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Identification and analysis of programmed cell death genes in Drosophila melanogaster and human cancer.. Pleasance, Erin Dael 2006

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IDENTIFICATION A N D A N A L Y S I S OF P R O G R A M M E D C E L L D E A T H GENES IN DROSOPHILA MELANOGASTER A N D H U M A N C A N C E R USING BIOTNFORMATIC A N A L Y S I S OF G E N E EXPRESSION D A T A  by ERBSf D A E L P L E A S A N C E B . S c , The University of British Columbia, 2000  A THESIS SUBMITTED IN P A R T I A L F U L F I L M E N T OF THE REQUIREMENTS FOR THE D E G R E E OF DOCTOR OF PHILOSOPHY in THE F A C U L T Y OF G R A D U A T E STUDIES  (Medical Genetics)  THE UNIVERSITY OF BRITISH C O L U M B I A December 2005 © Erin Dael Pleasance, 2005  Abstract Programmed cell death (PCD), or cell suicide, encompasses multiple pathways including apoptosis and autophagy and is essential for development, cellular homeostasis, and prevention of cancer cell growth. I describe here the development and use of bioinformatic methods to identify and analyze genes involved in P C D , both in the model organism Drosophila melanogaster and in human cancer, by analysis of large-scale gene expression data. A n approach was developed to correctly identify genes from serial analysis of gene expression (SAGE) data, distinguish the set of genes not accessible to the SAGE method, and determine the optimal set of enzymes for Drosophila, C. elegans, and human SAGE library construction. In Drosophila metamorphosis the salivary gland undergoes autophagic PCD, whereby cellular components are engulfed and degraded by cytoplasmic vacuoles, with additional hallmarks of apoptosis. This is an excellent model in which to study the genes involved in PCD. Transcriptional profiling of this tissue by expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE) identified many genes differentially regulated prior to cell death, including genes known to be death regulators, genes in related pathways, genes of no known function, and potentially novel unannotated genes. The PCD-associated genes found in this analysis were then used to identify similar genes in the human genome that are differentially expressed in cancer, which have the potential to be involved in PCD and in oncogenesis. The pattern of genes expressed suggests a role for autophagy-associated processes in cancer progression. To examine this further, expression of the autophagy gene LC3 was examined in multiple cancer types, subtypes, and stages. LC3 expression is decreased significantly in several cancer types and also during cancer progression, suggesting a tissue- and stage-specific role for autophagy in regulating oncogenesis.  ii  Table of Contents Abstract Table of Contents List of Tables List of Figures List of Abbreviations Acknowledgements Chapter 1: Introduction 1.1 Thesis overview 1.2 Programmed cell death 1.2.1 Functions and types of programmed cell death 1.2.2 Apoptosis 1.2.3 Autophagy 1.2.4 Autophagic cell death 1.3 Cancer and programmed cell death 1.3.1 Molecular mechanisms of cancer 1.3.2 Apoptosis and cancer 1.3.3 Autophagy and cancer 1.4 Programmed cell death in the development of Drosophila melanogaster 1.4.1 Drosophila as a model for programmed cell death 1.4.2 Molecular mechanisms of programmed cell death in Drosophila 1.4.3 Programmed cell death in the Drosophila salivary gland 1.5 Gene expression analysis 1.5.1 Gene expression and mRNA levels '. 1.5.2 Microarrays 1.5.3 Expressed Sequence Tags 1.5.4 Serial Analysis of Gene Expression S A G E experimental method SAGE tag processing and statistics SAGE tag-to-gene mapping Advantages, disadvantages, and applications of S A G E 1.6 Thesis objectives and chapter summaries '. Chapter 2: Assessment and analysis of SAGE for transcript identification 2.1 Introduction 2.2 Methods 2.2.1 A C E D B 2.2.2 Transcript construction 2.2.3 Evaluating tag-to-gene mapping accuracy 2.2.4 Choice of tagging and anchoring enzymes, tag length 2.2.5 Mapping experimentally derived tags 2.2.6 Human tag mapping with RefSeq 2.3 Results 2.3.1 Tag mapping using genomic sequence 2.3.2 Transcript construction and analysis  iii  ;  ii iii vi vii viii ix 1 2 2 2 3 7 10 11 11 13 15 17 17 18 20 21 21 23 24 25 25 30 32 37 39 44 45 47 47 48 50 51 51 52 52 52 53  2.3.3 Tag-to-gene mapping....: 2.3.4 Analysis of transcriptomes with S A G E 2.3.5 Mapping experimentally derived tags 2.3.6 Assessment of human tag mapping with RefSeq 2.4 Discussion 2.4.1 Ambiguity in S A G E 2.4.2 Choice of tag length, anchoring, and tagging enzyme 2.4.3 Tags with no gene mapping 2.4.4 Limitations of conceptual transcripts 2.4.5 Conclusions Chapter 3: SAGE and EST analysis of PCD in the Drosophila salivary gland 3.1 Introduction 3.2 Methods 3.2.1 EST sequence processing and clustering 3.2.2 B L A S T analysis of ESTs 3.2.3 SAGE tag processing 3.2.4 SAGE tag-to-gene mapping 3.2.5 SAGE statistics 3.2.6 A C E D B database 3.3 Results 3.3.1 Processing and clustering of EST sequences 3.3.2 Genes identified by ESTs 3.3.3 Processing and differential expression of S A G E tags 3.3.4 Genes identified by SAGE tags 3.4 Discussion 3.4.1 Use of EST and S A G E to identify genes 3.4.2 Expression of PCD genes 3.4.3 Novel putative PCD genes 3.4.4 Conclusions Chapter 4: Identification of PCD genes in Drosophila and cancer gene expression data 4.1 Introduction 4.2 Methods 4.2.1 Drosophila PCD expression data 4.2.2 Human cancer expression data 4.2.3 Drosophila-human orthology.... 4.2.4 False discovery rate 4.2.5 Expected proportion of differentially expressed and PCD genes 4.2.6 GO analysis 4.3 Results 4.3.1 Drosophila PCD and human cancer expression 4.3.2 Differentially expressed genes ; 4.3.3 Roles of genes in PCD and cancer 4.3.4 Functional categorization and overrepresentation 4.4 Discussion 4.4.1 Cross-species integration 4.4.2 Functions of genes in PCD and cancer  iv  .•  ;  •  55 58 62 64 66 66 68 72 74 75 76 77 78 78 79 79 80 80 81 81 81 83 86 87 93 93 95 97 98 99 100 101 101 102 103 103 104 104 105 105 106 108 110 113 113 114  4.4.3 Conclusions Chapter 5: The autophagy gene MAP1LC3B in cancer 5.1 Introduction 5.2 Methods 5.2.1 Processing of microarray expression data 5.2.2 Processing of S A G E expression data 5.2.3 Tag mapping and EST analysis of secondary LC3 tag 5.3 Results and discussion 5.3.1 Overview of LC3 regulation in multiple cancer and normal tissues 5.3.2 LC3 in cancer stages 5.3.3 LC3 in breast cancer progression 5.3.4 LC3 in cancer subtypes 5.3.5 LC3 and Beclin 1 expression 5.3.6 Conclusions Chapter 6: Summary and Conclusions 6.1 Summary 6.2 Large-scale gene expression analysis with S A G E and other techniques 6.2.1 Measurement and comparison of gene expression 6.2.2 Novel genes and alternative transcripts 6.3 Genes and pathways associated with programmed cell death 6.3.1 In Drosophila 6.3.2 In cancer 6.4 Roles of autophagy in PCD and cancer 6.4.1 In Drosophila PCD 6.4.2 In cancer 6.5 Conclusions References  v  117 118 119 120 120 121 122 123 123 126 128 131 133 134 136 137 137 137 139 140 140 142 143 143 144 145 146  List of Tables Table 1.1 Acquired molecular changes in cancer progression 13 Table 1.2 Variations in the S A G E procedure affecting S A G E tag length 30 Table 2.1 SAGE tag mapping accuracy using conceptual transcripts compared to other available sequences 58 Table 3.1 Summary of sequences matched by representative Drosophila ESTs 84 Table 3.2 Cell death genes identified by ESTs : 84 Table 3.3 S A G E library tag counts and frequency distributions 87 Table 3.4 Differentially expressed genes associated with salivary gland autophagic cell death in Drosophila SAGE libraries 92 Table 4.1 Tissues and S A G E libraries from C G A P used for analysis of cancer expression 106 Table 4.2 Functions overrepresented in the pcd-cancer set 112 Table 5.1 Composition of normal and cancer samples shown in Figures 5.1 and 5.5 126  vi  List of Figures Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure  1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5 2.6 2.7 3.1 3.2 3.3 3.4 4.1 4.2 4.3 5.1 5.2 5.3 5.4 5.5  Mammalian apoptosis pathway Mammalian autophagy pathway Programmed cell death during Drosophila development Ecdysone-triggered transcriptional cascade controlling salivary gland P C D Diagram of the SAGE procedure Conceptual transcript construction for SAGE tag mapping U T R size distributions in Drosophila and C. elegans S A G E tag ambiguity varies with tag length '. Number of genes not resolvable by S A G E varies with anchoring enzyme Experimental SAGE tags mapped to conceptual transcripts SAGE tag mapping with RefSeq Creating shorter LongSAGE tags Processing and clustering of Drosophila 3' ESTs ESTs representing known and potentially novel genes Mapping of Drosophila SAGE tags to genes, ESTs, and genomic sequence Expression of known salivary gland cell death related genes in S A G E libraries. Expression categories of human pcd-cancer genes Categorized roles of human genes in pcd-cancer set as found in the literature Functions associated with human genes in the pcd-cancer set LC3 expression in multiple normal and cancer tissues LC3 expression in liver cancer stages LC3 expression in breast cancer progression LC3 expression in lung and breast cancer subtypes Beclin 1 expression in multiple normal and cancer tissues  vii  6 9 18 21 29 54 55 60 61 63 65 72 82 85 90 91 108 110 112 125 128 130 132 134  List of Abbreviations ACEDB  A C. elegans Database  BDGP  Berkeley Drosophila Genome Project  BLAST  Basic Local Alignment Search Tool  BLAT  BLAST-Like Alignment Tool  DCIS  Ductal Carcinoma In Situ  EST  Expressed Sequence Tag  FDR  False Discovery Rate  GFP  Green Fluorescent Protein  GO  Gene Ontology  IAP  Inhibitor of Apoptosis Protein  LC3  M A P 1 L C 3 B ; microtubule-associated protein 1 light chain 3 beta  MGC  Mammalian Gene Collection  MMP  Matrix Metalloprotease  NCBI  National Center for Biotechnology Information  RT-PCR  Reverse Transcriptase Polymerase Chain Reaction  SAGE  Serial Analysis of Gene Expression  TIMP  Tissue Inhibitor of Metalloproteases  TOR  Target of Rapamycin  UCSC  University of California, Santa Cruz  UTR  Untranslated Region  viii  Acknowledgements I would like to thank my graduate supervisor Dr. Steven Jones for the opportunity to work in the Genome Sciences Centre's excellent and stimulating research environment, and for his support, encouragement, and guidance during my studies. In particular, I appreciate the opportunities to expand my knowledge and experience as a researcher, through attending conferences and exploring multiple different aspects of my project. I am grateful for salary and travel funding from the Natural Sciences and Engineering Research Council, the Canadian Institutes for Health Research, and the Michael Smith Foundation for Health Research. I would also like to thank others for their guidance and the time they have taken to work with me, particularly Drs. Sharon Gorski and Marco Marra, from whom I have learned a great deal. Thanks also to thesis committee members Drs. Dixie Mager and A l y Karsan for guidance and advice. I have enjoyed and appreciated the friendship and help of fellow bioinformatics graduate students, among them Angelique, Obi, Steve, and Monica. Thanks to Scott, Richard, and Mehrdad, along with others in the Gene Expression group, for help with aspects of SAGE analysis, and to members of the Programmed Cell Death group (Sugi, Doug, Ian, Claire, Melissa, Qadir and others), and the Gene Regulation Informatics group. I am grateful to have worked with so many outstanding people. Finally, I would like to thank my parents, Jane and Will, for their encouragement and my husband Steve for his constant love and support.  ix  Chapter 1: Introduction  Portions of this chapter have been published. Pleasance, E.D. and Jones, S.J.M. 2005. Evaluation of S A G E tags for transcriptome study. In: Wang, S.M., ed., SAGE Technologies: Current Technologies and Applications. Horizon Scientific Press, Norwich, U K . 1  1.1 Thesis overview The aim of this thesis was to identify and analyze genes involved in programmed cell death (PCD) in Drosophila melanogaster and in human cancer systems, using a bioinformatics approach. Programmed cell death, referring to a number of processes of cellular suicide, is essential for the development of multicellular organisms and the prevention of unwanted cell growth that can lead to cancer, and underlies the pathology of some neurodegenerative and other diseases. Several decades of research have defined complex pathways that control cell death, but many of the genes involved are still unknown, especially in less well studied forms of P C D such as autophagic cell death. In addition, the role of PCD genes in the process of oncogenesis is both important and complex; understanding of the functions of P C D in cancer has the potential to impact cancer research, diagnosis, and treatment. I approached these research questions by first examining the process of P C D during the development of the model organism Drosophila melanogaster, and then applying these results, in combination with further studies, to investigate the role of PCD in cancer. The primary method employed was bioinformatic analysis of largescale gene expression data, with additional use of genomic, expressed sequence, comparative genomic, and functional data. This comprehensive approach takes advantage of the large amount of high-throughput biological data that is being generated at an astounding rate, and applies it to the understanding of essential cellular pathways and their role in the complex causes of cancer.  1.2 Programmed cell death 1.2.1 Functions and types of programmed cell death Programmed cell death is precisely initiated and executed cellular suicide, and requires the activation of specific genes and pathways. Thus, it is differentiated from non-programmed  2  necrotic cell death which occurs when cells lose homeostasis often due to environmental insult. PCD is essential in development, where the specific removal of cells acts together with cell proliferation to shape tissues such as digits and the neural tube, and to select the cells of the immune system (reviewed in Baehrecke 2002). It also acts in a protective manner by deleting damaged or diseased cells, such as those, that are virally infected. However, excess PCD can be detrimental and play a role in disease, particularly neurodegenerative diseases (reviewed in Dlamini et al. 2004; Marino and Lopez-Otin 2004). PCD was categorized by Schweichel and Merker (1973) into three types: type I, apoptotic; type II, autophagic; and type III, non-lysosomal programmed cell death. Little research has gone into the study of non-lysosomal programmed cell death, described morphologically as cellular disintegration with no involvement of the lysosomal pathways and no cellular condensation (Clarke 1990), and it will not be discussed further here. Both apoptosis and autophagic cell death are important in development and disease, though autophagic cell death has only recently become a major research focus. Indeed, the relationship between apoptosis, autophagic cell death and the morphologically related process of autophagy is unclear and thus these processes are described separately here.  1.2.2 Apoptosis Classical apoptosis as described by Kerr et al (1972) is caspase-dependent and marked by chromatin and cytoplasm condensation, and D N A cleavage and fragmentation (reviewed in Baehrecke 2002; Zornig et al. 2001). The cell condenses to form apoptotic bodies which are cleared by phagocytes recognizing the phosphatidylserine which becomes exposed on the plasma membrane. Controlled apoptosis is necessary for development and maintenance of multicellular organisms. Most mice deficient in caspases, the effectors of apoptosis, die during development.  3  Apoptosis of self-reactive lymphocytes prevents autoimmune diseases, and apoptosis of virally infected cells prevents virus replication and spread. However, inappropriate apoptosis of neurons is thought to play a role in neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's diseases (reviewed in Friedlander 2003). The genes responsible for control of apoptosis were first elucidated in C. elegans with the identification and characterization of three ced genes (Ellis and Horvitz 1986). Discoveries of functionally homologous genes in insects, mammals, and many other organisms have shown that the apoptosis pathway is essentially conserved in many multicellular eukaryotes. However, the complexity is substantially increased compared to the C. elegans system, as there are hundreds of proteins that are involved in mammalian apoptosis. There are two major apoptotic pathways in mammals (Figure 1.1, reviewed in Hengartner 2000; Zornig et al. 2001). The extrinsic pathway responds to signals from outside the cell such as binding of T-cell Fas ligand, and is mediated by death receptors of the tumor necrosis factor family and a variety of adaptor proteins such as T R A D D and F A D D . The intrinsic pathway responds to internal indicators such as p53 signals due to D N A damage, and is mediated by the mitochondria which, regulated by pro- and anti-apoptotic Bcl-2 family proteins, can be triggered to release pro-apoptotic molecules including cytochrome c and Smac/DIABLO. Both pathways, which also have means of cross-talk, ultimately result in recruitment, cleavage and activation of cysteine proteases known as caspases. Their proteolytic action on a wide variety of substrates results in the observed apoptotic morphology: D N A fragmentation, protein degradation, and eventual cellular disintegration and phagocytosis. Numerous regulators such as the IAPs (Inhibitor of Apoptosis Proteins) tightly control the activity of apoptosis proteins so that the process is only activated when specific environmental or genetic triggers are present. Control of  4  apoptosis is also exerted by regulators of growth and differentiation, such as the  NFKB,  Ras, and  Jun kinase pathways. The number of genes with roles in apoptosis is ever-increasing, with some genes only recently described as associated with Bcl-2 proteins (Zhao et al. 2005), death receptors (Kamradt et al. 2005) and control of  NFKB  (Park and James 2005). Given the  complexity of this process, there are undoubtedly more genes involved in regulation of apoptosis yet to be discovered.  5  Plasma membrane  TNF 1 I N r  + •  TNFR i  i  TRADD FADD  Fas-L  TRAIL  1  1  Death receptor ligands on neighbouring cells  1  Fas  Death receptors  DR4 i  i  t  1  FADD  Adaptor proteins  Unknown adaptors  Initiator caspases  Caspase-8  Targets:  Caspase-3 T J Q)  Caspases Lamins PARP P-catenin  Effector caspases  zr SE  03 *<  o  — 1  XIAP  Inhibitor of apoptosis proteins  Survivin Caspase-9  o cn cn •  Apoptosome  Apaf-1  7T  Omi/HtrA2  t--  Cytochrome c  Mitochondrial  MffocJHorfina^)  Smac/DIABLO  -*• Bid  Bax  Bak  factors  Pro-apoptotic Bcl-2 proteins Anti-apoptotic Bcl-2 proteins Internal cell signals  p53  Figure 1.1 Mammalian apoptosis pathway. Not all proteins known to be involved in apoptosis are shown, only representatives of the major pathway components. The mechanism of p53 effects are multi-fold, but include transcriptional regulation of Bax. Many of the Bcl-2 proteins shown are actually located on the mitochondrial membrane. Initiator caspases cleave effector caspases, which cleave both other caspases and various cellular targets. (Hengartner 2000; Zornig et al. 2001)  6  1.2.3 Autophagy The term "autophagy" refers to multiple processes (Cuervo 2004b), but it is the process of macroautophagy which has been most extensively studied and implicated as having a role in development and disease. Macroautophagy, hereafter referred to as simply autophagy, is a mechanism of protein and organelle degradation and turnover that is conserved in organisms as distantly related as yeast and humans (reviewed in Levine and Klionsky 2004). It involves the sequestration of cellular components in double-membraned structures thought to be derived from the endoplasmic reticulum. These structures, called autophagic vacuoles or autophagosomes, fuse with lysosomes to form autolysosomes in which the sequestered cellular components are degraded. It is this pattern of autophagosomal activity that forms the morphological definition of autophagy. Autophagy in yeast is a mechanism to survive nutrient starvation, and it may have a similar function in mammals (Kuma et al. 2004). A role for autophagy in aging is suggested by its necessity for the formation of the long-lived C. elegans dauer larva (Melendez et al. 2003) and its association with increased lifespan in mammals (Bergamini et al. 2003). It also functions in cellular responses to bacterial infection and may be involved in cell differentiation (reviewed in Bursch 2001; Gozuacik and Kimchi 2004; Levine and Klionsky 2004). Defects in autophagy are implicated in muscular diseases and in neurological diseases such as Huntington's Disease and Alzheimer's Disease (reviewed in Cuervo 2004a; Marino and Lopez-Otin 2004; Shintani and Klionsky 2004). The majority of the molecular components of autophagy were first identified using genetic screens in yeast. Three screens identified aut (Thumm et al. 1994), apg (Tsukada and Ohsumi 1993), and cvt (Harding et al. 1995) gene sets, which were subsequently shown to be partially overlapping; the nomenclature has since been unified and all genes are referred to as atg  7  (autophagy-related) genes (Klionsky et al. 2003). A n overview of the mammalian autophagy pathway is shown in Figure 1.2. Initiation of autophagy, regulated by nutrient availability and mitogenic signals, is controlled by TOR (target of rapamycin). Early stages of autophagic vacuole formation are dependent on class III PI3K which is found in a complex with Beclin 1 (orthologous to Atg6 in yeast). Beclin 1 can induce and is required for normal autophagy (Liang et al. 1999; Qu et al. 2003; Yue et al. 2003). Expansion of the autophagosomal membrane occurs through the action of two ubiquitin-like systems, with the result that the Atg8 protein is tethered to the autophagosomal membrane. In mammals, several homologs of yeast Atg8 have been identified, but only M A P 1 L C 3 B , known as L C 3 , has a clear role in autophagy (reviewed in Tanida et al. 2004). LC3 is used as a marker for autophagosomes (Mizushima et al. 2004), as it is the only protein known to remain membrane-bound after the autophagosome  has been  completely formed. Beclin 1 and L C 3 are among the most extensively studied mammalian autophagy genes, and both are integrally involved in the autophagic process.  8  Nutrient availability Mitogenic signalling  Class I PI3K  Autophagosome nucleation  Atg12 conjugation  Atg8 conjugation  Autophagosome expansion  Autophagosome completion  Lysosomal fusion  Figure 1.2 Mammalian autophagy pathway. Atg6 is also known as Beclin 1. In mammals, there are at least three Atg8 homologs, the best studied of which is LC3. There are four Atg4 homologs, known also as Autophagin-1 to -4. PE, phosphatidylethanolamine, is a lipid inserted into the autophagosomal membrane anchoring Atg8. Several other pathways that are not shown, including the Ras pathway, also impact autophagy. (Baehrecke 2005; Levine and Klionsky 2004; Lum et al. 2005; Ng and Huang 2005; Tanida et al. 2004).  9  1.2.4 Autophagic cell death Autophagic cell death has morphological features of autophagy, as the formation of large vacuoles which engulf and degrade both cytoplasm and organelles is observed prior to nuclear collapse (reviewed in Bursch 2001). The level of autophagic vacuole formation can be very extensive, such that a majority of the area in the cell is contained within vacuoles. Autophagic PCD is distinct both in morphology and molecular control from apoptosis. In addition to the presence of large autophagic vacuoles and early organelle degradation, autophagic P C D is marked by the preservation of the cytoskeleton, which is cleaved early in apoptosis but is presumably necessary for the movement of autophagic vesicles in type II P C D (Bursch et al. 2000). Molecularly, autophagic P C D is not caspase-dependent, but can be prevented by inhibitors of the autophagy pathway which target the P B K pathway or prevent the fusion of autophagosomes with lysosomes, and can require autophagy genes such as Beclin 1 (Shimizu et al. 2004; Y u et al. 2004). However, little work has yet been done to define the molecular pathways controlling autophagic. P C D , and thus the relationship between autophagy and autophagic P C D is unclear. For instance, autophagy is a protective mechanism of removing potentially detrimental protein aggregates in the brain, but high levels of autophagy are also observed in neurodegenerative disorders associated with cell death (reviewed in Levine and Klionsky 2004). In such cases, the observed autophagy-associated P C D may represent a progression of the autophagy pathway past the point at which the cell can survive, thus leading to PCD. Many tissues which were previously thought to undergo only apoptosis, such as the tadpole tail, sexual structures during mammalian development, and various organs during insecf metamorphosis, have since been shown to have additional hallmarks of autophagy, and thus the  10  mechanisms of P C D are in some cases being redefined (reviewed in Gozuacik and Kimchi 2004). It appears that autophagic PCD may play an important role when a large number of cells or an entire tissue need to be deleted. This will be discussed in more detail in the context of the Drosophila salivary gland in Section 1.4. Autophagic cell death and apoptosis are not mutually exclusive, and there may be some cross-talk between the respective signaling pathways as indicated by the regulation of Beclin 1 by Bcl-2 (Liang et al. 1998; Pattingre et al. 2005), and the triggering of autophagy when apoptosis is prevented, for instance, by Caspase-8 inhibition (Yu et al. 2004). However, as apoptosis can occur with no evidence for autophagy, so can autophagy occur with no evidence for apoptosis. In particular, there is growing evidence that under certain environmental or genetic conditions cancer cells undergo caspase-independent cell death with no apoptotic features, which can be prevented by autophagy inhibitors (Kitanaka et al. 2002). The role of autophagy and autophagic PCD in cancer will be discussed further in Section 1.3.3.  1.3 Cancer and programmed cell death 1.3.1 Molecular mechanisms of cancer Cancer is a disease of uncontrolled cell growth caused by genetic changes that alter cell physiology. This process is exceptionally complex, and thus only a broad overview of concepts related to cancer progression and associated molecular changes is given here. These are discussed in more detail in Kufe et al (2003). A recent review identified 291 genes demonstrated to be involved in cancer, representing over 1% of human genes (Futreal et al. 2004). For a cancer cell to survive, it must initially start to grow in an uncontrolled manner, beyond what is specified by various extracellular signals. Concurrently, it must circumvent the normal cellular mechanisms for recognizing and preventing this uncontrolled growth. It then must sustain this growth by obtaining sufficient nutrients and oxygen, even in a large tumor cell 11  mass. To advance to a metastatic state, it must gain the ability to live outside of its normal cellular milieu. Many different types of genetic mutations contribute to this complex multistep process, which differ from cancer to cancer and from cell to cell. Hanahan and Weinberg (2000) summarize the common molecular characteristics of cancers into six categories which specify the genetic changes a cancer cell must acquire to progress (Table 1.1). The genes involved in cancer progression can be placed in two broad categories: oncogenes and tumor suppressors. Oncogenes promote cancer growth when active, and are often upregulated in cancers, for instance by increased expression or by mutations that increase their activity. Examples are the growth-promoting genes Ras and Myc. Tumor suppressors inhibit cell growth, and are often downregulated in cancers, for instance by decreased expression, inactivating mutations, or deletion. A very common example is p53, which can restrain the cell cycle or promote apoptosis, and is inactivated in an estimated 50% of cancers. A number of genes which regulate or execute the apoptotic program clearly fall into these categories. The role of autophagy genes in cancer is, however, more enigmatic.  12  Table 1.1 Acquired molecular changes in cancer progression.  Characteristic of cancer*  Molecular examples  . Production and secretion of extracellular growth factor PDGF results in autocrine growth signaling Self-sufficiency in growth signals • Mutations in Ras cause constitutive activation of intracellular growth signaling pathways . Mutation or inhibition of Rb allows cell cycle progression Insensitivity to growth-inhibitory signals • Overexpression of Myc inhibits cell differentiation . Bcl-2 upregulation blocks mitochondrial apoptosis pathway Evasion of programmed cell death . Deletion of p53 prevents apoptosis due to DNA damage • Upregulation of telomerase enzyme maintains telomere length which is otherwise lost after many cell divisions; loss of Limitless replicative potential telomeres otherwise results in crisis, characterized by chromosome fusions and cell death • Upregulation of VEGF promotes growth of blood vessels into tumor, providing nutrients and oxygen Sustained angiogenesis • Downregulation of angiogenesis inhibitor thrombospondin-1 promotes vessel formation . Loss of E-cadherin function removes inhibitory cell-cell signals and connections . Proteases such as MMPs activated due to loss of inhibition by Tissue invasion and metastasis TIMPs degrade the extracellular matrix, promoting metastasis and invasion * as categorized by Hanahan and Weinberg (2000).  1.3.2 Apoptosis and cancer Current knowledge of tumorigenesis suggests that all cancers must inhibit apoptosis at some stage in their growth, as the apoptotic response is linked to alterations in many other cellular pathways (reviewed in Evan and Vousden 2001; Zornig et al. 2001). Overexpression of oncogenes such as Myc, in addition to signaling cell proliferation, also triggers apoptosis (Hipfner and Cohen 2004). Similarly, detachment of endothelial cells from the extracellular matrix during metastasis removes survival signals from integrins and cadherins and disrupts the cytoskeleton, resulting in a form of apoptosis called anoikis (Frisch and Screaton 2001). Genomic instability and D N A damage due to mutations and erosion of telomeres also trigger  13  apoptosis, primarily through p53, and result in cancer cell death (Evan and Vousden 2001). As might be expected, then, it is genes that promote apoptosis (pro-apoptotic) that are generally found to be inactivated or deleted in cancer, and thus are potential tumor suppressors. Genes that prevent apoptosis (anti-apoptotic) are potential oncogenes and are expected to be overexpressed. Probably the most common mechanism resulting in inhibition of apoptosis is the mutation or deletion of p53, which protects the cell from apoptosis due to D N A damage and oncogene activation (Asker et al. 1999). If p53 is not mutated, its activity may be decreased by overexpression of its inhibitor Mdm2 or downregulation of the Mdm2 inhibitor A R F (Lowe and Lin 2000). Other common mutations seen in the apoptotic pathway include the overexpression of anti-apoptotic Bcl-2 proteins, for instance due to a translocation involving Bcl-2 in lymphoma (Zornig et al. 2001), or the downregulation of pro-apoptotic proteins such as the frameshift mutations of Bax seen in some colon carcinomas (Rampino et al. 1997), either of which can prevent apoptosis via the mitochondrial pathway. While the death receptor pathway is a less common target for oncogenic mutations, changes in Fas and other death receptors have been found in non-Hodgkin's lymphoma and gastric cancer (Gronbaek et al. 1998; Park et al. 2001), and the decoy receptor DcR3 that competes with death receptors for ligand binding is amplified in a significant proportion of lung and color cancers (Pitti et al. 1998). IAPs, especially survivin, are overexpressed in cell lines and many common tumor types (Ambrosini et al. 1997). Direct effects on caspases themselves are rare, possibly because it is difficult to stop the apoptotic program at the late stage of caspase activation, although methylation, deletion, or mutation of the Caspase-8 gene is seen in neuroblastomas (Takita et al. 2000; Teitz et al. 2000) and late stage gastric cancers (Soung et al. 2005). The central role of apoptosis in cancer and the commonality  14  of apoptosis-related alterations in cancer makes modulation of cell death pathways an attractive target for cancer therapies (Ghobrial et al. 2005). 1.3.3 Autophagy and cancer One of the avenues of research that broadly ignited interest in investigating the role of autophagy in cancer was the study of the autophagy gene Beclin 1. Beclin 1 is monoallelically deleted in 40% of breast cancer cell lines, and its protein expression is reduced in a majority of breast and ovarian tumors (Liang et al. 1999). The MCF-7 breast cancer cell line has undetectable expression of Beclin 1 protein, and reintroduction of expression increases autophagy, decreases cell proliferation, and reduces MCF-7-derived tumor formation in mice (Liang et al. 1999). Additionally, Beclin 1 has been shown to act as a haploinsufficient tumor suppressor, as mice heterozygous for a deletion of beclin 1 develop lymphomas, liver and lung cancers, and breast hyperplasias, with no evidence for additional mutations in the wild-type Beclin 1 gene (Qu et al. 2003; Yue et al. 2003). Although it is clear that inactivation of Beclin 1 can be an important step in the oncogenic process, it binds the apoptosis protein Bcl-2 (Liang et al. 1998; Pattingre et al. 2005) and may function through mechanisms other than autophagy (Furuya et al. 2005; Y u et al. 2004). Thus, an outstanding question is whether it is reduced autophagy that is responsible for the tumorigenic effects of Beclin 1 deletion mutants. Recently, a number of studies have demonstrated that autophagy has a role in cancer causation, progression, and therapy. Multiple lines of evidence suggest that autophagy has a role in suppressing oncogenesis, either through its role in autophagic cell death, or through other mechanisms such as regulation of cell growth (reveiwed in Gozuacik and Kimchi 2004). Rat models of hepatocellular carcinomas and pancreatic adenocarcinomas demonstrate decreased levels of autophagy (Schwarze and Seglen 1985; Toth et al. 2002). Autophagic cell death can be  15  induced by oncogenic forms of Ras in glioma and gastric cancer cell lines (Chi et al. 1999), and may contribute to spontaneous regression of neuroblastomas (Kitanaka et al. 2002). MCF-7 breast cancer cells undergo autophagic cell death in response to tamoxifen (Bursch et al. 1996), as do glioma cells when treated with the chemotherapeutic arsenic trioxide (Kanzawa et al. 2003). These data suggest that autophagy and autophagic cell death can act to prevent cancer, and thus induction of these pathways could be of therapeutic benefit in treatment of at least some cancers. However, autophagy does not only have an inhibitory role in oncogenesis; indeed, it may in some situations contribute to cancer progression (reviewed in Ogier-Denis and Codogno 2003). Some cancers exhibit high levels of autophagy, which may aid in cancer cell survival under conditions of nutrient starvation and hypoxia which are common in preangiogenic solid tumors. The potential survival advantage of active autophagy in early stage cancers is supported by the observation that although rat models of pancreatic carcinomas show decreased autophagic capacity compared to normal pancreatic cells, premalignant rat pancreatic cancer cells show increased levels of autophagy (Toth et al. 2002). Additionally, autophagy may diminish the effects of chemotherapy or irradiation, potentially by contributing to elimination or sequestration of toxic molecules and damaged organelles (Cuervo 2004a; Ogier-Denis and Codogno 2003; Paglin et al. 2001). Thus, it appears that autophagy may have dual roles in cancer. In certain cancer types or stages, it may aid in cancer cell survival. In other types or stages, or in response to other stimuli such as blocked apoptosis or presence of particular chemotherapeutics, autophagic cell death may be induced (Ogier-Denis and Codogno 2003).  16  1.4 Programmed cell death in the development of Drosophila melanogaster 1.4.1 Drosophila as a model for programmed cell death During development and metamorphosis of the fruitfly Drosophila melanogaster, a number of tissues are subject to programmed cell death (Figure 1.3A, reviewed in Baehrecke 2000; Rusconi et al. 2000). Tissues such as the salivary gland and midgut are removed completely during metamorphosis by autophagic cell death to make way for the adult organs (Figure 1.3B). In other tissues, such as the eye, it is only individual cells which are deleted by apoptosis in order to shape the tissue in a precise manner (reviewed in Brachmann and Cagan 2003). As discussed above, the pathways responsible for programmed cell death are conserved in many organisms, and many human P C D genes have homologs in the Drosophila system. Importantly, both the apoptotic and autophagic systems are in place in the fly. However, the pathways in Drosophila are less complex and less redundant; for instance, there have been at least 14 caspases identified in mammals (Zhang et al. 2003), and only seven in Drosophila (Kornbluth and White 2005). The conservation of PCD elements between species implies that novel genes found in this model system are likely to have relevant functional homologues in mammalian systems, but at the same time will be easier to study in Drosophila both because of the relative ease of genetic studies in this model, and the reduced complexity of the system (Richardson and Kumar 2002).  17  A pupanum formation  egg laid • ; embryo  larva  ; embryonic  late larval  prepupa  adult  pupa  prepupal  mid-pupal  //— -120  10  -100 midgut, anterior muscles  segment borders, midline glia, head  salivary glands, foregut, posterior muscles  12 14 * . * retina  t  96  abdominal muscles, neurons  developmental stage  ecdysone pulses hrs APF (25°C)  cell death  B  13 hr  14.5 hr  15 hr  26 hr  28 hr  30 hr  (APF, 25°C) (APF, 18°C)  Figure 1.3 Programmed cell death during Drosophila development. (A) Pulses of the steroid hormone ecdysone punctuate development. The late larval pulse in the 3 instar larva triggers destruction of the midgut, and the prepupal pulse triggers destruction of the salivary gland; both are destroyed by autophagic cell death. The time scale is expanded in the prepupal stage. Images from (Griffiths et al. 1999). (B) The prepupal ecdysone pulse triggers death and removal of the salivary gland by autophagy. Images from (Jiang et al. 1997). APF, after puparium formation. rd  1.4.2 Molecular mechanisms ofprogrammed cell death in Drosophila Apoptosis in Drosophila, as in the mammalian system, is executed by caspases. The caspase-inhibitory proteins, IAPs such as Diapl, have a central role in controlling apoptosis as lack of these proteins results in increased apoptosis and lethality (Hay et al. 1995). Primary control of IAPs is exerted by three IAP-inhibiting pro-death proteins, Hid, Reaper, and Grim (Goyal et al. 2000), and also by two recently discovered proteins which appear to have similar function, Sickle and Jafrac2 (Srinivasula et al. 2002; Tenev et al. 2002). Though these genes do  18  not have direct mammalian homologs, several can induce apoptosis by activating caspases in mammalian cells (Varghese et al. 2002) and they are analogous in function to mammalian Smac/DIABLO and Omi/HtrA2 (Du et al. 2000; Hegde et al. 2002). Expression of these proteins allows caspases to be activated and thus triggers death. A mitochondrial apoptosis pathway also exists, involving the Bcl-2 homologues dBORG-1 and 2 and the Apaf-1 homologue Dark, which initiates activation of Drone and downstream caspases (Kumar and Doumanis 2000). Several important PCD-related proteins, such as the Bcl-2 homologues (Colussi et al. 2000) and several more caspases (Doumanis et al. 2001; Harvey et al. 2001), have only recently been recognized, and there are undoubtedly more apoptosis regulators to be identified in Drosophila. For instance, the initiator caspase Dredd binds a protein very similar to human F A D D (Hu and Yang 2000) and thus resembles in function human Caspase-8, but the putative death receptors which may initiate this pathway have yet to be uncovered. In general, few upstream regulators of cell death have been identified in Drosophila, and thus research into potential upstream regulators of caspases, transcriptional regulators of Reaper, Hid and Grim, and other as yet unidentified control mechanisms has the potential to contribute significantly to the understanding of cell death in Drosophila (Hay et al. 2004). Although 11 putative homologs of autophagy genes have been identified in Drosophila (Baehrecke 2003), most have not been studied in detail. Drosophila Atg4 (Apg4/Aut2) can complement its yeast counterpart, but mutants have no phenotype in Drosophila; genetic studies have demonstrated that it is a modifier of Notch signaling but no studies on its role in autophagy have yet been done (Thumm and Kadowaki 2001). Atg3 (Apg3/Autl), on the other hand, is required for autophagy in Drosophila fat body cells and mutants die during metamorphosis, demonstrating this gene is part of the Drosophila autophagy pathway and that this pathway is  19  required for normal development (Juhasz et al. 2003). Several other putative autophagy genes have recently been shown by loss of function to be necessary for autophagy in Drosophila fat bodies (Scott et al. 2004). As in the human system, the exact role of autophagy in programmed cell death is unclear but intriguing. The Drosophila salivary gland has been a focus of research in this area. 1.4.3 Programmed cell death in the Drosophila salivary gland The Drosophila salivary gland is an excellent model in which to study gene regulation during PCD, because nearly every cell in the gland undergoes P C D within a well-defined, short time period (Figure 1.3B). This provides a large number of cells which are all in the same known stage of PCD for analysis. Developmental P C D in the salivary gland is triggered by a pulse of the steroid hormone ecdysone at the prepupal-pupal stage transition. Upon ecdysone binding to the ecdysone receptor, this complex along with PFTZ-F1 initiates a transcriptional cascade (Figure 1.4) by activating the early genes B R - C , E74A and E93, which in turn regulate downstream genes including the IAP inhibitors Hid and Reaper (Jiang et al. 1997; Jiang et al. 2000; Lee et al. 2002a; Martin and Baehrecke 2004). This results in activation of caspases such as Drone and death of the salivary gland. Intriguingly, although genes involved in the apoptotic pathway are required for salivary gland death, and the death has hallmarks of apoptosis such as D N A fragmentation (Jiang et al. 1997; Martin and Baehrecke 2004), the primary morphology of salivary gland death is autophagic (Lee and Baehrecke 2001). Large autophagic vacuoles are formed, and changes in the cytoskeleton and increased acid phosphatase activity indicate vesicular movement and protease degradation (Jochova et al. 1997). Inhibition of caspases by baculovirus p35 does prevent D N A fragmentation and death of the salivary glands, but they progress to a late stage of autophagic  20  activity. Mutants in the early genes BR-C and E74A similarly progress to late stages but salivary glands are not destroyed, whereas PFTZ-F1 and E93 mutants show early defects in vacuolarization (Lee and Baehrecke 2001). Our work (Gorski et al. 2003) as discussed in Chapter 3, along with a similar large-scale study published concurrently (Lee et al. 2003), has demonstrated that multiple autophagy genes are expressed during autophagy in the salivary gland, suggesting that the canonical autophagy pathway is involved in salivary gland death. Thus, programmed cell death in the salivary gland appears to be autophagic but with hallmarks of apoptosis, possibly with some level of overlap between pathways in the genes involved, and is a model for understanding the relationship between these very different types of cell death.  dark  1  E93 PFTZ-F1  BR-C  -• <  ecdysone ->  EcR/USP  E74A <  rpr hid  f drone 1 diap2  \\  drice  — • DEATH  E75  Figure 1.4 Ecdysone-triggered transcriptional cascade controlling salivary gland PCD. Binding of ecdysone to the ecdysone receptor, in the presence of the competency factor pFTZ-Fl, initiates transcription of early genes, which in turn act as transcription factors to express late genes. Repression of Diap2 allows caspases including Drone and Drice to cleave downstream substrates.  1.5 Gene expression analysis 1.5.1 Gene expression and mRNA levels Messenger R N A is the molecular intermediate between D N A and protein; and the quantity of mRNA for a gene present in a cell is often used as a surrogate measure of the activity of the functional gene product, generally a protein. mRNA levels are determined by several  21  levels of regulation, including transcription, splicing, and mRNA stability (Akker et al. 2001; Day and Tuite 1998). Although correlations between mRNA and protein levels vary, there is generally high consistency for abundantly expressed genes (Chen et al. 2002a; Gygi et al. 1999; Kern et al. 2003). Additionally, genes whose protein products are involved in the same protein complex are expressed similarly (Greenbaum et al. 2002), and it is generally thought that changes in mRNA levels, because they are specifically regulated, are indicative of changes in cellular function. Cellular activities are often measured at the mRNA level because, unlike protein abundance, mRNA abundance is relatively easy to assay. Both low throughput methods such as Northern blots and high-throughput methods such as microarrays rely on the natural R N A base-pairing to probe for the presence of particular mRNA molecules; other methods such as ESTs and SAGE rely on reverse transcription and sequencing of mRNAs. Several such highthroughput methods for gene expression analysis, that allow simultaneous assay of large numbers of mRNA molecules and thus generate large datasets requiring computational algorithms for analysis, are described below in more detail. Analysis of mRNA expression measurements can identify genes that change in expression between conditions and thus have a potential role in the system under study. The utility and potential power of these studies is indicated by the size of publicly available gene expression repositories such as the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/ geo/), containing tens of thousands of experiments representing hundreds of millions of measurements (Barrett et al. 2005). In the study of cancer, it is common to compare tumor cells to nontumor cells, compare types of cancers, or compare expression across a timecourse such as cancer progression or drug effects in cell culture. A subset of genes, including the autophagy gene Beclin 1 (Liang et al. 1999), are regulated at the protein level and thus are not identifiable  22  through mRNA expression profiling. In cancers, however, this subset may be reduced as genes normally regulated only at the protein level may be perturbed due to mutations causing deletions, over-, or under-expression. Large-scale analysis of gene expression is particularly powerful for indicating cellular pathways that are perturbed in the process under study, and for identifying new genes involved in the process without the necessity of prior functional knowledge. 1.5.2 Microarrays Microarray methods, using either cDNA or oligonucleotide-based chips, make use of hybridization rather than sequencing to measure mRNA expression levels. cDNAs or oligonucleotide sequences are printed or constructed in known locations on a glass slide or other substrate. mRNA is reverse transcribed to cDNA and labeled, and then allowed to hybridize with the D N A on the chip. After washing, the measured intensity of the fluorescence is related to the expression level of the mRNA in the sample. Two-color microarrays, as described originally (Schena et al. 1995), use long P C R products or complete cDNAs to measure relative hybridization levels of two samples. Oligonucleotide microarrays, such as those produced by Affymetrix (Lipshutz et al. 1999), use shorter sequences of 25-70 nucleotides designed to precisely and specifically match known genes and thus permit more direct rather than relative quantitiation. These methods are discussed in more detail in (Butte 2002; Lipshutz et al. 1999). Microarrays have significant advantages over other large-scale expression measurement methods as they are relatively inexpensive, widely available, and produce results quickly, but they also have disadvantages that can affect the accuracy of the resulting measurements. Probe design is a major determining factor in the efficacy of microarray analysis. Prior knowledge of the genes to be profiled is necessary to place the probes on the array, and thus microarrays, unless specifically designed to do so (Shoemaker et al. 2001), do not have the ability to detect  23  novel genes. Additionally, genes which are very similar in sequence may hybridize to each others' probes (cross-hybridization) and thus interfere with correct expression measurement (Evertsz et al. 2001; Zhang et al. 2005); this is a more significant problem with longer probes such as those used on cDNA arrays. Analysis of microarray data to determine true expression levels is also complicated by issues such as differences in c D N A labeling efficiencies and probe affinities, and requires sophisticated computational tools for steps including image processing, background subtraction, and normalization (discussed in Quackenbush 2001). Many cellular systems in a wide range of organisms have been subjected to microarray analysis, and microarrays have been used extensively in the study of cancer. Gene expression during Drosophila development from embryo to adult, and in specific tissues including the salivary gland and midgut, has been profiled with microarray time courses (Arbeitman et al. 2002; L i and White 2003). The applications of microarray analysis to cancer include using gene expression profiles to define subtypes of breast cancer (Sorlie et al. 2003), and differentiate patients with poor prognoses (van't Veer et al. 2002). Meta-analysis of multiple microarray experiments can identify sets of genes commonly deregulated in tumors (Rhodes et al. 2004). Because they are relatively economical compared to other large-scale gene expression methods, it is thought that microarrays may become practical for use in diagnosis. A l l large-scale gene expression methods have the potential to identify genes important in cancer progression that could potentially be markers or targets for treatment.  1.5.3 Expressed Sequence Tags Expressed sequence tags, or ESTs, are single-pass sequences representing the 3' or 5' ends of genes in a c D N A library that is constructed by reverse transcription of mRNAs extracted from a cell population. Each sequence read corresponds to one gene, and thus ESTs are usually  24  300-500 nucleotides long. As a result, ESTs are good for gene identification and genome annotation as they can be compared to other genes and to genome sequence, but are expensive and not best suited for quantitative large-scale expression profiling. Nearly 30 million ESTs have been sequenced and stored in the dbEST database (http://www.ncbi.nlm.nih.gov/dbEST/, Boguski et al. 1993), of which the largest proportion are human. Use of ESTs for quantitative profiling is limited by the common practice of normalizing cDNA libraries to avoid repeatedly sequencing the same abundant transcripts, and by some skewing of expression levels (Haverty et al. 2004). However, EST sequences are very valuable for identifying alternative splice variants, and combined with expression levels can identify tumor-associated variants (Hui et al. 2004). Additionally, ESTs are widely used in gene annotation (Curwen et al. 2004). Over 200,000 Drosophila ESTs have been sequenced, and used both for identification of full-length clones for the majority of Drosophila genes (Stapleton et al. 2002) and for gene prediction (Misra et al. 2002).  1.5.4 Serial Analysis of Gene Expression Serial analysis of gene expression (SAGE) is a sequence-based  rather than a  hybridization-based approach to expression profiling developed in 1995 (Velculescu et al. 1995) and now widely used. It is both more efficient and more quantitative than EST-based profiling. As this technique and the associated analysis are central to this thesis, the experimental method and computational analysis of SAGE data are described here in more detail. SAGE experimental method Serial analysis of gene expression, or SAGE, involves extraction of short sequences called S A G E tags from polyadenylated R N A by conversion to cDNA followed by a series of restriction  25  digestions. Tags are then P C R amplified, concatenated, and sequenced (Figure 1.5, Velculescu et al. 1995). The S A G E method is designed such that the frequency of S A G E tags in the final sequences should be directly proportional to the abundance of their parent mRNA molecules. The position in the transcript from which the tag is extracted is determined by the 4-cutter anchoring enzyme used, commonly Nlalll which recognizes " C A T G " , but also occasionally Sau3A which recognizes " G A T C " . As the 3' end of the cDNA is immobilized and all nonimmobilized fragments are removed after cleavage with the anchoring enzyme, only the portion of the transcript 3' to the anchoring enzyme site is retained. After the linker sequence containing the tagging enzyme recognition site and PCR primers is ligated, tagging enzyme cleavage results in a tag representing the sequence adjacent to the 3'-most anchoring enzyme site. BsmFl is the most common tagging enzyme used, but others such as Mmel are also employed, and this defines the length of the extracted SAGE tag (Table 1.2). There are several sources of bias in the SAGE method that can skew the resulting tag frequencies and lead to misinterpretation of gene expression levels. The efficiency of the bluntend ligation used in the standard SAGE procedure varies dependent on the terminal nucleotides of the sequences to be ligated (Yamamoto et al. 2001). In LongSAGE, 2 bp overhangs are retained for ligation. Both of these approaches can result in nonrandom pairing of tags for amplification and therefore influence overall tag frequency. A n independent bias in GC content has also been observed whereby room-temperature storage of ditags results in denaturation and loss of AT-rich tags (Margulies et al. 2001). Finally, PCR amplification has an inherent potential for bias, as not all sequences are amplified with equal efficiency (Warnecke et al. 1997). AT-rich ditags may produce as much as 7-fold more amplicon than GC-rich tags (Spinella et al. 1999). The potential for P C R bias was recognized as a concern in the original description of S A G E  26 i  (Velculescu et al. 1995), and it is partly to account for this problem that ditags are introduced, rather than simply amplifying and ligating tags individually. Because ditags are formed before amplification, and there are generally an extremely large number of different tag species, it is expected that only rarely will two tags be coupled to produce the same ditag more than once by chance. Therefore, if two identical ditags ("duplicate ditags") are observed in the final. S A G E sequences, such ditags are proposed to have arisen from biased PCR amplification (Velculescu et al. 1995). Duplicate ditags are observed in essentially every SAGE library, commonly at frequencies of 5-10% but as high as 50% in less complex tissues (Gorski et al. 2003). Several experimental issues can produce tags other than those expected. Only SAGE tags derived from the 3'-most anchoring enzyme (e.g. M a l l l ) site are canonical or expected tags in SAGE libraries. During cDNA synthesis, extended strings of A's can bind the oligo-dT primer, resulting in cDNA production starting at sites upstream of the poly-A tail, and thus can produce SAGE tags from farther upstream in the transcript. Similarly, tags derived from 2, 3 or even 4 anchoring enzyme sites upstream from the 3'-most are observed due to incomplete digestion by the tagging enzyme. Additionally, some proportion of S A G E tag sequences produced are incorrect. Sequence error rates in SAGE libraries have been estimated at 0.7%-1% per base, or 6.8%-9.6% per 10 bp tag (Colinge and Feger 2001a; Velculescu et al. 1997). Less frequently, similar errors in SAGE tags can result from misincorporation of bases during PCR. Formation of linker dimers during tag cleavage and ligation can produce artifactual SAGE tags whose sequence is derived from the linker sequence itself and not the transcript. Linker contamination in SAGE libraries usually ranges from less than 1% to 5% of sequenced tags (Cheng and Porter 2002; Velculescu et al. 1999). Incorrect "quasi-ditags" can also arise from genomic contamination or random nucleotide combinations, and are most likely to appear in clones with  27  only one or two ditags (Anisimov and Sharov 2004). A l l of these issues of sequence bias, sequence errors, and non-canonical tags must be dealt with where possible in the computational analysis of SAGE libraries.  28  Reverse transcribe mRNA to form cDNA internal poly-A tracts misprime to form cDNAs which produce upstream SAGE tags - A A A A A  Cut with anchoring enzyme, eg. Malll incomplete digestion results in upstream SAGE tags - A A A A A G T A C "  3'-most anchoring enzyme site  |  Primer " " ' R  C  L  Ligate linker sequences - A A A A A  G G G A C A T G ICCCTGTAC "  Cut with tagging enzyme, eg. BsmFI, blunt ends formation and cleavage of linker dimers results in linker contamination p rmiICI r  i  m  p  r  |  GGGACATG NNNNNNNNNN C T G T A C NNNNNNNNNN C C  Primer  Tag sequence derived from transcript  Anchoring enzyme site  Tagging enzyme site  I  Blunt-end ligate to form ditags  I  *efficiency of blunt end ligation depends on terminal nucleotides  G G G A C A T G N N N N N N N N N N N N N N N N N N N N C A T G T C C C C C C T G T A C NNNNNNNNNNNNNNNNNNNNGTACAGGGI  R  I  M  A  R  r H I net  IGGGACATG N N N N N N N N N N N N N N N N N N N N C A T G T C C C  |  C  C  C  T  G  T  A  r  m p r  PCR-amplify ditags *AT-rich sequences preferentially amplified  Ditag  P  p ;  C  NNNNNNNNNNNNNNNNNNNNGTACAGGGI  p j r  m p r  Cut with anchoring enzyme to isolate ditags *A T-rich ditags may be lost at room temperature  I  N N N N N N N N N N N N N N N N N N N N C A T G G T AC N N N N N N N N N N N N N N N N N N N N  I  Concatenate and sequence ditags *sequence errors produce incorrect tags  ..NNCATGNNNNNNNNNNNNNNNNNNNNCATGN ..NNGTACNNNNNNNNNNNNNNNNNNNNGTACNNNNNNNNNNNNNNOT  NN... NN...  Extract tag sequences, determine tag frequencies and map to genes Tag: CATGTTTTGTAGAG  Frequency:  Gene mapping:  5  p53  Figure 1.5 Diagram of the SAGE procedure. Steps marked in italics are subject to errors and biases. For more detailed procedure, see (Velculescu et al. 1995) or www.sagenet.org. From Pleasance and Jones (2005).  29  Table 1.2 Variations in the SAGE procedure affecting SAGE tag length.  Procedure  Anchoring enzyme  Tagging enzyme  Tag length  Reference  SAGE (original) SAGE (now in practice) SADE Modified SAGE LongSAGE SuperSAGE  Malll Nla\\\ Sau3f\ Malll Malll Malll  Fok\ BsmF\ BsmF\ Rsa\ Mme\ EcoP15\  13 bp 14 bp 14 bp 18 bp 21 bp 26 bp  (Velculescu et al. 1995) (Velculescu et al. 1997) (Virion et al. 1999) (Ryo et al. 2000) (Sahaetal. 2002) (Matsumura et al. 2003) SAGE tag processing and statistics Sequence reads from S A G E libraries consist of a series of ditag sequences separated by anchoring enzyme sites, and it is commonly assumed that all tags are the same length. However, variation in cleavage by the BsmYl tagging enzyme produces tags ranging from 13 to 17 bp including the anchoring enzyme site (Yamamoto et al. 2001). Therefore, there is no way to determine i f a particular ditag of 28 bp corresponds to ligation of two 14 bp tags, or a 13 bp tag and a 15 bp tag. Approximately 88% of ditags are 30 or 31 bp long, corresponding to tags of 1516 bp (Colinge and Feger 2001b), so the likelihood of one base of a longer tag being erroneously assigned to a 13 bp tag is small and therefore 14 bp tags are commonly extracted. This variation in tag length, however, provides an opportunity to extend an extracted S A G E tag to the 15 base th  if a longer tag is needed to distinguish between two possible gene assignments. This extension can be done by determining the most common 15 base for a particular tag using statistical th  methods to determine confidence (Colinge and Feger 2001b), but still increases the likelihood of misassigning a base to the wrong tag and so should be used with caution. Many of the issues discussed in Section can be mitigated by appropriate computational processing to remove incorrect S A G E tags. The most common way to deal with PCR bias is to remove all but one copy of repeated ditags (Lash et al. 2000; Velculescu et al. 30  1995). However, i f genes of extremely high abundance are present, for instance those representing >1% of total tags, they may pair in a significant fraction of ditags to produce nonartifactual duplicate ditags in the final sequence. In such cases, it has been suggested that, based on observed tag frequencies, a certain calculable proportion of duplicate ditags should not be removed from analysis as they may represent true transcript expression (Vilain et al. 2003). Noncanonical tags produced due to mispriming or incomplete digestion can be included in an analysis i f they can be correctly assigned to genes (see Section, in which case their frequency can be added to that of the canonical tag for the same gene. Tags resulting from incorporation of linker sequence can be easily identified computationally and simply removed. Sequence errors can be reduced by selecting tags only of high sequence quality (Gorski et al. 2003; Pleasance et al. 2003). However, there is no way to detect P C R errors through Phred scores. For relatively small libraries, the chance of seeing the same sequence or PCR error twice is very low and thus the approach of removing all tags seen only once ("singletons") will alleviate almost all such errors (Lash et al. 2000). For tags with expression high enough to produce multiple redundant errors, all possible permutations of a tag must be determined, and tags corresponding to these permutations identified as potentially affected by sequence error. These tags can either be excluded from the library i f their expression is not significantly above that expected due to error (Velculescu et al. 1999), or adjusted in frequency by estimating the original tag count without sequencing error (Akmaev and Wang 2004; Beissbarth et al. 2004; Colinge and Feger 2001a). As SAGE tag counts are absolute, SAGE libraries from different tissues or conditions can be directly compared once all sequence processing is complete and tag frequencies are determined. A number of statistical tests for S A G E library comparison have been developed.  31  Tests generally involve the comparison of two libraries, where the frequency of a tag in one library is compared to the frequency of that tag in another and the probability (p-value) that the observed difference in frequencies would have occurred by chance due to sampling error is determined. Some statistical tests have been developed particularly for S A G E analysis (Audic and Claverie 1997), while others are more generic tests applied to S A G E data (Man et al. 2000). A comparison of the number of tags found to be differentially expressed in various situations with each of these tests revealed that most of them produce equivalent results (Ruijter et al. 2002). Several web-based tools have been developed to apply statistical tests to experimental S A G E libraries (Lai et al. 1999; Pylouster et al. 2005). SAGE tag-to-gene mapping A n essential step in SAGE is the unambiguous correlation of the 14 bp S A G E tag with the transcript from which it is derived, a process termed "tag mapping". It generally involves automated searching for the tag sequence in sequence databases. This process is complicated by variation  in  gene  structure  and  sequence  including alternative  splicing,  alternative  polyadenylation site usage, polyadenylation cleavage heterogeneity, and single nucleotide polymorphisms (SNPs). It is estimated that between 35% and 75% of human genes are alternatively spliced (Johnson et al. 2003; Xie et al. 2002; X u et al. 2002), producing different transcripts by use of different exons. Computational extraction of S A G E tags from alternatively spliced transcripts suggests 38% of human genes produce more than one tag due to alternative splicing (Unneberg et al. 2003), and 24% of Caenorhabditis elegans genes produce multiple S A G E tags (Jones et al. 2001). 30-50% of genes are likely to use multiple different polyadenylation signals, producing 3' ends that vary in length by up to several kb (Beaudoing et al. 2000; Iseli et al. 2002). As this variation is focused at the 3' end of the transcript, and  32  generally changes the length of the transcript by several hundred nucleotides, use of different polyadenylation signals can result in extraction of different S A G E tags. In addition, the exact site of polyadenylation cleavage varies from 10-25 nucleotides downstream of the polyadenylation signal. The approximately 3% of human SAGE tags affected by cleavage heterogeneity (Pauws et al. 2001) can be difficult to map to genes as part of their sequence is often derived from the poly-A tail itself, and thus a one-base-pair variation in cleavage (eg. G G T C G A A A A A vs. G G T C A A A A A A ) can result in a tag that no longer matches any known sequence. Finally, SNPs result in slight changes in a gene's nucleotide sequence, and thus can produce a SAGE tag that does not exactly match the expected sequence for that gene; as many as 8.6% of genes may produce such tags (Silva et al. 2004). It can also be difficult to distinguish true gene variations from errors in SAGE tags due to sequencing errors and incomplete digestion. Full-length cDNA sequences which exactly match the transcripts from which SAGE tags are experimentally derived would be ideal for making the most accurate assignment of S A G E tags to genes. A number of full-length cDNA sequencing projects are ongoing (Ota et al. 2004; Stapleton et al. 2002; Strausberg et al.' 1999), but complete transcript sequences of all gene variants are not currently available for any organism. Sequences that are not full-length will result in tags that cannot be assigned to genes. For instance, in 54% of Drosophila full-length cDNAs, S A G E tags are derived from either the 5' or 3' U T R and thus full-length coding sequences alone are not sufficient for tag mapping (Pleasance et al. 2003). Having an incomplete set of gene sequences can also be very misleading, as some tags will not be assignable, and others may be assigned to the wrong gene. A common issue in S A G E tag mapping is that, because of the limited length of a S A G E tag, two or more different genes may produce identical S A G E tags and thus a tag sequence may not have a unique gene assignment. If not all gene  33  sequences are known, a tag may appear to match one gene uniquely, when in reality it also matches another gene as well and thus its frequency may represent the expression of one or both genes. Choosing the sequences and methods used for SAGE tag mapping has a significant effect on the completeness and accuracy of the resulting gene expression profiles. For simpler organisms such as yeast, simply mapping to ORFs and 500 bp of downstream sequence can assign 88% of tags, and mapping the remaining tags to genomic sequence assigns in total 93% (Velculescu et al. 1997). For more complex and larger genomes, however, this method is not applicable. Instead, a number of different methods have been developed for assigning SAGE tags to genes, most of which use a combination of different sequence databases. The first comprehensive human SAGE tag mapping resource to be developed was SAGEmap, available from N C B I (Lai et al. 1999; Lash et al. 2000). SAGEmap extracts SAGE tags from GenBank mRNA and 3' EST sequences that are grouped into UniGene clusters. Tags extracted from the 3'-most M a l l l or Sau3A site are assigned reliability based on the type of sequence they are derived from, where mRNAs are more reliable than ESTs, and ESTs with polyadenylation signals and poly-A tails are more reliable than those without as they can be correctly assigned an orientation. There are a number of difficulties with using ESTs for tag mapping. Because ESTs are single-pass sequences, they have an error rate of approximately 1% and thus an overall tag error rate of-10% (Lash et al. 2000). In addition, clustering of ESTs into gene groups as is done in the UniGene database (Schuler 1997) is a difficult process due to sequence errors, sequence artifacts such as chimeras, incomplete gene sequences, and the existence of highly similar genes. ESTs belonging to different genes may be clustered together, or the same gene may be represented in different EST clusters. Thus, EST-based tag mapping can result in erroneous or highly nonunique SAGE tag assignments. To accommodate the  34  expected EST sequence errors, SAGEmap removes the 10% of least commonly observed taggene pairs to create a "reliable" mapping. In this reliable mapping, 87% of tags can be assigned uniquely to one gene and 59% of genes have only one tag, compared to mappings using mRNAs only from which 96% of tags were unambiguous, and 80%) of genes had a single tag. The S A G E Genie tool refines the SAGEmap approach by incorporating full-length sequences from the Mammalian Gene Collection (MGC, Strausberg et al. 1999) and the Reference Sequence (RefSeq) database at NCBI (Maglott et al. 2000), and devising a datasource scoring method based on experimentally observed SAGE tags (Boon et al. 2002). Similar approaches have since been applied by others (Bala et al. 2005). For S A G E Genie, tag databases were created from M G C , RefSeq, mitochondrial, clustered mRNA, clustered EST, and unclustered EST sequences, divided based on presence or absence of polyadenylation signals and poly-A tails. Tags were extracted from the four 3'-most M a l l l sites, as well as sites upstream of poly-A tracts i f they were confirmed by EST or cDNA evidence to be potential sites of internal priming. A confident SAGE tag list of 194,126 different experimental tags was also produced, using 6.8 million tags from 171 SAGE libraries filtered to remove linker sequences, sequence errors, and singletons. Each of the tag databases was then scored based on the percentage of computationally extracted tags that were represented in the confident S A G E tag list, with the expected result that M G C sequences were the most reliable, followed by RefSeq, clustered sequences, and finally unclustered ESTs. For tag-to-gene mapping, the final "best" assignment is then made based on the database the tag was extracted from, the position relative to the 3' end, whether the site is internally primed, and the expression level of the gene based on the confident tag list where more highly expressed genes are considered more reliable. A limited manual  35  confirmation of 77 tag-to-gene assignments suggests the method produces fairly accurate tag mappings. As more eukaryotic genome sequences are completed (Adams et al. 2000; Arabidopsis Genome Initiative 2000; C. elegans Sequencing Consortium 1998; Lander et al. 2001; Waterston et al. 2002), it is possible to assign SAGE tags directly to genomic sequence. This eliminates the problems arising from incomplete representations of the transcriptome by sequenced cDNAs, difficulties with clustering sequences, and effects of sequence errors, as finished genome sequences have error rates typically below 1/10,000 bp (C. elegans Sequencing Consortium 1998). However, S A G E tags derived from sites in the transcript that cross exon splice junctions will not exist directly in genomic sequence, and are very computationally difficult to search for. Tags with sequence derived partly from the poly-A tail will also not be found in genomic sequence, although these tags are difficult to map by any means due to slight variations in polyadenylation cleavage sites as discussed above. Finally, depending on S A G E tag length and the size of the genome, it is often not possible to uniquely map S A G E tags to genomic sequence. For instance, only 59% of Drosophila and C. elegans S A G E tags are unique in their genomes, even though these organisms only have genome sizes of 120 M B and 100 M B respectively (Pleasance et al. 2003). Statistically, it is expected that essentially every 14 bp S A G E tag will be found more than once in the human genome making tag mapping to genomic sequence infeasible (Saha et al. 2002). It is also important to include the mitochondrial genome in SAGE tag mappings, as mitochondrial genes produce a significant proportion of S A G E tags (Boon et al. 2002; Jones et al. 2001). Thus, genomic sequence is best used as part of an iterative process of tag mapping using multiple data sources (Gorski et al. 2003; Robinson et al. 2004).  36 A chantages, disadvantages, and applications o/SA GE SAGE is highly reproducible in the expression patterns it produces, and has reasonable concordance with other measurements of gene expression. S A G E libraries produced from the same R N A sample show correlations of 0.96 (Dinel et al. 2005). Similarly, MicroSAGE libraries constructed from the same R N A , the same ditag ligation, or the same pool of sequences in sequencing vectors, have correlations of between 0.94 and 0.98, with all of the differences due only to variability in tag sampling. As this sampling variability has a larger effect on tags of lower frequency, tags with counts under 100 show correlations closer to 0.8 (Blackshaw et al. 2003). Comparisons to RT-PCR and to oligonucleotide microarrays show correlations generally between 0.4 and 0.8 (Huminiecki et al. 2003; Ishii et al. 2000; K i m 2003). The biases and errors in SAGE discussed above can largely be accounted for by appropriate computational analysis, which contributes to the high reproducibility of SAGE. As S A G E does not depend on hybridization, issues such as cross-hybridization and saturation, which are more difficult to measure and resolve, do not influence observed expression measurements. The ability of SAGE to determine transcript abundance is dependent on the depth to which the library is sequenced, which may typically be as few as 20,000-30,000 tags or as many as over 100,000 tags from a single R N A sample. S A G E is much more sensitive than EST sequencing for identifying low abundance transcripts (Sun et al. 2004), and at moderate library sizes it has similar detection efficiency to Affymetrix oligonucleotide arrays (Evans et al. 2002). Deep sequencing makes detection of very low abundance transcripts possible, which is particularly important in understanding complex tissues and transcriptional regulation (Boheler and Stern 2003). However, SAGE library construction is substantially more expensive than microarray profiling, especially when many tags are sequenced to produce large libraries, and  37  thus the number of libraries constructed is often more limited. This is offset by the increased utility of S A G E libraries for multiple experiments and comparisons. S A G E tag counts are absolute rather than relative as many hybridization-based methods are, and are not dependent on array design or prior knowledge of sequences. Thus, S A G E tag frequencies from different tissues or conditions can be directly compared, even across experiments. Additionally, as more genome and gene sequence information becomes available, tag-to-gene mappings can be updated and thus available S A G E data will be able to be reanalyzed and used for years to come. Like ESTs and unlike most microarrays, SAGE does not require prior knowledge of the genes to be profiled, and thus it is an ideal method for novel gene identification. While the rate of novel sequence discovery by ESTs has decreased over time, a significant proportion of newly sequenced S A G E tags are unique and do not represent known genes (Boheler and Stern 2003). 35% of SAGE tags from mouse embryonic stem cells cannot be mapped to any known gene (Anisimov et al. 2002a; Anisimov et al. 2002b), suggesting there could be 16,000-39,000 novel genes and transcripts yet to be identified (Boheler and Stern 2003). Additionally, S A G E has been used to identify antisense transcripts (Quere et al. 2004), and the procedure modified to produce 5' SAGE tags for genome annotation (Wei et al. 2004). The ability of SAGE to profile expression of a gene is dependent on the presence of specific sequence motifs corresponding to endonuclease recognition sites, particularly of the commonly used anchoring enzyme J V M I I and the occasionally employed Sau3A (Virion et al. 1999) and Rsal (Ryo et al. 2000). Additionally, gene profiling is dependent on unique representation of genes by S A G E tags. These issues are examined and discussed in Chapter 2. Tens of millions of SAGE tags have been generated for hundreds of studies of normal and diseased tissues, cell lines, and model organisms. A limited number of studies have produced  38  Drosophila SAGE libraries, including experiments on the J N K signaling pathway (Jasper et al. 2001) and sex-specific differences (Fujii and Amrein 2002), as well as our work on programmed cell death (Gorski et al. 2003). SAGE libraries produced from multiple Drosophila tissues have been used to identify many novel low-abundance transcripts in Drosophila, demonstrating the utility of SAGE for gene identification (Lee et al. 2005). As is the case with microarray technologies, the SAGE method is commonly applied to studies of cancer. Many of these studies are collected in the Cancer Genome Anatomy Project (http://cgap.nci.nih.gov/) catalog of SAGE tags from hundreds of libraries representing close to 30 tissues, which also provides tools for identification of genes differentially expressed between normal and cancer samples (Boon et al. 2002) . 1.6 Thesis objectives and chapter summaries Programmed cell death is a complex cellular response that encompasses multiple pathways including apoptosis and autophagy. This response is essential in development and cellular homeostasis, but when misregulated can result in diseases including cancer. The death of the Drosophila salivary gland during development is an excellent model for studying PCD due to the precisely timed and complete nature of the destruction of this tissue; additionally, this death has hallmarks of both autophagy and apoptosis, and the genes involved are regulated at the transcriptional level. The mechanisms of programmed cell death, and the role of autophagy in PCD, are not completely understood, and identification of genes and pathways involved in these processes would contribute to our understanding both of PCD and its role in cancer. Genomescale gene expression profiling and comparison of expression between species is a powerful approach that has the potential to identify such genes and pathways. Analysis of expression  39  datasets requires development of computational methods and approaches to examine the patterns of gene expression in cells undergoing cell death or tumorigenesis. The primary aim of this thesis was to identify, using gene expression data, genes and associated pathways involved in programmed cell death in two systems, the Drosophila salivary gland and human cancer. A l l of the analyses described here are computational, involving development of methods, algorithms and databases appropriate to the assembly of data from disparate sources, and analysis for the purpose of extracting biological meaning. The first part of this analysis involved the development of methods to analyze gene expression data, particularly serial analysis of gene expression data, for the purpose of gene discovery and monitoring differential expression and also to define the limitations of the technique. Using these methods, I analyzed SAGE and EST data from the Drosophila salivary gland to identify genes expressed and regulated prior to programmed cell death in this tissue. The PCD-associated genes found in this analysis were then used to identify similar genes in the human genome that are differentially expressed in cancer, which have the potential to be involved in P C D and its regulation in oncogenesis. As both of these analyses pointed toward an important role for autophagy and associated processes in cancer, I subsequently examined the expression of an autophagy marker in various cancers, which indicated that autophagy is likely to be altered in cancer in a tissuespecific manner. A summary of these analyses as presented in Chapters 2 through 5 are given here. In Chapter 2 (as published in Pleasance and Jones 2005; Pleasance et al. 2003), I describe an approach to constructing full-length "conceptual transcripts" from genome, EST, and gene prediction data, which I implemented in the Drosophila and C. elegans genomes. I hypothesized that using these transcripts constructed from multiple data sources would allow for more  40  effective assignment of SAGE tags to genes. The method I developed was indeed superior to previous tag-to-gene mapping methods available for Drosophila, and also allowed the assessment of SAGE for its ability to correctly, completely, and unambiguously identify genes. I demonstrated the effects in the Drosophila, C. elegans, and human genomes of varying the restriction enzymes used in S A G E that affect SAGE tag length and position. This analysis provides S A G E tag-to-gene mappings for these organisms, defines the limitations of SAGE, and suggests optimal choices of enzymes for a SAGE experiment. In Chapter 3, I describe the analysis of an EST library and three S A G E libraries derived from developmental timepoints leading up to programmed cell death in the Drosophila salivary gland (Gorski et al. 2003). ESTs were clustered and assigned to genes, and novel ESTs identified. S A G E tags were assigned to genes using the method described in Chapter 2, and novel tags assigned to ESTs and genomic locations. Confirming the hypotheses that genes involved in P C D could be identified by gene expression analysis and that both apoptosis and autophagy genes are regulated in Drosophila PCD, genes which change in expression between S A G E libraries were found to include genes known to be part of the salivary gland P C D transcriptional hierarchy, genes belonging to other cellular pathways, and genes of no known function. These genes are potential regulators or effectors of programmed cell death in Drosophila. Chapter 4 describes my approach based on the hypothesis that similar genes are involved in programmed cell death in cancer and in Drosophila, and that these genes can be identified based on a cross-species gene expression filter. Human orthologs of genes differentially expressed in the Drosophila salivary gland were identified, and the expression of these genes examined in multiple human cancers based on a database of SAGE data. Analysis of Drosophila  41  and human datasets identified a subset of genes which are regulated in both systems. These candidates for involvement in PCD in cancer were examined for known roles in PCD or cancer, and overall functions determined. Chapter 5 (Pleasance et al, manuscript submitted) focuses on the analysis of the human LC3 autophagy gene in cancer based on multiple expression datasets, from both SAGE and microarray technologies. Consistent with the hypothesis that LC3 expression is altered in cancers, potentially reflecting the impact of autophagy on oncogenesis, differential expression of this gene was observed in multiple cancers compared to corresponding normal tissues. Differences in expression were also seen in cancer subtypes and stages of cancer progression. M y observations suggest potential roles for the process of autophagy in cancer which are tissuespecific. The bioinformatic methods used in these analyses can be applied to studies of other cellular processes, and the results suggest roles for multiple processes including autophagy in programmed cell death and cancer. This work contributes to our overall understanding of the functions of cell death in oncogenesis, and to the goal of a comprehensive view of the genetic changes that occur in cancer.  In addition to the work described in this thesis, I have been involved in several other collaborative projects at the Genome Sciences Centre which have been described in publications or submitted manuscripts. I worked with Obi Griffith and others to identify human genes which are coexpressed across many conditions by comparing and combining S A G E and microarray expression datasets, as described in Griffith et al (2005). I was also involved in work done by Peter Huang, examining the expression of genes in operons in C. elegans based on large-scale  42  GFP and S A G E data (Huang, Pleasance, et al, manuscript submitted). Finally, I am currently working on both the collection of known regulatory elements in a public database (Montgomery et al, manuscript accepted in Bioinformatics) and the discovery of novel regulatory elements in Drosophila based on genome sequence data as part of a pipeline and database system known as cisRED (Robertson et al, manuscript accepted in Nucl Acids Res.).  43  Chapter 2: Assessment and analysis of SAGE for transcript identification  A version of this chapter has been published. Pleasance, E.D., Marra, M . A . , and Jones, S.J.M. 2003. Assessment of S A G E in transcript identification. Genome Res 13: 1203-1215. Pleasance, E.D. and Jones, S.J.M. 2005. Evaluation of S A G E tags for transcriptome study. In: Wang S M , ed., SAGE Technologies: Current Technologies and Applications. Horizon Scientific Press, Norwich, U K .  44  2.1 Introduction An essential step in Serial Analysis of Gene Expression (SAGE) is tag-to-gene mapping, or tag mapping, which refers to the unambiguous determination of the gene represented by a SAGE tag. At the time of the work described in this chapter, the importance of full-length, complete transcript sets for SAGE tag mapping had not been discussed in the literature. The primary tag mapping resource at the time of our analysis was SAGEmap (Lash et al. 2000), which makes use of EST and mRNA sequences, with the corresponding drawbacks as described in Section Additionally, this resource was only available for mammalian and Arabidopsis thaliana sequences, while the SAGE technique had been increasingly applied to other organisms (Jasper et al. 2001; Jones et al. 2001; Steen et al. 2002). In particular, there was no publicly available tag mapping resource for Drosophila, and such a resource was necessary for our analysis of Drosophila S A G E libraries. Many of the difficulties encountered when tag mapping based solely on expressed sequences could be addressed by using genomic sequence and annotation. As genomic sequence is more accurate, with a low estimated error rate of <1/10,000 (Adams et al. 2000; C. elegans Sequencing Consortium 1998), the potential for results to be obfuscated due to sequence errors in the tag mappings is reduced. Also, annotated gene sets are potentially more complete than expressed sequence sets alone due to the use of additional information such as genefinder predictions and exons detected through conserved protein similarity, and the curation of this data to form the annotation (FlyBase Consortium 2002; Stein et al. 2001). Basing tag mappings entirely on predicted genes from genomic databases is problematic, however, as gene annotations rarely include untranslated regions (UTRs) of expressed transcripts.  Since SAGE tags will  correlate to the 3'-most anchoring enzyme site, typically that of the four-cutter restriction  45  enzyme Nlalll which occurs on average every 256 bp, many S A G E tags are expected to be derived from 3' UTR sequence. It is essential when using the SAGE technique to be aware of the transcripts that will not be identifiable using this method, due to shared sequence resulting in ambiguity or due to lack of an appropriate anchoring enzyme site, as the expression of such transcripts will not be accurately determined. Identification and quantification of these refractory transcripts would potentially permit the choice of appropriate modifications to the SAGE procedure, which could minimize the number of transcripts that will not be profiled using S A G E and increase the utility of this expression profiling method. Thus, in addition to developing a tag mapping method and resource, I assessed for the first time the ability of S A G E to uniquely and completely identify transcripts. To overcome some of the limitations in SAGE tag mapping and to assess the utility and restrictions of the S A G E approach in transcript identification, I devised a method for constructing a complete predicted transcript set ("conceptual transcripts") and deriving S A G E tags from it. These conceptual transcripts are based on a combination of genomic sequence, annotated predicted genes, and expressed sequences, and thus are sufficiently complete to allow this assessment. I applied this method to the model organisms Drosophila and C. elegans, as more mature genomic annotation resources were available for these organisms than for the human genome. Also, although the SAGE technique has been utilized in these organisms, there were at the time of our work no publicly available tag mappings. In addition to providing tag mappings for these organisms, I was able to determine the number of genes lacking a suitable anchoring enzyme site, and establish the extent to which S A G E tags will be correlated ambiguously to genes. I also determined the most efficient anchoring enzyme choice for each  46  particular organism. I applied a similar analysis, though more limited as it did not include construction of conceptual transcripts, to the human transcriptome. The method described here permits assessment of the efficacy of the SAGE approach in transcript identification by distinguishing genes refractory to this profiling method, and facilitates S A G E analysis in both Drosophila and C. elegans as well as in other organisms to which this method can potentially be applied. 2.2 Methods 2.2.1 ACEDB An A C E D B (Durbin and Thierry-Mieg 1991) database is available for C. elegans (Stein et al. 2001) and contains a large variety of data including genomic, gene, expression, and sequence similarity information. This database is vital in the construction of the conceptual transcript set for tag mapping, as it can be automatically queried using existing software. It was accessed through a remote connection to WormBase (http://www.woi-mbase.org) version WS78 (May 2002). This database includes EST alignments determined by B L A T (Kent 2002). The Drosophila databases GadFly (http://www.bdgp.orR/annot/index.html) and FlyBase (2002), contain similar information, but do not allow the direct automated manipulation of data in the manner necessary for the integration of sequence information into conceptual transcripts. Accordingly, I constructed an A C E D B database for Drosophila, based on the GadFly resources, which contains data on the genomic sequence as well as predicted and known genes, and EST and cDNA alignments. This database is directly queryable in an automated manner. Genomic sequence and predicted gene coding sequence positions were obtained from the GadFly Release 2 mySQL database gadb2c on Apr 8 2002, accessible from headcase.lbl.gov. EST and cDNA sequences were obtained from the BDGP at ftp.fruitfly.org/pub/genomic/fasta/ on April 11 2002. 47  ESTs and cDNAs were aligned to genomic segments identified as having similarity by B L A S T N (Altschul et al. 1990) (v. 2.0.14) (E value < le-100, word size 32) using the dynamic, intronaware E S T _ G E N O M E (Mott 1997) alignment program (min. score 100). E S T _ G E N O M E output was converted into A C E D B format using custom Perl scripts. In addition, the gene represented by each of the ESTs was determined by B L A S T N similarity (E value < le-3, word size 16) processed by MSPcrunch (Sonnhammer and Durbin 1994) (v. 2.3), requiring that an EST match no more than one gene locus. A l l other input/output processing was performed using custom Perl scripts. This Drosophila A C E D B database is available for download from http://sage.bcgsc.ca/ tagmapping/. Besides its utility in constructing the conceptual transcript set, the Drosophila A C E D B database is useful for viewing gene structure, expression and similarity information. In these respects, it is similar to FlyBase's (2002) GeneSeen (http://www.flvbase.org/annot/ geneseen-launch-static.html). However, it has the added advantages that it can incorporate userspecific data and be queried directly or through the AcePerl (Stein and Thierry-Mieg 1998) interface to extract sequences and associated information, allowing further whole-genome analysis.  2.2.2 Transcript construction Transcripts were constructed using custom Perl scripts for each organism which interact with the A C E D B database described above through the AcePerl modules (Stein and ThierryMieg 1998). UTRs were added to predicted genes using the genomic sequence as determined by alignments with ESTs and cDNAs for genes where such data is available. If expressed sequences were unavailable, UTRs were estimated to be a length that would encompass 95% of known UTR sequences based on empirical UTR size distributions and polyadenylation signals.  48  In C. elegans, UTRs were added based on EST evidence i f the EST was assigned to the gene (in the MatchingcDNA field of the gene in A C E D B ) and was in the correct orientation (e.g. only 3' ESTs used to construct 3' UTRs) and started within 1 kb of the end of the gene (for reasons of efficiency; UTRs in C. elegans are not expected to extend this far). UTRs were extended to encompass the furthest EST from the gene. In Drosophila, UTRs were added based on EST/cDNA evidence i f the EST/cDNA both overlapped with the gene's coding region (by EST_GENOME) and was more similar to that gene than any other (by BLAST). As Drosophila UTRs are occasionally spliced, breaks in the EST alignments corresponding to introns were excluded from the final transcript sequence. Only ESTs or cDNAs starting within 20 kb of genomic sequence from the end of the gene were considered. As in C. elegans, ESTs were required to be in the correct orientation. For genes that did not have expressed sequence corresponding to their UTRs, 5' UTRs were extended to 836 bp in Drosophila and 388 bp in C. elegans, and 3' UTRs were extended to 1039 and 574 bp respectively. This corresponds to a length > 95% of the known UTRs as determined by EST/cDNA alignments. If the most common polyadenylation signal A A T A A A (Graber et al. 1999; Riddle et al. 1997) was found in the estimated 3' UTR, the U T R was truncated 35 bp downstream of the end of this signal. Also, i f there was a gene nearby, the estimated UTRs were truncated so as not to overlap ESTs associated with another gene nor extend over one-half the distance to the next nearest coding region, so as to prevent adjacent UTRs from overlapping. A 30 bp poly-A sequence was added to the 3' end of all constructed transcripts to represent the poly-A tail present on mRNAs, as occasionally S A G E tags contain part of this poly-A sequence. 30 bp was chosen because it extends further than the longest S A G E tags (25  49  bp) used in this analysis, thus permitting S A G E tags to extend their full length into the poly-A sequence i f necessary. SAGE tags derived from poly-A sequence will complicate S A G E analysis in any case. A l l such tags will end in a varying number of As, and thus will be more likely to be ambiguous. Also, the variance in polyadenylation cleavage sites will result in multiple tags derived from the same gene (Pauws et al. 2001). Conceptual transcript sequences and corresponding tag mappings, to both gene loci and individual alternative transcripts, are available at http://sage.bcgsc.ca/tagmapping/. Perl code is made available upon request. 2.2.3 Evaluating tag-to-gene mapping accuracy To evaluate the accuracy of tag-to-gene mappings derived from the conceptual transcripts, I used a set of "test" SAGE tags extracted from the 3'-most position of 6614 fulllength Drosophila  cDNA  sequences  (Stapleton  et  al. 2002)  obtained  from  BDGP  (http://www.bdgp.org/EST/index.shtml; entire available set as of April 11 2002). The accuracy of mapping was calculated as the percentage of tags that could be correctly and unambiguously assigned to genes, as this is the goal of tag-to-gene mapping. First, I attempted to assign each of the cDNA and 252,362 EST sequences (from BDGP sequencing project, http://www.bdgp.org/ EST/index.shtml) to one of the 13,489 Drosophila predicted genes by B L A S T N (E value< e-50); conceptual transcripts, as they are constructed based on predicted gene sequences, are already associated with genes. In order to correctly compare the accuracy of EST mapping approaches, only cDNAs and genes to which EST sequences could be mapped were considered. Also removed were ESTs that did not match predicted genes from genomic annotation. I thus used for the analysis 5606 SAGE tags extracted from cDNA sequences, 13,489 conceptual transcripts, and 204,380 EST sequences, each assigned to a gene.  50  Each of the sequence sets described in Table 2.1 were then used to map the "test" S A G E tags to genes. I constructed sets of conceptual transcripts with and without using EST or c D N A sequences to determine UTRs; i f no ESTs or cDNAs were used, the UTRs were estimated as described above. In mapping to EST sequences, tags were assigned to ESTs which were grouped based on gene assignments so that clustering ESTs based on sequence similarity was unnecessary. Ambiguous mappings would only occur in cases where two ESTs from different genes contained the same SAGE tag. The number of tags assigned to a single gene using these various sequence sets and the number of those assignments that were correct were determined.  2.2.4 Choice of tagging and anchoring enzymes, tag length A l l tags of a given size for a given enzyme site were extracted from all conceptual transcripts for all loci in the genomes using Perl scripts. A locus was considered not to contain an enzyme site i f none of its alternative transcripts contained that site, and a locus was considered ambiguous if any of its transcripts shared a tag at the 3'-most enzyme site with a transcript from a different locus. If, for instance, a particular tag was found in the 3'-most site in one transcript and a site closer to the 5' end in another transcript, that was not considered to be an ambiguity and that tag would be assigned to the gene in which it was found at the 3'-most site. Probability of uniqueness was calculated from the formula used in (Saha et al. 2002), assuming 13,489 tags of 14 bp each.  2.2.5 Mapping experimentally derived tags 4007 different experimentally derived Drosophila tags (Gorski et al. 2003) and 9159 different C. elegans tags (Halaschek-Wiener et al. 2005), of at least 99% quality (1% chance of sequence error) and occurring more than once, were derived using software under development  51  in our laboratory (R. Varhol and S. Zuyderduyn, unpublished). A l l tags of 14 bp were extracted using Perl scripts from M a l l l enzyme sites at every position in the Drosophila and C. elegans genomes derived from A C E D B . Experimental tags were compared to genomic tags, and the number of occurrences of each tag determined. The same libraries of experimental tags, with varying frequency cutoffs, were mapped to conceptual transcripts and the percentage mapped unambiguously and ambiguously determined. 2.2.6 Human tag mapping with RefSeq Human nucleotide sequences from the RefSeq database were downloaded Oct 10 2002 from the N C B I FTP server (ftp://ftp.ncbi.nih.gov/refseq/H sapiens/1. A l l 15,274  mRNA  sequences (those beginning with N M _ J then available were used in analysis. Tags were extracted from the 3'-most enzyme site, with varying length and varying enzymes as described in Section 2.2.4. To estimate the actual level of ambiguity based on the incomplete set of RefSeq sequences, random subsamples of the sequence set of varying sizes were chosen and ambiguity calculated for each subsample. This was done in triplicate and the average ambiguity for each subsample size determined.  2.3 Results 2.3.1 Tag mapping using genomic sequence Mapping S A G E tags directly to genomic sequence could potentially deal with the issues of sequence quality and incomplete transcript sets. However, the genome sizes of complex eukaryotes are large enough that tag sequences may be present more than once by chance. To determine the impact of this issue, I mapped experimental C. elegans and Drosophila S A G E tags to the corresponding genomes, which are -100 M B and -120 M B respectively (Adams et al.  52  2000; C. elegans Sequencing Consortium 1998). Only 59% of mapped S A G E tags occur unambiguously in the C. elegans genome, and 20% of C. elegans S A G E tags occur 3 or more times in the genome. 59% of Drosophila SAGE tags are also unambiguous, and 18% occur 3 or more times in the genome. Also, only approximately 60% of S A G E tags map to the Drosophila or C. elegans genomes at all, due in part to some SAGE tags crossing splice boundaries and thus not being present in the genome. These results suggest that it is necessary to use transcript sequences rather than genomic sequence for S A G E tag-to-gene mapping.  2.3.2 Transcript construction and analysis In order to organize and access the data required to produce the conceptual transcript set, I first constructed a queryable A C E D B (Durbin and Thierry-Mieg 1991) database containing genomic, gene, and expressed sequence information for Drosophila (see Methods). A.similar database is already available for C. elegans (Stein et al. 2001). Using these databases, conceptual transcripts were constructed as described in Methods (Figure 2.1). In Drosophila, a total of 8213/14335 (57%) conceptual transcripts have 3' UTRs constructed based directly on expressed sequence evidence, with an average size of 343 bp and a median of 224 bp. The remaining transcripts have predicted 3' ends based on empirical size distributions (Figure 2.2A and B) indicating that 95% of 3' UTRs are 1039 bp or less. In C. elegans, the 6608/20448 transcripts (32%) found to have 3' UTRs constructed based on direct sequence evidence were an average of 195 bp and a median of 137 bp, while the remaining 3' UTRs were estimated based on empirical size distributions (Figure 2.2C and D) that show 95% of known UTRs to be 574 bp or less.  53  Coding sequence  Genomic sequence  cDNAs and ESTs UTRs  B  Coding sequence  1  ^  Genomic sequence  AATAAA  Estimated 5' UTR  Estimated 3' UTR  i  AATAAA  Truncated 3' UTR AATAAA Figure 2.1 Conceptual transcript construction for SAGE tag mapping. Genomic sequence was used to form conceptual transcripts, taking advantage of its high sequence quality. Coding sequences were derived from current genomic annotation. (A) If expressed sequences that extend beyond the predicted coding sequence were available, the alignment position of these sequences was used to determine the extent of the UTR. (B) If no expressed sequences were available, Drosophila 5' and 3' UTRs were extended to 836 bp and 1039 bp respectively, and C. elegans UTRs were extended to 388 bp and 574 bp. These UTR size estimates encompass 95% of known UTRs determined in (A). If the polyadenylation signal "AATAAA" was found within the estimated 3' UTR, the UTR was truncated 35 bp downstream of the signal.  54  B 1600 1400 1200 to  a.  5  1000  °  800  f  600  3 400  200  200  400 600 800 1000 1200 1400 1600 1800 2000  I .i  lllllllllllllllll 200  400 600 800 1000 1200 1400 1600 1800 2000  0  UTR size (bp)  UTR size (bp)  700 600 » 500  a. => 1500  I-  2  400  E  Il  100  Z  300  200 100  • mmmm  200  300  400  500  0  600 700  IIIIIUH. 0  100  200  300  400  500  600  700  800 900  UTR size (bp)  UTR size (bp)  Figure 2.2 UTR size distributions in Drosophila and C. elegans. Lengths of conceptual transcript UTRs were determined based on expressed sequence alignments (Figure 2.1). (A) Drosophila 5' UTR. (B) Drosophila 3' UTR. (C) C. elegans 5' UTR. (D) C. elegans 3' UTR.  2.3.3 Tag-to-gene mapping Based on this conceptual transcript set, tag-to-gene mappings were derived by extracting all tags from the transcripts, that is, the 10 bp downstream of each MoIII (CATG) site for a total of 14 bp for each tag. It is important for this tag mapping method to consider all anchoring enzyme sites and not just the 3'-most, for two reasons. First, gene prediction programs often have difficulty correctly defining the ends of genes (Rogic et al. 2001), so that the apparent 3'most tag site may not be correct. Second, estimated UTRs added when no expressed sequence is available are a length that would encompass 95% of known UTRs, and are thus known to be  55  overestimates of real UTR length. In many cases, therefore, the true 3' end of the transcript and the associated 3'-most SAGE tag will be upstream of the estimated one. S A G E tags are then assigned to the transcript containing the correct tag sequence. In cases where a S A G E tag is found in more than one transcript, we take advantage of the fact that the S A G E procedure is expected to derive the SAGE tag from the 3'-proximal position (Figure 1.5), and resolve this ambiguity where possible by assigning the correct tag as the one closest to the 3' end of the transcript. If a tag is found in the same relative position in more than one transcript, that tag cannot be resolved and is considered to be ambiguous. In this tag to gene mapping, tags derived from alternative transcripts are consolidated and assigned to a single gene locus, to avoid labeling unique assignment to a single locus "ambiguous" when a tag matches two alternative transcripts. This also means that the same gene may be represented unambiguously by two or more different SAGE tags from different alternative transcripts. These tag mappings, as they are derived from accurate (< 1/10,000 error rate) (Adams et al. 2000; C. elegans Sequencing Consortium 1998) genomic sequence and incorporate expressed sequence information with the predicted transcriptome, are expected to be more complete than mappings from expressed sequences alone. To assess our tag mapping method, a test set of 5606 tags were extracted from sequences of full-length cDNAs, which are assumed to be accurate representations of true expressed transcripts. The accuracy of each mapping method, including mapping to ESTs or to conceptual transcripts, was determined by its ability to correctly assign tags to genes from this validated test set (Table 2.1). As relatively few full-length cDNAs are available for C. elegans, this comparison was done for Drosophila only, although the results are expected to be similar for both organisms. I found that conceptual transcripts constructed using EST data in conjuction with gene predictions produced significantly more accurate tag mappings  56  (85% correct) compared to EST sequences alone (70% correct). There were also slightly more tags assigned ambiguously when mapping to EST sequences alone (6% of tags compared to 4%), most likely to due sequencing errors in the EST sequences that produce extra, erroneous tags, or due to chimeric ESTs. Simulating the situation in which a set of genes have no associated ESTs and thus only predicted protein coding genes from genomic annotation are available for tag mapping, we found that tag mappings derived from such sequences alone (48% correct) were significantly less accurate than tag mappings from conceptual transcripts constructed with estimated UTRs (81% correct). This is not unexpected, given that 56% of SAGE tags extracted from Drosophila cDNAs are derived from the U T R sequence, which is lacking in the predicted protein coding sequences. The high proportion of tags derived from U T R sequence emphasizes the importance of estimating UTRs when no expressed sequence evidence is available. Overall, between 81% and 85% of tag mappings derived from conceptual transcripts are correct when full-length cDNA data is not incorporated, a significant improvement over tag mapping with ESTs or genomic annotations alone. When full-length c D N A data is incorporated into the conceptual transcripts, as is normally the case where possible, 93% of test tags are mapped correctly (Table 2.1); the remaining incorrectly mapped tags are due to gene prediction errors.  57  Table 2.1 SAGE tag mapping accuracy using conceptual transcripts compared to other available sequences.  Test tags assigned to genes by mapping to ...  Tags mapped unambiguously  Unambiguously mapped tags correctly assigned to genes  Full-length cDNAs (5606) EST sequences (204,380) Predicted protein coding sequences from genomic annotation (13,489) Conceptual transcripts constructed from predicted protein coding sequences from genomic annotation, UTRs estimated (13,489) Conceptual transcripts constructed from predicted protein coding sequences from genomic annotation, UTRs derived from EST data or estimated if ESTs unavailable (13,489) Conceptual transcripts constructed from predicted protein coding sequences from genomic annotation, UTRs derived from EST and cDNA data or estimated if ESTs and cDNAs unavailable (13,489)  99% 94% 97%  100% 70% 48%  96%  81%  96%  85%  96%  93%  2.3.4 Analysis of transcriptomes with SAGE As the conceptual transcript sets for Drosophila and C. elegans are based on essentially complete genomic sequence, representing all known and predicted genes, quantification of the genes that are not amenable to SAGE in these organisms is possible. For this analysis, alternative transcripts were considered to be a single gene, so as not to erroneously assign tags as ambiguous i f they are derived from multiple alternative transcripts belonging to the same locus, as described above. Tag mappings are also available (see Methods) that assign tags to individual alternative transcripts. As described above, genes that produce ambiguous tags, shared with other genes, will not be uniquely identifiable in a SAGE library. 6% of Drosophila genes and 12% of C. elegans genes fall in this category, in the common situation of 14 bp S A G E tags extracted using the Nlalll anchoring enzyme. As recent work has yielded a SAGE procedure that produces a 21 bp  58  tag (Saha et al. 2002), it is relevant to ask how SAGE tag length influences the ambiguity of the tag mappings for each organism. We observe (Figure 2.3A and B) that increasing S A G E tag length by 2-3 bp decreases ambiguity in tag assignments, after which increasing length has little effect. This information can be used to make an informed decision about the ideal tag length for a particular S A G E experiment. One drawback of the S A G E technique is that genes that lack the appropriate anchoring enzyme recognition sequence will not be represented in S A G E libraries. For instance, genes lacking a " C A T G " site will not be represented in SAGE libraries constructed with M a l l l . However, judicious choice of anchoring enzyme could improve the utility of the SAGE approach. Based on the conceptual transcript set, there are 261 genes (2% of the annotated transcriptome) lacking Nlalll sites in Drosophila and 563 (3%) in C. elegans. However, when tags are extracted with other four-cutter anchoring enzymes, differing numbers of genes contain no anchoring enzyme site or produce ambiguous SAGE tags (Figure 2.4A and B). Sau3A, which has occasionally been used in S A G E library construction (Virion et al. 1999), allows recovery of more genes unambiguously in Drosophila and C. elegans than Nlalll. Interestingly, however, at higher tag length in Drosophila, Nlalll has this property (Figure 2.3). It is important to consider, however, that MoIII is compatible with the BsmFl tagging enzyme in such a way that an extra base pair of tag length can be obtained, resulting in the potential for a 15 bp rather than 14 bp tag (see Section The Acil (CCGC) enzyme, which has not been used in S A G E library construction, also has the same property and allows more genes to be resolved in Drosophila than M a l l l . This knowledge allows the preselection of an anchoring enzyme, which may be organism-specific, which produces the best representation of the expressed genes within an mRNA population.  59  6% — " — T a g s , Nlalll —A—Genes, Nlalll  5%  ••o--  Tags, Sau3A  ••  Genes, Sau3A  -  4% m  3  o  k...  3  g> !a E  '  i>  i1  il  it  ,  l  •  ,  3  €j  1  1  Ik  2%  1%  y  >  — 1  [J  C  -g  0% 14  15  16  17  18  19  20  21  22  23  24  25  Tag length (bp)  B  18  19  20  21  Tag length (bp) Figure 2.3 SAGE tag ambiguity varies with tag length. Number of ambiguous genes and number of ambiguous tags derived from conceptual transcripts is shown with varying tag length (length includes 4 bp anchoring enzyme site) and anchoring enzyme. (A) Drosophila, total 13,489 gene loci (B) G elegans, total 19,432 gene loci.  60  Figure 2.4 Number of genes not resolvable by SAGE varies with anchoring enzyme. Number of ambiguous genes and genes with no anchoring enzyme site are shown for various restriction enzymes used as the anchoring enzyme. (A) Drosophila, total 13,489 gene loci (B) C. elegans, total 19,432 gene loci. 61  2.3.5 Mapping experimentally derived tags To further analyse the tag-to-gene mappings and compare predicted levels of ambiguity with experimental levels, I mapped C. elegans and Drosophila experimental SAGE tags using this method of tag-to-gene mapping (Figure 2.5). In this comparison, the number of ambiguous genes cannot be compared, as we do not know how many genes are represented by an ambiguous experimental SAGE tag. Instead, the number of SAGE tags that are ambiguous is compared. In both organisms, an even higher proportion of experimental S A G E tags map ambiguously to genes than expected. For C. elegans, 5.0% of 14 bp tags were predicted to be ambiguous (Figure 2.3B), and 7.5% of experimental tags were ambiguous; for Drosophila, the predicted (Figure 2.3A) and experimental ambiguities were 2.5% and 3.6% respectively. It is also notable that the proportion of tags that are mapped to genes increases with expression level, for both organisms.  62  100% 90%  • Ambiguous ^Unambiguous  g_ 80% Q.  ra E  70%  3  60%  CO  50%  IA  o < c  cu  E  cu  Q.  as  i  40% 30% 20% 10% 0% >= 5  >= 1  >= 10  50  Tag frequency  100% 90% Q. Q.  n E U)  I Ambiguous 3 Unambiguous  80% 70%  DJ  iS  LU  60%  <  V) 50% ~B c 40% cu  E  cu  Q. X  cu  30% 20% 10% 0% >= 1  >=2  >=5  10  50  Tag frequency Figure 2.5 Experimental SAGE tags mapped to conceptual transcripts. Subsets of experimental SAGE tags with varying minumum frequency were mapped to conceptual transcripts, and the percentage that mapped ambiguously and unambiguously determined. (A) Drosophila (B) C. elegans.  63  2.3.6 Assessment of human tag mapping with RefSeq A complete gene set is not available for the human transcriptome, and thus SAGE tag ambiguity cannot be directly determined as for Drosophila and C. elegans. However, ambiguity can be estimated from the incomplete set of full-length, high-quality sequences in the RefSeq database by examining the increase in ambiguity with the number of sequences in a transcriptome (Figure 2.6A). 5000 sequences produce -4.5% ambiguous tags, while the full set of 15,274 sequences produces -7.5% ambiguous tags. Extrapolation from this trend suggests that 10% of 14 bp tags will be ambiguous in a set of 30,000 transcripts, 15% in 50,000 transcripts, 22% in 75,000 transcripts, and 27% in 100,000 transcripts. The actual transcriptome size is not yet known. As expected, increasing tag size decreases SAGE tag ambiguity, by approximately 50% for a 21 bp tag (Figure 2.6B), and less thereafter. Less than 1% of human RefSeq sequences lack an Nlalll site, compared to nearly 3% which lack a Sau3A site, and Sau3A also derives tags with higher overall ambiguity (Figure 2.6C). In fact, of all the restriction enzymes that are compatible in recognition sequence with the BsmFl tagging enzyme (see Section 2.3.4), M a l l l appears to allow the most human genes to be resolved by SAGE.  64  8% i  2%  1% 0%  -I  1  0  1  3000  1  6000  1  9000  12000  15000  Number of RefSeq sequences  B  2 0 %  i  i  i  i  i  i  i  i  i  i  i  1  13  14  15  16  17  18  19  20  21  22  23  24  1  25  Tag length (bp)  c  35%  Figure 2.6 SAGE tag mapping with RefSeq. (A) Effect of transcriptome size on unique SAGE tag assignment. Varying size random subsets of RefSeq sequences were sampled and the percentage of genes producing ambiguous 14 bp tags determined. (B) Percentage of 15,274 RefSeq transcripts that produce ambiguous SAGE tags. (C) Percentage of RefSeq transcripts that produce ambiguous tags or have no anchoring enzyme site for various choices of anchoring enzyme.  65  2.4 Discussion This work has shown that utilizing genomic annotation provides a more accurate strategy for the assignment of SAGE tags to gene transcripts. I have also estimated the total resolving power of the S A G E approach, determining that 93% and 86% of genes can be detected in Drosophila and C. elegans respectively, when optimum anchoring enzymes are utilized. Extrapolations for the human transcriptome suggest that between 75% and 90% of genes can be resolved by SAGE. These findings suggest that the use of M a l l l , most commonly used in S A G E experiments, is most advantageous for studies in human cells but is potentially sub-optimal for other organisms.  2.4.1 Ambiguity in SAGE  The observed rate of SAGE tag ambiguity is higher than would have been expected based on statistical calculations that predict 13,489 genes would produce tags with a 98.7% probability of being unique (see Methods). The observed increase in ambiguity is likely due to the nonindependent nature of gene sequences, as there is similarity within gene families and even between distantly related genes. In addition, the presence of repetitive elements in the 3' UTRs of genes can contribute to increased rates of ambiguity. The 6% increase in ambiguity seen in C. elegans compared to Drosophila may be due to a combination of increased gene number and expansion of gene families (Friedman and Hughes 2001). Similarly, increasing transcriptome size as determined by subsampling of human sequences yields higher levels of ambiguity. The same factors of non-independent and repetitive sequence are likely to be responsible for the higher than expected rates of over 40%) ambiguity observed when mapping directly to genomic sequence, which makes such an approach less feasible even for relatively small genomes of ~100  66  M B . For the human genome of 3,000 M B , 14 bp tags cannot be usefully mapped to the genome, as even random sequence would result in each tag occurring by chance 10 times in the genome. The  ambiguity predicted based on the conceptual transcripts is likely to be an  underestimate of the true ambiguity. This is demonstrated by the higher than predicted proportion of ambiguous tags seen in experimental data. There are two factors that contribute to this. First, the genomic annotations remain under constant revision and our current understanding of both transcription and gene prediction suggest that complete transcript predictions will not be available for some time. Thus, the total gene count may be an underestimate as not all genes have necessarily been identified. EST data in Drosophila suggests there may be as many as 1020% more genes than currently predicted (Andrews et al. 2000; Gorski et al. 2003), and 5% of full-length cDNAs do not match known or predicted genes (data not shown). Increased gene number increases the chance of shared sequence and thus shared S A G E tags. Also, increasing numbers of alternative transcripts, which are poorly predicted by genefinders and are constantly being discovered based on expressed sequences, can also increase ambiguity i f an alternative transcript produces a tag that is already present in a transcript from a different locus. Second, 4368%) of 3' UTRs predicted in this analysis are estimated and thus are likely to be longer than the actual UTRs, and so will extend into less conserved intergenic sequence. It is expected that this sequence, as it is more random, will be less likely to be shared between genes, and thus less likely to yield ambiguous tags. As this hypothesis would predict, updating the transcripts with increasing amounts of expressed sequence data has resulted in shorter UTRs and increased ambiguity as more tags are derived from conserved sequence (data not shown). The increase in ambiguity with expression level that occurs in C. elegans is likely because ambiguous tags have the potential to represent the sum of expression of several genes,  67  thus increasing the observed tag frequency. For instance, the tag " A A A A A A A A A A " which can be derived from the poly-A tail of many transcripts is seen at a relatively high frequency in most S A G E libraries (data not shown). Such an increase may not be visible for Drosophila in Figure 2.5 because of the lower overall ambiguity, and fewer tags available with high frequencies in the libraries under study (less than 200 tags with expression > 50 tags). 2.4.2 Choice of tag length, anchoring, and tagging enzyme Based on Figure 2.3, the ideal tag lengths are approximately 16 bp for Drosophila and 17 bp for C. elegans. It is at these points that the curves of ambiguity vs. tag length level off and little is gained from further tag length. The residual level of ambiguity is most likely due to high similarity within gene families and repeats in 3' UTRs that result in sequences identical over regions > 25 bp. This suggests that approximately half of the ambiguity seen for 14 bp tags is due to random production of identical tags by dissimilar genes, which can be distinguished by longer tags. Human sequences similarly produce tags that are less ambiguous at higher tag lengths. However, unlike the curves of Drosophila and C. elegans, the human curve in Figure 2.6 does not level off completely, suggesting that tags of 21, 25 bp or longer continue to allow an increase in resolving power. SAGE procedures that produce tags of 14 bp (Velculescu et al. 1995) and 21 bp (Saha et al. 2002) are commonly in use, and the SuperSAGE procedure produces tags of 26 bp (Matsumura et al. 2003). Although the currently described procedures cannot produce tags of arbitrary length, it is possible to modify any of these methods to produce shorter tags. Shorter tags may be desirable for organisms such as Drosophila and C. elegans where tags of 21 bp do not provide more resolving power than tags of 16 or 17 bp, and are more error-prone and expensive to produce as discussed below. Normally, the linker sequence used in SAGE consists  68  of a P C R primer attached to the tagging enzyme recognition site, where the recognition site is placed at the far 3' end of the linker to maximize the resulting tag length (Figure 2.7A). However, i f one or more bases are appended to the 3' end of the linker, the tagging enzyme site will be further from the tag sequence and thus a shorter tag will be extracted (Figure 2.7B), allowing tags of arbitrary length up to 26 bp to be extracted. A n important additional factor to consider when comparing SAGE tag lengths is the crucial role of longer S A G E tags in novel gene discovery, as longer tags can be mapped directly to the genome. 99.8% of 21 bp tags are expected to occur only once in the human genome, though mappings indicate that only 70% of tags are unique in practise, due to repeats and related genes. Even this lower proportion is still practical, and allows tag mapping to be done independent of the cDNA sequences available in current databases. Approximately 7.5% of human RefSeq full-length mRNA sequences produce ambiguous 14 bp SAGE tags, while 4% produce ambiguous 21 bp tags. As RefSeq is incomplete, these levels are underestimates; extrapolation from ambiguity levels in smaller sets of sequences suggests that ambiguity is between 10% (in a transcriptome of 25,000) and 27% (in a transcriptome of 100,000). These results are in line with those published after our work, estimating that 9% of RefSeq sequences produce ambiguous 14 bp MoIII-derived S A G E tags, but increasing to a tag of 34 bp only decreases ambiguity to 6% (Unneberg et al. 2003). In contrast to tags extracted from RefSeq, tags extracted from UniGene clusters appear to have extremely high ambiguity, as 89%) of 14 bp tags are ambiguous, and even tags of 39 bp are 80% nonunique (Clark et al. 2002; Lee et al. 2002b). Using only high quality non-EST sequences in this mapping decreases ambiguity to 56%, still significantly above the RefSeq estimates. The actual number of unique genes is likely to be somewhere between these estimates, as RefSeq  69  represents only a fraction of the transcriptome and so will underestimate ambiguity, while UniGene, due to problems with clustering and other artifacts, may overestimate ambiguity. A n intermediate level of ambiguity along the lines of our estimates is also suggested by a model incorporating sequence errors and the nonrandom nature of D N A sequences, which predicts 6% of 14 bp tags will be ambiguous in a set of 15,000 sequences, similar to the size of RefSeq, while 25% will be ambiguous in a set of 78,600 sequences, more similar to the size of UniGene (Stollberg et al. 2000). The true level of ambiguity will be dependent on the actual number of genes transcribed. It is important also to consider that increasing tag length decreases the efficiency of sequencing SAGE tags due to both longer S A G E tag length and thus fewer tags per sequence read, and also an increased per-tag error rate. At 1% sequence error, 13% of 14 bp tags are expected to contain sequence errors, while 21 bp tags will be erroneous in 19% of cases. A more recent detailed analysis of sequencing errors in SAGE data has indeed shown that 17.3% of LongSAGE tags contain one or more incorrect bases due to sequencing and P C R amplification errors (Akmaev and Wang 2004). Thus, for Drosophila and C. elegans, a tag length of 14 bp is probably an efficient and cost-effective choice, unless a more complete snapshot of the transcriptome is desired or a particular gene or set of genes of interest is identified as ambiguous with a 14 bp tag and not with a longer tag. Alternatively, if a tag of particular interest is found to be ambiguous, it can be resolved with further experimental procedures such as GLGI (Chen et al. 2000) which derive a larger portion of the expressed sequence represented by a SAGE tag allowing more likelihood of unambiguous gene assignment. As shown in Figure 2.4 and Figure 2.6A, there can be a decrease in the number of genes that cannot be accurately analyzed using SAGE depending on the anchoring enzyme chosen. The  70  number of genes that are not unambiguously identifiable with a 14 bp tag can be decreased by 12% (883 vs. 1001) in Drosophila and 5% (2823 vs. 2962) in C. elegans by using Sau3A instead of M a l l l . Conversely, Nlalll performs 37% better than Sau3A in the human transcriptome (629 vs. 1001 not unambiguously identifiable). Other work has since confirmed our findings that less than 1%> of human genes represented in RefSeq do not contain M a l l l sites and 3% do not contain Sau3A sites (Boon et al. 2002; Unneberg et al. 2003). Notably, 7%> of RefSeq genes do not contain Rsal sites, an enzyme used in an alternative S A G E procedure (Ryo et al. 2000), suggesting this enzyme is not a good choice. Making use of the extra base pair of tag length provided by M a l l l by using a 15 bp tag in analysis would further increase the effectiveness of M a l l l . This would not involve a change in the S A G E procedure, only a change in the way in which SAGE tags are extracted from raw sequence derived from serially ligated tags. The tradeoff of such an analysis would be the loss of the small proportion of tags which, due to the slight variation in cutting position of BsmFl, would only be 14 bp even with M a l l l . M y analysis of the effects of the choice of anchoring enzyme allows the potential for tailoring of the SAGE procedure to produce the most comprehensive results for each organism individually.  71  21 bp tag I  \  TCCRACATGNNNNNNN1TOJNNNNNNNN  Primer AGGYTGTACNNNNNNNNNNNNNNN Anchoring enzyme site  Tagging enzyme site  Tag sequence derived from transcript  B 17 bp tag A  i  \  TCCRACXXXCATGNNNNNNNNNNNNN Primer AGGYTGXXXGTACNNNNNNNNNNN Tagging enzyme site  Additional nucleotides  Anchoring enzyme site  Tag sequence derived from transcript  Figure 2.7 Creating shorter LongSAGE tags. (A) Current LongSAGE procedure produces tags of 21 bp (Saha et al. 2002). (B) Designing the linker sequence with the appropriate number of nucleotides added to the 3' end could produce shorter tags of arbitrary length under 21 bp.  2.4.3 Tags with no gene mapping It is notable that the proportion of experimental S A G E tags which cannot be mapped to genes or genomic sequence is significant (Figure 2.5). This is due in part to the presence of sequencing errors in experimental SAGE data. In Figure 2.5A, 55% of all experimental SAGE tags are mapped to genes, whereas 68% of tags that occur 2 or more times are mapped. This suggests that a significant proportion of the unmapped tags are singletons (occur only once) in the S A G E library under consideration. A similar trend occurs in Figure 2.5B. Singleton tags are more likely to represent sequencing errors, which generally produce tags that do not match  72  known sequences. In rare cases, a sequencing error may produce a tag that, by chance, matches another gene, which can confound analysis. Using more sequences for tag-to-gene mapping, such as a large set of EST sequences or clusters, increases the chance of this occurrence because there is a larger body of sequence to which a match may occur. Using the conceptual transcripts for mapping makes this unlikely, and thus a significant proportion of infrequently occurring, unmapped tags may be due to sequencing errors. Many of these errors can be removed by using only S A G E tags derived from sequence of high quality as determined, for instance, by Phred (Ewing et al. 1998). Techniques have also been proposed (Akmaev and Wang 2004; Velculescu et al. 1999) to remove tags that are likely to represent sequence errors of more highly expressed tags as discussed in Section These methods can help to limit the effect of sequence errors in S A G E analysis. The general increase in proportion of mapped tags with increasing expression level may also be related to the quality of genome annotation. On average, highly expressed genes are easier to study and survey and so are more likely to be known and thus included in the predicted gene set, while rarely expressed genes may not be annotated. Quality of annotation may in part explain why fewer tags are assigned to genes in Drosophila than C. elegans in Figure 2.5, as the C. elegans genome has been available for a longer period and therefore may have more correct annotations. It is somewhat surprising that the proportion of non-singleton tags mapped to genomic sequence (61%) is even lower than the proportion mapped to genes (68%) in C. elegans). This may be because SAGE tags that cross splice boundaries will not exist in genomic sequence. Another reason that experimental SAGE tags may not be mapped to transcripts or genomic  sequence  is  the  presence  of polymorphisms, especially single nucleotide  73  polymorphisms (SNPs). The Drosophila and C. elegans strains used to produce the SAGE data described here are slightly different or derived from those used for genomic sequencing, and so there are expected to be base pair changes in a fraction of S A G E tags that prevent exact matches to genomically derived sequence. The same polymorphism effect can be expected in humans. Large bodies of EST sequence from multiple strains or multiple individuals are likely to represent these polymorphisms; however, it is very difficult to separate true polymorphisms from EST sequence and clonal errors which can cause erroneous mappings as discussed above, and thus this issue is not simplified by the use of EST sequences for tag mapping. The polymorphism rate in most experimental situations is relatively low, thought to be below 1/1000 bp in human sequences (Lander et al. 2001; Venter et al. 2001). However, more recent work suggests a polymorphism rate as high as 1/100 to 1/300 bases, and an analysis using dbSNP to map polymorphisms to transcripts and SAGE tags indicates that 8.6% of human genes may produce tags with sequence variations due to SNPs (Silva 2004). We find that even after comprehensive mapping to the known transcriptome, 15-30%) of highly expressed (>50 copies) SAGE tags are not identified, which cannot be accounted for by sequence errors and polymorphisms. This emphasizes the current incompleteness of genomic annotation and underscores the role that SAGE can play in novel gene discovery.  2.4.4 Limitations of conceptual transcripts As the conceptual transcript sets rely heavily on curated sets of known and predicted genes, errors and omissions in gene predictions result in inaccuracies and incompleteness in the tag mappings. For instance, 7% of tags from known Drosophila cDNAs in the test set were not mapped correctly, due to gene prediction errors. This is mostly likely due to the latency in the process of incorporating experimental data into the curated genomic databases. It is anticipated  74  that as gene predictions are updated, refined, and consolidated with more expression data, the constructed transcript sets will become a more accurate reflection of the true transcriptome and thus yield more accurate tag mappings. For this purpose, the full-length cDNA sequencing projects currently being carried out in multiple organisms have positive implications for S A G E analysis. 2.4.5 Conclusions The conceptual transcript sets and tag mapping procedure described here provide tag-togene mappings for the Drosophila and C. elegans genomes, thus facilitating SAGE analysis in these organisms. They also permit analysis of the limitations of S A G E for transcript identification. Without complete transcript sets, incomplete sets of full-length sequences such as the RefSeq database of human transcripts can be used both for tag mapping and to estimate levels of ambiguity. This analysis allows researchers to make informed decisions as to the SAGE procedure most appropriate for the organism under study and for the applications of the research. Since this research has been published, an equivalent method utilizing U T R length distributions, gene predictions, and genome sequence to create conceptual transcripts has been used for SAGE tag mapping and SAGE assessment in Arabidopsis (Robinson et al. 2004). This demonstrates our method can be usefully applied to other model organisms. Cheaper and more accurate sequencing has already decreased significantly the cost and errors involved in SAGE library construction. Genome annotations are constantly being improved and, in combination with full-length cDNA sequencing projects, will result in more reliable tag-to-gene mappings. Thus, SAGE is likely to continue to be a significant tool for gene expression analysis and genome annotation.  75  Chapter 3 : SAGE and EST analysis of PCD in the Drosophila salivary gland  A version of this chapter has been published. Gorski, S.M., Chittaranjan, S., Pleasance, E.D., Freeman, J.D., Anderson, C.L., Varhol, R.J., Coughlin, S.M., Zuyderduyn, S.D., Jones, S.J., and Marra, M . A . 2003. A S A G E approach to discovery of genes involved in autophagic cell death. Curr Biol 13: 358-363. Co-authorship details: I was responsible for all of the computational analysis described in this chapter with the exception of raw SAGE sequence processing and implementation of statistical methods, and partially responsible for the data interpretation. Where others contributed to the work described, they are credited in the text.  76  3.1 Introduction During Drosophila metamorphosis, the larval salivary glands undergo programmed cell death that is regulated by a transcriptional cascade induced by the steroid hormone ecdysone. Death occurs with features of both autophagy and apoptosis. Transcription of several known ecdysone-induced and cell death genes is upregulated. Subsequently, the entire larval salivary gland undergoes cell death in a rapid, stage-specific, and virtually synchronous manner. These features combine to make Drosophila salivary gland death an ideal system for analysis of gene expression associated with autophagic cell death. Large-scale analysis of the genes involved in this process would indicate which known P C D and hormone signaling genes are involved, as well as indicating which other genes are candidates for involvement and what other processes are important for PCD. This would contribute to the understanding of the regulation of cell death in this tissue and in other systems which undergo PCD, and provide insight into the relationship between the molecular mechanisms of apoptotic and autophagic cell death. Gene expression analysis techniques permit simultaneous measurement of the majority of mRNA species expressed in a tissue, and as genes involved in Drosophila hormone-regulated cell death are transcriptionally up- and down-regulated prior to PCD, differential gene expression is an indicator of potential involvement in PCD. A combination of EST and SAGE analysis is an ideal way to examine gene expression, as no prior knowledge of the genes expressed or present in the genome is necessary. ESTs in particular can identify novel genes and transcripts, and SAGE provides quantitative measurements of gene expression. S A G E and EST libraries created by Suganthi Chittaranjan, Doug Freeman, and Sharon Gorski at the Genome Sciences Centre were the first libraries to comprehensively measure gene expression associated with autophagic cell death during normal metazoan development in vivo. S A G E libraries were created from  77  salivary glands dissected from developmental stages leading up to cell death, 16 hours, 20 hours, and 23 hours (at 18°C) after puparium formation (APF; see Section 1.4). A n EST library was created, representing mixed stages between 16 h and 24 h. At 24 h APF, acridine orange staining is visible and salivary glands begin to be degraded. This chapter describes the computational analysis of this EST and S A G E data, for the purpose of identifying genes and pathways potentially involved in cell death in the salivary gland. EST analysis involved removing poor quality sequences, grouping redundant ESTs, identifying the genes represented, and discovery of potentially novel genes. S A G E analysis involved removing poor quality SAGE  tags, determining differential expression, and  identification of the genes represented. EST sequences and all available Drosophila sequences including genes and genomic sequence were used for S A G E tag mapping using the method described in Chapter 2. This analysis identified known PCD genes, differentially expressed genes not previously known to be involved in PCD, and many genes of unknown function, some not previously predicted. These genes are of potential interest based on their expression patterns. Our data indicates that both apoptosis and autophagy genes are involved in salivary gland death, as are several other pathways, and identifies a number of genes of unknown function that are candidates for involvement in programmed cell death.  3.2 Methods 3.2.1 EST sequence processing and clustering EST sequences were trimmed for vector using crossjmatch (P. Green, unpublished) and for poor quality sequence using Phred (Ewing and Green 1998). The minimum requirement for inclusion in analysis was at least 50 bp of non-vector sequence at Phred 20 quality, corresponding to average 99% accuracy at each base. Sequences were masked for low 78  complexity and repetitive regions using Dustn (NCBI toolkit release 6.1) and RepeatMasker (A. Smit, unpubl.; version available 04042000) using the Drosophila library from Repbase Update (Jurka 2000) vol. 7 no. 2, March 2002, option -dr. Clustering was based on cross_match alignments between all possible EST pairwise comparisons and required at least 95% identity over 80 bp with no greater than a 10 bp sequence end overhang. 3.2.2 BLAST analysis of ESTs B L A S T N (v. 2.0.14) comparison to genes revealed 3 chimeric sequences that were removed from further analyses. Representative EST sequences were chosen as (1) the longest sequence from a cluster or (2) any singleton EST sequence that aligned to genomic sequence (Release 2 of the Drosophila genome) by B L A S T N with at least 95% identity over 80 bp. Comparisons were then conducted between representative EST sequences and all Drosophila annotated genes (14,350 including mitochondrial genes, Release 2 gene annotations), enhanced to include UTRs as described in Chapter 2, and all Drosophila expressed sequences (both ESTs and full-length cDNAs; total of 259620 as of May 14 2002) using B L A S T N with a minimum requirement of 95% identity over 80 bp.  3.2.3 SAGE tag processing S A G E tags were extracted from sequences and counted using a pipeline of Perl scripts written by Scott Zuyderduyn and Richard Varhol. Phred was used to call bases (Ewing et al. 1998) and assign quality scores (Ewing and Green 1998), and vector sequence was detected using crossjnatch (P. Green, unpublished). Ditags significantly shorter or longer than expected (due to errors creating or deleting C A T G sites) were removed. Duplicate ditags, with identical sequences, were identified and all but one copy removed from analysis as described (Velculescu  79  et al. 1995). This can result in removal of true ditags, especially when certain mRNAs and therefore certain tags are highly represented in the library, but this only reduces the quantity of very highly expressed tags by a small percentage, and does not significantly affect differential expression. 14 bp tags were extracted from ditags, and tags were removed i f (1) they were derived from linker sequences, (2) their overall quality, as calculated by multiplying the Phred error probability for each base, was less than 95%, i.e. >5% chance of at least one error, or (3) they were only observed once in all three libraries combined. 3.2.4 SAGE tag-to-gene mapping Conceptual transcripts representing Drosophila genes were constructed and used for tagto-gene mapping, as described in Chapter 2. The sequenced salivary gland ESTs were used in transcript construction to maximize the number of tags that could be mapped. Tags which did not match to genes were matched to Drosophila EST sequences (both 5' and reverse complemented 3'); the public ESTs used are described in Section 3.2.2. When such ESTs overlapped with a gene, or their clone partner overlapped with a gene, the S A G E tag was assigned to the gene as well. Multiple EST matches that resolved to the same gene or genomic location were considered unambiguous. Remaining SAGE tags were mapped to genomic sequence, and also to the reverse orientation of genes and ESTs to identify antisense tags. Gene annotations, including Gene Ontology functional categories and chromosomal locations, were obtained from the FlyBase database (FlyBase Consortium 2002).  3.2.5 SAGE statistics Differential gene expression in SAGE libraries was determined in three pairwise comparisons. Statistical differences were determined using the formulas of Audic and Claverie  80  (1997), a SAGE-specific statistical method which accounts for sampling error. For each tag, the probability (p) that the observed difference in tag frequencies between two libraries would occur by chance is determined, given the respective library sizes. Tags with p<0.05 were considered differentially expressed; multiple testing was not taken into account in this analysis. This algorithm was implemented in Perl scripts and in the DiscoverySpace software (Scott Zuyderduyn, Richard Varhol et al, unpubl.) as well as in Java (Mehrdad Oveisi).  3.2.6 ACEDB database The Drosophila A C E D B database as described in Section 2.2.1 was expanded to include and visualize the newly sequenced ESTs, S A G E tags, and other data such as protein and genome alignments. ESTs were aligned as described in Section 2.2.1. Mapped S A G E tags were positioned on the genome and associated with genes to facilitate searching for tags matching genes, tags matching ESTs, and tags matching genomic sequence only. A l l Drosophila proteins and all SWISSPROT-TREMBL proteins (obtained via SRS Sep 27 2002) were aligned against the genome with B L A S T X (EO.001) to facilitate identification of novel genes. In areas of particular interest, T B L A S T X (E<0.001) alignments of Drosophila pseudoobscura (downloaded from the Baylor Human Genome Sequencing Centre June 12 2003) and Anopheles gambiae (downloaded from N C B I October 2 2002) partially sequenced genomes were also incorporated.  3.3 Results 3.3.1 Processing and clustering of EST sequences A library of 3' ESTs was constructed from mixed-stage Drosophila OreR salivary glands (16 h to 24 h APF). A total of 7680 sequence reads were obtained, which were processed to select high-quality sequences and analyzed to group the ESTs based on sequence similarity  81  (Figure 3.1). Three ESTs were identified as chimeric and removed from analysis. Each of the 1696 representative EST sequences, 1323 of which were single-EST clusters, potentially represents a gene or transcript. The quality of this clustering was determined by comparing the ESTs to annotated Drosophila genes. Two genes were represented by two clusters each instead of one, and one cluster erroneously contained ESTs corresponding to two genes. In addition, 374 singleton ESTs matched genes also matched by other representative EST sequences; these singleton ESTs were not clustered due to small sequence differences, so that the correct cluster membership could not be assured. Overall, the clustering was very successful in grouping ESTs, as few clusters split or joined genes.  7680 sequence reads for 3' E S T s Remove vector and low-quality sequence Mask repetitive regions 5161 high quality 3' E S T s Cluster based on sequence overlap  Unclustered E S T s  Clustered E S T s  Choose only E S T s that align against genomic sequence  Choose longest sequence as representative for each cluster 373 clusters contain 3858 E S T s  1323 singleton E S T s  1696 representative E S T sequences  Figure 3.1 Processing and clustering of Drosophila 3' ESTs.  82  3.3.2 Genes identified by ESTs Comparison of ESTs to predicted Drosophila genes and to publicly available ESTs (Table 3.1) identified subsets of ESTs that represent known genes, and potentially novel genes. The most abundant gene, a mitochondrially encoded ribosomal R N A , represented 14% of the ESTs. Other abundant genes were genes known to be ecdysone inducible, or genes of unknown function. 4 of the 10 most abundant genes were not already represented by any publicly available ESTs. Of the 1043 genes matched by the ESTs, 56 had no previous expressed sequence evidence; thus, our ESTs confirmed the expression of these predicted genes. Notably, 196 representative ESTs (16 clusters and 180 singletons, 38 of which are spliced) did not match any known or predicted genes, or any previously sequenced ESTs or cDNAs (Table 3.1). These ESTs represent potentially novel genes or transcripts. Examples of ESTs corresponding to known genes, novel transcripts, and potentially novel genes are shown in Figure 3.2. A l l representative ESTs, matching genes, and matching public ESTs are listed at http://sage.bcasc.ca/tagmapping/ SG_representative_ESTs.txt. A number of known PCD genes were also identified by ESTs (Table 3.2), representing several parts of the salivary gland death pathway as well as several autophagy genes. A l l of these genes were identified by very few ESTs, as the EST library was not very deep, but further confirmation of the expression of PCD genes was found in the S A G E data (see below).  83  Table 3.1 Summary of sequences matched by representative Drosophila ESTs.  Match predicted genes  Do not match predicted genes  Total  1280  145  • 1425  Do not match public ESTs  75  196  271  Total representative ESTs  1355  341  1696  Match public ESTs  Table 3.2 Cell death genes identified by ESTs.  Gene BR-C Rpr Diap-1 / Th Dark / Apaf-1 CG7188 Dcp-1 Drone / Nc Drice Crq Ced-6 CG6194 CG1643  Description Ecdysone PCD signaling Pro-death gene Inhibitor of apoptosis Caspase activator Probable inhibitor of pro-apoptotic Bax Caspase Caspase Caspase Croquemort, engulfment receptor Similar to C. elegans apoptosis engulfment gene Atg4-like, autophagy gene Atg5-like, autophagy gene  84  Number of ESTs 1 1 4 3 3 1 1 3 1 3 2 1  AE  B  ESL0112d_rut.3pNme  S e l e c t e d DNA  Inr.n.n: I H  ESG0112ai_B10.3p-ime  M  H  i  i  166B96 167861  Iflett^. m ,  II P II  11  S»l«ot.d  MSUI—l Urj-.n  DNA  1656) E S T . M  ICqJurcnsIIZcow In..IIZoow O u t . . I I C l o a r l i R e v - C o n p . . 1 IBnalHsls. . | IGeneFlnd.. I  IZoow  t?tm  Out..[IClearl  |Rev-Comp.7l IDWB., I  [-  3'  D Gene  • 66k  3-  • l—l—l-i  1  ESTs  1 1  r --  ,Exon  Selected  ENS  |0-t£ln:It  ESCO110a_BO3. J p r l r m Colunnsl IZoon  *  -101 xj  [jESG0115bj:i0.3prirne  -Intron  Motive z i  M H S  166449 156539  •  <90J  EST_C  In.71I Zoom OutTTI IClearl  rJfflP  R c » - C o m p 771 l O W . . I IflnalmlsTTI iGene-Find  s-  rj] LJ-  1 8 k  236*21  1 E  S  T  s  tacaaaeacaaaatcata  1 156*51 t t # ' . t t d s c U « « * t t U t t * g a M a t f t t -  .101 t I K U W t l W M H i l W W M p f g M  156511 fBtcctstacttggttcrttgacatagtct • 156541 f c « * f tat e ggcataataat t f t c  1  tHH»LtlLKWt>HatKUt|t 156601 •  _ _ __  atitctaftaja  136£-31^  t b  at-tctc-acct  IS  - . „ . ..tctct nta«na :c•etcec•f«ccggt«« o~c tec tee8 I56'21 f M t a c c a c c t t f | K S ( t a t » t c t c c t C c  [ 156751 1 ) L U ' . > . . U I V . ; I : , « ' . , B L ' . , . . . . ' , 156~R1 (-t.»j«(rits*>Mflt.(MBni^i-.*r>f 1  J I  J  156811 aat-cagaattaaaetcaeaccaacttacs  1  156841 1S6871 156901 156331  tsjcaagcagcgattegctetcatjeaatt ttc*eecce**a3aea,c*»ccet«ttsa.a gacccgacaaacgctstccaccactgacca eKC»*atccttst»8taflt8*Stf-c*t.ct  356791 c g t ' . t ; t c t g t t c t t c a t L g * e « t g ( * a a a t 15* 21 * c c a c c c « a t * * t * * t * * * * t t a « t t « < » ( - • J ' t . ctttac|*cc*cB«est°attatta(cac 157061 a c a c a a c t K a u c n t t l t & c t c c c i c t t t 3571li t » t  Figure 3.2 ESTs representing known and potentially novel genes. Images generated from the Drosophila ACEDB database. (A) ESTs are in the 3' UTR of a gene. (B) ESTs are in the intron of a gene, possibly representing a novel exon or an entirely separate gene. (C) ESTs are not near any predicted genes and are spliced, and therefore are likely to represent novel genes.  85  3.3.3 Processing and differential expression of SAGE tags Three S A G E libraries were constructed from 16 h, 20 h, and 23 h APF salivary glands (Suganthi Chittaranjan and Doug Freeman). Sequences containing S A G E tags were processed to extract tags (Scott Zuyderduyn and Richard Varhol); tags of poor sequence quality, tags potentially derived from amplification bias, and tags only seen once in all the libraries combined were removed. Each library was sequenced to a depth of over 30,000 high-quality tags (Table 3.3). In all libraries, a small number of tags were very highly abundant; at the 23 h timepoint, one tag species accounted for 9.1% of the tags in the library. The salivary gland is predominantly made up of a single cell type, and thus it is expected that this tissue will have reduced complexity compared to libraries constructed from mixed tissues or mixed timepoints. Nevertheless, a large number of tags are seen only a few times, suggesting that despite this reduced complexity there are many genes expressed at low levels, and the number of different genes expressed is most likely even higher than observed. S A G E libraries were compared pairwise to identify genes up- or down-regulated in the three stages profiled. In the 16 h vs. 23 h comparison, 522 (12.1%) transcripts were upregulated significantly (p<0.05), and 331 (7.7%) transcripts were downregulated significantly (p<0.05) prior to cell death. Together, these transcripts account for almost 20% of all transcripts expressed in the salivary gland during these two stages. In the 16 h vs. 20 h and 20 h vs. 23 h comparisons, 288 (7.0%) and 459 (11.2%) transcripts were significantly upregulated, and 255 (6.2%) and 287 (7.0%i)  transcripts were significantly downregulated, respectively.  86  Table 3.3 SAGE library tag counts and frequency distributions.  Tags Library 16 h 20 h 23 h Total  Total  Unique  1  34,989 31,215 30,823 97,027  3,126 3,034 2,963 4,628  32.7% 38.0% 33.3%  % of tag species seen at frequency: 2-10 11-100 100+ 55.7% 50.9% 54.0%  10.4% 9.7% 11.2%  1.2% 1.4% 1.4%  3.3.4 Genes identified by SAGE tags The genes represented by the 4628 different S A G E tags were identified by a series of comparisons to available sequence databases (Figure 3.3). Tags were mapped to conceptual transcripts enhanced to include UTRs as described in Chapter 2, as well as to ESTs and genomic sequence. This comprehensive mapping method was successful in mapping all but 7% of the tags. Of the 4628 tags, 53% were matched to genes directly; half of these tags mapped to UTRs, demonstrating the necessity of using conceptual transcripts rather than simple gene predictions. A n additional 7% were matched to genes through matches to ESTs. 217 tags (5%) unambiguously match ESTs not corresponding to predicted genes; 55 of these match ESTs from the salivary gland library only. 225 antisense tags were also found, where a tag matched both genomic D N A and the reverse strand of an EST, and the tag mapped to a single genomic location and all matching ESTs also corresponded to the same location. Notably, 294 S A G E tags (6%) matched only to genomic sequence, suggesting potential locations for genes which have not been predicted and do not have previously available expression evidence. Genes identified by SAGE tags were associated with Gene Ontology (Ashburner et al. 2000) terms describing their function, as well as chromosome band location. A complete list of salivary  gland SAGE  tag  sequences,  frequencies,  87  and  mappings  can be  found  at  http://sage.bcgsc.ca/tagmapping/SG  SAGE_tags.txt and summary tables at http://www.bcgsc.ca/  lab/fg/dsage. As in the EST libraries, some of the most abundant genes identified by SAGE were ecdysone-inducible genes and mitochondrially-encoded rRNAs, as well as genes of unknown function. Several abundant SAGE tags corresponded only to ESTs. Examination of this gene set as described below, in particular determining the pathways of interest that were found to be differentially expressed, was done in conjunction with Sharon Gorski and Suganthi Chittaranjan based on the results of my analysis. S A G E tags corresponding to known cell death and ecdysone-induced genes associated previously with salivary gland cell death (Jiang et al. 1997; Jiang et al. 2000; Lee et al. 2000) were detected in the S A G E libraries (Figure 3.4). B R - C , E74 and E75 are general ecdysoneinduced primary response genes shown previously to regulate salivary gland expression of cell death genes. E93 is a stage and tissue-specific ecdysone-induced primary response gene required in prepupal salivary glands for maximal expression of both ecdysone-induced and cell death genes. Consistent with a previous report (Lee et al. 2000), we detected increased expression from 16 to 23 h APF of the pro-death genes Dark, Drone and Crq. The genes E93, Rpr and Diap2 were detected but expressed at low levels. Some of the genes showing differential expression that are involved in pathways other than those previously known to be active in the salivary gland are shown in Table 3.4. Multiple apoptosis genes not previously associated specifically with salivary gland cell death were identified both in the EST and SAGE libraries, as were a number of autophagy genes. The caspases Drone, Dcp-1, Drice, and Dredd were present, as were Diap-1 and Diap-2. The Bcl-2 gene Debcl and the pro-death gene Sickle were upregulated. Nine putative homologs of yeast autophagy genes were detected, with marked increase in expression of Atg4/apg4/aut2. Multiple  88  cathepsins, proteins involved in lysosomal degradation which is part of the autophagic process, were also upregulated. These data identify specific autophagy genes which may be involved in salivary gland death, and indicate that this autophagic cell death may use some of the same molecular components as does apoptotic cell death.  89  4628 unique tags from 3 SAGE libraries Map tags to genes (conceptual transcripts)  2067 tags do not map to^ genes  107 tags (2%) with ambiguous gene mappings  2454 tags (53%) unambiguously represent 2191 genes  Map tags to ESTs and to genomic sequence  533 tags map to ESTs unambiguously  303 tags (7%) do not match any known sequences  937 tags (20%) with ambiguous EST and/or genomic mappings  294 tags (6%) map unambiguously to genomic sequence only  217 tags (5%) map unambiguously to novel ESTs  Figure 3.3 Mapping of Drosophila SAGE tags to genes, ESTs, and genomic sequence. SAGE tags were mapped iteratively, first to genes, then to ESTs and genomic sequence. Mappings were to the 3'-most enzyme site. Ambiguous mappings refer to SAGE tags that exactly matched to two or more sequences.  90  0.0006 0.0005 0.0004  1  BFTZ-F1  ->  EcR/USP - >  Rpr Hid Dark Drone Crq  BR-C E74 E93 E75  —I  • SG16 • SG20 • SG23  Cell * Death  Diap2  0.0003 0.0002 0.0001  in LU  o  co CD  p  UJ  CM CO  E o  Figure 3.4 Expression of known salivary gland cell death related genes in SAGE libraries. Observed number of SAGE tags corresponding to known salivary gland death related genes were converted to frequencies for purposes of comparison. SGI 6, SG20 and SG23 refer to the 16 h, 20 h, and 23 h SAGE libraries, respectively. The inset of a simplified cell death pathway indicates the relative timing of expression for the genes indicated (Jiang et al. 2000; Lee et al. 2000).  91  Table 3.4 Differentially expressed genes associated with salivary gland autophagic cell death in Drosophila SAGE libraries. Tag Sequence  GO id  GO Molecular Function  3746 8079 3743 3743 3743 3743 3743 3743 3743  translation elongation factor translation termination factor translation initiation factor translation initiation factor translation initiation factor translation initiation factor translation initiation factor translation initiation factor translation initiation factor  4879 4879  ecdysone-induced protein ecdysone-induced protein ecdysone-induced protein ligand-dependent nuclear receptor ligand-dependent nuclear receptor  5509 8113  calcium binding protein-met-S-oxide reductase  CG3350  3702 16563 3700 3700  RNA pol II transcription factor transcriptional activator transcription factor transcription factor  3.03E-19 1.95E-02 8.10E-12 1.95E-02 8.24E-04 4.22E-02 4.12E-04  Akap200  5079  protein kinase A anchoring  1.95E-02 5.69E-03  CG16708  4674 4674 16308 4725 4713 17050 4702  protein serine/threonine kinase protein serine/threonine kinase 1-PI-4-phosphate 5-kinase protein tyrosine phosphatase protein tyrosine kinase D-erythro-sphingosine kinase receptor signaling S/T kinase  1.32E-02 2.21E-02 3.64E-02 8.35E-02 4.24E-11  Emp CG3829  5044 5044 5044  scavenger receptor scavenger receptor scavenger receptor  Debet  16506  apoptosis activator  3928 3796 4565 4213 4192 4193 16946 4217 4217  apg4/aut2-like; cysteine protease RAB small monomeric GTPase lysozyme beta-galactosidase cathepsin B cathepsin D cathepsin E cathepsin F cathepsin L cathepsin L  16 h  23 h  P-value  Gene  11 1 1 0 0 4 0 0 60  24 9 25 10 10 15 5 4 81  8.34E-03 5.69E-03 5.82E-08 4.12E-04 4.12E-04 4.35E-03 1.95E-02 4.22E-02 7.70E-03  Ef1  65 1103 1371 2 2 32 121 2 1  0 38 540 36 8 532 2339 *11 *7  3.28E-18 1.99E-239 3.09E-58 1.79E-10 3.64E-02 2.32E-135 0.00E+O0 6.83E-03 2.50E-02  Eig71Ed  1 1 0 0  *6 *8 *5 *11  4.79E-02 1.30E-02 2.16E-02 2.34E-04  Bun  3 0 0 0 2  67 5 33 5 14 4  Protein Synthesis ATATTGTCAA AGCAGGGGGA ATGAAAAACA TGGGAGGATG# TGGGAGGATG# ACCCACGAGC GGGTGTCTCT ATGAGCTATG TTTGAATAAC Ecdysone/hormone AACTGTAATG AACGAGGGAT AGACGGATTC GGTTTATTGT TAGCAACTAG AGTCAAAAGG GATCCAGCCA TGGATTCATA GCCGAATCTG  gamma  CG5605 CG3845 CG8277 elF-4E CG9769 CG10192 CG7439 elF-5A  Eig71EI Eig71Ej Hr78 Hr78 CG15505 CG7592 Eip63F-1 Eip71CD  Transcription Factors TTAAGTTCGT TAGCTGGTGT TCCAATTCCG GAGCAGGAGT  EP2237 CG9954  Signal Transduction CGAATAATCC AGAATCCAAC TGTACACTTC CTGCGCTTGT TAAATAAAGG AGAAGATAAA CAAGTAACCA TAGCTCTTAG TGAACGAGGA  0 0 0 1  10 5 9  4 1 2 4 1  13 7 8 *0 38  Trafl Doa Doa Sktl Ptpmeg PR2 CG8655  Cell Death TTCCGCATAT GCTTTCGTGT CCCGTTCCAC GGCACCAGTC TATTTTCTTT  CG12789  Sickle  Autophaqy 8.20E-11 30 0 TAGCGCTTAG 12 1.44E-01 7 TAAAATTGCT 4.22E-02 4 0 GATCCAGCCC 3.86E-160 566 19 CATCATCATC 1.53E-03 3 15 GTTTCTTCCG 43 2.23E-08 8 GGCAACGATC 1.04E-30 66 240 AAATAAATTG 4.22E-02 4 0 TTCTTCAACC 1.04E-02 15 ATGGCAGAGA 5 2.63E-139 620 58 TATGATATAG * tag number corresponds to the SG20 SAGE library # ambiguous mapping to two similar genes  CG6194 Rab-7 CG11159 CG3132 CG10992 cathD CG17283 CG12163 Cp1 Cp1  92  3.4 Discussion This study represents the first comprehensive analysis of genes associated with autophagic cell death in vivo. Our work was published alongside similar research by Lee et al (2003), who used oligonucleotide microarrays to examine gene expression in Drosophila salivary gland PCD and radiation-triggered cell death. Their findings were consistent with those described in this chapter; they similarly reported differential expression of apoptosis and autophagy genes, and changes in other cellular pathways. In addition to providing important clues to the molecules involved in PCD, however, our work also contributes significantly to knowledge of the Drosophila genome. With respect to the latter, this work provides evidence for the expression of more than 4,000 transcripts, including over 500 previously unpredicted, almost 300 previously undetected, and at least 225 overlapping and divergently transcribed. In total, 1244 different transcripts were expressed differentially prior to salivary gland cell death, and 377 of these did not correspond to predicted genes. Detection of these transcripts exemplifies the advantage of the SAGE and EST methods, ideally suited for the discovery of new genes.  3.4.1 Use of EST and SAGE to identify genes The EST set, though small, comprised significant novelty. 341 ESTs (20%) did not match to predicted genes, and 196 of these (12%) did not correspond to any previously sequenced ESTs at the time of our analysis. Many of these ESTs not corresponding to genes likely represent previously unannotated genes, but some will represent novel splice variants or 3' ends of already predicted genes. This data therefore suggests that the number of genes in Drosophila may be substantially underestimated, as suggested by previous EST analysis studies (Andrews et al. 2000; Posey et al. 2001) and more recent expression analyses (Hild et al. 2003). Strikingly, despite the previous sequencing of over 200,000 Drosophila ESTs and cDNAs, over half of the  93  ESTs not matching genes were unrepresented in public databases. This demonstrates the uniqueness of this salivary gland-specific resource, and suggests that deeper sequencing from specific tissues will be necessary to identify all Drosophila genes and transcripts. The genes represented by such ESTs are likely to have a unique role in the salivary gland, possibly in PCD. The SAGE tag-to-gene mapping results highlight one of the main advantages of the SAGE method compared to other large-scale profiling methods such as oligonucleotide- or cDNA array-based analyses. SAGE has the potential to reveal transcripts not previously identified, and indeed 45% of the SAGE tags did not correspond to known or predicted genes, consistent with more recent analyses of Drosophila S A G E libraries (Lee et al. 2005). It is possible that some of these S A G E tags do not correspond to predicted genes because they are derived from noncoding transcripts, as recent work in several species has demonstrated that such transcripts may account for a significant fraction of expressed mRNAs (Ota et al. 2004; Tupy et al. 2005) but only protein-coding transcripts were included in the Drosophila genome annotation at the time of my analysis. 217 tags, or 5% of the set, corresponded unambiguously to ESTs only. 55 of these mapped specifically and unambiguously to the novel salivary gland ESTs, thus confirming  the  expression of these putative  salivary gland-specific novel genes and  demonstrating the advantages of a complementary tissue-specific 3' EST and SAGE approach. 294 tags, or 6%, mapped uniquely and unambiguously to genomic D N A and may represent novel genes or novel 3' ends or splice forms of already predicted genes. Our S A G E data provides the first evidence of expression for these putative transcripts. In at least 225 cases, SAGE tags represent apparent antisense transcription. Some fraction of these antisense tags could arise out of mispriming in cDNA library construction as discussed in Section, but such errors are likely to account for only a few percent of the observed  94  tags-. These tags suggest the existence of previously unpredicted transcripts that may represent divergently transcribed overlapping gene sequences; this is not surprising as current gene finding programs are unable to readily detect overlapping genes (Rogic et al. 2001). More recent work analyzing SAGE and other expressed sequence data has indeed identified many such overlapping genes and antisense transcripts in mammals (Wahl et al. 2005; Yelin et al. 2003), and pairing of sense-antisense overlapping transcripts is conserved in evolution (Dahary et al. 2005). The significance of these transcripts is not known, but it is possible that they act as a form of antisense regulation at the transcriptional or post-transcriptional level. Even after mapping to all Drosophila sequences available at the time of this analysis, 303 tags, 7% of the SAGE set, were left unassigned. The unmapped tags could be due to issues discussed in Section 2.4.3 such as sequence polymorphisms or common sequencing errors, or to lack of representation in the available sequence resources. The latter could occur i f tags represented genes in heterochromatic regions, which at the time of my analysis were not sequenced but have since been shown to contain significant numbers of genes (Hoskins et al. 2002). It is also possible that unmapped tags span adjacent exons that are currently not represented in the EST or cDNA data set, and as such would not be identified in genomic sequence. Overall, the salivary gland EST and SAGE data confirms expression of predicted genes and can also aid in gene discovery, demonstrating the importance of expression data for genome annotation, especially data from specific tissues.  3.4.2 Expression of PCD genes In general, the gene expression profiles of known salivary gland cell death genes as generated by S A G E are consistent with previous reports and can temporally distinguish known upstream transcriptional regulators from downstream death effector molecules. The genes E93,  95  Rpr and Diap2, which were detected only at very low levels, were analyzed further by quantitative RT-PCR by Suganthi Chittaranjan and Shaun Coughlin and this indicated expression profiles consistent with previous studies (Jiang et al. 1997; Lee et al. 2000). RT-PCR analysis of 96 genes chosen to have varying expression levels were concordant with respect to the direction of change in expression for 91/96 (95%) of the genes tested and the overall correlation in folddifference of 0.48 was significantly positive. Thus, the S A G E data is confirmed by and consistent with lower-throughput, quantitative RT-PCR results. Genes known to be expressed during salivary gland P C D but not detected in our SAGE and EST libraries were EcR, USP, pFTZ-Fl and hid (Figure 3.4 inset). A l l of these genes possess putative M a l l l recognition sites and thus theoretically can be associated with a S A G E tag. However, EcR, USP and PFTZ-F1 act upstream of the primary response genes BR-C, E74, E75, and E93, and thus may be expressed maximally prior to 16 h APF. This interpretation is consistent with Northern analysis of pTTZF l (Jiang et al. 2000). Alternatively, these genes may be expressed at very low levels. Failure to detect Hid was not surprising because RT-PCR analysis indicates it is expressed at levels lower than Rpr which was detected only two times at the 23 h timepoint. The degradation phase of autophagic cell death appears to utilize components of the machinery required for autophagy. While autophagic cell death was shown previously to share morphological features with autophagy, there had been no prior connection between the molecules involved in these two processes at the time of our study. Expression was detected in the EST library of a gene similar to Atg5, involved in one of the ubiquitin-like pathways required for autophagy in yeast (Ohsumi 2001). Particularly highly induced in the S A G E libraries was a gene similar to Atg4 which encodes a novel cysteine protease, whose yeast homolog processes and activates Atg8, an ubiquitin-like protein. Multiple lysosomal enzymes, including cathepsins,  96  were also upregulated prior to autophagic cell death. It is not expected that these genes would be specifically induced in apoptotic cells because the bulk of cellular degradation occurs within a macrophage or neighboring cell. Our analyses showed that multiple genes involved in apoptotic cell death are also expressed during autophagic cell death, supporting the view that these two processes occur simultaneously or utilize common pathway components (Baehrecke 2002; Lee and Baehrecke 2001; Lee et al. 2000). It is reasonable to expect, then, that some of the novel autophagic cell death associated genes identified in this study may also be associated with apoptotic cell death. Given the relationship of both autophagic and apoptotic cell death to disease, and the concomitant use of apoptotic genes as therapeutic targets (Reed 2002), it is essential to develop a detailed understanding of the molecules required for both processes. The genes discovered here provide a powerful starting point for protein function-based studies to determine the mechanisms essential for the execution of autophagic cell death and to understand how its unique components are integrated with those of known apoptotic cell death pathways.  3.4.3 Novel putative PCD genes Many genes involved in other pathways, not specifically related to cell death, also demonstrated changes in gene expression levels between the three S A G E libraries. Multiple other ecdysone- and hormone-inducible genes were differentially expressed. Upregulation of several translation initiation, elongation, and termination factors is consistent with the concept that autophagic cell death requires active protein synthesis. Multiple transcription factors, including several with no known function in Drosophila, were differentially expressed between timepoints. Components of multiple signal transduction pathways, including genes related to cytoskeletal remodelling, Ras, and defense response signaling, changed in expression level, indicating what is likely to be a complex interplay of pathways in autophagic death.  97  In addition to assigning a possible new role to genes already annotated functionally, we have implicated in the autophagic cell death process more than 732 differentially expressed genes with unknown function. As these genes change significantly in expression during stages leading up to cell death, their expression implicates them as having a role in salivary gland death, either directly or indirectly. 377 of these differentially expressed genes were unpredicted and 48 of these are represented solely by our salivary gland ESTs. 3.4.4 Conclusions This work has demonstrated the value of large-scale expression data for examining gene expression patterns in Drosophila, and the utility of analysing S A G E and EST data for identification of novel genes. The genes observed and differentially expressed indicate that programmed cell death in the Drosophila salivary gland utilizes components of both the apoptosis and autophagy systems, and suggests a complex regulation of this process by multiple signalling pathways. A major challenge is to identify which genes, both previously described and newly discovered, are likely to play an important role in the autophagic cell death process. One computational approach to this analysis is to examine the human homologs of these genes, not only in terms of function but also in terms of expression, to identify which genes have expression patterns consistent with a role in programmed cell death across multiple species.  98  Chapter 4: Identification of PCD genes in Drosophila and cancer gene expression data  99  4.1 Introduction Programmed cell death (PCD) is essential for cell homeostasis, and cells which lose the ability to initiate or execute this process have the potential to grow in an uncontrolled manner. Genes involved in the apoptosis pathway are commonly altered in sequence or expression in cancers, as inhibition of this pathway is one of the necessary steps in oncogenesis. However, the pathways regulating cell death are complex, and in many cases the genes that are most relevant to oncogenesis are unknown. There is great potential for the use of gene expression data to identify the relevant genes and pathways which are altered in cancers. In mammals, posttranscriptional regulation at the translation or protein activity level is widespread and thus changes in mRNA levels alone can be insufficient to identify active pathways. However, the widespread gene deregulation that occurs in cancers can cause genes normally regulated at the protein level, such as caspases, to change in expression at the mRNA level and thus be detectable by analysis of differential gene expression (Takita et al. 2000). In this chapter, I aim to identify genes involved in regulation or execution of PCD, specifically those which are altered during oncogenesis either by activation or suppression. The larval salivary glands of Drosophila melanogaster undergo programmed cell death during development in a concerted, precisely timed, transcriptionally regulated manner, making this tissue an ideal system in which to identify genes involved in PCD by gene expression analysis. i  As P C D is altered in cancer and conserved from Drosophila to humans, genes which are differentially expressed during cancer development and have orthologs differentially expressed during Drosophila P C D are candidate PCD-related genes with involvement in oncogenesis. Using S A G E data as described in Chapter 3, genes differentially expressed during Drosophila PCD were identified. Human orthologs for these Drosophila genes were found, and the 100  Drosophila S A G E tags linked to their human SAGE tag counterparts. The expression of human S A G E tags was compared in cancerous and non-cancerous libraries representing a variety of tissues. Choosing appropriate statistical cutoffs, genes differentially expressed during cancer progression with orthologs differentially expressed during Drosophila P C D were identified as candidate PCD-related genes. Included in this set were genes with a previously defined role in PCD, or an association with PCD or cancer. The genes were classified by functional categories, and the functions overrepresented in the candidate gene set determined. The observed functions point to a role for several cellular processes related to autophagy in both cancer progression and PCD.  4.2 Methods 4.2.1 Drosophila PCD expression data SAGE libraries were obtained and processed as described in Chapter 3, such that only tags of minimum quality 99% and minimum total count of 2 were considered. The A C E D B database and associated Drosophila gene annotations and genomic sequence were updated to Release 3.1 (Misra et al. 2002). SAGE tags were mapped to genes from this new release using methods described in Chapters 2 and 3; tags mapped to ESTs and genomic sequence were not considered in this analysis, as no gene predictions are available and so human orthologs of these putative novel genes could not be identified. Three library vs. library statistical comparisons were performed: 16 h vs. 20 h, 16 h vs. 23 h, and 20 h vs. 23 h, to identify all genes which changed in timepoints leading up to cell death. P-values were determined for each SAGE tag for each comparison using the AudicClaverie algorithm used in Chapter 3 (Audic and Claverie 1997). A mysql database was created to hold tags, counts, p-values, genes, and associated gene data such as gene functions for easy 101  querying across datasets. Data from a similar experiment on the salivary gland using Affymetrix oligonucleotide arrays (Lee et al. 2003), giving gene expression levels at 6 h and 12 h APF (at 25°C, equivalent to 12 h and 24 h A P F at 18°C) and associated p-values for differential expression, were also obtained and included in the database. 4.2.2 Human cancer expression data 265 human normal and cancer S A G E libraries were downloaded from the Cancer Genome Anatomy Project website (http://cgap.nci.nih.gov/) on October 15 2004. From these, the libraries included in analysis were (1) not derived from cell lines or cultured cells, (2) short S A G E (14 bp tags), and (3) derived from tissues for which both normal and cancer data were available. S A G E tags were mapped using SAGE Genie (Boon et al. 2002) "best gene for tag" mappings linking tags to UniGene entries, downloaded at the same time as the S A G E libraries. S A G E Genie was chosen as the mapping method as this database utilizes full-length cDNA sequences from the RefSeq and M G C databases as well as mRNAs from GenBank and ESTs to compile a set of mappings that takes into account the reliability of each data source. For each tissue, a comparison of the expression of each tag in normal libraries vs. cancer libraries was done and p-values computed with the Audic-Claverie algorithm, implemented as a commandline program by Mehrdad Oveisi. RefSeq, LocusLink, and UniGene database identifiers were also downloaded from NCBI (June 22 2004) so that SAGE tags could be linked to genes in LocusLink and RefSeq databases. SAGE tags, counts, p-values, mapping and library information, and all database mapping links were included in the same mysql database used for Drosophila data.  102  4.2.3 Drosophila-human orthology The InParanoid program (Remm et al. 2001) was used to determine Drosophila-human orthologs; this program processes B L A S T results to identify orthologs and paralogs. The publicly available InParanoid ortholog pairs for these species were only computed on SwissProt sequences, which do not represent the complete set of predicted proteins in Drosophila and cannot be completely mapped to NCBI databases used for S A G E tag mapping. Thus, the InParanoid software was run locally on compiled sequence sets. 18,489 Drosophila predicted proteins (Release 3.1) were extracted from A C E D B , 18691 human RefSeq proteins were downloaded from UCSC (ftp://genome.cse.ucsc.edu; April 2003 genome build), and 9478 predicted yeast proteins were downloaded from the Saccharomyces Genome Database (June 11 2003). RefSeq proteins were used because they are high quality and full-length. Reciprocal pairwise B L A S T P comparisons were performed between each species pair, a score threshold of 50 was applied, the results were input into InParanoid, which identified Drosophila-human orthologs with yeast as an outgroup. The output was parsed using a script written by Keith Boroevich, and integrated into the mysql database to link Drosophila genes to RefSeq sequences.  4.2.4 False discovery rate To determine a p-value cutoff for differential expression for each of the Drosophila and human S A G E library comparisons, a desired false discovery rate (FDR) was chosen and then a p-value approximating this F D R determined. A range of p-values cutoffs for Drosophila comparisons (0.001, 0.01, 0.05 and 0.1) and human comparisons (0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05 and 0.1) were applied and the number of genes differentially expressed in each comparison determined. The F D R was then calculated for each p-value as described in Storey (2002):  103  Estimate of FDR =  (# observations not significant) x (p-value cutoff) (# observations significant) x (1 - p-value cutoff)  The actual F D R is different for each of the three Drosophila comparisons and the nine human comparisons performed, and so an average FDR for Drosophila and an average FDR for human were calculated. A n average F D R close to 30% was achieved for Drosophila p-value cutoff of 0.05 and human p-value cutoff of 0.01. For an average F D R close to 10%, as used in the analysis, the Drosophila p-value cutoff was 0.01 and the human p-value cutoff was 0.001. 4.2.5 Expected proportion of differentially expressed and PCD genes To determine whether the overlap between the P C D and cancer sets was greater than expected given a random selection of genes, the expected number of human genes in the pcdcancer set was determined to be:  Expected # human genes =  (# human genes differentially expressed) x (% of all human genes that have Drosophila orthologs) x (% of all Drosophila orthologs that are differentially expressed)  The expected number of Drosophila genes in the pcd-cancer set was determined in an equivalent manner. Similarly, the number of human known PCD genes expected to be identified by chance in the pcd-cancer set was:  Expected # human PCD genes =  (# human genes appearing in pcd-cancer set) x (% of human genes with orthologs that are known PCD genes)  4.2.6 GO analysis Gene Ontology (Ashburner et al. 2000) terms were obtained for Drosophila genes from FlyBase (FlyBase Consortium 2002). Human genes were linked to GO terms using DiscoverySpace (Varhol, Zuyderduyn et al, unpubl.). For the pie chart of GO terms (Figure 4.3),  104  genes associated with terms below level 4 of the GO hierarchy were mapped to parent terms at level 4. The GoMiner software (Zeeberg et al. 2003) for identification of overrepresented functions in a gene set requires UniProt IDs, and thus Ensembl was used to associate the LocusLink IDs of the human pcd-cancer genes with UniProt IDs (as of Oct 26 2004). This set of genes was input into GoMiner, and associated GO categories from any of the hierarchies (Molecular Function, Biological Process, or Cellular Component) determined. The P C D upregulated subset of the pcd-cancer set consisted of genes which (1) increased in expression between 16 h and 23 h, or (2) did not change in expression between 16 h and 23 h, and increased in expression between 16 h and 20 h. The remaining genes, which decrease in expression in the 16 h vs. 23 h or 16 h vs. 20 h comparisons, made up the PCD-downregulated subset. In GoMiner, each GO category is given a p-value representing the probability that the observed number of genes would be associated with that category by chance, based on a Fisher exact test. The GO categories with the lowest p-values are the most highly overrepresented.  4.3 Results 4.3.1 Drosophila PCD and human cancer expression In total 97,027 high-quality SAGE tags were sequenced for the three Drosophila SAGE libraries constructed from salivary glands at developmental stages leading up to PCD, at 16 h, 20 h, and 23 h after puparium formation at 18°C (Chapter 3, Gorski et al. 2003). 2960 of the 4628 different tags in these libraries could be unambiguously mapped to predicted genes, giving a set of 2313 genes expressed prior to cell death in Drosophila. SAGE libraries constructed from human cancerous and normal tissues were obtained from the Cancer Genome Anatomy Project (Boon et al. 2002). 9.1 million S A G E tags from nine 105  tissues were included in analysis, with the largest proportion of tags derived from brain and breast samples (Table 4.1). Of the 541325 different tag species, 233614 were unambiguously mapped to 40213 human UniGene identifiers using CGAP's S A G E Genie tool (Boon et al. 2002). These corresponded to 16655 LocusLink records, which approximates the number of genes. Orthologous genes between the Drosophila and human genomes, those thought to be derived from a common ancestor and thus expected to have related function, were identified using the InParanoid algorithm (Remm et al. 2001) using yeast (Saccharomyces cerevisiae) as an outgroup. This identified 5792 Drosophila genes which were putative orthologs of 8723 human genes. Cases where more than one human gene was associated with a Drosophila gene could arise from gene duplication since the divergence of the human and Drosophila lineages.  Table 4.1 Tissues and SAGE libraries from CGAP used for analysis of cancer expression. Brain  Tissue N* C* T*  Libraries Tags"  Libraries Tags  Libraries Tags  Breast  Colon  Kidney  Lung  Pancreas  Peritoneum  Prostate  Stomach  Total  9  8  2  1  1  1  1  2  2  27  769K  446K  98K  41K  89K  22K  54K  123K  51K  1.693K  69  22  2  1  3  2  1  3  4  107  5.364K  1.255K  97K  100K  159K  67K  33K  154K  249K  7,478  78  30  4  2  4  3  2  5  6  134  6.133K  1.701K  195K  141K  248K  89K  87K  277K  300K  9.171K  * N-Normal, C=Cancer, T=Total ** "K" refers to thousands of tags  4.3.2 Differentially expressed genes To identify genes which change in expression in both PCD in Drosophila and in cancer, the genes differentially expressed in each system need to be identified, orthologs linking them established, and the overlap between the two systems determined. Each Drosophila and human S A G E tag was assigned a p-value for each library comparison; three pairwise comparisons  106  between the three Drosophila libraries were performed, and nine normal vs. cancer comparisons were performed, one for each human tissue. Comparisons were not done across tissues, as substantial, non-cancer related differences in gene expression would be expected. Based on the distribution of p-values obtained, a p-value cutoff of 0.01 for Drosophila tags and 0.001 for human tags was chosen, to correspond with a false discovery rate of approximately 10% (see Methods). This indicates that for each species, the chance that a gene will be identified as differentially expressed when in fact there is no real difference and the apparent difference is due to chance is estimated to be 10% or 0.1. These p-value cutoffs resulted in 426 Drosophila genes and 4509 human genes identified as differentially expressed. Using the orthologs as identified above, and linking between the UniGene and RefSeq databases, the genes that are differentially expressed in both Drosophila PCD and human cancer were identified. In total, 171 human genes, orthologous to 143 Drosophila genes, changed in expression in both systems, hereafter referred to as the "pcd-cancer set" (full listing of genes and expression at http://sage.bcgsc.ca/tagmapping/pcd cancer table.txf). This set contains 2-fold more genes than expected by chance, given the number of genes differentially expressed in each system and the number of orthologs (see Methods), indicating the two processes of PCD and cancer have significant overlap. The most common pattern in this set is opposite expression in PCD and cancer; 48 human genes upregulated in cancer have orthologs downregulated in PCD in Drosophila, and 18 human genes are downregulated in cancer with counterparts upregulated in PCD (Figure 4.1). This suggests that overall P C D is likely to be inhibited in cancer. The 44 genes which show the same direction of change in PCD and in cancer may have more complex roles, or may represent pathways within tumors that are actively attempting to prevent tumor  107  growth. 61 genes are both up- and down-regulated in cancer in different tissues, indicating the variety of responses to cancer and the variety of pathways that may be active in different tissues.  Upregulated in PCD  J |  Downregulated J in PCD  Upregulated in cancer  Downregulated in cancer  Figure 4.1 Expression categories of human pcd-cancer genes. The 171 human genes in the pcd-cancer set were divided into categories based on their expression in cancer and the expression of their Drosophila orthologs in PCD. Genes shown in both circles were upregulated in cancer in one or more of the nine tissues examined and also downregulated in one or more tissues.  4.3.3 Roles of genes in PCD and cancer Gene annotations and Gene Ontology terms were searched to identify 434 human and 50 Drosophila known programmed cell death genes in the respective genomes. Three of the human genes in the pcd-cancer set were in this list of known human PCD genes: P R K A G 1 , a subunit of the A M P K kinase involved in the regulation of autophagy (Samari and Seglen 1998), SGPL1, involved in ceramide-induced apoptosis (Reiss et al. 2004), and IkBa, a pro-apoptotic molecule (Castro-Alcaraz et al. 2002). Two of the Drosophila genes in the pcd-cancer set were in the list of known Drosophila PCD genes: Sec61a, involved in neuronal cell death (Kanuka et al. 2003), and BR-C, an early gene in ecdysone death signaling (Jiang et al. 2000) that was identified in my  108  analysis as orthologous to a human transcription factor that controls cell proliferation and death (Kamio et al. 2003). In both cases, the number of known P C D genes identified is not significantly greater than expected by chance given the size of the pcd-cancer set. This analysis was repeated based on Affymetrix microarray data from the Drosophila salivary gland (Lee et al. 2003) in place of the SAGE data, similarly identifying differentially expressed genes and human orthologs, and again no more known PCD genes were identified than expected by chance (data not shown). Many genes, although not core components of PCD pathways, have been associated with cell death in literature. For instance, a gene may induce cell death when overexpressed or knocked out, but the mechanism may be unknown. Brief annotation and literature database searches were performed for each of the 171 human genes in the pcd-cancer set to identify which genes had been associated with cell death in this way. Similarly, implied roles in cancer, for example inhibition or promotion of tumor cell growth, involvement in translocations, or mutations in tumor cells were investigated. The majority of genes in the pcd-cancer set have some known function, and a significant portion of these were associated with cell death or cancer in the literature (Figure 4.2).  109  Known function  Role in PCD  Role in cancer  Figure 4.2 Categorized roles of human genes in pcd-cancer set as found in the literature. Searches of PubMed and other NCBI databases for each gene identified genes associated with cell death or cancer in previous experiments. Genes marked as no known function have not been studied at a molecular level.  4.3.4 Functional categorization and overrepresentation To understand the cellular pathways and functions represented by the human genes in the pcd-cancer set, the Gene Ontology (GO) Biological Process terms associated with each gene were determined. 101 of the 171 genes had such annotated functions, representing a wide range of cellular pathways (Figure 4.3). Processes such as protein metabolism and biosynthesis appear highly represented, but this is partly due to the unequal nature of the GO hierarchical tree classification, where some processes are more highly represented due to a greater level of study or a difference in the branching pattern of the tree (e.g. some parts of the tree branch greatly near the root, while others branch at levels further down the hierarchy). To gain an unbiased view of the functions in the pcd-cancer set, the functions most overrepresented in the set were identified using the GoMiner software (Zeeberg et al. 2003). This analysis takes into account the unequal distribution of genes and GO terms in the GO hierarchy,  110  and determines which GO terms are found associated with genes in a gene set more often than would be expected by chance. Terms and functions thus enriched in the pcd-cancer set are more likely to represent pathways altered in the systems under study. The pcd-cancer set, limited to 168 genes with UniProt IDs for use in GoMiner, was divided into two categories: a P C D upregulated set of 110 human genes whose Drosophila homologs are upregulated in PCD, and a PCD-downregulated set of 58 human genes whose Drosophila homologs are downregulated in PCD. 121 genes were found to have at least one GO term in any GO category (Biological Process and others). A summary of the categories of genes found to be enriched in each set are shown in Table 4.2. Cathepsins, lysosomal proteins, and cytoskeletal proteins were primarily downregulated in cancers. Ribosomal proteins were found to be enriched both in the pcdupregulated and the pcd-downregulated sets, as well as in several cancers. Metalloprotease inhibitors (TIMPs) changed in expression in cancers of multiple tissues: they are upregulated in brain and pancreatic cancer, and downregulated in lung and prostate cancer. Carbonic anhydrases and V-ATPases also showed varied expression in cancers of different tissues.  Ill  neurophysiological process biosynthesis  amine metabolism  regulation of cell proliferation  organismal movement cell death  regulation of metabolism  electron transport  sexual reproduction  organic acid metabolism  nucleic acid metabolism  lipid metabolism  carbohydrate metabolism  cell adhesion  response to external stimulus  organogenesis coenzyme metabolism  energy pathways  phosphorus metabolism  response to stress cellular morphogenesis catabolism  protein metabolism cell growth and/or maintenance  amino acid and derivative metabolism cell motility  Figure 4.3 Functions associated with human genes in the pcd-cancer set. The proportion of 101 genes associated with each G O Biological Process term were determined, and terms at level 4 of the G O hierarchy are shown. 70 genes did not have an associated GO Biological Process term.  Table 4.2 Functions overrepresented in the pcd-cancer set.  PCD-upregulated subset Cathepsins and lysosomal proteins Carbonic anhydrases Cytoskeletal proteins Signaling and adaptor proteins Ribosomal proteins  PCD-downregulated subset V-ATPases Metalloendopeptidase inhibitors Protein carriers Ribosomal proteins  112  4.4 Discussion In this study I took a novel approach to understanding the cellular pathways important in cancer by combining expression analysis with cross-species similarity information to pinpoint genes altered in both programmed cell death and cancer. Human orthologs of Drosophila genes differentially expressed during salivary gland P C D were identified and the expression of these genes determined in cancers of nine tissues. 171 human genes and 143 Drosophila genes were found to be differentially expressed in both systems, and these are candidates for involvement in cancer through a function in P C D . Although few of these genes were found to be core components of apoptosis or autophagy pathways, a significant proportion are associated with cancer or cell death in the literature. The genes represent a wide range of functions, and the functions overrepresented in the set point to pathways regulated in both programmed cell death and cancer.  4.4.1 Cross-species integration Model organisms are an essential aspect of molecular biology, allowing experiments that are inappropriate or impossible to perform in humans or mammals to be carried out in other, often simpler, organisms. Advent of genomic technologies has only increased the utility of such models, as comparative genomics allows identification of genes, regulatory sequences, and other genomic features (Ureta-Vidal et al. 2003). M y approach expands the concept of model organism analysis to include analysis of expression data in multiple organisms concurrently. The Drosophila salivary gland has advantages as a model of programmed cell death that are not available in mammalian systems. The method used here directly applies gene expression studies in Drosophila to the much more complex system of cancer in humans, of which programmed cell death is one aspect, and thus permits the identification of genes involved in both processes. Some  113  previous work has been done using this concept, for instance, for identification of genes involved in breast cancer by concurrent analysis of mouse and human SAGE data (Hu et al. 2004), but overall cross-species analyses of genome-wide data have rarely been applied to gene expression. Cross-species analysis presents several difficulties which must be considered. It is estimated that over half a billion years has elapsed since the divergence of human and Drosophila lineages (Hedges et al. 2004), during which time varying evolutionary requirements can result in multiple gene deletions, duplications, and mutations and thus make identification of true orthologs, rather than paralogs, difficult. The InParanoid algorithm is specifically designed to account for such complexities, as it takes into account all relevant gene-gene distances both between and within genomes (Remm et al. 2001). Indeed, several cases examined in detail showed that given the sequence alignments, the ortholog relationship found appeared most likely to be correct. However, as the full complement of human genes, and to a lesser extent Drosophila genes, is not yet determined, in some cases the correct ortholog relationship may not be discovered. In addition, even when genes are orthologous, there is no certainty that the genes perform the same function in both organisms; the pathway may serve a different purpose, or the gene's activity may have changed. Thus, absolute inferences cannot be made about the role of human genes in cases where their orthologs are involved in programmed cell death. However, given the similarities in the known pathways of apoptosis and autophagy in humans and Drosophila, in many cases the results are expected to be relevant to both systems.  4.4.2 Functions of genes in PCD and cancer 2-fold more genes were found in the pcd-cancer set than expected given the number of genes identified as differentially expressed in the PCD and cancer systems individually; this overlap indicates commonality between these processes. Similarly, the observation that a higher  114  proportion of genes have opposite expression tendencies in cell death and in cancer points to an inhibitory role for cell death processes in cancer, consistent with current understanding as discussed in Section 1.3. If the observed decrease in expression of genes in cancer is due to a mutation associated with cancer progression, those genes are potential tumor suppressors; similarly, genes that increase in expression have the potential to be oncogenes. However, it is notable that the pcd-cancer gene set is not enriched for genes involved in programmed cell death, as might be expected given that their expression in Drosophila indicates a potential role in this process. There are several issues that may contribute to this. It is not expected that all programmed cell death genes would be regulated at the transcriptional level. In Drosophila,  many are, and in cancer, many genes that are normally regulated post-  transcriptionally are altered in expression due to drastic changes in gene regulation caused by mutations in cancer. However, genes not regulated at the mRNA level in both systems would be excluded from this analysis. Also, many of the genes known to be involved in programmed cell death are expressed at very low levels in the Drosophila S A G E libraries (see Chapter 3), and therefore do not have the opportunity to be recognized as differentially expressed. Analysis of Affymetrix oligonucleotide array data from the same tissue (Lee et al. 2003) gave similar results. This is a common problem in quantitation of gene and protein expression, as frequently molecules such as transcription factors which can have the greatest influence on cellular activities are expressed at levels that make them difficult to detect. Our analysis suggests that it is not the core P C D pathways that undergo the most dramatic changes in gene expression in cancer, but associated pathways which also have the potential to be important in controlling cancer cell survival and cancer progression.  115  Many of the genes identified in the pcd-cancer set have a previous association with cell death or cancer (Figure 4.2). Our analysis suggests that the genes with known roles in cancer may act through effects on PCD-related cellular processes. It also implicates a number of other genes as having a role in cancer, when no such role was previously described, and suggests that these genes may also function through PCD. In future, it would be beneficial to examine the pcdcancer set for enrichment of known cancer genes as well as known programmed cell death genes, using a defined set of known cancer genes such as compiled in a recent cancer census (Futreal et al. 2004). Several classes of genes in the PCD-upregulated subset (Table 4.2) are related to the process of autophagy. Cathepsins and other lysosomal proteins are responsible for the degradation of cellular proteins and organelles in the autolysosome (reviewed in Bursch 2001), and cytoskeletal proteins are necessary for the large amount of vesicular movement involved in autophagy (Bursch et al. 2000). Carbonate anhydrases and V-ATPases are responsible for cellular pH regulation, and proper acidification of vesicles is required for autophagy (Yamamoto et al. 1998). pH regulation is also particularly important in hypoxic cells, and V-ATPase expression can prevent apoptosis due to cellular acidosis (reviewed in Izumi et al. 2003). Multiple signaling pathway components and adaptor proteins were identified in the pcdcancer set, several with no known function; not surprisingly, this suggests that cell signaling is important both in P C D and in cancer. Interestingly, several metalloendopeptidase inhibitors (TIMPs) were identified as downregulated in PCD and changing in expression in cancer. TIMPs inhibit matrix metalloproteases which promote metastasis by breaking down the extracellular matrix. Although this activity could suppress tumorigenesis, the role and expression of TIMPs is complex (Noel et al. 2004), and they have also been described as anti-apoptotic (Hojilla et al.  116  2003). The downregulation of these genes during PCD in Drosophila and upregulation in cancers of the brain and pancreas suggests that their role in apoptosis may be important in cancer. Functions associated with ribosomes and protein synthesis were found to be enriched both in the pcd-upregulated and the pcd-downregulated sets. This may reflect a potential bias in this analysis, as genes which are highly conserved will be more likely to appear as orthologs and thus become highly represented in the pcd-cancer set. Many genes involved in basic cellular processes, such as ribosomal proteins, are also often highly expressed and therefore more likely to be differentially expressed in any system. Their appearance in this set, as well as in many other studies of gene expression, may simply reflect these properties. However, changes in expression of genes associated with protein translation, including ribosomal proteins, may actually be related to changes in cellular growth pathways (Martin et al. 2004) and thus may truly be an important indicator of changes in pathway regulation.  4.4.3 Conclusions M y novel cross-species analysis of programmed cell death in cancer demonstrates the use of genome-scale data from multiple systems to better understand the roles of genes and pathways in complex processes. Such potentially powerful approaches are likely to become more common as the amount of genome, gene expression, and other large-scale molecular data increases and demands new analysis methodologies. The results described here suggest that pathways associated with autophagy may be regulated in cancer, and thus point to the importance of studying autophagy gene expression in cancer.  117  Chapter 5: The autophagy gene MAP1LC3B in cancer  A version of this chapter has been submitted for publication. Pleasance, E.D., Jones, S.J.M, and Gorski, S.M.. 2005. The autophagy gene M A P 1 L C 3 B downregulated in multiple human cancers. Manuscript submitted.  118  5.1 Introduction Cancer is characterized by multiple molecular genetic alterations, including changes in growth regulation, cellular aging, and cell death pathways (reviewed in Hanahan and Weinberg 2000). Recently, the cellular mechanism of autophagy has been implicated in cancer, and represents an additional pathway whose modulation potentially impacts cancer progression (Edinger and Thompson 2003). However, the role of autophagy in cancer is enigmatic; there is evidence for its role in cancer inhibition through regulating cell growth and death, but also for contributing to cancer cell survival in conditions of nutrient- or oxygen-limitation or chemotherapy and radiation (reviewed in Ogier-Denis and Codogno 2003). The gene Beclin 1, which is essential for autophagy, has been demonstrated to be a tumor suppressor which can influence cell growth and tumorigenesis in cell lines and mice (Qu et al. 2003; Yue et al. 2003). To date, no other autophagy genes have been specifically studied in cancer. To initiate further investigation into the role of autophagy in cancer and examine autophagy's possible dual role in oncogenesis, I describe in this chapter a comprehensive gene expression approach utilizing publicly available data. This approach was chosen and the analysis focused on the gene M A P 1 L C 3 B , or LC3, because previous studies indicated LC3 is the only autophagy gene demonstrated to be transcriptionally regulated in mammals. LC3 is.indeed unique in this regard since regulation of other mammalian autophagy genes is not known to be at the transcriptional level. The localization of the LC3 protein to autophagosomes is used as a marker for autophagy (Mizushima et al. 2004), and importantly the level of expression of LC3 mRNA is correlated both with this localization and with onset of autophagy (Kanzawa et al. 2004). Additionally, transcriptional regulation of LC3 is evolutionarily conserved. The yeast LC3 homolog Atg8 is transcriptionally regulated in autophagy (Kirisako et al. 1999), multiple  119  Arabidopsis thaliana Atg8 homologs show increased expression during starvation when autophagy is observed (Rose et al. 2005), and expression of the Drosophila melanogaster LC3 homolog CG32675 correlates with autophagy in the midgut (E. Pleasance, based on data from L i and White 2003) and fat body (G. Juhasz and M . Sass, unpublished data), and autophagy stimuli including starvation and chemotherapeutic treatment increase LC3 expression in human systems (Kanzawa et al. 2004; Kanzawa et al. 2005; Nara et al. 2002). This conservation suggests that transcriptional control is an important mechanism of regulating L C 3 function. Together, the above observations suggest that levels of LC3 gene expression correlate with autophagy levels. Thus, the expression patterns of LC3 not only may provide insight into the roles of LC3 itself, but also may provide insight into the possible roles of autophagy in different cancerous tissues, subtypes and stages. Results show that LC3 is indeed differentially expressed in multiple cancers, consistent with the tissues in which Beclin 1 effects are seen.  5.2 Methods 5.2.1 Processing of microarray expression data Affymetrix oligonucleotide microarray data from Ramaswamy et al (2001) were downloaded from the authors' website. LC3 was represented by probes RC_AA283759_at and W28106_at on array Hu35KsubA, and Beclin 1 was represented by probe L38932_at on array Hu6800, as annotated by NetAffx (http://www.affymetrix.com/analvsis/, March 30 2005). Only samples for which comparable normal and cancer tissues were available were considered in this analysis. Intensities were provided as absolute readings; values were converted to log(2) and, for the two LC3 probes, averaged. Affymetrix oligonucleotide microarray data from Bhattacharjee et al (2001) were downloaded from the authors' website. LC3 was represented by probe 39370_at on array U95A, as annotated by NetAffx (Feb 10 2005). Intensities were provided as absolute 120  readings, and log(2) was applied. For all cDNA and oligonucleotide microarray data, p-values for differences in group means were calculated with two-sample two-tailed Welch's t-tests. Two-color cDNA microarray data from Chen et al (2002b) and Sorlie et al (2003) were obtained from the Stanford Microarray Database (http://genome-www5.stanford.edu/)  as log(2)  normalized ratios. Both studies used cDNA arrays (Perou et al. 2000), and for all samples standardized R N A was used as a reference in hybridization. LC3 was represented by two clones, IMAGE:796650 and IMAGE:795604, mapping to UniGene Hs. 121849. It was confirmed by B L A S T N that ESTs from these clones correctly align to LC3 RefSeq record NM_022818. The average of the log-ratio values for the two LC3 probes was computed. As full-length clones are spotted on these cDNA microarrays, there is potential for cross-hybridization of the M A P 1 L C 3 B probe sequence with other genes. B L A S T N analysis of the LC3 RefSeq sequence compared to all RefSeq mRNA sequences identified a -100 bp region of 80% identity between M A P 1 L C 3 B and L C 3 A gene sequences, as well as a region of >300 bp of >90% identity between M A P 1 L C 3 B and two hypothetical genes, only one of which has expression evidence. This indicates that a low-to-moderate level of cross-hybridization may be expected (Evertsz et al. 2001), which could alter the observed hybridization intensities.  5.2.2 Processing of SAGE expression data The LC3 gene was mapped to sequences in the RefSeq, UniGene (via S A G E Genie, Boon et al. 2002), M G C , and Ensembl databases using DiscoverySpace (Varhol, Zuyderduyn et al, unpubl.). This identified the 14 bp tag ( C A T G ) T G A G T G G T C A , at the 3'-most M a l l l site in the 3' UTR, as unambiguously representing the LC3 gene. The tag ( C A T G ) C T G A G G G G T G was identified as the next 3'-most tag corresponding to the second N M I I site from the 3' end of the LC3 transcript. S A G E libraries were downloaded from the Cancer Genome Anatomy Project  121  website (http://cgap.nci.nih.gov/) as described in Chapter 4. Breast cancer libraries were classified based on descriptions in the associated publications (Lai et al. 1999; Porter et al. 2003; Porter et al. 2001); libraries that could not clearly be classified were excluded. P-values were determined both by summing tag counts in each group and using the Audic-Claverie method (Audic and Claverie 1997), which may produce overly optimistic p-values when used on summed libraries, and also using a two-sample two-tailed Welch's t-tests, which may be overly conservative for S A G E data (Baggerly et al. 2003).  5.2.3 Tag mapping and EST analysis of secondary LC3 tag The  14 bp tag ( C A T G ) C T G A G G G G T G , which corresponds to the M a l l l site just  upstream of the 3'-most M a l l l site in LC3, also corresponds to an upstream tag in the gene FBXW11. This ambiguity was resolved by examining the 15 base pair in the ditags (Colinge th  and Feger 2001b) from which the observed S A G E tags were derived. In the LC3 gene, the sequence C A T G C T G A G G G G T G is followed by " A " ; in the FBXW11 gene, it is followed by " G " . Kornelia Polyak, author of the breast cancer S A G E studies, kindly provided raw SAGE sequences. These were processed to extract ditags, and ditags containing the tag of interest that were of length 22-26 (30-34 including flanking C A T G sites) were analyzed to determine the 15  th  base. In all cases where suitable length ditags were available (between 1 and 6 cases per library), the 15 base pair was " A " , indicating unambiguous representation of the LC3 mRNA transcript. th  The locations of the primary and secondary LC3 tags in the LC3 3' UTR were visualized in the UCSC Genome Browser (http://genome.ucsc.edu/), and it was noted that the tags reside -100 bp from the 3' end of the transcript, and only 10 bp separates the two tags. For these two tags to be produced legitimately, there must be two forms of the LC3 transcript, one of which ends after the secondary tag but before the primary tag. To look for other evidence of such a  122  transcript, which would only differ from the canonical, full-length transcript by ~100 bp, EST alignments at UCSC were examined for 3' ESTs which ended between the two tags. 16 such ESTs were found, 10 of which had discernable poly-A tails and all of which ended in the short region between the two tags. Thus, ESTs and SAGE data indicate that a second, slightly shorter, transcript of LC3 is expressed. 5.3 Results and discussion 5.3.1 Overview ofLC3 regulation in multiple cancer and normal tissues To gain a comprehensive view of LC3 expression in normal and cancer tissues, I first examined oligonucleotide microarray data produced by Ramaswamy et al (2001) which profiles 90 normal and 190 cancer samples obtained from tissue biopsies prior to cancer treatment (Table 5.1). This dataset is particularly suitable for LC3" expression analysis as the Affymetrix oligonucleotide microarray platform used in this study produces consistent results (Nimgaonkar et al. 2003), and the LC3 gene can be uniquely identified by probes on this array. In addition, the number of cancer and normal samples available for each tissue combined with the variety of tissues allows for a comprehensive comparison and statistical assessment of LC3 expression patterns. Overall, a trend is observed toward decreased LC3 expression in cancerous tissues, but this varies substantially between tissues (Figure 5.1). Decreased LC3 expression (p<0.05, Welch's t-test), suggesting a decrease in levels of autophagy, is particularly pronounced in cancer of the bladder, colon, breast and ovary. These observations are consistent with reports of decreased Beclin 1 protein expression in cancer of the breast and ovary (Liang et al. 1999). In contrast to the observation of decreased LC3 expression in some cancers, several other cancer types do not show differences in LC3 expression, including brain, kidney, and prostate cancers, and leukemias and lymphomas. Overall expression of LC3 in the brain is higher than in 123  many tissues, as has been observed previously (Mizushima et al. 2004; Tanida et al. 2004), but no difference in LC3 expression is seen in brain cancer compared to normal brain tissue, or in medulloblastoma or glioblastoma subtypes. This is confirmed by my analysis of alternate expression data (Boon et al. 2004; Lai et al. 1999; Siu et al. 2001) in medulloblastomas, glioblastomas, epyndymomas, astrocytomas, and meningiomas (data not shown). Although glioma cell lines have previously been observed to undergo autophagic cell death in response to therapy and thus have autophagic capacity (Kanzawa et al. 2004; Kanzawa et al. 2003), there is no evidence from LC3 expression for a change in autophagy in gliomas. Similarly, no difference in LC3 expression is seen in prostate cancer despite that autophagic death that has been observed in the LnCAP prostate cancer cell line in response to neuregulin (Tal-Or et al. 2003). This may reflect that, in the absence of certain chemotherapeutic agents, autophagic cell death does not occur and thus there is no survival advantage to the cancer cells in reducing autophagy. Additionally, there may be a difference between the behavior of cell lines and cancers in vivo; indeed, I have observed disparities in LC3 expression in some cell lines compared to bulk samples from the same tissue (data not shown). In only one tissue, the pancreas, was observed an increase rather than a decrease in LC3 expression in cancer. The higher expression of LC3 in pancreatic adenocarcinomas compared to normal pancreatic tissues may simply reflect an unusually low expression of LC3 in normal pancreas compared to any other normal tissue examined. Alternatively, it may indicate that expression of LC3 has been elevated, potentially reflecting a different role for autophagy in this type of cancer. Although rat pancreatic carcinomas have decreased autophagic capacity, which is a different pattern than we observe in human pancreatic adenocarcinomas, early stage rat pancreatic cancers show increased levels of autophagy (Toth et al. 2002). This may imply that  124  the proliferation of this type of cancer, which also has a particularly high mortality rate (Lowenfels and Maisonneuve 2004), can be enhanced by increased autophagy levels.  .12  S  1 0  fl  0) T3  Oi  N  r-ffl  p<0.05  • Normal  *p<0.01  El Cancer  8 n  75 E  c5 6  z  CO  o  4 V  o  °"  CO  2  O  c "T3  ro  co.  to  CQ  <A  re ca  c o o o  >> c  E a>  ro E  o a. E  OJ c  3  (A  re >  O  re v t_  o c re CL  (A  (A  O  3 a> 3  Tissue Figure 5.1 LC3 expression in multiple normal and cancer tissues. Expression values are log transformed from Affymetrix oligonucleotide microarray data published by Ramaswamy et al (2001). P-values are calculated with a Welch's t-test for each cancer with respect to equivalent normal tissue; error bars represent standard error. Number and type of samples represented for each tissue are given in Table 5.1.  125  Table 5.1 Composition of normal and cancer samples shown in Figures 5.1 and 5.5. Tissue* Bladder  Cancer status"  Type"  Normal Cancer  Normal bladder tissue  Normal Brain Cancer Normal Breast  Cancer Normal Cancer Normal Cancer  Colon Kidney  Leukemia  Normal Cancer Normal  Lymphoma  Lung Ovary Pancreas Prostate Uterus  a  b  Cancer Normal Cancer Normal Cancer Normal Cancer Normal Cancer Normal Cancer  # Samples  Transitional cell carcinoma Cerebellum Whole brain Glioblastoma Medulloblastoma Normal breast tissue Breast carcinoma in situ Invasive breast carcinoma Normal colon Colorectal carcinoma Normal kidney Renal cell carcinoma Peripheral blood polymorphonuclear leukocytes Peripheral blood monocytes Acute myelogenous leukemia Germinal centers Follicular lymphoma Large B-cell lymphoma Normal lung Lung adenocarcinoma Normal ovary Ovarian adenocarcinoma Normal pancreas Pancreatic adenocarcinoma Normal prostate Prostate adenocarcinoma Normal uterus Uterus adenocarcinoma  7 11 3 5 10 10 5 7 4 11 11 12 11 3 2 10 6 11 11 7 11 4 11 10 11 9 10 6 10  As in Figures 5.1 and 5.5 As described and analyzed in Ramaswamy et al (2001)  5.3.2 LC3 in cancer stages To corroborate and expand on the observations above, I examined complementary expression data from different studies, using different expression technologies. In particular, we were interested to observe whether there were any stage-specific differences in autophagy gene expression in cancers, as one hypothesis is that increased autophagy enhances cancer cell survival in early-stage preangiogenic tumors, while later in cancer development autophagy is detrimental to cancer cells and may lead to cell death. Breast cancer was of particular interest, as  126  LC3 shows a difference in mRNA expression (Figure 5.1) and Beclin 1 differs in protein expression in this tissue (Liang et al. 1999), and Beclin l's tumor suppressor activity has been demonstrated specifically in breast cancer cell lines. Liver and lung cancers were also of interest due to the observation of increased rates of these cancers in Beclin 1 knockout mice (Qu et al. 2003; Yue et al. 2003). Data generated from liver cancers (Chen et al. 2002b) shows a decrease in expression of LC3 during cancer progression (pO.OOl, Welch's t-test; Figure 5.2), consistent with a previously observed decrease in autophagy in liver cancer models (Schwarze and Seglen 1985), and the presence of hepatocellular carcinomas in Beclin 1 knockout mice (Qu et al. 2003; Yue et al. 2003). Unlike the trend observed in pancreatic cancer (Toth et al. 2002), no increase in autophagy in early stages was seen. This liver cancer dataset was derived from cDNA microarrays, and unlike oligonucleotide microarrays, the probe design of c D N A microarrays is such that the expression of LC3 observed may be influenced by the expression of other genes. Even so, the data are consistent with reduced LC3 expression levels in hepatocellular carcinoma.  127  -0.5 * p<0.05 **p<0.01  '35  c oE •  -0.6  -0.7  A  >  ** >  <  > V  -1.1 -  ..;  -1.2  Normal  Carcinoma  Hyperplasia  Metastatic  Stage Figure 5.2 LC3 expression in liver cancer stages. Expression values in normal, early, and late-stage liver cancers are averages of two cDNA microarray probes, with intensities given relative to reference RNA, from data published by Chen et al (2002b). P-values are calculated with a Welch's t-test for each stage relative to normal. Number of samples represented for each stage are: 76 (normal), 7 (hyperplasia), 104 (carcinoma) and 7 (metastatic).  5.3.3 LC3 in breast cancer progression  To investigate a possible stage-specific role for autophagy in cancer, I analyzed serial analysis of gene expression (SAGE) data from breast cancer samples representing cancer progression (Lai et al. 1999; Porter et al. 2003; Porter et al. 2001). Genes can have multiple S A G E tags that can represent alternative transcripts. For LC3, both the most common S A G E tag for LC3 and a secondary tag were sufficiently abundant in both normal and cancerous breast libraries to be statistically significant, and our analysis shows that these tags unambiguously represent expression of the LC3 gene. Consistent with the data shown in Figure 5.1, levels of LC3 were observed to decrease in ductal carcinoma in situ, as well as in invasive and metastatic breast carcinomas (Figure 5.3A; p<0.05 for Welch's t-test comparing each stage to normal).  128  Interestingly, however, the secondary SAGE tag for LC3 shows a similar decrease in DCIS and invasive stages, but increases again at the metastatic carcinoma stage (Figure 5.3B; p<0.05 for Welch's t-test comparing metastatic to DCIS). Additionally, invasive library INV5, which shows higher expression of the secondary LC3 tag (Figure 5.3B), is derived from the same patient as the metastatic library MET1. Analysis of Expressed Sequence Tag (EST) evidence suggests that this secondary S A G E tag represents a shorter version of the LC3 mRNA transcript which lacks -100 bp of the 3' U T R (data not shown), possibly due to a difference in usage of polyadenylation recognition sequences. This is an example where subtle differences in transcript structure can be observed with SAGE, where such differences would be unlikely to be observed with microarray technologies. While we cannot rule out the possibility that the difference in expression level of the LC3 transcript variant is due to the effects of cancer treatments, there are numerous examples of genes with alternative splice forms which are differentially associated with cancer progression (Kirschbaum-Slager et al. 2004). 3' U T R sequences are known to contain regulatory elements that can control mRNA levels (Yan and Marr 2005), which may explain the difference in expression level. The differential regulation of this transcript variant in metastatic breast tissue suggests that LC3 may have a different role in different stages in breast cancer.  129  A 40  s a. o z 10  o o  s a. o  " ^ n n" Normal (NORM)  .EL  ...a.  Ductal carcinoma in situ (DCIS)  Invasive (INV)  Metastatic (MET)  H UJ  in  S o  •- t« w o  i  ^1 ^ Normal (NORM)  Ductal carcinoma in situ (DCIS)  i Invasive (INV)  Metastatic (MET)  Figure 5.3 LC3 expression in breast cancer progression. (A) LC3 primary SAGE tag frequency in breast cancer progression SAGE libraries from CGAP (Lai et al. 1999; Porter et al. 2003; Porter et al. 2001). Each bar represents a SAGE library; libraries are sorted from lowest to highest LC3 expression in each category. (B) LC3 secondary SAGE tag frequency in breast cancer progression SAGE libraries. Note that INV5 and MET1 are derived from the same patient, and NORM7 represents a breast hyperplasia.  130  5.3.4 LC3 in cancer subtypes I also found evidence for a cancer subtype-specific pattern of LC3 expression. Analyzing oligonucleotide microarray data profiling specific histological subtypes of lung cancer (Bhattacharjee et al. 2001), a significant decrease in LC3 expression in lung adenocarcinoma was observed (Figure 5.4A), which was not significant in the dataset analyzed for Figure 5.1 due to a smaller number of samples. Interestingly, the expression of LC3 is not consistent between lung cancer subtypes. Small cell lung carcinomas and squamous carcinomas show the greatest decrease in expression. In contrast, LC3 expression in carcinoid tumors, a less common and lower grade subtype (Kufe et al. 2003), is in fact higher than in normal lung samples. I also examined breast cancer expression profiles from cDNA arrays (Sorlie et al. 2003) and found differences in LC3 expression in breast cancer subtypes (Figure 5.4B). In particular, significantly decreased expression of LC3 in the luminal A subtype is observed; this subtype also shows the lowest mortality rate. Results from both of these datasets suggest that autophagy may not only have different functions in different cancer stages, but may in fact be differentially regulated in different cancer subtypes.  131  Normal  Adenocarcinoma  Carcinoid  Small cell  Squamous  Subtype  B  in  c  0)  <u  CC  < z a  o eo O _l  V O)  ra  v > <  Normal  Normal-like  Luminal A  Luminal B  ERBB2+  Basal  No subtype  Subtype Figure 5.4 LC3 expression in lung and breast cancer subtypes. (A) LC3 expression in lung cancer subtypes; values are log transformed from Affymetrix oligonucleotide microarray data published by Bhattacharjee et al (2001). P-values are calculated with a Welch's t-test with respect to normal lung tissue expression for each cancer type; error bars represent standard error. Number of samples represented: 17 (normal), 139 (adenocarcinoma), 20 (carcinoid), 6 (small cell), 21 (squamous). (B) LC3 expression in breast cancer subtypes; values are averages of two cDNA microarray probes, with intensities given relative to reference RNA, from data published by Sorlie et al (2003) where subtypes were also defined. P-values are calculated with a Welch's t-test comparing each subtype. Number of samples represented for each subtype are: 3 (normal), 6 (normal-like), 28 (luminal A), 11 (luminal B), 11 (ERBB2+), 19 (basal), 43 (no subtype given).  132  5.3.5 LC3 and Beclin 1 expression My observations of L C 3 , the second autophagy gene to be investigated in cancer, are strikingly consistent with the previously reported behavior of the autophagy gene Beclin 1 in cancer, and provide strong support for a role of autophagy in this disease. Decreased expression of the autophagy gene LC3 in breast, ovarian, lung, and liver cancer was observed, while previous work showed that protein expression of the autophagy gene Beclin 1 is decreased in breast and ovarian cancer, and that mice lacking one copy of Beclin 1 develop lung and liver cancers as well as breast hyperplasias. M y analysis thus lends support to the hypothesis that it is indeed Beclin l's role in autophagy that is responsible for its tumor suppressor effects, as the major autophagy marker LC3 has an expression pattern suggesting decreased autophagy in cancers - and this is observed in the same tissues affected by decreased Beclin 1. Unlike the data available for L C 3 , there is no evidence for transcriptional regulation of mammalian Beclin 1 or its yeast counterpart. Analysis of Beclin 1 expression in multiple cancers (Figure 5.5) confirms that Beclin 1 mRNA levels are not consistent with protein levels (Aita et al. 1999), and when multiple testing is accounted for, the differences in Beclin 1 expression in cancers are insignificant. This emphasizes the importance of evaluating protein expression of Beclin 1 when studying the role of this gene in autophagy and cancer.  133  14  in §  12  *  10  oi  O _l  • Normal  *p<0.01  H Cancer  ft  !• z  p<0.05  ft  „ 6  <u  Si  O  =  4  2  o o  CO „ TJ T»  JS  m  c '« CQ  (A (0 0) CO  c o o o  c  E ^£ 3  0)  ra E o  £ Q.  E  O)  c 3  to  > O  in  ra Q)  ra o  ra  0) 3  Tissue Figure 5.5 Beclin 1 expression in multiple normal and cancer tissues. Expression values are log transformed from Affymetrix oligonucleotide microarray data published by Ramaswamy et al (2001). P-values are calculated with a Welch's t-test for each cancer with respect to equivalent normal tissue; error bars represent standard error. Number of samples represented for each tissue are given in Table 5.1.  5.3.6 Conclusions In summary, we have analyzed for the first time the autophagy gene LC3 in the context of multiple human cancers. We have observed decreased expression of LC3 in several cancers, corroborating previous findings with Beclin 1 and indicating that downregulation of autophagy may indeed be an important step in oncogenesis. Not all cancers show this difference, suggesting that autophagy levels and the potential for autophagic cell death may be an important factor in some but not all tumors. We have also observed differences in LC3 expression during cancer progression, and in different cancer subtypes, lending support to the hypothesis that autophagy may play a dual role in oncogenesis and can be beneficial to cancer cell survival. Overall, these  134  results underline the importance of better understanding the role of autophagy in oncogenesis, and point to the modulation of the pathways of autophagy and autophagic cell death as a possible means of therapeutic intervention.  135  Chapter 6: Summary and Conclusions  136  6.1 Summary This thesis describes the testing of the hypotheses that gene expression analysis can be used to identify genes potentially involved in programmed cell death, and that related apoptosis and autophagy genes play a role in Drosophila P C D and cancer; additionally, this work generates further hypotheses regarding the involvement of other genes in these processes. The aims of this work were therefore to develop and utilize methods for large-scale gene expression analysis, and identify genes and pathways regulated in programmed cell death and cancer. Methods for analyzing SAGE data and assessment of SAGE for gene identification were described and shown to be more effective than other available approaches. These methods were applied to expression data describing programmed cell death in the Drosophila salivary gland. The PCD-associated genes identified as differentially expressed in this analysis were used in conjunction with large-scale expression data from human cancers in an analysis of the role of programmed cell death in oncogenesis. Additionally, multiple datasets were examined for expression of the autophagy marker LC3. These analyses identified genes and pathways with potential roles in PCD and cancer.  6.2 Large-scale gene expression analysis with SAGE and other techniques 6.2.1 Measurement and comparison of gene expression For large-scale gene expression analysis methods to be successful, correct identification of the genes under study is necessary. I found that the standard S A G E procedure has the ability to profile and unambiguously identify all but 7-20% of the transcriptome, depending on the organism under study. Use of different anchoring enzymes or use of longer S A G E tags reduces this intractable fraction to only 5-10% of genes. The ideal SAGE tag length and enzyme differs  137  for each organism; for instance, longer tags are more beneficial in analysis of human genes than in Drosophila. The ability of microarrays to correctly identify genes is not inherent to the procedure, as it is for SAGE, but is greatly dependent on the probes placed on the microarray. In the analysis of cDNA microarray data for the study of autophagy in cancer, it was noted that the probes corresponding to the gene of interest had the potential to match other genes as well, making unambiguous gene profiling difficult. The vast repositories of gene expression data continuously being collected in databases such as the SAGE catalog at the Cancer Genome Anatomy Project or the array data in the Stanford Microarray Database provide an opportunity to study cellular pathways by data mining. Comparison of expression across experiments performed in different contexts is necessary to make full use of this data. This is difficult when analyzing microarray data, especially if the array probe design is not identical or different references are used for cDNA arrays. As such, in the examination of LC3 expression in cancer, the analysis looked at each microarray experiment individually rather than analyzing the results collectively; this can reduce the power of an analysis, but has advantages such as reduced variability due to differences in procedures. S A G E data, on the other hand, does not depend on array design and does not require a reference; SAGE tag counts are absolute and easily normalized to library size. These properties allowed direct comparison of breast cancer S A G E data from multiple publications, and the success of this comparison was demonstrated by the consistency of expression of the L C 3 autophagy gene across different SAGE libraries representing the same stage of breast cancer and the significant differences between stages. Similarly, SAGE libraries from multiple cancer and normal tissues were mined for differential gene expression, and the genes found linked to genes differentially expressed in Drosophila PCD. In addition to variability in gene expression due to technical  138  issues or conditions, however, the contribution of biological variability due to differences between individuals or individual occurrences of cancer must be recognized, as many differences may be measured that may or may not be relevant to the system under study. Recognizing the most relevant expression changes in any system will require continued collection and further refined analysis of gene expression data. 6.2.2 Novel genes and alternative transcripts Despite intense research focused on gene discovery and genome annotation, the entire gene complement of any organism is not yet established. S A G E and EST analyses have the potential for novel gene discovery to aid in genome annotation, while typical microarrays representing only known genes do not. Indeed, analysis of Drosophila salivary gland S A G E and EST data identified many potential novel genes. With an equivalent amount of sequencing, SAGE has a greater potential to identify novel genes than do ESTs, due to greater depth of sampling which reaches genes expressed at lower levels. Many genes of interest in the Drosophila salivary gland are observed at levels of less than 5 tags. A significant proportion of Drosophila salivary gland ESTs did not correspond to known or predicted genes and could be mapped directly to the genome. Drosophila S A G E tags could be used to confirm expression of potential novel genes by mapping directly to ESTs, and in a limited fashion could be mapped directly to the genome, identifying potential novel expressed transcripts. In smaller genomes such as that of Drosophila, mapping 14 bp SAGE tags directly and uniquely to the genome is possible in approximately 60% of cases, while in larger vertebrate genomes this is not practical unless longer S A G E tags are used. The finding that only 55-65% of Drosophila SAGE tags could be mapped to genes, and less than half of human S A G E tags in C G A P could be mapped  139  unambiguously, underscores the possibility that there are a large number of expressed transcripts which are not yet annotated. In addition to the potential number of unannotated gene loci, many alternative transcripts for known genes may not yet have been described. Alternative splicing and alternative 5" and 3' ends increase the number of different transcripts and proteins that can be derived from coding sequences in the genome. cDNA microarrays have little potential for identifying alternative transcripts, as different transcripts are likely to cross-hybridize. Oligonucleotide microarrays, depending on probe design, can potentially target specific transcripts. S A G E has the potential to distinguish alternative transcripts if the variation occurs near the 3' end of the gene. In mapping of Drosophila SAGE tags, the ratio of mapped tags to genes was approximately 1.1:1. The situation is even more extreme in human tag mapping, where hundreds of thousands of tags mapped to tens of thousands of genes. This indicates that multiple S A G E tags are extracted from each gene, which may be accounted for in part by alternative transcripts. Indeed, SAGE data from breast cancers not only identified an alternative, shorter LC3 transcript, but indicated a difference in expression of this transcript in metastatic breast cancer. Continued research into identifying both novel genes and novel transcript forms through analysis of large-scale expression data will be necessary for the understanding of cellular regulatory networks.  6.3 Genes and pathways associated with programmed cell death  6.3.1 In Drosophila Changes in gene expression measured by S A G E in the Drosophila salivary gland indicated that significant transcriptional changes are associated with programmed cell death, and that genes known to be involved in autophagic P C D and apoptosis are expressed and transcriptionally regulated. Transcriptional profiling using both ESTs and S A G E was successful 140  in detecting expression of the ecdysone-triggered transcriptional cascade known to regulate PCD in the salivary gland. Several of these genes were expressed at very low levels, however, indicating that much of the control of P C D may be exercised by genes whose differential expression is difficult to measure; accordingly, some of the many genes that were not found to be differentially expressed due to low expression levels may also play important roles in P C D . Interestingly, in addition to expression of autophagy genes (discussed in Section 6.4.1), many apoptosis genes including multiple caspases, that were not previously known to be involved in salivary gland cell death were expressed prior to autophagic PCD. Death of the salivary gland, although it is autophagic as demonstrated by the accumulation of autophagic vacuoles, cytoskeletal rearrangements, and degradation with little phagocyte involvement, shows marked apoptotic features such as cytoplasm and D N A fragmentation (Jiang et al. 1997; Lee and Baehrecke 2001; Martin and Baehrecke 2004). Interestingly, caspase inhibitors that prevent apoptosis also prevent complete salivary gland destruction, but do not avert autophagic vacuole formation and rearrangements of the actin cytoskeleton (Martin and Baehrecke 2004). The nature of the relationship between apoptosis and autophagy is not known. The two pathways may be independent but triggered by the same upstream signal; for instance, the E93 transcription factor is required for both autophagic and apoptotic morphologies (Lee and Baehrecke 2001). Alternatively or additionally, there may be direct crosstalk between the pathways, as is suggested to be the case in mammals (Yu et al. 2004). The changes in expression of ecdysone cascade genes, autophagy genes, and apoptosis genes suggest an intriguing picture whereby both autophagy and apoptosis are required to correctly and completely remove a tissue by PCD. Nearly one quarter of the transcripts expressed in the Drosophila salivary gland in stages prior to cell death show changes in expression, pointing to a vast reorganization of the  141  transcriptome preceding PCD. Most likely transcriptional control is mediated in part by the activity of many transcription factors which are observed at low levels and peak several hours before death is initiated. There may be additional upstream factors that peak at even earlier time points and so were not observed in the time frame examined, as is probably the case with the ecdysone cascade initiators EcR/USP and PFTZF-1. These various regulators act on downstream pathways such as those controlling protein synthesis and defense responses, and possibly trigger signaling through kinase cascades including the protein kinase A and Ras pathways. Future research may determine the role of each of these transcription factors and interaction of these signaling pathways in cell death. Not surprisingly, it seems the death of an entire tissue is complex and requires significant cellular changes and regulation.  6.3.2 In cancer Transcriptional profiling of cancers has become increasingly common, and is carried out with goals of identifying pathways which are altered and understanding the underlying causes of these alterations for the purposes of diagnosis and therapy. M y analysis of S A G E data indicates thousands of human genes are altered in expression in cancers of various tissues, representing a broad spectrum of cellular functions. Given the number of changes observed, recognizing the pathways that are related to a specific cellular process such as PCD can be difficult. Using the better defined system of the Drosophila salivary gland as a filter to pick out genes more likely to be involved in PCD, my approach identified genes known or suspected of being involved in cell death that are changed in cancer. Additionally, pathways related to p H regulation, the cytoskeleton, protein synthesis, and metalloprotease regulation were implicated in cell death. Although some changes are consistent between cancers of different tissues, in other cases a gene may show a significant increase in expression in some cancers, and a significant decrease  142  in others. This may reflect tissue-specific differences that are not directly relevant to the cancer, for instance, a higher or lower basal level of activity of a pathway in that tissue. Alternatively, such differences could be related to variations in cellular environments and growth requirements in different tissues. They may also be specific to the individual cancer or the individual patient, especially in cases where only one or two SAGE libraries, derived only from one or two patients, were available to represent a tissue. The large number of changes observed which are common to multiple cancers represent common mechanisms in cancer which are the most promising targets for generally applicable therapies, whereas genes that behave significantly differently between individuals suggest targets that would require individualized therapies. In future, application of this filtering principle with other gene expression datasets, possibly focused on mammalian systems, may be a powerful tool for dissecting pathway deregulation in cancer.  6.4 Roles of autophagy in P C D and cancer 6.4.1 In Drosophila PCD Large-scale expression data, both from our Drosophila EST and S A G E libraries and from co-published microarray data (Lee et al. 2003) provided the first direct evidence for expression of multiple Drosophila homologs of yeast autophagy genes in salivary gland PCD. Many genes in the autophagy pathway were observed and several were differentially expressed, indicating that the majority of the putative autophagy pathway is present and active in the Drosophila salivary gland. Though this pathway has not until recently been examined in detail in Drosophila (Scott et al. 2004), it is thought to function similarly to the yeast pathway in regulating the formation of autophagic vacuoles, which then engulf and degrade cytoplasmic contents. Not surprisingly, significant changes in the expression of cytoskeletal genes are observed in the SAGE libraries, as the cytoskeleton is required for the increased vesicular movement in 143  autophagy. Autophagy is observed in situations where, as in the death of the salivary gland, an entire tissue must be removed simultaneously. Death through apoptosis produces apoptotic bodies which must be removed by phagocytes, whereas death by autophagy internalizes much of the required cellular degradation, leading to more efficient destruction and removal of the tissue. To what extent the activity of the autophagic pathway itself is responsible for cell death is as yet an unanswered question.  6.4.2 In cancer Evidence for the important role of autophagy in cancer is growing. M y analysis of the LC3 gene has supported the suggestion that autophagy has a dual role in cancer, as the expression of this autophagy gene differs between tissues, stages, and subtypes of cancer. Marked decreases in LC3 expression are consistently observed in breast tumors and several other cancers. The role of autophagy may be dependent on the cellular environment or the pathways naturally active in a tissue, on other cellular changes such as inhibition of apoptosis pathways, or on external conditions such as lack of oxygen or treatment by chemotherapy. M y analysis of genes regulated in both Drosophila P C D and cancer identified changes in other cellular machinery such as lysosomal activity, pH regulation and cytoskeletal rearrangements that could relate to activity of the autophagy pathway. Increases in expression of autophagy genes and associated pathways could reflect positive selection within the tumor, where autophagy is advantageous due to its protective effects. Alternatively, increases may reflect the triggering of natural cellular mechanisms which are in place to prevent unwanted cell growth, which the cancer may need to counter to survive. Which of these circumstances may be the case in different cancers will be better understood as the genes and pathways responsible for initiating autophagy are examined further.  144  6.5 Conclusions Large-scale gene expression analysis has the potential to discover genes and reveal molecular components of complex cellular processes. S A G E is a powerful technique, facilitating comparative analyses of multiple systems, but has important limitations which must be understood. Analyses of expression data in Drosophila P C D and human cancer reveal that pathways of autophagy and apoptosis may act together to execute programmed cell death. Additionally, autophagy is clearly important in oncogenesis, as both core autophagy genes and potentially associated processes are regulated in cancer progression in multiple tissues. 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