Understanding Temporal Patterns in
Cell Cycle Regulation through
isualization
V
Maria Secrier, Schneider Group
05 April 2011
Biological processes are dynamic!
• Time: a bottleneck
in systems biology
( © John Kyrk )
• How to efficiently visualize time course data?
Ex: gene expression, knockdown experiments etc.
http://www.visualcomplexity.com/vc/
Cell cycle regulation: inference of temporal patterns
from knockdown effects
siRNA mediated gene silencing
(http://www.genengnews.com/gen-articles/next-generation-rna-interference/1321/)
The Mitocheck Project
(Neumann et al., Nature, 2010)
Morphological classes
(Neumann et al., Nature, 2010)
Looking at phenotypic transitions within populations of cells
Time
Gene
kif11
Phenotype
0
1
...
91
mitotic delay
0.223
0.437
...
0.014
binuclear
0.437
0.324
...
0.000
polylobed
0.129
0.034
...
0.954
.....
Binuclear
aurkb
Mitotic Delay
Polylobed
mitotic delay
0.436
0.428
...
0.424
binuclear
0.722
0.134
...
0.853
polylobed
0.274
0.543
...
0.023
.....
Binuclear Polylobed
How do populations of cells
transition between phenotypes
throughout the cell cycle?
?
Binuclear
Visualizing phenotypic transitions
GO term network (from BiNGO)
transitions between phenotypes
number of
knockdown events
time
phenotypes
time slider
choose GO representation
0.004
0.004
0.004
0.004
0.004
0.004
0.000
0.000
0.000
0.000
0.000
0.000
0
40
80
0
40
80
80
0
0
40
40
80
80
80
Grape
80
0
0
40
40
80
80
0
40
80
0
40
80
Large
80
0
40
80
0.004
40
0.000
0
0.004
80
0.000
40
0.004
0
0.000
0.004
40
0.000
0
0.004
80
0.000
40
0.004
0.004
0
0.000
0.000
80
0.004
40
40
0.000
0
0
0.004
80
0.000
Polylobed
0.004
80
0.000
0.004
0.004
40
0.004
0.000
0.004
0
0.004
0.000
0.000
80
0
40
80
0
40
80
0
40
80
0
40
80
0
40
80
Dynamic
0.004
40
80
40
0.000
0
40
0.000
0.004
0.004
80
0
0.004
80
0.000
0.000
0.000
Binuclear
0.004
0
40
80
0.000
0
40
0.000
40
80
0.004
0.004
0.004
0
0.004
0
40
80
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.004
0.004
0.004
0.004
0.004
0.004
Mitotic Delay
0.000
0
40
0.004
0.004
0.004
0
40
0.000
0.000
0.000
0
0.004
0.004
0.004
transition
0.000
0.000
0.000
TO phenotype
Specific transitions: phen.A >> phen.B
FROM phenotype
0
40
80
0
40
80
0
40
80
0
40
80
0
40
80
0
40
80
Apoptosis
Are there gene knockdowns that cause (quasi-)simultaneous transitions?
knockdown
gene G1
...
...
knockdown
gene G2
...
phen.A
phen.B
t = 27
...
...
phen.C
phen.D
t = 56
...
...
phen.E
phen.F
t = 78
Hypothesis: genes G1 and G2 could be activated together or act in the same pathway
...
Genes quasi-synchronized in terms of transitions
Transition 1
Transition 2
Transition 3
Phen.changes (1..7)
adra2c | znf527
4|3
9 | 11
27 | 22
6>>2>>3>>2
aifm3 | aldoa
6|4
17 | 16
55 | 50
6>>3>>2>>3
aldoa | rpl27a
4|7
16 | 21
50|49
6>>3>>2>>3
aurkb | fam92b
18 | 20
37 | 35
54 | 53
1>>2>>3>>2
bahcc1 | fam112a
4|5
32 | 31
78 | 83
6>>3>>2>>3
bahcc1 | sap30bp
4|6
32 | 33
78 | 81
6>>3>>2>>3
dock2 | lrp1
10 | 10
18 | 16
65 | 69
6>>3>>2>>3
dock2 | vdac4
10 | 15
65 | 67
84 | 87
6>>3,2>>3>>2
ela3b | proc
17 | 17
24 | 24
33 | 36
6>>3>>2>>3
fam112a | sap30bp
5|6
31 | 33
83 | 81
6>>3>>2>>3
fam112a | spatc1
5|4
31 | 30
83 | 88
6>>3>>2>>3
fanci | vcx
17 | 18
36 | 38
57 | 56
3>>2>>3>>2
ky | slc2a8
10 | 5
11 | 16
35 | 38
5>>3>>2>>3
ky | znf664
4|4
10 | 9
11 | 11
7>>5>>3>>2
leprot | rpl27a
5|7
17 | 21
44 | 49
6>>3>>2>>3
mfsd4 | ncoa6
9|5
33 | 33
48 | 53
5>>3>>2>>3
ntn21 | sf3b5
6|4
12 |17
82 | 86
6>>3>>2>>7
q8na17_human | rhot2
9|7
56 | 57
83 | 81
6>>2>>3>>2
sez612 | trmu
10 | 5
11 | 7
87 | 87
6>>3>>2>>3
wbp2nl | znf575
3|4
7|5
75 | 70
6>>3>>2>>3
Genes causing quasi-simultaneous phenotypic transitions:
network of potentially synchronous activations
bahcc1
slc2a8
aifm3
ky
aldoa
znf664
fam112a
spatc1
sap30bp
rpl27a
leprot
znf527
vdac4
wbp2nl
sez612
trmu
q8na17_human
rhot2
adra2c
znf575
dock2
lrp1
sf3b5
mfsd4
fanci
cela3b
vcx
aurkb
proc
ntn21
ncoa6
fam92b
Genes causing quasi-simultaneous phenotypic transitions:
network of potentially synchronous activations
coexpression
colocalization
physical interaction
other
shared protein domains
potential synchronous
activation
wbp2nl
lrp1
vdac4
znf575
dock2
spatc1
znf664
ncoa6
sf3b5
leprot
ntn21
fam112a
rpl27a
mfsd4
ky
sez6l2
sap30bp
bahcc1
aldoa
aifm3
slc2a8
rhot2
q8na17_human
cela3b
vcx
aurkb
proc
trmu
adra2c
fanci
fam92b
znf527
sez612
Gene-drug-disease maps
in the cell cycle context
Gene-disease associations
(diseases mapped from OMIM Morbid Map)
Cell cycle regulation: an evolutionary perspective
How does the human ortholog landscape map to the cell cycle events ?
P.troglodytes
H.sapiens
F.catus
M.fuliginosus
(Gauthier et al, NAR 2009)
How does cell cycle regulation occur for different organisms?
Which are the most conserved cell cycle sub-phases?
Which temporal events contain most novel (i.e. least conserved) genes?
no. of conserved genes
at time point
Which temporal events contain most novel (i.e. least conserved) genes?
no. of conserved genes
at time point
Which temporal events contain most novel (i.e. least conserved) genes?
no. of conserved genes
at time point
Time-point conservation of genes that peak throughout the cell cycle
-1: no genes peaking at that time point
Time-point conservation of genes that peak throughout the cell cycle:
farther species removed
-1: no genes peaking at that time point
time
Time-point conservation of genes that peak throughout the cell cycle:
identifying not-so-well-conserved time point-peaking genes
t27 - least conserved
t60-64
-1: no genes peaking at that time point
1h
4h
11 h
time
8h
H.sapiens
G1
0
t51
S
45
G2 M
80
96 100
Visualizing genetic variation in a phenotypic context
•susceptibility to disease
•drug response
•reaction to environmental factors
(©Jane Ades, NHGRI)
Experiment setup
BY
gene expression
genotyping
phenotypic responses to small molecules
RT
Genetic variation and phenotypic plasticity
Genetic variation and phenotypic plasticity
positive phenotype
peaks above defined threshold
parent
strains
genes with highest expression change
negative phenotype
stress factors
Acknowledgements
Schneider Group
Reinhard Schneider
Venkata Satagopam
Sean O’Donoghue
Salvador Santiago
Afshin Khan
Janos Binder
TAC members
Toby Gibson
Lars Steinmetz
Rob Russell
Ellenberg Group
Huber Group
Dudley Group (ISB)
Jean-Karim Hériché
Wolfgang Huber
Patrick May
Thomas Walter
Gregoire Pau