Function and evolution of Protein kinase A splice variant Cβ2 and its role as a biomarker in prostate cancer

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Function and evolution of Protein kinase A splice variant Cβ2 and its role as a biomarker in prostate cancer

Line Victoria Moen

Thesis for the degree of Philosophiae doctor Department of Nutrition

Institute of Basic Medical Sciences Faculty of Medicine

UNIVERSITETET I OSLO

2016

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© Line Victoria Moen, 2017

Series of dissertations submitted to the Faculty of Medicine, University of Oslo

ISBN 978-82-8333-390-9

reproduced or transmitted, in any form or by any means, without permission.

Cover: Hanne Baadsgaard Utigard.

Print production: Reprosentralen, University of Oslo.

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Papers included

I. Moen LV, Sener Z, Volchenkov R, Svarstad AC, Eriksen AM, Holen HL and Skålhegg BS.

Ablation of immune specific Cβ2 of PKA leads to increased susceptibility to collagen- induced arthritis. Submitted

II. Moen LV, Ramberg H, Zhao S, 4, Grytli HH, Sveen A, Berge V, Skotheim RI, Taskén KA, Skålhegg BS. Observed correlation between the expression levels of catalytic subunit, Cβ2, of cAMP-dependent protein kinase and prostate cancer aggressiveness. In press, Urol Oncol. 2016 Nov 9. pii: S1078-1439(16)30307-6.

III. Søberg S, Moen LV, Skålhegg BS, Lærdahl J. Evolution of the cAMP-dependent protein kinase (PKA) catalytic subunit isoforms. Manuscript

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Acknowledgement

The work presented in this thesis was carried out in the Department of Nutrition and

Department of Biochemistry, Basic Medical Sciences, University of Oslo and was supported by grants from UiO and the Norwegian Cancer Society.

First of all, I would like to thank my supervisor Bjørn Steen Skålhegg for giving me the opportunity to work in your research group. Thanks for always taking the time to answer questions and for all the constructive feedback and encouragement during the time in your group. Your research group has been an awesome place to work in!

Next, I would like to thank Kristin Austlid Taskén for sharing her prostate cancer expertise and for her supervision and helpful comments during the writing process. Thanks to Håkon and Helene and all the other members of your group.

Thanks to Sissel for the all the help with various lab techniques, lunches and especially all the time you have used on genotyping. Thanks to Anja for teaching me the kinase assay, your meticulous attention to details is truly impressive. Thanks to Kristoffer, for interesting and intriguing discussions about PKA, evolution and alternative medicine. Thanks for somewhat correct medical advice and for sharing all the coffee walks to nutrition. I’m grateful to Halvor for taking the time to proof reading every last detail and for always having insightful

comments. Thanks to Roman for all the hours in the flow room, and for all the fast reading at the end of this project. Many thanks to Tore Jahnsen useful comments and guiding during the PhD. Thank you to all the other past and present members in the Skålhegg group; To

Henning for all the patient help with the Äkta and your never ending positivity. To Aud, thanks for introducing me to the cell lab, always taking time to help out and for the funny, but sometimes slightly improper comments. Thanks to Aud Marit for being the best master student, your hard work was admirable and thanks for sharing so many nice food memories.

Thanks to Sam for being an awesome office mate the first months of my PhD. To Tuva, thanks for your helpful scientific discussions and for my (hopefully) less than 15 minutes of fame. To Claudia for all the wedding discussions and for your laughter which always lifts the mood. Thanks to Ken and Steffi for all your insight in protein purifications skills. To Bora for your prompt help with vacuum drying my protein.

Thanks to Sophia for doing the orderings and help with solving all kinds of practical stuff, always with a smile. Thanks to Per Eugen for the collaboration, your knowledge about NMR is impressive. Thanks to all the other colleagues in the biochemistry and nutrition

departments.

To all the #thesuperphdgirls; Zeynep for being my mouse expert and craft beer companion, Simona for all the positivity and fashion advice and to Graciela for your inspiring dedication and for “Las Rosas”. You all made the long hours in lab and office shorter, and for that I will be forever grateful.

Thanks to my sister Aurora for all the “breakfast meetings” sharing up and downs of our PhDs, finally we are both finished! I am grateful to all my friends and family for listening to me and helping me out when needed. Finally, thanks to my husband John André for putting things in perspective, picking up the slack at home and supporting me endlessly through this process.

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Abbreviations

4K 4-kallikrein

Ab Antibody

AC Adenylyl Cyclase

AKAP A-Kinase Anchoring Proteins

APC Antigen presenting cells

AR Androgen receptor

Atf-1 Activating transcription factor 1

ATP Adenosine triphosphate

BEAS-2B Human bronchial epithelium BPH Benign prostate hyperplasia

C Catalytic

CAIA Collagen antibody-induced arthritis cAMP Cyclic adenosine monophosphate CD Cluster of differentiation

CI Confidence interval

CIA Collagen induced arthritis

CII Collagen Type II

COPD Chronic obstructive pulmonary disease

CRE cAMP response element

CREB cAMP response element-binding protein Crem cAMP response element modulator CRPC Castration resistant PCa

cT-stage Clinical T-stage

DRE Digital rectal exam

Epac Exchange protein directly activated by cAMP ES cells Embryonic stem cells

FFPE Formalin-fixed, paraffin-embedded

FoxP3 Forkhead box P3

FRT Flp recombination target

G-CSF Granulocyte colony-stimulating factor ICER Inducible cAMP early repressor

IL Interleukin

KO Knockout

LPS Lipopolysaccharide

MHC Major histocompatibility complex

MIP1-α Macrophage inflammatory protein 1 alpha

MR Magnetic resonance

MS Multiple sclerosis

NCI National Cancer Institute

NED Neuroendocrine like differentiation

Neo Neomycin

NFκB Nuclear factor kappa-light-chain enhancer of activated B-cells

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NHGRI National Human Genome Research Institute

NK Natural killer

PCA-3 Prostate cancer antigen-3

PDE Phosphodiesterase

Phi Prostate health index

PKA Protein Kinase A

PKA-I PKA type I

PKA-II PKA type II

PSA Prostate specific antigen pT-stage Pathological T-stage

R Regulatory

RA Rheumatoid arthritis

SRA Sequence Read Archive

TCGA The cancer genome atlas

TCM Central memory T cell

TCR T cell receptor

TEM Effector memory T cell

TLR Toll-like receptor

TNF-α Tumor necrosis factor alpha

TNM Tumor-nodes-metastasized

Treg Regulatory T-cell

TRUS Trans-rectal ultrasound

T-stage Tumor -stage

WT Wild type

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1 Introduction

Protein Kinase A 1.1

1.1.1 Overview of the Protein kinase A pathway

Protein kinase A (PKA) was discovered already in 1968 and serves as a model kinase for studying the function and structure of most protein kinases identified today [1, 2]. PKA is a cyclic adenosine monophosphate (cAMP) activated holoenzyme which is composed of a regulatory (R) subunit dimer bound to two catalytic (C) subunits and is ubiquitously

expressed in all human tissues examined [3]. Four genes have been identified that encodes for RI (RIα, RIβ) and RII (RIIα and RIIβ), and the holoenzyme consisting of either RI or RII are called PKA type I (PKA-I) or PKA type II (PKA-II) respectively. The R-subunits normally forms homodimers; however heterodimer of RIαRIβ have been described [4]. Two major PKA C subunit genes, designated PRKACA and PRKACB, have been identified, which encode the C subunits Cα and Cβ respectively. A third C variant gene, PRKACG that encodes Cγ has also been reported, however there is limited evidence that this gene is actually

translated. Cγ is a retroposon only identified in higher primates, and most likely represents a pseudogene [5]. Finally, the genes PRKX and PRKY are also identified as C subunit genes, with only PRKX shown to be translated into protein [6-8]. Activation of PKA is initiated when an exogenous signal such as hormones and neurotransmitters binds to a G-Protein coupled receptor that stimulates enzymes adenylyl cyclase (AC), which converts adenosine triphosphate (ATP) to cAMP [9]. When the cAMP concentration in the cytosol increases as response to the exogenous signal, four cAMP molecules bind to the R subunit dimers, which leads to changed conformation and the two C subunits are released (Figure 1). The unbound C subunits are catalytically active and phosphorylate several downstream targets [4, 10].

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Figure 1 Activation of Protein kinase A by cAMP. When the endogenous concentration of cAMP increases, four cAMP molecules (yellow) bind the R- subunit dimer (grey). The two C-subunits (pink) are released and are activated for phosphorylation of downstream protein targets in the cytoplasm and nucleus.

PKA phosphorylation of downstream targets regulates signal transduction pathways that are involved in differentiation, proliferation and maturation of a number of different cells [3]. A change in cAMP concentration thus leads to several different responses in the cell, and how the differential responses are regulated has been intensively studied.

Cyclic AMP is an important signaling factor and may have a diverse response in different tissues. Some of the modulating effects by cAMP are facilitated through PKA, but also some can be modulated by exchange protein directly activated by cAMP (Epac) independent of PKA, and cyclic nucleotide gated ion channels [11]. Some of the differential effects are probably due to different expressions levels of PKA subunit isoforms [12]. In the human teratocarcinoma cell line Ntera 2, it was found that when cells were differentiated, RIIβ increased together with the Cβ splice variants and resulted in the formation of novel PKA holoenzymes [12]. PKA activity is regulated at several levels, including use of alternative forms of cAMP phosphodiesterases (PDEs) which are degrading cAMP by cleaving the phosphodiester bond, hence controlling PKA activity [13]. In addition, a family of proteins denoted A-Kinase Anchoring Proteins (AKAPs) also have an important role in regulating the PKA effect. AKAPs bind the R-subunits through the PKA-binding domain and keep the

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holoenzyme nearby specific target, thus modulating the cAMP response [14, 15]. AKAPs also bind other proteins and enzymes including PDE, thus also controlling the local cAMP concentration [16]. Finally, AKAPs bind more strongly to RII subunits [17, 18], but there also exists AKAPs that binds to RI subunits [19, 20] as well as dual-specific AKAPs binding both RII and RI [21, 22].

PKA is not considered to be an oncogene, but there is evidence that both R and C subunits may contribute to cancer progression and differentiation. Also the well-known oncogene c- MYC has been shown to upregulate PRKACB [23]. Expression of PRKACA is elevated in some types of breast cancer [24]. A PRKACA mutation has been identified in 50 % of all adrenocortical tumors [25, 26], and it has been shown that dysregulation of PRKACA (Cα1 gain of function) is associated with Cushing’s syndrome and also associated with the rare liver cancer fibrolamellar hepatocellular carcinoma [27, 28]. PRKACB is also involved in Carney complex, however with a very low incidence [29, 30]. Finally, PRKACB is

downregulated in non-small cell lung cancer [31], and have been reported to be up-regulated in prostate cancer [32]. Thus, precise control of C subunit gene expression is evidently critical for normal functioning of cells.

1.1.2 The regulatory subunits of PKA

The R subunits are the cAMP-responsive part of the holoenzyme and are also inhibiting the C subunits in absence of cAMP. The R subunits variants are differentially expressed and

localized in mammal cell types [3]. RIα is expressed in most cells, and is crucial for the regulation of PKA catalytic activity in early embryonic development [33]. RIβ on the other hand is mainly identified in the testis, brain, and in T- and B- cells. Correspondingly, RIIα appears ubiquitously expressed, while RIIβ expression is restricted to adipose, brain and endocrine tissue [4, 34]. The two different variants are also localized at different sites inside the cell, PKA-I is mostly restricted to the cytoplasm, while PKA-II seems to be localized closer to membranes and subcellular structures through AKAPs [35].

The R subunits are crucial for cell function, which has been shown in different knock out (KO) studies in mice. RIα KO mice are not viable since the ablation of RIα leads to

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developmental defects and early embryonic lethality [36]. Characterization of RIIα KO mice showed some alterations in long term potentiation and ocular dominance of the visual cortex in the brain [37], apart for this the RIIα KO mice appear normal [38, 39]. In the RIIβ KO mice a compensatory increase in RIα was observed. The change in PKA holoenzyme composition from PKA-II to PKA-I was associated with a series of altered physiological processes including an increased basal level of PKA catalytic activity. The latter was likely caused by the fact that RIα binds the C subunit with lower affinity for the C subunit

compared to RIIβ [40].

1.1.3 The catalytic subunits of PKA

1.1.3.1 Cα and Cβ splice variants

As mentioned there are two major C subunit genes, PRKACA and PRKACB, which encode the C subunits Cα and Cβ. PRKACA encodes two different splice variants, Cα1 and Cα2. Cα1 is expressed in all tissues examined, whereas the Cα2 variant is exclusively expressed in testis [17, 41, 42]. PRKACB encodes several splice variants including Cβ1, Cβ2, Cβ3, Cβ4 as well as several variants of Cβ3 and Cβ4 with varying inclusion of the three short exons denoted a, b, and c (Figure 2). While Cβ1 is expressed in most tissues, Cβ2 is enriched in lymphoid tissue [43, 44]. Cβ2 is found in several organisms including mouse and human and its size of 46 kDa is significantly larger than the other variants and is the largest known C variant, parts of the tail have been predicted to form an alpha helix [44-46]. Cβ3 and Cβ4, including the a,b and c variants are solely expressed in neuronal tissue [12, 47].

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Figure 2 Overview of the Cβ gene structure and amino acid sequence of the different splice variants. A) Exon 1-10, introns and the 5’ and 3’ end UTR, of the Cβ gene encoded by PRKACB (human). The exons 2-10 that are common for all the splice variants are marked with a red rectangle. B) The peptide sequence of the different splice variants of Cβ for human (Cβ1, Cβ2, Cβ3, Cβ4) and mouse (Cβ2), all the variants includes exon 2-10, where the first encoded amino acids of exon 2 are shown in the red rectangle. In addition there exist several splice variants combining either Cβ3 or Cβ4 with a,b or c (not shown). The predicted alpha helix forming part in Cβ2 is marked in pink. The figure is based on [48].

There have been several studies on the effects of KO of C subunit genes in mice (Table 1).

The double KO offspring of Cα and Cβ are not viable, and die during embryogenesis,

demonstrating the importance of the C subunits during embryonal development [49]. The Cα KO mice are smaller than their WT littermates, and more than 70 % of the mice die before adulthood. The male mice that survive through adulthood have dysfunctional sperm resulting in infertility [41, 42, 49]. Cβall KO mice have been shown to be protected against diet

induced- obesity [50]. A follow up in the same group showed that Cβall KO mice were also protected against angiotensin II-induced cardiac hypertrophy and dysfunction [51]. Neither Cβall nor Cβ1 KO mice have been found to have an immune related phenotype, although Cβ2 is expressed in immune cells. There is, however, some evidence that Cβ1 KO mice might have reduced plasticity in hippocampus [49, 52]

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Table 1 Overview over C subunit KO mice

C subunit KO mice

Phenotype References

Cαall(-/-)Cβall(-/-) Die under embryogenesis Huang et al. 2002 Cαall (-/-)

Cαall (+/-)

73 % of the mice dies before adulthood Males are infertile (those that do reach adulthood)

Anxiety

Skålhegg et al. 2002, Huang et al. 2002 Briassoulis et al. 2016

Cα2(-/-) Males are infertile Nolan et al. 2004

Cβall(-/-) No altered phenotype

Protected for metabolic dysfunction Protected against cardiac dysfunction

Howe et al. 2002, Funderud et al. 2009 Enns et al. 2009

Enns et al. 2010 Cβ1(-/-) Cβ1 KO mice might have reduced plasticity

in hippocampus

Huang et al. 1995, Qi et al. 1996

Cβ2(-/-) Hypersensitive to Collagen induced arthritis, more CD4+ and CD8+ naïve T- cells

Moen et al. 2016 (Paper I)

There exist studies both in KO mice and in cells lines that imply that different C subunit isoforms have different effects within a single cell. KO studies in mice [53] show that the T- cell activation marker CD69 was upregulated in Cα KO mice and not in Cβ KO mice. Also, Cα but not Cβ was required for inhibition of T-cell activation through PKA, despite

comparable PKA catalytic activity in the two cells.[53]. Studies on human prostate cancer cell lines suggested differential regulation of the Cβ subunit splice variants [32]. In human bronchial cell lines, Cβ but not Cα was required for induction of the PDE variant PDE4B, thus showing a divergent role of two catalytic subunits in different cell types [54].

It has also been shown that when one allele of RIα is knocked out in mice, basal PKA kinase activity is increased. This is most likely caused by loss of R subunit-dependent inhibition of C subunit activity, as Cα subunits have quantitatively fewer RI subunits to bind to. This leads to anxiety-like behavior in mice [55]. The same group later showed that mice with PKA- defects in RIα and Cα1 (Prkar1a+/- /Prkaca+/-) also showed increased anxiety-like behavior compared to wild type (WT) animals, which was not expected as they hypothesized that the

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crossing with Cα(+/-) would reverse the effect [56]. This also highlights the complex relation between the different C and R subunits of PKA.

1.1.3.2 The structure and catalytic properties in the C subunit

All respective splice variants encoded by PRKACA and PRKACB are homologous between exon 2 and exon 10. Thus, most amino acid sequence variation between the respective Cα and Cβ variants are located at the N-terminus of the protein. Most eukaryotic protein kinases share a highly conserved region known as the catalytic core domain, containing most of the catalytic machinery necessary for an active protein kinase[1]. The catalytic core domain is located in Cα1 and Cβ1 residues 40-300. The amino acid segments located N- and C-terminal to the catalytic core are denoted the N- and C-tail, respectively, and are more variable among different eukaryotic protein kinases. The sequence of all the PKA C subunits encoded by exon 2 through 10 is highly conserved between the C isoforms encoded by PRKACA and PRKACB [57]. Consistent with the location of the catalytic core domain, shared among all respective catalytically active Cα and Cβ proteins, the catalytic activity is comparable between the Cα subunits Cα1 and Cα2 [58]. Similarly, it is likely that the catalytic activities of all Cβ variants are comparable as well. other eukaryotic protein kinases also share similar properties, especially in the catalytic core [1]. PKA Cα was the first eukaryotic protein kinase crystal structure to be solved [59] and has served as a model for understanding protein kinase structure and function in general. The conserved catalytic core of the C subunit consists of a small lobe and large lobe [60]. The small lobe contains most of the ATP binding residues and mainly consists of β sheets, while the large lobe has binding sites that facilitates binding to the R subunits and substrates, and consists mostly of α –helices [59-61] (Figure 3). Between the large and small lobes is the active site cleft, which may bind an ATP molecule and two divalent cations that are critical for catalysis. Since all the respective isoforms of Cα and Cβ exclusively differ through the use of alternative exons 5’ of exon 2, the proteins have different N-tails. The mammalian lines of C subunits have been shown to be myristylated at the N-terminus [62].Myristic acid may bind to a hydrophobic pocket near the N-terminus of C. It is believed that myristic acid may flip out of the hydrophobic pocket and associated with membranes, and when myristylated C subunits bind to RII, and not RI, subunits, possibly

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resulting in altered subcellular localization to the membrane of C subunits [63]. Cα1 and Cβ1 are the only C isoforms exhibiting the myristylation site.

Figure 3 Crystal structure of Cα1 (1L3R form PDB) Ribbon diagrams of the 3D structure of myristylated (yellow) Cα1 (purple) bound to PKI (red). A) shows myristylated N-terminus and with myristic acid binding to the hydrophobic pocket. B) shows the active site cleft. Made in Pymol v.1.3 based on PDB: 1cmk

As mentioned the Cβ2 variant encoded by exon 1-2 combined with exon 2-10 is larger compared to any other C subunit identified. This exon encodes 62 amino acids at the N- terminus of Cβ (Figure 2). Based on helical wheel prediction, it has been suggested that Cβ2 has α-helix forming capabilities [46]. The precise function of Cβ2 is still unknown, but it seems to be involved in the differentiation of immune cells and it is speculated that the α- helix forming capabilities may contribute to isoform-specific effect [44, 53]. It is known that one single ancestor subunit gene duplicated to the paralogs PRKACA and PRKACB

approximately 500 mill years ago, and some results suggest that this gene duplication occurred at the same time as RIα/RIβ in addition to RIIα/RIIβ occurred [57, 64]. There are few residues that differ between Cα and Cβ, but those that do may be important for selection of target and the differential response of Cα and Cβ [57]. It should however be pointed out that little if anything is known of the evolution of the alternative first exons of PRKACA and PRKACB. Since most of the variability among the C subunits is located in the N-terminus, and are expected to lead to functional differences, we have investigated the evolutionary paths of the exons 1-1 and 1-2 of the PRKACA and PRKACB genes, respectively (Paper III).

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Immune system 1.2

1.2.1 Overview of the immune system

The immune system protects the body from endogenous and exogenous pathogens and is classified into two subsystems, the innate (“unspecific”) and the adaptive (“specific”) system.

The innate immune system represents the first line defense against pathogens; primarily represented by physical barriers (skin, mucous, saliva and stomach acid), but also cellular components such as phagocytes (neutrophils, dendritic cells and macrophages) and natural killer (NK) -cells. The innate immune system is the first line defense to pathogens, and works fast and is less specific compared to the adaptive immune system. The cellular components of the innate system recognize pathogens through binding to Toll-like receptor (TLR) that activates immune cells [65]. TLR binds to conserved pattern of microbial metabolic products [66]. The innate immune system both recognizes and removes pathogens. The phagocytes engulf foreign substances, such as neutrophils and macrophages, but macrophages may destroy a lot more of bacterial cells before they die compared to neutrophils that are more short-lived[67]. Even though the main role of macrophages is to engulf foreign substances, macrophages also produce cytokines to alert other cells of danger [68]. The cytokines released from macrophages includes the pro-inflammatory cytokines tumor necrosis factor alpha (TNF-α), macrophage inflammatory protein 1 alpha (MIP1-α), and the anti-

inflammatory interleukin 10 (IL-10). The macrophages together with dendritic cells are also presenting antigens to T-cells after digesting pathogens and are designated antigen presenting cells (APC)[69]. The dendritic cells are considered the most important APC, and are the link between the innate and the adaptive immune system [70].

Whereas the innate immune system may respond to pathogens in seconds, the adaptive immune system takes longer time to be activated, hours to days [71]. However, the adaptive system is faster and more effective upon a second encounter, as the immune exhibits acquired memory. The adaptive immune system is divided into “cellular” and “humoral immunity”, which consists of mostly T- and B-cells respectively. These cells derive from a common precursor stem cell. B-cells mature in the bone-marrow and are involved in the humoral immune defense because they secrete antibodies systemically, while T-cells are matured in the thymus, and are involved in cell-mediated immune defense. T-cells need to encounter antigens through binding of APC for activation; these may be macrophages, dendritic cells or

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B-cells. The expansion of the naïve T-cells is rapidly increasing during an infection when the APC presents antigen to T-cells, and the number of T-cells may increase up to 10⁵ [72].

There exist several subtypes of T- and B-cells, and they are broadly categorized into naïve cells, effector cells and memory cells.

1.2.2 T-cell activation and differentiation

T–cells are mainly divided into cluster of differentiation (CD) 8+ and CD4+ cells depending on which type of co-receptor they express. CD8+ and CD4+ cells are activated when the T- cell antigen receptor (TCR) binds to the major histocompatibility complex (MHC) I and II respectively. MHC I is expressed on the surface of all nucleated cells and present endogenous antigens (antigens from the cells interior) and bind to CD8+ T-cells: CD8+ T-cells also called cytotoxic T-cells are able to lyse infected cells. CD4+ T-cells on the other hand binds to MHC II expressing APC and tune and facilitate the immune response to exogenous antigens [73]. CD4+ and CD8+ cells have a different response time upon stimulation [74], and in addition CD8+ cells divide quicker with a faster proliferation rate than CD4+ cells [75].

MHC II is present on APCs such as dendritic cells, macrophages and B-cells. CD4+ T-cells are activated when encountering antigen on APCs. Finally, CD4+ cells may further be divided into TH1 and TH2 based on functional subsets, defined mainly on their cytokine profile.

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Figure 4 Overview over subsets of CD8 + and CD4+ memory T-cells Figures based on Seder RA, 2003

CD4+ and CD8+ memory cells may be divided into central memory (TCM) and effector memory (TEM) based on the expression of the cell surface molecules CD62L and CCR7, which both are lymphoid homing receptors, meaning that they bind to cell and tissue specific cell surface molecules [76]. TCM cells are considered as the reactive memory cells and reside in the secondary immune tissues such as the lymph system, where they are ready to

differentiate or proliferate when challenged by antigens. TEM cells on the other hand, are the protective memory cells and migrate readily to peripheral tissue [77]. In mice TCM are

characterized by CD44^highCD62L^high and TEM are characterized by CD62L^lowCD44^high (Figure 4) [78]. Both TH1 and TH2 cell lineages may develop into memory T-cells and the CD4+

pathway seems more complex than CD8+ cells that develops more linearly from naïve to TEM

througheffector andTCM [75].

Another important component of the adaptive immune system is the regulatory T-cells (Tregs) that are regulating the immune cell homeostasis. Tregs are mostly developed from naïve CD4+ cells and the differentiation may start in thymus [79]. Tregs in mice are defined by the expression of the CD25 activation marker and the transcription factor Forkhead box P3

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(FOXP3), and defects in the FOXP3 protein leads to autoimmune diseases [80]. Even though the T-cell responses are mostly beneficial for the organism, if the immune cells are not properly regulated, it may cause uncontrolled responses that may lead to autoimmune diseases such as diabetes mellitus type 1 (also known as type 1 diabetes), systemic lupus erythematosus and rheumatoid arthritis (RA).

1.2.3 PKA and the immune system

In T-cells the PKA holoenzyme consists of RIα (PKAI) and RIIα (PKAII) dimers bound to Cα1, Cβ1 or Cβ2 subunits [12, 43, 47]. The function of PKAII is ambiguous in T-cells, while PKA-I has been reported to inhibit antigen-induced activation of T and B-cells [81, 82] in addition to regulating the cytotoxicity of NK cells [83]. PKA-I has also been shown to regulate the TCR/CD3 complex activation by phosphorylation of the C-terminal Src kinase (Csk), which further regulates the TCR/CD3 complex associated tyrosine kinase Lck [84].

Whereas Cα1 is expressed in a number of cells, Cβ2 appears as mentioned to be expressed mainly in immune cells residing in lymph nodes, thymus and spleen both in human and mice [43, 44, 47]. Cα KO immune cells have shown to be hyper reactive to antigens as well as having significant upregulation of the activation marker CD69, in the same study none of the effects was observed in immune cells from Cβ KO mice [53].

PKA regulates immune cells through several different pathways. One of these pathways are phosphorylation of the transcription factor cAMP response element-binding protein (CREB) that binds to a cAMP response element (CRE) which is in the promoter regions of a number of different genes [85]. CREB is in this way regulating transcription rates of several genes involved in proliferation in T-cells [86, 87].Creb is closely related to cAMP response element modulator (Crem) and activating transcription factor 1(Atf-1). Crem encodes, among several other isoforms, inducible cAMP early repressor (ICER) that is transcribing genes involved in down-regulation of early response genes in the immune system [88, 89]. The cytotoxicity of NK-cells modulated by dopamine receptors has also been shown to require CREB signaling [90]. PKA is also regulating another transcription factor, nuclear factor kappa-light-chain enhancer of activated B-cells (NFκB), which is responsible for regulating a number of genes expressed in immune cells that are associated with inflammation [91]. More specific, Cβ is involved in stimulating c-Rel (a component of NF-κB) in a cAMP-independent process [92].

There is no evidence of differential expression or various levels of total amount of PKA in

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Tregs, compared to other CD4+ and CD8+ cells [11], despite that downstream targets such as ICER/CREM are expressed at higher levels in Tregs compared to CD4+ and CD8+ cells [93], which is a way of regulating the cAMP response in the cells. Moreover, Tregs induce cAMP- PKA as well as Epacs in other T-cells. Interestingly, it seems like the suppression of dendritic cells by Tregs are mediated by activation of EPAC [94].

In macrophages, cAMP has been shown to have an anti-inflammatory effect involving PKA and AKAP95, as showed in a mouse macrophage cell line (RAW 264.7) [95]. In the study Cα, Cβ or both isoforms were cell-specifically depleted. This showed that Cα and Cβ double knockdown cells resulted in upregulation of TNF-α mRNA in cells treated with

lipopolysaccharide (LPS) and cAMP compared to control [95]. In the same study it was observed that PKA through anchoring to AKAP95, suppressed TNF-α and MIP1-α expression, and that granulocyte colony-stimulating factor (G-CSF) was up-regulated. In addition, Wall et al demonstrated that neither expression of TNF-α, MIP1-α nor G-CSF required phosphorylation of CREB. In contrast, regulation of IL-10 expression did not require anchoring by AKAP95 and was dependent on signaling through CREB [95]. It has also been a report suggesting that cAMP-PKA signaling is involved in the regulation of the IL-33 response in RAW 264.7 cells [96]

In the present thesis we investigated the role and function of the Cβ2 protein in mice that were targeted deleted for exon 1-2 in the PRKACB gene and analyzed the effects of this deletion on the innate and adaptive immune system.

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Prostate cancer 1.3

1.3.1 The prostate gland

Figure 5 Overview over the male reproduction system Illustration from The Web site of the National Cancer Institute (http://www.cancer.gov)

The prostate is a small exocrine gland in the male reproduction system that is placed under the urine bladder and close to the rectum (Figure 5). The prostate excretes a fluid that nourishes the sperm and makes up about 30 % of the semen volume [97]. The fluid consists of Prostate Specific Antigen (PSA), which liquefies the semen, so that the sperm cells have better motility [97, 98]. The enlargement of the prostate is called benign prostate hyperplasia (BPH), and its prevalence is about 50 % of men above 50, and 70 % of men above 70 [99- 101]. In comparison, the autopsy prevalence of prostate cancer in U.S. men aged 60-70 years is 65-70% and slightly above 80% in men aged 70-80 years [102]. Prostate cancer is the most common cancer inflicting men in the western world, and makes up 15 % of all cancers diagnosed in men [103-105]. Furthermore, prostate cancer is the second most common cause of cancer-related death [106]. Despite this, prostate cancer is usually slow growing, and prostate cancer patients are the largest group of cancer survivors in the world [107]. It has been debated if it exist a causal link between BPH and prostate cancer, but it seems that both

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conditions are affected by the same mechanisms and that BPH is associated with prostate cancer [108].

1.3.2 Diagnosis, risk stratification and treatment of prostate cancer

Early stage prostate cancer does not cause any symptoms. If the tumor is pressing on the urethra, urinary problems similar to BPH appear. Locally advanced or metastatic prostate cancer may give rise to skeleton pain and bleedings. Asymptomatic prostate cancer can be detected due to elevated level of PSA and/or abnormal growth detected by trans-rectal

ultrasound (TRUS) or digital rectal exam (DRE), where the prostate is felt through the rectum to determine the size of the possible tumor. The diagnosis is made by the pathologist who performs microscopic analysis of tissue biopsies taken from the prostate. To improve the chance of diagnosing the clinically significant tumors, magnetic resonance (MR) guided biopsies are performed [109-111]. Thus, MR-guided biopsies reduce the chance of overtreatment of tumors that will never give clinical symptoms. To stratify patients for treatment, PSA is used together with Gleason score and tumor -stage (T-stage) to divide prostate cancer into low -, intermediate - and high risk.

Prostate cancer is staged using the tumor-nodes-metastasized (TNM) system. cT1 (clinical T1-stage)(Figure 6) is a small tumor confined to one lobe of the prostate that normally undetectable by DRE, T2 is larger tumors that are still confined within the prostate and ranges from the tumor being less than half of one side (T2a), more than half of one side (T2b) or in both sides of the prostate (T2c), while T3 means that the cancer has outgrown the

prostate (T3a) or has grown into the seminal vesicles (T3b). T4 is a score given to cancers invading nearby tissue in addition to the seminal vesicles.

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Figure 6: Overview over T1-3 stages of prostate cancer

Illustration attributed to Cancer Research UK / Wikimedia Commons.

The pathologist determines the aggressiveness of the tumor by pathological T-stage (pT-stage) of the radical prostatectomy specimen and by Gleason grading of the tissue sample. The Gleason score describes the growth pattern of the cancer cells and how the glands are spaced.

Gleason grading range from 1 to 5, where grade 1 is a normal differentiated tissue, grade 5 not resembling normal tissue at all is called undifferentiated tissue. Gleason grades from 3-5 are considered cancerous. A Gleason score is given for the two most prominent patterns in the tumors, and then added together for a score from 5-10. The Gleason score of the tumors can also be classified in a new system (Table 2), where the Gleason score is categorized as grading groups (1-5) to better grade the tumor according to the range of risk. Grade group 1 is tumors with only Gleason pattern 3, Grade group 2 is disease where 3 is the dominate pattern, and Grade group 4 is where the Gleason score 4 is dominant over 3. Group 4 and 5 are

assigned to Gleason score 8 and ≥ 9. Since the grade scoring differentiates between 3+4 and 4+ 3 tumors, it is made as an attempt to grade the tumor according to the range of risk [112].

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Table 2 The Gleason grade system

≤ 6 Grade group 1 3+4 = 7 Grade group 2 4 + 3 = 7 Grade group 3

8 Grade group 4

≥ 9 Grade group 5

The D’Amico system is used to estimate the risk of aggressive prostate cancer and the cancer patients are divided into three risk groups; low, intermediate and high, based on PSA level, Gleason score and cT stage. The most widely used definition for aggressive prostate cancer is the occurrence of at least one of the following: Gleason score ≥8, PSA greater than 20 ng/ml, or clinical tumor stage ≥T3a [104]. Low risk patients are candidates for active surveillance.

This is a treatment with curative intent were the patients are closely followed and offered curative treatment if the disease progresses. Watchful waiting is a similar approach for patients who are not candidates for curative treatment, but who has slow growing tumors.

These are often elderly men that are given palliative treatment when required. Active

surveillance and watchful waiting are becoming more common and may be a good strategy to reduce overtreatment [113]. Patients with intermediate risk cancers are candidates for radical prostatectomy (removal of the prostate) or radical radiotherapy, both representing curative treatment options. Surgical castration (removing the testicles producing testosterone) or medical castration (luteinizing hormone agonists or antagonists or anti-androgens) is an option for palliative treatment and is based on the fact that most prostate cancer cells are dependent on androgens for survival and growth. However, over time castration resistance evolves and the prostate cancer progresses. This is called castration resistant prostate cancer (CRPC). Genomic effects of androgens are mediated by the nuclear steroid transcription factor, androgen receptor (AR). Multiple mechanisms are involved in CRPC development including amplification and mutations in the AR and ligand-independent activation of AR.

The PKA signaling pathway is one of several shown to activate AR in the presence of castration levels of androgens [114, 115].

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1.3.3 Biomarkers

A biomarker can be any measurable molecule that makes it possible to diagnose or predict disease progression. This current staging system based on PSA, T-stage and Gleason score is not perfect and leads to complicated judgments when determining prostate cancer prognosis.

Which means that there is a need for prostate cancer biomarkers in order to determine: 1) which patients to biopsy, 2) when to re-biopsy patients and 3) which patients in need of therapy [116].

PSA is not prostate cancer-specific and PSA screening is not recommended. Nevertheless, PSA is frequently used and is a major cause of overdiagnosis. In the age group 50-69; the risk of overdiagnosis has been estimated to be 5 - 45 % [117]. Therefore, in order to determine with more specificity which patients need biopsy, two new tests have been chosen as

probable candidates: prostate health index (Phi) and serum 4-kallikrein (4K).Phi is a formula ((proPSA/free PSA) × √PSA), and has been suggested for patients with increased PSA levels, where the DRE fails to find a tumor. PSA is formed by cleavage of the precursor proPSA, which is an inactive form of PSA. Circulating PSA exists in the blood by bound to other proteins or as unbound PSA (free PSA). Phi has shown to be better than PSA in patients with PSA 5- 10 ng/ml [118, 119], as well recently shown in patients with PSA >10 ng/ml [120].

The 4K is a similar test that measures the amount of four specific kallikrein immunoassays:

total PSA, free PSA, intact PSA, and human kallikrein 2. The 4K test gives a probability for the patient to have a cancer detectable by biopsy [121]. For repeated biopsies after a negative biopsy, Prostate cancer antigen-3 (PCA-3), which is a non-coding RNA expressed in the prostate and is a urinary marker used for both initial and the repeated biopsy have been suggested [122]. ConfirmMDx is another example of a test to determine the patients that needs repeated biopsy. ConfirmMDx measures the methylation profile of several biomarkers on negative biopsies, which indicates the likeliness of a new biopsy being negative or not [123, 124]. To determine patients in the high risk group, some tests use an mRNA expression profile to stratify the patients in need of treatment; examples of such test are Prolaris,

Decipher and Oncotype DX [125-127] It has been shown that patients with higher risk of metastasis have a distinct transcriptomic fingerprint that may be detected several years before metastasis [128, 129]. Despite this battery of tests, there is still a need for biomarkers that distinguish more accurately between aggressive and non-aggressive disease [127, 130, 131].

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1.3.4 PKA and prostate cancer

Most research on prostate cancer and PKA has focused on the R subunit [132]. RIα /β (PKA-I) has been shown to be overexpressed in various cancers, including prostate cancer [133]. RIα has been studied as a predictor of prostate cancer outcome in patient groups either treated with radiation therapy or short-term androgen deprivation therapy, and they argued that RIα is a potential biomarker to predict outcome [134, 135]. It has been shown in several studies that cAMP/PKA activates the AR pathway independent of androgen, which means that PKA is important in the development of CRPC [32, 132, 136, 137]. It has also been suggested that the AR and the PKA pathways cross-talk [138]. In a study by Eder (2013), in which they combined inhibition of AR with inhibition of RIα, and showed that the combination was more effective than any of them alone, leading to inhibition of cancer growth in vivo [139].

Various subunits of PKA C are differentially expressed in prostate cancer, depending on the differentiation stage of the tumor. Cβ2 has been found to be down regulated, and Cβ1, Cβ3 and Cβ4 were upregulated in LNCap-Rf cells that mimic neuroendocrine-like differentiation (NED) in humans [32]. NE like cells are involved in the development of CRPC [140, 141], and PKA has also been suggested as the mediator of NED in prostate cancer [142]. Each C subunit seems to have differential roles in regulating proliferation and differentiation of the cancer, which means that not only the total concentration of PKA matters, but also the isoform composition. In this thesis we investigated whether Cβ2 mRNA expression can be used as a biomarker alone or in combination with known biomarkers to predict prostate cancer aggressiveness (Paper II).

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2 Aims

Based on what has been described in the introduction the aim of the present work was to investigate the expression, function and evolution of the catalytic subunit Cβ2 PKA according to the following objectives:

1. Characterize the functional consequences of PKA Cβ2 ablation in mice.

2. Can Cβ2 mRNA expression be used as a biomarker for prostate cancer aggressiveness?

3. Investigate the evolution of exon 1 variants in the PRKACA and PRKACB genes.

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3 Summary of papers

Paper I 3.1

Ablation of immune specific Cβ2 of PKA leads to increased susceptibility to collagen- induced arthritis.

In an attempt to characterize the function of Cβ2, we made mice that were ablated for the Cβ2 protein of PKA. First, Cβ2 specific exon mutation was confirmed by PCR and Cβ2 protein ablation were confirmed by western blot analysis and antibodies for the C subunit of PKA. Ablation of Cβ2 resulted in reduced cAMP-induced PKA-specific catalytic activity in lymph nodes, spleen and thymus. As the PKA-specific catalytic activity was significantly reduced by 35-40 % in the different immune tissues, the immune system of Cβ2 KO mice was examined and challenged to identify to the role and function of Cβ2 in vitro and in vivo.

This demonstrated that anti-CD3/CD28 induced CD4+ T-cell proliferation was unaltered, whereas the proportion of both CD4+ and CD8+ cells was increased in the KO mice. As Cβ2 was found to be expressed in macrophages both the innate and adaptive immune systems of Cβ2 KO mice were challenged by analyzing the consequences of Lipopolysaccharides (LPS) injections and collagen injections to investigate collagen induced arthritis (CIA). The Cβ2 KO mice were hypersensitive to CIA and LPS injections lead to increased levels of TNF-α l and decreased levels of IL-10 in plasma.

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Paper II 3.2

Observed correlation between the expression levels of catalytic subunit, Cβ2, of cAMP- dependent protein kinase and prostate cancer aggressiveness

It has been shown that Cβ2 mRNA and protein is expressed in prostate cancer cells. Here PKA Cβ2 mRNA and exon expression was studied in a total of 241 patients from three independent prostate cancer cohorts. We found that the Cβ2 mRNA expression was higher in cancer samples than the paired normal controls. Furthermore, we found that the Cβ2 mRNA was inversely correlated to Gleason score, but not to PSA and pT-stage. In addition, Cβ2 mRNA expression was significantly lower in biopsies from patients that later died from prostate cancer in comparison to patients that were alive 10 years after the diagnosis.

Moreover, uni- and multivariable COX regression analysis of low and high Cβ2 mRNA level and death from prostate cancer showed that the patients with low Cβ2 mRNA died earlier than patients with higher Cβ2 mRNA levels. To our knowledge, this is the first study to identify a correlation between PKA C subunit and death from PCa.

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Paper III 3.3

Evolution of the cAMP-dependent protein kinase (PKA) catalytic subunit isoforms

In this paper, both genomic and transcriptomic databases for 5’ exon encoded variants of PKA Cα/Cβ sequences in several different organisms from all major vertebrate groups as well as selected non-vertebrates were explored. Short exons in PRKACA were found in all vertebrate groups except the Coelacanth, which in mammals was identified as the sperm- specifically expressed exon 1-2. Eutherian exons 1-1 of PRKACA and PRKACB encode very similar amino acid sequences, but have amino acids in certain positions that were invariably conserved in one of the variants, but different in the other. We found that the PRKACB exon 1-2 was universally conserved among vertebrates, and we identified a likely orthologous exon, denoted exon 1-L, in the PRKACA gene in all major vertebrate groups except birds and mammals. We named the predicted protein encoded by PRKACA exon 1-L CαL, and

demonstrated that the CαL sequence shares an N-terminally located segment with PKA Cβ2.

The fact that this long variant is conserved, suggests that it likely has a functional role, and it was predicted to contain an alpha-helix possibly interacting with isoform specific interaction partners.

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4 Discussion

Discussion of methods and model systems 4.1

4.1.1 SRA database

The NCBI Sequence Read Archive (SRA, http:https://www.ncbi.nlm.nih.gov/Traces/sra) is an archive where sequencing data and alignment information primarily from high-throughput sequencing platforms can be stored. The SRA archive has grown exponentially the last years, and in 2011, the archive had more than 100 Terrabases of DNA (Figure 7) [143]. The dataset is publically available, which improves quality of knowledge produced based on the sequence information. Public availability also makes the research more reproducible. Lately many journals such as PLOS One (http://journals.plos.org/plosone/s/data-availability) require that data are stored in a public archive. The SRA database comprises transcriptomes of an

increasing amount of organisms, making it ideal to also search for sequences for evolutionary studies. However, many of the sequencing data are not verified, as they represent raw data that are simply uploaded to the database. However, each SRA experiment includes a

minimum amount of information how the material was sequenced. Due to the increase in data volume, a big challenge is to have enough storage space, and the SRA no longer guarantee to store raw signal data from the next generations sequencing systems Illumina GA and SOLiD, however better compressing methods are currently in development [144, 145].

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Figure 7 Overview of databases of submitted to the SRA database from 2009 until 2016 (https://trace.ncbi.nlm.nih.gov/Traces/sra/)

4.1.2 TCGA database public data

The cancer genome atlas (TCGA) is a freely available open data source, which was initiated as a three-year pilot in 2006 funded by National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI) in the USA. The goal was to make an infrastructure of genome changes. TCGA has samples from several cancer types including prostate cancer (http://www.cancer.gov/). However, even though the TCGA data are available to the public, it can still be time-consuming to develop pipelines for interpretation and analysis [146].

These types of data portals make it possible to increase the size of participants and make the research reproducible since the data is accessible for everyone. It has also been shown that public health databases promote scientific research [147, 148]. There are, however, some new challenges posed by large databases with genomic data. One of them is the ethical

consideration for the patients donating their tissue for research. Some studies have shown that

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it is possible to identify anonymized samples by genome wide associations studies [149].

Also the increasing amount of data makes it difficult to store and handle the data, making IT structures an important issue [150]. Finally, most databases require a lot of resources to annotate, process and normalize the different data types, as well organize the samples from different labs or that were derived from different biological conditions [151]. Although the TCGA initiative will stop by early 2017, other initiatives at NCI will build up on this dataset, and will include even more datasets in the Genomic Data Commons program, which will make it a valuable source for research (https://gdc.nci.nih.gov). Another advantage with using data from a large database such as TCGA is the guidelines that ensure the data deposited in their database are sampled by the same method

(http://cancergenome.nih.gov/cancersselected/biospeccriteria).

4.1.3 Nanostring Technology

The RNA of formalin-fixed, paraffin-embedded (FFPE) samples is in most cases degraded and crosslinked after prolonged storage [152]. Due to the crosslinking in FFPE sample, it is an advantage to use the Nanostring technology compared to similar methods. Nanostring is based on probes that recognize shorter segments compared to qPCR (100 bp for PRKACB).

In our study, the normal and tumor areas were marked for RNA extraction from each patient based on hematoxylin and eosin stained sections of the biopsy. This could lead to bias in this study, in case the area where the pathologist makes the cut for analysis varies from sample to sample. Nanostring is considered as a very sensitive method [153], and the sensitivity of Nanostring has been described to be between qPCR and other hybridization microarray technologies [154]. In this thesis, the total RNA from each sample used was shipped to NanoString Technologies in Seattle for nCounter gene expression analysis [155].To ensure that the FFPE samples could be used, we performed a pilot test with fresh frozen tissue compared to FFPE tissue. This pilot showed that although the expression values was different between the fresh frozen tissue and FFPE samples, the ratio between tumor and normal samples remained comparable.

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4.1.4 Mice as model animals

4.1.4.1 Biological similarity to humans

In many aspects mice and humans are considered to be fairly similar as humans share 95 % of the genomes with mice [156]. Mice are also small and have short generation time which makes them easy to handle and is a well-used model animal. There exist several methods to create transgenic, knockout as well as knockin mice, which also makes them a potent tool for researchers to study gene specific alterations [157]. Examples of such models are the prostate cancer mice models, where it has been shown that several approaches are needed to mimic all aspects of human disease [158-160]. Also, several important findings were first discovered in mouse research [161]. However, as there also are considerable differences between human and mouse, especially in RNA expression and epigenetics [162], generalization of data from mice to humans should be done with caution. Mice and humans diverged from a common ancestral species around 85 million years ago [163], and developed under very different conditions. Not only do they differ in size, the also survive on very different diets, have highly different life spans which is associated with pronounced differences in many

physiological processes including metabolic rate. Moreover, as mice and humans normally have evolved to live in very different environments they have developed closely related but different immune systems [164, 165]. As an example Foxp3 is necessary and sufficient for CD4+CD25+Treg development and function in the mouse, whereas in humans this is not the case [166]. Nevertheless, mouse experiments are an important source of new knowledge, but the experiments should be designed and interpreted carefully to ensure that the conclusions are trustworthy. Good design includes reporting the sample size, mean, the distribution of data, strain (genetic) background, as well as characteristics of mice such as their age, weight, sex and source, if purchased (review;[167]). Despite this many papers do not require

information about what BL6 substrain that is used. This is an issue as substrains can be associated with altered phenotypes [168].

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34 4.1.4.2 Ethical considerations in mice research

In 1958, Russel and Burch developed the three R’s (Replacement, Reduction and Refinement), which is an international approach to all animal research; including mice research [169]. Replacement means substitution of animals where appropriate with e.g. cell cultures. Refinement means to minimize the pain of the animal participating in the

experiments. Whilst Reduction is to minimize the number of animals needed for the

experiment. In Norway you have to have a license to work with research animals, and apply for usage of animals in experiments according to “The regulation on animal

experimentation” (https://lovdata.no/dokument/SF/forskrift/2015-06-18-761). Even though animal research has contributed to a lot of important breakthrough discoveries, it is important that the animals are having rights to ensure humane treatment and welfare of animals in research [170]. All animal experimentation in this study has been approved by the Norwegian Animal FOTS ID 5929; 8549; 8744).

4.1.4.3 The creation of Cβ2 mice

A genetic null mutation of the Cβ2 subunit was generated by GenOway (www.genoway.com) (Appendix A). The genomic sequence encoding Cβ2 specific protein sequence was removed by targeted mutation of exon 1-2 in the PRKACB gene. This process is time consuming and involves several steps, and required both Flp-FRT site directed recombination and Cre-loxP site-specific recombination. Both of these methods are similar - Flp derived from

Saccharomyces cerevisiaeand Cre derived from Bacteriophage p1, and both catalyze

excision, inversions, insertion as well as translocation of DNA between their two recognition target sites [171]. Flp is recognizing Flp recombination target (FRT), and Cre recognize LoxP sites. A gene that is flanked by two loxPsites is called a floxed gene. The advantage with this system is that it is possible to KO the gene in specific tissue [172].

To make Cβ2 KO mouse line used in paper I, GenOway made a targeting construct with flanked exon 1-2 at the 5’end with FRT-neomycin-FRT-loxPcassette and with a loxPsite in the 3’ direction (Figure 8A). The targeting construct was introduced in Embryonic stem (ES) cells on an Agouti-129Sv/Pas background with homologous recombination, and the

neomycin (neo) resistance sites ensured resistance to the antibiotic for positive selection of in ES cells. The ES cells were then injected into host blastocyst, which subsequently was

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introduced in pseudo pregnant mice on a C57BL/6J background. Since the ES cells had Agouti background, which have a dominant yellow coat color, the offspring with the targeting construct inserted would show color chimerism and have a mixture of black and yellow color. For ensuring mutation in germ cell genome, male mice with chimeras > 85 % were selected for further breeding. To allow germline excision of the neo cassette these mice were crossed with Flp-deleter mice on a C57BL/6J background (Figure 8B). Since the male mice carry the Agouti gene, this means that yellow pups are carrying the targeting construct.

The resulting floxed exon 1-2 of PRKACBmice were delivered from GenOway (Figure 8C).

The floxed Cβ2 mice were crossed in house with Cre-deleter mice carrying active Cre in all tissues (A gift from Dr. Michael Leitges, Biotechnology center of Oslo) to delete the floxed exon, which gives constitutive Cβ2 KO mice (Figure 8D). These mice were further bred to homozygosity.

Figure 8 Overview over the generation of the Cβ2 KO mice A) Overview over the exons in thePRKACBB) The floxed targeting construct of exon 1-2 with the neo cassette (yellow) flanked by FRT site (green) on each side, and the loxP site (red) of each side of exon 1-2. C) After the mice are crossed with FLP deleter mice the neo cassette was deleted in the offspring, giving an Flp-excised allele. D) In the next round the mice were bred with a Cre mice carrying Cre in all tissues generating heterozygous constitutive Cβ2 KO mice.

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36 4.1.4.4 The genetic background of the Cβ2 mice

Homozygote Cβ2 KO mice were initially on a mixed C57BL6/Ntac and C57BL/J background. This required backcrossing to obtain inbreed C57BL6 mice. In our case we backcrossed to C57BL6/Ntac strain, and also used this mouse strain as controls in the present study. After every 6-7 generations the Cβ2 line was crossed with WT C57BL6/Ntac to keep the correct background followed by crossing the mice back to homozygosity. An important uncertainty with backcrossing mice is that KO mice and flanking genes may follow your gene of interest, meaning that the backcrossed mice can differ in their genetic background [173].

Although, laboratory mice are supposed to be genetically similar, there are also other factors that could influence the results, such as stalling factors [174]. Inbred mice are not perfectly genetically isogenic, but the genetic difference occurs at a low rate [175]. It is important to take into consideration the genetic background in mice before making conclusions about the gene of interests, and ideally one should test the KO on several strains [176]. This is

unfortunately very time-consuming and costly, and was not done in the paper I.

4.1.4.5 Collagen induced arthritis

CIA in mice is a commonly used animal model for studying various aspects of pathogenesis of RA [177, 178]. Collagen Type II (CII) is only expressed in the articular cartilage of joints, meaning that induced immunity against CII will induce inflammation in the joints [179]. This has been proven to be effective in rat [180], mice [181] and also in monkeys [182].

Historically, DBA mice are used for the CIA, as BL6 mice have been shown to be less sensitive to the development of CIA [183]. Reduced sensitivity to CIA is thought to be caused by an H-2^ballele in the BL6 mice [183]. BL6 show defects in secondary but not primary immune response to bovine collagen [184]. CIA has, however, been reported to be inducible in mice by injecting chicken CII, and the reported incidence of RA is 40-75 % in BL6 [185, 186]. IgG anti CII antibody (Ab) is necessary for CIA, but the amount of anti CII Ab does not necessarily correlates with CIA [183, 187], which means that higher Ab levels do not mean that the mice are more likely to develop clinical signs. CD8+ T-cells have controversial and mixed roles in CIA, but CD8+ T-cells may be important in effector phase of disease (review [188]. Using the CIA model might not be the best choice for BL6 mice due to the variable incidence. However, the disease in BL6 is milder compared to DBA. At the same time it has a tendency to develop chronic CIA more often, and has more persistent T-

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cell responses. Therefore, CIA in BL6 might resemble human disease more closely [185].

Another animal model of RA is a collagen antibody-induced arthritis (CAIA) that has been developed recently. CAIA has been reported to be faster, have higher incidence, and works in most mice strains and is more reliable than CIA [178, 189]. CAIA might have been a better choice for BL6, but is more expensive, and is still mostly used in the industry.

Discussion of results 4.2

4.2.1 General considerations of results

Although PKA signaling is well characterized, the R- subunit and the AKAPs have been more extensively studied compared to the C subunits. The R-subunit has been thought to be more important for regulating downstream targets of PKA, even though the C subunit directs the catalytic activity by phosphorylating substrates. The specificity in the cAMP-PKA signaling pathway is controlled at several different levels, from R binding to AKAPs that keeps the holoenzyme in specific positions, the composition of R subunits regulation the free C in the cell and localized concentration of cAMP. Some articles suggest that C-subunit composition and levels are crucial features that determine the downstream effect in cells.

Most of the C subunits have high sequence homology, except the part that is encoded by exon 1 variants in the PRKCAand PRKCBgenes. The catalytic core is highly conserved in all the C subunits confirming that all C subunit share the same catalytic function and most probably substrate specificity. However, the existence of several similar splice variants, and that various C subunits are cell-specifically expressed suggest differential roles for Cα and Cβ.

Littleis known about the Cβ-specific signaling pathway, and the function of Cβ2 has not been characterized earlier. In this thesis the functional role of PRKACB2 in the immune system of Cβ2 KO mice was investigated and we found that Cβ2 has a role in priming immune sensitivity in mice (Paper I). Moreover, Cβ2 mRNA expression correlates with clinical characteristics and may be used as biomarker for prostate cancer aggressiveness (Paper II). Finally, we have studied the evolution of the alternative 5’ exon of the C subunits and show that the N-terminal part of Cβ2 encoded by exon 1-2 in the PRKACBgene was conserved among vertebrates, and a likely orthologous exon, denoted exon 1-L, was identified in the PRKACAgene in all major vertebrate groups except birds and mammals (Paper III).

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4.2.2 The role of Cβ2 in the immune system

Earlier studies have yet to resolve the function of the different splice variants of PRKACB in the immune system, and it has been shown that despite that the kinase activity was

downregulated in immune cells, there was seemingly no phenotypical difference between WT controls and Cβall or Cβ1 KO animals associated with the immune system [41, 44, 52, 53]. It has been shown that Cα but not Cβ is involved in proliferation of T-cells [53], and that Cα and Cβ contributes toapproximately 50 % each of the PKA activity in lymphoid tissue [44].

The results from the Cβ2 KO mice showed that Cβ2 contributes to PKA catalytic activity by 39% in spleen, 52 % in lymph nodes and 37 % in thymus (Paper I). This is in line with the previous results where cAMP-induced PKA catalytic activity in spleen was reduced by 66 % in Cβall KO mice and 27 % Cβ1 KO mice [44]. Since the lymphoid tissue expresses solely Cα, Cβ1 and Cβ2 [12, 43, 47], it seems that the Cβ2 is the main contributor to kinase activity in immune tissues of two PKA Cβ subunits. Studies on Cβall KO and Cβ1 KO mice did not show altered levels of immune cells or changed immune response [44, 53, 190]. This is in contrast to the Cβ2 KO animals which had altered levels of both CD4+ and CD8+ cells, suggesting that Cβ2 plays a role in regulating differentiation of T-cells (Paper I). An earlier studied has shown that T-cells are suppressed through cAMP-PKA pathway, while dendritic cells are suppressed through cAMP-Epac [94]. Interestingly we only found an altered composition of T-cells, but not other cell types in the Cβ2 KO mice.

The fact that the Cβ2 phenotype was not observed in the Cβall KO was unexpected as the Cβall animals are also KO for Cβ2. We do not know the physiological reason for this but speculate that Cβ2 and Cβ1 may have an opposite role in immune cells. However, the exact pathways and function of the two subunits remains to be investigated. Altered differentiation of immune cells may influence the normal immune function and alterations of the immune cell ratio may lead to failure of mounting an immune response to pathogens, but also a variety of diseases including serious conditions such as autoimmunity. Based on this and that we observed increased levels of CD4+ and CD8+ T-cells, we challenged the Cβ2 KO mice with CII. This demonstrated that the Cβ2 KO mice were more sensitive to CII and had higher incidence of arthritis (Paper I). That PKA has a role in autoimmune disease has also been reported in T-cell enriched PBMCs from multiple sclerosis (MS) patients, where they showed that the antioxidant lipoic acid inhibited the immune response through PKA pathway [191].

Due to complexity of these signaling pathways it is hard to resolve all the different upstream

Function and evolution of Protein kinase A splice variant Cβ2 and its role as a biomarker in prostate cancer