Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect

Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3d individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion- aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of "soft" intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli.Comment: PLoS Computational Biology (2013

Pre-Disposition and Epigenetics Govern Variation in Bacterial Survival upon Stress

<div><p>Bacteria suffer various stresses in their unpredictable environment. In response, clonal populations may exhibit cell-to-cell variation, hypothetically to maximize their survival. The origins, propagation, and consequences of this variability remain poorly understood. Variability persists through cell division events, yet detailed lineage information for individual stress-response phenotypes is scarce. This work combines time-lapse microscopy and microfluidics to uniformly manipulate the environmental changes experienced by clonal bacteria. We quantify the growth rates and RpoH-driven heat-shock responses of individual <em>Escherichia coli</em> within their lineage context, stressed by low streptomycin concentrations. We observe an increased variation in phenotypes, as different as survival from death, that can be traced to asymmetric division events occurring prior to stress induction. Epigenetic inheritance contributes to the propagation of the observed phenotypic variation, resulting in three-fold increase of the RpoH-driven expression autocorrelation time following stress induction. We propose that the increased permeability of streptomycin-stressed cells serves as a positive feedback loop underlying this epigenetic effect. Our results suggest that stochasticity, pre-disposition, and epigenetic effects are at the source of stress-induced variability. Unlike in a bet-hedging strategy, we observe that cells with a higher investment in maintenance, measured as the basal RpoH transcriptional activity prior to antibiotic treatment, are more likely to give rise to stressed, frail progeny.</p> </div

Increased auto-correlation of the stress response within micro-colonies after induction.

<p>The mathematical derivation of the auto-correlation function (AF) can be found in the <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003148#pgen.1003148.s020" target="_blank">Text S1</a>. The black curve shows the AF of non-induced micro-colonies (average of 4). The yellow area indicates the standard deviation. The blue, cyan, green and red curves are the AFs calculated with starting points that are 70 minutes, 90 minutes, 110 minutes, and 140 minutes after induction respectively. The induced AF data is from the micro-colony shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003148#pgen.1003148.s022" target="_blank">Video S2</a>. The generation is determined by the number of cells in the micro-colony. All the curves are truncated at the 8–16 cell stage due to increased fluctuations for small sample sizes.</p

Increased cellular membrane permeability following stress induction.

<p>Exponential phase cells of a strain carrying both p<i>^terR</i> - and p<i>^ibpAB</i> -driven fluorescence reporters are plated on LB-agar pads with or without streptomycin (3 µg/ml) and ATC (25 ng/ml). After 2–3 hours of colony growth, the reporter intensity is quantified by fluorescence microscopy. The intensity is normalized to that of the non-induced state ([streptomycin] = [ATC] = 0). The black dot in the middle of each data cloud shows the mean value of both fluorescence channels. R² and k are the coefficient of determination and slope for linear fitting. For each condition, at least 5 micro-colonies are quantified.</p

Growth inhibition and clonal cell death correlate with a high stress response.

<p>(A) Correlation between reporter intensity and growth rate in response to stress. The growth rate and fluorescence intensity of single cells are measured 130 minutes after Streptomycin treatment. Red indicates dead cells; blue, live cells. (B) Histogram of the growth rate distribution from the same data set as (A). The dashed line indicates the threshold we chose to distinguish alive from dead cells. (C) Life history of a sub-lineage from the micro-colony (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003148#pgen-1003148-g003" target="_blank">Figure 3</a> for the full lineage tree and the legend therein). The colour code represents the fluorescence intensity. Dead cells are indicated by a red dot at the end of the lineage tree. In addition, the cellular growth rate is represented inversely by line width (e.g., bold line the slow growers). The dashed line indicates the time of induction by streptomycin. Data correspond to <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003148#pgen.1003148.s022" target="_blank">Video S2</a>.</p

Size-dependence of the diffusion constants.

<p>Trajectories from the LF movies (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003038#pcbi-1003038-g002" target="_blank">Fig. 2</a>) were clustered into 5 classes of increasing initial median fluorescence (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003038#pcbi-1003038-t001" target="_blank">Table 1</a>) and the corresponding MSD were averaged in each class. Symbols (open circles) show the MSD for the <i>x</i>- (<i>A</i>) and <i>y</i>-directions (<i>B</i>) for each class. Curve colors correspond to the classes from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003038#pcbi-1003038-t001" target="_blank">Table 1</a>(with median fluorescence increasing from top to bottom). The corresponding full lines show the results of the fitting procedure for each class (see text and Material and Methods). Panels (<i>C</i>) and (<i>D</i>) show the corresponding log-log plots, to explore for possible anomalous diffusion. The straight lines are linear fits over the initial regimes (first 21 seconds), before movement restriction starts saturating the MSDs. The slopes of these lines are the anomalous exponents as defined by <i>MSD</i>(<i>t</i>)∼<i>t^α</i>. Each panel indicates the average (+/− s.d.) of the exponents determined for the 4 smallest aggregates classes (thus excluding the largest class, represented by black circles). The resulting values of the diffusion constant <i>D</i> are plotted against the radius <i>r</i> in (<i>E</i>), keeping the same color code as in (A–D). Full circles indicate the values determined from fitting the MSD in the <i>x</i>-direction, while full squares show the values from the fit in the <i>y</i>-direction. The full line is a fit to a Stock-Einstein law <i>D</i>(<i>r</i>) = <i>C</i>₀/<i>r</i>, yielding <i>C₀</i> = 47.23×10³ nm³/s. The inset replots these data as a function of 1/<i>r</i>.</p

Single-aggregate tracking analysis inside <i>E. coli</i> cells.

<p>Coordinates along the <i>x</i> and <i>y</i>-axis are shown in red and black, respectively. Low frequency sampling trajectories (LF) are displayed using full lines and high frequency ones (HF) using open symbols. Light red and black swaths indicate + and −95% confidence intervals for the <i>x</i>- and <i>y</i>-axis data, respectively (for clarity, − and + intervals for the x- and y-axis data, respectively, are omitted) (<i>A</i>) Corrected mean displacement where <i>u_c</i>(<i>t</i>) is the applied correction. For the <i>y</i>-component, the correction is the time-average of the <i>y</i>-coordinate. For the <i>x</i>-component, the applied correction is cell growth : where <i>L</i>(<i>t</i>) is the cell half-length at time <i>t</i> and Δ<i>t</i> is the time interval between two consecutive images. (<i>B</i>) Corresponding mean squared displacements . The inset shows a magnification of the HF results and their close-to-linear behavior for the first 10–15 seconds (dashed line).</p

Localization of the detected aggregates in the cells.

<p>(<i>A</i>) In each image on the time-lapse fluorescence movies, the bacterial cells are automatically isolated (each individual cell is given a unique random color). The aggregates appearing during the movie are automatically detected and their trajectory within the cell quantified (internal trajectories). (<i>B</i>) By convention, we referred to the projection of the aggregate location on the long axis of the cell as the <i>x</i>-component and that along the short axis as the <i>y</i>-component. (<i>C</i>) Histogram of the <i>x</i>-component of the initial position of the trajectories (total of 1,644 trajectories). Since the cell length at the start of the trajectory is highly variable, the <i>x</i>-component was rescaled by division by the cell half-length. After this normalization, the cell poles are located at locations −1.0 and 1.0 respectively, for every trajectory. (<i>D</i>) Experimentally measured positions of the aggregates detected in the poles (both poles pooled, <i>n</i> = 9,242 points). The green-dashed curves in (<i>D</i>–<i>F</i>) locate the 2d projection of the 3d semi-ellipsoid that was used to approximate the cell pole. (<i>E</i>) Synthetic data for bulk positions: 10,000 3d positions were drawn uniformly at random in the 3d semi-ellipsoid pole. The figure shows the corresponding 2d projections. (<i>F</i>) Synthetic data of membranary positions: 10,000 3d positions were drawn uniformly at random in the external boundary (membrane) of the 3d semi-ellipsoid pole. The figure shows the corresponding 2d projections. (<i>G</i>) To quantify figures D–F, the correlation function <i>ρ</i>(<i>s</i>) was computed as the density of positions located within crescent <i>D</i>(<i>s</i>) (gray). See text for more detail. (<i>H–I</i>) Local density of aggregate positions <i>ρ</i>(<i>s</i>) in the synthetic (<i>H</i>) and experimental (<i>I</i>) data shown in <i>E</i> (bulk, blue), <i>F</i> (membranary, red) and <i>D</i> (experimental, orange). The dashed black line shows the local density computed for 10,000 synthetic <i>2</i>d positions that were drawn uniformly at random in the 2d semi-ellipse resulting from the 2d projection of the 3d pole ellipsoid (green dashed curve in <i>D–F</i>).</p