Novel indole derivatives targeting HuR-mRNA complex to counteract high glucose damage in retinal endothelial cells

Background ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. Results We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Conclusions Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1934-z) contains supplementary material, which is available to authorized users.

Careful regulation of mRNA half-lives is a fundamental mechanism allowing cells to quickly respond to changing environmental conditions. The mRNA-binding Hu proteins are important for stabilization of short-lived mRNAs. Here we describe the identification and mechanistic characterization of the first low-molecular-weight inhibitors for Hu protein R (HuR) from microbial broths (Actinomyces sp.): dehydromutactin (1), MS-444 (2) and okicenone (3). These compounds interfere with HuR RNA binding, HuR trafficking, cytokine expression and T-cell activation. A mathematical and experimental analysis of the compounds' mode of action suggests that HuR homodimerizes before RNA binding and that the compounds interfere with the formation of HuR dimers. Our results demonstrate the chemical drugability of HuR; to our knowledge HuR is the first example of a drugable protein within the Hu family. MS-444, dehydromutactin and okicenone may become valuable tools for studying HuR function. An assessment of HuR inhibition as a central node in malignant processes might open up new conceptual routes toward combatting cancer.

Protein molecules work as a whole, so that any local perturbation may resonate on the entire structure: allostery deals with this general property of protein molecules. It is worth noting a perturbation does not necessarily involve a conformational change but, more generally, it travels across the structure as an 'energy signal'. The atomic interactions within the network provide the structural support for this 'signaling highway'. Network descriptors, capturing network signaling efficiency, explain allostery in terms of signal transmission. In this review we will survey the key applications of graph theory to protein allostery. The complex network approach introduces a new perspective in biochemistry; as for applications, the development of new drugs relying on allosteric effects (allo-network drugs) represents a promising avenue of contact network formalism.