Noise characteristics of the Escherichia coli rotary motor

The chemotaxis pathway in the bacterium Escherichia coli allows cells to detect changes in external ligand concentration (e.g. nutrients). The pathway regulates the flagellated rotary motors and hence the cells' swimming behaviour, steering them towards more favourable environments. While the molecular components are well characterised, the motor behaviour measured by tethered cell experiments has been difficult to interpret. Here, we study the effects of sensing and signalling noise on the motor behaviour. Specifically, we consider fluctuations stemming from ligand concentration, receptor switching between their signalling states, adaptation, modification of proteins by phosphorylation, and motor switching between its two rotational states. We develop a model which includes all signalling steps in the pathway, and discuss a simplified version, which captures the essential features of the full model. We find that the noise characteristics of the motor contain signatures from all these processes, albeit with varying magnitudes. This allows us to address how cell-to-cell variation affects motor behaviour and the question of optimal pathway design. A similar comprehensive analysis can be applied to other two-component signalling pathways.Comment: 22 pages, 7 figures, 3 tutorials, supplementary information; submitted manuscrip

Predicting chemical environments of bacteria from receptor signaling

Sensory systems have evolved to respond to input stimuli of certain statistical properties, and to reliably transmit this information through biochemical pathways. Hence, for an experimentally well-characterized sensory system, one ought to be able to extract valuable information about the statistics of the stimuli. Based on dose-response curves from in vivo fluorescence resonance energy transfer (FRET) experiments of the bacterial chemotaxis sensory system, we predict the chemical gradients chemotactic Escherichia coli cells typically encounter in their natural environment. To predict average gradients cells experience, we revaluate the phenomenological Weber's law and its generalizations to the Weber-Fechner law and fold-change detection. To obtain full distributions of gradients we use information theory and simulations, considering limitations of information transmission from both cell-external and internal noise. We identify broad distributions of exponential gradients, which lead to log-normal stimuli and maximal drift velocity. Our results thus provide a first step towards deciphering the chemical nature of complex, experimentally inaccessible cellular microenvironments, such as the human intestine.Comment: DG and GM contributed equally to this wor

Optimal receptor-cluster size determined by intrinsic and extrinsic noise

Biological cells sense external chemical stimuli in their environment using cell-surface receptors. To increase the sensitivity of sensing, receptors often cluster, most noticeably in bacterial chemotaxis, a paradigm for signaling and sensing in general. While amplification of weak stimuli is useful in absence of noise, its usefulness is less clear in presence of extrinsic input noise and intrinsic signaling noise. Here, exemplified on bacterial chemotaxis, we combine the allosteric Monod-Wyman- Changeux model for signal amplification by receptor complexes with calculations of noise to study their interconnectedness. Importantly, we calculate the signal-to-noise ratio, describing the balance of beneficial and detrimental effects of clustering for the cell. Interestingly, we find that there is no advantage for the cell to build receptor complexes for noisy input stimuli in absence of intrinsic signaling noise. However, with intrinsic noise, an optimal complex size arises in line with estimates of the sizes of chemoreceptor complexes in bacteria and protein aggregates in lipid rafts of eukaryotic cells.Comment: 15 pages, 12 figures,accepted for publication on Physical Review

Chemotactic response and adaptation dynamics in Escherichia coli

Adaptation of the chemotaxis sensory pathway of the bacterium Escherichia coli is integral for detecting chemicals over a wide range of background concentrations, ultimately allowing cells to swim towards sources of attractant and away from repellents. Its biochemical mechanism based on methylation and demethylation of chemoreceptors has long been known. Despite the importance of adaptation for cell memory and behavior, the dynamics of adaptation are difficult to reconcile with current models of precise adaptation. Here, we follow time courses of signaling in response to concentration step changes of attractant using in vivo fluorescence resonance energy transfer measurements. Specifically, we use a condensed representation of adaptation time courses for efficient evaluation of different adaptation models. To quantitatively explain the data, we finally develop a dynamic model for signaling and adaptation based on the attractant flow in the experiment, signaling by cooperative receptor complexes, and multiple layers of feedback regulation for adaptation. We experimentally confirm the predicted effects of changing the enzyme-expression level and bypassing the negative feedback for demethylation. Our data analysis suggests significant imprecision in adaptation for large additions. Furthermore, our model predicts highly regulated, ultrafast adaptation in response to removal of attractant, which may be useful for fast reorientation of the cell and noise reduction in adaptation.Comment: accepted for publication in PLoS Computational Biology; manuscript (19 pages, 5 figures) and supplementary information; added additional clarification on alternative adaptation models in supplementary informatio

Two-state approach to stochastic hair bundle dynamics

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The <i>Escherichia coli</i> chemosensory system is adapted to its chemical environment by evolution.

<p>Influences such as motile behavior, chemical sources (e.g. patchy food gradients in the human intestine), and the multitude of other organisms shape the typical concentrations sampled by a bacterium, leading to typical input distributions of chemical concentrations. Through signal transduction the sensory system produces (intracellular) output distributions. Evolution is expected to have selected the optimal shape of the input-output (dose-response) curve to allow for an appropriate response to typical stimuli.</p

Comparison of Microbiomes from Different Niches of Upper and Lower Airways in Children and Adolescents with Cystic Fibrosis

Changes in the airway microbiome may be important in the pathophysiology of chronic lung disease in patients with cystic fibrosis. However, little is known about the microbiome in early cystic fibrosis lung disease and the relationship between the microbiomes from different niches in the upper and lower airways. Therefore, in this cross-sectional study, we examined the relationship between the microbiome in the upper (nose and throat) and lower (sputum) airways from children with cystic fibrosis using next generation sequencing. Our results demonstrate a significant difference in both α and β-diversity between the nose and the two other sampling sites. The nasal microbiome was characterized by a polymicrobial community while the throat and sputum communities were less diverse and dominated by a few operational taxonomic units. Moreover, sputum and throat microbiomes were closely related especially in patients with clinically stable lung disease. There was a high inter-individual variability in sputum samples primarily due to a decrease in evenness linked to increased abundance of potential respiratory pathogens such as Pseudomonas aeruginosa. Patients with chronic Pseudomonas aeruginosa infection exhibited a less diverse sputum microbiome. A high concordance was found between pediatric and adult sputum microbiomes except that Burkholderia was only observed in the adult cohort. These results indicate that an adult-like lower airways microbiome is established early in life and that throat swabs may be a good surrogate in clinically stable children with cystic fibrosis without chronic Pseudomonas aeruginosa infection in whom sputum sampling is often not feasible

Reconstruction of distributions of sampled gradients.

<p>(A) Using <i>m</i>^* = 4 (QEQE) as an example, predicted distribution of inputs from information theory (black lines) and distributions of sampled concentrations (blue lines) obtained for cells swimming in increasing relative linear gradients, 0.1 mm⁻¹ (left), 0.5 mm⁻¹ (central), and 1.5 mm⁻¹ (right) (gradients relative to <i>c</i>^* = 0.065 mM). To imitate cell-external noise, the base concentration of the gradients was fluctuating every 0.1<i>s</i> with standard deviation 0.001 mM. To imitate cell-internal noise, modification level was selected from normal distribution with relative standard deviation in line with previous results <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003870#pcbi.1003870-Clausznitzer2" target="_blank">[70]</a>. (B) Overlap between distribution of sampled concentrations and predicted distribution (blue shades), chemotactic index (CI, red shades) and drift velocity (green shades) with modification level <i>m</i> = <i>m</i>^*. Symbols indicate modification level: squares, circles and triangles stand for <i>m</i>^* = 4 (QEQE, mM), <i>m</i>^* = 4.6 (WT 2, mM), and <i>m</i>^* = 6 (QEQQ, mM), respectively. <i>m</i>^* = 8 (QQQQ, <i>c</i>^* = 0.63 mM) is not included as Tar-only cells do not adapt at high values of <i>c</i>^*. Horizontal arrow illustrates range of relative gradients over which the overlap is within 20% of maximal value on average between the three modification levels. (C) Sampled distributions from different relative gradients (0.1–1.75 mm⁻¹) indeed fit prediction (overlap 90%). (D) Range of exponential gradients predicted to be sensed best (blue area), according to the range indicated by horizontal arrow in (B).</p