Systems Biology approach: cell cycle (sixth framework program: DIAMOND)

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Systems Biology approach cell cycle (sixth
framework program DIAMOND)

• Integrative approach to build a basic model of
  cell cycle in 4 different species
• S.cerevisae (budding yeast)
• S. Pombe (fission yeast)
• A. Thaliana (weed)
• Human
• Objectives
• Identification of cell cycle targets of major
  signaling pathways affecting cell division
• knowledge base for further cell cycle control
  research
• design of a mathematical model of cell cycle
  network on basis of genome wide
• data sets
• Analysis
• transcriptome
• targeted proteomics approaches
• literature
• annotated databases

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Why cell cycle?

• Cell division is regulated by highly conserved
  networks
• The comparative approach will illuminate the
  variation in the intrinsic stability of
• cell cycle controls in the different species
• Cell cycle progression regulating mechanisms in
  eukaryotes are largely conserved
• cells as different as yeast and mammalian cells
  use the same kind of regulatory molecules to
  trigger cell cycle progression
• new insights in cell cycle regulation and the
  mechanisms that prevent uncontrolled
  proliferation of cells ? way to novel anti-tumor
  drugs

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different research areas of the project

• I. Cell cycle events in synchronized cell
  cultures
• II. Cell cycle exit/entry in response to
  extracellular signaling
• III. DNA damage checkpoint

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Criteria for selection of studies

• synchronization method, validation, quality
• number of samples biological variations
• number of arrays experimental variations
• sufficient amount of time points
• number of represented genes on array
• validation of array results
• availability of data sets

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1. Cell cycle events in synchronized cell cultures

• ? measure regular cyclic changes under
  standard growth conditions
• double thymidine block and
  nocodazole/aphidicolin
• samples for RNA 12 or 24 time
  points after release from block
• study available Identification of Genes
  Periodically Expressed in the
• Human
  Cell Cycle and Their Expression in Tumors
• 
  Michael L. Whitfield, Gavin Sherlock, Alok J.
  Saldanha, John I. Murray,
• Catherine A. Ball, Karen E.
  Alexander, John C. Matese, Charles M. Perou,
• Myra
  M. Hurt, Patrick O. Brown, and David Botstein
• 
  Departments of Genetics and
   Biochemistry,  Howard Hughes Medical Institute,
  Stanford University School of
• Medicine, Stanford, California 94305
   Department of Biomedical Sciences, College of
  Medicine, Florida State
• 
  University, Tallahassee,Florida
  32306 and  Department of Genetics, Lineberger
  Comprehensive Cancer Center,
• University of North Carolina at
  Chapel Hill, Chapel Hill, North Carolina 27599

• HeLa cells
• cDNA micro array representing 16332 different
  human genes
• 5 independent arrays

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Identification of Genes Periodically Expressed
in the Human Cell Cycle and Their Expression in
Tumors
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Available data sets

• http//genome-www.standford.edu/Human-CellCycl
  e/Hela/
• human cell cycle study raw data (unfiltered) -
  individual data files as well as the entire
  experiment set
• complete data and scores for all clones in all
  five experiments.
• complete gene list and annotations for all 1134
  cell cycle regulated clones.
• for all 1134 genes
• the pre-clustered, filtered data
• the complete data table matrix
• the gene tree correlations

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II. Cell cycle exit/entry in response to
extracellular signaling

• ? measure gene response to
  proliferative stimuli (cytokines)
• study available The Transcriptional Program in
  the Response of Human Fibroblasts to Serum
• Vishwanath
  R. Iyer, Michael B. Eisen, Douglas T. Ross,
• Greg
  Schuler, Troy Moore, Jeffrey C. F. Lee, Jeffrey
  M. Trent,
• Louis M.
  Staudt, James Hudson Jr., Mark S. Boguski,
• Deval
  Lashkari, Dari Shalon, David Botstein, Patrick O.
  Brown
• V. R. Iyer and D. T. Ross, Department of
  Biochemistry,Stanford University School of
  Medicine,
• 
  Stanford CA94305, USA. M. B. Eisen and D.
  Botstein, Department of Genetics, Stanford
  University
• 
  School of Medicine, Stanford CA 94305, USA.
• SCIENCE VOL 283 1 JANUARY 1999

• normal diploid human fibroblast cell line
  derived from foreskin
• cDNA micro array representing 8613 different
  human genes
• 3 independent arrays

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The Transcriptional Program in the Response of
Human Fibroblasts to Serum
Figure 2. Cluster image showing the different
classes of gene expression profiles. Five hundred
seventeen genes whose mRNA levels changed in
response to serum stimulation were selected (7).
This subset of genes was clustered hierarchically
into groups on the basis of the similarity of
their expression profiles by the procedure of
Eisen et al. (6). The expression pattern of each
gene in this set is displayed here as a
horizontal strip. For each gene, the ratio of
mRNA levels in fibroblasts at the indicated time
after serum stimulation ("unsync" denotes
exponentially growing cells) to its level in the
serum-deprived (time zero) fibroblasts is
represented by a color, according to the color
scale at the bottom. The graphs show the average
expression profiles for the genes in the
corresponding "cluster" (indicated by the letters
A to J and color coding). In every case examined,
when a gene was represented by more than one
array element, the multiple representations in
this set were seen to have identical or very
similar expression profiles, and the profiles
corresponding to these independent measurements
clustered either adjacent or very close to each
other, pointing to the robustness of the
clustering algorithm in grouping genes with very
similar patterns of expression.
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Available data sets
http//genome-www.stanford.edu/serum/data.html

• Raw Data
• Image File

Individual arrays Detailed data for each of
the array hybridizations. Data for cluster
A tab-delimited text file with the normalized
R/G ratios for all genes reported in the cluster.
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Research areas of the project
III. DNA damage checkpoint
? monitoring the effects of DNA damage,
changes in gene expression profile after various
time points double strand
breaks, induction of ATM dependent checkpoint by
ionizing radiation single strand breaks,
induction of ATR dependent checkpoint by UV
problems with available studies -
usage of cancer cell lines -
arrays representing to less genes
- fold changes in expression levels of known DNA
damage responsive genes quite
low (e.g. p21) - focus on
either G1 or G2 checkpoint -
influence of biological and experimental variance
? too few samples ?
too few arrays
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Experimental procedure
p53 deficient human diploid fibroblasts (Tig3)
normal human diploid fibroblasts (Tig3)
Cell lines
UVC (25 J/m2)
?-irradiation (10GY)
non stressed
Treatment
harvest cells after 0, 3, 6 12h
microarray
FACS
expression p53,p21,Puma (qPCR, immunoblotting)
Analysis
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Tig3-tert cells show a distinct DNA damage
response depending on the kind of damage
gamma irradiation
cells
0 3 6
12 24 h
0 3 6 12 24
h
UVC
cells
0 3 6
12 24 h
0 3 6 12 24
h
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Tig3 cells show a distinct DNA damage response
depending on the kind of damage
cells
0 3 6 12 24
h
cells
0 3 6 12 24
h
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Tig3 cells show a distinct DNA damage response
depending on the kind of damage
gamma irradiation
cells
0 3 6
12 24 h
0 3 6 12 24
h
UVC
cells
0 3 6
12 24 h
0 3 6 12 24
h
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Tig3 cells show elevated expression levels of p53
target genes after UVC treatment ?-irradiation
P21CIP1
0 3 6 12
24 h
0 3 6 12
24 h
PUMA
0 3 6 12
24 h
0 3 6 12
24 h
UVC (25J/cm2)
?-irradiation (10GY)
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p53 knock down in Tig3-tert cells
P53
ctr p53 kd
P21Cip1
ctr p53 kd
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knock down of p53 influences the cell cycle
distribution of Tig3 cells after UVC treatment
ctr p53 kd
more ctr
? cells
cells
more p53 kd
0 0.5 3 6 12 24
0 0.5 3 6 12 24
0 0.5 3 6 12 24
G1 S G2
G1 S G2
G1 S G2
G1 S G2
G1 S G2
G1 S G2
G1 S
G2
0 0.5 3 6
12 24 h
? cells (controll-p53 knockdown)
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Work planning time table
(Nature R Report P Prototype. Level PU
Public PP Restricted to other programme
participants RE Restricted to a group
specified by the consortium.)
No. deliverable title month nature level
1-D1.1 Optimised synchronisation protocol for yeasts, Arabidopsis, human cells 12 R PU
2-D1.2 Transcript data providing a detailed description of transcriptome evolution through cell cycle 24 R PP, PU
3-D1.3 Macro arrays for core cell cycle genes in A. thaliana 24 P PU
4-D1.4 Protein complex data showcasing the dynamics of key complexes through cell cycle 24 R PP, PU
5-D1.5 Y2H interaction data on a core set of cell cycle proteins 30 R PP, PU