chemotaxis and motility in e. coli examples of biochemical and genetic networks background...

Chemotaxis and Motility in E. coliExamples of Biochemical and Genetic Networks

• Background• Chemotaxis- signal transduction network• Flagella gene expression – genetic network

Dictyostelium- adventures in multicellularity

Julie Andreotti – Oscillations in a Biochemical 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)

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 or 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.


Bacteria are added to the center of the plate and allowed to grow.


As the bacteria grow to higher densities, they generate a gradient of attractant as they consume it in the media.

cells cells

AttractantConcentration


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).


‘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.

Videos of motile bacteria:

1) Free swimming bacteria2) Swimming in soft agar3) Tethered cells4) Latex bead tethered to flagellum5) Surface swarming behavior6) Swarm cells mixed with swim cells7) Aggregation / patterns formation


1) Free swimming bacteria2) Swimming in soft agar3) Tethered cells4) Latex bead tethered to flagellum5) Surface swarming behavior6) Swarm cells mixed with swim cells7) Aggregation / patterns formation

Watch for sudden changes of direction = tumbles


1) Free swimming bacteria

2) Swimming in soft agar3) Tethered cells4) Latex bead tethered to flagellum5) Surface swarming behavior6) Swarm cells mixed with swim cells7) Aggregation / patterns formation

Cells are stuck most of the time but when the video is run at 5X they look almost like cells in aqueous environments.GFP labeled cells


1) Free swimming bacteria2) Swimming in soft agar

3) Tethered cells4) Latex bead tethered to flagellum5) Surface swarming behavior6) Swarm cells mixed with swim cells7) Aggregation / patterns formation

A cell is stuck to the coverslip by a sheared flagella. The motor still turns but since the flagella can’t the cell body rotates.

wt - motor switches regularly cheY – motor does not switchcheZ – motor switched more frequently


1) Free swimming bacteria2) Swimming in soft agar3) Tethered cells

4) Latex bead tethered to flagellum5) Surface swarming behavior6) Swarm cells mixed with swim cells7) Aggregation / patterns formation

A cell is stuck to the coverslip and a latex bead is attached to a single flagella. The flagella rotation can be visualized by the bead.


1) Free swimming bacteria2) Swimming in soft agar3) Tethered cells4) Latex bead tethered to flagellum

5) Surface swarming behavior6) Swarm cells mixed with swim cells7) Aggregation / patterns formation

Bacteria can move over a solid surface in a process call swarming. The movement is relatively slow compared to swimming and is coordinated.


1) Free swimming bacteria2) Swimming in soft agar3) Tethered cells4) Latex bead tethered to flagellum5) Surface swarming behavior

6) Swarm cells mixed with swim cells7) Aggregation / patterns formation

Swarms cells are elongated relative to normal swimming cells.


1) Free swimming bacteria2) Swimming in soft agar3) Tethered cells4) Latex bead tethered to flagellum5) Surface swarming behavior6) Swarm cells mixed with swim cells

7) Aggregation / patterns formation

Dilute cells placed under conditions where they release attractants will aggregate into large masses of cells (~30’ video ~2’).

The Molecular Machinery of Chemotaxis

OUTPUT

SignalTransduction

INPUT Attractant concentration

Directionof

rotation


OUTPUT

SignalTransduction

INPUT

Directionof

rotation

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

TarTapTrg


OUTPUT

INPUT

Directionof

rotation

The MCPs regulate the activity of a histidine kinase - autophosphorylates on a histidine residue.Tsr

TarTapTrg

CheA(CheW)

P~


OUTPUT

INPUT

Directionof

rotation

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

TarTapTrg

CheA(CheW)CheY

P~

P~


OUTPUT

INPUT

Directionof

rotation

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

TsrTarTapTrg

CheA(CheW)CheYCheZ

P~

P~

MCPCheA

(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.


OUTPUT

INPUT

Directionof

rotation

TsrTarTapTrg

CheA(CheW)CheYCheZ

CheRCheB

P~

P~

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

Adaptation Pathway

MCPCheA

(CheW)

MCP~CH3

CheA(CheW)

CheR

CheB~P

Less active More active

Adaptation Pathway

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

MCP-(CH3)0

+AttractantMCP-(CH3)3




+Attractant

CheR

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.

The conformational transitionbetween T and R states of the MCP-CheA-CheW ternary complex probably involves analteration in the positioning of methylatedhelices within a coiled coil structure. Thistransition is modulated by changes in theelectrostatic potential between helices effectedby the conversion of anionic glutamyl sidechains to neutral methyl glutamyl groups andvice versa. Ligand binding between the sensorydomain would act to perturb the T/Requilibrium by altering the relative positioningof monomers within the cytoplasm (see Fig. 6).This interplay between methylation andstimulation could operate to control the relativepositioning of signaling domains and theirassociated CheA subunits so as to regulate thetransphosphorylation activity of CheA, whichthrough CheY controls the swimming behaviorof the bacterial cell.

Some Issues in Chemotaxis:

• Sensing of Change in Concentration not absolute concentrationi.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. USA94: 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 networksN. 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 m(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 frequencySteady-State Tumble Frequency

Adaptation TimeAdaptation 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 M. 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

Regulation of flagella gene expression: A three tiered transcriptional hierarchy

Positive transcriptional regulators

Alternative sigma factors

Ant-sigma factors

Temporal regulation

The Flagellar Transcription Hierarchy

1. The Master Regulon

2. The FlhCD Regulon

3. The FliA Regulon

FlhCD

FliAFlgM

Basal Bodyand Hook

Filament

Chemotaxisproteins

Motorproteins

CRP,H-NS,OmpRother?

other?

outside

inside

flhDC

The flhDC promoter integrates inputs from multiple environmental signals

?

CRP - catabolite repression, carbohydrate metabolismOmpR - osmolarityIHF - growth state of cell?HdfR - ?

FliA Regulation by FlgM

outside

inside

FlhDC expression leads to activation of Level 2 genes including the alternative sigma factor FliA and an anti sigma factor FlgM

Level 3 Genes

FlgM accumulates in the cell and binds to FliA blocking its activity (i.e. interaction with RNA polymerase) preventing Level 3 gene expression.


outside

inside

Other level 2 genes required for Basal body and hook assembly are made and begin to assemble in the membrane.

Level 3 Genes

Basal Bodyand HookAssembly


outside

inside

The Basal body and hook assembly are completed.

Level 3 Genes

Completed Basal Bodyand Hook


outside

inside

The Basal body and hook assembly are completed.

Level 3 Genes


FlgM is exported through the Basal Body and Hook Assembly


outside

inside

Level 3 gene expression is initiated.

Level 3 Genes


FlgM is exported through the Basal Body and Hook Assembly.

FliA can interact with RNA polymerase and activate Level 3 gene expression.


outside

inside

Filament

Level 3 gene products are added to the motility machinery including the flagella filament, motor proteins and chemotaxis signal transduction system.

flhD flhC

flhDC promoter

Regulator

RNA polymerase

Using reporter genes to measure gene expression

Organization of operon on chromosome.

flhD flhC

flhDC promoter

Regulator

RNA polymerase


Organization of operon on chromosome.

Reporter gene

Clone a copy of the promoter into a reporter plasmid.

flhD flhC

Regulator

RNA polymerase


Reporter gene

Both the flhDC genes and the reporter plasmid are regulated in the same way and thus the level of the reporter indicates the activity of the promoter.

Note that the strain still has a normal copy of the genes.

Gene Expression in Populations

Gene Expressionin Single Cells

Video microscopy

- “individuality”- cell cycle regulation- epigenetic phenomenon

Multi-well plate reader

- sensitive, fast reading- high-throughput screening- liquid cultures- colonies- mixed cultures

Automation: Both approaches are amenable to high throughput robotics

Time [min]

Fluorescencerelative to max

0.01

0.1

0.6

Class

Operon

0 600

Fluorescence of flagella reporter strains as a function of time

Cluster 1

Cluster 2

Cluster 3

Class 1 flhDC

Class 2 fliLClass 2 fliEClass 2 fliFClass 2 flgAClass 2 flgBClass 2 flhBClass 2 fliAClass 3 fliDClass 3 flgKClass 3 fliC

Class 3 mecheClass 3 mochaClass 3 flgM

Early

Late

Activator of class 3

Master regulator

The order of flagellar gene expression is the order of assembly

Time

[protein]

Simple Mechanism for Temporal Expression Within an Regulon

Induction of positive regulator

Promoters with decreasing affinity for regulator

[protein]

Simple Mechanism for Temporal Expression Within an Regulon

Using Expression Data to Define and Describe Regulatory Networks

With the flagella regulon, current algorithms can distinguish Level 2 and Level 3 genes based on subtleties in expression patterns not readily distinguished by visual inspection.

Using our methods for expression profiling (sensitive, good time resolution) we have been able to demonstrate more subtle regulation than previously described.

The Challenge:

Can this type of experiment and analysis be used to describe the details of the flagella regulon? (our ‘model’ network)

Can this be applied on a genomic scale?

Time [min] Condition A

(No pre-existing flagella)

Time [min] Condition B

(Pre-existing flagella)

0 600 6000

Synchronization of the population occurs only under some growth conditions

flhDC activation

Level 2 genes

Level 3 genes

Level 2 & 3 genes

1:600 dilution 1:60 dilution

0

100000

200000

300000

400000

500000

100

1000

10000

100000

1000000

Rel

ativ

e Pr

omot

er A

ctiv

ity

(max

)

Variability in 22 E. coli flhDC Promoters

* * *

* * *

The Promoter for flhDC varies significantly between E. coli Isolates

• In several randomly cloned E. coli flhDC promoters, there is a large distribution in promoter strength

• Quantitative differences in promoter strength can not be inferred from promoter sequence nor from swim rates on soft agar plates.

• The same promoter behaves differently in different strain backgrounds which implies variability in regulators acting on the promoter (CRP,OmpR etc.)

• Correct temporal patterning of gene expression and assembly of flagella occurs despite significant variation in the level of gene expression between strains. Where is the source of the ‘robustness’ in this genetic network?

chemotaxis and motility in e. coli examples of biochemical and genetic networks background...

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