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

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Life or Cell DeathDeciphering 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

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

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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

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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

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

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Transgenic Model c-MycERTAM
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
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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
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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
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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

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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
4 mRNA Extraction Isolate mRNA from target cells
5 2-Cycle IVT Preparation of cRNA for microarray
hybridisation
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
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Experimental Setup
GeneExpression
GeneExpression
GeneExpression
GeneExpression
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Laser Capture Microdissection

• Heterogeneity of tissue may cause problem with in
  vivo studies
• ß-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

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Laser Capture Microdissection
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Laser Capture Microdissection
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Laser Capture Microdissection
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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 optimisation9 months from
  tissue to microarray results!

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RNA Integrity
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Effect of RNA Quality on Yield

• General trend between RNA quality (RIN) and yield
  (Starting cRNA)
• Only 1 low starting cRNA samples below RIN5
  cutoff
• Implies RIN may not be a great estimator of
  overall RNA yield

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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

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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
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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

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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
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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
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Tissue-Specific Differentiation Markers
Involucrin
Insulin
4-fold down in pancreas
2-fold down in skin
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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
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Apoptosis
Fas Receptor
p19ARF
6-fold up in pancreas
2-fold up in pancreas
6-fold up in pancreas

• 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
No change
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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

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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 e is some
error term
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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.

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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

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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

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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?

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Acknowledgements
Project SupervisorsMichael KhanDavid
EpsteinStella Pelengaris
Special thanksHelen BirdLesley WardSue
DavisHeather Turner Ewan Hunter
Advisory CommitteeRobert OldManu VatishJames
Lynn
Sponsors EPSRC, BBSRC, AICR, Eli Lilly and
Amylin Pharmaceuticals Inc.
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Acknowledgements
Luxian
Mike
Vicky
Sevi
David
Stella
Sylvie