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Identification of differentially expressed genes in AHI-1-mediated leukemic transformation in cutaneous… Kennah, Erin 2008

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IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES IN AHI-1-MEDIATED LEUKEMIC TRANSFORMATION IN CUTANEOUS T-CELL LYMPHOMA by ERIN KENNAH B.Sc., The University of British Columbia, 2005  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in  THE FACULTY OF GRADUATE STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2008 © Erin Kennah, 2008  ABSTRACT The oncogene Ahi-1 was recently identified through provirus insertional mutagenesis in murine leukemias and lymphomas. Its involvement in human leukemogenesis is demonstrated by gross perturbations in its expression in several leukemic cells lines, particularly in cutaneous T-cell lymphoma (CTCL) cell lines (Hut 78 and Hut 102). Hut 78 is derived from a patient with Sezary syndrome, a common leukemic variant of the human CTCL mycosis fungoides. Aberrant expression of AHI-1 mRNA and protein has been found in CD4+CD7- leukemic Sezary cells from patients with Sezary syndrome. Moreover, stable suppression of AHI-1 using retroviral-mediated RNA interference in Hut 78 cells inhibits their transforming activity in vitro and in vivo. In an effort to identify genes involved in AHI-1-mediated leukemic transformation in CTCL, microarray analysis was performed to compare six RNA samples from AHI-1 suppressed Hut 78/sh4 cells to five samples from Hut 78 control cells. Limma and dChip analyses identified 218 and 95 differentially expressed genes, respectively, using a fold change criteria of > or < 2 and a p-value threshold of ≤ 0.01. After evaluation of both analyses, 21 genes were selected based upon interesting structural and functional information, specificity to hematopoietic cells or T-cells, and previous connections to cancer. Expression patterns of these 21 genes were validated by qRT-PCR with p-values < 0.05 ranging from 1.97 x 10-10 to 6.55 x 10-3, with the exception of BRDG1 at 5.88 x 10-2. The observed upregulation of both BIN1 and HCK in AHI-1 suppressed Hut 78/sh4 cells as compared to control cells further confirmed at the protein level. The tumor suppressor BIN1 is known to physically interact with c-MYC, which also exhibits differential protein expression in these cells. Characterization of BIN1 identified 4 isoforms all of which contain exon 10 ii  and demonstrate alternative splicing of exons 12A and 13. Additionally, qRT-PCR results from primary Sezary samples indicate there is clinical significance in the expression changes detected for BIN1, HCK, REPS2, BRDG1, NKG7 and SPIB. These findings identify several new differentially expressed genes that may play critical roles in AHI-1-mediated leukemic transformation of human CTCL cells.  iii  TABLE OF CONTENTS ABSTRACT....................................................................................................................... ii TABLE OF CONTENTS ................................................................................................ iv LIST OF TABLES ........................................................................................................... vi LIST OF FIGURES ........................................................................................................ vii LIST OF ABBREVIATIONS ....................................................................................... viii ACKNOWLEDGEMENTS ............................................................................................. x CHAPTER 1: BACKGROUND ..................................................................................... 1 1.1 OVERVIEW ................................................................................................................ 1 1.2 T-CELL BIOLOGY ...................................................................................................... 2 1.2.1 T-cell Function............................................................................................. 2 1.2.2 T-cell Development ..................................................................................... 7 1.3 CUTANEOUS T-CELL LYMPHOMA (CTCL) ............................................................. 11 1.3.1 Mycosis fungoides ..................................................................................... 11 1.3.2 Sezary Syndrome ....................................................................................... 12 1.3.3 Current Clues to Pathogenesis ................................................................... 13 1.4 ABELSON HELPER INTEGRATION SITE-1 (AHI-1)................................................... 16 1.4.1 Structural Analysis and Expression Studies .............................................. 16 1.4.2 Connecting Ahi-1 to Cancer....................................................................... 19 1.4.3 Beyond the Cancer Connection ................................................................. 25 1.5 EXPERIMENTAL OUTLINE ....................................................................................... 26 1.5.1 Hypothesis.................................................................................................. 26 1.5.2 Objective .................................................................................................... 26 1.5.3 Specific Aims............................................................................................. 27 CHAPTER 2: MATERIALS AND METHODS ......................................................... 28 2.1 CELL CULTURE ....................................................................................................... 28 2.2 RNA ISOLATION ..................................................................................................... 28 2.3 MICROARRAY ANALYSIS ........................................................................................ 28 2.4 CDNA SYNTHESIS .................................................................................................. 29 2.5 QUANTITATIVE REVERSE TRANSCRIPTION-POLYMERASE CHAIN REACTION.......... 29 2.6 CELL LYSATE ......................................................................................................... 30 2.7 PROTEIN LYSATE QUANTIFICATION........................................................................ 30 2.8 WESTERN BLOTTING .............................................................................................. 31 2.9 PEPTIDE COMPETITION ........................................................................................... 32 2.10 REVERSE TRANSCRIPTION-POLYMERASE CHAIN REACTION................................. 32 2.11 CLONING AND SEQUENCING ................................................................................. 33 2.12 STATISTICAL ANALYSIS........................................................................................ 33  iv  CHAPTER 3: RESULTS .............................................................................................. 34 3.1 3.2 3.3 3.4 3.5  MICROARRAY IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES ............... 34 VALIDATION OF MICROARRAY RESULTS ................................................................ 40 RNA AND PROTEIN EXPRESSION CORRELATIONS: HCK AND BIN1....................... 48 ISOFORM CHARACTERIZATION OF BIN1................................................................. 53 CORRELATIONS IN PRIMARY SEZARY SYNDROME SAMPLES................................... 59  CHAPTER 4: DISCUSSION ......................................................................................... 63 CHAPTER 5: CONCLUSION....................................................................................... 73 REFERENCES................................................................................................................ 74 APPENDIX...................................................................................................................... 82 A.1 A.2 A.3 A.4 A.5 A.6  QUANTITATIVE RT-PCR PRIMER SEQUENCES ....................................................... 82 EFFICIENCY ANALYSIS OF QUANTITATIVE RT-PCR PRIMERS ............................... 83 PRIMARY AND SECONDARY ANTIBODY CONDITIONS ............................................ 85 BIN1 EXON SPECIFIC PRIMERS FOR RT-PCR........................................................ 86 QUANTITATIVE RT-PCR VALIDATION .................................................................. 87 WESTERN BLOTS OF SELECT GENES ...................................................................... 92  v  LIST OF TABLES Table 1: Hut 78 cell lines compared in microarray analysis............................................. 36 Table 2: Justification of genes selected for further study and validation ......................... 42 Table 3: Fold changes of the selected 21 differentially expressed genes ......................... 44 Table 4: Summary of the correlations between mRNA and protein expression............... 49 Table 5: Several previously described BIN1 isoforms with tissue specificity.................. 54 Table A.1: Sequences of the quantitative RT-PCR primers ............................................. 82 Table A.2: Primary and secondary antibody conditions for Western blotting ................. 85 Table A.3: Sequences of the BIN1 exon specific primers ................................................ 86  vi  LIST OF FIGURES Figure 1: Complete activation of T-cells requires two stimulatory signals. ....................... 4 Figure 2: T-cell development in the thymus. .................................................................... 10 Figure 3: Protein structure of human AHI-1 isoforms...................................................... 17 Figure 4: Aberrant expression of AHI-1 in human leukemic cell lines. ........................... 21 Figure 5: Deregulated expression of AHI-1 in Sezary syndrome primary samples.......... 22 Figure 6: Effects of stable suppression of AHI-1 in Hut 78 cells..................................... 23 Figure 7: Volcano plot of differentially expressed probes selected by Limma analysis. . 37 Figure 8: Clustering results of differentially expressed gene identified by refined Limma (BH) analysis. ........................................................................................................... 38 Figure 9: Venn diagram of differentially expressed probes selected by both Limma and dChip analyses. ......................................................................................................... 39 Figure 10: Validation of differential expression in biological replicates. ........................ 45 Figure 11: Western blot analysis of AHI-1 expression..................................................... 49 Figure 12: Up-regulation of HCK in Hut 78/sh4 cell lines............................................... 51 Figure 13: Up-regulation of BIN1 in Hut 78/sh4 cell lines. ............................................. 52 Figure 14: Schematic diagram of BIN1 protein domains and exon organization............. 54 Figure 15: BIN1 isoform characterization in Hut 78 cell lines. ........................................ 57 Figure 16: Validation of expression in primary RNA samples......................................... 60 Figure A.1: Determination of the cycle threshold difference for cDNA dilutions. .......... 83 Figure A.2: Calculation of the efficiency of the LMCD1 primer set. ............................... 84 Figure A.3: Quantitative RT-PCR validation of 21 differentially expressed genes. ........ 87 Figure A.4: Western blots of genes with no differential protein expression. ................... 92  vii  LIST OF ABBREVIATIONS Ahi-1/AHI-1 = Abelson helper integration site-1 AML = Acute myeloid leukemia A-MuLV = Abelson-murine leukemia virus APC = Antigen presenting cell B-ALL = B-cell acute lymphoblastic leukemia BAR = BIN1/Amphiphysin/RVS167-related BCAS4 = Breast carcinoma amplified sequence 4 BCR = B-cell receptor BH = Benjamini and Hochberg’s adjustment BIN1 = Bridging integrator 1 BRDG1 = BCR downstream signaling 1 CCNG2 = Cyclin G2 CDKN1C = Cyclin-dependent kinase inhibitor 1C CML = Chronic myeloid leukemia CMTM7 = CKLF-like MARVEL transmembrane domain containing 7 CTCL = Cutaneous T-cell lymphoma D = Diversity dChip = DNA-Chip Analyzer DN = Double negative DP = Double positive ELAVL1 = ELAV (embryonic lethal, abnormal vision, Drosophila)-like 1 FACS = Fluorescence-activated cell sorting GAPDH = Glyceraldehydes 3-phosphate GRHL1 = Grainyhead-like 1 (Drosophila) HCK = Hemopoietic cell kinase IL = Interleukin IL1RN = Interleukin 1 receptor antagonist IL4I1 = Interleukin 4 induced 1 INF-γ = Interferon gamma J = Joining JS = Joubert syndrome LAPTM5 = Lysosomal associated multispanning membrane protein 5 LIM and cysteine-rich domains 1 LMCD1 = Linear Model for Microarray Data (Limma) MBD = MYC binding domain MF = Mycosis fungoides MHC = Major histocompatibility complex MLLT1 = Myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, Drosophila); translocated to, 11 NKG7 = Natural killer cell group 7 sequence NTS = Neural tissue specific domain ORF = Open reading frame OXCT1 = 3-oxoacid CoA transferase 1 PALM2-AKAP2 = Paralemmin 2-A kinase (PRKA) anchor protein 2 viii  PDCD6 = Programmed cell death 6 Ph+ = Philadelphia chromosome positive qRT-PCR = Quantitative reverse transcription-PCR REPS2 = RALBP1 associated Eps domain containing 2 RNAi = RNA interference RT-PCR = Reverse transcriptase PCR SH3 = Src homology 3 shRNA = Short hairpin RNA SP = Single positive SPIB = Spi-B transcription factor (Spi-1/PU.1 related) SS = Sezary syndrome SV2B = Synaptic vesicle glycoprotein 2B TBST = Tris-Buffered Saline Tween-20 Tcm = Central memory T-cell TCR = T-cell receptor Tem = Effector memory T-cell TH = T-helper TNF = Tumor necrosis factor Treg = T-regulatory V =Variable WD40 = Tryptophan-aspartic acid 40  ix  ACKNOWLEDGEMENTS Firstly, I would like to thank Dr. Xiaoyan Jiang, my supervisor, for the opportunity to complete this project and for her endless support both academically and personally. I would also like to thank both Dr. Keith Humphries and Dr. Gregg Morin, my committee members, for their valuable guidance over the course of my project. Additionally, I would like to recognize Kyi-Min Saw, Leon Zhou, and Margaret Hale for their technical expertise and patience in helping me learn new techniques in the lab. Members of both the Jiang and Eaves labs have provided helpful critiques and suggestions over the course of my project, for which I am very appreciative. Beyond the lab, I must thank my family and friends for their incredible support throughout my graduate studies. Special thanks to Kathryn Kennah, John Kennah, Leslie Kennah and Daniel Dressler for their constant patience and understanding in every aspect of this experience. I feel very fortunate to have had the opportunity to pursue graduate work in the unique environment of the Terry Fox Laboratory and have found the experience to be overwhelmingly enriching both academically and personally.  x  CHAPTER 1: BACKGROUND 1.1 Overview Cancer originates when normal cells undergo transformations which allow them to proliferate uncontrollably, often with serious biological consequences. The challenge in cancer treatments stems from the extreme heterogeneity of this complex disease where almost all cancers are considered unique at a molecular level. Many different types of cancers can arise with one subset being hematologic malignancies which affect the blood, bone marrow and lymph nodes. Within the hematologic subset, leukemias and lymphomas can develop in various different cell types in the blood lineage, such as myeloid cells, B-cells and T-cells. Several molecular mechanisms can contribute to malignant transformation, namely activation of oncogenes, loss of tumor suppressor genes, chromosome breaks, translocations, amplifications, and deletions. The oncogene AHI-1 exhibits highly elevated expression in human leukemic cell lines of myeloid, B-cell and T-cell origin, suggesting aberrant expression of this gene contributes to tumor development (1). This overexpression is most strikingly evident in cutaneous T-cell lymphoma which is a proliferation of malignant T-cells. Currently the functional role of the AHI-1 oncogene is unknown and the focus of this project is to investigate its involvement in the leukemic transformation in cutaneous T-cell lymphoma. As cancers arise from perturbations in normal biology, knowledge of normal T-cell function and development is critical to attempt to elucidate the mechanisms behind the AHI-1 malignant transformation.  1  1.2 T-cell Biology Investigations in T-cell malignancies, such as cutaneous T-cell lymphoma, must commence with an understanding of the developmental paths and functional roles of normal T-cells to gain insights into the specific perturbations occurring in the malignant cells. The primary role of the human immune system is to protect the body from infection and disease; more simply to defend “self” from “nonself” (2). The immune system is composed of two branches: the first-line, non-specific defense mechanisms of the innate immune system, and the antigen-specific responses of the adaptive immune system (3). Lymphocytes are the major players in the immune system and include natural killer cells for innate immunity, and T-cells and B-cells for adaptive immunity (2, 3). Both T-cells and B-cells express antigen-specific receptors, called T-cell receptors (TCR) and B-cell receptors (BCR), respectively, which facilitate the recognition of “self” from “nonself” within the body (2). Orchestration of an adaptive immune response is coordinated by T-cells who recruit leukocytes and stimulate the elimination of intracellular pathogens, while B-cells generate specific antibodies against extracellular pathogens (2).  1.2.1 T-cell Function As critical members of the adaptive immune system, T-cells work in concert with professional antigen presenting cells (APC) to mount effective immune surveillance for the body. APCs possess major histocompatibility complex (MHC) molecules and have the ability to process both extracellular and intracellular antigens into linear peptides 1012 amino acids long (2). These peptides are then presented in the antigen-presenting groove of the MHC molecules on the cell surface and subsequently allow for a physical 2  interaction with the cognate TCR and/or BCR (2). There are two classes of MHC molecules which differ in the origin of peptide presentation: MHC class I molecules present peptides derived from self/nonself intracellular proteins, and MHC class II molecules presents peptides from extracellular proteins (2). T-cell activation is initiated when the TCR binds to an agonist linear peptide presented in the MHC complex on the surface of an APC (4, 5). This is referred to as “Tcell priming” and complete activation further requires the interaction of additional costimulatory receptors, such as CD28 and lymphocyte function-associated 1 (LFA-1) which bind to their respective ligands, B7 and intercellular adhesion molecule-1 (ICAM1), expressed by the APC (Figure 1) (3-5). Complete activation via signaling through both the TCR and a costimulatory receptor is necessary for the production of interleukin (IL)-2, a cytokine that stimulates the clonal expansion of the activated antigen-specific Tcell (2). This clonal expansion is rapid and dramatic as it can increase the prevalence of the antigen-specific T-cell from 0.001% to >30% of the entire T-cell population (2). As activation is a two-signal paradigm, absence of the secondary costimulatory signal leads to T-cell anergy, a phenomenon critical in the prevention of self-reactive T-cells and autoimmunity (2, 3).  3  MHC  TCR 1  APC  T- cell  2  B7  CD28  Figure 1: Complete activation of T-cells requires two stimulatory signals. The first signal (1) is the interaction of the TCR with its cognate antigen (red diamond) presented on the surface of the APC in the MHC groove. There is a second costimulatory signal (2) required for complete activation, such as the interaction between CD28 and B7; absence of the second signal results in T-cell anergy.  4  Two predominant types of mature T-cells exist in the blood: CD4+ effector T-cells which interact with MHC class I complexes, and CD8+ cytotoxic T-cells that bind to MHC class II molecules (2). Normally the ratio of CD4+:CD8+ T-cells in peripheral blood is 1.5 (± 0.5):1 (6-8). Primed CD4+ effector T-cells, commonly called T-helper (TH) cells, can further differentiate into several subsets defined by specific cytokine profiles and effector functions: TH1, TH2, TH17, and T-regulatory (Tregs) cells (2, 3). TH1 cells specialize in macrophage activation through production of interferon gamma (INF-γ) and thus are responsible for intracellular pathogen clearance (3). TH2 cells, on the other hand, are focused on the generation of B-cell antibody responses by production of IL-4, IL-5, and IL-13 stimulation, and therefore play a role in extracelluar pathogen elimination (3). The more recently described TH17 subset is responsible for recruitment and proliferation of neutrophils via production of IL-17, a powerful inflammatory cytokine (3). Lastly, the Treg population, which expresses the IL-2 receptor and the transcription factor FOXP3, is a regulatory subset which possesses the critical ability to suppress T-cell responses and thus regulate adaptive immunity and prevent autoimmunity (3). Conversely, unlike the recruitment and activation abilities of CD4+ effector Tcells, CD8+ cytotoxic T-cells are responsible for mediating cell death using two cytotoxic mechanisms (2, 9). Activated CD8+ T-cells have the ability to secrete proteins, such as perforin and granzymes, at the point of contact with the APC which specifically kill the target cell without damaging bystander cells (3). Perforin is a protein which disrupts membranes and subsequently allows granzymes to enter the cell and induce apoptosis (3). In addition to secretion of cytotoxic proteins, CD8+ T-cells can induce cell death via  5  engagement of the Fas death receptor with its ligand, FasL, which triggers an apoptotic cascade to activate caspases (3, 9). Beyond cytolytic activities, CD8+ T-cells also produce the cytokines INF-γ and tumor necrosis factor (TNF) (3, 9). Once CD4+and CD8+ T-cells have executed a successful antigen response, the clonal expansion of the lymphocyte compartment shrinks due to gradual up-regulation and stimulation of inhibitory receptors coupled with an additional mechanism called activation-induced apoptosis (2). However, there are several antigen specific CD4+and CD8+ T-cells which persist after an adaptive immune response and these cells serve the role of memory T-cells to allow an accelerated and vigorous response if the antigen is reencountered (2, 3). Two subsets of memory T-cells exist: central memory T-cells (Tcm) which express CD45RO, CD62L and CCR7 and home to lymph nodes; and effector memory T-cells (Tem) that express CD45RO but neither CD62L nor CCR7 and circulate the peripheral tissue (2, 3). The two memory subsets complement each other since Tcm cells are able to rapidly divide and differentiate into effector cells, while Tem cells possess limited proliferative capacity and instead have effector functions (2, 3). Thus, it is the Tem cells that are responsible for immune surveillance in peripheral tissues and initiation of a swift effector response, while Tcm cells persist to generate the back-up reservoir of effector cells able to migrate to the required site (2, 3). In summary, effector and cytotoxic T-cells coordinate an efficient adaptive immune response by simultaneously recruiting leukocytes and initiating targeted cell death. Since roles of these T-cell populations are critical to immune response, each subset must be carefully modulated, not only to ensure functional precision but also to accurately discriminate “self” from “nonself”. As a result, there are many stringent  6  checkpoints in T-cell development which exist to ensure that only functionally normal cells are able to fully mature. By understanding these specific functional roles of T-cells, the impacts of the uncontrolled proliferations in T-cell malignancies can be appreciated. 1.2.2 T-cell Development Hematopoietic stem cells are self-renewing bone marrow resident cells which are able to give rise to both myeloid and lymphoid progenitors that in turn generate all lineages of mature myeloid and lymphoid blood cells, respectively (10). Lymphoid progenitors give rise to B-cells, T-cells, natural killer cells, and dendritic cells, but the generation of T-cells is unique since these are the sole hematopoietic cells that do not undergo development in the bone marrow (10-12). The thymus is the specialized organ which serves as the site for T-cell development and provides specific microenvironments critical for certain stages of T-cell maturation (12). The mature thymus is composed of several lobules, each with an inner medulla and an outer cortex and the junction between the two is where lymphoid progenitors enter the thymus from the bone marrow (13). As T-cell development occurs the maturing cells migrate out towards the cortex and then back towards the medulla before entering the bloodstream (13). Therefore, the medulla and the cortex each have functionally distinct populations of thymus epithelial cells which are critical to supporting specific stages of T-cell development (12, 14). Most immature lymphoid progenitors enter the thymus as “double-negative” (DN) cells who lack expression of both CD4 and CD8 which are the cell surface markers associated with mature T-cells (15). Developmental studies in mice demonstrate these DN cells undergo a series of changes through several developmental stages (DN1-DN4) and that the first branch of differentiation initiates during the DN2 stage as TCR  7  rearrangement commences (Figure 2) (12, 15). There are two distinct lineages of T-cells which develop in the thymus classified according to their type of successful TCR rearrangement: αβ T-cells and γδ T-cells (12, 15, 16). αβ T-cells are primarily responsible for MHC-restricted antigen-specific immune responses, while γδ T-cells are involved in non-MHC-restricted responses that both complement and regulate the role of αβ T-cells (15). The vast repertoire of unique TCRs, on the magnitude of >1014, is created through the somatic rearrangement of the α, β, γ, and δ TCR chains (2). As members of the immunoglobulin family, these TCR chains consist of two (α and γ chains) or three (β and δ chains) gene segments: variable (V), diversity (D), and joining (J) (2, 3). Since VJ recombination of α and γ chains and V(D)J recombination of β and δ chains is rather imprecise and a translational reading frame is required to yield a functional antigen receptor, only cells with successful rearrangement survive and continue through development; this occurs approximately one third of the time (3, 16). TCR γ and δ genes undergo rearrangement between DN2 and DN3, slightly earlier than the αβ lineage which initiates TCRβ rearrangement at DN4 (3, 12). The mechanism of lineage commitment and precise point of divergence of γδ and αβ T-cells has yet to be defined and TCR rearrangement is currently thought to either instruct or reinforce commitment to a particular lineage (3, 12). A cell that commits to the αβ lineage must first successfully pass the β-selection checkpoint to continue with recombination of the α loci to finally yield an αβ T-cell (3, 16). The bulk of mature T-cells in peripheral blood are of the αβ lineage with reports of only 1-5% of circulating cells accounting for the γδ lineage; consequently, there is still much to learn about this rare γδ+ population (2, 16, 17).  8  A T-cell who successfully commits to the αβ lineage then progresses to a “double positive” (DP) stage of development, characterized by expression of both CD4 and CD8 (3, 12). Positive selection occurs at this point as only DP cells with sufficient affinity to bind an MHC molecule are selected to survive (3, 12). Successful interactions with either MHC class II or MHC class I molecules further differentiates the DP cells into single positive (SP) CD4+ or CD8+ mature cells, respectively, as they migrate back towards the medulla (3, 12, 15). A final negative selection step occurs when SP cells that react too strongly with self antigens presented by APCs from the bone marrow and medullary epithelia are eliminated by apoptosis (3, 12). These positive and negative selection steps in development ensure the production of viable T-cells capable of accurately identifying and responding to foreign antigens, and the checkpoints are so stringent that only ~2% of DP cells survive (3). These SP T-cells which have successfully undergone both positive and negative selection are considered naïve mature cells which require further stimulation to differentiate into activated CD4+ or CD8+ T-cells (2, 3, 18). Priming of the naïve cells occurs through interaction of their specific TCR with a cognate antigen and this triggers the final differentiation step into either CD8+ cytotoxic T-cells or CD4+ TH1, TH2, TH17, or Treg cells (Figure 1) (2, 3, 18). This process of T-cell development is carefully controlled and is critical for maintenance of robust adaptive immunity; any disruption or disorder of the system can have severe consequences as in the case of T-cell malignancies and specifically cutaneous T-cell lymphoma.  9  Medulla  Cortex  †  Blood vessel DN2 TH1  DN3  TH2 DN1 γδ TH17  γδ-TCR  CD4+ Treg  αβ-TCR  +  CD8  pre-TCR  DN4  CD4+ CD8+ DP  †  Adapted and reprinted, with permission, from the Annual Review of Cell and Developmental Biology, Volume 23 © 2007 by Annual Reviews www.annualreviews.org  Figure 2: T-cell development in the thymus. Murine T-cell development proceeds through four developmental stages (DN1-DN4). The first differentiation branch begins with TCR rearrangement at DN3 where the γδ and αβ lineages diverge. αβ T-cells pass though a DP stage before further differentiation into either CD4+ or CD8+ T-cells. CD4+ cells further specialize into TH1, TH2, TH17, or Treg effector cells. Over the course of differentiation in the thymus, the cells migrate out towards the cortex and then back to the medulla. Daggers (†) mark cells undergoing apoptosis which fail to pass developmental checkpoints.  10  1.3 Cutaneous T-cell Lymphoma (CTCL) Cutaneous T-cell lymphomas (CTCLs) represent a group of lymphoproliferative disorders that are characterized by the homing of malignant T-cells to the surface of the skin (19-21). As a heterogeneous group, CTCLs are the most common of the T-cell lymphomas with an incidence rate of ~1500-2000 new cases annually in the United States (22). The two main types of CTCLs are mycosis fungoides (MF) and its leukemic variant Sezary syndrome (SS) which together represent ~65-70% of all CTCL cases (19, 22, 23). Both MF and SS are characterized by a monoclonal proliferation of small to medium sized mature memory CD4+ CD45RO+ T-cells, and the precise genetic pathogenesis of these diseases still needs to be determined (19, 24, 25). 1.3.1 Mycosis fungoides Patients presenting with MF are usually older adults with a median age at diagnosis of 55-60 years, yet the disease has also been documented less frequently in children and adolescents (25). Further, males are more commonly affected than females at a ratio of 1.6-2:1 (24, 25). MF manifests on the skin as itchy, dry patches, which can thicken into plaques, and finally develop into tumors, often in areas infrequently exposed to sunlight (24, 25). At later stages of disease some patients can have involvement of the lymph nodes and the visceral organs (25). Small to medium-sized atypical T-cells with highly indented (cerebriform) nuclei are frequently seen in the epidermis of patients with MF and these malignant T-cells commonly accumulate on the basal layer, resembling a string of pearls (24).  11  As an indolent disease, MF has a 5-year survival rate of 88%, but this is highly dependent upon the stage of diagnosis, the extent of skin lesions and any evidence of extracutaneous involvement (25). Currently, curative therapy is not available for any CTCL and as a result skin targeted therapies, such as photo (chemo)-therapy, topical steroid therapies, or radiotherapy are the first line treatments for MF as long as the disease is confined to the skin (25). Presently, there is also an increase in use of biologicals such as cytokines, retinoids, and receptor-targeted cytotoxic fusion proteins both as single-agent and combinational therapies (25). One of the greatest challenges that remains in the treatment of MF and other CTCLs is the similarities they have with more benign skin diseases which can often confound diagnosis (22). 1.3.2 Sezary Syndrome Historically, SS had been classified as the leukemic variant of MF but currently the World Health Organization in conjunction with the European Organization for Research and Treatment of Cancer (WHO-EORTC) lists MF and SS as two independent diseases (25). Although closely related, the link between the two is not well understood as there are two defined disease patterns: usually primary SS develops independent of pre-existing MF, but in rare events secondary SS can develop after a systemic spread of MF (22, 26). Additionally, one study reports that the unusual transition of SS to MF is also possible by documenting a case where a SS patient who achieved complete remission subsequently developed typical MF plaques (20). Regardless of the disease pattern, SS is characterized by erythroderma, general lymphadenopathy, and the presence of Sezary cells in the skin, lymph nodes, and peripheral blood (22, 24, 25). This disease occurs exclusively in adults and diagnosis must include one or more of the following: an  12  absolute Sezary cell count of > 1000 cells/mm3, a CD4/CD8 ratio > 10, and demonstration of a T-cell clone in the peripheral blood (21, 25). Sezary cells are circulating malignant mature memory CD4+ CD45RO+ T-cells with atypical cerebriform nuclei and these cells often lose expression of CD7 and CD26 (21, 22, 24, 25). Additionally, Sezary cells have a TH2 cytokine profile characterized by the production of IL-4, IL-5 and IL-10 and the lack of expression of the TH1 cytokines IL-12 and IFN-γ (22, 27, 28). Patients with SS also suffer from a reduction in T-cell receptor diversity resulting in immunosuppression which increases their susceptibility to opportunistic infections (29). Although MF is a more common CTCL, SS is a very aggressive disease in comparison with a 5-year survival rate of only 24% (25). The most effective treatment for SS currently is extracorporeal photopheresis (ECP) where the patient’s blood is removed from the body and treated with photoactivatable 8-methoxypsoralen (8-MOP) and ultraviolet light which is thought to trigger leukocyte apoptosis (25, 30). ECP is given either alone or in combination with other treatments, such as the cytokine interferon α, but the efficacy varies with overall response rates of 30 – 80% and complete response rates only 14 – 25% of the time (25).  1.3.3 Current Clues to Pathogenesis Recently, several gene expression studies on MF and SS primary samples have been published in an attempt to gain greater insight into the pathogenesis of these two closely related diseases to identify diagnostic and therapeutic target molecules. Deciphering the specific genetic events responsible for disease initiation compared to secondary molecular changes that result is a current challenge, along with improving the  13  purification strategies for Sezary cells which lack a unique and distinct malignant phenotype (25, 31). Loss of expression of several tumor suppressor and apoptotic-related genes are frequently reported in MF and SS: p53, p16, p15, p14, PTEN, TGFBR2, FAS, and FASL (24, 32). Conversely, constitutive activation of genes involved in survival pathways such as STAT3 and NFκB have also been described in SS (33-35). Some research groups are focused on developing a genetic signature for diagnosis of SS, while others are attempting to selectively discriminate SS from MF. Recently, five genes were described (STAT4, GATA-3, PLS3, CD1D, and TRAIL) which reliably identified SS patients with 90% accuracy who had 5-99% circulating tumor cells (31). As well, overexpression of CDO1 and DNM3 has been reported to distinguish SS from MF and other inflammatory skin diseases (21). Furthermore, a set of TH1 specific genes (TBX21, NKG7, and SCYA5) has been identified which are down-regulated in both MF and SS (27). Although no recurrent chromosomal translocations have been implicated in the pathogenesis of SS, chromosomal amplifications of JUNB, a member of the activator protein-1 transcription complex which is involved in TH2 cytokine expression and cell proliferation, have been documented (25). Additionally, a recent study on recurrent genetic alterations in SS identified the gain of cMYC in 75% of patients coupled with loss of cMYC antagonists (MXI1 and MNT) in 40% and 55%, respectively (36). Further another study reported gain of STAT3/STAT5, which are transducers of IL-2 signaling, in 75% of patients while loss of DUSP2, an inhibitor of IL-2 signaling, was found in 55% (36). It is evident there is still much ground to cover to determine the genetic pathogenesis behind SS and MF with the hopes of eventually developing effective targeted therapies for these diseases. The connection of these CTCLs to the AHI-1 gene  14  is an exciting avenue for investigation and elucidation of the functions of this oncogene may contribute to understanding the genetic causes behind these cancers.  15  1.4 Abelson Helper Integration Site-1 (AHI-1) Abelson-murine leukemia virus (A-MuLV) is a replication-defective murine retrovirus that contains the v-abl oncogene and is highly lymphomagenic (37). The induced pre-B-cell lymphomas are usually oligoclonal in nature which indicates a second event is most likely required for transformation (37). Due to its replication deficiency, AMuLV relies upon a non-defective helper virus to replicate both in vitro and in vivo, and studies reveal that the specific strain of the helper MuLV has some contribution to the resulting lymphomagenesis (37). Specifically, the helper Molony MuLV has been shown to strongly influence the incidence of lymphomas induced by A-MuLV and it was further speculated that this helper virus might act as an insertional mutagen and activate protooncogenes (37). In a search for provirus insertional sites, the Abelson helper integration site-1 (Ahi-1) locus was identified as a common integration site (37). Subsequent studies, also revealed provirus integrations at the Ahi-1 locus in both Myc induced T-cell lymphomas and Nf-1 generated acute myeloid leukemias (AMLs) (38, 39).  1.4.1 Structural Analysis and Expression Studies The murine Ahi-1 gene encodes a modular protein with several Src homology 3 (SH3) binding sites (PxxP), an SH3 domain, and seven tryptophan-aspartic acid 40 (WD40) repeat domains (38). Further, there are many potential phosphorylation sites, including two tyrosine kinase phosphorylation sites, an amino acid-rich domain and three potential PEST sequences, enriched in proline (P), glutamic acid (E), serine (S) and threonine (T) (38, 40). All these described domains and motifs are known to be important mediators of protein-protein interactions (38, 41). The human AHI-1 protein  16  contains an additional N-terminal coiled coil domain, absent in the mouse, which is frequently described to be involved in both intra and inter-molecular interactions and in homo and hetero-dimerization (Figure 3) (42, 43).  Human AHI-1 Y PEST  I  Y  PEST  C  N PXXP  Coiled-Coil  PXXP  WD40  SH3  Y  II N  C PXXP  PXXP Y  Y  III N  C PXXP  PXXP  Adapted by permission from Macmillan Publishers Ltd: Leukemia 20, 1593-1601, © 2006  Figure 3: Protein structure of human AHI-1 isoforms. There are at least 3 human isoforms of AHI-1. The full length AHI-1 isoform I contains an N-terminal coiled-coil domain, several SH3 binding sites (PxxP), PEST sequences, a WD40 domain, an SH3 domain and potential tyrosine phosphorylation sites (Y). Differing at the C-terminus and shorter due to in frame termination codons, isoform II lacks the SH3 domain and III contains additional coding sequences (striped box).  17  Conserved in mammals, Ahi-1 is situated on the mouse chromosome 10 and the human chromosome 6 and the gene contains at least 27 and 33 exons, respectively (38). Ahi-1 encodes two major RNA species, at 5 kb and 4.2 kb in mice and rats and at 6.5 kb and 4.2 kb in humans, along with several other smaller splice variants (1, 38). There appears to be at least three human isoforms of AHI-1, which differ in their C-terminus sequence (1, 41). Isoforms II and III are shorter due to in frame termination codons; the former lacks the SH3 domain while the later contains additional unique coding sequences (Figure 3) (1, 38). In normal human bone marrow isoform III transcripts are most prevalent, closely followed by transcripts of isoform I and ~10-fold fewer transcripts of isoform II (1). The Ahi-1 gene is expressed in mouse embryos and further studies in rodent tissues reveal Ahi-1 expression in several organs, with the highest levels detected in the brain and testes (38). Additionally, different isoforms seem to exhibit some degree of tissue specificity which may be indicative of controlled splice variants (38). Upon closer examination of hematopoietic cells, in both mice and humans the highest Ahi-1/AHI-1 expression is found in the most primitive stem cells and the gene is down-regulated upon differentiation into different lineages (1). A pattern is observed where granulocyte/macrophage lineage cells show stronger down-regulation in comparison to T-lymphoid, B-lymphoid, and erythroid lineage cells (1). This down-regulation of Ahi1/AHI-1 during normal hematopoietic cell development is an important conserved step and suggests that Ahi-1/AHI-1 gene products may play a role in regulation of differentiation (1).  18  1.4.2 Connecting Ahi-1 to Cancer The oncogenic potential of Ahi-1, suggested by A-MuLV induced lymphomas, was further established in several other murine leukemias and lymphomas (1, 37, 38). Subsequently, highly deregulated expression of AHI-1 was also confirmed in 15/16 human leukemic cell lines of myeloid, B-cell and T-cell origin, suggesting aberrant expression of this gene contributes to tumor development (Figure 4) (1). The most striking up-regulation of AHI-1 is observed in the CTCL cell lines Hut 78 (~40-fold) and Hut 102 (~30-fold) which are derived from patients with SS and MF, respectively (1). Additionally, both the chronic myeloid leukemia (CML) cell line K562 and Philadelphia chromosome positive (Ph+) primary leukemic cells also show marked up-regulation of AHI-1 (1). Recent studies to further investigate the oncogenic role of this gene in CML have identified a direct physical interaction between BCR-ABL and AHI-1 in K562 cells, and overexpression of AHI-1 has been shown to sustain BCR-ABL phosphorylation and enhance activation of the JAK2/STAT5 pathway (Zhou et al, revised manuscript has been resubmitted to J Exp Med). Further studies to examine how AHI-1 contributes to leukemic transformation have focused on the Hut 78 human SS cell line, which exhibits significant overexpression of this gene. Moreover, aberrant expression of AHI-1 at both RNA and protein levels, particularly the truncated isoforms, has been detected in fluorescence-activated cell sorting (FACS) purified CD4+CD7- Sezary cells from several patients with SS (Figure 5) (41). To determine if there is direct involvement in malignant transformation a stable knockdown of endogenous AHI-1 isoforms was achieved by retroviral-mediated RNA interference (RNAi) (Figure 6A) (41). This RNAi strategy reduces the autocrine  19  production of IL-2, IL-4 and TNFα in these cells and inhibits their transforming activity in vitro and in vivo (Figure 6B, C, and D) (41). Thus, this clearly demonstrates the lymphomagenic activity of Hut 78 cells to be directly dependent on the expression of AHI-1. Although there is substantial evidence that Ahi-1/AHI-1 can act as an oncogene in hematopoietic cells, the specific molecular mechanisms involved in this transformation have yet to be determined.  20  Absolute transcript no/µg RNA (fold change)  50  25  Normal BM Myeloid Lymphoid  Hut 78 Hut 102  15 10 5  N B orm M a  l M o AM7E K L5 G TF 1 U 1 9 H 37 L6 K 0 5 TH 62 P1 D IM9 au R di Ju aji C rk C at L M 1 19 H T- 4 ut H 102 ut 78  0 AML/CML BC B-cell T-cell  This research was originally published in Blood. Jiang et al. Deregulated expression in Ph+ human leukemias of AHI-1, a gene activated by insertional mutagenesis in mouse models of leukemia. Blood. 2004;103:3897-3904. © The American Society of Hematology.  Figure 4: Aberrant expression of AHI-1 in human leukemic cell lines. Quantitative RT-PCR of AHI-1 transcript levels in 16 human leukemic cell lines of myeloid (AML/CML blast crisis), B-cell and T-cell origin. AHI-1 expression in total normal human bone marrow (BM) is included for comparison. The fold change values presented are the mean ± SEM from 3 biological replicates. The primers used for this expression analysis identify all AHI-1 isoforms. AML = acute myeloid leukemia; CML BC = chronic myeloid leukemia blast crisis.  21  A 12345678  AHI-1 transcript levels (fold change)  Normal samples  B 123 456789  KDa  6  170  5  130  4  100  3  72  3  5  6 8  SS-2 4 6  8  9  Hut 78  AHI-1  2 55  1 0 Normal samples  SS-1  SS-2  42  Actin  SS-1 = < 5 % Sezary cells SS-2 = > 5% Sezary cells or CD4+:CD8+ >10 Adapted by permission from Macmillan Publishers Ltd: Leukemia 20, 1593-1601, © 2006  Figure 5: Deregulated expression of AHI-1 in Sezary syndrome primary samples. A) Quantitative RT-PCR AHI-1 transcript levels in primary samples from both normal individuals and patients with Sezary syndrome (SS). More aberrant AHI-1 expression is seen in patients with typical SS (SS-2) in comparison to those with atypical SS (SS-1). B) Western blotting of AHI-1 protein expression in the same primary samples of normal individuals and patients with SS-2. Several AHI-1 isoforms are detected and upregulation of truncated forms of AHI-1 is observed in SS-2 patients.  22  Figure 6: Effects of stable suppression of AHI-1 in Hut 78 cells. A) A retroviral-mediated RNAi strategy was used to knockdown AHI-1 in Hut 78 cells, by transducing the cells with either an empty RPG vector or a RPG vector containing short-hairpin RNA (shRNA) designed against AHI-1 (sh4). Successfully transduced cells were identified by GFP expression and FACS sorted in purified RPG and AHI-1/sh4 populations. B) AHI-1/sh4 cells demonstrate stable suppression of AHI-1 expression in addition to a reduction in the autocrine production of IL-2, IL-4 and TNFα when compared to the control populations of parental Hut 78 cells and Hut 78 cells transduced with the empty RPG vector. C) The AHI-1/sh4 cells have reduced growth factor independence as measured with colony forming cell (CFC) assays. The presence of three growth factors (+3GF), IL-2, IL-4 and TNFα, was able to rescue this phenotype. Further, the sole addition of either IL-4 or TNFα, but not IL-2, was also able to restore the growth deficiency of AHI-1/sh4 cells. D) Suppression of AHI-1 resulted in a loss of the ability to form tumors in NOD/SCID β2m-immunodeficient mice.  23  A  Transduction AHI-1 shRNA constructs  RPG (7kb) GFP  40%  GFP  Transcript levels relative to control Hut 78  B  AHI-1  120 100 80 60 40 20 0  IL-4  120 100 80 60 40 20 0  Hu  C  78 PG h4 R I/s AH  IL-2  120 100 80 60 40 20 0  TNF α  120 100 80 60 40 20 0  78 PG h4 Hu R HI/s A  No. of CFC per 103 input cells  300  RPG AHI-1/sh4  250 200 150 100 50 0 -GF  RPG -GF  +3GF  +IL-2  AHI-1/sh4 –GF  +IL-4 +TNFα  RPG +IL-2  AHI-1/sh4 +IL-2  D Hut 78/RPG  Cells (2x107)  Tumor Growth (# of sick mice/total)  Hut 78  4/4  Hut 78/RPG  6/6  Hut 78/AHI-1/sh4  0/8 Hut 78/AHI-1/sh4  Adapted by permission from Macmillan Publishers Ltd: Leukemia 20, 1593-1601, © 2006  24  1.4.3 Beyond the Cancer Connection In addition to evidence for an oncogenic role, mutations in AHI-1 have recently been documented in Joubert syndrome (JS) (44-46). JS is an autosomal recessive disease characterized by a malformation in the mid-hindbrain, developmental delay/mental retardation, and hypertonia (46). Although no consistent mutation has been described, nonsense, missense, splice-site, and insertional mutations in AHI-1 have been documented in ~10% of JS patients (46). Interestingly, the most frequent type are truncating mutations (80%) that abolish completely, or partially, two critical domains of AHI-1: WD40 and SH3 (44). These affected individuals are also at risk for developing the additional symptoms of retinal dystrophy and progressive kidney failure (46). Strong expression of Ahi-1 isoforms I and II have been documented in the embryonic hindbrain and forebrain of mice suggesting the gene may play a role in development of the cerebellum and the cortex (45). Lastly, recent studies report the AHI-1 locus to be associated with schizophrenia and type-2 diabetes (47, 48). Therefore, these findings suggest that changes in AHI-1 gene functions are likely to be critical in the development of these diseases and more detailed characterization of AHI-1 could lead to important insights into disease processes.  25  1.5 Experimental Outline AHI-1 has been shown to have direct biological involvement in several human leukemias and lymphomas, and the aberrant expression of AHI-1 observed both in human leukemia/lymphoma cell lines and in primary cells from patients supports the rationale it acts as an oncogene in transformed hematopoietic cells. The most notable overexpression of AHI-1 has been described in the CTCL Hut 78 cell line and further studies support that this up-regulation translates clinically to SS patient samples. Based upon these significant findings, the overall goal of this thesis project is to attempt to elucidate new genes connected to the AHI-1 transformation occurring in this CTCL. If the molecular and cellular mechanisms of AHI-1 transformation can be determined in Tcell lymphomagenesis, these findings can fuel improvements in both diagnosis and treatment of not only SS, but also other related CTCLs.  1.5.1 Hypothesis AHI-1 modulates the regulation of human malignant T-cell proliferation, differentiation and apoptosis by mediating the activity of specific molecular partners critical for control of these cellular programs.  1.5.2 Objective To identify and characterize differentially expressed genes dependent on AHI-1-mediated leukemic transformation of human CTCL cells by microarray analysis.  26  1.5.3 Specific Aims 1. Identify and validate differentially expressed genes in a microarray analysis comparing the Hut 78 control cell lines to Hut 78/sh4 cell lines, in which short hairpin (shRNA) has been transduced to successfully knockdown AHI-1 expression.  2. Investigate these candidate genes to determine if the observed differential expression translates to the protein level.  3. Characterize the function and/or structure of these candidate genes in an effort to determine their role in AHI-1-mediated leukemic transformation.  27  CHAPTER 2: MATERIALS AND METHODS 2.1 Cell Culture The Hut 78, Hut 78 RPG, Hut 78/sh4 bulk and Hut 78/sh4 clone 1 cells were cultured in RPMI 1640 Medium with 10% fetal bovine serum, 100 U/mL penicillin, 0.1 mg/mL streptomycin, and 1 x 10-4 M β-mercaptoethanol (StemCell Technologies, Vancouver, BC). These cells were maintained as suspension cultures, at a cell density less than 1 x 106 cells/mL, in 25 cm2 rectangular canted neck cell culture flasks with vent caps (Corning Inc., Lowell, MA).  2.2 RNA Isolation RNA was isolated by pelleting 1 x105 to 1 x 106 cells and washing them in 10 mL of Dulbecco’s Phosphate Buffered Saline (PBS) (StemCell Technologies, Vancouver, BC). The washed cells were then re-pelleted and the Absolutely RNA® Miniprep Kit (Stratagene, La Jolla, CA) was used to purify total RNA using the procedure described for tissue culture cells grown in suspension. A total of 70 µL (40 µL followed 30 µL) of elution buffer was used to extract the RNA and the yield was quantified by measuring the optical density at 260 nm and 280 nm using a Nanodrop ND-100 Spectrophotometer.  2.3 Microarray Analysis 1 µg of purified total RNA from Hut 78, Hut 78 RPG, Hut 78/sh4 bulk, and Hut 78/sh4 clone 1 cells was provided for microarray analysis (Michael Smith Genome Sciences Centre, BC Cancer Agency). The controls consisted of two RNA samples of Hut 78 cells and three RNA samples of Hut 78 RPG cells, while the experimental  28  samples included three RNA samples of Hut 78/sh4 bulk cells and three RNA samples of Hut 78/sh4 clone 1 cells. The Affymetrix GeneChip® Human Genome U133 Plus 2.0 Array was used to determine the differential gene expression between the two groups. The data was analyzed by both DNA-Chip Analyzer (dChip) and Linear Model for Microarray Data (Limma) software programs to identify significant differentially expressed genes (49, 50).  2.4 cDNA Synthesis cDNA was synthesized according to the First-strand cDNA synthesis procedure included with SuperScript™ III Reverse Transcriptase (Invitrogen, Burlington, ON). First-strand cDNA was synthesized in 20 µL reaction volumes from 100-500 ng of purified RNA using 3 µg/µL random primers (Invitrogen, Burlington, ON).  2.5 Quantitative Reverse Transcription-Polymerase Chain Reaction Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed using 12.5 µL Power SYBR® Green PCR Master Mix (Applied Biosystems, Foster City, CA), 1 µL 20 µM gene specific primer (Invitrogen, Burlington, ON) (Appendix A.1), 1 µL cDNA, and 10.5 µL water for a total reaction volume of 25 µL. Quantification of gene expression was performed using the 7500 Real Time PCR System (Applied Biosystems, Foster City, CA) by first running a relative quantification plate followed by the respective dissociation cycle. The thermal profile of the reaction was: 50˚C for 2 min, 95˚C for 10 min, and 45 or 50 cycles of 95˚C for 15 sec followed by 60˚C for 1 min. Fluorescence measurements were recorded using SYBR® as the reporter  29  dye and ROX™ as the passive reference and the resulting data was normalized to an endogenous control, glyceraldehyde 3-phosphate (GAPDH). Efficiency analysis was performed on all primers prior to use in qRT-PCR analysis using serial dilutions of cDNA (1x, 1/10x, 1/100x, and 1/1000x) (Appendix A.2). The 7500 Real Time PCR System Software was used for data analyses (Applied Biosystems, Foster City, CA).  2.6 Cell Lysate Cell lysate was prepared by pelleting cells washed with Dulbecco’s Phosphate Buffered Saline (PBS) (StemCell Technologies, Vancouver, BC) and placing them at -70˚C to freeze dry. The pellet was then resuspended in an appropriate volume of lysis buffer (1 x 106 cells / 30 µL lysis buffer) and the mixture was incubated for 1 hour on a rotator at 4˚C. The lysis buffer consisted of 1 mL phosphorylation solubilization buffer (PSB), 10 µL phenylmethylsulfonyl fluoride (PMSF) (Sigma-Aldrich, Oakville, ON), 50 µL protease inhibitor cocktail (PIC) (Sigma-Aldrich, Oakville, ON), and 50 µL NP-40 Alternative, Protein Grade® Detergent (Calbiochem, Gibbstown, NJ). After incubation, the lysed cells were centrifuged at 12,000 RPM for 10 min at 4˚C and the supernatant was harvested and stored at -70˚C.  2.7 Protein Lysate Quantification Bradford assays were used to determine lysate concentration by generation of a standard curve with bovine serum albumin (BSA) (Bio-Rad Laboratories, Mississauga, ON) using concentrations ranging from 50-1000 ng/µL. Samples were diluted to 1/20x to ensure a reading in the linear range of the standard curve and 20 µL of each diluted sample and standard were aliquoted into an untreated flat-bottom 96 well plate. Next,  30  200 µL of Bio-Rad Protein Assay Dye Reagent (Mississauga, ON) was mixed in each well and the plate incubated at room temperature for at least 5 minutes. The absorbance of samples/standards was measured at 630 nm using the ELx808™ Absorbance Microplate Reader (BioTek Instruments, Winooski, VT).  2.8 Western Blotting Protein expression was assessed by Western blotting with the NuPAGE® Novex® Bis-Tris Gel Electrophoresis system (Invitrogen, Burlington, ON). Samples were prepared using 20 µg protein lysate, 2.5 µL NuPAGE® LDS Sample Buffer (4x), 1.0 µL NuPAGE® Reducing Agent (10x), and up to 6.5 µL deionized water for a final volume of 10 µL (Invitrogen, Burlington, ON). To analyze 40 µg of protein this reaction was simply doubled. The samples were then heated at 70°C for 10 min and loaded into a NuPAGE® Novex 4-12% Bis-Tris Gel 1.0 mm, 10 well (Invitrogen, Burlington, ON) along with the PageRuler™ Prestained Protein Ladder (Fermentas, Burlington, ON). Using the XCell Surelock™ Mini-cell and NuPAGE® MOPS SDS Running Buffer (Invitrogen, Burlington, ON) the gel was performed under reducing conditions at 200 V for 50 min. Proteins were then transferred from the gel onto Immobilon-P PVDF 0.45µm membrane (Millipore, Billerica, MA) using NuPAGE® Transfer Buffer (Invitrogen, Burlington, ON) in the XCell II™ Blot Module (Invitrogen, Burlington, ON). The transfer was performed at 30 V for 1 hr. The membrane was next dried, blocked in TrisBuffered Saline Tween-20 (TBST) with 5% skim milk for 1 hr at room temperature, and washed 2 x 5 min with TBST. The membrane was then incubated with primary antibody overnight at 4°C, washed 3 x 10 min with TBST, and incubated with secondary antibody for 1 hr at room temperature, followed again by 3 x 10 min washes with TBST  31  (Appendix A.3). Western Lightning® Western Blot Chemiluminescence Reagent Plus and KODAK™ BioMax® XAR autoradiography film (PerkinElmer Life and Analytical Sciences, Waltham, MA) was used to image the membrane with the Fuji RGII x-ray film processor. Relative protein expression was determined by densitometry using the image analysis program ImageQuant Version 5.2 (Molecular Dynamics, Sunnyvale CA).  2.9 Peptide Competition Peptide competition experiments were performed on the HCK primary antibody (sc-72; Santa Cruz, Santa Cruz, CA) using the HCK epitope (sc-72 P; Santa Cruz, Santa Cruz, CA) and also a random peptide, AHI-1 (Imgenex, San Diego, CA). Neutralization of the primary antibody was achieved by incubating 10 µL 0.2 µg/µL HCK antibody with either 50 uL 0.2 µg/µL HCK blocking peptide and 440 µL PBS, or 20 µL 0.5 µg/µL AHI1 peptide and 470 µL PBS. The final 500 µL reaction volume incubated overnight at 4°C while gently shaking and was subsequently diluted with 9.5 mL of TBST to a final primary antibody concentration of 1:1000. The neutralized antibody was then used as described in the Western blotting procedure.  2.10 Reverse Transcription-Polymerase Chain Reaction To perform reverse transcription-polymerase chain reaction (RT-PCR) 1.0 µL 10 mM dNTP, 2.0 µL 20 µM exon specific primer, 5.0 µL 10x PCR buffer , 1.5 µL 50 mM MgCl2, 1.0 µL Platinum® Taq DNA Polymerase, 2.0 µL cDNA and 37.5 µL water were mixed together for a total reaction volume of 50 µL (Invitrogen, Burlington, ON). To determine the presence of exons 10, 12A-D, and 13 in BIN1 transcripts, exon specific primers were used (Appendix A.4). The thermal profile of the reaction was: 95˚C for 5  32  min; 35 cycles of 94˚C for 30 sec, 66˚C for 30 sec, and 72˚C for 45 sec; followed by 72˚C for 10 min.  2.11 Cloning and Sequencing The RT-PCR products were analyzed on a 2% Agarose SFR™ (Amresco, Solon, OH) Tris-acetate-EDTA (TAE) gel at 90 V for 2.5 hrs. The resulting bands were excised and purified using the MinElute Gel Extraction Kit (Qiagen, Mississauga, ON). The extracted PCR products were then ligated into the pCR®2.1 vector using the TOPO TA Cloning® Kit (Invitrogen, Burlington, ON). This was followed by transformation into the MAX Efficiency® DH5 αT1 Phage Resistant One Shot® Competent E. coli (Invitrogen, Burlington, ON) which were plated on 100 µg/mL ampicillin LB selection plates with 40 µL 20 µg/µL X-gal. Positive colonies were expanded in liquid cultures and the plasmids were purified with the GeneJET™ Plasmid Miniprep Kit (Fermentas, Burlington, ON). Both M13 PCR and digestion using Hind III and EcoR V restriction enzymes (Invitrogen, Burlington, ON) were subsequently used to verify the positive colonies, and those clones which successfully confirmed were sent for forward and reverse sequencing at the McGill University and Genome Québec Innovation Centre.  2.12 Statistical Analysis Data analysis utilized two sample student’s t-tests to determine the statistical significance of the results. The two arrays compared consisted of the Hut 78 and Hut 78 RPG data versus the Hut 78/sh4 bulk and Hut 78/sh4 clone 1 data. The analysis included two tails and assumed unequal variance since the data sets were relatively small (N = 6, 8, 10, or 12).  33  CHAPTER 3: RESULTS 3.1 Microarray Identification of Differentially Expressed Genes To identify genes involved in AHI-1-mediated leukemic transformation in human CTCL cells, a microarray experiment was designed to compare parental Hut 78 cells to those which have a stable knockdown of all AHI-1 isoforms (Table 1). These AHI-1 knockdown cells were previously generated by both former and current lab members. They cloned a short hairpin transcript (sh4) derived from an N-terminal 19 nucleotide sequence of AHI-1 into a modified retroviral vector (RPG) which has the H1 promoter of RNA polymerase III and a GFP marker gene (41). The sh4 construct was introduced into Hut 78 cells by retroviral transduction for 48 hours and FACS sorting was then used to purify the GFP+ cells; as an experimental control, the same strategy was used with the empty RPG vector (41). qRT-PCR analysis confirmed this sh4 construct specifically inhibited total AHI-1 expression by 80% in these stably transduced cells and several stable clonal lines with similar AHI-1 suppression were subsequently generated by single cell sorting (41). Successful AHI-1 suppression was further confirmed with Northern blotting, Western blotting and additional qRT-PCR analyses using isoform specific primers (41). Using these previously generated cell lines, this project began with a microarray to compare both parental Hut 78 cells and Hut 78 cells transduced with the empty RPG vector (Hut 78 RPG) to a bulk population of AHI-1 suppressed cells (Hut 78/sh4) and a clonal population (Hut 78/sh4 clone 1). Hut 78 RPG cells were included as simply presence of the vector could result in gene expression changes and the sh4 bulk and  34  clonal cells were assessed to determine if the expression changes identified were true in both a large population and a representative individual cell. The Affymetrix GeneChip® Human Genome U133 Plus 2.0 Array was used to quantify gene expression with 54,330 probe sets, which represent over 47,000 transcripts. Six RNA samples from AHI-1 suppressed cells (Hut 78/sh4) were evaluated against five control samples and the resulting pooled data was analyzed by two independent software programs: Linear Model for Microarray Data (Limma) and DNA-Chip Analyzer (dChip) (49, 50). Differentially expressed genes were identified with these two programs using the threshold values of a fold change ≥ or ≤ 2 and a p-value ≤ 0.01. The Limma analysis of the resulting data initially listed 283 differentially expressed probes sets (218 genes) that were further refined to a list of 33 (27 genes) (Figures 7, 8). Refinement of this Limma list occurred by performing the Benjamini and Hochberg’s (BH) adjustment on the p-values to correct for the discovery of false positives in the data set (51). Using the same threshold values, the dChip analysis identified 119 probe sets (95 genes) with differential expression, and encouragingly the selected genes completely overlapped with those detected in the initial Limma analysis (Figure 9). Notably, significant down-regulation of AHI-1 itself (p-value < 0.001) was validated by microarray analysis in all six Hut 78/sh4 RNA samples studied (Figure 8).  35  Table 1: Hut 78 cell lines compared in microarray analysis*. CTCL Control Cell Lines Hut 78 (parental line)  Hut 78 RPG (parental line transduced with the empty RPG vector)  N  AHI-1 suppressed Cell Lines  N  Hut 78/sh4 bulk 2  3  (bulk population of the parental line transduced with the RNAi construct for AHI-1)  Hut 78/sh4 clone 1 (clonal population of the Hut 78/sh4 bulk cell line)  3  3  * Hut 78 cell lines were compared to AHI-1 suppressed Hut 78/sh4 cell lines in a microarray. The 5 controls were the Hut 78 and Hut 78 RPG cell lines and the 6 test samples were the Hut 78/sh4 bulk and Hut 78/sh4 clone 1 cell lines. N represents the number of technical replicates of each cell line that were included for analysis.  36  Figure 7: Volcano plot of differentially expressed probes selected by Limma analysis. Initial Limma analysis of the microarray data listed 283 differentially expressed probe sets using the threshold values of a fold change ≥ or ≤ 2 and a p-value ≤ 0.01 (red = upregulated; green = down-regulated). Performing Benjamini and Hochberg’s (BH) adjustment on the p-values refined this list to 33 probe sets (+). Note: Log scales used.  37  sh 4 sh 4 sh 4 sh 4 sh clon e 4 sh clo 1 4 c ne Hu lon 1 Hut78 e1 Hu t78 t Hu 78 R t 7 8 PG Hu t78 RPG RP G  Abelson helper integration site 1 CKLF-like MARVEL transmembrane domain containing 7 3-oxoacid CoA transferase 1 Abelson helper integration site 1 programmed cell death 6 synaptic vesicle glycoprotein 2B ELAV (embryonic lethal, abnormal vision, Drosophila)-like 1 (Hu antigen R) programmed cell death 6 A kinase (PRKA) anchor protein 2 /// PALM2-AKAP2 protein hypothetical protein LOC642730 RALBP1 associated Eps domain containing 2 Transcribed locus, strongly similar to NP_071746.1 LIM and cysteine-rich domains 1 RALBP1 associated Eps domain containing 2 hypothetical protein LOC642730 Transcribed locus,similar to NP_071746.1 EF hand calcium binding protein 1 lysosomal associated multispanning membrane protein 5 myeloid/lymphoid or mixed-lineage leukemia; translocated to, 11 lysosomal associated multispanning membrane protein 5 grainyhead-like 1 (Drosophila) BCR downstream signaling 1 BCR downstream signaling 1 glutathione peroxidase 7 breast carcinoma amplified sequence 4 bridging integrator 1 natural killer cell group 7 sequence microfibrillar-associated protein 4 heat shock 70kDa protein 2 cytochrome P450, family 1, subfamily A, polypeptide 1 hydroxyprostaglandin dehydrogenase 15-(NAD) chromosome 1 open reading frame 162 interleukin 4 induced 1 Spi-B transcription factor (Spi-1/PU.1 related)  -5.0  0  5.0  Figure 8: Clustering results of differentially expressed gene identified by refined Limma (BH) analysis. The Benjamini and Hochberg’s (BH) adjusted Limma list of up-regulated (red) and down-regulated (blue) genes when comparing gene expression of Hut 78/sh4 bulk and Hut 78/sh4 clone 1 samples to Hut 78 and Hut 78 RPG samples.  38  dChip  Limma BH  0  33  86  Figure 9: Venn diagram of differentially expressed probes selected by both Limma and dChip analyses. In total, the microarray screened 54,330 probe sets for differential expression. The Limma analysis selected 283 probe sets which was further refined to 33 with the BH adjustment. Importantly, the 119 probes sets identified by dChip analysis completely overlapped the initial Limma results.  39  3.2 Validation of Microarray Results The differentially expressed genes identified by both Limma (27 genes) and dChip (95 genes) analyses were carefully reviewed to select genes with interesting functional or structural information. More specifically, focusing on the Limma BH and dChip lists, genes with previous documentation in SS, CTCL, or other cancers; roles in cell cycle, proliferation, or apoptosis; specificity to hematopoietic cells and particularly T-cells; or involvement in IL-2, IL-4, or TNFα signaling were noted. Additionally, since AHI-1 has several domains known to be involved in protein interactions, genes with complementary structural domains were also of interest. After careful evaluation of both analyses, 15 genes found to be up-regulated and 6 genes found to be down-regulated in AHI-1 suppressed Hut 78/sh4 cells as compared to control Hut 78 cells were selected for validation by qRT-PCR (Table 2). The genes described as up and down-regulated in AHI-1 suppressed cells are defined by their pooled expression levels in Hut 78/sh4 bulk and Hut 78/sh4 clone 1 cells as compared to their pooled expression levels in Hut 78 and Hut 78 RPG cells. qRT-PCR validation was performed on four technical replicates using the same RNA samples provided for microarray analysis. Firstly, suppression of AHI-1 was verified using both C-terminal and N-terminal primer sets with p-values of 1.01 x 10-5 and 2.84 x 10-6, respectively. This was followed by quantification of the 21 selected genes. The results validated the differential expression of the 21 genes with p-values < 0.05 ranging from 1.97 x 10-10 to 6.55 x 10-3 (Table 3, Appendix A.5). The only gene with a p-value > 0.05 was BRDG1 at 5.88 x 10-2.  40  To further evaluate the 21 differentially expressed genes, several were selected for more comprehensive validation in two additional biological replicates of RNA from Hut 78, Hut 78 RPG, Hut 78/sh4 bulk and Hut 78/sh4 clone 1 samples. Aliquots of the previously generated cell lines were placed in culture and new RNA isolations were performed. 6 genes were selected based upon encouraging functional and structural information, and connections to T-cells and cancer. In each biological replicate, defined by an independent RNA isolation, qRT-PCR expression analysis began again with verification of AHI-1 suppression, using both C-terminal and N-terminal primer sets, followed by expression analyses of CDKN1C, HCK, BIN1, REPS2, PDCD6 and ELAVL1 (Figure 10). Overall, all the 6 selected genes validated their differential expression in these biological replicates, although small variations in expression samples were observed for a few genes between Hut 78 and Hut 78 RPG samples and among Hut 78/sh4 bulk and Hut 78/sh4 clone 1 samples.  41  Table 2: Justification of genes selected for further study and validation. GENE IL1RN CDKN1C  (p57, KIP2)  UP-REGULATED  LMCD1 LAPTM5 (E3) MLLT11 (AF1Q) NKG7 GRHL1 HCK SPIB CCNG2 IL4I1 BIN1 BRDG1 (STAP1) REPS2 (POB1) BCAS4  SELECTION JUSTIFICATION • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •  Receptor antagonist of the inflammatory cytokine IL-1; low IL1RN expression described in both CML and AML (52) Up-regulation of IL1RN described in lesional skin biopsies of CTCL patients (53) Cyclin dependent kinase inhibitor involved in negative regulation of cell cycle; involved in T-cell cycle control (54) Suppression/methylation of CDKN1C described in several lymphoid malignancies (54) Down-regulation of CDKN1C observed in a patient with CTCL (55) Contains 2 LIM domains commonly involved in protein interactions, specifically with GATA family proteins (56) LMCD1 interacts with the transcription factor GATA6 to inhibit DNA binding (56) In T-cells, IL-4 stimulation up-regulates GATA3 which is important for TH2 differentiation (18) LAPTM5 is a lysosomal transmembrane protein specific to hematopoietic cells (57) LAPTM5 is found to be frequently inactivated in multiple myeloma (58) MLL fusion gene highly expressed in primitive hematopoietic cells and down-regulated in differentiated cells (59) Initially identified in a pediatric AML (59) Down-regulated in Sezary syndrome PBMC and mycosis fungoides CD4+ cells (60) Transcription factor shown to be important in murine development (61) Src-related protein tyrosine kinase with an SH3 and SH2 domain predominantly expressed in hematopoietic cells (62) Involved in signaling that triggers caspase-mediated apoptosis (63, 64) Up-regulation of HCK described in MF patient tissue samples (65) An Ets family transcription factor with restricted expression to hematopoietic cells (66) Important in T-cell lineage commitment and differentiation (67) Cyclin G2 is a ubiquitous cell cycle regulator that inhibits cell cycle progression at the G1/S phase transition (68) IL-4 inducible L-phenylalanine oxidase with expression restricted to lymphoid tissues (69) IL4I1 expression by dendritic cells inhibits T-cell proliferation (69) Tumor suppressor able to physically interact with and inhibit the malignant transformation of MYC (70) Frequently inactivated in human cancers and contains an SH3 domain (70, 71) Participates in downstream signaling in the BCR signal cascade (72) Possesses a proline-rich sequence that forms a potential binding site for SH3 and several SH2 binding sites (72) Negatively regulates signaling and endocytosis of growth factors (73) REPS2 contains a C-terminal coiled-coil region and 2 proline-rich motifs, which interact with SH3 domains (73) REPS2 binds to the NFκB subunit p65 and is down-regulated in prostate cancer progression (74) Undergoes amplification, overexpression, and fusion in breast cancer (75)  42  Table 2: Justification of genes selected for further study and validation (continued).  DOWN-REGULATED  PALM2-AKAP2 SV2B CMTM7 PDCD6 (ALG-2) OXCT1 (OXCT, SCOT ) ELAVL1 (HUR)  • • • • • • • • • • • • •  Naturally occurring co-transcribed product of the neighboring PALM2 and AKAP2 genes with unknown function (76) Contains the N-terminal coiled-coil domain from PALM2 (76) Glycosylated synaptic vesicle membrane protein involved in calcium-mediated synaptic transmission and vesicle trafficking (77) Expressed in small-cell neuroendocrine prostate cancer xenografts (78) Shares homology with chemokine-like factor (CKLF), but gene function undetermined (79) Expressed in CD25+CD4+ natural regulatory T cells and induced in naive T cells by Foxp3 transduction (79) Calcium binding protein involved in T cell receptor-, Fas-, and glucocorticoid-induced programmed cell death (80) ALG-2 up-regulated in hepatomas and lung cancer tissue (81) Member of the 3-oxoacid CoA-transferase gene family and plays a central role in extrahepatic ketone body catabolism (82) Expressed in lymphoblasts (82) Down-regulation of OXCT1 described in Sezary Syndrome PBMC patient samples (83) Ubiquitously expressed member of the RNA binding ELAV protein family which selectively binds AU-rich elements to stabilizes mRNAs (84) Early exposure of CD4+ T cells to endogenous IL-4 increased IL-4 mRNA stability via ELAVL1 (85)  43  Table 3: Fold changes of the selected 21 differentially expressed genes #. UP-REGULATED GENES IL1RN * CDKN1C * LMCD1 LAPTM5 MLLT11 NKG7 GRHL1 HCK * SPIB CCNG2 * IL4I1 BIN1 BRDG1 REPS2 BCAS4  Microarray 10.78 5.31 5.09 4.64 4.41 4.30 4.11 3.92 2.79 2.73 2.69 2.59 2.52 2.46 2.24  P-Value ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01  qRT-PCR 4.97 3.55 3.23 3.22 3.33 2.63 1.98 1.63 1.87 1.44 1.90 1.82 1.37 6.65 1.94  P-value 2.27 x 10-4 6.70 x 10-4 3.25 x 10-6 6.73 x 10-6 1.97 x 10-10 1.12 x 10-5 4.20 x 10-4 1.84 x 10-3 2.45 x 10-5 6.55 x 10-3 6.41 x 10-5 1.17 x 10-3 5.81 x 10-2 3.51 x 10-4 1.52 x 10-5  DOWN-REGULATED GENES PALM2-AKAP2 SV2B CMTM7 PDCD6 OXCT1 ELAVL1  0.12 0.33 0.34 0.38 0.44 0.48  ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01 ≤ 0.01  0.03 0.14 0.18 0.41 0.27 0.29  3.92 x 10-3 6.45 x 10-3 2.06 x 10-3 1.51 x 10-5 7.11 x 10-4 6.59 x 10-5  AHI-1  0.44  ≤ 0.01  0.27  2.84 x 10-6  #  Microarray and qRT-PCR validation data for the 21 select genes in addition to  verification of AHI-1 suppression. Fold changes represent the ratio of expression in Hut 78/sh4 bulk and Hut 78/sh4 clone 1 samples to Hut 78 and Hut 78 RPG samples. Four technical replicates were performed for qRT-PCR validation using the same RNA samples used in the microarray analysis. The qRT-PCR p-values are the result of two sample t-tests comparing the expression of AHI-1 suppressed cells lines to Hut 78 controls. Genes denoted with * identified in dChip but not in refined Limma BH analysis.  44  Figure 10: Validation of differential expression in biological replicates. Further validation of differential expression of select genes was performed using two additional biological replicates. Mean mRNA fold change values are presented with the standard error of the mean (n = 3). A) Verification of AHI-1 suppression was first validated with both C-terminal and N-terminal primer sets. B) Validation of the upregulation of CDKN1C, HCK, BIN1, and REPS2 as a result of AHI-1 suppression. C) Validation of the down-regulation of PDCD6 and ELAVL1 as a result of AHI-1 suppression. The p-values shown were generated by two sample t-tests comparing the Hut 78/sh4 AHI-1 suppressed populations to the Hut 78 control populations.  45  0.0  1  0.5  e  1.0  on  1.5  cl  2.0 10  4  P-value = 0.07  sh  4  e  bu on  4  cl  sh  1  lk  sh  4  e  1  lk  PG bu  R  on  4 cl  sh  78  78  1  mRNA Fold Change 1.4  sh  3.5  lk  BIN1  bu  0  4  CDKN1C  sh  2  PG  4  R  6  78  ut  ut  e  AHI-1 (C-terminal)  PG  14  R  ut  H  on  H  cl  lk  0.0  78  H  8  mRNA Fold Change  10  78  1  P-value = 0.01  ut  e  4  bu  PG  0.2  H  on  sh  4  R  0.4  ut  2.5  mRNA Fold Change  cl  lk  sh  78  78  0.6  H  1  4  bu  PG  ut  ut  0.8  78  e  sh  4  R  H  H  1.0  ut  on  lk  sh  78  78  P-value = 0.00008  H  cl  bu  PG  ut  ut  1.2  4  4  R  H  H  mRNA Fold Change 1.4  sh  sh  78  78  3.0  ut  ut  mRNA Fold Change 12  H  H  mRNA Fold Change  A AHI-1 (N-terminal)  1.2  P-value = 0.000009  1.0  0.8  0.6  0.4  0.2  0.0  B HCK  10 8 P-value = 0.02  6  4  2  0  REPS2  12  P-value = 0.06  8  6  4  2  0  46  4  cl  on  e  bu  1  lk  1.2  sh  4  PDCD6  sh  PG  0.0  R  0.2  78  0.4  mRNA Fold Change  0.6  ut  1  0.8  78  e  1.0  ut  on  P-value = 0.1  H  cl  lk  PG  78  bu  R  ut  1.2  H  4  4  78  sh  ut  sh  H  H  mRNA Fold Change  C ELAVL1  1.0  P-value = 0.0005  0.8  0.6  0.4  0.2  0.0  47  3.3 RNA and Protein Expression Correlations: HCK and BIN1 The next analytical step was to assess whether changes observed in mRNA actually translated to changes at the protein level. As protein is commonly the functional unit of a gene, those genes which demonstrated correlated changes in protein expression as a result of AHI-1 suppression may have a biological or functional connection to the oncogene. Protein expression was assessed by Western blotting and 8 genes were examined: IL1RN, CDKN1C, HCK, BIN1, REPS2, PALM2-AKAP2, PDCD6 and ELAVL1. Once again these genes were selected due to their differential expression, intriguing function and structure, convincing biological connections, and lastly a technical requirement for antibody availability. An additional motive for selection of IL1RN and PALM2-AKAP2 was that they were the most significantly up and downregulated genes, respectively, that resulted from AHI-1 suppression. Importantly, the first gene examined by Western blotting was AHI-1 to confirm that there was knockdown of the gene not only in mRNA but also protein. The murine Ahi-1 antibody used has a C-terminal epitope that cross reacts with human AHI-1 (41). Full length AHI-1 protein (150 kDa) is very weakly expressed in the Hut 78 cell lines and the 4 predominant bands detected were shorter forms at 120, 110, 70 and 50 kDa (Figure 11). A knockdown in AHI-1 expression was seen most clearly at 120 kDa, but also evident at 110 kDa. Conscious of the fact that correlations between mRNA and protein expression are not inherently strong, it was not completely surprising to next discover that only 2 of the 8 selected genes, HCK and BIN1, demonstrated differential protein expression (Table 4). There was no change detected in IL1RN, CDKN1C, REPS2, PALM2-AKAP2, PDCD6 or ELAVL1 expression (Appendix A.6).  48  B  AHI-1 120 kDa 110 kDa  1.2  0.4 0.2  bu  cl 4  R  H  ut  78  H  lk  0.0  ut  Actin  0.6  sh  50  P-value = 0.5  4  70  P-value = 0.02  0.8  sh  Anti-AHI-1  1.0  PG  120 110  1  sh4 clone 1  e  sh4 bulk  on  Hut 78 RPG  78  kDa  Hut 78  Fold Change  A  Figure 11: Western blot analysis of AHI-1 expression. A) Isoforms of AHI-1 were detected at 120, 110, 70 and 50 kDa in the Hut 78 and Hut 78/sh4 cell lines. AHI-1 knockdown was most evident at 120 kDa but also observed at 110 kDa. B) Quantification of the AHI-1 knockdowns at 120 kDa and 110 kDa relative to Actin. Mean fold change values are presented with standard error of the mean (n = 2).  Table 4: Summary of the correlations between mRNA and protein expression. Gene  Microarray (mRNA)  qRT-PCR (mRNA)  Western (Protein)  IL1RN CDKN1C HCK BIN1 REPS2 PALM2-AKAP2 PDCD6 ELAVL1  10.78 5.31 3.92 2.59 2.46 0.12 0.38 0.48  4.97 3.55 1.63 1.82 6.65 0.03 0.41 0.29  No change No change Up-regulated Up-regulated No change No change No change No change  49  Examination of the HCK expression change showed clear up-regulation in both Hut 78/sh4 bulk and Hut 78/sh4 clone 1 cells as compared to the control cells, which correlated well with the earlier mRNA expression data (Figure 12A and B). Two isoforms of HCK have been reported to be generated by alternative translation: p59HCK initiates translation at the ATG codon, while p61HCK starts 21 codons upstream (86). Distinct up-regulation of HCK was observed in a double band at ~65 kDa, indicating these two isoforms became up-regulated in AHI-1 suppressed cells. Additionally, both a positive control cell lysate (HL-60) and peptide competition experiments with the primary antibody confirmed the observed expression to be specific to HCK (Figure 12C and D). There was also a strong 85 kDa band evident in the both the Hut 78 cell line lysates and in the HL-60 lysates (Figure 12A). Although, this band has not been previously described for HCK peptide competition experiments confirmed this band was highly specific for the HCK antibody (Figure 12C). The other protein exhibiting differential expression was BIN1 which also showed up-regulation in both the Hut 78/sh4 bulk and Hut 78/sh4 clone 1 cells (Figure 13A and B). Again, this correlated well with the previous mRNA expression data and in the three isoforms observed (65, 60, and 55 kDa) up-regulation was most evident at 65 kDa but also seen at 60 kDa. Since it is well documented that BIN1 physically interacts with cMYC, this protein was also investigated although there were no significant changes in cMYC mRNA expression as it did not appear in the earlier microarray results (70, 87, 88). Noticeable down-regulation of c-MYC, however, was observed in the Hut 78/sh4 bulk and Hut 78/sh4 clone 1 cells and the connection to BIN1 suggests this could be of functional significance (Figure 13C and D).  50  A kDa  Hut 78 RPG  Hut 78  sh4 bulk  C  sh4 HL-60 clone 1 +ve  sh4 bulk  HL-60 +ve  AntiHCK  85  AntiHCK  85  Hut 78  65  65  Actin  Actin  HCK Peptide Competition  HCK 7 6 5 4 3 2 1 0  D  p61 P-value = 0.03  Hut 78  sh4 bulk  HL-60 +ve  AntiHCK  85  p59 P-value = 0.008  65  Actin  1  4  cl  on  e  bu sh  sh  78 ut H  4  PG R  ut H  lk  AHI-1 Peptide Competition  78  Fold Change  B  Figure 12: Up-regulation of HCK in Hut 78/sh4 cell lines. A) Up-regulation of 2 isoforms of HCK was evident in AHI-1 suppressed Hut 78/sh4 cell lines at 65 kDa. HL-60 cell lysate served as a positive control for the antibody. B) Quantification of the differential expression of HCK for both the p61HCK and p59HCK isoforms relative to Actin. Mean fold change values are presented with standard error of the mean (n = 2). C) Peptide competition experiment where the HCK primary antibody was incubated with 5-fold of its cognate epitope prior to use in the Western. Dissipation of the 65 kDa doublet bands indicated these results were specific for the HCK antibody. D) Additional peptide competition experiment where the HCK antibody was incubated with 5-fold of a random peptide (AHI-1) to show that the previous competition experiment was a direct result of saturation of the HCK antibody.  51  A kDa  Hut 78 RPG  Hut 78  sh4 bulk  C  sh4 clone 1  kDa  65 60  Hut 78 RPG  sh4 bulk  sh4 clone 1  Anti-c-MYC  65  AntiBIN1  55  Hut 78  Actin  Actin  B  D  BIN1 65 kDa  c-M YC  P-value = 0.03  4 2  1.0  P-value = 0.1  0.8 0.6 0.4 0.2  e  1  lk 4  cl  on  bu sh  sh  4  PG R 78  ut H  on cl 4  ut  e  1  lk bu sh  sh  R 78 ut  H  4  PG  78 ut H  78  0.0  0  H  6  1.2  Fold Change  Fold Change  8  Figure 13: Up-regulation of BIN1 in Hut 78/sh4 cell lines. A) Up-regulation of BIN1 was evident in AHI-1 suppressed Hut 78/sh4 cell lines. Of the three isoforms detected at 65, 60 and 55 kDa, the most significant up-regulation was observed in the 65 kDa isoform. B) Quantification of the differential expression of the 65 kDa isoform of BIN1 relative to Actin. Mean fold change values are presented with standard error of the mean (n = 2). C) Down-regulation of c-MYC was found in AHI-1 suppressed Hut 78/sh4 cell lines. D) Quantification of the differential expression of cMYC relative to Actin. Mean fold change values are presented with standard error of the mean (n = 2).  52  3.4 Isoform Characterization of BIN1 Detection of BIN1 up-regulation as a result of AHI-1 suppression in Hut 78/sh4 cells was an intriguing result since certain isoforms of this c-MYC interacting gene have been described to have tumor suppressor properties (70, 89, 90). Additionally, BIN1 attenuation is frequently described in several cancers, including pancreatic, liver, lung, breast and prostate cancer (71, 91-97). Furthermore, in melanoma cells and primary neuroblastoma cells aberrant splicing of BIN1 has been documented yielding isoforms unable to bind c-MYC and subsequently inhibit malignant transformation (88, 91, 95). For these reasons, characterization of the BIN1 isoforms expressed in Hut 78, Hut 78 RPG, sh4 bulk and sh4 clone 1 cells was performed to gain more information about how AHI-1 expression may mediate expression changes and possibly generate aberrantly spliced isoforms of BIN1. Several isoforms of BIN1 have been previously described and based upon these reports, the regions of the gene susceptible to differential splicing are exons 10, 12A-D, and 13 (Table 5, Figure 14) (90, 91, 98). Therefore, RT-PCR exon specific primers for these segments of BIN1 were designed to elucidate the presence or absence of these exons in the isoforms observed (Figure 15A, Appendix A.4).  53  Table 5: Several previously described BIN1 isoforms with tissue specificity. Tissue Specificity  Molecular Weight (kDa)  BIN1 (10-, 12A/D-, 13-) BIN1 (10-, 12A/D-, 13+) BIN1 (10+, 12A/D-, 13+)  Ubiquitous (90) Ubiquitous (90) Muscle (98)  ~ 45 ~ 48 ~ 50  BIN1 (10-, 12A/D+, 13-) BIN1 (10-, 12A/D+, 13+)  Neural (90)  ~ 62 ~ 65  Cancer (91, 95)  ~ 50 ~ 53  BIN1 Isoform  BIN1 (10-, 12A+, 13-) BIN1 (10-, 12A+, 13+)  BAR 1  2  3  4  U1 U3 U2  5  6  7  8  9  NTS  MBD  SH3  11 12A 12B 12C 12D 13 14 15 16  10  BAR = BIN1/Amphiphysin/RVS167-related U1 = unique 1  Muscle Specific  U3 = unique 3 U2 = unique 2  BAR  U1  U3  U2  9  10  11  MBD  SH3  NTS = neural tissue specific domain MBD = Myc-binding domain  1  2  3  4  5  6  7  8  13 14 15 16  SH3 = Src homology 3  Brain and Neural Specific  Ubiquitously Expressed BAR  1  2  3  4  5  6  7  U1  U2  MBD  9  11  13 14 15 16  8  SH3  BAR  1  2  3  4  5  6  7  8  U1  U2  NTS  9  11  12A 12B 12C 12D  MBD  SH3  13 14 15 16  Cancer BAR  1  2  3  4  5  6  7  U1  U2  9  11  8  BAR  SH3  14 15 16  1  2  3  4  5  6  7  8  U1  U2  9  11  MBD  12A  SH3  13 14 15 16  Figure 14: Schematic diagram of BIN1 protein domains and exon organization. Full length BIN1 consists of 19 exons and differential splicing has been observed for exons 10, 12A-D, and 13. Two ubiquitously expressed isoforms, a muscle specific isoform, a brain and neural specific isoform and cancer specific isoforms have been previously described.  54  Detection of exon 10 utilized two primer sets: Exon 9/10 with a forward primer in exon 9 and a reverse in exon 10, and Exon 9/11 which used the same exon 9 forward primer paired with a reverse in exon 11. The rationale was that successful formation of a product from Exon 9/10 primers would confirm the presence of exon 10 and a sole product using Exon 9/11 primers would further verify its inclusion. qRT-PCR was performed on the 4 Hut 78 cell lines which resulted in a single product from each primer set along with a single amplicon from Exon 9/11 (Figure 15B). Thus the inclusion of exon 10 was confirmed in all splice isoforms of BIN1. Further, qRT-PCR analysis using the BIN1 9/10 primer set which should thus detect all isoforms, confirmed notable upregulation of BIN1 in the AHI-1 suppressed cell lines relative to Hut 78 controls (Figure 15C). To identify the presence of exons 12A-D and 13 in the BIN1 transcript, primers were designed to span exons 11 – 14 of the gene. After RT-PCR optimization, 4 products were detected in the Hut 78 cell lines which were subsequently cloned and sequenced (Figure 15D). These 4 PCR products revealed alternative splicing of exons 12A and 13 and sequence analysis further confirmed there were no mutations in the any BIN1 splice isoforms (Figure 15E). Interestingly, characterization of BIN1 transcripts have not been previously described in hematopoietic cells and it was surprising to discover all splice isoforms contained the uncommon exon 10, previously described to be tightly regulated and specific to muscle tissue (90, 98). Additionally, identification of the presence of exon 12A in this CTCL cell line is also intriguing as this neuron specific exon has been previously described to be aberrantly spliced in melanoma and abolishes tumor suppressor activity by interfering with MYC binding (88, 95). It is important to note,  55  however, that although 4 distinct RNA isoforms were isolated and characterized, only 3 protein isoforms were observed in Western blotting. This could suggest that not all of the RNA isoforms are successfully translated, for example if certain isoforms are sequestered in the nucleus, or that specific post-translational modifications result in very close molecular weight of the resulting proteins which does not allow them to be resolved in the Western blotting gels that were performed.  56  Figure 15: BIN1 isoform characterization in Hut 78 cell lines. A) Schematic diagram of protein domains and exon organization of BIN1 and the specific primers sets generated to detect exons 10, 12 A-D, and 13: BIN1 9/10, BIN1 9/11, and BIN1 11/14. BIN1/Amphiphysin/RVS167-related (BAR), unique (U), neural tissue specific (NTS), MYC binding domain (MBD), Src homology 3 (SH3). B) Yield of a single product in each primer set confirmed the presence of exon 10 in all isoforms by qRT-PCR. Lane 1: Hut 78, Lane 2: Hut 78 RPG, Lane 3: Hut 78/sh4 bulk, and Lane 4: Hut 78/sh4 clone 1. C) BIN1 up-regulation observed in AHI-1 suppressed cells as compared to the control Hut 78 cells using BIN1 9/10 primers in qRT-PCR. D) 4 distinct RT-PCR products were identified and cloned in Hut 78 cell lines using BIN1 11/14 primers. Lane 1: Hut 78, Lane 2: Hut 78 RPG, Lane 3: Hut 78/sh4 bulk, Lane 4: Hut 78/sh4 clone 1, Lane 5: negative control (no RNA), Lane 6: positive control (Hut 78 RPG with GAPDH primer set). E) Sequencing results of cloned RT-PCR products of BIN1 isoforms shown in D. F) Schematic diagram of the structure of the 4 BIN1 isoforms characterized in the Hut 78 cell lines.  57  A  8  9  10  11  12A 12B 12C 12D  C  Amplicons Exon 9/11  1 2 3 4 1 2 3 4 150 bp 100 bp  1.0 0.5 0.0  4  5  PG R 78  ut  3  6  H  2  H  Exon 11/14 Amplicons 1  500 bp 400 bp  16  1.5  ut  D  15  BIN1 9/10  78  50 bp  14  2.0  mRNA Fold Change  Exon 9/10  13  1  7  e  6  SH3  on  5  MBD  4  4  NTS  cl  B  3  U2  4  2  U3  sh  1  U1  sh  BAR  E BIN1 (11+, 12A+, 13+, 14+) = 423 bp  300 bp  BIN1 (11+, 12A+, 13-, 14+) = 333 bp  200 bp  BIN1 (11+, 12-, 13+, 14+) = 294 bp 100 bp  BIN1 (11+, 12-, 13-, 14+) = 204 bp  F 1  2  3  4  5  6  7  8  9  10  11  12A  13 14 15 16  1  2  3  4  5  6  7  8  9  10  11  12A  14 15 16 Size  1  2  3  4  5  6  7  8  9  10  11  13 14 15 16  1  2  3  4  5  6  7  8  9  10  11  14 15 16  58  3.5 Correlations in Primary Sezary Syndrome Samples Although cell lines are often excellent biological models and experimental tools to study disease, it is imperative that the findings carry clinical significance for them to be useful. For this reason, several of the genes demonstrating differential expression as a result of AHI-1 suppression in the Hut 78 cell lines were screened in primary samples obtained from Dr. Youwen Zhou in the UBC and VGH Department of Dermatology and Skin Science. Due to the limited amount of these primary samples, only qRT-PCR analysis could be performed to evaluate the expression of AHI-1, NKG7, HCK, SPIB, BIN1, BRDG1, REPS2, PALM2-AKAP2 and ELAVL1 in 6 CD4+ CD7- SS patient samples compared to 5 CD4+ normal controls. Genes that were found to be down-regulated in AHI-1 suppressed Hut 78/sh4 cell lines were expected to be up-regulated in SS patients compared to controls, and vice versa. Thus, all the genes, with the exception of ELAVL1 (data not shown) demonstrated up or down-regulation which corresponded to the differential expression observed in the Hut 78 cell lines (Figure 16). Discovery of these expression correlations between primary samples and AHI-1 suppressed cell lines suggests that these genes originally identified as a result of AHI-1 suppression could actually have importance in the pathogenesis of SS in patients.  59  Figure 16: Validation of expression in primary RNA samples. The expression of several genes identified in the microarray comparing AHI-1 suppressed Hut 78/sh4 cells to Hut 78 control cells were evaluated by qRT-PCR in both CD4+CD7primary leukemic cells from 6 SS patients (S-1, S-4, S-7, S-9, S-10, and S-11) and CD4+ normal controls (C-1, C-2, C-3, C-4, and C-5). Mean mRNA fold change values are presented with the standard error of the mean (n = 2). A) Expression of AHI-1 was shown to be up-regulated in primary SS samples compared to normal controls. B) Validation of down-regulation of NKG7, HCK, SPIB, BIN1, BRDG1 and REPS2 in primary SS samples compared to normal controls. C) Validation of up-regulation of PALM2-AKAP2 in primary SS samples compared to normal controls. The p-values presented were the result of two-sample t-tests comparing the SS patient expression data to that of normal controls.  60  P-value = 0.004  10  8  6  4  2  0  5  S9 S10 S11 C -1 C -2 C -3 C -4 C -5  12  7  SPIB 4 7  S9 S10 S11 C -1 C -2 C -3 C -4 C -5  S-  0  S-  16 1  5  S-  10 25  4  15  S-  20  1  35  S-  25  mRNA Fold Change  P-value = 0.04  mRNA Fold Change  4  1  7 S9 S10 S11 C -1 C -2 C -3 C -4 C -5  S-  S-  S-  mRNA Fold Change  4  1  7 S9 S10 S11 C -1 C -2 C -3 C -4 C -5  S-  S-  S-  mRNA Fold Change 10  S-  S9 S10 S11 C -1 C -2 C -3 C -4 C -5  7  14  4  1  mRNA Fold Change 30  S-  S-  S-  A AHI-1  12 P-value = 0.1  8  6  4  2  0  B NKG7 HCK  30 P-value = 0.01  20  15  10  5  0  BIN1  6  P-value = 0.03  4  3  2  1  0  61  S9 S10 S11 C -1 C -2 C -3 C -4 C -5  7  4  1  mRNA Fold Change  4  1  6 7 S9 S10 S11 C -1 C -2 C -3 C -4 C -5  0  4  4  S-  8  1  12  mRNA Fold Change  P-value = 0.001  S-  mRNA Fold Change 20  S-  7 S9 S10 S11 C -1 C -2 C -3 C -4 C -5  S-  S-  S-  16  S-  S-  S-  BRDG1 REPS2  10 8 P-value = 0.001  6  4  2  0  C PALM2-AKAP2  7  P-value = 0.04  5  4  3  2  1  0  62  CHAPTER 4: DISCUSSION Microarray analysis identified several differentially expressed genes in AHI-1 suppressed Hut 78/sh4 cells compared to control Hut 78 cells. Once these results were verified and validated by qRT-PCR, several of these differentially expressed genes were further investigated at the protein level with Western blotting. Of the 8 proteins examined only 2, HCK and BIN1, demonstrated differential expression which correlated with the observed mRNA changes. Although these results may seem surprising, they are in fact quite typical and several studies report similar expression discrepancies. Investigations in yeast revealed correlations between protein and mRNA abundance were insufficient for prediction of protein expression from quantitative mRNA data; the projected correlation for all yeast proteins was expected to be less than 0.4 (99). More specifically, this study identified several genes with similar mRNA expression which had corresponding protein expressions that varied more than 20-fold, and conversely other genes with relatively equal protein expression had variations in mRNA expression as great as 30-fold (99). mRNA and protein expression discordance has also been described in primary lung adenocarcinomas which reported Spearman correlation coefficients ranging from -0.467 to 0.442 for the 98 genes assessed with DNA microarrays and quantitative proteomic techniques (100). Moreover, another study in mammalian cells concluded differential mRNA expression only explains at most 40% of the differential protein expression (101). This comprehensive study investigated not only steady-state conditions in mammalian cell lines, but also dynamic conditions in cells from mice treated with multiple drug perturbations over time (101). Together these results  63  demonstrate and argue that although many genes are under transcriptional control, several are more tightly regulated by translational control. There are two modes of transcriptional control: global control which regulates translation of the majority of the mRNA in the cell, and mRNA-specific control where a defined group of mRNAs are modulated (102). Modification of translation initiation factors is the predominant mechanism for global control while mRNA-specific control is thought to result from regulatory protein complexes that recognize particular elements in the 5’ and/or 3’ untranslated regions (UTRs) of their targets (102). Thus specific structural and regulatory sequences within mRNA transcripts can determine their translational fate. These influential elements include: 5’ cap structure and 3’ poly-A tail which promote translation initiation, internal ribosome-entry sequences (IRESs) which allow for cap-independent translation, and upstream open reading frames (uORFs) which can act as negative regulators by reducing translation from the main ORF (102). Additionally, secondary and tertiary mRNA structures may also have translational influence such as hairpins and pseudoknots which may act as translational blocks (102). In fact, most of the regulatory mechanisms that have been discovered and described are inhibitory in nature, for example steric blockage, interference with the eukaryotic initiation factor complexes, and targeted degradation by micro RNAs (miRNAs) (102). Aside from translation control, protein expression can also be affected by stability and post-translational modifications. Therefore, discordant RNA and protein expression could be an artifact if the protein has a very short half life due to poor stability or conversely a long half-life as a result of more robust stability. Protein stability is intrinsic since it is based upon the 3-dimensional structure of the protein, however  64  protein-protein interactions and post-translational modifications are able to modulate this intrinsic stability (103, 104). Some examples of post-translational modifications known to affect stability are acetylation, glycosylation, hydroxylation, disulfide bond formation, sumoylation and ubiquitination (104, 105). Additionally, acylation, sulfonation, and deamidation are also able to regulate protein-protein interactions which could subsequently affect stability (104). Other modifications are responsible for subcellular localization, protein and transcriptional activities, and further post-translational modifications (104). Thus there are many factors which can influence the observed expression of a particular protein. Although there is value in evaluating gene expression profiles of cells and tissues, particularly when such cells and tissues are limited, poor correlations between mRNA and protein expression highlight the importance of integrating genomic (DNA and RNA) and proteomic (proteins and peptides) profiling (106). Attempting to interpret mRNA changes without additional information is very challenging; for example mRNA levels for a particular gene can increase as a result of low levels of the corresponding protein through a feedback circuit or conversely, the increased mRNA expression may translate to increased protein expression (106). However, to successfully couple genomic and proteomic profiling or analyses, caveats and limitations must be identified for each technique (106). In this study, the Affymetrix GeneChip® Human Genome U133 Plus 2.0 Array was used to determine the differential gene expression in the AHI-1 suppressed Hut 78/sh4 cells and control Hut 78 cells with 54,330 probe sets, representing over 47,000 transcripts. Like most arrays, this array usually offers a single reporter for each gene.  65  Potential sources of error that arise are that certain probes could hybridize to multiple targets or fail to detect certain isoforms considering that 50-70% of all human genes are predicted to undergo alternative splicing (107, 108). Additionally, since there is a degree of subjectivity in the data filtration, the fold change and p-value criteria could be either too stringent or slack for detection of the most important differentially expressed genes. There is also value in critiquing the experimental design for this project where differentially expressed genes were identified by comparing Hut 78 control cells to Hut 78 cells with stable AHI-1 suppression. It is important to consider that stable suppression may cause cells to undergo adaptation which may skew the gene expression profile as some changes may be indirect and not closely related to AHI-1. Therefore, it would be interesting to investigate gene expression changes in transiently AHI-1 suppressed Hut 78 cells as well. Common genes in both of these expression analyses could be more closely connected to AHI-1. Additionally, Western blotting was used for assessment of the corresponding protein levels for several select genes. As a laborious technique there are several potential sources of error to consider: lysate volume restrictions provided challenges in detection of less abundant proteins, both primary and secondary antibodies had variable affinities and specificities, and use of x-ray film and chemiluminesence had imaging limitations. Since film has a small dynamic range it can quickly become saturated in an exposure; more sensitive imaging with chemiluminesence can be achieved with the use of a charge coupled device (CCD) camera which has higher sensitivity. Moreover, although chemiluminesence is a popular imaging method there are superior techniques available, such as chemifluorescence, which allow for stronger protein quantifications.  66  Interestingly, two proteins examined in this study demonstrated differential expression which also correlated with their observed mRNA expression changes. HCK (hemapoietic cell kinase) was found to be up-regulated in AHI-1 suppressed Hut 78/sh4 cells as compared to control cells. As a member of the Src family tyrosine kinases, HCK has a kinase or SH1 domain, an SH2 domain, and an SH3 domain (109). Alternative translation of the transcript generates two isoforms, p59HCK and p61HCK, both found to be up-regulated in AHI-1 suppressed Hut 78/sh4 cells (86). Expression of HCK has been reported to be restricted to hematopoietic cells with predominant expression in myeloid lineage cells, particularly mature granulocytes and monocytes, and B lymphocytes (64, 110). Increased expression has been observed with differentiation of myeloid cells suggesting this gene may have a signaling role in more mature cells (64, 111). HCK also appears to be involved in monocyte activation since stimulation of these cells with IL-2 resulted in significant up-regulation of HCK mRNA, protein and p59HCK tyrosine phosphorylation (112). Commonly, constitutive activation of Src family kinases can contribute to oncogenic transformation of a cell (109). This oncogenic potential of HCK has been demonstrated in the Philadelphia (Ph) chromosome positive leukemias chronic myeloid leukemia (CML) and B-cell acute lymphoblastic leukemia (B-ALL) which are characterized by the presence of the BCR-ABL oncogene (113). In myeloid cells BCRABL has been shown to bind and activate both Hck and Lyn, a fellow Src family tyrosine kinase. (114, 115). Further, overexpression and/or activation of both HCK and LYN have been linked to disease progression in CML patients (116). Interestingly, a recent study from our lab has demonstrated that AHI-1 also physically interacts with BCR-ABL  67  and modulates BCR-ABL transforming activity and imatinib response of CML progenitor cells (Zhou et al, revised manuscript has been resubmitted to J Exp Med). It is possible that HCK may also be associated with the AHI-1-BCR-ABL interaction complex involved in leukemic transformation of CML cells to either stabilize the interaction or transduce signals down the cascade. Contrary to the oncogenic suggestions in Ph+ leukemias, there is also evidence of potential tumor suppressor properties for HCK in Ph- leukemias and other cancers (117). Recently, aberrant methylation of the CpG island in the HCK promoter was detected in 13/23 hematopoietic and 8/10 non-hematopoietic cancer cell lines but not in normal controls (117). Additionally, HCK methylation was observed in 9/44 Ph- ALL patients, and in only 1/16 Ph+ ALL patients (117). Furthermore those Ph- ALL patients with HCK methylation were linked to poorer prognosis (117). There are also two independent reports that Hck can trigger caspase-mediated apoptosis which adds to the tumor suppressor proposal (63, 64). Since up-regulation of HCK has been observed in the AHI1 suppressed Hut 78/sh4 cells, and protein kinases are responsible for transmitting signals within a cell, HCK could be a critical player in a signaling cascade in Hut 78 cells which leads to activation of caspase-mediated apoptosis. AHI-1 may directly affect HCK expression by inhibiting genes involved in its specific transcription and translational control, or instead may modulate methylation of the HCK promoter. Alternatively, upregulation of HCK could be a cellular response to the oncogenic activities of AHI-1 in an effort to stimulate apoptosis in these malignant cells. The other protein which exhibited differential expression was BIN1 (bridging integrator 1) which was also up-regulated in AHI-1 suppressed Hut 78/sh4 cells. The full  68  length BIN1 protein contains an N-terminal BIN1/Amphiphysin/RVS167-related (BAR) domain important in nuclear localization, an internal neural tissue specific domain (NTS) thought to have endocytic functions in neural isoforms, a MYC-binding domain (MBD), and a C-terminal SH3 domain (Figure 14) (87, 88, 90). As a nucleocytosolic adapter protein, BIN1 is a tumor suppressor able to physically interact with and inhibit the transforming activity of c-MYC (70, 87). The physical interaction is generally thought to occur through the MBD domain of BIN1, although a recent study identified the SH3 domain as the site of interaction (70, 88). BIN1 has been shown to inhibit cell proliferation and activate cell death processes through multiple mechanisms that are both c-MYC-dependent and c-MYC-independent (87, 118, 119). Elucidation of the specific function of BIN1 is challenging as there are more than 10 splice isoforms that exist, each with unique roles, specific cellular localization and tissue specificity (90, 94). Neural tissue specific isoforms have been shown to have a role in endocytosis while muscle specific isoforms are thought to be involved in differentiation (98, 120). Those isoforms which localize to the nucleus exhibit tumor suppressor properties and loss of heterozygosity and attenuated BIN1 expression have been described in breast cancer, prostate cancer and malignant melanoma (95-97, 118). Moreover, aberrant splicing of the NTS exon 12A in BIN1 transcripts has been described in both primary neuroblastoma cells and melanoma cell lines (91, 95). Inclusion of this 12A exon has recently been shown to abolish the ability of BIN1 to bind c-MYC as an intramolecular interaction occurs where exon 12A binds to its C-terminal SH3 domain (88).  69  For these reasons, the observed up-regulation of BIN1 in AHI-1 suppressed Hut 78 cells was quite intriguing. This was the first study to characterize BIN1 expression in hematopoietic cells and described 4 splice isoforms in Hut 78 cells. Interestingly, all BIN1 splice isoforms contained the uncommon exon 10, previously described to be tightly regulated and specific to muscle tissue, and exhibited alternative splicing of exons 12A and 13. The unusual presence of exon 10 in all isoforms may indicate a general function in hematopoietic cells or more specifically in T-cells, or alternatively a role in leukemic cells. Importantly, 2 of the 4 BIN1 isoforms characterized confirmed the presence of exon 12A that is specifically associated with BIN1-MYC interaction. Further work to elucidate the expression patterns and localization of these specific isoforms in the Hut 78 cells with and without AHI-1 suppression needs to be performed. Additionally, as BIN1 expression has not be previously examined in hematopoietic cells, the specific functions of these isoforms and the general role of the gene need to be elucidated. It was also intriguing to note that although c-MYC did not demonstrate any significant differential mRNA expression in the microarray analysis, down-regulation of the c-MYC protein was observed in AHI-1 suppressed Hut 78/sh4 cells. Since up-regulation of BIN1 is observed in AHI-1 suppressed Hut 78/sh4 cells, AHI-1 may directly or indirectly inhibit expression of BIN1 to enhance its oncogenic transformation in CTCL cells. This could occur by inhibiting transcription of this tumor suppressor or manipulating genes involved in both its mRNA and/or protein stability. Additionally or alternatively, AHI-1 may mediate the splicing events of BIN1 to yield perturbed expression of isoforms which incorrectly express exon 12A which would abolish c-MYC binding and tumor suppressor properties. It has recently been reported  70  that splicing regulation could occur by manipulation of the SF2/ASF splicing factor to increase inclusion exon 12A in BIN1 transcripts (121). Further investigation is needed to determine if either BIN1 or AHI-1 expression could influence the levels of c-MYC in Hut 78 cells. Conversely, BIN1 expression could be a response by the cell to combat the oncogenic activities of AHI-1 and modulations in expression and specific splicing may occur to inhibit the proliferation of these oncogenic cells. However, since this gene has not been studied in hematopoietic cells and current literature describes several isoforms each with unique functional roles and localization, it is difficult to speculate on the specific functions of the BIN1 isoforms in T-cells. Finally, several differentially expressed genes identified in AHI-1 suppressed Hut 78 cells were evaluated in CD4+CD7- primary SS samples compared to CD4+ normal controls. Expression patterns correlated with the microarray observations as genes that exhibited down-regulation in AHI-1 suppressed Hut 78/sh4 cell lines showed upregulation in SS patients compared to controls, and vice versa. These findings are significant since they infer that these genes with a connection to the AHI-1 oncogene could also be involved in the pathogenesis of SS. Further investigations could reveal the specific roles of these genes in T-cells and the effects of their perturbed expression in the both the AHI-1 suppressed Hut 78/sh4 cells and primary SS cells. The hypothesis for this project was that AHI-1 modulated the regulation of human malignant T-cell proliferation, differentiation, and apoptosis by mediating the activity of specific molecular partners critical for control of these cellular programs. The identification of both HCK and BIN1 to be differentially expressed in the AHI-1 suppressed Hut 78/sh4 cells supports this idea since both of these proteins have roles in  71  apoptosis and inhibition of proliferation. Whether or not these genes are in fact downstream targets of AHI-1 or simply a cellular response to combat this oncogene remains unclear. These findings, however, have scratched the surface in the attempt to clearly define how the AHI-1 oncogene operates, and these results suggest AHI-1 has an effect on apoptotic and proliferation inhibition mechanisms within the cell.  72  CHAPTER 5: CONCLUSION In conclusion, in an attempt to elucidate genes involved in AHI-1-mediated leukemic transformation in human CTCL cells, a microarray was used to compare AHI-1 suppressed Hut 78/sh4 cells to Hut 78 controls. 21 differentially expressed genes were selected on the basis of interesting structural and functional information and their expression patterns were successfully validated. The differential expression of 6 genes (CDKN1C, HCK, BIN1, REPS2, PDCD6, and ELAVL1) was further validated with biological replicates and these genes, along with IL1RN and PALM2-AKAP2 were investigated at the protein level. Poor correlations between mRNA and protein were observed as only 2/8 genes demonstrated differential protein expression. Both HCK and BIN1 exhibited up-regulation in AHI-1 suppressed Hut 78/sh4 cells in mRNA and protein expression. Subsequent characterization of 4 BIN1 isoforms revealed all transcripts contained exon 10 and exhibited differential splicing of exons 12A and 13. Lastly, evaluation of NKG7, HCK, SPIB, BIN1, BRDG1, REPS2, and PALM2-AKAP2 expression in primary CD4+ CD7- SS patient samples compared to CD4+ normal controls revealed correlating expression changes with the earlier microarray results. Encouragingly, these results indicated that the differentially expressed genes identified through AHI-1 suppression may have significance in SS and thus could help elucidate the pathogenesis of this disease.  73  REFERENCES 1.  Jiang X, Zhao Y, Chan WY, Vercauteren S, Pang E, Kennedy S, et al. Deregulated expression in ph+ human leukemias of AHI-1, a gene activated by insertional mutagenesis in mouse models of leukemia. Blood. 2004 May 15;103(10):3897-904.  2.  Alam R, Gorska M. 3. lymphocytes. 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Gene AHI-1 (C-terminal) AHI-1 (N-terminal) IL1RN CDKN1C LMCD1 LAPTM5 MLLT11 NKG7 GRHL1 HCK SPIB CCNG2 IL4I1 BIN1 BRDG1 REPS2 BCAS4 PALM2-AKAP2 SV2B CMTM7 PDCD6 OXCT1 ELAVL1  Primer Sequence Fwd 5’- CTG TCA CAG AGG TGA TAC GTT C -3’ Rv 5’- GAC TGT TGT GAG GAA ACT GCT G -3’ Fwd 5’- GCC GAG ATA GCC CGG TTT ATC -3’ Rv 5’- TCA GTT CGG TGA ATG TAA ACT CC -3’ Fwd 5’- GGC CCT CCC CAT GGC TTT AG -3’ Rv 5’- CTT TGA GTC AGC ATT GTC TTC ACC -3’ Fwd 5’- CTC TCC TTT CCC CTT CTT CTC G -3’ Rv 5’- GCT CCA TCG TGG ATG TGC TG -3’ Fwd 5’- GCC GCC ATT ACT GCG AGA GTC -3’ Rv 5’- CCA GGC CAG ATC TTC CAC ACG -3’ Fwd 5’- GTG CTT CTG GAG CAT CTT GGA G -3’ Rv 5’- CGT GTG GCG GTG CTA CAG ATT G -3’ Fwd 5’- CCA TCT TTG GAA CAC GCC AGT AA -3’ Rv 5’- CAG CAG ATC CAG TTC TGG GAT GG -3’ Fwd 5’- GGG CAT GGG GAC ATC ATA TC -3’ Rv 5’- GGA CAG GAC CAG GAA GCT CAC G -3’ Fwd 5’- GCC GTA CGG CAC AGA AGA TGA C -3’ Rv 5’- CCT TTC GAA CGT AGA GCA GCA C -3’ Fwd 5’- CGG ATC CCA CAT CCA CCA TCA -3’ Rv 5’- ACC ACG ATG ATG TCC TCA GAG C -3’ Fwd 5’- GCA TAC CCC ACG GAG AAC TTC G -3’ Rv 5’- GGC TGT CCA ACG GTA AGT CTT CC -3’ Fwd 5’- GGG GGG TTG TTT TGA TGA AAG TG -3’ Rv 5’- GAT CAC TGG GAG GAG AGC TGC T -3’ Fwd 5’- CAG AAC ACG GGC TGG ATT GG -3’ Rv 5’- GGC AGA GCT TGT GGA GGA TCC -3’ Fwd 5’- GCA ACG TGC AGA AGA AGC TCA C -3’ Rv 5’- GCT CAA ACT GCT CAT CCT TGG TC -3’ Fwd 5’- GCC GAA AGA GGA AGT ACA ACT GAA G -3’ Rv 5’- ACT GTA AGA ATG AAG CCT CTC CAT TC -3’ Fwd 5’- GGC CAC AGA AAA CCC ATT CCA G -3’ Rv 5’- GGT GGG GGT AAC AAT CCT GAC T -3’ Fwd 5’- CCT CAG GCT GGA AGA GTT TTG C -3’ Rv 5’- GGC CTT AAG GAC TGG GAT GTT TTC -3’ Fwd 5’- CCC TCC AGA ACA TGC TAC AAA AC -3’ Rv 5’- CCT CTT CAG GGG CTA AGT CAG G -3’ Fwd 5’- CTT CGT GCA GGG ATA TGG AGC C -3’ Rv 5’- CGG TAG AGC ACC CCC AAT ACC -3’ Fwd 5’- GTA TCA GCT GGC CCC TGT CG -3’ Rv 5’- CTC TTG GAA GCT GCC ACA ATG G -3’ Fwd 5’- GGC CCT TGG CTG CCT ACT CC -3’ Rv 5’- CGT TCC ACA GGA AGC TCT GGT C -3’ Fwd 5’- GTG GAG CTG ACA CCA CAG GGC -3’ Rv 5’- CCC TGT TGG GGT GTA AAA TGC -3’ Fwd 5’- CCA AAC CAA CGG AAA AGG CTT C -3’ Rv 5’- ACG GAG ACT TTG ACA GGA CTG GAT -3’  82  A.2 Efficiency Analysis of Quantitative RT-PCR Primers Efficiency analysis was performed on all primer sets prior to their use in qRTPCR validation of microarray results. Serial dilutions of cDNA (1x, 1/10x, 1/100x, and 1/1000x) were prepared and expression of each primer set was assessed with qRT-PCR. The number of transcripts in each cDNA dilution differed from the next by 10-fold and the number of transcripts would have doubled in each cycle of qRT-PCR. This information was used to calculate the cycle threshold (Ct) difference between sequential  Fluorescence  cDNA dilutions to be 3.32 (Figure A.1).  x  cDNA Dilutions: 1x 1/10x 1/100x 1/1000x  1 cycle → 2-fold ↑ transcripts x cycles → 10-fold ↑ transcripts 2x = 10 x = 3.32  Cycle Number  Figure A.1: Determination of the cycle threshold difference for cDNA dilutions. The difference between the average cycle threshold values for serial cDNA dilutions (1x, 1/10x, 1/100x, 1/1000x) was calculated to be 3.32 cycles. This was calculated by determining that each in each cycle of qRT-PCR the number of transcripts increases by two-fold and there is a 10-fold difference in the number of transcripts between each cDNA dilution (2^3.32 = 10).  83  Once qRT-PCR has been performed on each primer set using the cDNA dilutions, the resulting cycle threshold values were plotted against log10 values of the cDNA concentrations. The slope of this graph was ∆ Ct / ∆ log10[cDNA] and therefore, if a primer was working with 100% efficiency the absolute value of the slope would be 3.32. Primer efficiency was subsequently calculated by dividing the absolute value of the slope by 3.32 and multiplying by 100. For example, the LMCD1 primer set was determined to have an efficiency of 93% (Figure A.2).  LMCD1 40  Average Cycle Threshold (Ct)  35  Efficiency = |slope| x 100 3.32 = 3.08 x 100 3.32 = 93%  30 25  y = -3.0809x + 24.952 R2 = 0.999  20 15 10 5 0  -3.5  -3  -2.5  -2  -1.5  -1  -0.5  0  Log[cDNA]  Figure A.2: Calculation of the efficiency of the LMCD1 primer set. Primer efficiency was calculated with linear regression by plotting the average cycle threshold (Ct) value against the logarithm in base 10 of the cDNA concentration. The absolute value of the resulting slope was divided by 3.32 and multiplied by 100 to determine the percent efficiency. For the LMCD1 primer set the efficiency was calculated to be 93%.  84  A.3 Primary and Secondary Antibody Conditions Table A.2: Primary and secondary antibody conditions for Western blotting. Antibody  Supplier  Primary Conditions  Secondary Conditions  Comments Murine C-terminal antibody - cross reacts with human isoforms I and III  Ahi-1 (polyclonal)  In house  1:1,000  1:5,000 anti-rabbit  IL1RN (polyclonal)  Applied Biological Materials (Richmond, BC) Cat.#:Y054942  1:1,500  1:10,000 anti-rabbit  CDKN1C (p57) (polyclonal)  Santa Cruz (Santa Cruz, CA) Cat.#: sc-8298  1:200 in 5% skim milk  1:10,000 anti-rabbit in 5% skim milk  HCK (polyclonal)  Santa Cruz (Santa Cruz, CA) Cat.#: sc-72  1:1,000  1:10,000 anti-rabbit in 5% skim milk  Load 40 µg of protein lysate  BIN1 (monoclonal)  Abnova (Taipei city, TW) Cat.#: H00000274-M01  1:1,500 in 5% skim milk  1:10,000 anti-mouse in 5% skim milk  Use PBST instead of TBST for washes and dilutions  c-MYC (monoclonal)  Santa Cruz (Santa Cruz, CA) Cat.#: sc-40  1:250  1:5,000 anti-mouse in 5% skim milk  REPS2 (polyclonal)  Santa Cruz (Santa Cruz, CA) Cat.#: sc-50177  1:100 in 5% skim milk  1:5,000 anti-goat in 5% skim milk  PALM2 (monoclonal)  Abcam (Cambridge, MA) Cat.#: ab57710  1:1,000  1:10,000 anti-mouse  PDCD6 (ALG-2) (polyclonal)  Santa Cruz (Santa Cruz, CA) Cat.#: sc-11255  1:100 in 5% skim milk  1:5,000 anti-goat  ELAVL1 (HuR) (monoclonal)  Santa Cruz (Santa Cruz, CA) Cat.#: sc-5261  1:2,000  1:10,000 anti-mouse In 5% skim milk  ACTIN AC40 (monoclonal)  Sigma-Aldrich (Oakville, ON) Cat.#: A4700  1:500  1:4,000 anti-mouse in 5% skim milk  Load 40 µg of protein lysate  Transfer for only 45 min - small molecular weight  Incubate primary for only 1 hr at room temperature  85  A.4 BIN1 Exon Specific Primers for RT-PCR Table A.3: Sequences of the BIN1 exon specific primers. BIN1 Region  Exon 10  Exon 11-14  Exon Specific Primer Set  Primer Sequence  BIN1 Exon 9  Fwd  5’- GAA GCA ACA CGG GAG CAA CAC C -3’  BIN1 Exon 10  Rv  5’- CTG TTC TTC TTT CTG CGC AGC C -3’  BIN1 Exon 9  Fwd  5’- GAA GCA ACA CGG GAG CAA CAC C -3’  BIN1 Exon 11  Rv  5’- GAG CCA TCT GGA GGC GAA GG -3’  BIN1 Exon 11  Fwd  5’- CCT CCA GAT GGC TCC CCT GC -3’  BIN1 Exon 14  Rv  5’- CCT GGG GGC AGG TCC AAG CG -3’  86  A.5 Quantitative RT-PCR Validation Figure A.3: Quantitative RT-PCR validation of 21 differentially expressed genes. Quantitative RT-PCR conformation was performed on the 21 differentially expressed genes using 4 technical replicates of the same RNA samples that were sent for microarray analysis. Mean mRNA fold change values are presented with the standard error of the mean (n = 4). A) Validation of AHI-1 suppression using both C-terminal and N-terminal primer sets. B) Validation of differential expression in the 15 up-regulated genes in AHI1 suppressed Hut 78 cells compared to Hut 78 control cells. C) Validation of the 5 downregulated genes in AHI-1 suppressed Hut 78 cells as compared to control Hut 78 cells. The p-values for each graph are listed in Table 3.  87  cl  on  e  1  0.0  4  1.0  sh  2.0  lk  4.0  bu  LMCD1 sh 4  e  bu on  4  cl  sh  1  lk  0  4  2  PG  4  R  16  78  IL1RN  sh  ut  6  4  cl  1  e  1  lk  PG  78  e  bu  R  ut  on  4  78 sh  ut  sh  H  H  on  mRNA Fold Change  AHI-1 (C-terminal)  PG  H  8  78  10  ut  12  mRNA Fold Change  14  H  1  0.0  R  3.0  mRNA Fold Change  e  cl  0.2  78  1  on  lk  PG  78  bu  R  ut  0.4  78  e  cl  4  4  78  sh  ut  sh  H  H  0.6  ut  on  lk  PG  bu  R  78  0.8  H  cl  4  4  78  sh  ut  sh  H  ut  1.0  ut  4  lk  PG  bu  R  78  H  mRNA Fold Change 1.2  H  sh  4  78  sh  ut  ut  mRNA Fold Change  B  H  H  mRNA Fold Change  A AHI-1 (N-terminal)  1.2  1.0  0.8  0.6  0.4  0.2  0.0  CDKN1C  8.0  6.0  4.0  2.0  0.0  LAPTM5  5.0  4.0  3.0  2.0  1.0  0.0  88  cl  on  e  1  0.0  4  0.5  sh  1.0  lk  1.5  bu  SPIB  4  2.0  sh  4  e  1  lk  PG  bu  R  on  4  cl  sh  78  GRHL1  sh  3.0  PG  ut  0.0  R  H  1.0  78  2.0  mRNA Fold Change  3.0  ut  1  4.0  H  e  4  cl  1  e  1  lk  PG  78  e  bu  R  on  4  78 sh  ut  sh  H  ut  on  H  cl  0.0  78  2.5  mRNA Fold Change  on  lk  PG  78  bu  R  ut  mRNA Fold Change  1.0  ut  1  cl  4  4  78  sh  ut  sh  H  H  2.0  78  e  lk  PG  78  bu  R  ut  3.0  ut  on  4  4  78  sh  ut  sh  H  H  mRNA Fold Change 4.0  H  cl  lk  PG  78  bu  R  ut  mRNA Fold Change 5.0  H  4  4  78  sh  ut  sh  H  H  mRNA Fold Change  MLLT11 NKG7  6.0  5.0  4.0  3.0  2.0  1.0  0.0  HCK  3.0  2.5  2.0  1.5  1.0  0.5  0.0  CCNG2  2.5  2.0  1.5  1.0  0.5  0.0  89  4  cl  e  1  lk  PG  78  bu  R  ut  on  4  78  sh  ut  sh  H  H  mRNA Fold Change  4  cl  on  e  1  0.0  lk  0.5  bu  BRDG1  4  1.0  PG  1.5  R  2.5  78  2.0  mRNA Fold Change  4  cl  1  e  1  lk  PG  78  e  bu  R  ut  on  4  78 sh  ut  sh  H  H  on  0.0  sh  ut  1  cl  mRNA Fold Change  1.0  78  e  4  lk  PG  78  bu  R  ut  2.0  ut  on  4  78  sh  ut  sh  H  H  3.0  H  cl  lk  PG  78  bu  R  ut  mRNA Fold Change 4.0  sh  H  4  4  78  sh  ut  sh  H  H  mRNA Fold Change  IL4I1 BIN1  4.0  3.0  2.0  1.0  0.0  REPS2  14  12  10  8  6  4  2  0  BCAS4  3.0  2.5  2.0  1.5  1.0  0.5  0.0  90  cl  on  e  1  0.0  4  0.2  lk  0.4  sh  OXCT 1  bu  0.6  sh 4  e  bu  on  4  cl  sh  1  lk  PG  CMT M7  4  0.8  4  cl  1  e  1  lk  PG  78  e  bu  R  on  4  78 sh  ut  sh  H  ut  on  mRNA Fold Change  PALM2-AKAP2  sh  1.2  R  0.0  78  1.2  PG  ut  0.2  H  cl  0.0  R  H  0.4  78  0.6  mRNA Fold Change  0.8  ut  1  1.0  H  e  0.2  78  1.0  mRNA Fold Change  on  lk  PG  78  bu  R  ut  0.4  ut  1  cl  4  4  78  sh  ut  sh  H  H  0.6  78  e  lk  PG  78  bu  R  ut  0.8  ut  on  4  4  78  sh  ut  sh  H  H  mRNA Fold Change 1.0  H  cl  lk  PG  78  bu  R  ut  mRNA Fold Change 1.2  H  4  4  78  sh  ut  sh  H  H  mRNA Fold Change  C SV2B  1.2  1.0  0.8  0.6  0.4  0.2  0.0  PDCD6  1.2  1.0  0.8  0.6  0.4  0.2  0.0  ELAVL1  1.2  1.0  0.8  0.6  0.4  0.2  0.0  91  A.6 Western Blots of Select Genes A  B kDa  Hut 78  Hut 78 RPG  sh4 bulk  sh4 clone 1  kDa  Anti-IL1RN  30  Hut 78  Hut 78 RPG  sh4 bulk  sh4 clone 1  Anti-p57  57  Actin  C  Actin  D kDa  Hut 78  Hut 78 RPG  sh4 bulk  sh4 clone 1  kDa  Hut 78 RPG  sh4 bulk  sh4 clone 1  170 130  95 72  Hut 78  95  Anti-REPS2  Anti-PALM2  72  55  55  Actin  Actin  E  F kDa  Hut 78  Hut 78 RPG  57  sh4 bulk  sh4 clone 1  kDa  Anti-ALG2  Hut 78  Hut 78 RPG  sh4 bulk  sh4 clone 1  40  Actin  Anti-HuR Actin  Figure A.4: Western blots of genes with no differential protein expression. There were 8 genes investigated by Western blotting for differential expression at the protein level and 6 demonstrated even expression across all cell lines examined. A) IL1RN; B) CDKN1C (p57); C) REPS2, two isoforms: 56 and 72 kDa; D) PALM2AKAP2, PALM2 at 56 kDa and PALM2-AKAP2 at 130 kDa; E) PDCD6 (ALG-2); and F) ELAVL1 (HuR).  92  


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