In the intestine, finger-like villi provide abundant surface area for nutrient absorption. During murine villus development, epithelial Hedgehog (Hh) signals promote aggregation of subepithelial mesenchymal clusters that drive villus emergence. Clusters arise first dorsally and proximally and spread over the entire intestine within 24 h, but the mechanism driving this pattern in the murine intestine is unknown. In chick, the driver of cluster pattern is tensile force from developing smooth muscle, which generates deep longitudinal epithelial folds that locally concentrate the Hh signal, promoting localized expression of cluster genes. By contrast, we show that in mouse, muscle-induced epithelial folding does not occur and artificial deformation of the epithelium does not determine the pattern of clusters or villi. In intestinal explants, modulation of Bmp signaling alters the spatial distribution of clusters and changes the pattern of emerging villi. Increasing Bmp signaling abolishes cluster formation, whereas inhibiting Bmp signaling leads to merged clusters. These dynamic changes in cluster pattern are faithfully simulated by a mathematical model of a Turing field in which an inhibitor of Bmp signaling acts as the Turing activator. In vivo, genetic interruption of Bmp signal reception in either epithelium or mesenchyme reveals that Bmp signaling in Hh-responsive mesenchymal cells controls cluster pattern. Thus, unlike in chick, the murine villus patterning system is independent of muscle-induced epithelial deformation. Rather, a complex cocktail of Bmps and Bmp signal modulators secreted from mesenchymal clusters determines the pattern of villi in a manner that mimics the spread of a self-organizing Turing field.

This article investigates the dynamics of an important bacterial pathogen, Xylella fastidiosa, within artificial plant xylem. The bacterium is the causative agent of a variety of diseases that strike fruit-bearing plants including Pierce's disease of grapevine. Biofilm colonization within microfluidic chambers was visualized in a laboratory setting, showing robust, regular spatial patterning. We also develop a mathematical model, based on a multiphase approach that is able to capture the spacing of the pattern and points to the role of the exopolymeric substance as the main source of control of the pattern dynamics. We concentrate on estimating the attachment/detachment processes within the chamber because these are two mechanisms that have the potential to be engineered by applying various chemicals to prevent or treat the disease.

Intrinsically disordered proteins fail to adopt a stable three-dimensional structure under physiological conditions. It is now understood that many disordered proteins are not dysfunctional, but instead engage in numerous cellular processes, including signaling and regulation. Disorder characterization from amino acid sequence relies on computational disorder prediction algorithms. While numerous large-scale investigations of disorder have been performed using these algorithms, and have offered valuable insight regarding the prevalence of protein disorder in many organisms, critical proteome-based descriptive statistical guidelines that would enable the objective assessment of intrinsic disorder in a protein of interest remain to be established. Here we present a quantitative characterization of numerous disorder features using a rigorous non-parametric statistical approach, providing expected values and percentile cutoffs for each feature in ten eukaryotic proteomes. Our estimates utilize multiple ab initio disorder prediction algorithms grounded on physicochemical principles. Furthermore, we present novel threshold values, specific to both the prediction algorithms and the proteomes, defining the longest primary sequence length in which the significance of a continuous disordered region can be evaluated on the basis of length alone. The guidelines presented here are intended to improve the interpretation of disorder content and continuous disorder predictions from the proteomic point of view.

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