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

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Outline

1. Introduction to c-Myc

2. Transgenic in vivo models – skin versus pancreas

3. Methods

4. Results

5. Generalised linear models

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

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

c-Myc Regulated Processes

c-Mycc-MycProliferation

Growth

Apoptosis

ExternalSignals(eg. mitogens,

survival factors)

Loss of Differentiation

p27KIP1

Cell-Cycle ProgressionGene Activation

CACGTGCyclin D2 CDK4

CCND2CDK4

CUL1CKS

Proteosome

Cyclin E CDK2CAK

Inactive Active

Ub

MYC MAX

E-Box sequence in promoter sequence of target gene

p27KIP1

p27KIP1

Cyclin E CDK2

P

MYC MAXMIZ-1

p15Ink4b (CDKN2B)p27 (not known if Miz-1 is required)

MYCSp1/Sp3

p15Ink4b (CDKN2B)p21Waf1 (CDKN1A)

Apoptosis – Cell Death

FAS “Death Receptor” Death Induced Signalling Complex (DISC)

Apoptosis

Procaspase 8

Mitochondrion

Effectorcaspases

FAS Ligand

FADD

tBID

BID BCL-2

APAF-1

ATP

Cytochrome c

Apoptosome

Procaspase 9

IAPs

Cellular targets

AIFEndo G

Caspase Cascade

Effector caspases

c-Myc

BIM

IAPs

p53

PUMANOXA

SmacDIABLO

Omi/Htra2

BAX/BAK

FLIP

ARF MOMP

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 to drastically different phenotypes – Replication in skin, apoptosis in pancreas

Transgenic Model – c-MycERTAM

Myc Box IMyc Box IIBasic

Helix-Loop-HelixLeucine ZipperEstrogen Receptor

Legend

ERTAM HSP90

MycInactive MycERTAM

Bound Heat Shock Protein 90

4-Hydroxytamoxifen

4-OHT binds estrogen receptor opening up bHLHz domain.

Max Max binds Myc at leucine helix-loop-helix zipper region

Active MycERTAM

Myc-Max complex binds E-box sequence of target gene

TR

RA

P

Transformation-Transcription domain Associated Protein (TRRAP) binds to MBII with help from MBI

HA

T

RNA Polymerase

TRRAP recruits a histone acetyltransferase (HAT). This acetylates nucleosomal histones resulting in chromatin remodelling, allowing access by RNA Polymerase for gene transcription

CACGTG

c-MycERTAM Activation

Pelengaris et al. (2002), Cell, Vol. 109(3), 321-334

Skin

Inactive Active

Pancreas

Pelengaris et al. (1999), Molecular Cell, Vol. 3(5), 565-577

Suprabasallayer

Suprabasallayer

c-MycERTAM Activation

• SkinUnchecked proliferation, no apoptosis - Replication

• PancreasSynchronous cell cycle entry and apoptosis – Death

• Myc activation regulates two opposing phenotypes

Pelengaris et al. (2002), Cell, Vol. 109(3), 321-334

Pelengaris et al. (1999), Molecular Cell, Vol. 3(5), 565-577

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

Workflow1: Treatment of

TransgenicsControlled activation of

c-Myc in two diverse tissues

2: Extraction of Tissue

Excision of target tissue

3: Laser Capture

MicrodissectionIsolation of homogenous

tissue

4: mRNA Extraction

Isolate mRNA from target cells

5: 2-Cycle IVTPreparation of cRNA for microarray hybridisation

6: Microarray HybridisationHybridise cRNA to

microarrays

7: Microarray Data AnalysisAnalysis of microarray

data

QCQC

QC

8: Validation Studies

Validation studies to confirm results

9: Functional Validation

Linking results to the biology of the system

Experimental SetupUntreated with 4-OHT Treated with 4-OHT

Skin Tissue

Pancreas Tissue

x3 x3

x3x3

Time course Time course

Time course Time course

4 8 16 32

Gen

eE

xpre

ssio

n4 8 16 32

Gen

eE

xpre

ssio

n4 8 16 32

Gen

eE

xpre

ssio

n

4 8 16 32

Gen

eE

xpre

ssio

n

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

Laser Capture Microdissection1: Find Islet 2: Cut Islet

3: Lift Islet 4: Extracted Islet

Laser Capture Microdissection1: Find Islet 2: Cut Islet

3: Lift Islet 4: Extracted Islet

Laser Capture Microdissection1: Find Islet 2: Cut Islet

3: Lift Islet 4: Extracted Islet

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!

RNA Integrity

Poor quality:

Majority of peaks at lower levels

Okay quality:

18S and 28S peaks more prominent, but many 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

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

RNA Yield vs Quality

0

10

20

30

40

50

60

70

80

90

100

3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

RIN

Sta

rtin

g c

RN

Au

g

Effect of RNA Quality on Yield

• 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

Skin

Pancreas

Microarray Analysis

• Each feature measures one 25-mer nucleotide 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

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

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

Gene Expression Analysis

• 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

Pancreas Skin

Tissue-Specific Differentiation Markers

Insulin Involucrin ~4-fold down

in pancreas~2-fold down

in skin

Cell-Cycle ProgressionCDK4

p27KIP1

Cyclin D2~2-fold up

in skin~4-fold up

in skin

• 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

Cyclin E~4-fold up

in pancreas

Apoptosis

• 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

p53

p19ARF

~2-fold up in pancreas

No change

Fas Receptor ~6-fold up

in pancreas

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

Generalised Linear Models

• Unsupervised linear regressive technique.• Model gene expression data as a linear

combination of parameter variables:

ppxbxbxby ...2211

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

• Can be used in the following ways:

1.To check how much of an effect other parameters have on gene expression (eg batching effects)

2.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.

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

Generalised Linear Models

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

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?

AcknowledgementsProject Supervisors:

Michael KhanDavid Epstein

Stella Pelengaris

Special thanks:Helen Bird

Lesley WardSue Davis

Heather Turner Ewan Hunter

Sponsors:EPSRC, BBSRC, AICR, Eli Lilly and Amylin Pharmaceuticals Inc.

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Advisory Committee:Robert Old

Manu VatishJames Lynn

AcknowledgementsLuxian Mike Vicky Sevi

David Stella Sylvie