The complex three-dimensional organization of epithelial tissues.

Understanding the cellular organization of tissues is key to developmental biology. In order to deal with this complex problem, researchers have taken advantage of reductionist approaches to reveal fundamental morphogenetic mechanisms and quantitative laws. For epithelia, their two-dimensional representation as polygonal tessellations has proved successful for understanding tissue organization. Yet, epithelial tissues bend and fold to shape organs in three dimensions. In this context, epithelial cells are too often simplified as prismatic blocks with a limited plasticity. However, there is increasing evidence that a realistic approach, even from a reductionist perspective, must include apico-basal intercalations (i.e. scutoidal cell shapes) for explaining epithelial organization convincingly. Here, we present an historical perspective about the tissue organization problem. Specifically, we analyze past and recent breakthroughs, and discuss how and why simplified, but realistic, in silico models require scutoidal features to address key morphogenetic events.

Mechanics and self-organization in tissue development.

Self-organization is an all-important feature of living systems that provides the means to achieve specialization and functionality at distinct spatio-temporal scales. Herein, we review this concept by addressing the packing organization of cells, the sorting/compartmentalization phenomenon of cell populations, and the propagation of organizing cues at the tissue level through traveling waves. We elaborate on how different theoretical models and tools from Topology, Physics, and Dynamical Systems have improved the understanding of self-organization by shedding light on the role played by mechanics as a driver of morphogenesis. Altogether, by providing a historical perspective, we show how ideas and hypotheses in the field have been revisited, developed, and/or rejected and what are the open questions that need to be tackled by future research.

TiFoSi: an efficient tool for mechanobiology simulations of epithelia.

MOTIVATION: Emerging phenomena in developmental biology and tissue engineering are the result of feedbacks between gene expression and cell biomechanics. In that context, in silico experiments are a powerful tool to understand fundamental mechanisms and to formulate and test hypotheses. RESULTS: Here, we present TiFoSi, a computational tool to simulate the cellular dynamics of planar epithelia. TiFoSi allows to model feedbacks between cellular mechanics and gene expression (either in a deterministic or a stochastic way), the interaction between different cell populations, the custom design of the cell cycle and cleavage properties, the protein number partitioning upon cell division, and the modeling of cell communication (juxtacrine and paracrine signaling). AVAILABILITY AND IMPLEMENTATION: http://tifosi.thesimbiosys.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC.

Immune checkpoint inhibitors remained the standard-of-care treatment for advanced non-small cell lung cancer (NSCLC) for the past decade. In unselected patients, anti-PD-(L)1 monotherapy achieved an overall response rate of about 20%. In this analysis, we developed a pharmacokinetic and pharmacodynamic module for our previously calibrated quantitative systems pharmacology model (QSP) to simulate the effectiveness of macrophage-targeted therapies in combination with PD-L1 inhibition in advanced NSCLC. By conducting in silico clinical trials, the model confirmed that anti-CD47 treatment is not an optimal option of second- and later-line treatment for advanced NSCLC resistant to PD-(L)1 blockade. Furthermore, the model predicted that inhibition of macrophage recruitment, such as using CCR2 inhibitors, can potentially improve tumor size reduction when combined with anti-PD-(L)1 therapy, especially in patients who are likely to respond to anti-PD-(L)1 monotherapy and those with a high level of tumor-associated macrophages. Here, we demonstrate the application of the QSP platform on predicting the effectiveness of novel drug combinations involving immune checkpoint inhibitors based on preclinical or early-stage clinical trial data.

Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager.

Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients' immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy.

A quantitative biophysical principle to explain the 3D cellular connectivity in curved epithelia.

Epithelial cell organization and the mechanical stability of tissues are closely related. In this context, it has been recently shown that packing optimization in bended or folded epithelia is achieved by an energy minimization mechanism that leads to a complex cellular shape: the "scutoid". Here, we focus on the relationship between this shape and the connectivity between cells. We use a combination of computational, experimental, and biophysical approaches to examine how energy drivers affect the three-dimensional (3D) packing of tubular epithelia. We propose an energy-based stochastic model that explains the 3D cellular connectivity. Then, we challenge it by experimentally reducing the cell adhesion. As a result, we observed an increment in the appearance of scutoids that correlated with a decrease in the energy barrier necessary to connect with new cells. We conclude that tubular epithelia satisfy a quantitative biophysical principle that links tissue geometry and energetics with the average cellular connectivity.

Self-sustained planar intercalations due to mechanosignaling feedbacks lead to robust axis extension during morphogenesis.

Tissue elongation is a necessary process in metazoans to implement their body plans that is not fully understood. Here we propose a mechanism based on the interplay between cellular mechanics and primordia patterning that results in self-sustained planar intercalations. Thus, we show that a location-dependent modulation of the mechanical properties of cells leads to robust axis extension. To illustrate the plausibility of this mechanism, we test it against different patterning models by means of computer simulations of tissues where we implemented mechano-signaling feedbacks. Our results suggest that robust elongation relies on a trade-off between cellular and tissue strains that is orchestrated through the cleavage orientation. In the particular context of axis extension in Turing-patterned tissues, we report that different directional cell activities cooperate synergetically to achieve elongation. Altogether, our findings help to understand how the axis extension phenomenon emerges from the dynamics of individual cells.

Mycobacterium Phage Butters-Encoded Proteins Contribute to Host Defense against Viral Attack.

A diverse set of prophage-mediated mechanisms protecting bacterial hosts from infection has been recently uncovered within cluster N mycobacteriophages isolated on the host, Mycobacterium smegmatis mc2155. In that context, we unveil a novel defense mechanism in cluster N prophage Butters. By using bioinformatics analyses, phage plating efficiency experiments, microscopy, and immunoprecipitation assays, we show that Butters genes located in the central region of the genome play a key role in the defense against heterotypic viral attack. Our study suggests that a two-component system, articulated by interactions between protein products of genes 30 and 31, confers defense against heterotypic phage infection by PurpleHaze (cluster A/subcluster A3) or Alma (cluster A/subcluster A9) but is insufficient to confer defense against attack by the heterotypic phage Island3 (cluster I/subcluster I1). Therefore, based on heterotypic phage plating efficiencies on the Butters lysogen, additional prophage genes required for defense are implicated and further show specificity of prophage-encoded defense systems.IMPORTANCE Many sequenced bacterial genomes, including those of pathogenic bacteria, contain prophages. Some prophages encode defense systems that protect their bacterial host against heterotypic viral attack. Understanding the mechanisms undergirding these defense systems is crucial to appreciate the scope of bacterial immunity against viral infections and will be critical for better implementation of phage therapy that would require evasion of these defenses. Furthermore, such knowledge of prophage-encoded defense mechanisms may be useful for developing novel genetic tools for engineering phage-resistant bacteria of industrial importance.