer et al. 2007) and analysed by AROS Applied Biotechnology (Aarhus, Denmark) using the Affymetrix GeneChip Human Genome U133 Plus 2.0 array (Affymetrix, Santa Clara, CA, USA). In microarray analysis double-stranded cDNA is synthesized from RNA samples. In vitro transcription, including a biotin-labelled nucleotide, is performed to produce biotin-labelled complementary RNA (cRNA) from the cDNA. The cRNA is purified to remove unincorporated nucleotides, inorganic phosphates and salts and then

Materials and Methods fragmentated and hybridized to the GeneChip array. The hybridized microarray is stained with phycoerytrin-conjugated streptavidin and scanned (excitation wavelength 488 nm, emission wavelength 570 nm). The emitted light is proportional to the amount of bound target sequence and hence the gene expression level. The image files (cel format) were imported to the GeneSpring GX 11 software (Agilent Technologies, Santa Clara, CA, USA). Data was background corrected and normalized by the robust multiarray analysis, RMA, algorithm (Irizarry et al. 2003).

Bioinformatic tools Gene sequence information (Paper I) was retrieved through National center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/) and the Genome Browser (http://genome.ucsc.edu). PCR primers were designed in PRIMER3 available through the Biology Workbench (http://workbench.sdsc.edu/). Primer specificity was controlled using the NCBI BLAST server (http://blast.ncbi.nlm.nih.gov/Blast.cgi). In Paper I, sequence-specific primers for pyrosequencing were designed with the Pyrosequencing® assay design software (Biotage AB, Qiagen since 2008). In Paper IV, microarray probe set annotations and corresponding TaqMan® assays were obtained via Affymetrix NetAffx analysis center (http://www.affymetrix.com) and Applied Biosystem UMapIt (http://www4.appliedbiosystems.com/tools/umapit/). Gene set enrichment analysis (GSEA) was performed on microarray data, using the Broad Institute GSEA software (http://www.broadinstitute.org/gsea/index.jsp), version 3.0 (Mootha et al. 2003; Subramanian et al. 2005) and the C2 molecular signatures database of pathways of the Kyoto Encyclopedia of Genes and Genomes (KEGG). Based on knowledge of the thiopurine metabolism, and by exploring the Pharmacogenomics knowledge base (http://www.pharmgkb.org/) and the KEGG pathway of purine metabolism (http://www.genome.jp/kegg/pathway.html#nucleotide) microarray data of genes potentially related to the thiopurine metabolism were extracted and analysed separately (Paper IV). The interaction between gene products was evaluated by the Search Tool for the Retrieval of Interacting Genes/Proteins database, STRING (http://string-db.org/), version 8.3 (von Mering et al. 2005) (Paper IV).

Statistics Statistical analyses were performed using the Statistical Package for Social Sciences version 15.0 (SPSS Inc, Chicago, IL, USA) (Paper I-III) or Statistica version 9.1 (StatSoft Inc, Tulsa, OK, USA) (Paper III-IV). Correlations between variables were evaluated using the Spearman rank order correlation coefficient, rs. For group comparisons of continuous variables, the Mann-Whitney U-test, or the Kruskal Wallis test were used. Median (range) values are given. For categorical variables, Fisher´s exact test was used. Two-sided testing was used and considered statistically significant if P <0.05.

Materials and Methods

To compensate for differences in doses between patients, dose-normalized metabolite concentrations (expressed as pmol metabolite per mg AZA) were used when investigating relationships between enzyme activities (Paper II and III) or gene expression data (Paper IV) and metabolite concentrations (Hindorf et al. 2004). 6-MP doses were converted to AZA doses, assuming a conversion factor of 2.08 (Sandborn 1996). Data from the in vitro experiments in Paper II were expressed as the mean ± S.D. One-way ANOVA with Bonferroni post hoc tests or two-sided t tests were used and considered statistically significant if P <0.05. Linear and logistic regression analyses were used in Paper III and IV to evaluate the relationship between a dependent variable (meTIMP/6-TGN concentration ratio, metabolite concentrations or metabolite profile) and several independent variables, after testing that the residuals were close to normally distributed. The subsets of genes that best predicted metabolite profiles were identified by using discriminant analyses followed by leave-one-out cross-validation in Paper IV. Similarities between gene expression profiles were investigated using unsupervised hierarchical cluster analysis with Ward’s linkage rule and 1-Pearson’s r as distance metric.

Results and Discussion

RESULTS AND DISCUSSION

TPMT genotypes in Sweden Until Paper I was written, mainly quantitative methods had been available to determine a persons TPMT activity. However, in patients transfused with RBC within 120 days before sampling these methods may over or underestimate activity (Yates et al. 1997; Cheung and Allan 2003). Also, there are disease conditions, e.g. leukaemia, that may affect the relative blood composition of old and young RBC and hence the TPMT activity (Lennard et al. 2001; de Boer et al. 2008). Furthermore, TPMT activity is induced in uremic patients by an unknown factor removed by haemodialysis (Weyer et al. 2001). In these circumstances genotyping will provide a more accurate result. Therefore a pyrosequencing method was developed to detect all ten, at that time, known genetic variants of TPMT, in addition to wild-type TPMT*1 (Paper I).

Allele frequencies in a general background population Next to wild-type, the most common genetic variants of TPMT in Europe and in the Nordic countries are TPMT*3A, *3C and *2 (Collie-Duguid et al. 1999; Loennechen et al. 2001; Rossi et al. 2001). Genotyping of G460A, A719G, and G238C in 800 individuals representing a normal background population in southeast Sweden confirmed these observations. The majority, ~91% (n = 730), did not carry any of the investigated gene variants. They were therefore considered to be wild-type TPMT*1/*1 and predicted to exhibit normal TPMT activity. The remaining 9% (n = 70) were all heterozygous for one variant allele and would express intermediate activity. None of the samples contained two variant alleles, although we had expected to find at least 2-4 individuals with a homozygous genotype based on the frequencies of homozygous subjects (0.3-0.6%) reported in other populations (Weinshilboum and Sladek 1980; Schaeffeler et al. 2004).

Table III. Variant TPMT alleles identified in a Swedish background population. TPMT genotype Allele frequency (n = 1600) TPMT*3A (G460A, A719G) 3.75 TPMT*3C (A719G) 0.44 TPMT*3B (G460A) 0.13 TPMT*2 (G238C) 0.06

TPMT*1/*3A was the most common genotype, followed by TPMT*1/*3C, TPMT*1/*3B and TPMT*1/*2 (Table III). The allele frequencies of TPMT*3A and TPMT*3C were in agreement with results in other studies (Rossi et al. 2001;

Results and Discussion

Schaeffeler et al. 2008; Serpe et al. 2009). TPMT*3B is uncommon and has only been detected in few subjects (Otterness et al. 1997; Nasedkina et al. 2006; Serpe et al. 2009). The allele frequency of TPMT*2 was approximately as in other populations (Collie-Duguid et al. 1999; Rossi et al. 2001; Schaeffeler et al. 2008).

Concordance between TPMT genotype and phenotype Thirty individuals were selected to represent a wide range of TPMT activity and genotyped for 10 genetic variants (Table IV). Nine individuals had normal TPMT activity (>8.9 U/mL pRBC), thirteen had intermediate activity (range 6.5-8.9 U/mL pRBC) and eight had low TPMT activity (range 0.3-0.8 U/mL pRBC). None of the individuals with normal activity carried an alternative allele and were considered to be wild-type TPMT*1/*1. All individuals with intermediate activity carried one variant TPMT allele and were genotyped as either TPMT*1/*3A (n = 12) or TPMT*1/*2 (n = 1). Among eight patients with low TPMT activity, four were TPMT*3A/*3A and two were TPMT*3A/*3C. Two discordant cases were discovered and genotyped as TPMT*1/*3A (Table IV and Figure 8). TPMT*3A comprise two nucleotide substitutions (G460A and A719G) which may occur on separate alleles as TPMT*3B/*3C. It is therefore possible that these two individuals were compound heterozygous. However, pyrosequencing like many other genotyping methodologies is unable to distinguish between TPMT*1/*3A (intermediate activity) and TPMT*3B/*3C (low activity). Haplotyping with long-range PCR may separate between these genotypes, but is much more labour intense (McDonald et al. 2002; von Ahsen et al. 2004). The results would have been the same if only three genetic variants had been studied (G238C, G460A, A719G). Taken together, the concordance between genotype and phenotype in this small selected population was 93%.

Figure 8. Concordance between TPMT genotype and phenotype in 30 individuals.

Results and Discussion

Table IV. Summary of results for 30 subjects selected to represent a wide range of TPMT activity and genotyped with pyrosequencing. No. Sex Age Diagnosis Treatment1 Phenotype Genotype at sampling TPMT activity in U/mL pRBC 1 F2 21 IBD3 AS, GC 0.3 *1 / *3A 2 M4 32 IBD TP 0.3 *3A / *3C 3 M 28 IBD NT 0.4 *1 / *3A 4 F 12 IBD NT 0.4 *3A / *3A 5 F 19 IBD TP, AS 0.4 *3A / *3A 6 F 29 IBD GC 0.8 *3A / *3A 7 M 59 IBD TP, AS 0.8 *3A / *3A 8 M 59 IBD NT 0.8 *3A / *3C 9 M 33 IBD GC 6.5 *1 / *3A 10 M 28 HV5 NT 6.9 *1 / *2 11 F 21 IBD NT 7.2 *1/*3A 12 F 35 OD6 TP, GC 7.2 *1 / *3A 13 M 46 IBD AS 7.3 *1 / *3A 14 M 56 IBD AS 7.4 *1 / *3A 15 F 22 IBD AS 7.6 *1 / *3A 16 M 50 IBD AS 7.7 *1 / *3A 17 M 52 IBD TP 7.8 *1 / *3A 18 M 49 IBD AS, GC 8.1 *1 / *3A 19 F 35 IBD AS, GC 8.5 *1 / *3A 20 M 58 IBD AS 8.6 *1 / *3A 21 M 46 IBD TP 8.9 *1 / *3A 22 F 60 HV NT 9.8 *1 / *1 23 F 61 IBD AS 11.0 *1 / *1 24 F 51 HV NT 11.6 *1 / *1 25 F 18 IBD TP, AS 12.4 *1 / *1 26 M 61 HV NT 13.2 *1 / *1 27 M 28 HV NT 14.8 *1 / *1 28 M 59 HV NT 15.9 *1 / *1 29 F 18 IBD TP, AS, GC 17.1 *1 / *1 30 M 12 IBD AS 19.6 *1 / *1 1AS, aminosalicylate; GC, glucocorticoid; TP, Thiopurine; NT, no treatment 2Female, 3Inflammatory bowel disease, 4Male, 5Healthy volunteer, 6Other diagnosis

The two discordant patients (number 1 and 3 in Table IV), including their parents, were followed up by sequencing of exon III-X and the exon/intron boundaries of TPMT. Two new variant alleles [TPMT*14 (A1G) and TPMT*15 (IVS7-1G>A)] were identified and described in a separate publication (Lindqvist et al. 2004). The genotypes of the discordant individuals were TPMT*3A/*14 and TPMT*3A/*15, respectively. After this study was performed up to TPMT*28 different variant allels have now been described. TPMT*1-*26 was reviewed by Fotoohi et. al (Fotoohi et al. 2010). TPMT*27 (Feng et al. 2010) and TPMT*28 (Appell et al. 2010; Landy et al. 2010) were recently discovered. In addition to TPMT*14 and TPMT*15, TPMT*23 (Lindqvist et al. 2007) and TPMT*28 (Appell et al. 2010) have been described by our laboratory. However, apart from TPMT*2 and TPMT*3, most of these more newly discovered variants are rare and in some cases have only been identified in a few subjects. All

Results and Discussion known variant alleles of TPMT are schematically summarized in Figure 9 (Garat et al. 2008).

Results and Discussion

Figure 9. Schematic overview of TPMT*1-TPMT*28. Modified and reprinted with permission from Garat et.al. Biochemical Pharmacology 2008;76:404-15.

Results and Discussion

Genotyping vs. phenotyping The main advantages of genotyping compared with phenotyping is the small sample volume required, that it is unaffected by concomitant treatment or blood transfusion, and only needs to be performed once. Difficulties may concern primer design and, obviously, a possible influence of yet unknown genetic variants. To avoid amplification of the pseudogene in PCRs it is necessary to use at least one intronic primer. Overall the concordance between TPMT phenotyping and genotyping is 72-100% depending on sample size and the number of TPMT variants studied (Yates et al. 1997; Spire-Vayron de la Moureyre et al. 1998; Rossi et al. 2001; Schaeffeler et al. 2004; Ford et al. 2006; Schaeffeler et al. 2008; Serpe et al. 2009). Other SNP detection techniques described in the literature include PCR with restriction fragment polymorphism analysis (RFLP) (Yates et al. 1997), single-strand conformation polymorphism (SSCP) analysis (Spire-Vayron de la Moureyre et al. 1998), denaturing HPLC (Schaeffeler et al. 2001), horizontal conformation-sensitive gel electrophoresis (HCSGE) (Alves et al. 2000), TaqMan® technology with hydrolysis probes (Hiratsuka et al. 2000), LightCycler® with hybridization probes (Schuetz et al. 2000), minisequencing primer extension (Kapoor et al. 2009), arrayed primer extension (Lu et al. 2005), and multiplex PCR with DNA micro-chip (Nasedkina et al. 2006). The majority of these methods cover a small proportion of known variant alleles. Recently, a multiplexed mass spectrometry based method able to detect TPMT*2-*18 and TPMT*20-*23 was described (Schaeffeler et al. 2008). Pyrosequencing and genotyping with hydrolysis or hybridization probes may be regarded as the most convenient techniques due to a high degree of automation and short turnaround times. Pyrosequencing requires special equipment, whereas the TaqMan® probe and hybridization probe based assays may be run on most commercially available real-time PCR instruments and without need for post-PCR manipulations. One advantage with the latter two techniques is that most manufacturers offer so called “fast” reagents that considerably reduces PCR cycling time. Pyrosequencing was chosen as it generates sequence data, provides easily interpretable results and avoids the use of expensive fluorescently labelled probes. The total turnaround time including the prepyrosequencing PCR step (excluding DNA extraction) was 3.5 h. Figure 10 illustrates a representative pyrogram of G460A, TPMT*3B (that also is part of TPMT*3A and TPMT*3D). In most techniques whose aims are to genotype multiple polymorphisms, the bottleneck is PCR multiplexing. Multiplexing is possible with pyrosequencing, but results in complex pyrograms. Further, multiplex PCR with DNA micro-chips could be a promising future alternative.

Results and Discussion

Figure 10. Pyrograms of TPMT*3B (G460A). The analysed sequence was (G/A)CATTAGTTG.

Wild-type G/G (A)

Heterozygous G/A (B)

Homozygous A/A (C)

Besides the question of yet unknown genetic variants, genotyping requires knowledge about the prevalence of different genotypes in different populations. With an increased mobility of people this becomes a challenging task. Therefore, in a routine laboratory, the most informative approach to identify patients with reduced TPMT activity would be phenotyping and not genotyping (Winter et al. 2007; Ford and Berg 2010). Long-term thiopurine therapy is better tolerated in patients with normal TPMT activity (TPMT wild-type) than in carriers of variant TPMT alleles with reduced activity (Black et al. 1998; Hindorf et al. 2006). However, the latter group of patients can be treated successfully with reduced thiopurine doses. Identification of these patients before initiating therapy will allow direct dose adjustments and avoidance of adverse reactions (Kaskas et al. 2003; Hindorf et al. 2004; Hindorf et al. 2006; Relling et al. 2011). This study showed a high concordance between TPMT genotype and phenotype, but the results also highlight the risk to miss rare variant alleles using genotyping only. Phenotyping in RBC and/or genotyping has become a routine procedure before initiating thiopurine therapy in several clinical centres (Fargher et al. 2007; Ford and Berg 2010). It is recommended by the Food and Drug Administration since 2004. This approach has been shown to be cost-effective by reducing the need of control visits and hospitalization (Winter et al. 2004; Dubinsky et al. 2005). Since 2006 our laboratory performs both phenotyping and genotyping for the most common TPMT alleles (TPMT*3A, *3B, *3C and *2) in parallel as routine. The combined analyses overcome the limitations of both tests and in cases where TPMT

Results and Discussion genotype is discrepant from phenotype, the DNA sequence is analysed. At present we have results from 16 000 patient samples in our database and have identified 645 patients with low activity (heterozygotes) and 36 with practically no enzyme activity (homozygotes).

IMPDH in thiopurine metabolism Approximately 25% of Swedish patients with IBD display unexpectedly low concentrations of 6-TGN and high concentrations of meTIMP despite normal TPMT activity. This is in agreement with observations in other populations (Gerich et al. 2010). IMPDH is considered a rate limiting enzyme in the de novo synthesis of guanine nucleotides from IMP (Weber 1983) and in the synthesis of thioguanine nucleotides from 6-TIMP (Elion 1989). Thus, variation in IMPDH activity could explain variations in the balance between 6-TGNs and meTIMP. In current clinical praxis TPMT activity as well as thiopurine metabolite concentrations are measured in RBC. Although data are scarce (Bergan et al. 1997; Lancaster et al. 2002), these metabolite concentrations have been proposed to reflect those in MNC. If this is true one could expect the enzyme activity of IMPDH in MNC to correlate to RBC metabolite concentrations.

IMPDH activity in healthy blood donors and in patients with IBD In Paper II, an IP-RP HPLC method for the analysis of IMPDH activity in MNC previously described by Glander et. al (Glander et al. 2001) was adopted. The intraindividual variation was investigated in three healthy individuals over one month (four measurements). The observed variation of 9.5-21% was in agreement with the intraindividual variation detected in healthy individuals by others (Glander et al. 2004). A wide range of IMPDH activity, similar to that described by others (Glander et al. 2001), was observed both in healthy blood donors and in patients (Paper II and III). The activity in 50 randomly selected patients was similar to that in healthy blood donors, a result in agreement with other studies of patients on thiopurine therapy (Devyatko et al. 2006). Compared with the trimodal activity distribution of TPMT (Weinshilboum and Sladek 1980; Pettersson et al. 2002), a similar genetic influence on IMPDH activity did not seem likely (Figure 11). Measurement of IMPDH activity is unable to distinguish between IMPDH 1 and IMPDH 2 as the activity represents the sum of both isoforms. Real-time qPCR has been used in attempts to quantify the relative contribution of each isoform (Bremer et al. 2007; Sanquer et al. 2008; Bremer et al. 2009). However, in renal transplant patients there was no correlation between enzyme activity and gene expression levels (Sombogaard et al. 2009). These results are in accordance with our observation in Paper IV. No correlation was observed between IMPDH activity and the expression of

Results and Discussion

IMPDH1 (rs = 0.28, P = 0.25), but we found a negative correlation to the expression of IMPDH2 (rs = - 0.56, P = 0.01). Our results may be due to the small number of patients evaluated or that different forms of transcripts recognized by real-time qPCR are not reflected by the IMPDH activity.

Figure 11. IMPDH activity in 100 healthy blood donors (left) and in 50 randomly selected thiopurine treated patients with IBD (right), Paper II.

Genetic variants of both IMPDH1 (Roberts et al. 2007; Wang et al. 2008; Wu et al. 2010) and IMPDH2 (Wang et al. 2007; Grinyo et al. 2008; Garat et al. 2009; Sombogaard et al. 2009; Winnicki et al. 2010; Wu et al. 2010) have been described and characterized in vitro. These variants offer an explanation to the wide range of activity observed by us and others (Glander et al. 2001; Glander et al. 2004), but most of them have not been investigated in the context of thiopurine metabolism. However, one promoter variant of IMPDH1 that disrupts a CRE was described in one patient resistant to AZA, who displayed a high meTIMP/6-TGN concentration ratio (Roberts et al. 2007). Garat et. al described a promoter variant in IMPDH2 with similar effect (Garat et al. 2009). Simultaneous mutagenesis of tandem CRE reduced IMPDH2 promoter activity by 83% in a luciferase reporter gene assay (Zimmermann et al. 1997). Even though genetic variants of IMPDH may have clinical significance many of them are rare. Also, it remains to be established their consequences on IMPDH activity and metabolite profiles in vivo.

IMPDH activity and dose-normalized metabolite concentrations The IMPDH activity in MNC was inversely correlated to the dose-normalized concentration of meTIMP in RBC but not with dose-normalized 6-TGN concentrations

(Figure 12, Paper II) or the meTIMP/6-TGN concentration ratio (rs = - 0.25, P = 0.08). Presence of the ITPA 94C>A polymorphism in seven patients did not influence these results.

Results and Discussion

Figure 12. IMPDH activity and dose-normalized metabolite concentrations of meTIMP (A) and 6-TGN (B) in patients with IBD (n=50), Paper II.

The inverse correlation between IMPDH activity and the concentration of meTIMP found in Paper II, was confirmed in patients with normal TPMT activity selected based on their metabolite profiles (Paper III). No correlation to 6-TGN was found, thus extending the results in Paper II to patients with different metabolite profiles (Figure 13). In 26 patients with meTIMP/6-TGN concentration ratios >20 the IMPDH activity (median 14.3 nmol/mg protein/h) was lower than in 21 patients with meTIMP/6-TGN concentration ratios <20 (median 19.9 nmol/mg protein/h) (P <0.001). Although we had expected the subgroup of patients with the lowest metabolite ratios (<4) to present the highest IMPDH activities only a trend to higher IMPDH activity was observed (median 17.3 nmol/mg protein/h) in comparison with patients with metabolite ratios >20. Thiopurine doses were similar among patients with metabolite ratios above or below 20. We did not observe any difference in TPMT activity between the different patient groups. This is in accordance with the study by Dubinsky et. al (Dubinsky et al. 2002), indicating that TPMT alone does not predict the metabolite profile. Ultra high TPMT activity may explain some of the cases with meTIMP/6-TGN concentration ratios >20, but such high activity is present only in 1-2% of the population (Schaeffeler et al. 2004). Furthermore, other groups have shown that the majority of patients with this metabolite profile display normal and not ultra high TPMT activity (Roberts et al. 2007; Sparrow et al. 2007; Gardiner et al. 2008). IMPDH activity was similar in patients with normal and in those with reduced TPMT activity. We conclude that normal TPMT activity is a prerequisite for a meTIMP/6-TGN concentration ratio >20 to develop, as all ten patients with reduced TPMT activity displayed a metabolite ratio <10. There was no correlation between IMPDH activity and the dose-normalized concentration of meTIMP

(rs = - 0.06, P = 0.87) or 6-TGN (rs = 0.30, P = 0.39) in patients with reduced TPMT activity.

Results and Discussion

Figure 13. IMPDH activity and dose-normalized metabolite concentrations in patients with normal TPMT activity, selected based on their metabolite profiles (n = 50), Paper III.

The role of XO has been considered in patients presenting high meTIMP/6-TGN concentration ratios, as the combination of thiopurine in reduced dose with the XO inhibitor allopurinol reverses the metabolite pattern and is effective in IBD (Sparrow et al. 2007; Ansari et al. 2008; Leung et al. 2009; Ansari et al. 2010; Gerich et al. 2010). By this combination therapy corticosteroid requirements and liver transaminases, as well as nonhepatic side effects (nausea, myalgia and fatigue) were reduced (Sparrow et al. 2007; Ansari et al. 2008; Ansari et al. 2010). However, high XO activity per se does not explain a high meTIMP/6-TGN ratio (Wong et al. 2007). A logical explanation to the metabolic change after introduction of allopurinol would be inhibition of TPMT (Duley et al. 2005), but neither allopurinol nor oxypurinol are inhibitors of TPMT in RBC or in liver cytosol preparations (Sparrow et al. 2007). However, the plasma concentration of 6-thioxanthine, a potent inhibitor of TPMT (Deininger et al. 1994; Kroplin et al. 1999), increases upon coadministration with allopurinol (Keuzenkamp- Jansen et al. 1996). This mechanism could present one explanation to the reversal, and is in agreement with the observation of low metabolite ratios among patients with reduced TPMT activity. However, other explanations need to be investigated. One can also argue that if genetic variants were the only explanation to a high meTIMP/6-TGN ratio, then allopurinol should fail to reverse the metabolite profile.

Results and Discussion

RBC vs. MNC In clinical practise, characterization of thiopurine metabolism in RBC is used as a surrogate compartment for MNC. This is mainly based on the correlation between TPMT activity measured in RBC and in hepatic tissues, kidney and leukaemic blasts (Van Loon and Weinshilboum 1982; Szumlanski et al. 1992 McLeod, 1995 #371; Coulthard et al. 1998). Metabolite concentrations in RBC have been suggested to reflect those in less accessible tissues, although supporting data are scarce (Bergan et al. 1997; Lancaster et al. 2002). Therefore these assays were used in our studies. Even though low activity of IMPDH has been observed in RBC (Pehlke et al. 1972; Montero et al. 1995), previous studies have suggested that IMPDH is essentially nonfunctional in these cells (Parks et al. 1975; Nelson et al. 1977; Lennard and Maddocks 1983). The target cells of thiopurine therapy are the MNC and therefore analysis of IMPDH activity in these cells seems more appropriate (Jain et al. 2004; Thomas et al. 2005; Poppe et al. 2006). The absence of a positive correlation between MNC IMPDH activity and RBC 6-TGN may be because of measurement in different cell types or that IMPDH is not as central for metabolite formation in vivo as has been thought (Elion 1989). However, it is generally appreciated that 6-TGNs are synthesized via IMPDH and therefore our result casts doubt on the use of RBC as a surrogate compartment, especially since RBC lack DNPS (Parks et al. 1975). Our results bring the difference in thiopurine metabolism between MNC and RBC to the fore and support a model in which RBC synthesize 6-TGN from purine bases such as 6-TG and thioxanthine, produced via hepatic or other tissue metabolism, either via salvage by HGPRT (Parks et al. 1975; Weber 1983; Rowland et al. 1999; Duley and Florin 2005) or via a pathway with cytosolic 5´-nucleotidase (cN-II) (Barsotti et al. 2005). Interindividual differences in transport proteins may therefore also contribute to the variability in RBC and MNC metabolite concentrations, and to explain the modest correlation between RBC 6-TGN and clinical response described in the literature (Osterman et al. 2006). Ideally, all metabolite concentrations and enzyme activities should be measured in MNC, as these cells present an active pathway for the synthesis of 6-TGN. In vitro studies have shown a paradoxically self-limiting effect with decreased toxicity, and reduced 6-TGN and ATP concentrations with increasing concentrations of 6-MP. These findings indicate that not only the regulation of IMPDH, but probably also that of GMPS (Nakamura and Lou 1995), is central to the production of 6-TGN (Liliemark et al. 1990).

ITPA 94C>A ITPase deficiency could hypothetically affect the metabolite pofile (Lin et al. 2001; Bierau et al. 2007). Seven patients in Paper II and five patients in Paper III were heterozygous ITPA 94C>A. We did not find any interaction between metabolite concentrations and this genotype. However, based on the small number of patients we

Results and Discussion conclude that, in patients with IBD, the importance of ITPA polymorphisms remains to be clarified in relation to metabolite concentrations (Stocco et al. 2009). Two studies have shown that patients with wild-type TPMT/ITPA 94C>A present increased concentrations of meTIMP, without effects on 6-TGN, suggesting a role of ITPase in patients with high meTIMP/6-TGN concentration ratios (Stocco et al. 2010; De Beaumais et al. 2011). The influence of other variant alleles of ITPA was not investigated in our studies. In a recent study by De Beaumais et.al (De Beaumais et al. 2011) the intronic IVS2 +21A>C polymorphism was, however, not associated with the concentration of 6-MP metabolites in patients with acute lymphoblastic leukaemia. In contrast, a study by Hawwa et. al showed higher concentrations of 6-TGN in carriers of this polymorphism (Hawwa et al. 2008). The clinical significance of ITPA polymorphisms is a matter of controversy. A recently published meta analysis did therefore not show any correlation between thiopurine toxicity and the most extensively studied ITPA variant; ITPA 94C>A (Van Dieren et al. 2007).

IMPDH activity and metabolite concentrations in HEK293 cells To measure the effects of different IMPDH activities on metabolite concentrations in one cell type, an in vitro model was established. Both mRNA expression of IMPDH2 and IMPDH activity were reduced following treatment with siRNA against IMPDH2 in HEK293 cells (Table V). In cells cotreated with 6-MP and the IMPDH inhibitor mycophenolic acid (MPA) both mRNA expression of IMPDH2 and IMPDH activity were higher than in cells treated with 6-MP alone. Since IMPDH activity was measured in washed cells, in the absence of MPA, it is possible that an induction of the basal IMPDH activity, similar to that observed in MOLT-4 cells (Bremer et al. 2007; Vethe et al. 2008), could have occurred despite the presence of siRNA. Unlike the effect in MOLT-4 cells, MPA reduced the expression of IMPDH1 in HEK293 cells. MPA has been suggested to induce IMPDH in RBC (Sanquer et al. 1999; Weigel et al. 2001) but not in MNC (Glander et al. 2003). Studies of RBC from renal transplant patients showed increased GTP concentration during MPA treatment, which was proposed to be caused by a stabilization of IMPDH. This effect was not observed in MNC in which GTP decreased (Goldsmith et al. 2004; Jagodzinski et al. 2004). Whether such a stabilizing effect also applies to IMPDH in HEK293 cells cotreated with 6-MP and MPA is unknown. Induction of IMPDH2 has been observed in human hepatocytes exposed to 6-MP (Petit et al. 2008). In HEK293 cells both IMPDH1 and IMPDH2 were unaffected by 6-MP (data not shown). Downregulation of IMPDH2 in combination with 6-MP alone, or in combination with MPA, increased the concentration of meTIMP, but unexpectedly, also that of 6-TGMP (Table VI). This phenomenon is difficult to explain if IMPDH is assumed to

Results and Discussion be the rate limiting enzyme in the phosphorylation of 6-TIMP and suggests effects of pharmacogenes other than IMPDH and TPMT.

Table V. IMPDH activity and mRNA expression of IMPDH1 and IMPDH2 in low (uninduced) and high (induced) TPMT expressing HEK293 cells after downregulation of IMPDH2. TPMT- mRNA expression IMPDH status Condition IMPDH1 IMPDH2 activity Uninduced siNeg ctrl 100 100 100 siIMPDH2 85 ± 19 15.0 ± 3.6*** 32.3 ± 4.6*** siIMPDH2 + 0.5 µM MPA 70 ± 2.7* 22.8 ± 8.1*** 48.3 ± 6.1*** Induced siNeg ctrl 100 100 100 siIMPDH2 81 ± 19 27.0 ± 1.2*** 34.0 ± 4.8*** siIMPDH2 + 0.5 µM MPA 55 ± 4.0** 35.3 ± 3.2*** †54.0 ± 1.7*** Cells were treated with 3 µM 6-MP in all settings. Each result is based on four experiments. Results are given as percent of the non-silencing negative control. The statistical comparisons are in relation to the the non-silencing negative control. Mean ± S.D is given. †One measurement excluded due to a technical problem; *, P <0.05; **, P <0.01; ***, P <0.001. siNeg ctrl, nonsilencing negative control; si, siRNA down-regulated.

Table VI. The effects of IMPDH2 downregulation on thiopurine metabolite concentrations in low (un-induced) and high (induced) TPMT expressing HEK293 cells. Ratio TPMT- Condition meTIMP 6-TGMP meTIMP status 6-TGMP Uninduced siNeg ctrl 2.5 ± 0.7 3.8 ± 0.6 0.7 ± 0.3 siIMPDH2 41.4 ± 19.4*** 19.3 ± 8.5*** 2.1 ± 0.4** siIMPDH2 + 0.5 µM MPA 146.0 ± 13.8*** 19.6 ± 2.6*** 7.5 ± 1.2*** Induced siNeg ctrl 2.2 ± 0.9 1.6 ± 0.5 1.4 ± 0.3 siIMPDH2 46.3 ± 12.5*** 4.0 ± 0.9*** 11.5 ± 1.5** siIMPDH2 + 0.5 µM MPA 204.6 ± 16.7*** 6.5 ± 1.5*** 33.3 ± 9.0*** Cells were treated with 3 µM 6-MP in all settings. Each result is based on four experiments. Data are given as pmol per 1x106 cells. The statistical comparisons are in relation to the non-silencing negative control Mean ± S.D is given. **, P <0.01; ***, P <0.001. siNeg ctrl, non-silencing negative control; si, siRNA downregulated.

Muristerone A had no effect on IMPDH activity (P = 0.93) or the mRNA expression of IMPDH1 and IMPDH2 (P = 0.88 and 0.44). A meTIMP/6-TGN concentration ratio >20 was observed only when TPMT had been induced. Of 6-TGNs, 6-TGMP was the predominant metabolite in HEK293 cells. This is in accordance with observations in WEHI-3b cells and MOLT-4 cells (Liliemark et al. 1990; Stet et al. 1993), but in contrast to RBC where 6-TGTP predominates (Neurath et al. 2005; Vikingsson et al. 2009). Our results in HEK293 cells are in accordance with those observed in MOLT-4 cells when incubated with similar 6-MP concentrations. In both celltypes meTIMP, as well as 6-TGMP concentrations increased after 72h by the combination of 6-MP and MPA. In MOLT-4 cells the concentrations of endogenous nucleotides initially

Results and Discussion decreased, but were recovered after 48 h onwards (Bokkerink et al. 1993; Stet et al. 1993), which is in agreement with our observations of similar endogenous nucleotide concentrations in IMPDH2 downregulated and control cells. Inhibition of DNPS by meTIMP increases the availability of PRPP (Bokkerink et al. 1993) which can be used in salvage of (thio)purine precursors from dead cells to recover 6-TGMP and endogenous nucleotides. However, PRPP was not measured in our study.

5-aminosalicylic acid and corticosteroid therapy Paper II, III, IV The proportion of males and females, disease condition (UC/CD), smokers, age, and AZA/6-MP doses were similar over groups (Paper III and IV), as were blood counts in the gene expression analysis in Paper IV. There is a potential effect of 5-ASA on TPMT activity and the concentration of 6-TGN in vivo, but controversy exists about the clinical relevance based on the high concentration of 5-ASA required to inhibit TPMT in vitro compared with the plasma concentration received after oral administration (Szumlanski and Weinshilboum 1995; Sparrow et al. 2005; Xin et al. 2005). Some studies have shown increased concentrations of 6-TGN in vivo (Lewis et al. 1997; Lowry et al. 2001; Gilissen et al. 2005; Hande et al. 2006; de Boer et al. 2007) but without significant effects on meTIMP levels and TPMT (Gilissen et al. 2005; Hande et al. 2006; de Boer et al. 2007). In addition, there are studies that have shown similar metabolite concentrations among patients treated with 5-ASA or not (Dubinsky et al. 2000; Hindorf et al. 2006; Ansari et al. 2008; Daperno et al. 2009), which is in agreement with our results in Paper II and III. In Paper IV, the distribution of 5-ASA between metabolite profiles was similar. Nevertheless, 5-ASA was part of the subsets of variables that best predicted metabolite profiles and individual metabolite concentrations. Corticosteroid dependency represents one criterion defining a general unresponsiveness to therapy. Nine of sixty patients (15%) in Paper III and 8 of 52 patients (15%) in Paper IV were on corticosteroids. The majority (n = 7, and n = 6, respectively) was found in the group with meTIMP/6-TGN concentration ratios >20. The effect of corticosteroid therapy on thiopurine metabolism is largely unknown. Corticosteroid dependency and high meTIMP/6-TGN concentration ratios are associated with therapeutic failure (Dubinsky et al. 2002; Sparrow et al. 2007). However, when metabolite ratios are reversed by the addition of allopurinol, corticosteroid requirements are reduced (Sparrow et al. 2007). We therefore suggest that the presence of corticosteroid therapy is a proxy of disease activity rather than a cause of a high metabolite ratio. In addition the validity of our data was strengthened in our cell model with no influence of corticosteroids or 5-ASA.

Results and Discussion

Pharmacotranscriptomics in patients with different metabolite profiles

In Paper IV we explored the molecular basis of differences in metabolite profiles by using whole genome expression profiling analysis (Affymetrix 3’IVT U133 plus 2.0 array) of blood samples from patients representing three distinct groups; low (R4), normal (Median) and high meTIMP/6-TGN (R20) concentration ratios.

Screening of microarray data Ten genes differently expressed between the three metabolite profiles were identified. Large interindividual differences in gene expression levels were observed, but only small differences between patient groups. Nevertheless, this set of genes was able to distinguish between metabolite profiles as judged from principal component analysis (Figure 14). Similar magnitudes of relative gene expression ratios have been described in studies comparing transcriptional profiles between UC and CD in peripheral blood MNC (Mannick et al. 2004; Burakoff et al. 2010).

Figure 14. Principal component analysis of ten genes identified from microarray data from the exploratory patient cohort (n = 20). Two principal components explained 62% of variation in data. The ten genes were; FAM46A, FAR1, SLX1A/SLX1B, IFIT1, PLCB2, RELA, RNPEPL1, RER1, TGOLN2, UBE2A. R20, meTIMP/6-TGN concentration ratio >20; Median, median metabolizer; R4, meTIMP/6-TGN concentration ratio ≤ 4. One outlier excluded from the initial 21 patients (metabolite ratio >3 SD above upper quartile).

Seven genes were further evaluated by reverse transcription qPCR (Table VIIA), including the subset of five genes that best predicted metabolite profiles according to

Results and Discussion discriminant analysis and leave-one-out cross-validation, and the four genes with the lowest P-values in the statistical analyses of microarray data. The accuracy of a prediction model based on five genes was 90% (18/20) as judged by leave-one-out cross-validation with microarray data, and was 78% by using the ∆Ct values from reverse transcription qPCR. However, when the five genes were applied on a validation cohort, the overall accuracy was at best 36%. By groups, the median relative gene expression ratios were at most 1.39 in the validation cohort (Table VIIB, PLCB2), whereas the relative gene expression by individual was up to 17.07.

Table VIIA and VIIB. Median relative gene expression by groups and relative gene expression by individual in the exploratory cohort of patients (Table VIIA) and in the validation cohort of patients (Table VIIB). Reverse transcription qPCR data.

Table VIIA exploratory cohort Relative gene expression Relative gene expression by individuala by groups Gene symbol R4 vs R20 vs R20 vs R4 R20 Median R4 All Median Median (n = 9) (n = 4) (n = 6) (n = 19) FAM46Ab 1.06 0.86 0.81 1.99 1.75 1.80 2.97 SLX1A/SLX1Bb 1.43 1.27 0.89 1.68 1.29 1.43 2.02 IFIT1b 0.51 0.37 0.72 4.49 3.88 7.04 13.28 PLCB2b 1.31 0.96 0.74 2.77 1.49 2.01 3.71 RELAb 1.12 1.07 0.96 2.22 1.50 1.47 2.22 TGOLN2 1.04 0.99 0.95 1.95 1.42 1.65 1.95 RER1 1.08 1.05 0.97 1.61 1.51 1.70 1.85

Table VIIB validation cohort Relative gene expression Relative gene expression by individuala by groups Gene symbol R4 vs R20 vs R20 vs R4 R20 Median R4 All Median Median (n = 8) (n = 12) (n = 12) (n = 32) FAM46A 0.84 0.86 1.03 2.07 2.09 2.06 2.25 SLX1A/SLX1B 0.98 1.20 1.22 2.43 1.63 1.53 2.43 IFIT1 0.90 1.14 1.27 17.07 12.48 8.18 17.07 PLCB2 0.93 0.72 0.78 2.07 2.11 1.95 2.32 RELA 0.95 0.93 0.98 1.53 1.45 1.55 1.58 TGOLN2 0.97 0.87 0.90 1.62 1.85 1.58 1.94 RER1 0.99 0.92 0.93 1.25 1.54 1.45 1.54 aRelative gene expression by individual is calculated as the sample with highest expression level/the sample with lowest expression level. bThe expression levels of 5/7 genes were significantly correlated to the microarray data, P <0.05, rs range 0.45 to 0.81. R20, meTIMP/6-TGN concentration ratio >20; Median, median metabolizer; R4, meTIMP/6-TGN concentration ratio ≤ 4 (Table VIIA) or ≤ 5.7 (Table VIIB); FAM46, Family with sequence similarity 46 member A; SLX1A/SLX1B, SLX1 structure-specific endonuclease subunit homolog A/B; IFIT1, interferon-induced protein with tetratricopeptide repeats 1; PLCB2, phospholipase C beta 2; RELA, nuclear factor NF-kappa-B p65 subunit; TGOLN2, trans-golgi network integral membrane protein 2 precursor, RER1, retention in endoplasmatic reticulum 1. One outlier excluded (metabolite ratio >3 SD above upper quartile, Table VIIA and Table VIIB) and one sample due to insufficient amount of RNA (Table VIIA).

Results and Discussion

The statistical analyses were performed without correction for multiple testing. The inconsistency in gene expression patterns between metabolite profiles observed when the exploratory and the validation cohorts were compared (Table VIIA and VIIB) indicate type I errors, where small non-reproducible differences may explain the poor performance of the classification algorithms. Thus, the identified genes do probably not represent true pharmacogenes in the context of thiopurine metabolism. The same problem was observed when Csillag et. al (Csillag et al. 2007) investigated gene expression profiles in colonic biopsies from CD patients classified according to their response to thiopurine therapy. The results did not improve by using a less stringent filtering of microarray data (data not shown).

Genes related to the thiopurine metabolic pathway Microarray data from 42 genes related to the pathways of purine and thiopurine metabolism were extracted and included in a discriminant analysis. All subsets of five genes that best distinguished between metabolite profiles were analysed by reverse transcription qPCR. Again, large interindividual differences in gene expression levels were observed, but no differences in ∆Ct values between metabolite profiles (data not shown). Accordingly, discriminant analysis using leave-one-out cross-validation and the best subset of five genes (ENTPD5, GMPR1, ITPA, NT5M, SLC29A1), predicted only nine out of thirty-three patients (27%) of the validation cohort correctly.

Combined cohorts of patients – thiopurine related genes To improve detection of subtle relationships between gene expression levels and metabolite profiles the exploratory and the validation cohort were combined. When genes were evaluated individually the dose-normalized concentration of

6-TGN correlated to the expression of HPRT1 (rs = - 0.31, P = 0.03) and SLC29A1

(rs = 0.33, P = 0.02), whereas the dose-normalized concentration of meTIMP correlated to the expression of NT5E (rs = 0.33, P = 0.02) and TPMT (rs = - 0.37, P = 0.007). A high meTIMP/6-TGN concentration ratio was associated with low SLC29A1 expression and concurrently high NT5E expression (Figure 15). The expression levels of these two genes explained 16% of the variation in the meTIMP/6-TGN concentration ratio in a multiple linear regression model (P = 0.01).

Results and Discussion

Figure 15. 3D surface plot illustrating the relationship between the meTIMP/6-TGN concentration ratio and the expression of NT5E and SLC29A1 in the combined cohorts of patients (n = 52). Low SLC29A1 expression (rs = - 0.31, P = 0.02) and concurrently high NT5E expression (rs = 0.34, P = 0.01) was associated with a high meTIMP/6-TGN concentration ratio. Note the orientation of the axes. One outlier excluded (metabolite ratio >3 SD above upper quartile) and one sample due to insufficient amount of RNA.

The plasma membrane is relatively impermeable to nucleosides and nucleotides and therefore interactions between transport proteins and intracellular or extracellular kinases and nucleotidases may affect the amount of active thiopurine metabolites within cells (Rosman et al. 1974; Pieters et al. 1992; Baldwin et al. 1999; Wielinga et al. 2002). ENT1 (encoded by the gene SLC29A1) belongs to the equilibrative nucleoside transporter family, important for cellular uptake of chemotherapeutic agents and preformed nucleosides (Baldwin et al. 1999). Among patients with acute lymphoblastic leukaemia the concentration of 6-TGN in leukaemic blasts exposed to AZA, both in vivo and in vitro, was associated with the expression level of SLC29A1 (Zaza et al. 2005). Our results confirm this relationship and suggest that ENT1 may be involved in regulating the intracellular metabolite concentration also in patients with IBD. The dose-normalized concentration of meTIMP correlated positively to the expression of ecto-5´-nucleotidase (NT5E). This ecto-nucleotidase hydrolyses nucleotides to their corresponding nucleosides at the outer surface of the plasma membrane (Zimmermann 1992). Recently, the role of NT5E and ABCC4 in a cellular circulation scheme for 6-TGN was described (Li et al. 2010). Based on studies of

Results and Discussion nucleoside and nucleotide transport in MOLT-4 cells and in HEK293 cells, it is possible that similar pathways exist also for meTIMP (Wielinga et al. 2002; Fotoohi et al. 2006). Transport proteins may confer resistance to thiopurines (Wielinga et al. 2002; Peng et al. 2008) as well as protection against the accumulation of toxic concentrations of thiopurine metabolites. Genetic variants of ABCC4 have been associated with the intracellular accumulation of 6-TGNs and myelotoxicity in a Japanese population (Krishnamurthy et al. 2008; Ban et al. 2010). In our data we did, however, not find any correlation between metabolite concentrations and the expression level of ABCC4. Unexpectedly, negative correlations between the dose-normalized concentration of meTIMP and the expression of TPMT, and between the dose-normalized concentration of 6-TGN and the expression of HPRT1 were observed. Measurement of metabolite concentrations in RBC and gene expression in MNC represents one possible explanation. When all genes and the presence of 5-ASA and corticosteroid therapy were considered, the best multiple linear regression model with five variables explaining the variation in the meTIMP/6-TGN concentration ratio comprised; IMPDH2, NME1/2, SLC29A1, 5-ASA and corticosteroid therapy (Table VIII). This model explained 41% of the variation in the meTIMP/6-TGN concentration ratio (P <0.001). Considering the positive relationship between IMPDH2 expression and the meTIMP/6-TGN concentration ratio, a similar phenomenon was observed in the knock-down model of IMPDH2 in HEK293 cells in Paper III. The variation in the dose-normalized concentration of 6-TGN was best explained by a model of PNP, SLC29A2, TPMT, 5-ASA and corticosteroid therapy (R2 = 0.25, P = 0.02). The variation in the dose-normalized concentration of meTIMP was best explained by a model that comprised ENTPD1, NME1/2, SLC29A2, 5-ASA and corticosteroid therapy (R2 = 0.37, P <0.001).

Table VIII. Multiple linear regression analysis in the combined cohorts of patients (n = 52), with meTIMP/6-TGN concentration ratio as the dependent variable. Parameter Estimate 95% CI P-value Intercept 10.35 -30.45 51.14 0.61 IMPDH2 25.70 6.61 44.79 0.009 NME1/2 -12.61 -32.41 7.19 0.21 SLC29A1 -2.04 -4.69 0.61 0.13 5-ASA Yes -6.68 -16.25 2.88 0.17 Corticosteroid Yes 11.02 1.82 20.22 0.02 5-ASA x Corticosteroid -0.25 -10.09 9.58 0.96 CI; confidence interval. One outlier excluded (metabolite ratio >3 SD above upper quartile) and one sample due to insufficient amount of RNA.

Hierarchical cluster analysis of gene expression Three main clusters of genes (A, B, and C) were defined based on correlations between gene expression levels (Figure 16). Three of the four genes (SLC29A1, NT5E,

Results and Discussion

HPRT1, TPMT) with strongest individual correlations to either meTIMP or 6-TGN concentrations, belonged to the same cluster of genes. Based on the Spearman rank correlation coefficients, the expression pattern of the fourth gene (SLC29A1) was not closely correlated to any of the three main groups of expression patterns. The metabolite concentrations clustered closest to TPMT, NME1/2, HPRT1, ITPA, and GMPS in cluster C and to SLC29A1. The cluster analysis suggests that not only individual genes with significant correlation to the metabolite concentration merit further attention, but also that co-regulated genes should be investigated for their combined influence on the meTIMP/6-TGN concentration ratio. The small differences noticed in median gene expression levels between metabolite profiles, as compared with those observed between individuals, suggest that differences between distinct metabolite profiles are explained either by interactions between several genes, each with a small contribution (Figure 15 and 16), or at the post-transcriptional level. Search for more precise tools in order to explain differences in metabolite profiles is needed.

Figure 16. Unsupervised hierarchical cluster analysis of variables (19 genes related to the purine and thiopurine metabolic pathways and dose-normalized metabolite concentrations). Three main clusters; A, B and C, and SLC29A1 were defined. The metabolite concentrations clustered closest to TPMT, NME1/2, HPRT1, ITPA, and GMPS in cluster C and to SLC29A1. The dendrogram is based on data from the combined cohorts of patients (n = 52). One outlier excluded (metabolite ratio >3 SD above upper quartile) and one sample due to insufficient amount of RNA.

Conclusions

CONCLUSIONS

Based on the findings in Paper I-IV, the following conclusions can be drawn:

Pyrosequencing offers a flexible and convenient method for genotyping. The combination of genotyping and phenotyping overcomes the limitations of both tests. The allele frequencies of TPMT*3A, *3B, *3C and *2 in a Swedish background population were in agreement with those in other Caucasian and European populations.

The IMPDH activity in MNC was distributed over a wide range and was similar in thiopurine treated patients and healthy blood donors. We did not observe any distinct genetic influence on the IMPDH activity.

Patients with high meTIMP/6-TGN concentration ratios (>20) had lower IMPDH activity than patients with lower metabolite ratios, explained by an inverse correlation to the dose-normalized concentration of meTIMP. No correlation to the dose-normalized concentration of 6-TGN was observed. The TPMT activity was similar in the two groups of patients.

A meTIMP/6-TGN concentration ratio >20 was observed only in patients with normal TPMT activity. IMPDH activity was not affected by TPMT status and did not correlate to RBC metabolite concentrations in patients with reduced TPMT activity. The results indicate that a normal TPMT activity is a necessary albeit insufficient prerequisite for a high meTIMP/6-TGN concentration ratio to develop.

Downregulation of IMPDH activity in vitro was associated with an increased concentration both of meTIMP and 6-TGMP. This suggests contributing effects of pharmacogenes other than IMPDH and TPMT. Although the in vitro and in vivo situations are not entirely comparable our results indicate that 6-TGNs both in vitro and in vivo are regulated by yet unidentified pharmacogenes.

The whole genome expression analysis did not identify any significant differences between three distinct metabolite profiles in IBD-patients.

Analysis of thiopurine related genes revealed large interindividual variations in gene expression levels, but only small differences between metabolite profiles, indicating

Conclusions that complex interactions between expression levels of multiple genes exist and may explain differences between metabolite profiles.

Three main clusters of co-regulated thiopurine related genes were defined based on correlations between gene expression levels. The dose-normalized concentration of meTIMP in RBC correlated positively to the expression of NT5E and inversely to TPMT. The dose-normalized concentration of 6-TGN correlated positively to the expression of SLC29A1 and inversely to HPRT1. With the exception of SLC29A1, these genes belonged to the same main cluster of genes. A high meTIMP/6-TGN concentration ratio was associated with low SLC29A1 expression and concurrently high NT5E expression.

Taken together, the results illustrate the complexity of the thiopurine metabolism and suggest that differences between metabolite profiles are explained either by interactions between several genes, each with a small contribution, or at the post-transcriptional level. Search for more precise tools in order to explain differences in metabolite profiles is needed. Also, in order to investigate small effects it is necessary to analyse metabolite concentrations and gene expression levels, as well as enzyme activities in the target cells of therapy (MNC). These analyses are not feasible in routine clinical praxis today, mainly because of the large number of cells needed. However, from a scientific perspective this would be a highly prioritized area of methodological development to improve the understanding of the thiopurine metabolism in vivo.

Future aspects

FUTURE ASPECTS

Despite several efforts a clinical program for routine metabolite monitoring of the thiopurine drugs that can guide dosing has not been established. The thiopurine metabolism is complex and an approach which relies on the analysis of TPMT and metabolite concentrations in RBC is probably an oversimplification. The 6-TGNs found in RBC are probably taken up as precursors synthesized in other tissues or cells, as RBC lack DNPS. Therefore RBC do not necessarily reflect the concentration in the target cells correctly, which may explain the modest correlation between RBC 6-TGN and clinical response reported in the literature. Furthermore, the routine methods used today co-determine a number of metabolites that probably have varying efficacy. In order to advance the knowledge of the thiopurine metabolism and clinical effects in vivo it is necessary to analyse metabolite concentrations and gene expression levels, as well as enzyme activities in the target cells of therapy (MNC). Thus, there is a demand for continued methodological development. The correlation between RBC and MNC metabolite concentrations needs to be evaluated in an adequate patient cohort as data hitherto are scarce. Also, in this context, the importance of various metabolites in MNC for clinical effects and adverse events needs to be studied. Systematic data over the intraindividual variation in metabolite concentrations on stable doses are needed. In order to improve treatment outcomes there will be a demand for increased knowledge of factors that influence the metabolism and clinical efficacy. Many patients intolerant to AZA tolerate a shift to 6-MP, whereas others fail both therapies for different reasons. Genetic characterization of intolerant and/or refractory patients will improve the understanding of factors that influence clinical efficacy and metabolite formation in order to avoid adverse events or lack of response. It may also bring economical benefits both from a patient perspective with diminished loss of productivity, and for the economical impact on the health care system as concerns number of control visits etc. Also, increased knowledge of mechanistic similarities and differences between the different thiopurine drugs is needed. The combination of the XO inhibitor allopurinol and thiopurine in reduced dose reverses a high meTIMP/6-TGN concentration ratio and is effective in IBD. The mechanism for this metabolic change needs to be resolved, and studied in MNC. In addition, further studies are needed to explore the molecular basis of differences in metabolite profiles, preferable with all parameters measured in the target cells of therapy. The cotreatment with thiopurines and biological drugs is increasing. Because of the high costs of biological drugs it would be interesting to compare the ability of cotherapy with biological drugs to induce mucosal healing to that of cotherapy with allopurinol.

Acknowledgements

ACKNOWLEDGEMENTS

The studies in this thesis were conducted at the Division of Gastroenterology and Hepatology, Department of Clinical and Experimental Medicine, Faculty of Health Sciences, Linköping University, Linköping and at the Division of Medical Diagnostics, Laboratory medicine, Ryhov Hospital, Jönköping, Sweden. During the years several people have contributed to this thesis in different ways and I would like to express my gratitude to you all. In particular, the following co-workers, friends and family should be mentioned:

My tutor Sven Almer, for his never ending enthusiasm and support, for encouragement, sound advice and scientific knowledge. Also for his kindness and for being a valued friend.

My tutor Jan Söderman, for his generous support, inspiration, scientific advices and for always being there to discuss science and molecular biology.

My tutor Curt Peterson, for his great enthusiasm, for sharing his knowledge about pharmacology, for all support and constructive criticism.

Lena Svensson, for valuable support, her positive spirit and our great teamwork. For travel company around Swedish hospitals, friendship and a welcoming bed now and then.

Malin Lindqvist Appell, my co-author, for fruitful collaborations, discussions and friendship, and for letting me stay over now and then.

My other co-authors Ulf Hindorf, Svante Vikingsson, Christer Grännö, Margareta Danelius, and Sally Coulthard for fruitful discussions and for making my articles possible.

Britt Sigfridsson, Monica Häger and Gunilla Graffner for excellent skills in analysing TPMT and metabolites.

The physicians, drs Christer Grännö, Ulf Jansson, Henrik Stjernman and the nurses Inga-Lena Hultman, Monica Wåhlin, Anette Persson, Berit Kindvall-Nilsson, Lotta Granberg, Britt Brage, Ewa Petterstedt, Anneli Pousette at Ryhov Hospital 71

Acknowledgements

Jönköping and ”EM-mottagningen” Linköping University Hospital for all help with the patients in this thesis, and the staff at the Clinical chemistry laboratory, Blekinge Hospital for all help.

Björn Norlander for sharing his knowledge about chromatography.

Karin Skoglund for valuable laboratory discussions, friendship and fun travel company.

All my research colleagues at the Division of Medical Diagnostics, Laboratory medicine, Länssjukhuset Ryhov, Jönköping, for being great support and friends both in triumph and disappointment, among others; Bettan, Hanna, Sara, Lisa, Malin, Olaf, Kiki, Karin, Emma and Inga. Bettan I wish you the best luck with the little ones! All my other colleagues in Jönköping for their friendship and general support; Ingalill and Annica for valuable support.

Mats Nilsson for help with statistics and Zlatko Mujanovic for help with figures.

My present and past head of operations Pia Rundgren, Håkan Ossowicki and Bente Transö, for believing in me and my project, the support and encouragement during these years.

My parents Ulla and Krister for your endless love, encouragement, understanding and support and for always being there for me. Ingegerd and Sten, for love and encouragement, tasty lunchboxes and carpentry!

My love David, for being such a tremendous support throughout these years. You are my best! I promise I will be a little bit more present in the future!

To those I have not mentioned in this list, thank you!

This work was supported by grants from Futurum - the Academy for Healthcare, County Council Jönköping, the Medical Research Council of Southeast Sweden (FORSS), the Swedish Society of Medicine, the County Council of Östergötland, the Swedish Cancer Society, the Swedish Childhood Cancer Foundation, Rut and Richard Juhlin´s foundation, Jenny Nordqvist Foundation and the Swedish Research Council.

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