Upload
winka
View
22
Download
1
Tags:
Embed Size (px)
DESCRIPTION
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 - PowerPoint PPT Presentation
Citation preview
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
M A
o c
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.
M A
o c
Advisory Committee:Robert Old
Manu VatishJames Lynn
AcknowledgementsLuxian Mike Vicky Sevi
David Stella Sylvie