The Discovery of Myc
Regulated Genes in Islet
Tumours using Microarray
Analysis
Stella Pelengaris
Sam Robson
ACTIVATION OF MYCERTAM PROTEIN
Inactive
MycERTAM
Myc
Bound HSP90
ERTM HSP90
+ 4-hydroxytamoxifen
Active
MycERTAM
Myc Unbound HSP90
ERTM
HSP90
Myc
Max
ERTM
Myc Activation Promotes  cell
Proliferation
MycERTAM inactive
MycERTAM activated 24 hrs
Ki67
Myc induces concomitant
apoptosis
MycERTAM inactive
MycERTAM activated 72 hrs
TUNEL
-cells are almost totally ablated
MycERTAM inactive
MycERTAM activated 6 days
H&E
Blocking Myc-induced
apoptosis in islet ß-cells
…….
By over-expressing Bcl-XL
(RIP- Bcl-XL Transgenic mice from Doug
Hanahan)
Blocking Myc-induced
apoptosis: Ins-MycERTM x RIPBcl-xL mice TAM
MycERTAM inactive
MycER
H&E
activated 7-14 days
Loss of  cell differentiation
Decreased insulin content (also loss of Pdx-1, GLUT 2),
moreover, animals develop transient diabetes.
The tumour suppressor function of c-Myc
c-Myc ON
Proliferation
c-Myc
OFF
Adult Pancreatic
islet beta cells
Apoptosis
Islet involution
+Bcl-xL (Apoptosis blocked)
Hyperplasia
Loss of Differentiation
Loss of cell-cell contact
Local invasion
Angiogenesis
The oncogenic potential of c-Myc
Rapid tumour regression following Myc
deactivationDay 10
Day 0
Day 4
Day 38
Ki-67
E-cadherin
Experimental Design
• Islet tumour reversal experiment.
• Timepoints:
–
–
–
–
Time 0 (Transgenic untreated)
1 day ON (4-OHT administered for 1 day)
14 days ON (4-OHT administered for 14 days)
14 days ON 7 days OFF (4-OHT administered for 14
days, injections stopped for 7 days)
• 3 replicates for each timepoint.
• Randomization or litter mates?
Affymetrix GeneChips
• Each gene represented by 11-20 ‘probe pairs’.
• Probe pairs are 3’ biased.
• ‘Probe Pair’ consists of Perfect Match (PM) and
MisMatch (MM) probes.
• MM has altered middle (13th) base. Designed to measure
non-specific binding (NSB).
GeneChip Scanning
• RNA sample prepared, labelled and hybridised
to chip.
• Chip fluorescently scanned. Gives a raw
pixelated image - .DAT file.
• Grid used to separate pixels related to individual
probes.
• Pixel intensities averaged to give single intensity
for each probe - .CEL file.
• Probe level intensities combined for each probe
set to give single intensity value for each gene .CHP file.
Affymetrix MicroArray Suite (MAS)
v5.0
• Current method employed by Affymetrix.
• Weighted mean using one-step Tukey Biweight Estimate:
signal  log 1 Tukey Biweight log PM j  CTj 
• CTj is a quantity derived from MMj never larger than PMj.
• Weights each probe intensity based on its distance from
the mean.
• Robust average (insensitive to small changes from any
assumptions made).
Problems with Mis-Match Data
• MM intensity levels are greater than PM intensity
levels in ~1/3 of all probes.
• Suggests that MM probes measure actual
signal, and not just non specific binding.
• Removal of MM results in negative signal
values.
• Subtracting MM data will result in loss of
interesting signal in many probes. Several
methods have been proposed using only PM
data.
Robust Multiarray Average
(RMA)
• Subtraction of MM
data corrects for NSB,
but introduces noise.
• Want a method that
gives positive
intensity values.
• Normalising at probe
level avoids the loss
of information.
Analysis Setup
• Use both RMA normalization and MAS5.0
normalization.
• Have higher confidence in genes that show
differential expression by both methods.
• Setup experiment with both RMA and MAS5.0
normalized data. Allows direct comparison.
• Be sure to apply per chip and per gene
normalization steps on RMA and MAS5.0
normalized data separately.
Analysis Setup
Quality Control - Samples
• Measure how similar replicates are.
• Methods include condition trees, principle
component analysis (PCA) and scatter plots.
• See here that one time point has variation
amongst replicates compared to the others. This
raises an interesting question regarding
randomization.
• For now, remove these samples from analysis.
Quality Control - Samples
Genespring N...
Genes...
Selected Condition Tree:
Branch color parameter:
Genespring ...
Time (days)
Colored by: Non QC'd - Genespring No...
Gene List:
test (920), 1455802_x_at s...
Condition Tree
Quality Control - Samples
1
Y: PCA component 2 (11.76% variance)
0d ON
0
14d O...
0
0
Z: PCA component 3 (10.54% variance)
1
14d ON
X: PCA component 1 (13.07% variance)
1
1d ON
X-axis: PCA component 1 (13.07% varia...
Y-axis: PCA component 2 (11.76% varia...
Z-axis: PCA component 3 (10.54% varia...
Conditions:
Colored by:
Non QC'd - Genespring No...
Parameter Time
Principle Component Analysis – MAS5.0
Quality Control - Samples
1Y: PCA component 2 (29.82% variance)
0d ON
0
14d O...
0
0
X: PCA component 1 (31.67% variance)
1
Z: PCA component 3 (10.96% variance)
1
14d ON
1d ON
X-axis: PCA component 1 (31.67% variance)
Y-axis: PCA component 2 (29.82% variance)
Z-axis: PCA component 3 (10.96% variance)
Conditions:
Colored by:
Non QC'd - RMA Preprocessed Experiment, All Samples
Parameter Time
Principle Component Analysis – RMA
Quality Control - Genes
• Remove all AFFX probe data (standard probes present
on all Affymetrix Genechips).
• Filter on flags (removed ‘absent in all’ genes)
• Filter genes based on Standard Deviation of replicate
data. “Interesting data lies within 1.4 SDs of mean”.
• Filter out non-changing genes to leave differentially
expressed genes (0.8< fold change <1.2). Be sure to use
ratio mode with all fold change analyses.
• Venn diagram tool to make a list of ∩ RMA and MAS 5
QC’d genes.
• Replace samples removed from sample QC prior to
further analyses.
Supervised vs. Unsupervised
Analysis
• Unsupervised analysis uses iterative
methods to cluster expression profiles.
• Useful to see coexpressed genes.
• Supervised analysis probably better in this
case. We know what expression profiles to
expect.
• Compare all genes to an expected
expression profile.
Expected Expression Profile
Use of Gene Ontology
• Genes listed based on Molecular Function,
Biological Process and Cellular
Component.
• Comparison of gene lists with GO lists
offers insight into gene function.
• Can split window to group genes with
similar molecular functions.
Use of Gene Ontology
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Time
Normalized Intensity
1 (log scale)
Time
Normalized Intensity
1 (log scale)
Time
Time
Time 0
Time 0
GC-RMA
MAS5.0
antioxidant activity (GO...
Time 0
Time 0
GC-RMA
MAS5.0
apoptosis regulator ac...
Time 0
Time 0
GC-RMA
MAS5.0
binding...20 in list
Time 0
Time 0
GC-RMA
MAS5.0
catalytic activity...
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Time
Time
Time
Time
Time 0
Time 0
GC-RMA
MAS5.0
cell adhesion molecul...
Time 0
Time 0
GC-RMA
MAS5.0
chaperone activity (GO...
Time 0
Time 0
GC-RMA
MAS5.0
chaperone regulator a...
Time 0
Time 0
GC-RMA
MAS5.0
defense immunity prot...
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Time
Time
Time
Time
Time 0
Time 0
GC-RMA
MAS5.0
Time 0
Time 0
GC-RMA
MAS5.0
Time 0
Time 0
GC-RMA
MAS5.0
Time 0
Time 0
GC-RMA
MAS5.0
enzyme regulator activi...
molecular_function un...
motor activity (GO:000...
protein stabilization ac...
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Time
Normalized Intensity
1 (log scale)
Time
Normalized Intensity
1 (log scale)
Time
Time
Time 0
Time 0
GC-RMA
MAS5.0
Time 0
Time 0
GC-RMA
MAS5.0
Time 0
Time 0
GC-RMA
MAS5.0
Time 0
Time 0
GC-RMA
MAS5.0
signal transducer activ...
structural molecule act...
transcription regulator ...
translation regulator (...
Normalized Intensity
1 (log scale)
Normalized Intensity
1 (log scale)
Time
Time 0
Time 0
GC-RMA
MAS5.0
transporter activity...
Time
Time 0
Time 0
GC-RMA
MAS5.0
Unclassified...14 in list
Y-axis: Comparison of Normalization methods, Time course - Log mode
Split by: Gene Lists/Gene Ontology (GO SLIMS)/GO SLIMS Molecular Functio...
Colored by: Time 0 GC-RMA
Gene List:
All (64)
Use of gene Pathways
• GenMAPP and KEGG have pathways
available to GeneSpring.
• Able to show expression profiles directly
onto these pathways.
Use of Gene Pathways
Selected Pathway:
Colored by:
Gene List:
Adherens junction - Mus musculus
Comparison of Normalization methods, Time course - Log mode
all genes (45101)
Use of Gene Pathways
Selected Pathway:
Colored by:
Gene List:
Apoptosis - Mus musculus
Comparison of Normalization methods, Time course - Log mode
all genes (45101)
Use of Gene Pathways
Selected Pathway:
Colored by:
Gene List:
Cell Cycle
Comparison of Normalization methods, Time course - Log mode
all genes (45101)
Differences seen between RMA
and MAS5.0
• Every analysis step performed on both sets of
data.
• Those genes found with both methods have
greater confidence.
• See more genes pulled out with MAS5.0 than
RMA.
• Is MAS5.0 producing false positives, or is RMA
producing false negatives?
• RMA seen to ‘squash’ low expression genes
compared to MAS5.0. These could be lost in QC
process.
• Probable that RMA loses interesting data.
Hand Curation of Genes
• First pull out genes with at least two-fold
change between time points.
• Use GO lists to find genes that could be
interesting.
• Use literature to find possible targets.
• Which genes are not interesting, and
which are novel targets?
• Biologist intellectual input required.
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
14 days ON
MAS5.0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Mmp9 (2)
Mmp9 – Matrix metalloproteinase 9
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
14 days ON
MAS5.0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Mmp12 (1)
Mmp12 – Matrix metalloproteinase 12
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
14 days ON
MAS5.0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Ipf1 (2)
Ipf1 – Insulin promoter factor 1
Some Results
100 Normalized Intensity
(log scale)
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
14 days ON
MAS5.0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Insulin (2)
Ins1 – Insulin I and II
Some Results
100 Normalized Intensity
(log scale)
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
14 days ON
MAS5.0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Cad (2)
Cad – Carbamoyl-phosphate synthetase 2, aspartate
transcarbamylase and dihydroorotase
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Caspase1 (1)
Casp1 – Caspase 1
14 days ON
MAS5.0
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Caspase4 (1)
Casp4 – Caspase 4
14 days ON
MAS5.0
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Caspase7 (2)
Casp7 – Caspase 7
14 days ON
MAS5.0
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Cdk8 (1)
Cdk8
1 day ON
14 days ON
MAS5.0
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
p15 (1)
p15
1 day ON
14 days ON
MAS5.0
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
p57 (1)
p57
1 day ON
14 days ON
MAS5.0
Some Results
100 Normalized Intensity
(log scale)
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
1 day ON
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Somatostatin (1)
Somatostatin
14 days ON
MAS5.0
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Vegf (4)
Vegf
1 day ON
14 days ON
MAS5.0
Some Results
100
10
1
0.1
0.01
Time 0
Time
1 day ON
14 days ON
GC-RMA
Time 0
Y-axis:
Comparison of Normalization methods, Time course - Log mode
Colored by: Time 0 GC-RMA
Gene List:
Vimentin (1)
Vimentin
1 day ON
14 days ON
MAS5.0
Pathways
Problems seen in data
• Pancreatic tissue notorious for producing low
quality RNA (can be prevented with experience).
• Use whole pancreatic tissue, yet only interested
in changes in the islets. Exocrine tissue may
mask important changes.
• Islet mass not constant throughout the pancreas.
• Islet mass increases with 4-OHT administration.
Thus later time points contain more islet tissue
than earlier time points.
• Many suspected target genes show unexpected
expression profiles.
Future work
• More in silico work before wet lab work?
• Wet lab work to confirm hypothesise:
– Real Time PCR
– Western Blots
– In situ PCR (Not quantitative but would allow us to concentrate
on islet tissue)
– Micro Fluidic Cards?
• Run microarrays on pure exocrine tissue.
Subtract from whole pancreas data to see islet
changes.
• Laser microdissection to concentrate on islet
tissue. Method requires work to prevent RNA
degradation. Paradise FFPE kit?