Vertebrate proteins that fulfill multiple and seemingly disparate functions are increasingly recognized as vital solutions to maintaining homeostasis in the face of the complex cell and tissue physiology of higher metazoans. However, the molecular adaptations that underpin this increased functionality remain elusive. In this Commentary, we review the PACS proteins -which first appeared in lower metazoans as protein traffic modulators and evolved in vertebrates to integrate cytoplasmic protein traffic and interorganellar communication with nuclear gene expression -as examples of protein adaptation 'caught in the act'. Vertebrate PACS-1 and PACS-2 increased their functional density and roles as metabolic switches by acquiring phosphorylation sites and nuclear trafficking signals within disordered regions of the proteins. These findings illustrate one mechanism by which vertebrates accommodate their complex cell physiology with a limited set of proteins. We will also highlight how pathogenic viruses exploit the PACS sorting pathways as well as recent studies on PACS genes with mutations or altered expression that result in diverse diseases. These discoveries suggest that investigation of the evolving PACS protein family provides a rich opportunity for insight into vertebrate cell and organ homeostasis.
ROS1 fusion proteins resulting from chromosomal rearrangements of the ROS1 gene are targetable oncogenic drivers in diverse cancers. Acquired resistance to targeted inhibitors curtails clinical benefit and response durability. Entrectinib, a NTRK/ROS1/ALK targeted tyrosine kinase inhibitor (TKI), was approved for the treatment of ROS1 fusion-positive non–small cell lung cancer (NSCLC) in 2019. In addition, lorlatinib and repotrectinib are actively being explored in the setting of treatment-naïve or crizotinib-resistant ROS1 fusion driven NSCLC. Here, we employed an unbiased forward mutagenesis screen in Ba/F3 CD74-ROS1 and EZR-ROS1 cells to identify resistance liabilities to entrectinib, lorlatinib, and repotrectinib. ROS1F2004C emerged as a recurrent entrectinib resistant mutation and ROS1G2032R was discovered in entrectinib and lorlatinib-resistant clones. Cell-based and modeling data show that entrectinib is a dual type I/II mode inhibitor, and thus liable to both types of resistant mutations. Comprehensive profiling of all clinically relevant kinase domain mutations showed that ROS1L2086F is broadly resistant to all type I inhibitors, but remains sensitive to type II inhibitors. ROS1F2004C/I/V are resistant to type I inhibitors, entrectinib and crizotinib, and type II inhibitor, cabozantinib, but retain sensitivity to the type I macrocyclic inhibitors. Development of new, more selective type II ROS1 inhibitor(s) or potentially cycling type I and type II inhibitors may be one way to expand durability of ROS1-targeted agents.
Regulated RNA turnover is vital for the control of gene expression in all cellular life. In Escherichia coli, this process is largely controlled by a stable degradosome complex containing RNase E and a variety of additional enzymes. In the Firmicutes phylum, species lack RNase E and often encode the paralogous enzymes RNase J1 and RNase J2. Unlike RNase J1, surprisingly little is known about the regulatory function and protein interactions of RNase J2, despite being a central pleiotropic regulator for the streptococci and other closely related organisms. Using crosslink coimmunoprecipitation in Streptococcus mutans, we have identified the major proteins found within RNase J2 protein complexes located in the cytoplasm and at the cell membrane. In both subcellular fractions, RNase J2 exhibited the most robust interactions with RNase J1, while additional transient and/or weaker “degradosome-like” interactions were also detected. In addition, RNase J2 exhibits multiple novel interactions that have not been previously reported for any RNase J proteins, some of which were highly biased for either the cytoplasmic or membrane fractions. We also determined that the RNase J2 C-terminal domain (CTD) encodes a structure that is likely conserved among RNase J enzymes and may have an analogous function to the C-terminal portion of RNase E. While we did observe a number of parallels between the RNase J2 interactome and the E. coli degradosome paradigm, our results suggest that S. mutans degradosomes are either unlikely to exist or are quite distinct from those of E. coli.
Oligonucleotide-based probes offer the highest spatial resolution, force sensitivity, and molecular specificity for cellular tension sensing and have been developed to measure a variety of molecular forces mediated by individual receptors in T cells, platelets, fibroblasts, B-cells, and immortalized cancer cell lines. These fluorophore−oligonucleotide conjugate probes are designed with a stem−loop structure that engages cell receptors and reversibly unfolds due to mechanical strain. With the growth of recent work bridging molecular mechanobiology and biomaterials, there is a need for a detailed spectroscopic analysis of DNA tension probes that are used for cellular imaging. In this manuscript, we conducted an analysis of 19 DNA hairpin-based tension probe variants using molecular dynamics simulations, absorption spectroscopy, and fluorescence imaging (epifluorescence and fluorescence lifetime imaging microscopy). We find that tension probes are highly sensitive to their molecular design, including donor and acceptor proximity and pairing, DNA stem−loop structure, and conjugation chemistry. We demonstrate the impact of these design features using a supported lipid bilayer model of podosome-like adhesions. Finally, we discuss the requirements for tension imaging in various biophysical contexts and offer a series of experimental recommendations, thus providing a guide for the design and application of DNA hairpin-based molecular tension probes.
Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.
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