Shear force enhances adhesion of Pseudomonas aeruginosa by counteracting pilus-driven surface departure.

Fluid flow is thought to prevent bacterial adhesion, but some bacteria use adhesins with catch bond properties to enhance adhesion under high shear forces. However, many studies on bacterial adhesion either neglect the influence of shear force or use shear forces that are not typically found in natural systems. In this study, we use microfluidics and single-cell imaging to examine how the human pathogen Pseudomonas aeruginosa interacts with surfaces when exposed to shear forces typically found in the human body (0.1 pN to 10 pN). Through cell tracking, we demonstrate that the angle between the cell and the surface predicts if a cell will depart the surface. We discover that at lower shear forces, type IV pilus retraction tilts cells away from the surface, promoting surface departure. Conversely, we show that higher shear forces counterintuitively enhance adhesion by counteracting type IV pilus retraction-dependent cell tilting. Thus, our results reveal that P. aeruginosa exhibits behavior reminiscent of a catch bond, without having a specific adhesin that is enhanced by force. Instead, P. aeruginosa couples type IV pilus dynamics and cell geometry to tune adhesion to its mechanical environment, which likely provides a benefit in dynamic host environments.

R-package LNIRT for joint modeling of response accuracy and times.

In computer-based testing it has become standard to collect response accuracy (RA) and response times (RTs) for each test item. IRT models are used to measure a latent variable (e.g., ability, intelligence) using the RA observations. The information in the RTs can help to improve routine operations in (educational) testing, and provide information about speed of working. In modern applications, the joint models are needed to integrate RT information in a test analysis. The R-package LNIRT supports fitting joint models through a user-friendly setup which only requires specifying RA, RT data, and the total number of Gibbs sampling iterations. More detailed specifications of the analysis are optional. The main results can be reported through the summary functions, but output can also be analysed with Markov chain Monte Carlo (MCMC) output tools (i.e., coda, mcmcse). The main functionality of the LNIRT package is illustrated with two real data applications.

Type-IV pili tune an adhesion-migration trade-off during surface colonization of Pseudomonas aeruginosa.

Bacterial pathogenicity relies on both firm surface adhesion and cell dissemination. How twitching bacteria resolve the fundamental contradiction between adhesion and migration is unknown. To address this question, we employ live-cell imaging of type-IV pili (T4P) and therewith construct a comprehensive mathematical model of Pseudomonas aeruginosa migration. The data show that only 10% to 50% of T4P bind to substrates and contribute to migration through random extension and retraction. Individual T4P do not display a measurable sensory response to surfaces, but their number increases on cellular surface contact. Attachment to surfaces is mediated, besides T4P, by passive adhesive forces acting on the cell body. Passive adhesions slow down cell migration and result in local random motion on short time scales, which is followed by directionally persistent, superdiffusive motion on longer time scales. Moreover, passive adhesions strongly enhance surface attachment under shear flow. Δ pilA mutants, which produce no T4P, robustly stick to surfaces under shear flow. In contrast, rapidly migrating Δ pilH cells, which produce an excessive number of T4P, are easily detached by shear. Wild-type cells sacrifice migration speed for robust surface attachment by maintaining a low number of active pili. The different cell strains pertain to disjunct regimes in a generic adhesion-migration trait space. Depending on the nature of the adhesion structures, adhesion and migration are either compatible or a trade-off is required for efficient bacterial surface colonization under different conditions.

Shear force enhances adhesion of Pseudomonas aeruginosa by counteracting pilus-driven surface departure.

Fluid flow is thought to prevent bacterial adhesion, but some bacteria use adhesins with catch bond properties to enhance adhesion under high shear forces. However, many studies on bacterial adhesion either neglect the influence of shear force or use shear forces that are not typically found in natural systems. In this study, we use microfluidics and single-cell imaging to examine how the human pathogen Pseudomonas aeruginosa interacts with surfaces when exposed to shear forces typically found in the human body (0.1 pN to 10 pN). Through cell tracking, we demonstrate that the angle between the cell and the surface predicts if a cell will depart the surface. We discover that at lower shear forces, type IV pilus retraction tilts cells away from the surface, promoting surface departure. Conversely, we show that higher shear forces counterintuitively enhance adhesion by counteracting type IV pilus retraction-dependent cell tilting. Thus, our results reveal that P. aeruginosa exhibits behavior reminiscent of a catch bond, without having a specific adhesin that is enhanced by force. Instead, P. aeruginosa couples type IV pilus dynamics and cell geometry to tune adhesion to its mechanical environment, which likely provides a benefit in dynamic host environments.

Eco-friendly Synthesis and Characterization of Silver Nanoparticles using Juglans regia Extract and their Anti-Trichomonas vaginalis, Anticancer, and Antimicrobial Effects.

BACKGROUND: Green synthesis is an efficient and eco-friendly method that has been used frequently in silver nanoparticle production in recent years. This method facilitates the production of nanoparticles using various organisms, such as plants, and is also cheaper and easier to apply than the other techniques. AIMS: This study aims to find possible mechanisms and pharmacological effects of cubic silver nanoparticles (AgNPs). OBJECTIVES: This study characterizes cubic AgNPs and describes in detail their anticancer, antimicrobial, and anti- Trichomonas vaginalis abilities. METHODS: Silver nanoparticles were produced by green synthesis using Juglans regia (walnut) leaf aqueous extract. We validated the formation of AgNPs by UV-vis spectroscopy, FTIR analysis, and SEM micrographs. To determine the pharmacological effects of the AgNPs, we conducted anti-cancer, anti-bacterial, and anti-parasitic activity experiments. RESULTS: Cytotoxicity data revealed that AgNPs have cellular inhibitory properties on cancerous MCF7 (breast), HeLa (cervix), C6 (glioma), and HT29 (colorectal) cell lines. Similar results are also obtained with anti-bacterial and anti- Trichomonas vaginalis activity experiments. At certain concentrations, AgNPs displayed stronger anti-bacterial activities than the sulbactam/cefoperazone antibiotic combination in five bacteria species. Furthermore, the 12-h AgNPs treatment exhibited satisfactory anti-Trichomonas vaginalis activity similar to the FDA-approved metronidazole. CONCLUSION: Consequently, AgNPs produced by the green synthesis method by Juglans regia leaves showed remarkable anti-carcinogenic, anti-bacterial, and anti-trichomonas vaginalis activities. We propose the potential usefulness of green synthesized AgNPs as therapeutics.

Generic self-stabilization mechanism for biomolecular adhesions under load.

Mechanical loading generally weakens adhesive structures and eventually leads to their rupture. However, biological systems can adapt to loads by strengthening adhesions, which is essential for maintaining the integrity of tissue and whole organisms. Inspired by cellular focal adhesions, we suggest here a generic, molecular mechanism that allows adhesion systems to harness applied loads for self-stabilization through adhesion growth. The mechanism is based on conformation changes of adhesion molecules that are dynamically exchanged with a reservoir. Tangential loading drives the occupation of some states out of equilibrium, which, for thermodynamic reasons, leads to association of further molecules with the cluster. Self-stabilization robustly increases adhesion lifetimes in broad parameter ranges. Unlike for catch-bonds, bond rupture rates can increase monotonically with force. The self-stabilization principle can be realized in many ways in complex adhesion-state networks; we show how it naturally occurs in cellular adhesions involving the adaptor proteins talin and vinculin.

Substrate-rigidity dependent migration of an idealized twitching bacterium.

Mechanical properties of the extracellular matrix are important determinants of cellular migration in diverse processes, such as immune response, wound healing, and cancer metastasis. Moreover, recent studies indicate that even bacterial surface colonization can depend on the mechanics of the substrate. Here, we focus on physical mechanisms that can give rise to substrate-rigidity dependent migration. We study a "twitcher", a cell driven by extension-retraction cycles, to idealize bacteria and perhaps eukaryotic cells that employ a slip-stick mode of motion. The twitcher is asymmetric and always pulls itself forward at its front. Analytical calculations show that the migration speed of a twitcher depends non-linearly on substrate rigidity. For soft substrates, deformations do not lead to build-up of significant force and the migration speed is therefore determined by stochastic adhesion unbinding. For rigid substrates, forced adhesion rupture determines the migration speed. Depending on the force-sensitivity of front and rear adhesions, forced bond rupture implies an increase or a decrease of the migration speed. A requirement for the occurrence of rigidity-dependent stick-slip migration is a "sticky" substrate, with binding rates being an order of magnitude larger than unbinding rates in absence of force. Computer simulations show that small stall forces of the driving machinery lead to a reduced movement on high rigidities, regardless of force-sensitivities of bonds. The simulations also confirm the occurrence of rigidity-dependent migration speed in a generic model for slip-stick migration of cells on a sticky substrate.

Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells.

Adherent cells exert traction forces on to their environment which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures during developmental morphogenesis. Traction force microscopy (TFM) enables the measurement of traction forces on an elastic substrate and thereby provides quantitative information on cellular mechanics in a perturbation-free fashion. In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular tractions in different conditions.

Multilevel segmentation of histopathological images using cooccurrence of tissue objects.

This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship. As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images. Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function. This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the final result. The experiments on 200 colon tissue images reveal that the proposed approach--the object cooccurrence features together with the multilevel segmentation algorithm--is effective to obtain high-quality results. The experiments also show that it improves the segmentation results compared to the previous approaches.