Borrelia burgdorferi modulates the physical forces and immunity signaling in endothelial cells.

Borrelia burgdorferi (Bb), a vector-borne bacterial pathogen and the causative agent of Lyme disease, can spread to distant tissues in the human host by traveling in and through monolayers of endothelial cells (ECs) lining the vasculature. To examine whether Bb alters the physical forces of ECs to promote its dissemination, we exposed ECs to Bb and observed a sharp and transient increase in EC traction and intercellular forces, followed by a prolonged decrease in EC motility and physical forces. All variables returned to baseline at 24 h after exposure. RNA sequencing analysis revealed an upregulation of innate immune signaling pathways during early but not late Bb exposure. Exposure of ECs to heat-inactivated Bb recapitulated only the early weakening of EC mechanotransduction. The differential responses to live versus heat-inactivated Bb indicate a tight interplay between innate immune signaling and physical forces in host ECs and suggest their active modulation by Bb.

A Stiff Extracellular Matrix Favors the Mechanical Cell Competition that Leads to Extrusion of Bacterially-Infected Epithelial Cells.

Cell competition refers to the mechanism whereby less fit cells ("losers") are sensed and eliminated by more fit neighboring cells ("winners") and arises during many processes including intracellular bacterial infection. Extracellular matrix (ECM) stiffness can regulate important cellular functions, such as motility, by modulating the physical forces that cells transduce and could thus modulate the output of cellular competitions. Herein, we employ a computational model to investigate the previously overlooked role of ECM stiffness in modulating the forceful extrusion of infected "loser" cells by uninfected "winner" cells. We find that increasing ECM stiffness promotes the collective squeezing and subsequent extrusion of infected cells due to differential cell displacements and cellular force generation. Moreover, we discover that an increase in the ratio of uninfected to infected cell stiffness as well as a smaller infection focus size, independently promote squeezing of infected cells, and this phenomenon is more prominent on stiffer compared to softer matrices. Our experimental findings validate the computational predictions by demonstrating increased collective cell extrusion on stiff matrices and glass as opposed to softer matrices, which is associated with decreased bacterial spread in the basal cell monolayer in vitro. Collectively, our results suggest that ECM stiffness plays a major role in modulating the competition between infected and uninfected cells, with stiffer matrices promoting this battle through differential modulation of cell mechanics between the two cell populations.

Confined Cell Migration and Asymmetric Hydraulic Environments to Evaluate the Metastatic Potential of Cancer Cells.

Metastasis, a hallmark of cancer development, is also the leading reason for most cancer-related deaths. Furthermore, cancer cells are highly adaptable to micro-environments and can migrate along pre-existing channel-like tracks of anatomical structures. However, more representative three-dimensional models are required to reproduce the heterogeneity of metastatic cell migration in vivo to further understand the metastasis mechanism and develop novel therapeutic strategies against it. Here, we designed and fabricated different microfluidic-based devices that recreate confined migration and diverse environments with asymmetric hydraulic resistances. Our results show different migratory potential between metastatic and nonmetastatic cancer cells in confined environments. Moreover, although nonmetastatic cells have not been tested against barotaxis due to their low migration capacity, metastatic cells present an enhanced preference to migrate through the lowest resistance path, being sensitive to barotaxis. This device, approaching the study of metastasis capability based on confined cell migration and barotactic cell decisions, may pave the way for the implementation of such technology to determine and screen the metastatic potential of certain cancer cells.

Mechanical competition triggered by innate immune signaling drives the collective extrusion of bacterially infected epithelial cells.

Intracellular pathogens alter their host cells' mechanics to promote dissemination through tissues. Conversely, host cells may respond to the presence of pathogens by altering their mechanics to limit infection. Here, we monitored epithelial cell monolayers infected with intracellular bacterial pathogens, Listeria monocytogenes or Rickettsia parkeri, over days. Under conditions in which these pathogens trigger innate immune signaling through NF-κB and use actin-based motility to spread non-lytically intercellularly, we found that infected cell domains formed three-dimensional mounds. These mounds resulted from uninfected cells moving toward the infection site, collectively squeezing the softer and less contractile infected cells upward and ejecting them from the monolayer. Bacteria in mounds were less able to spread laterally in the monolayer, limiting the growth of the infection focus, while extruded infected cells underwent cell death. Thus, the coordinated forceful action of uninfected cells actively eliminates large domains of infected cells, consistent with this collective cell response representing an innate immunity-driven process.

Integration of in vitro and in silico Models Using Bayesian Optimization With an Application to Stochastic Modeling of Mesenchymal 3D Cell Migration.

Cellular migration plays a crucial role in many aspects of life and development. In this paper, we propose a computational model of 3D migration that is solved by means of the tau-leaping algorithm and whose parameters have been calibrated using Bayesian optimization. Our main focus is two-fold: to optimize the numerical performance of the mechano-chemical model as well as to automate the calibration process of in silico models using Bayesian optimization. The presented mechano-chemical model allows us to simulate the stochastic behavior of our chemically reacting system in combination with mechanical constraints due to the surrounding collagen-based matrix. This numerical model has been used to simulate fibroblast migration. Moreover, we have performed in vitro analysis of migrating fibroblasts embedded in 3D collagen-based fibrous matrices (2 mg/ml). These in vitro experiments have been performed with the main objective of calibrating our model. Nine model parameters have been calibrated testing 300 different parametrizations using a completely automatic approach. Two competing evaluation metrics based on the Bhattacharyya coefficient have been defined in order to fit the model parameters. These metrics evaluate how accurately the in silico model is replicating in vitro measurements regarding the two main variables quantified in the experimental data (number of protrusions and the length of the longest protrusion). The selection of an optimal parametrization is based on the balance between the defined evaluation metrics. Results show how the calibrated model is able to predict the main features observed in the in vitro experiments.