Transcript CheW

Sept 25
Biochemical Networks
Chemotaxis and Motility in E. coli
Examples of Biochemical and Genetic Networks
• Background
• Chemotaxis- signal transduction network
Bacterial Chemotaxis
Flagellated bacteria “swim” using a reversible rotary motor linked by
a flexible coupling (the hook) to a thin helical propeller (the flagellar
filament). The motor derives its energy from protons driven into the
cell by chemical gradients. The direction of the motor rotation
depends in part on signals generated by sensory systems, of which
the best studied analyzes chemical stimuli.
Chemotaxis - is the directed movement of cells towards an
“attractant” or away from a “repellent”.
• For a series of QuickTime movies showing swimming bacteria with fluorescently
stained flagella see: http://www.rowland.org/bacteria/movies.html
• For a review of bacterial motility see Berg, H.C. "Motile behavior of bacteria".
Physics Today, 53(1), 24-29 (2000). (http://www.aip.org/pt/jan00/berg.htm)
A photomicrograph of three cells
showing the flagella filaments.
Each filament forms an extend helix
several cell lengths long.
The filament is attached to the cell
surface through a flexible ‘universal
joint’ called the hook.
Each filament is rotated by a reversible rotary motor, the direction of the motor
is regulated in response to changing environmental conditions.
The E. coli Flagellar Motor- a true rotary motor
Rotationally averaged reconstruction of electron micrographs of purified hook-basal
bodies. The rings seen in the image and labeled in the schematic diagram (right)
are the L ring, P ring, MS ring, and C ring. (Digital print courtesy of David DeRosier,
Brandeis University.)
Tumble
(CW)
Smooth Swimming
or Run
(CCW)
Chemotactic Behavior of Free Swimming Bacteria
No Gradient
Increasing attractant
Increasing repellent
A ‘Soft Agar’ Chemotaxis Plate
A mixture of growth media and a low concentration of agar are mixed in
a Petri plate. The agar concentration is not high enough to solidify the
media but sufficient to prevent mixing by convection.
The agar forms a mesh like network making
water filled channels that the bacteria can
swim through.
A ‘Soft Agar’ Chemotaxis Plate
Bacteria are added to the center of the plate and allowed to grow.
A ‘Soft Agar’ Chemotaxis Plate
As the bacteria grow to higher densities, they generate a gradient
of attractant as they consume it in the media.
Attractant
Concentration
cells
cells
A ‘Soft Agar’ Chemotaxis Plate
The bacteria swim up the gradients of attractants to form
‘chemotactic rings’ .
This is a ‘macroscopic’ behavior. The chemotactic ring is the
result of the ‘averaged” behavior of a population of cells. Each
cell within the population behaves independently and they
exhibit significant cell to cell variability (individuality).
A ‘Soft Agar’ Chemotaxis Plate
‘Serine’ ring
‘Aspartate’ ring
Each ‘ring’ consists of tens of millions of cells. The cells outside the rings are
still chemotactic but are just not ‘experiencing’ a chemical gradient.
Serine and aspartate are equally effective “attractants”, but in this assay the
attractant gradient is generated by growth of the bacteria and serine is
preferentially consumed before aspartate.
Assays of Bacterial Motility
Brownian
Motion
Latex Beads
Swimming
E. coli
Fluorescent
Flagella
Bundle
Tethered
E. coli
Tracking
E. coli
Assays of Bacterial Motility
Flow
Chamber
Assay
Surface Swarming
Salmonella
Pattern
Formation
Laser
Trap
The Molecular Machinery of Chemotaxis
INPUT
Attractant concentration
Signal
Transduction
OUTPUT
Direction
of
rotation
The Molecular Machinery of Chemotaxis
INPUT
Tsr
Tar
Tap
Trg
Attractants bind receptors at the cell
surface changing their “state”.
(methylated chemoreceptors MCPS).
Signal
Transduction
OUTPUT
Direction
of
rotation
The Molecular Machinery of Chemotaxis
INPUT
Tsr
Tar
Tap
Trg
CheA
(CheW)
The MCPs regulate the activity of a
histidine kinase - autophosphorylates
on a histidine residue.
P~
OUTPUT
Direction
of
rotation
The Molecular Machinery of Chemotaxis
CheA transfers its phosphate to a
signaling protein CheY to form
CheY~P.
INPUT
Tsr
Tar
Tap
Trg
CheA
(CheW)
CheY
P~
P~
OUTPUT
Direction
of
rotation
The Molecular Machinery of Chemotaxis
CheY~P binds to the “switch” and
causes the motor to reverse direction.
The signal is turned off by CheZ
which dephosphorylates CheY.
INPUT
Tsr
Tar
Tap
Trg
CheA
(CheW)
CheY
CheZ
P~
P~
OUTPUT
Direction
of
rotation
Excitatory Pathway
At ‘steady state’, CheY~P levels in the cell are constant and there is some
probability of the cell tumbling. Binding of attractant of the receptorkinase complex, results in decreased CheY~P levels and reduces the
probability of tumbling and the bacteria will tend to continue in the same
direction.
MCP
CheA
(CheW)
CheY~P
CheZ
Motor
+ attractant
CheY
inactive
The Molecular Machinery of Chemotaxis
Adaptation involves two proteins, CheR
and CheB, that modify the receptor to
counteract the effects of the attractant.
INPUT
Tsr
Tar
Tap
Trg
CheA
(CheW)
CheY
CheZ
CheR
CheB
P~
P~
OUTPUT
Direction
of
rotation
Adaptation Pathway
CheR
MCP
CheA
(CheW)
Less active
CheB~P
MCP~CH3
CheA
(CheW)
More active
Adaptation Pathway
CheR
MCP-(CH3)0
MCP-(CH3)1
MCP-(CH3)2
MCP-(CH3)3
MCP-(CH3)4
MCP-(CH3)0
MCP-(CH3)1
MCP-(CH3)2
MCP-(CH3)3
MCP-(CH3)4
+Attractant
+Attractant
+Attractant
+Attractant
+Attractant
CheB~P
In a receptor dimer there will 65 possible states (5 methylation states and two
occupancy states per monomer). If receptors function in receptor clusters,
essentially a continuum of states may exist.
Some Issues in Chemotaxis:
• Sensing of Change in Concentration not absolute concentration
i.e. temporal sensing
• Exact Adaptation
• Sensitivity and Amplification
• Signal Integration from different Attractants/Repellents
The range of concentration of attractants that will cause a chemotactic
response is about 5 orders of magnitude (nM  mM)
References on Modeling Chemotaxis
Barkai, N. & Leibler, S. (1997) Nature (London) 387: 913–917.
Spiro, P. A., Parkinson, J. S. & Othmer, H. G. (1997) Proc. Natl. Acad. Sci. US
94: 7263–7268.
Tau-Mu Yi, Yun Huang , Melvin I. Simon, and John Doyle (2000)
Proc. Natl. Acad. Sci. USA 97: 4649–4653.*
Bray, D., Levin, M. D. & Morton-Firth, C. J. (1998) Nature (London)
393: 85–88. *
* - these models have incorporated the Barkai model.
Robustness in simple biochemical networks
N. Barkai & S. Leibler
Departments of Physics and Molecular Biology, Princeton University,
Princeton, New Jersey 08544, USA
Simplified model
of the chemotaxis
system.
Mechanism for robust adaptation
E is transformed to a modified form, Em, by the
enzyme R; enzyme B catalyses the reverse
modification reaction. Em is active with a probability
of am(l), which depends on the input level l. Robust
adaptation is achieved when R works at saturation
and B acts only on the active form of Em. Note that
the rate of reverse modification is determined by
the system’s output and does not depend directly
on the concentration of Em (vertical bar at the end
of the arrow).
Some parameters used to characterize the network.
Tumble frequency
Steady-State Tumble Frequency
Adaptation Time
Adaptation precision
Chemotactic response and adaptation in the Model.
The system activity, A, of a model system which was subject to a series of
step-like changes in the attractant concentration, is plotted as a function of
time. Attractant was repeatedly added to the system and removed after 20
min, with successive concentration steps of l of 1, 3, 5 and 7 mM. Note the
asymmetry to addition compared with removal of ligand, both in the
response magnitude and the adaptation time.
How robust is the model with respect to variation in parameters?
Adaptation precision
Adaptation Time
Adaptation precision (i.e. exact adaptation) is Robust
Adaptation time is very sensitive to parameters
Testing the predictions of the Barkai model
Robustness in bacterial chemotaxis.
U. Alon, M. G. Surette, N. Barkai & S. Leibler
• The concentration of che proteins were altered as a simple method to
vary network parameters.
• The behavior of the cells were measured (adaptation precision,
adaptation time and steady-state tumble frequency).
• In each case the predictions of the model we observed.
Data for CheR
As predicted by the model the
adaptation precision was robust
while adaptation time and
steady-state tumble frequency
were very sensitive to
conditions.