Sept 25 Biochemical Networks Chemotaxis and Motility in E. coli - - PowerPoint PPT Presentation

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.

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.)

The E. coli Flagellar Motor- a true rotary motor

Tumble (CW) Smooth Swimming

• r Run

(CCW)

Increasing attractant No Gradient Increasing repellent

Chemotactic Behavior of Free Swimming Bacteria

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

• f attractant as they consume it in the media.

cells cells Attractant Concentration

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.

Swimming

• E. coli

Fluorescent Flagella Bundle Tethered

• E. coli

Tracking

• E. coli

Assays of Bacterial Motility Brownian Motion Latex Beads

Assays of Bacterial Motility Surface Swarming Salmonella Flow Chamber Assay Pattern Formation Laser Trap

The Molecular Machinery of Chemotaxis OUTPUT Signal Transduction INPUT

Attractant concentration Direction

• f

rotation

The Molecular Machinery of Chemotaxis OUTPUT Signal Transduction INPUT

Direction

• f

rotation Attractants bind receptors at the cell surface changing their “state”. (methylated chemoreceptors MCPS).

Tsr Tar Tap Trg

The Molecular Machinery of Chemotaxis OUTPUT INPUT

Direction

• f

rotation The MCPs regulate the activity of a histidine kinase - autophosphorylates

• n a histidine residue.

Tsr Tar Tap Trg CheA (CheW) P~

The Molecular Machinery of Chemotaxis OUTPUT INPUT

Direction

• f

rotation CheA transfers its phosphate to a signaling protein CheY to form CheY~P.

Tsr Tar Tap Trg CheA (CheW) CheY P~ P~

The Molecular Machinery of Chemotaxis OUTPUT INPUT

Direction

• f

rotation CheY~P binds to the “switch” and causes the motor to reverse direction. The signal is turned off by CheZ which dephosphorylates CheY.

Tsr Tar Tap Trg CheA (CheW) CheY CheZ P~ P~

MCP CheA (CheW) CheY~P CheZ CheY Motor

+ attractant

inactive

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 receptor- kinase complex, results in decreased CheY~P levels and reduces the probability of tumbling and the bacteria will tend to continue in the same direction.

The Molecular Machinery of Chemotaxis OUTPUT INPUT

Direction

• f

rotation

Tsr Tar Tap Trg CheA (CheW) CheY CheZ CheR CheB P~ P~

Adaptation involves two proteins, CheR and CheB, that modify the receptor to counteract the effects of the attractant.

Adaptation Pathway MCP CheA (CheW) MCP~CH3 CheA (CheW) CheR CheB~P Less active More active

Adaptation Pathway

MCP-(CH3)0 MCP-(CH3)3 MCP-(CH3)4 MCP-(CH3)1 MCP-(CH3)2 MCP-(CH3)0

+Attractant

MCP-(CH3)3

+Attractant

MCP-(CH3)4

+Attractant

MCP-(CH3)1

+Attractant

MCP-(CH3)2

+Attractant

CheR CheB~P

In a receptor dimer there will 65 possible states (5 methylation states and two

• ccupancy 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)

Spiro, P. A., Parkinson, J. S. & Othmer, H. G. (1997) Proc. Natl. Acad. Sci. US 94: 7263–7268. Barkai, N. & Leibler, S. (1997) Nature (London) 387: 913–917. 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. *

References on Modeling Chemotaxis

* - 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

• f 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

• f 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

• n the concentration of Em (vertical bar at the end
• f the arrow).

Some parameters used to characterize the network.

Tumble frequency Steady-State Tumble Frequency Adaptation Time Adaptation precision

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.

Chemotactic response and adaptation in the Model.

Adaptation precision Adaptation Time

How robust is the model with respect to variation in parameters?

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.

As predicted by the model the adaptation precision was robust while adaptation time and steady-state tumble frequency were very sensitive to conditions.

Data for CheR