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Adaptation in the TGFβ pathway and embryonic
patterning.
Aryeh Warmflash, Benoit Sorre
Ali Brivanlou (lab of molecular vertebrate embryology)
Qixiang Zhang and Alin Vonica
PNAS July 2012 & unpub.
What’s the problem?
Signaling pathways involved in cell fate determination
Smad
Nodals
Activins
Type II
Receptor
S
P
MAPK
STAT
Cytokine
Growth Factor
BMPs
GDFs
Type II
Receptor
S
S
P
S
GRB2
P
ALK4
Ky
Y
JAK
Sos
S
Smad4
P Effector
Y
P
Y
P
Smads
P
P
P
S
Y
P
Y
P
Ras
Raf
STAT
P
MAPK (ERK)
x
S P
P
IkS
PLC
75
Cl
50
x
DSH
60 50
60 50
x
TCR
PI[4,5]P
NICD
PKC
155
Cl
S P
155
Cl
65
GCRP
Wnt
105
P
X
P
Smad6
P
DELTAlike
X
Ks
NFAT
1G
P
P
Notch
IL-1
TNF
FGF
MEK
P
Wnt
Hh
Smad4
Effector
Smads
Ci/Gli
NFκB
CAT
Ks
Ca++
P
APC
ST
CAT
P
Ks
Gsk
PLC-6
CaN
NFAT P P P P
X
NFAT P
75
Cl
pS P
155
Cl
CAT
LEF
NICD
HLH
NKA
Transcription factors involved in cell fate determination
operate under the controls of signaling pathways
(Brivanlou and Darnell 2002, Science)
Signaling Pathways are Complex
Wnt’s for dummies
What’s the problem?
Pathways reused
TGFβ operates blastula....immune.....cancer
NFκB (inflammation) many inputs to many outputs
Are they simple ON/OFF switches, or rheostats from ligand to TF?
Periodic table for phenotype?
What pathways operate in what contexts?
What pathways work together?
Evolution artifact or signal processing?
(Gene centric models at best metaphors:)
Ignores cell biology, assumes cell well mixed!
Impossible to get all parameters (can not mix sources)
Single cell ≠ population (e.g., graded response?)
(Are linear pathways an artifact of genetics + biochem?)
TGFβ pathway (simplified)
Genetic and Biochemical Studies:
1. R-Smad phosphorylation and nuclear
accumulation stably reflect ligand levels
2. R-Smads nuclear synonymous with
transcription
Receptor Smads (e.g. 1,2) are
activated by specific receptors +
Smad4 -> nucleus.
3. Termination of signaling caused by
degradation or dephosphorylation of RSmads
Test with dynamic studies
Systems for TGFβ
C2C12 cell line (myoblast)
HaCaT (keratinocyte)
Xenopus animal caps
(fated to epidermis,
+- morphogens -->
neural, mesoderm)
(hESC)
Smad2 -> nuclear with stimulation
untreated
+TGF!1
5 ng/ml
0.1 ng/ml
!"#$%&'()*+,-$.+/"/0
3!!
#3!
Stably integrate RFP-Smad2,
GFP-NLS
#!!
Controls: level, response
vs
WT
via
IF
43!
D
$!!
+
56789&
123&4561)7
33!
3!!
#3!
#!!
43!
4!!
"3!
"!!
&3! +
!
E
"
#
$
%
'()*+,-./012
T=0 add ligand to dish
Nuc-Smad2
t=0h follows ligandt=1.5h
"!!
&3!
"
#
$
%
&!
&!!
!
'()*+,-./012
"
#
$
%
'()*+,-./012
&!
"Smad2/3
4!!
"3!
SB inhibits receptor kinase
kills response
Control: Same response via antibodies to pSmad2 for endogenous cells
t=7h
t=17h
t=40h
&!
!"#$%&'()*+,-$.+/"/0
Merge
GFP-NLS
RFP-Smad2
!"#$%&'()*+,-$.+/"/0
B
C
R-Smad activation is stable and graded
RFP-Smad2
R-Smad activation is stable and graded
RFP-Smad2
Transcription terminates during continuous stimulation
A
B0G#<10B5/D=46G.@
$*&
$
#*&
#
"*&
!*&
)
0>?8@
!*)
!*'
!*%
!*#
!
"
#
$
%
&
'
(
+,-./01/23/455,+,06/708+/9:;!"/<=0>?8@
"
!/
!
A0?-4B,C.5/B>D,1.?48./4D+,E,+F
2-45(
H4,"
264,B"
!*""
"
/
%
CAGA12
A0?-4B,C.5/B>D,1.?48./4D+,E,+F
%*&
3TP
#
%
'
+,-./<=0>?8@
)
"!
"#
)
%
!*!I
$
!*!(
#
!*!&
"
!*!$
!
!*!"
!
"
#
$
%
&
'
(
luciferase assay from synthetic promoters.
output does not increase after ~4 hrs
Assay
luc
Add SB
Add TGFβ
t=0
0
t
)
+,-./01/23/455,+,06/708+/9:;!"/<=0>?8@
qRT-PCR on standard TGFβ
targets (nb log scale)
TGFβ pathway adapts!
B
B0G#<10B5/D=46G.@
B
time
RFP-H2B
Nuclear Smad4 response tracks transcription
hUBC
CAG
B
0h
Zeo
mSmad2 pA
1h
*',-./A12324,/!"#$%&'()
GFP-Smad4
C
RFP-H2B
CAG
hUBC
4h
8h
4h
8h
RFP H2B
pA
D %&!
";'
";%
";#
"
!;)
!;'
!;%
!
"D %&! #
";'
627.89213-47-67-./:;2;5
*',-./A12324,/!"#$%&'()
CAG
hUBC
1h
0h
C
Puro
EGFP hSmad4 pA
627.89213-47-67-./:;2;5
Bsd
$
%
!"#$%&'()
'
(
&
"
!;)
!;'
"
#
$
%
&
*+,-./012345
12324,/!"#$%&'()
E
'
";#
";"
"(
";"
"
!;<
!;)
#&!
#!!
"
#
$
&
'
(
)
.
"$!
=&6>?,9.@AB!"
nuc Smad4
vs time for popul
=&6>?,9@AB!;<=;>1?/%@
""!
single cells: homogen, graded
"#!
=&.6>?,9.@AB!"
#&!
%
#!!
"!!
"
#
$
%
&
'
(
)
*+,-./012345
.
<!
)!
F
(!
=&6>?,9.@AB!"
Controls: dynamics
pSmad2
IF
~
WT,
qRT-PCR
~ WT
=&6>?,9@AB!;<=;>1?/%@
'!
!
=&.6>?,9.@AB "
!;)
HaCaT cells dense cultures endog Smad4
IF
&!
!;<
=!;".6>?,9.@AB !"
";#
F
$!!
"&! .
!
)
$!!
*+,-./012345
$&!
"$!
&'()/012/561.7/8'919:
!;%
!
*',-./012324,/!"#$%&'()
";#
$&!
*+,-./012345*"#$%&'(+
%!!
Integrate GFP-Smad4, RFP-H2B, Smad2
E
=!;".6>?,9.@AB !"
";%
*"#$%&'(+
"&! .
!
)
!"#$%&'()
%!!
!"#$%&'()/012/561.7/8'919:
A
"#!
""!
"!!
!;(
!;' .
!
#
<!
)!
(!
'!
%
'
)
"!
"#
%!
$!
.
!
#
%
'
)
"!
"#
"%
Smad4 is adaptive in response to a step increase in ligand
concentration
GFP-Smad4
Smad4 is adaptive in response to a step increase in ligand
concentration
GFP-Smad4
5()&
!"#$%&'
Adaptation requires transcription
!+
"
#!
#"
;!
&!+
!
'()*+,-./012
D
E
TGFb1+chx
"
?.@&8<%=2&)>"'?@:
Nuclear GFP−Smad4
1.3
1.2
1.1
+TGF
+TGF ,+chx
1
0.9
0.8
3
0
5
10
time (hours)
with CycloHex Smad4
remains nuclear
15
&$"
&
#$"
;
#
!$"
&
#
!
/49B
/;
!
0.7
;
+
AB(#
01"2A
0'"$=;
1.4
?.@&8<%=2&)>"'?@:
1.5
/; +
!
qRT-PCR
&
3
<
/;9B
>
'()*+,-./012
#!
Transcription lasts
longer, follows Smad4
--> Adaption not due to inhibitory ligands (eg lefty)
or inhibitory Smads, since would affect pSmad2
#&
/7 +
!
Imaging Smad proteins in Xenopus Animal Caps
Response to endogenous BMP
Block BMP receptor add activin
same behavior
rSmad nuclear, but Smad4
bursts ~20min ~cellcycle time
Animal caps multi-potent tissue
Inject mRNA constructs to 2 cell
stage, cut at ~4000 cell stage,
image
Behavior of Smads in Xenopus animal caps
GFP-Smad1
GFP-Smad1 is stably localized in the
nucleus of cells....
Venus-Smad4
... butVenus-Smad4 pulses
in and out of the nucleus
Behavior of Smads in Xenopus animal caps
GFP-Smad1
GFP-Smad1 is stably localized in the
nucleus of cells....
Venus-Smad4
... butVenus-Smad4 pulses
in and out of the nucleus
Behavior of Smads in Xenopus animal caps
GFP-Smad1
GFP-Smad1 is stably localized in the
nucleus of cells....
Venus-Smad4
... butVenus-Smad4 pulses
in and out of the nucleus
What’s new?
TGFβ activity == Smad4; pR-Smads necessary not suffic.
Functional Complex= [adaptation module] * [graded receptor]
~ 4*10^4 papers on TGFβ was adaptation seen?? NO
* end point assays
* population measurements for incoherent cells
* cell density can affect response in HaCaT
* used CHX to get direct effects
* cell type?
What’s causing nuclear Smad4 bursts in the embryo??
ligand/inhibitors move between cells?
autocrine?
tied to transcription
not via TGFβ inhib ligands or inhibitory Smads
Implications of adaptive pathway for embryonic patterning
Patterning by static morphogen (signal) the exception
Fly:
Wing disk: dpp and size control (d ln(dpp)/dt )
Eye: morphogenic furrow moves as wave
Vertebrates:
Dorsal-ventral by TGFβ activin/nodal (Schohl ... Whitman..
A-P Hox genes gastrulation time->space (Wacker )
Somite clock FGF, Wnt, RA (Pourquie..)
Neural tube D-V Shh (Briscoe)
Digits Hh, Hox
Images of time dep morpho
Anterior
Dorsal view
216
S.A. Wacker et al. / Developmental Biology 268 (2004) 207–219
Posterior
1.2mm egg, movie begins 5 hrs post
fertilization, lasts 17 hrs at 23C
Hox genes pattern the A-P axis
as it is formed.
(Wacker Durston 2004)
Fig. 6. The time space translator model. (A) False colour representation of expression of three Hox genes during gastrulation. WISH o
Images of time dep morpho
Anterior
Dorsal view
216
S.A. Wacker et al. / Developmental Biology 268 (2004) 207–219
Posterior
1.2mm egg, movie begins 5 hrs post
fertilization, lasts 17 hrs at 23C
Hox genes pattern the A-P axis
as it is formed.
(Wacker Durston 2004)
Fig. 6. The time space translator model. (A) False colour representation of expression of three Hox genes during gastrulation. WISH o
Morphogen patterning in an embryo?
How does an adaptive pathway facilitate dynamic patterning?
Can not control or directly measure morphogen in embryo -->
measure response to complex stimuli
Control the ligands
16
inputs
96 independent chambers 2 level PDMS**, multiplexed addressing.
Chambers: 1mm2 x 40μ, cells on PDMS
+FN
(Image phase + 2 colors)/15 min + feedings
Leica + matlab (microscope + fluidics)
**Quake
Lab www.stanford.edu/group/foundry/
Gómez-Sjöberg, et al. (2007) Anal Chem.
Tay, S.et al (2010). Nature
Cells grow normally (TGFβ +
imaging start at t=0)
Lots of plumbing
cell culture chip (46 hrs)
cell culture chip (46 hrs)
Single cell response to a ligand step at t=0
0.4
0.2
min2
min1
0
.5
0
.2
1
.8
.6
5
10
15
time (hrs)
te
20
t/
e
normalized cell count
1
Samd4 nuc/cyto
max
[TGF−β1] (ng/ml)
b
.5
1.2
1
0.8
0.6
0
5
10
time after stimulation start (hrs)
0.4 τ ~ 1hr
0.2
0.1 ng/ml
1 ng/ml
05
normalized cell count
0.2
0.3 ng/ml
1 ng/ml
0.1
0.1
0
0
0
i
Sm
0.4
0.001 0.001
0.01 0.01
0.1
1
10
0.1
1
[TGF−β1]
(ng/ml)(ng/ml)
[TGF−β1]
0.6
0.5 0.5 1
1 1.5
jump amplitude
jump amplitude
1.5
0.6
0.4
0.2
0.2
0
j
0.1 ng/ml
0.1 ng/ml
1 ng/ml1 ng/ml
0.4
0
0
10
h h
0.1 ng/ml
0.03 ng/ml
1 ng/ml
0.1 ng/ml
0.3
0.2
10 5 15 1020
time (hrs)
time (hrs)
0
15
normalized cell count
0
g g
0.3
0.4
All cells respond in graded manner
0.2 0.2to ligand step
0.7
0
[TGF
S
[TG
0.8
0.8
normalized cell count
GFP−S
0.2
0
0.5
0
1
1.5
0.5
1
tmax
t
2
2.5
1.5
2
max
Histograms for amplitude of Smad4 jump and time of max response as fit:
cst + ampl*t*exp(-t/tmax)
2.5
Population response nuc-Smad4 response ~ dish
RFP‐H2B
RFP‐H2B
GFP‐Smad4
t=1hr
GFP‐Smad4
t=1hr
0.4 0.4
1 1
0.9 0.9
0.2 0.2
0.8 0.8
0.7 0.7
gg
ell count
0.3
0.2
5 5
10 10
time (hrs)
time (hrs)
Ligand Step
0.3
1
0 0
15 15
0.03 ng/ml
0.03 ng/ml
0.1 ng/ml
0.1 ng/ml
0.3 ng/ml
Smad4 ~ L/(K+L) fits
1
Smad 4 jump
[TGF−β1] (ng/ml)
GFP−Smad 4 nuc/cyto
1.1 1.1
0 0
f f
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
hh
0.001
0.010.010.1 0.1 1 1 10
0.001
[TGF−β1] (ng/ml)
[TGF−β1] (ng/ml)
0.6
ell count
ee
Ligand
cell count
Response
t=5hrs
t=5hrs
[TGF−β1] (ng/ml)
Smad 4 jump
0 t=0
0.4
0.6
0.4
0.1 ng/ml
0.1 ng/ml
1 ng/ml
1 ng/ml
nor
nor
0
0
0
Nano-luc:
live
transcriptional
reporter
0.5
1
1.5
0
0.5
1
jump amplitude
tmax
i
j
RFP-H2B CAGA-Nluc
t=5hrs
Luminescence
not fluorescence, 1lower backgrounds
1
1
Substrate unstable in cell culture (microfluidics solves)
Nluc
Enzyme life timeCAGA
activity
dependent (lowers background)
12
0.5
0.5
[TGF−β1] (ng/ml)
erage Nluc signal (A.U.)
kNluc ~ 100x brighter than firefly/Renillal
ax luminescence (A.U.)
t=0
1.5
0.5
CAGA12-Nluc = synthetic TGFβ reporter (with H2B-RFP), (invisible
with GFP)
0
2
2.5
Population
response
RFP-H2B Nluc:
CAGA-Nluc
t=5hrs
l
1
1
CAGA12
Nluc
0.5
0.5
0
0
0
10
20
time (hrs)
[TGF−β1] (ng/ml)
average Nluc signal (A.U.)
k
Nluc ~ L/(K+L) fits
max luminescence (A.U.)
t=0
1
0.5
0
0.001
30
[Figure 1]
NB fold change! vs Smad4
0.01
0.1
1
[TGF−β] (ng/ml)
10
Response to pulses
0.5
0.8
c
0
10
20
30
time (hr)
time
(hrs)
0
40
GFP−Smad4 nuc/cyto
Complete response to pulses (1 hr
T =period
3hrs > adaptation time
on, 6 off),
1
Cells
1.2uncorrelated
1
0.5
0.8
0
10
20
30
0
40
1
0.5
0.5
CAGA12
Nluc
[TGF−β1] (ng/ml)
1
1
0
0
0
10
d
20
30
time (hr)
time (hrs)
Nluc: response to pulses -->
life time vs production of enzyme
1
1.2
response to step -->
internal response time
0.5
1
----- fits (below)
0.8
0
10
20
30
0
40
[TGFβ1] (ng/ml)
1.2
Luminescence (A.U.)
1
GFP−Smad4 nuc/cyto
T = 7hrs
[TGFβ1] (ng/ml)
b
[TGFβ1] (ng/ml)
GFP−Smad4 nuc/cyto
a
At the level of Smad4/transcription pathway is not a
ratchet -- responds independently to each pulse
Pulses block differentiation more effectively than continuous
myogenin
[TGF-!"]
[TGF-!"]
[TGF-!"]
*
es
ls
pu
ep
st
pu
ls
es
0
ep
ep
st
8
10
ly
log(I
) 0
myogenin
8 0
0
2
4
6
log(Imyogenin)
20
on
0
*
DAPI
myogenin
30
M
10
Fix
24
time (hours)
40
D
DM only
20
step
pulses
10
20
g
30
ls
es
% myogenin positive
30
8
*
DAPI
myogenin
e
*
Fix
step
pulses
e
Fix
Fix
*
24
24
time (hours)
on
ly
% myogenin
positive
DM only
step
pulses
40
6
DM only
gpulses
40
es
20
0.02
0
2
104
*
pulses
Fix
Fix
*
Fix
*
DAPI
0
myogenin
D
M
g
10
step
myogenin
40step TGFβ
30
0.04
Fix
dDAPI
e
fg
DM only
st
time (hours)
GM
DM
[TGF-!"]
[TGF-!"]
[TGF-!"]
[TGF-!"]
[TGF-!"]
[TGF-!"]
0
pu
4
6log(Imyogenin
8 )
log(I
)
log(myogenin)
*
Fix
*
pulses
24
es
6
e
ls
2
8
4
*
ls
4 0
6
log(Imyogenin2)
Fix
myogenin*
pu
0
0
Fix
nl
pyu
2
0.02
bstep
*
24
st DM
ep
o
0.02
0.04
DM only
step
0.02
pulses
0.04
0
DM only
30
step
0.06
40
pulses
20
Fix
*
myotubes
* Fix
step
ep
0.04
g
0.06
GM
DM DM only
DM only
d
st
0
0.06
e
% myogenin positive
0.02
f
DM only
step
pulses
0.06
normalized cell count
0.04
normalized cell count
f
f
DM
myoblast
c
d
0.06
normalized cell count
normalized cell count
f
pulses
ly
c
d
step
myotubes
b Fix
low serum
time (hours)
(DM)
pulses
Fix
DAPI
on
c
myoblast
c
DM only
time (hours)
0
D
M
d
0
[TGF-!"]
low serum
(DM)
DM
[TGF-!"]
myoblast
% myogenin positive
c
low serum
(DM)myotubes
myoblast
GM
DM
high serum
low serum
(GM)
TGF-!
(DM)
normalized cell count
myoblast
TGF-!
a
b
myotubes
GM
[TGF-!"]
high serum
low serum
(GM) (DM)
TGF-!
high serum
(GM)
[TGF-!"]
myotubes
TGF-!
b
GM
DM
D % myogenin positive
M
on
ly
a
high serum
TGF-!(GM)
[TGF-!"]
high serum
(GM)
a b
a
[TGF-!"]
a
100
100
CAGA12
Nluc
0.4
0.4
[TGF−β1] (ng/
[TGF−β1] (ng/
0.9
0.9
Luminescence
Luminescence(A(
0.4
[TGF−β1]
[TGF−β1](ng/m
(ng/
GFP−Smad4nuc/
nu
GFP−Smad4
1
Response
to ramps (transcription
~ d signal/dt) 0.20.2
0.2
50
0.2
0.8
GFP−Smad4
GFP−Smad4 nuc/cyto
nuc/cyto
ligand(t)
0.4
0.4
0.9
0.9
0.2
0.2
0.8
0.8
0.7
0.7
[TGF−β1]
[TGF−β1] (ng/ml)
(ng/ml)
11
00
10
10
20
30
time (hrs)
time
10
10
Nluc (--- fit)
50
50
0.2
0.2
00
10
10
gh
0.9
0.9
0.2
0.2
0.8
0.8
0.7
0.7
00
4040
0.4
0.4
00
0.4
0.4
20 3030
20
time(hrs)
(hrs)
time
100
100
40
11
1.1
00
00
1.1
1.1
10
20
30
10
20
30
time (hrs)
(hrs)
time
00
fg
1.1
1.1
00
d
40
40
00
[TGF−β1] (ng/ml)
[TGF−β1] (ng/ml)
time
time (hrs)
(hrs)
00
dc
30
30
[TGF−β1] (ng/ml)
(ng/ml)
[TGF−β1]
GFP−Smad4 nuc/cyto
nuc/cyto
GFP−Smad4
cb
10
20
10
20
Smad4
Luminescence (A.U.)
(A.U.)
Luminescence
00
0
0
50
h
4040
100
100
0.4
0.4
50
50
0.2
0.2
00
40
40
20
20 3030
time
time(hrs)
(hrs)
00
00
10
10
20
20 30
30
time
time(hrs)
(hrs)
40
40
[TGF−β1] (ng/ml)
[TGF−β1] (ng/ml)
0.7
0.7
Luminescence
Luminescence (A.U.)
(A.U.)
0.8
Is any useful modeling to be done?
Have nuclear Smad2, Smad4 fluorescent proteins(time),
Transcription
All for various ligand time courses.
???Many missing steps ???
Phenomenology!
e fewest free parameters and will prove adequate for our da
that the variable yMinimal
adaptsmodel
to a of
fixed
point
of
0.
We
use
I
adaption
ẋ
=
ẏ
=
Generic linear adaption system:
y returns to baseline after step in I(nput)
y
bx
I
cy + I
y
a positive value of x at the fixed point, and c > 0 for sta
time
o eigenvalue of the 2x2 matrix which are only degrees of fr
2
on real eigenvalues then c ⇥ 4b. The fastest adaptive res
the ytwo
eigenvalues
are
degenerate.
We
use
this
limit
henc
fastest response if two eigenvalues equal:
=0
t<0
in tolerably good
agreement with4b the
single
celltoSmad4
data.
= c2, one
time scale
fit.
ct/2
y2= Ite
t>0
ve (c = 4b)
y = Ite
ct/2
Transcription from ligand ramp
Ligand
cell
Some limiting component
upstream of adaption ->I(Ligand)
L(t)
I(t) =
KI + L(t)
ẋ = y
nucleus
DNA
ẏ = c2 x/4
cy + I(t)
ż1 = max(0, y)
ż2 = z1
z1 /
z2 /
Protein (z2)
3 parameters + scale !!
Adaption module between
receptor and DNA
y registers ~ d I/dt, keep positive
one τ since z1,2 in series.
need 2 z’s for shape
Model fits variable height steps vs time
Ligand vs Time
Nluc vs Time (--- model)
data= DATA130516dose
3
180
data= DATA130516dose
160
2.5
140
120
data ! /fit !!
ligand(t)
2
1.5
100
80
60
1
40
0.5
20
0
0
5
10
15
20
25
Time (hr)
30
35
40
45
50
0
0
5
10
15
20
25
30
35
40
45
KI,KC= 0.30 0.00, nK.,.,y2= 1.0 1.0 1.0 tau= 5.0 5.0 scl= 1050.0 cc= 3.000 2.250 0.006
Determines KI (ligand-receptor MM) and
τ transcription-transl time
Time (hr)
average. Thus the agreement is confirmation that microfluidics can be used to taylor dynamic stimuli
commensurate with rapid events internal to the signaling pathway, whose consequences can then be read
out through the much slower transcriptional time scales.
1 Pulse vs N pulses vs step fits c, τ
data= DATA130206pulse6hr
1.4
ch6
ch10
ch12
1.2
Ligand vs Time
Nluc vs Time (--- model, c=3)
data= DATA130206pulse6hr
140
1
c=3
100
0.6
data ! /fit !!
ligand(t)
120
0.8
80
0.4
60
0.2
0
40
20
0
10
20
30
40
50
Time (hr)
60
0
5
10
15
25
70KI,KC=
80
901.0 1.0 20
100
0.30
0.00, nK.,.,y2=
1.0
tau= 5.0 5.0
30
35
40
scl= 550.0 cc= 3.000 2.250 0.006
45
Time (hr)
Nluc vs Time (--- model, c=2)
y ~ t exp(-1.5*t) ~ Smad1
140
120
Nluc vs Time (--- model, c=4)
data= DATA130206pulse6hr
140
c=2
microfluidics + slow adder -->
Figure M 3. Nluc in response to ligand pulses. The TGF dose in (ng/ml) vs time (left) and
fast internal time scales
the Nluc transcriptional response (right), for corresponding colors. The overlay of the data for a single
80
80
pulse (yellow) vs the first pulse of a series (purple) is a measure of experimental reproducibility.
However the ligand step in this figure does not agree exactly with the prior figure displaying a series of
steps. The fit parameters are identical to Fig.M2 but the scale factor is different
60
60
40
40
20
0
5
10
15
20
25
30
35
40
45
KI,KC= 0.30 0.00, nK.,.,y2= 1.0 1.0 1.0 tau= 5.0 5.0 scl= 300.0 cc= 2.000 1.000 0.006
Time (hr)
c=4
100
data ! /fit !!
data ! /fit !!
100
120
data= DATA130206pulse6hr
20
0
5
10
15
20
25
30
35
40
45
KI,KC= 0.30 0.00, nK.,.,y2= 1.0 1.0 1.0 tau= 5.0 5.0 scl= 800.0 cc= 4.000 4.000 0.006
Time (hr)
Model of spreading ligand
Ligand spreads to x>0 from a fixed source==1 at x=0
Adaptive pathway will register initial slope and not final value.
Infer ‘x’ from slope
⇥t c(x, t) = D⇥x2 c(x, t)
kc(x, t)
0.7
x=1
Concentration
0.6
0.5
x=2
0.4
0.3
c(x, ⇥)
x=3
0.2
0.1
0
0
1
2
3
4
5
Time
6
7
8
9
10
e
⇤
D/kx
Ligand & Gene Expression(bars) vs X (T= 1, 4, 256)
1
Concentration/Gene Expression
Concentration/Gene Expression
1
T=256
T=1
0.5
T=4
0
1
2
3
4
0.5
0
1
2
X
k=0, conserved ligand
Model for adaptive signaling:
3
4
X
k>0, decaying ligand
⇥
transcription
(⇥t c(x, t))2 dt
x
2
0
(⇥t c(x, t))1 dt (static morphogen)
0
(morphogen)
Embryo (Xenopus)??
Thresholds
evidenceCellfor TGFβ as static morphogen:
in-vitro: ligand step/pulse
732
Epidermal
keratin
Xhox.?
XIHhox6
Green-Smith 1990,2: dispersed animal
cap cells RNA probe ~ much later:
discrete fates
Muscle
Actin
Brachyury
2. WholeXbra in Notochord
Goosecoid
Cytoskeletal
Anterior IS to the
probe was carried
actin
EFl-alpha
Figure 1. Induction
of Genes Showing
in-vivo: Whitman, Fagotto (2000-2)
α pSmad2, Dorsal to ventral gradient + wave
Frgure
by Activin of an Organizer-to-Posterior
Multiple Dose Thresholds
Spectrum
RNAase protection
assays of RNA extracted
from cell aggregates
at
stage 17.5. Single cells had been exposed
at blastula stages to a
1.7.fold dilution series of activin. This experiment
was performed four
timeswith thesame results. For each experiment,
two separate assays
were performed
on RNA from the same aggregates,
one with keratin
and actin probes (loading control: cytoskeletal
actrn), the other with all
other probes together (loading control: EFl-a). Embryo track corresponds to RNA from three whole stage 17.5 embryos.
Genes probed
were:
Epidermal
keratin XK81A (Jonas
et al., 1989); Xhox3 posteriorenriched homeobox
gene (Rub! i Altaba and Melton, 1989a, 1989b;
Saha and Grainger,
1992); XIHboxG antennapaedia
family posterolatera1 specific homeobox
(Fritz and De Robertis.
1988; Wright et al.,
1990); Actin, muscle and cytoskeletal
(Mohun et al., 1984); Xbra, Xeno-
cells in this h
consistent wi
Muscle and
Doses That
The correlation
with two Xbra
that the highe
Table 1 shows
cell aggregates
lyzed for not
show that no
at higher acti
the prediction
previous work
Dynamic morpho + adaptive
x-dependent
signal
Static morpho
(morphogen)
Embryos ?? II
⎨
G.R.N.
Thresholds
gsc --| Xbra
time
The transcriptional gene regulatory network
takes x-dependent activation and converts to
mutually exclusive gene expression
territories. Evolution can tune x-dependent
signal and leave GRN alone.
for x~organizer, first Xbra then gsc
Conclusions
Transcriptional response to step in TGFβ ligand adapts:
•
Response graded (development) vs NFκB binary (immuno)?
•
Smad4 reporter for transcriptional adaptation
(R-Smads reports receptor activation by ligand ≠ transcription)
•
Ligand pulses elicit more response than continuous stimulation in
context of differentiation of C2C12 to myotubes.
•
Transcription depends on rate of ligand presentation (ramp/step)
Embryos may read positional information from rate of change in response
to spreading ligand.
•
Development is quicker if cells acquire fates while signals are changing
Embryos
“To anyone with his normal quota of curiosity, developing embryos are
perhaps the most intriguing objects that nature has to offer. If you look at one
quite simply .... and without preconceptions .... what you see is a simple lump
of jelly that .... begins changing in shape and texture, developing new parts,
sticking out processes, folding up in some regions and spreading out in others,
until it eventually turns into a recognizable small plant or worm or insect...
Nothing else that one can see puts on a performance which is both so
apparently simple and spontaneous and yet, when you think about it, so
mysterious.”
C.H.Waddington 1966 Principles of Devel. Differentiation
(Current Concepts in Bio. Series)
"Perfect Adaptation
in the TGF-beta Pathway and its Implications for Embryonic Patterning"
>
> Genetics and biochemistry have defined the components and wiring of the signaling pathways that pattern the
embryo. Many of these pathways have the potential to behave as morphogens: in vitro experiments have clearly
established that these molecules can dictate cell fate in a concentration dependent manner. How morphogens convey
positional information in a developing embryo, where signal levels are changing with time, is less understood. New
data shows that the evolutionarily conserved TGF-b pathway responds transiently and adaptively to a step in ligand
stimulation. As a consequence it is shown using integrated microfluidic cell culture and time-lapse microscopy that
the speed of ligand presentation has a key and unexpected influence on signaling outcomes. Slowly increasing the
ligand concentration diminishes the response while well-spaced pulses of ligand combine additively resulting in
greater pathway output than is possible with constant stimulation. Our results suggest that in an embryonic context,
an adaptive pathway can extract positional information as ligand spreads dynamically from a source, thereby
providing an alternative to the static morphogen model. Thus the rate of change of ligand concentration, rather than
its level, is the instructive signal for patterning.
>
Phenotype of somitogenesis invariant, genes drift
Rostral
clock + growth velocity ->
spatial period
Somites
P
S
M
0 somites
MODEL PHENOTYPE
Somites
(Francois & EDS Curr Opin G&D)
4 somites
Tail
Bud
P
S
M
Caudal
RESEARCH ARTICLE 2783
Development 138, 2783-2792 (2011) doi:10.1242/dev.063834
17 somites
© 2011. Published by The Company of Biologists Ltd
Evolutionary plasticity of segmentation clock networks
Aurélie J. Krol1,2,*, Daniela Roellig3,*, Mary-Lee Dequéant1,*, Olivier Tassy1,2,4,*, Earl Glynn1, Gaye Hattem1,
Arcady Mushegian1, Andrew C. Oates3 and Olivier Pourquié1,2,4,†
SUMMARY
Chick
O. Pourquie lab
Overlap of oscillating genes among three
vertebrates is nil.
Among Wnt, FGF, N, RA pathways many
ways to make oscillators
The vertebral column is a conserved anatomical structure that defines the vertebrate phylum. The periodic or segmental pattern
of the vertebral column is established early in development when the vertebral precursors, the somites, are rhythmically
produced from presomitic mesoderm (PSM). This rhythmic activity is controlled by a segmentation clock that is associated with
the periodic transcription of cyclic genes in the PSM. Comparison of the mouse, chicken and zebrafish PSM oscillatory
transcriptomes revealed networks of 40 to 100 cyclic genes mostly involved in Notch, Wnt and FGF signaling pathways. However,
despite this conserved signaling oscillation, the identity of individual cyclic genes mostly differed between the three species,
indicating a surprising evolutionary plasticity of the segmentation networks.
KEY WORDS: Somitogenesis, Segmentation, Evolution, Cyclic genes, Segmentation clock, Microarray, Chicken, Zebrafish, Mouse, Notch,
Wnt, FGF
Phenotype of somitogenesis invariant, genes drift
Rostral
clock + growth velocity ->
spatial period
Somites
P
S
M
0 somites
MODEL PHENOTYPE
Somites
(Francois & EDS Curr Opin G&D)
4 somites
Tail
Bud
P
S
M
Caudal
RESEARCH ARTICLE 2783
Development 138, 2783-2792 (2011) doi:10.1242/dev.063834
17 somites
© 2011. Published by The Company of Biologists Ltd
Evolutionary plasticity of segmentation clock networks
Aurélie J. Krol1,2,*, Daniela Roellig3,*, Mary-Lee Dequéant1,*, Olivier Tassy1,2,4,*, Earl Glynn1, Gaye Hattem1,
Arcady Mushegian1, Andrew C. Oates3 and Olivier Pourquié1,2,4,†
SUMMARY
Chick
O. Pourquie lab
Overlap of oscillating genes among three
vertebrates is nil.
Among Wnt, FGF, N, RA pathways many
ways to make oscillators
The vertebral column is a conserved anatomical structure that defines the vertebrate phylum. The periodic or segmental pattern
of the vertebral column is established early in development when the vertebral precursors, the somites, are rhythmically
produced from presomitic mesoderm (PSM). This rhythmic activity is controlled by a segmentation clock that is associated with
the periodic transcription of cyclic genes in the PSM. Comparison of the mouse, chicken and zebrafish PSM oscillatory
transcriptomes revealed networks of 40 to 100 cyclic genes mostly involved in Notch, Wnt and FGF signaling pathways. However,
despite this conserved signaling oscillation, the identity of individual cyclic genes mostly differed between the three species,
indicating a surprising evolutionary plasticity of the segmentation networks.
KEY WORDS: Somitogenesis, Segmentation, Evolution, Cyclic genes, Segmentation clock, Microarray, Chicken, Zebrafish, Mouse, Notch,
Wnt, FGF
Biology is not simple
+ 33 TGFβ ligands in mammals.
Smad 1/5/8
non-canonical TGFβ-->
MAPK
Fig 5: Model for integrative role of TAK1 in plu
Smad1 as a convergence point for multiple signaling pathways
Wnt
TGFb
TGFβ
Autoc
BMP Sig
FGF
GSK3 p38 JNK Erk
Smad1
MH1
P
I
P
linker
MH2
I
Sma
1
TAK1
Smad
2/3
Ub
DNA Binding Domain
Proteosomal Degradation
MEK
Smurf1
Linker Phosphorylation represents an attractive mechanism
through which TAK1 tunes Smad1 activation
p38
JNK
Differen
Experimental Strategy
At 2-4 cell state: inject animal caps with mRNA encoding fluorescent
proteins
At late blastula stage: dissect the cap at late blastula stage and image
mRNAs encoding fluorescent proteins: GFP-XSmad1, GFP-XSmad2,VenusXSmad2, mCherry-XH2B
Misc conclusions
Mention R. Young paper, assumes Smad2 nuc == expression,
Schier diffusion is enough for activity, community effect etc.
Get new AW movies, more details on frog, note separated cells no smad4
flashes and no fates, but mixed up with no smad1
Questions:
activin on isolated frog cells, oscillates? yes pulse
CHX on animal caps? ND
arguments against endog ligands are inhibit BMP add activin (uniform)
and against moving lefty or smad7 etc, are uniform smad2 response
CCC movie with step stimulation and smad 4 or 2 + nuclear
5()&6-
!"#$%&'()*
!$%
Adaptation requires transcription
!+
"
#!
34
3456!
3456!738-9
3456!73:5#;&
;!
#"
&!+
!
"
'()*+,-./012
D
"
1.2
1.1
+TGF
+TGF ,+chx
1
0.9
0.8
3
10
time (hours)
with CycloHex Smad4
15
+TGF
+TGF ,+chx
55
&$"
50
&
45
#$"
;
40
#
AB(#
01"
0'"$
35
!$"
&
#
30
!
25
/49B
/; +
!
&
3
20
0
5
/;
qRT-PCR
!
5
;
+
AB(#
01"2A
0'"$=;
0.7
0
60
Nuclear RFP−Smad2
?.@&8<%=2&)>"'?@:
1.3
TGFb1+MG132
E
TGFb1+chx
1.4
Nuclear GFP−Smad4
'()*+,-./012
?.@&8<%=2&)>"'?@:
1.5
#!
10
15
time (hours)
/;9B
<
>
'()*+,-./012
#!
#&
Transcription lasts
longer, follows Smad4
--> Adaption not due to inhibitory ligands (eg lefty)
or inhibitory Smads, since would affect pSmad2
/7 +
!
&
3
<
>
'()*+,-./012
#!