The chemotaxis network of E. coli

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Presentation transcript:

The chemotaxis network of E. coli Ned Wingreen Boulder Summer School 2007 Thanks to: Robert Endres, Clinton Hansen, Juan Keymer, Yigal Meir, Monica Skoge, and Victor Sourjik Support from HFSP

Adaptation Adaptation uses methylation to adjust Δftotal ≈ 0, and thereby enhances sensitivity. Tsr Tar Δf > 0 Δf > 0 Δf > 0 C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Off Δftotal ≈ 0

Scaling of wild-type adapted response Sourjik and Berg: Δ[MeAsp] → ΔFRET{Tar(QEQE)} “Free energy” scaling: Δ[MeAsp] → Δ(Fon – Foff) ΔFRET for Tar(QEQE) strain Sourjik and Berg (2002) C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Includes zero-ambient data! And yields KDs: Doesn’t collapse zero-ambient data. KDoff = 25 µM, KDon ≈ 0.5 mM

Motor output also yields KDs Berg and Tedesco (1975)  KDoff = 27 µM, KDon ≈ 0.9 mM

2-state receptor model Originally proposed by Asakura and Honda (1984). Modified by Barkai and Leibler (1998) to explain precise and robust adaptation: Receptor complex has 2 states: “off”, i.e. inactive as kinase, and “on”, i.e. active as kinase. Demethylation only occurs in “on” state, Therefore, at steady state, Which implies precise and robust adaptation of each receptor complex to a fixed activity. off on CheB

Failure of precise adaptation? off on CheB Barkai-Leibler single-receptor adaptation model: Steady state → R B Activity C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Yields imprecise adaptation of receptor clusters:

Help from “assistance neighborhoods” C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Antommattei et al. (2004) Li and Hazelbauer (2005) Tethered CheR/CheB act on neighborhood of 5-7 receptors.

Precise adaptation saved! Assistance-neighborhood model Barkai-Leibler + assistance neighborhoods = precise adaptation: C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7.

Precision of adaptation with assistance neighborhoods Assistance neighborhood of ~ 6 receptors sufficient for precise adaptation: Adaptation not only precise but also robust to parameter choices in model. …compare this to single Tar receptor and 18 and 36 receptor clusters without AN. All perform worse than 18 with AN. Adaptation error:

Initial response and sensitivity of adapted receptors Experiment Sourjik and Berg (2002) Simulation (with assistance neighborhoods) Using our model, we can simulate time-dependent experiments. Notice two peaks leading to broad range of sensitivity…

Two peaks of sensitivity FRET Simulation Tar Tsr Kaoff Kaon Ksoff Kson Analytic result for single cluster Resolves long dispute among different proposals: Cluster size could get smaller with more methylation to decrease sensitivity K_D could get larger with methylation c) Peaks larger than constant since constant average over various methylation states at fixed ligand concentration.

Prediction: Two limits of adaptation fully methylated Tsr:Tar=2:1 Ser Asp Serine Berg and Brown (1972) Aspartate Saturation by ligand no response for [L] > KDon Full methylation before saturation adaptation stops Off Off Free Energy Reasons for 2 limits in cluster are different abundance of Tar and Tsr per cluster: More Tsrs bind Ser than Tar bind Asp. Cartoon explains 2 limits in terms of a single receptor and Different K_D’s. Pick lowest on free-energy state=fully methylated receptor. On left side, K_D^off smaller, so off states gets low quickly. On On Serine [L] Aspartate [L]

Open questions What determines cluster size and what is the mechanism of receptor-receptor coupling? Two limits of adaptation? What is being optimized? C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Stock (2000)

Conclusions E. coli chemotaxis network remarkable for: precise and robust adaptation signal integration sensitivity FRET studies reveal two regimes of receptor activity Model of mixed clusters of 2-state receptors accounts for network properties and for two regimes Precise adaptation of clusters requires assistance neighborhoods Prediction: two possible limits of adaptation

Outline Introduction to chemotaxis in E. coli The chemotaxis network Two regimes of activity Receptors function collectively Modeling Mixed clusters of receptors Precise adaptation through “assistance neighborhoods”

E. coli chemotaxis: runs and tumbles Division accuracy – F. J. Trueba, Arch. Microbiol. 131, 55-59 (1982). FtsZ ring accuracy – X.-C. Yu and W. Margolin, 32, 315-326 (1999). (Thanks to Howard Berg.)

The chemotaxis network http://www.rowland.harvard.edu/labs/bacteria/projects_fret.html

Chemoreceptor clustering Receptors are clustered globally into a large array, and locally into trimers of dimers. Gestwicki et al. (2000) Gestwicki et al. J. Bacteriol. 182, 6499-6502 (2000) – Immunofluorescence images using anti-Trg antibody. Kim et al. (1999); Studdert and Parkinson (2004)

Chemoreceptors Homodimer Tar - aspartate, glutamate (~900 copies) Tsr - serine (~1600) Trg - ribose, galactose (~150) Tap - dipeptides (~150) (Aer - oxygen via FAD (150?)) Sensor Transmembrane helices Linker region Attractant binding inhibits phosphorylation of CheA Adaptation: More attractant → increased methylation by CheR → faster phosphorylation of CheA Less attractant → increased demethylation by CheB → slower phosphorylation of CheA 380 A Methyl binding sites CheB, CheR Cytoplasmic domain CheA / CheW binding region Stock (2000)

In vivo FRET studies of receptor activity Real-time measurement of rate of phosphorylation of CheY. (FRET also allows subcellular imaging, Vaknin and Berg (2004).) IMPORTANT ADVANTAGE OF FRET IS THAT CHEY-P IS UPSTREAM OF MOTOR. A. Vaknin and H. C. Berg, Single-cell FRET imaging of phosphatase activity in the Escherichia coli chemotaxis system, Proc Natl Acad Sci U S A. 2004 Dec 7;101(49):17072-7. Sourjik and Berg (2002)

FRET data: two regimes of activity Sourjik and Berg (2002) Regime I: Activity moderate to low at zero ambient MeAsp (0.06,1) Ki small and almost constant Regime II: Activity high (saturated) at zero ambient MeAsp (1.3-1.9) Ki1 large and increasing with methylation Plateau in activity Ki2 approximately constant Regime II Regime I C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Two regimes of receptor activity consistent with 2-state receptor model.

Two regimes of a 2-state receptor On Off But first, a “1-state” receptor: Regime I Regime II Regime I: Activity low to very low at zero ligand concentration Ki = KDoff Regime II: Activity high (saturated) at zero ligand concentration Ki increasing as εon ↓ Off Off On On Free Energy Off Off C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. On KD Ligand Ligand Ligand However, single receptor does not account for low apparent Ki in Regime I.

Receptor-receptor coupling Duke and Bray (1999) Duke and Bray (1999) proposed that receptor-receptor coupling could enhance sensitivity to ligands. MWC model: if N receptors are all “on” or all “off” together, Regime I (Δε > 0): Low activity ~ e-NΔε at zero ligand concentration Ki = KDoff / N Hill coefficient = 1 Regime II (Δε < 0): Ki = KDoff e-Δε Hill coefficient = N C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Receptor-receptor coupling gives enhanced sensitivity (low Ki) in Regime I, and enhanced cooperativity (high Hill coefficient ) in Regime II.

Mixed cluster MWC model Tsr Tar Mello and Tu (2005) Keymer et al. (2006) Regime I: Ki = KDoff / N. Regime II: Plateaus: some clusters “on”, some “off”. Hill coefficient ≈ 1. C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Mixed clusters of size 14-16. Each cluster is an independent 2-state system.

Receptor homogeneity and cooperativity More Tars C. A. Hale, H. Meinhardt, and P. A. J. de Boer, EMBO J. 20, 1563-1572 (2001) – Fig. 7. Receptors are in Regime II: Hill coefficient increases with Tar homogeneity because more receptors bind ligand at transition. Ki (or Ki1) decreases with Tar homogeneity because fewer Tsrs need to be switched off.