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1. Mechanisms for Regulating and Organizing Receptor Signaling by Endocytosis

   https://doi.org/10.1146/annurev-biochem-081820-092427  

   von Zastrow, Mark ; Sorkin, Alexander ( June 2021 , Annual Review of Biochemistry)

   Intricate relationships between endocytosis and cellular signaling, first recognized nearly 40 years ago through the study of tyrosine kinase growth factor receptors, are now known to exist for multiple receptor classes and to affect myriad physiological and developmental processes. This review summarizes our present understanding of how endocytosis orchestrates cellular signaling networks, with an emphasis on mechanistic underpinnings and focusing on two receptor classes—tyrosine kinase and G protein–coupled receptors—that have been investigated in particular detail. Together, these examples provide a useful survey of the current consensus, uncertainties, and controversies in this rapidly advancing area of cell biology.
2. Mapping Tyrosine Kinase Receptor Dimerization to Receptor Expression and Ligand Affinities

   https://doi.org/10.3390/pr7050288  

   Mamer, Spencer B. ; Palasz, Alexandra A. ; Imoukhuede, P. I. ( May 2019 , Processes)

   Tyrosine kinase receptor (RTK) ligation and dimerization is a key mechanism for translating external cell stimuli into internal signaling events. This process is critical to several key cell and physiological processes, such as in angiogenesis and embryogenesis, among others. While modulating RTK activation is a promising therapeutic target, RTK signaling axes have been shown to involve complicated interactions between ligands and receptors both within and across different protein families. In angiogenesis, for example, several signaling protein families, including vascular endothelial growth factors and platelet-derived growth factors, exhibit significant cross-family interactions that can influence pathway activation. Computational approaches can provide key insight to detangle these signaling pathways but have been limited by the sparse knowledge of these cross-family interactions. Here, we present a framework for studying known and potential non-canonical interactions. We constructed generalized models of RTK ligation and dimerization for systems of two, three and four receptor types and different degrees of cross-family ligation. Across each model, we developed parameter-space maps that fully determine relative pathway activation for any set of ligand-receptor binding constants, ligand concentrations and receptor concentrations. Therefore, our generalized models serve as a powerful reference tool for predicting not only known ligand: Receptor axes but also howmore »unknown interactions could alter signaling dimerization patterns. Accordingly, it will drive the exploration of cross-family interactions and help guide therapeutic developments across processes like cancer and cardiovascular diseases, which depend on RTK-mediated signaling.« less
3. Recombinant expression in E. coli of human FGFR2 with its transmembrane and extracellular domains

   https://doi.org/10.7717/peerj.3512  

   Bajinting, Adam ; Ng, Ho Leung ( January 2017 , PeerJ)

   Fibroblast growth factor receptors (FGFRs) are a family of receptor tyrosine kinases containing three domains: an extracellular receptor domain, a single transmembrane helix, and an intracellular tyrosine kinase domain. FGFRs are activated by fibroblast growth factors (FGFs) as part of complex signal transduction cascades regulating angiogenesis, skeletal formation, cell differentiation, proliferation, cell survival, and cancer. We have developed the first recombinant expression system in E. coli to produce a construct of human FGFR2 containing its transmembrane and extracellular receptor domains. We demonstrate that the expressed construct is functional in binding heparin and dimerizing. Size exclusion chromatography demonstrates that the purified FGFR2 does not form a complex with FGF1 or adopts an inactive dimer conformation. Progress towards the successful recombinant production of intact FGFRs will facilitate further biochemical experiments and structure determination that will provide insight into how extracellular FGF binding activates intracellular kinase activity.
4. Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics

   https://doi.org/10.1093/bioinformatics/btab581  

   Kim, Mirae ; Hong, Soonwoo ; Yankeelov, Thomas E ; Yeh, Hsin-Chih ; Liu, Yen-Liang ( August 2021 , Bioinformatics)

   Xu, Jinbo (Ed.)

   Abstract Motivation Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 (triple negative, TN), MDA-MB-231 (TN) and BT549 (TN). Results The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial–mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells, but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classificationsmore »are primarily based on the optical transmission images of cell morphology and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible. Availability and implementation A python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification. Supplementary information Supplementary data are available at Bioinformatics online.« less
5. Cyclin-dependent Kinase 1 and Aurora Kinase choreograph mitotic storage and redistribution of a growth factor receptor

   https://doi.org/10.1371/journal.pbio.3001029  

   Cota, Christina D. ; Dreier, Matthew S. ; Colgan, William ; Cha, Anna ; Sia, Twan ; Davidson, Brad ( January 2021 , PLOS Biology)

   Martin, Sophie G. (Ed.)

   Endosomal trafficking of receptors and associated proteins plays a critical role in signal processing. Until recently, it was thought that trafficking was shut down during cell division. Thus, remarkably, the regulation of trafficking during division remains poorly characterized. Here we delineate the role of mitotic kinases in receptor trafficking during asymmetric division. Targeted perturbations reveal that Cyclin-dependent Kinase 1 (CDK1) and Aurora Kinase promote storage of Fibroblast Growth Factor Receptors (FGFRs) by suppressing endosomal degradation and recycling pathways. As cells progress through metaphase, loss of CDK1 activity permits differential degradation and targeted recycling of stored receptors, leading to asymmetric induction. Mitotic receptor storage, as delineated in this study, may facilitate rapid reestablishment of signaling competence in nascent daughter cells. However, mutations that limit or enhance the release of stored signaling components could alter daughter cell fate or behavior thereby promoting oncogenesis.