Chemotaxis and Motility in E. coli Examples 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: For a review of bacterial motility see Berg, H.C. "Motile behavior of bacteria". Physics Today, 53(1), (2000). ( behavior of bacteria

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.

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. 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.

Videos of motile bacteria: 1)Free swimming bacteria 2)Swimming in soft agar 3)Tethered cells 4)Latex bead tethered to flagellum 5)Surface swarming behavior 6)Swarm cells mixed with swim cells 7)Aggregation / patterns formation

Videos of motile bacteria: 1)Free swimming bacteria 2)Swimming in soft agar 3)Tethered cells 4)Latex bead tethered to flagellum 5)Surface swarming behavior 6)Swarm cells mixed with swim cells 7)Aggregation / patterns formation Watch for sudden changes of direction = tumbles

Videos of motile bacteria: 1)Free swimming bacteria 2)Swimming in soft agar 3)Tethered cells 4)Latex bead tethered to flagellum 5)Surface swarming behavior 6)Swarm cells mixed with swim cells 7)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

Videos of motile bacteria: 1)Free swimming bacteria 2)Swimming in soft agar 3)Tethered cells 4)Latex bead tethered to flagellum 5)Surface swarming behavior 6)Swarm cells mixed with swim cells 7)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 switch  cheZ – motor switched more frequently

Videos of motile bacteria: 1)Free swimming bacteria 2)Swimming in soft agar 3)Tethered cells 4)Latex bead tethered to flagellum 5)Surface swarming behavior 6)Swarm cells mixed with swim cells 7)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.

Videos of motile bacteria: 1)Free swimming bacteria 2)Swimming in soft agar 3)Tethered cells 4)Latex bead tethered to flagellum 5)Surface swarming behavior 6)Swarm cells mixed with swim cells 7)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.

Videos of motile bacteria: 1)Free swimming bacteria 2)Swimming in soft agar 3)Tethered cells 4)Latex bead tethered to flagellum 5)Surface swarming behavior 6)Swarm cells mixed with swim cells 7)Aggregation / patterns formation Swarms cells are elongated relative to normal swimming cells.

Videos of motile bacteria: 1)Free swimming bacteria 2)Swimming in soft agar 3)Tethered cells 4)Latex bead tethered to flagellum 5)Surface swarming behavior 6)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 Signal Transduction INPUT Attractant concentration Direction of rotation

The Molecular Machinery of Chemotaxis OUTPUT Signal Transduction INPUT Direction of 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 of rotation The MCPs regulate the activity of a histidine kinase - autophosphorylates on a histidine residue. Tsr Tar Tap Trg CheA (CheW) P~

The Molecular Machinery of Chemotaxis OUTPUT INPUT Direction of rotation CheA transfers its phosphate to a signaling protein CheY to form CheY~P. Tsr Tar Tap Trg CheA (CheW) CheY P~

The Molecular Machinery of Chemotaxis OUTPUT INPUT Direction of 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~

MCP CheA (CheW) CheY~P CheZCheY 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 of rotation Tsr Tar Tap Trg CheA (CheW) CheY CheZ CheR CheB 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~CH 3 CheA (CheW) CheR CheB~P Less activeMore 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 occupancy states per monomer). If receptors function in receptor clusters, essentially a continuum of states may exist.

The conformational transition between T and R states of the MCP-CheA- CheW ternary complex probably involves an alteration in the positioning of methylated helices within a coiled coil structure. This transition is modulated by changes in the electrostatic potential between helices effected by the conversion of anionic glutamyl side chains to neutral methyl glutamyl groups and vice versa. Ligand binding between the sensory domain would act to perturb the T/R equilibrium by altering the relative positioning of monomers within the cytoplasm (see Fig. 6). This interplay between methylation and stimulation could operate to control the relative positioning of signaling domains and their associated CheA subunits so as to regulate the transphosphorylation activity of CheA, which through CheY controls the swimming behavior of the bacterial cell.

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. USA 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 of the chemotaxis system.

Mechanism for robust adaptation E is transformed to a modified form, E m, by the enzyme R; enzyme B catalyses the reverse modification reaction. E m 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 E m. Note that the rate of reverse modification is determined by the system’s output and does not depend directly on the concentration of E m (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

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 FliA FlgM Basal Body and Hook Filament Chemotaxis proteins Motor proteins CRP,H-NS,OmpR other? outside inside

flhDC The flhDC promoter integrates inputs from multiple environmental signals ? CRP - catabolite repression, carbohydrate metabolism OmpR - osmolarity IHF - 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.

FliA Regulation by FlgM 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 Body and Hook Assembly

FliA Regulation by FlgM outside inside The Basal body and hook assembly are completed. Level 3 Genes Completed Basal Body and Hook

FliA Regulation by FlgM outside inside The Basal body and hook assembly are completed. Level 3 Genes Completed Basal Body and Hook FlgM is exported through the Basal Body and Hook Assembly

FliA Regulation by FlgM outside inside Level 3 gene expression is initiated. Level 3 Genes Completed Basal Body and Hook FlgM is exported through the Basal Body and Hook Assembly. FliA can interact with RNA polymerase and activate Level 3 gene expression.

FliA Regulation by FlgM 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 Using reporter genes to measure gene expression Organization of operon on chromosome. Reporter gene Clone a copy of the promoter into a reporter plasmid.

flhD flhC Regulator RNA polymerase Using reporter genes to measure gene expression 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 Expression in 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] Fluorescence relative to max Class Operon 0600 Fluorescence of flagella reporter strains as a function of time

Cluster 1 Cluster 2 Cluster 3 Class 1 flhDC Class 2 fliL Class 2 fliE Class 2 fliF Class 2 flgA Class 2 flgB Class 2 flhB Class 2 fliA Class 3 fliD Class 3 flgK Class 3 fliC Class 3 meche Class 3 mocha Class 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) Synchronization of the population occurs only under some growth conditions

flhDC activation Level 2 genes Level 3 genes Level 2 & 3 genes 1:600 dilution1:60 dilution

Relative Promoter Activity (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?