1. A SAGE Approach to Discovery of Genes Involved in Autophagic Cell Death

2. Acknowledgements Genome Sciences Centre Victor Ling Marco Marra Functional Genomics Suganthi Chittaranjan Doug Freeman Carrie Anderson Shaun Coughlin Sequencing Genome Sciences Centre Sequencing Team Bioinformatics Steven Jones Erin Garland Richard Varhol Scott Zuyderduyn SAGE team University of Maryland Biotech Institute Eric Baehrecke

3. Programmed Cell Death (PCD) Function Dysfunction Deleting damaged cells Cancer Culling cell number Deleting structures Sculpting tissues Autoimmune diseases Neurodegenerative diseases Developmental abnormalities

4. Programmed cell death Type I= Apoptotic cytoskeletal collapse condensation and fragmentation of chromatin and cytoplasm preservation of organelles phagocytosis by macrophages or neighbouring cells (heterophagy) occurs in isolated cells Type II = Autophagic preservation of cytoskeleton formation of vacuoles that engulf cytoplasm and organelles fusion of vacuoles with lysosomes for self-degradation late chromatin condensation and nuclear degeneration occurs in groups of cells

5. Autophagic cell death in normal physiology Dictyostelium sorocarp formation insect metamorphosis intersegmental muscle, gut, salivary glands mammalian embryogenesis regression of interdigital webs, sexual anlagen mammalian adulthood intestine, mammary gland post-weaning, ovarian atretic follicles

6. Autophagic cell death in disease human neurodegenerative diseases (Alzheimer and Parkinson) cardiomyocyte degeneration spontaneous regression of human neuroblastoma tamoxifen-treated mammary carcinoma cells (MCF-7) bcl-2 antisense treatment of human leukemic HL60 cells beclin-1 (apg6) promotes autophagy and inhibits tumorigenesis; expressed at decreased levels in human breast carcinoma

7. Aims 1.Identify the genes involved in autophagic cell death in vivo. 2.Determine which genes are necessary and sufficient for autophagic cell death. 3.Determine if genes function in mammalian autophagic cell death. 4.Identify the autophagic cell death genes associated with human disease and investigate potential as molecular markers and/or therapeutic targets.

8. Experimental Approach Gene expression profiling (SAGE): Comprehensive Gene Discovery Drosophila model system: Known cell death genes/pathways are conserved Genetic and molecular tools Sequence resources FlyBase and GadFly databases Multiple tissues undergo PCD; well-characterized

9. (Jiang et al., 1997) autophagic stage-specific synchronous hr (APF, 18°C) known cell death genes are regulated transcriptionally Reverse transcription - + - + - + 16 18 20 22 23 24 diap2 rpr hid RT-PCR analysis Drosophila salivary gland PCD

10. Overview of SAGE tag abundance SAGE Library Total tags analyzed Transcripts % of transcripts seen at frequency: 12-1011-100>100 16 hr34,9893,12632.755.710.41.2 20 hr31,2153,03438.050.99.71.4 23 hr30,8232,96333.354.011.21.4 Total number of different transcripts in all three libraries is 4,628.

11. Deriving SAGE tag abundance Total SAGE tags analyzed: remove linker sequences and duplicate ditags select a quality cut-off remove singletons i.e. found only once in all 3 libraries Number of different transcripts at each timepoint: combine libraries (e.g. 16a + 16b) in SAGEspace export to Excel and count lines/sort by tag numbers Total number of different transcripts: compare 16 + 20 vs 23 export to Excel and count lines Alternate: use Unix commands, e.g. grep – c, unique

12. Tag-to-gene Mapping in Drosophila (E. Pleasance, M. Marra and S. Jones, in preparation) Resources: Drosophila genomic sequence, full-length cDNAs, ESTs, salivary gland ESTs 4,628 transcripts: 2866 (61.9%) – known or predicted genes 289 ( 6.2 %) – genomic DNA and EST (but no predicted gene) 1170 (25.3%) – genomic DNA and/or reverse strand of gene 303 ( 6.5%) – no match

13. Comparison of SAGE and real-time quantitative RT-PCR Fold-difference by SAGE Fold-difference by QRT-PCR Correlation coefficient = 0.5 II. Correlation coefficient between fold-difference values (64 samples): I. Direction of Change: 91/96 samples = 95% concordance

14. Real time quantitative RT-PCR analyses Applied Biosystems 7900 Sequence Detection System gene specific primers designed (near SAGE tag) using Primer Express V software SYBR Green One-step RT-PCR reagent kit / Melting curve analysis sample volume reduced to 15 μl ; n = 3 “housekeeping” gene and known cell death gene as controls used to confirm expression profiles and to resolve ambiguous tag-to-gene mappings

15. An ecdysone induced transcriptional cascade regulates salivary gland cell death BFTZ-F1 EcR/USP BR-C E74 E93 rpr hid ark dronc crq diap2 Cell Death E75

16. SAGE Identifies Genes Associated Previously With Salivary Gland Death BFTZ-F1 EcR/USP BR-C E74 E93 rpr hid ark dronc crq iap2 Cell Death E75 Question: What genes didn’t we find? Do they contain an NlaIII site?

17. 1244 genes are expressed differentially (p <.05) prior to salivary gland PCD 512 genes have associated biological annotations (Flybase Gene Ontology) R. Varhol, S. Zuyderduyn 732 genes have unknown functions 377 of these were unpredicted

18. FlyBase Gene Annotation

19. Automating the association of differentially expressed genes with biological annotations Behind the scenes: Incorporation of FlyBase, GadFly, Swissprot, etc. into DISCOVERYdb Link database information with differential expression and tag-to-gene mapping data 6 files of pairwise comparisons with genes associated with annotations of interest Several trials……

20. Output of “flyannotation.pl” SG16 vs SG23 upregulated (p < 0.05)

21. Secondary screening of differentially expressed genes I. Data Mining II. Gene expression in salivary gland cell death-defective mutant (E93) III. Gene expression in other dying tissues IV. Loss-of-function and gain-of-function mutant analyses

22. Data mining related questions What differentially expressed Drosophila genes are similar to mammalian genes and associated previously with: cell death? autophagy? pathways of interest? tumorigenesis? cancer? other diseases?

23. I. Data mining by sequence similarity searches and keyword queries SAGE tag maps to Drosophila gene Extract gene sequence from GadFly Database GadFly – Swissprot Homology (tBLASTX) Keyword query of Swissprot comments, keywords and identification fields SAGE tag maps to novel EST tBLASTX search EST vs Swissprot Keyword query of FlyBase and PCD database

24. Keyword Data base SG 16 SG 23 Gene Sim (%) Score Length (aa) Swiss Prot id Database Description death apoptosis death survival autophagy hormone cancer tumor apoptosis TNF BH3 SP FB PCD 0 4 0 1 0 1 0 2 0 1 45 10 65 1 EST only 15 0 6 5 4 7 61 7 73 12 7 15 1 0 5 102 Nc debcl cact Traf1 CG4719 Ptpmeg CG4859 CG10777 Rpn2 stck chrw ciboulot CG8706 Atet CG13907 botv sp6 CG11335 CG10990 CG4091 CG2023 27.2 25.5 37.1 37.0 39.3 31.3 41.6 18.7 23.7 65.7 35.3 68.6 29.6 39.9 33.2 48.6 28.3 42.7 59.9 59.6 53.9 251 127 322 300 232 101 772 87 160 1276 257 114 1204 469 311 2044 427 423 592 330 247 253 133 203 179 116 80 407 444 267 321 170 34 984 240 203 838 361 182 469 188 228 ICE6_HUMAN BCL2_HUMAN IKBA_HUMAN TRA1_HUMAN BAR1_HUMAN PTND_HUMAN MM11_MOUSE WRN_HUMAN PSD2_HUMAN PINC_HUMAN RB24_MOUSE TYB4_HUMAN LRP2_HUMAN ABG2_HUMAN MOT1_HUMAN EXL3_HUMAN MASP_MOUSE LYOX_HUMAN CASPASE-6 PRECURSOR APOPTOSIS REGULATOR BCL-2 NF-KAPPAB INHIBITOR ALPHA TRAF1 BRCA1-ASSOCIATED RING DOMAIN FAS-ASSOCIATED PTP-1 STROMELYSIN-3 PRECURSOR WERNER SYNDROME HELICASE 26S PROTEASOME S2 PINCH PROTEIN RAS-RELATED PROTEIN RAB-24 THYMOSIN BETA-4 LDL RECEPTOR-RELATED PROT BREAST CANCER RESISTANCE MONOCARBOXYLATE TRANSPORT TUMOUR SUPPRESSOR EXL3-LIKE PROTEASE INHIBITOR 5 PROT-LYSINE 6-OXIDASE PREC. MM 'APOPTOSIS PROTEIN MA-3' HS 'TNF-INDUCED PROTEIN GG2-1' BH3

25. Data Mining - What next? Is it possible to include the “best” match from Swissprot? How can we best incorporate information from TrEMBL? What genes belong to the same metabolic or signal transduction pathways? A new release of GadFly will be available soon. How are the Drosophila databases updated? What differentially expressed genes are similar to mammalian genes up/downregulated in cancer? (e.g. compare to cancer SAGE libraries)

26. Secondary screening of differentially expressed genes I. Data Mining II. Gene expression in salivary gland cell death-defective mutant (E93) III. Gene expression in other dying tissues IV. Loss-of-function and gain-of-function mutant analyses

27. II. Analysis of differential gene expression in E93 mutant salivary glands E93 is a DNA binding protein required for salivary gland cell death (Lee et al. 2000) 65 different E93 chromosomal binding positions are known Expression of known cell death genes is reduced in E93 mutant salivary glands We tested 20 differentially expressed genes with map positions corresponding to E93 binding sites

28. Gene expression is reduced in E93 mutant salivary glands 19/20 upregulated genes showed a reduction in relative levels of transcription. Gene Cytological location OreR SAGE (16:23) OreR RT-PCR (16:23) E93 RT-PCR (16:23) E93 RT-PCR (16:30) CecB CG4091 CecA1 Doa CG4859 CG14995 BACR19J1.2 CG3845 Ptpmeg CG9321 Cp1 CG12789 CG1216 larp CG7860 Cyp1 Phm Sox14 CG13448 CG8149 ark (control) 3R 99E4-99E4 2R 59F5-59F5 3R 99E4-99E4 3R 98F1-98F2 2R 60D10-60D10 3L 64A7-64A8 X 1B10-1B10 2R 49E1-49E1 3L 61C1-61C1 2L 29E4-29E4 2R 50C20-50C20 2L 28A1-28A1 3L 61A6-61A6 3R 98C3-98C3 X 13E3-13E3 X 14B15-14B15 2R 60A14-60A14 3L 71E1-71E1 3R 85D25-85D25 N/A > 30 105 > 106 > 38 12 > 22 34 25 > 4 23 12 7 > 4 > 11 10.5 11 > 4 > 7 7.2 6 15 2765.7 206.7 177.5 71.9 47.2 40.8 34.6 29.6 14.2 13.7 12.8 10.7 9.1 7.8 4.9 3.5 3.4 2.9 2.5 2.1 2.0 0.1 0.8 0.4 2.1 1.1 0.6 1.4 0.8 0.5 2.0 0.8 0.7 0.9 0.4 0.2 0.5 0.7 3.2 0.5 0.7 3.0 34.2 2.7 2.6 1.2 1.5 0.5 1.9 1.8 0.5 2.2 0.9 1.5 0.8 1.8 0.8 0.5 0.7 2.9 0.2 0.5 3.2

29. III. Expression of salivary gland genes in the embryo coincides with patterns of PCD 3/15 genes examined have embryonic expression patterns that at least partially coincide with patterns of PCD CG3132 Cp1 akap200

30. IV. Loss-of-function mutant analyses. e.g. akap200 mutants indicate a possible defect in PCD: salivary glands embryos retinas wild-type (41 hr APF)akap200 EP2254 (41 hr APF)

31. Differentially expressed genes reveal molecular features associated with autophagic cell death Autophagic cell death involves the induction of genes required for protein synthesis e.g. 6 different translation initiation factors Novel transcription factors share transcription profiles with known salivary gland transcription factors e.g. maf-S, CG3350 Components of multiple signal transduction pathways are involved e.g. TNF-  like pathway, akap200, Doa

32. Differentially expressed genes and molecular features of autophagic cell death, continued. some apg-like genes are upregulated transcriptionally e.g. genes involved in two ubiquitin-like pathways CG6194 (apg4;novel cysteine protease) CG5429 (apg6/beclin-1) Other autophagy-associated genes are likely involved e.g. lysosomal enzymes, rab-7 Autophagic and apoptotic cell death appear to utilize at least some common pathways or pathway components

33. Acknowledgements Genome Sciences Centre Victor Ling Marco Marra Functional Genomics Suganthi Chittaranjan Doug Freeman Carrie Anderson Shaun Coughlin Sequencing Genome Sciences Centre Sequencing Team Bioinformatics Steven Jones Erin Garland Richard Varhol Scott Zuyderduyn SAGE team University of Maryland Biotech Institute Eric Baehrecke

35. SAGE (Velculescu et al. 1995) Stage 2 Stage 1 Expression level Bioinformatics: Quantitate tags Correlate tags to genes genes

36. Autophagic cell death shares morphological features with autophagy

37. Cp1 CG2444 Transcription Profiling of Autophagic Cell Death Gorski S, Anderson C, Chittaranjan S, Freeman D, Garland E, Jones S, Varhol R, Zuyderduyn S, Marra M Dr. Michael Smith 1932-2000 2. Salivary Gland Cell Death 1. Abstract 8. Data Mining Founding Director Multiple Pictures 10. Acknowledgements Salivary gland cell death is regulated by a transcriptional hierarchy induced by the steroid hormone 20-hydroxyecdysone (ecdysone). Following a pulse of ecdysone at the prepupal-pupal stage transition, the transcription of several known and highly conserved cell death genes is regulated. Shown below are the results of RT-PCR analyses using the known anti-death gene, diap2, and the pro-death genes, reaper ( rpr ) and hid. Developmental stages are indicated in hours (hr) after puparium formation (APF) at 18 ° C. To identify genes corresponding to SAGE tags, we created a Drosophila tag-to-gene mapping program that utilizes genomic DNA sequence and cDNA sequences from the Drosophila Genome Project. In addition, to facilitate specifically the mapping of our salivary gland SAGE tags, we constructed a salivary gland specific cDNA library and generated 3’ ESTs. 6681 quality 3’ ESTs were clustered and found to represent 1696 different transcripts. Of these, 1355 matched predicted genes and 341 (20%) are potentialy novel genes. These novel genes were used to map 210 tag species in the SAGE libraries. Align me! To annotate further the set of genes expressed prior to Drosophila salivary gland death, we are developing automated data mining tools that incorporate publicly available databases, sequence similarity, and user-defined keywords. During Drosophila metamorphosis, the larval salivary glands undergo steroid hormone-regulated programmed cell death (PCD). While dying salivary gland cells are detectable by markers of apoptosis, morphological analyses indicate that salivary gland destruction occurs by autophagy. Distinguished by the formation of acidic autophagic vacuoles that facilitate cellular self- destruction, autophagic cell death is a type of PCD that is not well characterized. To identify the genes involved in autophagic cell death, we used Serial Analysis of Gene Expression (SAGE) to examine salivary gland transcripts from three successive stages of wild-type salivary gland development leading up to PCD. We detected over 800 differentially expressed genes, including known cell death genes demonstrated previously to be upregulated prior to salivary gland PCD. Bioinformatic analyses revealed additional differentially expressed genes with sequence similarities to cell death related genes from other organisms. For a subset of the differentially expressed genes, we are currently verifying differential expression by quantitative real-time RT- PCR, examining gene expression in mutant backgrounds, and analyzing gene expression in cell death stage Drosophila embryos. S. Gorski is a Research Fellow of the National Cancer Institute of Canada supported with funds provided by the Terry Fox Run. E. Garland is a Michael Smith Foundation for Health Research Trainee. M. Marra is a Michael Smith Foundation for Health Research Scholar. We gratefully acknowledge the support of the BC Cancer Agency, BC Cancer Foundation, and NSERC. small vertical clearance check RT hr - + - + - + 16 18 20 22 23 24 diap2 rpr hid Following the induction of cell death gene expression, the entire salivary gland undergoes cell death in a rapid, stage-specific, and virtually synchronous manner. Acridine orange staining, shown below, indicates the temporal specificity of salivary gland cell death. Tissue destruction is evident by 24 – 25 hrs APF at 18 ° C. 24 hrs: staining in ALL nuclei23 hrs: staining in one nucleus22 hrs: no staining detected 3. Salivary gland SAGE libraries Micro-SAGE libraries were constructed from three successive stages of salivary gland development: 16 hrs APF (SG16), 20 hrs APF (SG20), and 23 hrs APF (SG23). The total number of SAGE tags analyzed includes tags of >95% sequence quality seen at least twice in all libraries combined. The total number of different tag species in all three libraries is 4,628. SAGE Library Total tags analyzed Tag species% of tag species seen at frequency: 12-1011-100>100 16 hr 20 hr 23 hr 34,989 31,215 30,823 3,126 3,034 2,963 32.755.710.41.2 38.050.99.71.4 33.354.011.21.4 4. Tag-to-Gene Mapping in Drosophila 5. Known salivary gland death-related genes identified 6. Differential gene expression 9. Pilot screen: embryonic gene expression Several ecdysone-induced and cell death genes associated previously with salivary gland death (e.g. Jiang et al., 1997; 2000; Lee et al. 2000) were identified in the SAGE libraries. BR-C, E74, E75 and E93 are ecdysone-induced primary response genes that act upstream of the cell death genes reaper (rpr), hid, (not detected), ark, dronc, iap2 and croquemort (crq). The gene expression profiles generated by SAGE indicate that upstream transcriptional regulators can be distinguished temporally from downstream death effector molecules. Verification of Differential Expression 7. Verification of Differential Expression The expression of several known cell death genes foreshadows death not only in the salivary gland but in other tissues as well. To identify other genes with a possible role in cell death beyond the salivary gland, we initiated a pilot screen to examine the expression pattern of upregulated salivary gland genes in cell death stage embryos. Preliminary results indicate some overlap with regions of embryonic apoptotic cell death. 522 genes (12%) are upregulated (p <.05) 331 genes (8%) are downregulated 16 hr 23 hr Correlation coefficient = 0.64 To verify independently the differential expression determined by SAGE, we conducted real-time quantitative RT-PCR analyses for a subset of 50 genes. The graph below indicates the fold- difference in expression (between 16 hr and 23 hr) as determined by SAGE and real-time RT-PCR. Fold-difference by SAGE Fold-difference by real-time RT-PCR 4628 tag species 2561 map to predicted genes (96% unambiguous) 1764 do not map to predicted genes 1461 map to ESTs &/or genomic sequence 303 have no match (6.5% of total tag species) 210 correspond to salivary gland ESTs that represent potentially novel genes To identify additional genes expressed differentially prior to salivary gland cell death, we conducted pairwise comparisons between the SAGE libraries. Below is a view of the 16 hr vs 23 hr comparison that was generated using SAGEspace, an in-house graphical tool for visualizing results of SAGE experiments. EcR/USP BR-C E74 E93 E75 rpr hid ark dronc crq diap2 cell death SAGE tag maps to Drosophila predicted gene Extract corresponding protein sequence From GadFly Database GadFly – Swissprot Homology (BLAST) Keyword search of Swissprot description SAGE tag maps to novel EST BLAST search EST vs Swissprot TUNEL RNA in situ Lateral view of mid-stage embryos. Cell death occurs mainly in the head region. Ventral view of late-stage embryos. Cell death occurs predominantly in the nervous system.

38. 1244 genes are differentially expressed prior to salivary gland PCD 16 hour 23 hour 522 genes (12%) are upregulated (p < 0.05) 331 genes (8%) are downregulated 1244 genes 512 genes with biological annotations (Gene Ontology) 732 genes with unknown function 377 of these were unpredicted

39. PCD is highly conserved Dcp-1 Drice Decay Damm Diap1 Diap2 Deterin Hid Grim Reaper Ras Raf Mapk dBorg-1 dBorg-2 Ark Cell Death Egfr Dm-P53 EcR/USP Dredd ? Strica Dronc cytC ? ? UV ? dFadd ?

40. Autophagy bulk degradation of cellular proteins through an autophagosomic- lysosmal pathway can be induced in yeast and in mammalian cells by starvation conditions genetic screens in yeast have identified the genes involved at least 16 autophagy-defective (apg) and autophagy (aut) genes identified genes encode components of two ubiquitin-like systems unknown whether the same molecular mechanisms are involved in autophagic cell death