Supplementary Materials from Dead bacterial absorption of antimicrobial peptides underlies collective tolerance

Supplementary text, methods, figures and tabl

Reproducibility of High-Throughput Plate-Reader Experiments in Synthetic Biology

Plate-reader assays are commonly conducted to quantify the performance of synthetic biological systems. However, on the basis of a survey of 100 publications, we find that most publications do not report critical experimental settings of plate reader assays, suggesting widespread issues in their reproducibility. Specifically, critical plate reader settings, including shaking time and covering method, either vary between laboratories or are not reported by the publications. Here, we demonstrate that the settings of plate reader assays have a significant impact on bacterial growth, recombinant gene expression, and biofilm formation. Furthermore, we show that the plate reader settings affect the apparent activity, sensitivity, and chemical kinetics of synthetic constructs, as well as alter the apparent effectiveness of antibiotics. Our results suggest the critical need for consistent reporting of plate reader protocols to ensure the reproducibility of the protocols. In addition, our work provides data for the setup of plate reader protocols in synthetic biology experiments

DataSheet1_Frequency dependent growth of bacteria in living materials.pdf

The fusion of living bacteria and man-made materials represents a new frontier in medical and biosynthetic technology. However, the principles of bacterial signal processing inside synthetic materials with three-dimensional and fluctuating environments remain elusive. Here, we study bacterial growth in a three-dimensional hydrogel. We find that bacteria expressing an antibiotic resistance module can take advantage of ambient kinetic disturbances to improve growth while encapsulated. We show that these changes in bacterial growth are specific to disturbance frequency and hydrogel density. This remarkable specificity demonstrates that periodic disturbance frequency is a new input that engineers may leverage to control bacterial growth in synthetic materials. This research provides a systematic framework for understanding and controlling bacterial information processing in three-dimensional living materials.</p

Unbalanced growth environments give rise to rich perturbations.

<p>A. A typical growth curve of MG1655z1 bacterial strain. Grey crosses represent original data. The black line represents the denoised growth curve using the “wden” function in Matlab with a Daubechies (db4) wavelet, a soft universal threshold and no rescaling. B. Wavelet transform of the raw growth curve (a) using a Daubechies (db4) wavelet. The heat map shows the amplitudes at each specific period and time-point. The black box indicates the range of periods that did not generate tight clusters of bacterial strains (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003751#pcbi.1003751.s002" target="_blank">Figure S2C</a>). C. Classification of bacterial strains using the corresponding wavelet transforms. All bacterial strains were classified correctly. mg = MG1655z1, dpro = DH5αPro, pao = PAO1, mds = MDS42, bpro = BL21Pro, etec = ETEC, jm109 = JM109, top 10 = Top10. All data was classified using the standard hierarchical clustering algorithm in Matlab with the average Euclidean distance as the metric. D. Classification of bacterial strains using the raw growth curves. One strain was classified incorrectly, as indicated by the red arrow.</p

Analysis of bacteriophage lambda infection dynamics.

<p>Classification of bacterial knockout strains using unbalanced growth dynamics perturbed by the infection of bacteriophage lambda. Wild type K12 strains were classified into one tight cluster (left panel). Furthermore, two clusters associated with either lipopolysaccharide synthesis or LamB regulation were identified by distinct clusters. Each distinct cluster is represented by the same color in the tree. The right panel shows the corresponding phenotypic signatures of each strain.</p

Minimizing Context Dependency of Gene Networks Using Artificial Cells

The functioning of synthetic gene circuits depends on their local chemical context defined by the types and concentrations of biomolecules in the surrounding milieu that influences gene transcription and translation. This chemical-context dependence of synthetic gene circuits arises from significant yet unknown cross talk between engineered components, host cells, and environmental factors and has been a persistent challenge for synthetic biology. Here, we show that the sensitivity of synthetic gene networks to their extracellular chemical contexts can be minimized, and their designed functions rendered robust using artificial cells, which are synthetic biomolecular compartments engineered from the bottom-up using liposomes that encapsulate the gene networks. Our artificial cells detect, interact with, and kill bacteria in simulated external environments with different chemical complexity. Our work enables the engineering of synthetic gene networks with minimal dependency on their extracellular chemical context and creates a new frontier in controlling robustness of synthetic biological systems using bioinspired mechanisms

Toggle switch: comparison with experiments.

<p>(A) A typical workflow for analyzing experimental data based on the framework for noise calculations presented in this study. Modes of the distribution(s) are determined and used with the deterministic model to estimate some reaction parameters (i.e. intrinsic noise parameters); prior knowledge may also be included in these estimates. Then, using the calculation methods (e.g. convolution for extrinsic noise) presented here, one can obtain a best fit for the full parameter set, which describes both intrinsic and extrinsic noise. (B) Theoretical (blue) and experimental (green) fraction of ON cells as a function of [NFX]. Inset shows the perturbation to the circuit. (C) Experimental distribution of GFP fluorescence in cells with 250 ng/mL NFX. (D) Predicted distribution of U molecule numbers (proportional to fluorescence) with 250 ng/mL NFX. Note that the distribution in (C) is used in (A) to illustrate the general computational procedure.</p

A modified Gibbs sampling method in comparison to previous methods.

<p>(A) The Hartree approximation can distort the joint distribution for multimodal distributions by generating false peaks. (B) Gibbs sampling method: sampling of each molecule number is based on current values of the other molecule numbers rather than on mean values, to avoid this distortion. This method can result in samples being “stuck” in one peak of the probability distribution. The blue arrows in (B) and (C) indicate sampling directions, which are used sequentially. (C) A modified Gibbs sampling method based on coordinate changes can avoid the sampling bias.</p

Calculating steady-state distributions for a simple birth-death process.

<p>Calculating the steady-state distribution of a (A) simple birth-death process; A is expressed in terms of molecule number for all distributions. (B) Simulated distribution using the Gillespie algorithm (histogram) as compared with the analytical solution (blue line) from the CME, which is a Poisson distribution. (C) Final distributions from this systems with extrinsic noise were generated by taking <i>k_s</i>/<i>k_d</i>∼Γ(20,1) (green in C) or <i>k_s</i>/<i>k_d</i>∼Γ(10,2) (green in D); best-fit distributions based on convolution with a Gaussian (red; standard deviations 4.4 for C and 6.1 for D) and the intrinsic-noise-only distribution (blue) are shown as well. (The shift to lower molecule numbers arising from extrinsic noise in the parameter-distribution representation is equivalent to changing the base parameter set in the convolution representation; the “base” parameter set is less well defined in the parameter-distribution representation).</p