skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Quantification of ligand and mutation-induced bias in EGFR phosphorylation in direct response to ligand binding
Signaling bias is the ability of a receptor to differentially activate downstream signaling pathways in response to different ligands. Bias investigations have been hindered by inconsistent results in different cellular contexts. Here we introduce a methodology to identify and quantify bias in signal transduction across the plasma membrane without contributions from feedback loops and system bias. We apply the methodology to quantify phosphorylation efficiencies and determine absolute bias coefficients. We show that the signaling of epidermal growth factor receptor (EGFR) to EGF and TGFα is biased towards Y1068 and against Y1173 phosphorylation, but has no bias for epiregulin. We further show that the L834R mutation found in non-small-cell lung cancer induces signaling bias as it switches the preferences to Y1173 phosphorylation. The knowledge gained here challenges the current understanding of EGFR signaling in health and disease and opens avenues for the exploration of biased inhibitors as anti-cancer therapies.  more » « less
Award ID(s):
2106031
PAR ID:
10519984
Author(s) / Creator(s):
; ;
Publisher / Repository:
Nature Communications
Date Published:
Journal Name:
Nature Communications
Volume:
14
Issue:
1
ISSN:
2041-1723
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The differential signaling of multiple FGF ligands through a single fibroblast growth factor (FGF) receptor (FGFR) plays an important role in embryonic development. Here, we use quantitative biophysical tools to uncover the mechanism behind differences in FGFR1c signaling in response to FGF4, FGF8, and FGF9, a process which is relevant for limb bud outgrowth. We find that FGF8 preferentially induces FRS2 phosphorylation and extracellular matrix loss, while FGF4 and FGF9 preferentially induce FGFR1c phosphorylation and cell growth arrest. Thus, we demonstrate that FGF8 is a biased FGFR1c ligand, as compared to FGF4 and FGF9. Förster resonance energy transfer experiments reveal a correlation between biased signaling and the conformation of the FGFR1c transmembrane domain dimer. Our findings expand the mechanistic understanding of FGF signaling during development and bring the poorly understood concept of receptor tyrosine kinase ligand bias into the spotlight. 
    more » « less
  2. Abstract Cell signaling by receptor protein tyrosine kinases (RTKs) is tightly controlled by the counterbalancing actions of receptor protein tyrosine phosphatases (RPTPs). Due to their role in attenuating the signal‐initiating potency of RTKs, RPTPs have long been viewed as therapeutic targets. However, the development of activators of RPTPs has remained limited. We previously reported that the homodimerization of a representative member of the RPTP family (protein tyrosine phosphatase receptor J or PTPRJ) is regulated by specific transmembrane (TM) residues. Disrupting this interaction by single point mutations promotes PTPRJ access to its RTK substrates (e.g., EGFR and FLT3), reduces RTK's phosphorylation and downstream signaling, and ultimately antagonizes RTK‐driven cell phenotypes. Here, we designed and tested a series of first‐in‐class pH‐responsive TM peptide agonists of PTPRJ that are soluble in aqueous solution but insert as a helical TM domain in lipid membranes when the pH is lowered to match that of the acidic microenvironment of tumors. The most promising peptide reduced EGFR's phosphorylation and inhibited cancer cell EGFR‐driven migration and proliferation, similar to the PTPRJ's TM point mutations. Developing tumor‐selective and TM‐targeting peptide binders of critical RPTPs could afford a potentially transformative approach to studying RPTP's selectivity mechanism without requiring less specific inhibitors and represent a novel class of therapeutics against RTK‐driven cancers. 
    more » « less
  3. Abstract Interactions between cells and their environment influence key physiologic processes such as their propensity to migrate. However, directed migration controlled by extrinsically applied electrical signals is poorly understood. Using a novel microfluidic platform, we found that metastatic breast cancer cells sense and respond to the net direction of weak (∼100 µV cm−1), asymmetric, non-contact induced Electric Fields (iEFs). iEFs inhibited EGFR (Epidermal Growth Factor Receptor) activation, prevented formation of actin-rich filopodia, and hindered the motility of EGF-treated breast cancer cells. The directional effects of iEFs were nullified by inhibition of Akt phosphorylation. Moreover, iEFs in combination with Akt inhibitor reduced EGF-promoted motility below the level of untreated controls. These results represent a step towards isolating the coupling mechanism between cell motility and iEFs, provide valuable insights into how iEFs target multiple diverging cancer cell signaling mechanisms, and demonstrate that electrical signals are a fundamental regulator of cancer cell migration. 
    more » « less
  4. 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 classifications 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. 
    more » « less
  5. 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 how 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. 
    more » « less