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Life or Cell Death: Deciphering c- Myc Regulated Gene Networks In Two Distinct Tissues

Life or Cell Death: Deciphering c- Myc Regulated Gene Networks In Two Distinct Tissues. Sam Robson MOAC DTC, Coventry House, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL. Outline. Introduction to c-Myc Transgenic in vivo models – skin versus pancreas Methods Results

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Life or Cell Death: Deciphering c- Myc Regulated Gene Networks In Two Distinct Tissues

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  1. Life or Cell Death:Decipheringc-MycRegulated Gene Networks In Two Distinct Tissues Sam Robson MOAC DTC, Coventry House, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL

  2. Outline • Introduction to c-Myc • Transgenic in vivo models – skin versus pancreas • Methods • Results • Generalised linear models

  3. Project Aims • Using two distinct switchable in vivo c-Myc models, we aim to: • Analyse differences in gene-expression • Identify c-Myc regulated genes in cell replication and cell death • Improve understanding of complex c-Myc activity in diseases such as cancer • To understand how and why c-Myc can regulate vastly different paradoxical phenotypes in vivo

  4. 1: Introduction to c-Myc • Transcription factor involved in wide range of cellular functions – “Dual function” • May regulate up to 15% of all genes • Deregulated in majority of human cancers • Therapeutic target? • Exact mechanisms not well understood – we know WHAT c-Myc does, but we want to know WHY it does it • In vitro studies miss complex interactions of surrounding environment on cell fate

  5. c-Myc Regulated Processes Growth c-Myc External Signals (eg. mitogens, survival factors) Proliferation Apoptosis Loss of Differentiation

  6. Cell-Cycle Progression Ub MYC MAX Cyclin D2 CDK4 p27KIP1 p27KIP1 p27KIP1 P Gene Activation CCND2 CDK4 CUL1 CKS CACGTG Proteosome E-Box sequence in promoter sequence of target gene CAK CDK2 Cyclin E CDK2 Cyclin E Inactive Active MIZ-1 MYC MAX p15Ink4b (CDKN2B) p27 (not known if Miz-1 is required) Sp1/Sp3 MYC p15Ink4b (CDKN2B) p21Waf1 (CDKN1A)

  7. Apoptosis – Cell Death FAS Ligand FAS “Death Receptor” Death Induced Signalling Complex (DISC) BCL-2 Apoptosome FADD BID Procaspase 8 Procaspase 9 Cytochrome c FLIP BAX/BAK tBID APAF-1 Caspase Cascade ATP SmacDIABLO Effectorcaspases c-Myc MOMP ARF Mitochondrion IAPs BIM IAPs PUMA Apoptosis AIF Omi/Htra2 Endo G NOXA p53 Cellular targets Effector caspases

  8. 2: Transgenic in vivo models • Controlled activation of c-Myc functions in target cells • Can analyse immediate effects of c-Myc activation • Targetted to pancreatic islet β-cells (insulin promoter) and skin supra-basal keratinocytes (involucrin promoter) • Activation of c-Myc can lead todrasticallydifferent phenotypes – Replication in skin, apoptosis in pancreas

  9. Transgenic Model – c-MycERTAM Legend Myc Box I Helix-Loop-Helix Leucine Zipper Myc Box II Basic Estrogen Receptor Max CACGTG TRRAP Myc-Max complex binds E-box sequence of target gene Transformation-Transcription domain Associated Protein (TRRAP) binds to MBII with help from MBI Inactive MycERTAM Active MycERTAM TRRAP recruits a histone acetyltransferase (HAT). This acetylates nucleosomal histones resulting in chromatin remodelling, allowing access by RNA Polymerase for gene transcription 4-Hydroxytamoxifen Myc HAT Max binds Myc at leucine helix-loop-helix zipper region RNA Polymerase 4-OHT binds estrogen receptor opening up bHLHz domain. Bound Heat Shock Protein 90 HSP90 ERTAM

  10. c-MycERTAM Activation Inactive Active Suprabasal layer Skin Suprabasal layer Pancreas Pelengaris et al. (1999), Molecular Cell, Vol. 3(5), 565-577 Pelengaris et al. (2002), Cell, Vol. 109(3), 321-334

  11. c-MycERTAM Activation • SkinUnchecked proliferation, no apoptosis - Replication • PancreasSynchronous cell cycle entry and apoptosis – Death • Myc activation regulates two opposing phenotypes Pelengaris et al. (1999), Molecular Cell, Vol. 3(5), 565-577 Pelengaris et al. (2002), Cell, Vol. 109(3), 321-334

  12. 3: Methods • Microarrays – High throughput technique • “Transcriptomics” – Analysis at mRNA level • LCM to ensure RNA homogeneity • mRNA very delicate! Degradation by RNAses • Huge amount of work to develop robust protocol for extraction of RNA of suitable quality and yield from LCM • Many technical problems to overcome

  13. Workflow 2: Extraction of Tissue Excision of target tissue 3: Laser Capture Microdissection Isolation of homogenous tissue 1: Treatment of Transgenics Controlled activation of c-Myc in two diverse tissues 6: Microarray Hybridisation Hybridise cRNA to microarrays 5: 2-Cycle IVT Preparation of cRNA for microarray hybridisation 4: mRNA Extraction Isolate mRNA from target cells QC QC QC 9: Functional Validation Linking results to the biology of the system 7: Microarray Data Analysis Analysis of microarray data 8: Validation Studies Validation studies to confirm results

  14. Experimental Setup Untreated with 4-OHT Treated with 4-OHT x3 x3 Time course Time course Skin Tissue x3 x3 Time course Time course Pancreas Tissue GeneExpression GeneExpression 8 8 8 32 32 32 4 4 4 4 16 16 16 16 8 32 GeneExpression GeneExpression

  15. Laser Capture Microdissection Heterogeneity of tissue may cause problem with in vivo studies β-cells make up only ~2% of pancreas LCM allows isolation of homogenous cell populations Optimisation of protocol for LCM of islets – No other protocols available LCM of skin not possible – too tough

  16. 1: Find Islet 2: Cut Islet 3: Lift Islet 4: Extracted Islet Laser Capture Microdissection

  17. 1: Find Islet 2: Cut Islet 3: Lift Islet 4: Extracted Islet Laser Capture Microdissection

  18. 1: Find Islet 2: Cut Islet 3: Lift Islet 4: Extracted Islet Laser Capture Microdissection

  19. Technical problems mRNA very unstable – Great care taken to prevent degradation Pancreas is notorious for being full of RNAses! Standard LCM protocols very long – Optimisation of suitable protocol for islets Small mRNA yield from LCM Logistics of 84 samples – Lots of preparation! Batching of samples – Randomisation to prevent systematic errors and batching effects ~1 year for LCM optimisation~9 months from tissue to microarray results!

  20. Okay quality: 18S and 28S peaks more prominent, but many peaks at lower levels Poor quality: Majority of peaks at lower levels Good quality: Fewer peaks at lower levels Excellent quality: 18S and 28S peaks clear with almost no peaks at lower levels RNA Integrity

  21. Effect of RNA Quality on Yield • General trend between RNA quality (RIN) and yield (Starting cRNA) • Only 1 low starting cRNA samples below RIN=5 cutoff • Implies RIN may not be a great estimator of overall RNA yield

  22. Effect of RNA Quality on Yield Skin Pancreas • In general, skin samples have higher RNA quality and yield than pancreas samples • Many differences between skin and pancreas • Greater number of ribonucleases in pancreas • Homeostasis maintained in skin • More intense processing for pancreas tissue RNA compared to skin

  23. Microarray Analysis • Each feature measures one 25-mernucleotide sequence. • Hundreds of identical 25mers per feature. • 11-20 features per gene. • 25-mer sequence specifically binds biotin labelled cRNA. • Fluorescence readings give relative mRNA concentration - gene expression • Very, very expensive! Courtesy of Affymetrix - www.affymetrix.com

  24. 4: Results • Quality control of microarray data – Several outliers but generally good quality data • Outliers increase variance – Remove for differential analysis • Outliers spread nicely amongst conditions – importance of randomisation! • Analysis of early time points – Direct c-Myc targets

  25. Skin vs Pancreas • Clustering – Group similar samples together • Branching tree like structure – samples on the same branch most similar • Data cluster nicely on tissue (some outliers) • Given the protocol, the data looks great! Skin Pancreas

  26. Gene Expression Analysis Pancreas Skin • Differential ExpressionLook for genes with changing expression across conditions • StatisticsCompare distributions between conditions to look for significant changes • ErrorBiological error, technical error, random error • Functional AnalysisSimilar expression profile implies related biological mechanisms

  27. Tissue-Specific Differentiation Markers Involucrin Insulin ~2-fold down in skin ~4-fold down in pancreas

  28. Cyclin D2 ~2-fold up in skin Cell-Cycle Progression CDK4 Cyclin E ~4-fold up in skin ~4-fold up in pancreas p27KIP1 • Ccnd2 and CDK4 upregulated in skin – Indicates G1/S cell cycle progression • No change in pancreas – Odd • CDK inhibitor p27 downregulated in both • Cyclin E upregulated in pancreas and not skin – Again, very odd ~2-fold down in pancreas ~4-fold down in skin

  29. Apoptosis Fas Receptor p19ARF • Increase in p19 – Oncogenic stress (p53 dependent pathway) • No change in p53 at transcriptional level – Changes may occur at protein level • Massive increase in Fas receptor expression – Extrinsic pathway • Myc seems to drive apotosis through extrinsic and intrinsic pathways ~6-fold up in pancreas ~2-fold up in pancreas ~6-fold up in pancreas p53 No change

  30. 5: Generalised Linear Models • Most microarray studies focus on one or two main parameters • Multi-factorial approach poses problems with significance analysis • Use of generalised linear models • Widely applicable particularly for clinical studies • Collaboration with Agilent – Implementation in Genespring GX

  31. Generalised Linear Models • Unsupervised linear regressive technique. • Model gene expression data as a linear combination of parameter variables: y = (y1,…,yn)T is the response variable (gene expression) for each sample xi = (x1,…,xn)T are the explanatory variables (1 ≤ i ≤ p) for each sample bi is the model coefficient for explanatory variable xi n is the number of samples, p is the number of parameters ε is some error term

  32. Generalised Linear Models • Can be used in the following ways: • To check how much of an effect other parameters have on gene expression (eg batching effects) • To find genes that change based on particular parameters while taking other parameters and interactions into account (eg clinical data) • Makes fewer assumptions of data distribution • Works with unbalanced experiment designs – useful for clinical data.

  33. Generalised Linear Models • Program written in statistical programming language R • Written as part of the Bioconductor project • Implemented in GeneSpring GX (Agilent) – Aim to translate into JAVA for complete integration • Close collaboration with Agilent • Currently testing the program on a number of diverse data sets • MOAC (Shameless plug) – First crop of inter-disciplinary scientists almost ready

  34. Further Work • Analysis of microarray data – Cluster analysis, differential analysis, network analysis, etc. • Use of GLM algorithm and comparison of results with standard methods (ANOVA) • Validation of results – Immunohistology, quantitative real time PCR, etc. • Functional validation – siRNA, ChIP-on-chip, etc. • Translation of GLM program to JAVA for implementation in GeneSpring GX version 8

  35. Conclusion • c-Myc regulates replication and cell death • Web of pathways to decipher – Tissue context in vivo • Seems to initiate apoptosis through combination of extrinsic and intrinsic pathways • Want to find the ‘suicide note’ for the pancreas – why choose death?

  36. Acknowledgements Project Supervisors:Michael KhanDavid EpsteinStella Pelengaris Special thanks:Helen BirdLesley WardSue DavisHeather Turner Ewan Hunter Advisory Committee:Robert OldManu VatishJames Lynn Sponsors: EPSRC, BBSRC, AICR, Eli Lilly and Amylin Pharmaceuticals Inc.

  37. Acknowledgements Luxian Mike Vicky Sevi David Stella Sylvie

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