The tricellular vertex-specific adhesion molecule Sidekick facilitates polarised cell intercalation during Drosophila axis extension.

In epithelia, tricellular vertices are emerging as important sites for the regulation of epithelial integrity and function. Compared to bicellular contacts, however, much less is known. In particular, resident proteins at tricellular vertices were identified only at occluding junctions, with none known at adherens junctions (AJs). In a previous study, we discovered that in Drosophila embryos, the adhesion molecule Sidekick (Sdk), well-known in invertebrates and vertebrates for its role in the visual system, localises at tricellular vertices at the level of AJs. Here, we survey a wide range of Drosophila epithelia and establish that Sdk is a resident protein at tricellular AJs (tAJs), the first of its kind. Clonal analysis showed that two cells, rather than three cells, contributing Sdk are sufficient for tAJ localisation. Super-resolution imaging using structured illumination reveals that Sdk proteins form string-like structures at vertices. Postulating that Sdk may have a role in epithelia where AJs are actively remodelled, we analysed the phenotype of sdk null mutant embryos during Drosophila axis extension using quantitative methods. We find that apical cell shapes are abnormal in sdk mutants, suggesting a defect in tissue remodelling during convergence and extension. Moreover, adhesion at apical vertices is compromised in rearranging cells, with apical tears in the cortex forming and persisting throughout axis extension, especially at the centres of rosettes. Finally, we show that polarised cell intercalation is decreased in sdk mutants. Mathematical modelling of the cell behaviours supports the notion that the T1 transitions of polarised cell intercalation are delayed in sdk mutants, in particular in rosettes. We propose that this delay, in combination with a change in the mechanical properties of the converging and extending tissue, causes the abnormal apical cell shapes in sdk mutant embryos

The evolution of strategy in bacterial warfare via the regulation of bacteriocins and antibiotics.

Bacteria inhibit and kill one another with a diverse array of compounds, including bacteriocins and antibiotics. These attacks are highly regulated, but we lack a clear understanding of the evolutionary logic underlying this regulation. Here, we combine a detailed dynamic model of bacterial competition with evolutionary game theory to study the rules of bacterial warfare. We model a large range of possible combat strategies based upon the molecular biology of bacterial regulatory networks. Our model predicts that regulated strategies, which use quorum sensing or stress responses to regulate toxin production, will readily evolve as they outcompete constitutive toxin production. Amongst regulated strategies, we show that a particularly successful strategy is to upregulate toxin production in response to an incoming competitor's toxin, which can be achieved via stress responses that detect cell damage (competition sensing). Mirroring classical game theory, our work suggests a fundamental advantage to reciprocation. However, in contrast to classical results, we argue that reciprocation in bacteria serves not to promote peaceful outcomes but to enable efficient and effective attacks

Small-molecule CaVα1⋅CaVβ antagonist suppresses neuronal voltage-gated calcium-channel trafficking

Extracellular calcium flow through neuronal voltage-gated CaV2.2 calcium channels converts action potential-encoded information to the release of pronociceptive neurotransmitters in the dorsal horn of the spinal cord, culminating in excitation of the postsynaptic central nociceptive neurons. The CaV2.2 channel is composed of a pore-forming α1 subunit (CaVα1) that is engaged in protein-protein interactions with auxiliary α2/δ and β subunits. The high-affinity CaV2.2α1⋅CaVβ3 protein-protein interaction is essential for proper trafficking of CaV2.2 channels to the plasma membrane. Here, structure-based computational screening led to small molecules that disrupt the CaV2.2α1⋅CaVβ3 protein-protein interaction. The binding mode of these compounds reveals that three substituents closely mimic the side chains of hot-spot residues located on the α-helix of CaV2.2α1 Site-directed mutagenesis confirmed the critical nature of a salt-bridge interaction between the compounds and CaVβ3 Arg-307. In cells, compounds decreased trafficking of CaV2.2 channels to the plasma membrane and modulated the functions of the channel. In a rodent neuropathic pain model, the compounds suppressed pain responses. Small-molecule α-helical mimetics targeting ion channel protein-protein interactions may represent a strategy for developing nonopioid analgesia and for treatment of other neurological disorders associated with calcium-channel trafficking

Impact of implementation choices on quantitative predictions of cell-based computational models

‘Cell-based’ models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.Publisher PDFPeer reviewe

How should we measure psychological resilience in sport performers?

Psychological resilience is important in sport because athletes must constantly withstand a wide range of pressures to attain and sustain high performance. To advance psychologists’ understanding of this area, there exists an urgent need to develop a sport-specific measure of resilience. The purpose of this paper is to review psychometric issues in resilience research and to discuss the implications for sport psychology. Drawing on the wider general psychology literature to inform the discussion, the narrative is divided into three main sections relating to resilience and its assessment: adversity, positive adaptation, and protective factors. The first section reviews the different ways that adversity has been measured and considers the potential problems of using items with varying degrees of controllability and risk. The second section discusses the different approaches to assessing positive adaptation and examines the issue of circularity pervasive in resilience research. The final section explores the various issues related to the assessment of protective factors drawing directly from current measures of resilience in other psychology sub-disciplines. The commentary concludes with key recommendations for sport psychology researchers seeking to develop a measure of psychological resilience in athletes

The TRPC6 inhibitor, larixyl acetate, is effective in protecting against traumatic brain injury-induced systemic endothelial dysfunction

BACKGROUND: The incidence of traumatic brain injuries (TBIs) is on the rise in the USA. Concussions, or mild TBIs without skull fracture, account for about 75% of all TBIs. Mild TBIs (mTBIs) lead to memory and cognitive deficits, headaches, intraocular pressure rises, axonal degeneration, neuroinflammation, and an array of cerebrovascular dysfunctions, including increased vascular permeability and decreased cerebral blood flow. It has been recently reported that besides vascular dysfunction in the cerebral circulation, mTBI may also cause a significant impairment of endothelial function in the systemic circulation, at least within mesenteric microvessels. In this study, we investigated whether mTBI affects endothelial function in aortas and determined the contribution of transient receptor potential canonical (TRPC) channels to modulating mTBI-associated endothelial dysfunction. METHODS: We used a model of closed-head mTBI in C57BL/6, 129S, 129S-C57BL/6-F2 mice, and 129S-TRPC1 and 129S-C57BL/6-TRPC6 knockout mice to determine the effect of mTBI on endothelial function in mouse aortas employing ex vivo isometric tension measurements. Aortic tissue was also analyzed using immunofluorescence and qRT-PCR for TRPC6 expression following mTBI. RESULTS: We show that in various strains of mice, mTBI induces a pronounced and long-lasting endothelial dysfunction in the aorta. Ablation of TRPC6 protects mice from mTBI-associated aortic endothelial dysfunction, while TRPC1 ablation does not impact brain injury-induced endothelial impairment in the aorta. Consistent with a role of TRPC6 activation following mTBI, we observed improved endothelial function in wild type control mice subjected to mTBI following 7-day in vivo treatment with larixyl acetate, an inhibitor of TRPC6 channels. Conversely, in vitro treatment with the pro-inflammatory endotoxin lipopolysaccharide, which activates endothelial TRPC6 in a Toll-like receptor type 4 (TLR4)-dependent manner, worsened aortic endothelial dysfunction in wild type mice. Lipopolysaccharide treatment in vitro failed to elicit endothelial dysfunction in TRPC6 knockout mice. No change in endothelial TRPC6 expression was observed 7 days following TBI. CONCLUSIONS: These data suggest that TRPC6 activation may be critical for inducing endothelial dysfunction following closed-head mTBI and that pharmacological inhibition of the channel may be a feasible therapeutic strategy for preventing mTBI-associated systemic endothelial dysfunction

A computational modelling approach for deriving biomarkers to predict cancer risk in premalignant disease.

The lack of effective biomarkers for predicting cancer risk in premalignant disease is a major clinical problem. There is a near-limitless list of candidate biomarkers and it remains unclear how best to sample the tissue in space and time. Practical constraints mean that only a few of these candidate biomarker strategies can be evaluated empirically and there is no framework to determine which of the plethora of possibilities is the most promising. Here we have sought to solve this problem by developing a theoretical platform for in silico biomarker development. We construct a simple computational model of carcinogenesis in premalignant disease and use the model to evaluate an extensive list of tissue sampling strategies and different molecular measures of these samples. Our model predicts that: (i) taking more biopsies improves prog-nostication, but with diminishing returns for each additional biopsy; (ii) longitudinally-collected biopsies provide slightly more prognostic information than a single biopsy collected at the latest possible time-point; (iii) measurements of clonal diversity are more prognostic than measurements of the presence or absence of a particular abnormality and are particularly robust to confounding by tissue sampling; and (iv) the spatial pattern of clonal expansions is a particularly prognostic measure. This study demonstrates how the use of a mechanistic framework provided by computational modelling can diminish empirical constraints on biomarker development

Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

Tracking of cells in live-imaging microscopy videos of epithelial sheets is a powerful tool for investigating fundamental processes in embryonic development. Characterizing cell growth, proliferation, intercalation and apoptosis in epithelia helps us to understand how morphogenetic processes such as tissue invagination and extension are locally regulated and controlled. Accurate cell tracking requires correctly resolving cells entering or leaving the field of view between frames, cell neighbour exchanges, cell removals and cell divisions. However, current tracking methods for epithelial sheets are not robust to large morphogenetic deformations and require significant manual interventions. Here, we present a novel algorithm for epithelial cell tracking, exploiting the graph-theoretic concept of a ‘maximum common subgraph’ to track cells between frames of a video. Our algorithm does not require the adjustment of tissue-specific parameters, and scales in sub-quadratic time with tissue size. It does not rely on precise positional information, permitting large cell movements between frames and enabling tracking in datasets acquired at low temporal resolution due to experimental constraints such as phototoxicity. To demonstrate the method, we perform tracking on the Drosophila embryonic epidermis and compare cell–cell rearrangements to previous studies in other tissues. Our implementation is open source and generally applicable to epithelial tissues

Adhesion-regulated junction slippage controls cell intercalation dynamics in an Apposed-Cortex Adhesion Model.

Funder: University of Cambridge Herchel Smith FundCell intercalation is a key cell behaviour of morphogenesis and wound healing, where local cell neighbour exchanges can cause dramatic tissue deformations such as body axis extension. Substantial experimental work has identified the key molecular players facilitating intercalation, but there remains a lack of consensus and understanding of their physical roles. Existing biophysical models that represent cell-cell contacts with single edges cannot study cell neighbour exchange as a continuous process, where neighbouring cell cortices must uncouple. Here, we develop an Apposed-Cortex Adhesion Model (ACAM) to understand active cell intercalation behaviours in the context of a 2D epithelial tissue. The junctional actomyosin cortex of every cell is modelled as a continuous viscoelastic rope-loop, explicitly representing cortices facing each other at bicellular junctions and the adhesion molecules that couple them. The model parameters relate directly to the properties of the key subcellular players that drive dynamics, providing a multi-scale understanding of cell behaviours. We show that active cell neighbour exchanges can be driven by purely junctional mechanisms. Active contractility and cortical turnover in a single bicellular junction are sufficient to shrink and remove a junction. Next, a new, orthogonal junction extends passively. The ACAM reveals how the turnover of adhesion molecules regulates tension transmission and junction deformation rates by controlling slippage between apposed cell cortices. The model additionally predicts that rosettes, which form when a vertex becomes common to many cells, are more likely to occur in actively intercalating tissues with strong friction from adhesion molecules