Transcript Presentation - University of Warwick

c-Myc regulated functional gene and protein
networks involved in tumourigenesis
Sam Robson Bsc Msc
Biomedical Research Institute, University of Warwick, CV4 7AL
Supervised by Michael Kahn, David Epstein and Stella Pelengaris
1: The c-Myc Oncogene
3: Pancreatic cancer
Deregulation of the c-Myc (Carcinoma Myelocytomatosis) proto-oncogene is seen
in many human cancers. The protein product is a transcription factor that
works in a heterodimeric complex with the protein Max (figure 1).
This complex controls cell cycle progression (G1 to S phase), inhibits terminal
differentiation, and (somewhat paradoxically) can induce apoptosis.
Control of aberrant c-Myc expression
has been the goal of several therapeutic
techniques (reviewed in Robson et al.,
2005). However, the precise genetic
pathways involved in c-Myc induced
cancer are not yet fully understood. It is
hoped that a better understanding can
be reached through analysis of changes
at the transcriptional level.
Legend
Myc Box I
Myc Box II
Basic
Amino
terminal
1
Helix-Loop-Helix
Leucine Zipper
Carboxyl
terminal
45 63
129 143
355 368
410
Amino acid
439
C-Myc
Protein
Max
Protein
Transactivation
domain
Figure 1: The structure of the c-Myc onco-protein
and its dimerization with the protein Max.
2: Transgenic c-Myc
Transgenic c-Myc provides a switchable model for Myc induced cancer. The cMyc onco-protein is inactive until application of 4-hydroxytamoxifen, a ligand
for the bound estrogen receptor (figure 2). Continued administration of 4-OHT
results in a prolonged cancer phenotype. Tumour regression occurs when
tamoxifen administration is halted.
Legend
Myc Box I
Myc Box II
Basic
1
Helix-Loop-Helix
Leucine Zipper
Estrogen Receptor
4: Results
The pancreas is responsible for regulating blood glucose levels. It
contains hundreds of colonies of cells within the exocrine tissue,
consisting predominately of β-cells, the main source of insulin within
the body.
Mouse models have been created with the chimeric c-MycERTAM gene
active in the pancreatic β-cells. Activation results in increased β-cell
proliferation, but also overwhelming apoptosis, leading to islet
involution and death (figure 3).
The anti-apoptotic gene
BclXL (beta-cell lymphoma
extra large) was introduced
as a second transgene to
suppress apoptosis.
Activation of c-MYCERTAM
in double transgenic mice
results in rapid,
synchronous entry of
nearly all β-cells into the
cell-cycle with no
discernable c-Myc induced
apoptosis, resulting in
grossly hyperplastic islets
(figure 3).
C-MycERTAM (Apoptosis)
INACTIVE
ACTIVE
+4OHT
Pancreatic
Islet β-Cells
Apoptosis
outweighs
proliferation
Proliferation
and Apoptosis
C-MycERTAM + BclXL (Apoptosis Blocked)
INACTIVE
ACTIVE
•Hyperplasia
•Loss of
differentiation
•Loss of cell-cell
contact
•Local invasion
•Angiogenesis
+4OHT
Pancreatic
Islet β-Cells
Proliferation
Insulinoma
Figure 3: The effect of activating c-Myc activity
is overwhelming apoptosis. A second
transgene, BclXL is introduced to block Mycinduced apoptosis, resulting in unchecked
proliferative activity.
Bound Heat
Shock Protein 90
4: Microarray Analysis
ERTAM HSP90
Microarrays are high density oligonucleotide chips able to measure
the expression levels of every gene within the genome in parallel by
measuring mRNA levels. Affymetrix Genechips are commercially
available microarrays with very high reproducibility.
Total RNA
2
3
cDNA
Reverse
Transcription
AAAA
In Vitro
Transcription
B
B
AAAA
B
Myc
HSP90
Max
4-OHT binds
estrogen receptor
opening up bHLHz
domain.
ERTAM
4
Myc-Max
complex binds
E-box sequence
of target gene
Myc
AAAA
Max binds Myc at
leucine helix-loophelix zipper
region
Fragmentation
of cRNA
Affymetrix Genechip
Microarray
ERTAM
5
Max
TRRAP
Active
MycERTAM Myc
Myc
Max
Genechip features
contain
oligonucleotide
25mers antisense
to cRNA strands
TransformationTranscription
domain
Associated
Protein (TRRAP)
binds to MBII with
help from MBI
+
B
B
B
B
B
B
HAT TRRAP
RNA
Polymerase
B
B
B
B
Max
B
B
B
B
B
18s and
28s peaks
different
heights
C
Airdried
section
Fixed
section
D
18s and
28s peaks
nonexistant
18s and
28s peaks
nonexistant
E
Stained
section
18s and
28s peaks
nonexistant
Figure 6: Reduction in RNA integrity during LCM protocol. A shows the bioanalyser readout for
intact RNA. The 28s and 18s ribosomal peaks are prominent and roughly the same size, indicating
good RNA quality. B-E shows the reduction in RNA quality as the LCM protocol progresses.
Airdrying alone is enough to destroy RNA integrity completely.
5: Conclusions
To analyse relevant gene changes within the β-cells, laser capture microscopy
was implemented, allowing isolation of pancreatic islets. However, the process
is relatively lengthy, involving sectioning, fixing, staining and dehydrating
before LCM can be performed.
Once LCM has been improved to a degree, work can commence on
microarray work to study the changes in gene expression levels during the
first few hours of c-Myc activation.
B
Acknowledgements
Analysis of gene
expression data in
Genespring
Import .CEL file
into Genespring
for analysis
CACGTG
Figure 2: Activation of the MycERTAM transgene by addition of the ligand 4-Hydroxytamoxifen, and
subsequent transcriptional activation following dimerization with the protein Max.
Fresh
section
B
Scan chip
and convert
to .CEL file
Scanned
microarray image
B
B
B
B
B
B
Wash and
Stain chip
Control
sample
Work continues on optimizing the LCM procedure, including trying various
staining protocols, and using a perfusion technique to allow RNA fixation in
vivo before extraction.
Biotin-labelled cRNA
hybridized to Genechip
B
B
Myc
B
Hybridization of
biotin-labelled
cRNA to
Microarray
CACGTG
TRRAP recruits a histone
acetyltransferase (HAT).
This acetylates nucleosomal
histones resulting in
chromatin remodelling,
allowing access by RNA
Polymerase for gene
transcription
Unfortunately, the LCM protocol allows RNase activity. As the protocol
progresses, RNA integrity is reduced (figure 6). By the time islet tissue is
collected, RNA is completely degraded (figure 6E). There are various
possible methods for preventing RNA degradation during LCM procedure,
and these will be used to optimise the technique.
During this time, RNA becomes gradually more degraded. By the time LCM
can be performed, RNA integrity is too poor to run on a microarray. Several
approaches have been attempted so far, including using RNAlater to protect
RNA, and DEPCs to prevent RNase activity, but so far have proved fruitless.
B
Fluorescently labelled
hybridized chip
CACGTG
Fragmented biotinlabelled cRNA strands
B
Figure 5: Scatter plots for replicate
samples. Ideally all points should lie
along diagonal. Curved graphs
indicate replicates have significant
discrepancies.
Analysis of the effects of c-Myc at the transcriptional level will provide an
insight into the role that the onco-protein plays in the onset of cancer.
Understanding the mechanisms by which cancer comes will help to improve
therapeutic methods. Microarrays allow parallel analysis of the expression
changes of thousands of genes, and so offer a powerful genomics tool.
Biotin-labelled
cRNA
AAAA
Unbound Heat
Shock Protein 90
Laser capture microscopy (LCM) can be
used to isolate islet tissue for microarrays.
18s and
28s peaks
similar
heights
Data is imported into the gene analysis software Genespring,
producing a timecourse of gene expression after Myc activation.
+4-Hydroxytamoxifen
Use of whole pancreas tissue poses a
problem for gene expression analysis – the
ratio of islet tissue to exocrine tissue is not
constant across time points. This means
that data is not necessarily comparable
across the time-course.
A
mRNA is extracted from pancreatic tissue at various timepoints from
the start of 4-OHT administration, and run on a mouse Affymetrix
Genechip (figure 4).
Inactive
MycERTAM Myc
6
Islet
involution
Triplicates are run for all time points for
statistical significance. Scatter plots of
replicates indicates that there are
significant discrepancies between replicate
samples (figure 5). Curved graphs (notably
the time 0 data) indicates dissimilarity in
the samples.
Figure 4: Gene expression analysis through 1-colour Microarray
analysis.
Many thanks to Mike, David and Stella for taking me on for this project, and to Vicky
Ifandi, Sylvie Abouna, Göran Mattson and Linda Cheung for always being willing to
help when I lost my way. Thanks also to Helen Bird, Sue Davis and Lesley for all
their work on the microarray side of things. This project is funded by the
Engineering and Physical Sciences Research Council, through the MOAC Doctoral
Training Centre.
Mco
A