Modeling signal transduction
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Modeling signal transduction. Phototransduction from frogs to flies. Eye as an evolutionary challenge.

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Modeling signal transduction

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Modeling signal transduction

Modeling signal transduction

Phototransduction from frogs to flies


Eye as an evolutionary challenge

Eye as an evolutionary challenge

“To suppose that the eye, with all its inimitable contrivances …, could have been formed by natural selection, seems, I freely confess, absurd in the highest possible degree. Yet reason tells me, … “

C. Darwin, The Origin of Species:

Darwin goes on to describe how eye may have evolved

through accumulation of gradual improvements

A number of different designs exist:

e.g. vertebrate, molluskan or jellyfish “camera” eyes

or insect “compound eye”

The eye may have evolved independently 20 times!!

(read “Cells, Embryos and Evolution” by Gerhart&Kirschner)


Eyes are present in most metazoan phyla how did they evolve

Eyes are present in most metazoan phyla: how did they evolve?

Copepod's 2 lens telescope

Scallop

Trilobite fossil 500 million years

House Fly

Black ant

Octopus

Cuttlefish

See Gerhart and Kirschener, “Cells, Embryos and Evolution”


The grand challenge

The grand challenge:

To understand the evolutionary processes that

underlie the appearance of a complex organ (an eye).

To what extent is the similarity between different

eyes (e.g. vertebrate and mollusk) is due to common

ancestry or is the result of evolutionary convergence

due to physical constraints?

What can we learn from the similarities and differences

in the architecture/anatomy, development and

molecular machinery of eyes (or light sensitive cells) in

different species?

BUT before we can address the question of evolution

we need first to learn how it works…


More broadly

More broadly:

Molecular pathway(s) of phototransduction are similar to many other signaling pathways:

olfaction, taste, etc

“Comparative Systems Biology”


Modeling molecular mechanisms of photo transduction

Modeling molecular mechanisms of photo-transduction

Case studies: 1) vertebrate and 2) insect

Phototransduction as a model signaling system


Vertebrate rods and cones

Vertebrate Rods and Cones


Vertebrate photoreceptor cell

100mm

Vertebrate photoreceptor cell

Rod

Outer

Segment

(2000

Discs)

hv

Rhodopsin

Na+

Na+, Ca2+

Discs

Light reduces the

“dark current” into the cell


Intracellular voltage in rods and cones

Turtle Rod: a flash response family shown

on different time scales

Turtle Cone

Intracellular Voltage in Rods and Cones


Measuring currents

Measuring currents


Patch clamp

Patch clamp


Single photon response

Single Photon Response


Patch clamp recordings

Patch clamp recordings


Electrophysiology of rods

Electrophysiology of Rods

In the dark a standing

current circulates

through the rod.

Light shuts off the

influx of current into

the outer segment, while K

continues to exit the inner

segment causing the cell

to hyperpolarize.

Na, Ca

cGMP

Na, Ca

cGMP

Ca,K

Ca,K

Na

Na

Na

Na

K

K

K

K

K

K

K

K

K

ATP driven

Na/K pump

Na,Ca channels of outer segment need cGMP to stay open


Light detector protein rhodopsin

Light detector protein: Rhodopsin

Rhodopsin

undergoes light-induced

conformational change

chromophore, 11-cis retinal

+

opsin, a membrane protein

with 7 transmembrane

segments


Cis trans retinal transition

cis/trans –retinal transition


Light induced conformational transition

Light-induced conformational transition

cytoplasmic side


Rhodopsin is a g protein coupled receptor

Rhodopsin is a G-Protein coupled receptor


Modeling signal transduction

Seven-Helix

G-protein-

Coupled

Receptors

form a large

class

(5% C. elegans

Genome !)

b2 adrenergic receptor

Rhodopsin

cytoplasmic end


Rhodopsin is a g protein coupled receptor1

Rhodopsin is a G-Protein coupled receptor

photoactivated rhodopsin

(Rh*) forms in about 1 ms

and serially activates 100 to

1000 Transducin molecules

per second

Transducin is a

heterotrimeric

G protein

specific to vision

light

Rhodopsin

about109 rhodopsin

molecules and about

108 G-protein molecules

per rod outer segment (ROS)

Rh*

Tabg

Ta* + Tbg

GDP

GTP


G proteins

G- proteins

a

b

a subunit: 35-45 kD

g

bsubunit: 35-40 kD

g subunit: 6-12 kD

guanine nucleotide

binding site on

a subunit

At least 15 different genes for

a subunit and several different

genes for b and g subunits


Steps in activation

Steps in activation

extracellular

cell membrane

Resting state:

(G protein off)

a

g

b

GDP

G protein coupled

receptor (GPCR)

b

1.

receptor activated by

action of external

signal

Binding of activated

receptor to G protein

opens guanine nucleotide

binding pocket on

a subunit

a

g

GDP


Modeling signal transduction

2.

GDP -- GTP exchange at nucleotide

binding site activates the G protein

a*

a

a*

g

g

g

GTP

GDP

b

b

b

GTP

The a and bg subunits

dissociate from activated

GPCR

3.

Activated a subunit

stimulates effector

proteins: enzymes or

ion channels

GTP


Effector

“Effector”

light

Rhodopsin

Rh*

Tabg-GDP

Ta*-GTP + Tbg

PDE

PDE*

cGMP

GMP

CNG Channels: OPEN CLOSED


Where did cgmp come from

Where did cGMP come from?

Guanylate Cyclase

GTP

cGMP + PPi

GMP

Phosphodiesterase


G protein effectors include

G Protein Effectors Include

hydrolyzes cyclic

nucleotide;

e.g. cGMP GMP

Phosphodiesterase (PDE)

Adenylyl cyclase

Phospholipase C

synthesizes cAMP

from ATP

Enzymes:

hydrolyzes PIP2 to produce

IP3 and diacylglycerol (DAG)

Ion channels:

e.g. K, Ca and Na channels


Shut off

Shut-off

light

Rhodopsin

Rh*

Tabg-GDP

Ta*-GTP + Tbg

PDE

PDE*

cGMP

GMP

CNG Channels: OPEN CLOSED

Next question:

How are the activated

intermediates shut off?


Rhodopsin inactivation

Rhodopsin inactivation


Transducin is inactivated by the a intrinsic gtpase activity which hydrolyzes gtp to gdp

Transducin is inactivated by the a intrinsic GTPase activity which hydrolyzes GTP to GDP

a*

g

g

g

In Time

b

b

b

a

GTP

GDP

a

GDP

+

Pi

Taandbg

re-associate

Accelerated

byPDE*

Ta-GDP

dissociates from PDE*

which re-inhibits PDE*


Modeling signal transduction

The intrinsic GTPase activity of the a subunit

is regulated by a GAP (GTPase accelerating protein)

RGS-9

a*

g

g

b

b

a

GDP

+

Pi

GTP

Ta*shut off is accelerated

by action of RGS-9

Regulator of G-protein signaling


Recovery to the resting state requires resynthesis of the cgmp that had been lost to hydrolysis

Recovery to the resting state requires resynthesis of the cGMP that had been lost to hydrolysis.

This is accelerated by low Ca (feedback signal!) that

stimulates Guanylate Cyclase

GTP

cGMP + PPi


Ca the second 2 nd messenger

Ca- the second “2nd messenger”

Na, Ca

cGMP

Na, Ca

cGMP

Ca,K

Ca,K

Na

Na

Na

Na

K

K

K

K

K

K

K

K

K

channel closure shuts off Ca

influx, while efflux by

Na:Ca,K exchange

continues and internal Ca

declines. The fall in [Ca] is the

[Ca] feedback signal.


Negative feedback

Negative feedback

Ca2+ Regulation of Guanylate Cyclase


Signaling cascade

Signaling cascade

  • Converts a microscopic stimulus

  • activation of a single molecule –

  • into a macroscopic response

Alberts et al, “Mol Bio of the Cell”


Phototransduction cascade as an enzymatic amplifier

Phototransduction Cascadeas an enzymatic amplifier

Total gain:

2 x 105– 106

cGMP / Rh*

light

Rhodopsin

Rh*

1st Stage of amplification:

200 - 1000 G* per Rh*

Gabg- GDP

G*a- GTP + Gbg

2nd Stage amplification:

each PDE* hydrolyzes

~ 100 cGMP molecules.

Phosphodiesterase (PDE)

PDE*

GC*

GTP

cGMP

GMP

channel closure

generates

electrical signal

CNG Channels: OPEN CLOSED


Olfaction

Olfaction:

olfactory cilium

mucus film

tight junction

olfactory knob

supporting cell

receptor dendrite

cell body

basement membrane

axon


Olfactory receptor cascade

Olfactory receptorcascade

Odor ligand

Olfactory receptor

R

R*

Gabg- GDP

G*a- GTP + Gbg

AC Adenylate cyclase

AC*

ATP

cAMP

AMP

PDE*

DEPOLARIZATION

OF THE CELL

CNG Channels: OPEN


Invertebrate phototransduction cascade

Rhodopsin

light

Invertebrate Phototransduction Cascade

Rh*

Gqa*- GTP + Gqbg

Gq- GDP

Phospholipase C

PLC*

PIP2

IP3 & DAG

( phosphoinositol 3,4 bisphosphate )

?

Trp Channels: CLOSED OPEN


Enzymatic amplifier modules

cGMP

GMP

GTP

GC

PDE*

Enzymatic amplifier modules

GDP

G*

GTP

1st stage:

G-protein (Transducin)

activation and deactivation

PDE*

Rh*

G

Pi

GDP

GTP

2nd stage:

cGMP hydrolysis

and synthesis

Note: [GTP] / [GDP] & [GMP] acts a metabolic “power supply”


More examples of amp modules

cGMP

GMP

GTP

More examples of amp modules

GDP

ADP

G*

SP

GTP

GAP

GEF

Phosphotase

Kinase

G

S

Pi

Pi

GDP

GTP

ATP

Small GTPases:

Ras, Rho, Rab, Rap, Reb,…

Vasodilation

Receptor

Guanylate

Cyclase

Nitric

Oxide

PDE5

Viagra


General push pull amplifier circuit

General “push-pull” amplifier circuit

Xc*

a

“Power supply”:

Eact

Ede-act

a+b

c

maintains high [c]/[a]

Xa

c

b

Steady state:

Note: [X*]/[X] = e-bDEbecause the system is held out of equilibrium by

fixed [c]/[a] maintained by metabolism


Amplifier gain and time constant

Amplifier gain and time constant

Small change in [X*] induced by a small change in [Eact]:

Linear response (in the Fourier domain):

Static gain

Time constant

Note: g ~ t the gain-bandwidth “theorem” !!


Linear analysis of vertebrate phototransduction

Linear analysis of vertebrate phototransduction

Rh* deactivation + 2 amplifier “modules”

with negative feedback via [Ca++]

Good approximation to weak flash (single photon) response


Linear analysis of the cascade

Linear analysis of the cascade

Assuming stoichiometric processes of

PDE* and Ch* activation are “fast” :

d PDE* ~ d G*

d Ch* ~ d cGMP*


Hard and soft parameters

“Hard” and “Soft” parameters

Kinetic constants/affinities -- “hard” parameters

change on evolutionary

time scale

Enzyme concentrations -- “soft” parameters that

can be regulated by

the cell.

e.g.

Golden rule of biological networks:

“anything worth regulating is regulated”


General behavior

General behavior

{

at short times

at times times

For:

With n=3

- good fit to

single-photon

response


Case of feedback

Case of feedback

+ gFd[Ca]

feedback

via dCh*


Consequences of negative feedback

Consequences of negative feedback

Reduction of static gain:

Accelerated recovery:

“Ringing”:


Signal and noise how much gain is enough

Signal and Noise: How much gain is enough?

Single photon “signal”:

}

dI ~ 2 pA

dI /I Dark ~ 3%

I Dark ~ 60 pA

What’s the dominant source of

background noise?

Thermal fluctuations?

ROS capacitance C ~ 20pF

Negligible !


Reaction shot noise

Reaction shot noise

Langevin noise

(i.e. Gaussian, White)

# of molecules

E.g. ifG- = 0 consider DX* produced over time Dt

Poisson

process


Channel opening noise

Channel opening noise

ChD* =< Ch*> = tChg ([cGMP]D)ChTot

Channel flicker noise:

Note: this Poisson law

holds generally for

mass action

< 3% signal

Plus fluctuations of d[cGMP]:


Cgmp fluctuations

cGMP fluctuations

Note: voltage response is coherent over the whole

rod outer segment, hence must consider

the whole volume Vros ~ 104mm3

#cGMP = [cGMP] Vros ~ 10mM 104mm3 ~ 107

Negligible fluctuations …


Locality of single photon response

Locality of single photon response

hv

cGMP

depletion

Na+, Ca2+

100mm

G* and PDE*

activity


Single photon response variability

Single photon response variability

Baylor-Riecke:

coefficient of

variation


What is the largest source of response variability

What is the largest source of response variability?

Spontaneous activation G -> G* ??

< 10-7 s-1 Negligible even with 108 G molecules !

Variability of Rh* “on”- time Dt ??

DG* ~ kDt <Dt> = tRh*

1storder Poisson process:

Baylor& Reicke measured:

Poisson process of order 20 !!!


Multistep deactivation of rh

Multistep deactivation of Rh*


Meta rhodopsin shut off

Rh Kinase is

turned on by

binding to Rh*

Rhodopsin

Kinase

Arrestin is a 48kD

accessory protein

Arrestin

Rh*

Rh*-PO4

Rh-PO4-Arrestin

ATP

(active)

(inactive)

Meta-Rhodopsin shut-off:


Summary

Summary:

  • What we have learned:

  • GPCR / cyclic nucleotide cascade

  • Enzymatic amplifier module

  • and linear response

  • Noise analysis

  • Tomorrow…

  • from frog to fly and from linear to non-linear


Acknowledgements

Acknowledgements

Anirvan Sengupta, (Rutgers)

Peter Detwiler (U. Washington)

Sharad Ramanathan (CGR/Lucent)


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