Signaling filopodia, termed cytonemes, are dynamic actin-based membrane structures that regulate the exchange of signaling molecules and their receptors within tissues. However, how cytoneme formation is regulated remains unclear. Here, we show that Wnt/planar cell polarity (PCP) autocrine signaling controls the emergence of cytonemes, and that cytonemes subsequently control paracrine Wnt/β-catenin signal activation. Upon binding of the Wnt family member Wnt8a, the receptor tyrosine kinase Ror2 becomes activated. Ror2/PCP signaling leads to the induction of cytonemes, which mediate the transport of Wnt8a to neighboring cells. In the Wnt-receiving cells, Wnt8a on cytonemes triggers Wnt/β-catenin-dependent gene transcription and proliferation. We show that cytoneme-based Wnt transport operates in diverse processes, including zebrafish development, murine intestinal crypt and human cancer organoids, demonstrating that Wnt transport by cytonemes and its control via the Ror2 pathway is highly conserved in vertebrates.

Controlled synthesis across several length scales, ranging from discrete molecular building blocks to size‐ and morphology‐controlled nanoparticles to 2D sheets and thin films and finally to 3D architectures, is an advanced and highly active research field within both the metal–organic framework (MOF) domain and the overall material science community. Along with synthetic progress, theoretical simulations of MOF structures and properties have shown tremendous progress in both accuracy and system size. Further advancements in the field of hierarchically structured MOF materials will allow the optimization of their performance; however, this optimization requires a deep understanding of the different synthesis and processing techniques and an enhanced implementation of material modeling. Such modeling approaches will allow us to select and synthesize the highest‐performing structures in a targeted rational manner. Here, recent progress in the synthesis of hierarchically structured MOFs and multiscale modeling and associated simulation techniques is presented, along with a brief overview of the challenges and future perspectives associated with a simulation‐based approach toward the development of advanced hierarchically structured MOF materials.

Significance: Protein–protein interactions are important to all facets of life, but their experimental and computational characterization is arduous and frequently of uncertain outcome. The current study demonstrates both the power and limitation to study protein interactions by utilizing sophisticated statistical inference technology to derive protein contacts from available sequence databases, more precisely from the coevolution between residues, that are in contact across the interaction interface of two proteins. By studying homo-oligomeric protein interactions, the current study expands from anecdotal evidence of the performance of this technology to systematic evidence of its value across close to 2,000 interacting protein families.

Nicotinamide adenine dinucleotide (NAD) provides an important link between metabolism and signal transduction and has emerged as central hub between bioenergetics and all major cellular events. NAD-dependent signaling (e.g., by sirtuins and poly–adenosine diphosphate [ADP] ribose polymerases [PARPs]) consumes considerable amounts of NAD. To maintain physiological functions, NAD consumption and biosynthesis need to be carefully balanced. Using extensive phylogenetic analyses, mathematical modeling of NAD metabolism, and experimental verification, we show that the diversification of NAD-dependent signaling in vertebrates depended on 3 critical evolutionary events: 1) the transition of NAD biosynthesis to exclusive usage of nicotinamide phosphoribosyltransferase (NamPT); 2) the occurrence of nicotinamide N-methyltransferase (NNMT), which diverts nicotinamide (Nam) from recycling into NAD, preventing Nam accumulation and inhibition of NAD-dependent signaling reactions; and 3) structural adaptation of NamPT, providing an unusually high affinity toward Nam, necessary to maintain NAD levels. Our results reveal an unexpected coevolution and kinetic interplay between NNMT and NamPT that enables extensive NAD signaling. This has implications for therapeutic strategies of NAD supplementation and the use of NNMT or NamPT inhibitors in disease treatment.

Motivation: The ongoing advances in sequencing technologies have provided a massive increase in the availability of sequence data. This made it possible to study the patterns of correlated substitution between residues in families of homologous proteins or RNAs and to retrieve structural and stability information. Direct coupling analysis (DCA) infers coevolutionary couplings between pairs of residues indicating their spatial proximity, making such information a valuable input for subsequent structure prediction. Results: Here, we present pydca, a standalone Python-based software package for the DCA of protein- and RNA-homologous families. It is based on two popular inverse statistical approaches, namely, the mean-field and the pseudo-likelihood maximization and is equipped with a series of functionalities that range from multiple sequence alignment trimming to contact map visualization. Thanks to its efficient implementation, features and user-friendly command line interface, pydca is a modular and easy-to-use tool that can be used by researchers with a wide range of backgrounds. Availability and implementation: pydca can be obtained from https://github.com/KIT-MBS/pydca or from the Python Package Index under the MIT License. Supplementary information: Supplementary data are available at Bioinformatics online.

Fully understanding biomolecular function requires detailed insight into the systems’ structural dynamics. Powerful experimental techniques such as single molecule Förster Resonance Energy Transfer (FRET) provide access to such dynamic information yet have to be carefully interpreted. Molecular simulations can complement these experiments but typically face limits in accessing slow time scales and large or unstructured systems. Here, we introduce a coarse-grained simulation technique that tackles these challenges. While requiring only few parameters, we maintain full protein flexibility and include all heavy atoms of proteins, linkers, and dyes. We are able to sufficiently reduce computational demands to simulate large or heterogeneous structural dynamics and ensembles on slow time scales found in, e.g., protein folding. The simulations allow for calculating FRET efficiencies which quantitatively agree with experimentally determined values. By providing atomically resolved trajectories, this work supports the planning and microscopic interpretation of experiments. Overall, these results highlight how simulations and experiments can complement each other leading to new insights into biomolecular dynamics and function.