A Large Scale Genetic
analysis of c-Myc-regulated
Gene Expression Patterns
Gene Expression Network
Dynamics
by Tomer Galon
A Large Scale Genetic Analysis
of c-Myc-regulated Gene
Expression Patterns
– Brenda C. O’Connell, Ann F. Cheung,
Carl P. Simkevich, Wanny Tam, Xiaojia
Ren,Maria K. Mateyak, and John M.
Sedivy
Department of Molecular Biology, Cell Biology, and Biochemistry,
Brown University,
Protein Production
Myc - Protein
Proto-Oncogenes
Transcriptional
regulators
Myc
c-myc, N-myc, L-myc
Human
malignancies
Not well
understood
Myc - Review
• Myc is a weak transcriptional regulator
• High Myc levels are found in tumor cells
Promotes
proliferation &
growth.
protection against
apoptotic
processes
harms cell cycle
withdrawal &
differentiation
harms cell
division and cell
growth
c-myc
• One of the Myc gene family.
• Sedivy and his team succeeded in
creating:
– c-myc -/- null rat fibroblast cells. (HO)
c-myc null cells had a slow cell cycle.
– Conditionally active c-myc cells. (MycER)
(using Tamoxifen, a specific c-myc estrogen
receptor)
About the experiments
• Goal: to create a biological system for
profiling Myc target genes.
• Conditions:
– Constant conditions of cell lines which
promised balanced steady state of growth.
– All of the experiments were performed on
three different independent occasions.
Experiment 1
• Revealing the total number of genes that
respond to loss of c-myc
• Cells used:
– c-myc +/+ (TGR) - wild type cells
– c-myc -/- (HO) - not producing c-myc
– c-myc transgened cells (HOmyc3) • over-expressing c-myc protein at 3-4 times the
level in TGR cells.
Experiment 1
• Total RNA was extracted out of the
exponential growth cell lines.
• Each of the corresponding RNAs was
hybridized to the three available Affymetrix
rat GeneChips (U34A, U34B, U34C)
• 9 total samples (3 replicates)
• Pairwise comparison between
cell lines were made using
Student’s t-test (p<0.05)
Experiment 1 - Results
• 5,732 probe sets displayed statistically
significant differences (p<=0.05) between
TGR and HO cells and between HO and
HOmyc3 cells.
• Adopting an expression differential cutoff
of 2-fold between the means of TGR and
HO and HO and HOmyc3 reduces the
number of probe sets to 1,527.
xI (i )
r (i ) 
xU (i )
Fold change ratio
Experiment 1 - Results
• Probe sets were grouped into 4 categories
– HO vs TGR & HO vs HOmyc3 using 2fold
– 599 (39%) activated by Myc
– 695 (46%) repressed by Myc
– 94 (6%) activated by overexpression of Myc
– 87 (6%) repressed by overexpression of Myc
• The remaining 52 probe sets (3%) exhibited patterns of
expression whose biological relevance to c-myc is not clear.
Experiment 1 - Results
Representative genes expressions of the
four functional categories
• To ascertain the accuracy of the microarray
analysis, the mRNA expression levels of 17
genes was checked using qPCR.
• In 17 of 17 cases the qPCR data confirmed the
microarray results.
Experiment 2
• Reveal the kinetics responses to c-myc
activation
• Conditionally active c-myc cells (MycER),
stimulated with 4-hydroxytamoxifen (OHT)
c-myc
Estrogen
Receptor
OHT (Estrogen)
nucleus
• RNA was extracted 0,2,4,8,16 hours after
treatment and hybridized with microarray.
Experiment 2
• MycER produced two cell lines types:
– HOmycER12 - slow growth. OHT dependent.
– HOmycER104 - rapid growth. Small leak in
c-myc expression without OHT present
• Cell lines in the experiments:
HOmycER12, HOmycER104
• 12 RNA samples, 3 replicates, 36 total
RNA samples
Experiment 2 - results
• Out of 611 probe sets which expressed
differently in experiment 1 on U39A chip, (after
reducing fold change threshold from 2.0 to 1.5),
218 probe sets were activated/repressed by
OHT = 180 genes. (p<0.05 and 1.5-Fold)
• Those genes were categorized to early (2-4h),
middle(8h), late(16h) respond
• Finally they were grouped according to their
general function
• HOmycER104 samples gave similar results, thus providing
additional verification.
Experiment 2 - results
c-myc activated
early responding
Function: trans. signaling
c-myc repressed
early responding
Function: cell-cell contact
and surface proteins
Experiment 3
• Reveal a subset of direct transcriptional targets
of c-myc.
• HOmycER12 + cycloheximide +/- OHT
• RNA samples were taken at 0,4,8,16 h after
treatment.
• Cycloheximide - a protein synthesis inhibitor.
cycloheximide
c-myc
DNA
RNA
Protein
Experiment 3 - results
• Direct targets: [cycloheximide +/- OHT]
- 21 genes identified, all were activated.
• Indirect targets: [+/-cycloheximide &
cycloheximide +/- OHT]
- 24 activated , 16 repressed
• All the rest (119) could not be identified
because of their significant response to
cycloheximide alone.
Experiment 2 vs. 3
Type:
D, A , E
I,A,M
X,R,E
X,R,E
Func:
signaling
DNA replication
Chemical responses
signaling
Summarized Results
Discussion
• The three Affymetrix GeneChips provide the
most extensive coverage of the rat genome
available (26,261 probe sets, 20,691 unique
genes and expressed sequence tag clusters).
• They assume that Myc activates ~2400 genes in
fibroblast cells.
• If the differential expression criterion is relaxed
to statistical significance only, the Mycresponsive transcriptome becomes greater than
50% of all active genes.
Discussion
• The large number of Myc-responsive
genes achieved is the result their ability to
modulate Myc expression from almost
zero to supraphysiological.
• 36% of differentially expressed probe sets
responded in the HOmycER12 cell line.
Possible explanation is that most of the
Myc-activated genes are indirect and late.
Discussion
• Cycloheximide limited resolution due to
short half-life of MycER and the effects of
cycloheximide on gene expression.
– No direct repressed genes found
– Other proteins needed for Myc regulation
• Estimation of 247 direct gene targets in
fibroblast cells.
Discussion
• The experiment might not represent the
total spectrum of Myc-regulated genes
– A gene may not be affected equally by Myc
under all growth conditions.
– Cell cycle effects
– Cell line - or cell type-specific effects
– Some genes are detected poorly or not at all
by the current U34 probe sets
Discussion
• Functions of MycER-activated responsive
genes identified in this profiling screen:
– Enzymes involved mostly in carbon
assimilation, anabolic pathways, and energy
metabolism
– Protein synthesis
– Rapid growth and proliferation
– Negative effectors of apoptosis
Discussion
• Myc-repressed genes are involved in the
interaction and communication of cells
with their external environment.
• This study revealed a number of new
candidates for drug targets.
• Eliminates drugs side effects
• Experiments on Human tissues
Gene Expression Network Dynamics: from
Microarray Data to Gene-Gene Connectivity
reconstruction.
Reconstruction of c-MYC protooncogene regulated genetic network
-G. C.Castellani, D.Remondini, N.Intrator, B. O’Connell,
JM Sedivy
Centro L.Galvani Biofisica Bioinformatica e Biocomplessità
Università Bologna and Physics Department Bologna
Institute for Brain and Neural System Brown University Providence
RI
One of the most recent theories that has been shown to have
promising applications in the Biological Sciences is the so called
Theory of Complex Networks that have been applied to protein-protein
interaction and to metabolic network (Jeong and Barabasi)
Extension to Random Graph Theory
During the last several years, considerable efforts have been made to
further analyze the statistics of Random Graphs.
The “Scale Free” network is created by two simple rules:
Network growth and Preferential Attachment (the most connected
nodes are the most probable sites of attachment)
The model gives a non Poisson degree distribution: Power Law
P(k )  k 
P(k )  (k  k0 ) e

( k  k0 )
kc
Moreover, this type of distribution was observed in real networks
such as
Internet, C.Elegans Brain, Methabolic Network with 2< < 3
exponent and
various values for the exponential cutoff kc and k0
The John Sedivy Lab at Brown University has designed a new generation of microarrays that cover
approximately one half of the whole rat genome (roughly 9000 genes).
The array construction aims at obtaining a precise targeting of the proto-oncogene c-MYC.
This gene encodes for a transcriptional regulator that is correlated with a wide array of
human malignancies, cellular growth and cell cycle progression.
The data base is organized in 81 array obtained by hybridisation with a cell line of rat fibroblats..
These gene expression measurements were performed in triplicate for a better statistical
significance. The complete data set is divided into three separate experiments; each of which
addresses a specific problem;.
Experiment 1: Comparison of different cell lines where c-myc is expressed at various degrees (null,
moderate, over-expressed). This experiment can reveal the total number of genes that respond to a
sustained loss of c-Myc as well as those genes that respond to c-MYC over-expression.
Experiment 2: Analysis of those cell-lines that over-express c-Myc following stimulation with
Tamoxifen (a drug that has been used to treat both advanced and early stages of breast cancer).
This data was collected during a 16 our time course. This experiment reveals the kinetics of the
response to Myc activation and may lead to the identification of the early- responding genes.
Experiment 3: Analysis of the time course of induction with Tamoxifen when it was performed in the
presence of Cycloheximide (a protein synthesis inhibitor). This experiment reveals a subset of
direct transcriptional targets of c-Myc.
Their approach to the determination of the
c-myc regulated network can be summarized in
3 points:
1) List of genes based on significance analysis over
time points between Myc and control and within
time point (between groups and within groups
(time)).
2) Time translation matrix calculated on microarray
treated with Tamoxifen and not treated
- T and NT raw data
The resulting time translation matrix will be used
to
reconstruct the connectivity matrix between
genes
3) Model validation for determination of the error
model
Significance Analysis
d (i ) between
xNT (i )  xT (i )

s(i )  s0
s(i ) 

(i )  (i )

n1
n1
2
NT
2
T
xT / NT (t  1)  xT / NT (t )
 T2 / NT (t  1)  T2 / NT (t )
d (i ) within 
s(i ) 

n1
n1
s (i )  s0
• S0 is an appropriate regularizing factor.
• Interesting genes are chosen as the union between the
genes selected with the above methods
• With this SA we obtain 776 significant genes (p<0.05) if we
require significance on 1 time point
Step 2: Linear “Markov” Model
The selected genes are used for step 2 of the analysis:
x(t )  Ax(t  1)
The x(t) are the gene expressions at time t and A is the
unknown linear transformation matrix that we estimate
from time course (0,2,4,8,16) of microarray data
(T and NT separately, An and At).
This is a so called inverse problem because the matrix
is recovered from time dependent data.
-> From appropriate thresholding on A’s we can
recover the connectivity matrix between the genes.
Network Topology
No Tamoxifen
With Tamoxifen
Changing Databases
In order to have a better understanding of the results,
both in terms of network topology and connectivity
distribution, we generated 2 databases:
1) One small database with those genes that were
without any doubt affected by Tamoxifen (50 genes)
2) One larger database with all the genes that give 2 P on
3 experiments i.e. those genes for which we have good
measurements (3444 genes)
50 Genes Database
NT
T
Results
For each of the 50 genes, they computed the
connectivity and the clustering coefficient that express
if the gene is connected to highly connected or poorly
connected genes.
It is possible to see that the treatment with Tamoxifen
causes a decrease in clustering in the network so it
seems that the network becomes “less scale free”. This
is confirmed by the network clustering coefficient:
N Overall graph clustering coefficient: 0.840
T Overall graph clustering coefficient: 0.241
The 3444 Genes Database
This large database is used in order to have a better statistics and
possibly a distribution fit.
N
T
Clearly these distributions are not Poisson and seem to be
Power law with exponential tail
Fitting the distributions
The distribution was fitted with a generalized power-law :
P(k )  (k  k0 ) e
N
N
k0  0.4 kc  4.8   2.5

( k  k0 )
kc
T
T
k0  1.04 kc  3.66   2.3
Network Structure (3444 genes)
N Overall graph clustering coefficient: 0.902
T Overall graph clustering coefficient: 0.893
From these results and from the fit parameters, it seems that the
N-Network is less scale free, but these results are strongly
affected by noise.
They have looked at the individual connectivity and clustering
coefficient, and their variation between N and T.
The results are encouraging: between those genes that have
changed their connectivity in a significant way there are c-Myc
targets
Network Structure (3444 genes)
As an example we report some connectivity change in C-Myc target
genes:
2379 rc_AI178135_at complement component 1, q subcomponent
binding protein 3 272
2796 U09256_at
transketolase 13 39
2772 U02553cds_s_at protein tyrosine phosphatase,
non-receptor type 16 133 146
390
D10853_at
Amidotransferase
0
phosphoribosyl pyrophosphate
7
933 M58040_at transferrin receptor 1 27
Conclusions
This assay tested the hypothesis that a treatment with Tamoxifen in
these engineered cells lead to c-Myc activation can be related to
connectivity changes between genes.
The results show that within the framework of scale free network
there are changes in gene-gene connectivity.
The connectivity distributions of N and T are far from Poisson with
parameters that are similar to those founded for other systems that
account for scale free distribution with exponential tail.
One clear result is that the global gene degree connectivity follows a
power law distribution both with and without Tamoxifen. This result
seems to point out that this type of behavior is very general.
Conclusions
Some points that need further analysis are the correlation between
connectivity change and c-Myc target method . This is not a
significance test it can only help to look gene activity as result of
interactions between genes at the previous time step
Looking at individual gene connectivity or looking at a smaller
database, we observe that there are significant changes induced by
the treatment. As example the clustering coefficient changes and
some c-Myc targets show connectivity and clustering coefficient
changes.
These results need to be confirmed and further analyzed, but, at
their knowledge this is the first attempt to monitor the network
connectivity changes induced by c-Myc activation in comparison with
a basal level.
References
•
O'Connell BC, Cheung AF, Simkevich CP, Tam W, Ren X, Mateyak MK, Sedivy JM. A large
scale genetic analysis of c-Myc-regulated gene expression patterns.
J Biol Chem. 2003 Apr 4;278(14):12563-73
•
Gene expression Network dynamics: from microarray data to gene-gene connectivity
reconstruction. Reconstruction of c-MYC proto-oncogene regulated genetic network.
G. C.Castellani, D.Remondini, N.Intrator, B. O’Connell, JM Sedivy.
Centro L.Galvani Biofisica Bioinformatica e Biocomplessità Università Bologna and Physics
Department Bologna
Institute for Brain and Neural System Brown University Providence RI
www.nettab.org/2003/docs/GastoneCastellani.ppt
•
Biology of the Cell 3rd Edition.
Bruce Alberts, Dennis Bray, Julian Lewis, Martin Raff, Keith Roberts, James D. Watson. Garland
Publishing.
Many Thanks to Yael Galon