Multisite EGFR phosphorylation is regulated by adaptor protein abundances and dimer lifetimes

Salazar-Cavazos, Emanuel; Nitta, Carolina Franco; Mitra, Eshan D.; Wilson, Bridget S.; Lidke, Keith A.; Hlavacek, William S.; Lidke, Diane S.

doi:10.1091/mbc.e19-09-0548

Cited by 26 publications

(22 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resulting rule-based networks are then used to interpret data by filter the arcs for the most consistent and, in the process, may reveal previously unappreciated pathways. For instance, a rule-based model was used to interpret single-molecule detection of multisite phosphorylation on intact EGFR to reveal a new role for the abundance of adapter proteins to redirect signaling 65 . Given the challenges with representing the various activation states of a 12-subunit Ca 2+ /calmodulin-dependent protein kinase II (CaMKII) holoenzyme that is essential for memory function, a rule-based model identified a molecular mechanism stabilizing protein activity that was obscured in prior reduced models 66 .…”

Section: Discussionmentioning

confidence: 99%

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

et al. 2022

View full text Add to dashboard Cite

Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results.

show abstract

Section: Discussionmentioning

confidence: 99%

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The 604 resulting rule-based networks are then used to interpret data 605 by filter the edges for the most consistent and, in the process, 606 may reveal previously unappreciated pathways. For instance, 607 a rule-based model was used to interpret single-molecule de-608 tection of multisite phosphorylation on intact EGFR to re-609 veal new a role for the abundance of adaptor proteins to redirect signaling (Salazar-Cavazos et al, 2020). Given the 611 challenges with representing the various activation states of 612 a 12-subunit Ca 2+ /calmodulin-dependent protein kinase II 613 (CaMKII) holoenzyme that is essential for memory function, 614 a rule-based model identified a molecular mechanism sta-615 bilizing protein activity that was obscured in prior reduced 616 models (Pharris et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Klinke

Fernández

Deng

et al. 2020

Preprint

View full text Add to dashboard Cite

1 ingly relies on mechanistic mathematical modeling and simu-2 lation. In immuno-oncology, models that capture causal rela-3 tions among genetic drivers of oncogenesis, functional plastic-4 ity, and host immunity provide an important complement to wet 5 experiments, given the cellular complexity and dynamics within 6 the tumor microenvironment. Unfortunately, formulating such 7 mechanistic cell-level models currently relies on hand curation 8 by experts, which can bias how data is interpreted or the pri-9 ority of drug targets. In modeling molecular-level networks, 10 rules and algorithms have been developed to limit a priori bi-11 ases in formulating mechanistic models. To realize an equivalent 12 approach for cell-level networks, we combined digital cytome-13 try with Bayesian network inference to generate causal models 14 that link an increase in gene expression associated with onco-15 genesis with alterations in stromal and immune cell subsets di-16 rectly from bulk transcriptomic datasets. To illustrate, we pre-17 dicted how an increase in expression of Cell Communication 18 Network factor 4 (CCN4/WISP1) altered the tumor microenvi-19 ronment using data from patients diagnosed with breast cancer 20 and melanoma. Network predictions were then tested using two 21 immunocompetent mouse models for melanoma. In contrast to 22 hand-curated approaches, we posit that combining digital cy-23 tometry with Bayesian network inference provides a less biased 24 approach for elaborating mechanistic cell-level models directly 25 from data. 26 Heterocellular networks | digital cytometry | deconvolution | anti-tumor 27 immunity | Bayesian network inference | functional plasticity Introduction 30 Tissues are dynamic structures where different cell types or-31 ganize to maintain function in a changing environment. For 32 instance, the mammary epithelium reorganizes during dis-33 tinct stages of the ovarian cycle in preparation for lactation 34 (Klinke, 2016). At the same time, immune cells clear dead 35 cells and defend against pathogens present in the tissue mi-36 croenvironment. Ultimately, the number and functional ori-37 entation of different cell types within a tissue interact to cre-38 ate a network, that is a heterocellular network. This hetero-39 cellular network is essential for creating and maintaining tis-40 sue homeostasis. While we know that tissue homeostasis is 41 disrupted during oncogenesis, our understanding of how ge-42 netic alterations quantitatively and dynamically influence the 43 heterocellular network within malignant tissues in humans 44 is not well developed despite large efforts, like The Cancer 45 Genome Atlas (TCGA), to characterize the genomic and tran-46 scriptomic landscape in human malignancy (Hoadley et al., 47 2018; Wells and Wiley, 2018). In parallel with these large 48 scale data gathering efforts, two informatic developments, 49 namely digital cytometry and Bayesian network inference, 50 may be helpful in interrogating these datasets. 51 87 Increases in size and information content of t...

show abstract

“…High- and low-affinity EGFR ligands differ even more profoundly since epigen, a low-affinity EGFR ligand, stabilizes an asymmetric ECD dimer different from the “canonical”, EGF- and TGFα-stabilized dimer. This feature is manifested in different dimer stability and longevity [ 61 ], which in turn results in distinct tyrosine phosphorylation patterns in the C-terminus [ 62 ]. These experiments also show that interpretation of dynamic properties of receptor interactions, which can only be studied in live cells, are indispensable for an accurate description of receptor activation, laying the foundation for the next section of the review.…”

Section: Dimerization-induced Activation Of Receptor Tyrosine Kinasesmentioning

confidence: 99%

It Takes More than Two to Tango: Complex, Hierarchal, and Membrane-Modulated Interactions in the Regulation of Receptor Tyrosine Kinases

Kovács

Zakany

Nagy

2022

Cancers

View full text Add to dashboard Cite

The search for an understanding of how cell fate and motility are regulated is not a purely scientific undertaking, but it can also lead to rationally designed therapies against cancer. The discovery of tyrosine kinases about half a century ago, the subsequent characterization of certain transmembrane receptors harboring tyrosine kinase activity, and their connection to the development of human cancer ushered in a new age with the hope of finding a treatment for malignant diseases in the foreseeable future. However, painstaking efforts were required to uncover the principles of how these receptors with intrinsic tyrosine kinase activity are regulated. Developments in molecular and structural biology and biophysical approaches paved the way towards better understanding of these pathways. Discoveries in the past twenty years first resulted in the formulation of textbook dogmas, such as dimerization-driven receptor association, which were followed by fine-tuning the model. In this review, the role of molecular interactions taking place during the activation of receptor tyrosine kinases, with special attention to the epidermal growth factor receptor family, will be discussed. The fact that these receptors are anchored in the membrane provides ample opportunities for modulatory lipid–protein interactions that will be considered in detail in the second part of the manuscript. Although qualitative and quantitative alterations in lipids in cancer are not sufficient in their own right to drive the malignant transformation, they both contribute to tumor formation and also provide ways to treat cancer. The review will be concluded with a summary of these medical aspects of lipid–protein interactions.

show abstract

Multisite EGFR phosphorylation is regulated by adaptor protein abundances and dimer lifetimes

Cited by 26 publications

References 69 publications

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

It Takes More than Two to Tango: Complex, Hierarchal, and Membrane-Modulated Interactions in the Regulation of Receptor Tyrosine Kinases

Contact Info

Product

Resources

About