1
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Trogdon M, Abbott K, Arang N, Lande K, Kaur N, Tong M, Bakhoum M, Gutkind JS, Stites EC. Systems modeling of oncogenic G-protein and GPCR signaling reveals unexpected differences in downstream pathway activation. NPJ Syst Biol Appl 2024; 10:75. [PMID: 39013872 PMCID: PMC11252164 DOI: 10.1038/s41540-024-00400-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 06/27/2024] [Indexed: 07/18/2024] Open
Abstract
Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas.
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Affiliation(s)
- Michael Trogdon
- Integrative Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, 92037, USA
- Pfizer, La Jolla, CA, 92037, USA
| | - Kodye Abbott
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Nadia Arang
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, 92093, USA
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Kathryn Lande
- Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, La Jolla, CA, 92037, USA
| | - Navneet Kaur
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Melinda Tong
- Integrative Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, 92037, USA
| | - Mathieu Bakhoum
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, 06520, USA
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, 06520, USA
| | - J Silvio Gutkind
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Edward C Stites
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, 06520, USA.
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, 06520, USA.
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2
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Gopich IV, Szabo A. Kinetics of diffusion-influenced multisite phosphorylation with enzyme reactivation. Biopolymers 2024; 115:e23533. [PMID: 36987692 PMCID: PMC10539481 DOI: 10.1002/bip.23533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/02/2023] [Accepted: 03/06/2023] [Indexed: 03/30/2023]
Abstract
The simplest way to account for the influence of diffusion on the kinetics of multisite phosphorylation is to modify the rate constants in the conventional rate equations of chemical kinetics. We have previously shown that this is not enough and new transitions between the reactants must also be introduced. Here we extend our results by considering enzymes that are inactive after modifying the substrate and need time to become active again. This generalization leads to a surprising result. The introduction of enzyme reactivation results in a diffusion-modified kinetic scheme with a new transition that has a negative rate constant. The reason for this is that mapping non-Markovian rate equations onto Markovian ones with time-independent rate constants is not a good approximation at short times. We then developed a non-Markovian theory that involves memory kernels instead of rate constants. This theory is now valid at short times, but is more challenging to use. As an example, the diffusion-modified kinetic scheme with new connections was used to calculate kinetics of double phosphorylation and steady-state response in a phosphorylation-dephosphorylation cycle. We have reproduced the loss of bistability in the phosphorylation-dephosphorylation cycle when the enzyme reactivation time decreases, which was obtained by particle-based computer simulations.
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Affiliation(s)
- Irina V Gopich
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Attila Szabo
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, 20892, USA
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3
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Shvartsman SY, McFann S, Wühr M, Rubinstein BY. Phase plane dynamics of ERK phosphorylation. J Biol Chem 2023; 299:105234. [PMID: 37690685 PMCID: PMC10616409 DOI: 10.1016/j.jbc.2023.105234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/12/2023] Open
Abstract
The extracellular signal-regulated kinase (ERK) controls multiple critical processes in the cell and is deregulated in human cancers, congenital abnormalities, immune diseases, and neurodevelopmental syndromes. Catalytic activity of ERK requires dual phosphorylation by an upstream kinase, in a mechanism that can be described by two sequential Michaelis-Menten steps. The estimation of individual reaction rate constants from kinetic data in the full mechanism has proved challenging. Here, we present an analytically tractable approach to parameter estimation that is based on the phase plane representation of ERK activation and yields two combinations of six reaction rate constants in the detailed mechanism. These combinations correspond to the ratio of the specificities of two consecutive phosphorylations and the probability that monophosphorylated substrate does not dissociate from the enzyme before the second phosphorylation. The presented approach offers a language for comparing the effects of mutations that disrupt ERK activation and function in vivo. As an illustration, we use phase plane representation to analyze dual phosphorylation under heterozygous conditions, when two enzyme variants compete for the same substrate.
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Affiliation(s)
- Stanislav Y Shvartsman
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; Center for Computational Biology, Flatiron Institute, New York, New York, USA.
| | - Sarah McFann
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, USA
| | - Martin Wühr
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
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4
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Andrianova EP, Marmion RA, Shvartsman SY, Zhulin IB. Evolutionary history of MEK1 illuminates the nature of deleterious mutations. Proc Natl Acad Sci U S A 2023; 120:e2304184120. [PMID: 37579140 PMCID: PMC10450672 DOI: 10.1073/pnas.2304184120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/24/2023] [Indexed: 08/16/2023] Open
Abstract
Mutations in signal transduction pathways lead to various diseases including cancers. MEK1 kinase, encoded by the human MAP2K1 gene, is one of the central components of the MAPK pathway and more than a hundred somatic mutations in the MAP2K1 gene were identified in various tumors. Germline mutations deregulating MEK1 also lead to congenital abnormalities, such as the cardiofaciocutaneous syndrome and arteriovenous malformation. Evaluating variants associated with a disease is a challenge, and computational genomic approaches aid in this process. Establishing evolutionary history of a gene improves computational prediction of disease-causing mutations; however, the evolutionary history of MEK1 is not well understood. Here, by revealing a precise evolutionary history of MEK1, we construct a well-defined dataset of MEK1 metazoan orthologs, which provides sufficient depth to distinguish between conserved and variable amino acid positions. We matched known and predicted disease-causing and benign mutations to evolutionary changes observed in corresponding amino acid positions and found that all known and many suspected disease-causing mutations are evolutionarily intolerable. We selected several variants that cannot be unambiguously assessed by automated prediction tools but that are confidently identified as "damaging" by our approach, for experimental validation in Drosophila. In all cases, evolutionary intolerant variants caused increased mortality and severe defects in fruit fly embryos confirming their damaging nature. We anticipate that our analysis will serve as a blueprint to help evaluate known and novel missense variants in MEK1 and that our approach will contribute to improving automated tools for disease-associated variant interpretation.
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Affiliation(s)
- Ekaterina P. Andrianova
- Department of Microbiology, The Ohio State University, Columbus, OH43210
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH43210
| | - Robert A. Marmion
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
| | - Stanislav Y. Shvartsman
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
- Department of Molecular Biology, Princeton University, Princeton, NJ08544
- Flatiron Institute, Simons Foundation, New York, NY10010
| | - Igor B. Zhulin
- Department of Microbiology, The Ohio State University, Columbus, OH43210
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH43210
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5
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Marmion RA, Simpkins AG, Barrett LA, Denberg DW, Zusman S, Schottenfeld-Roames J, Schüpbach T, Shvartsman SY. Stochastic phenotypes in RAS-dependent developmental diseases. Curr Biol 2023; 33:807-816.e4. [PMID: 36706752 PMCID: PMC10026697 DOI: 10.1016/j.cub.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 12/02/2022] [Accepted: 01/05/2023] [Indexed: 01/27/2023]
Abstract
Germline mutations upregulating RAS signaling are associated with multiple developmental disorders. A hallmark of these conditions is that the same mutation may present vastly different phenotypes in different individuals, even in monozygotic twins. Here, we demonstrate how the origins of such largely unexplained phenotypic variations may be dissected using highly controlled studies in Drosophila that have been gene edited to carry activating variants of MEK, a core enzyme in the RAS pathway. This allowed us to measure the small but consistent increase in signaling output of such alleles in vivo. The fraction of mutation carriers reaching adulthood was strongly reduced, but most surviving animals had normal RAS-dependent structures. We rationalize these results using a stochastic signaling model and support it by quantifying cell fate specification errors in bilaterally symmetric larval trachea, a RAS-dependent structure that allows us to isolate the effects of mutations from potential contributions of genetic modifiers and environmental differences. We propose that the small increase in signaling output shifts the distribution of phenotypes into a regime, where stochastic variation causes defects in some individuals, but not in others. Our findings shed light on phenotypic heterogeneity of developmental disorders caused by deregulated RAS signaling and offer a framework for investigating causal effects of other pathogenic alleles and mild mutations in general.
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Affiliation(s)
- Robert A Marmion
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
| | - Alison G Simpkins
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
| | - Lena A Barrett
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
| | - David W Denberg
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
| | - Susan Zusman
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
| | | | - Trudi Schüpbach
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
| | - Stanislav Y Shvartsman
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA; Flatiron Institute, Simons Foundation, New York, NY 10010, USA.
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6
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Marsh L, Dufresne E, Byrne HM, Harrington HA. Algebra, Geometry and Topology of ERK Kinetics. Bull Math Biol 2022; 84:137. [PMID: 36273372 PMCID: PMC9588486 DOI: 10.1007/s11538-022-01088-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 09/16/2022] [Indexed: 12/01/2022]
Abstract
The MEK/ERK signalling pathway is involved in cell division, cell specialisation, survival and cell death (Shaul and Seger in Biochim Biophys Acta (BBA)-Mol Cell Res 1773(8):1213–1226, 2007). Here we study a polynomial dynamical system describing the dynamics of MEK/ERK proposed by Yeung et al. (Curr Biol 2019, 10.1016/j.cub.2019.12.052) with their experimental setup, data and known biological information. The experimental dataset is a time-course of ERK measurements in different phosphorylation states following activation of either wild-type MEK or MEK mutations associated with cancer or developmental defects. We demonstrate how methods from computational algebraic geometry, differential algebra, Bayesian statistics and computational algebraic topology can inform the model reduction, identification and parameter inference of MEK variants, respectively. Throughout, we show how this algebraic viewpoint offers a rigorous and systematic analysis of such models.
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Affiliation(s)
- Lewis Marsh
- Mathematical Institute, University of Oxford, Oxford, UK.
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK.
| | | | - Helen M Byrne
- Mathematical Institute, University of Oxford, Oxford, UK
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK
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7
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Evangelou N, Wichrowski NJ, Kevrekidis GA, Dietrich F, Kooshkbaghi M, McFann S, Kevrekidis IG. On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability. PNAS NEXUS 2022; 1:pgac154. [PMID: 36714862 PMCID: PMC9802152 DOI: 10.1093/pnasnexus/pgac154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 08/11/2022] [Indexed: 02/01/2023]
Abstract
We present a data-driven approach to characterizing nonidentifiability of a model's parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established analytically.
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Affiliation(s)
| | | | - George A Kevrekidis
- Department of Mathematics and Statistics, University of Massachusetts, 710 N Pleasant St, Amherst, MA 01003, USA
| | - Felix Dietrich
- Department of Informatics, Technical University of Munich, Boltzmannstr. 3, Garching 85748, Germany
| | - Mahdi Kooshkbaghi
- The Program in Applied and Computational Mathematic, Princeton University, Washington Road, Princeton, NJ 08544, USA
| | - Sarah McFann
- Department of Chemical and Biological Engineering, Princeton University, 50–70 Olden St, Princeton, NJ 08544, USA,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
| | - Ioannis G Kevrekidis
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA,Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
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8
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Sharp JA, Browning AP, Burrage K, Simpson MJ. Parameter estimation and uncertainty quantification using information geometry. J R Soc Interface 2022; 19:20210940. [PMID: 35472269 PMCID: PMC9042578 DOI: 10.1098/rsif.2021.0940] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
In this work, we: (i) review likelihood-based inference for parameter estimation and the construction of confidence regions; and (ii) explore the use of techniques from information geometry, including geodesic curves and Riemann scalar curvature, to supplement typical techniques for uncertainty quantification, such as Bayesian methods, profile likelihood, asymptotic analysis and bootstrapping. These techniques from information geometry provide data-independent insights into uncertainty and identifiability, and can be used to inform data collection decisions. All code used in this work to implement the inference and information geometry techniques is available on GitHub.
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Affiliation(s)
- Jesse A Sharp
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Alexander P Browning
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kevin Burrage
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland, Australia.,Department of Computer Science, University of Oxford, Oxford, UK
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.,Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
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9
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Myers PJ, Lee SH, Lazzara MJ. MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 28:100349. [PMID: 35935921 PMCID: PMC9348571 DOI: 10.1016/j.coisb.2021.05.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.
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Affiliation(s)
- Paul J. Myers
- Department of Chemical Engineering, Charlottesville, VA 22904
| | - Sung Hyun Lee
- Department of Chemical Engineering, Charlottesville, VA 22904
| | - Matthew J. Lazzara
- Department of Chemical Engineering, Charlottesville, VA 22904
- Department of Biomedical Engineering University of Virginia, Charlottesville, VA 22904
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10
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Marmion RA, Yang L, Goyal Y, Jindal GA, Wetzel JL, Singh M, Schüpbach T, Shvartsman SY. Molecular mechanisms underlying cellular effects of human MEK1 mutations. Mol Biol Cell 2021; 32:974-983. [PMID: 33476180 PMCID: PMC8108529 DOI: 10.1091/mbc.e20-10-0625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Terminal regions of Drosophila embryos are patterned by signaling through ERK, which is genetically deregulated in multiple human diseases. Quantitative studies of terminal patterning have been recently used to investigate gain-of-function variants of human MEK1, encoding the MEK kinase that directly activates ERK by dual phosphorylation. Unexpectedly, several mutations reduced ERK activation by extracellular signals, possibly through a negative feedback triggered by signal-independent activity of the mutant variants. Here we present experimental evidence supporting this model. Using a MEK variant that combines a mutation within the negative regulatory region with alanine substitutions in the activation loop, we prove that pathogenic variants indeed acquire signal-independent kinase activity. We also demonstrate that signal-dependent activation of these variants is independent of kinase suppressor of Ras, a conserved adaptor that is indispensable for activation of normal MEK. Finally, we show that attenuation of ERK activation by extracellular signals stems from transcriptional induction of Mkp3, a dual specificity phosphatase that deactivates ERK by dephosphorylation. These findings in the Drosophila embryo highlight its power for investigating diverse effects of human disease mutations.
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Affiliation(s)
- Robert A Marmion
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Liu Yang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Yogesh Goyal
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544.,Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Granton A Jindal
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544.,Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544.,Department of Medicine, University of California San Diego, La Jolla, CA 92093
| | - Joshua L Wetzel
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544.,Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544.,Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Trudi Schüpbach
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544
| | - Stanislav Y Shvartsman
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544.,Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544.,Department of Molecular Biology, Princeton University, Princeton, NJ 08544.,Flatiron Institute, Simons Foundation, New York, NY 10010
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11
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Gopich IV. Multisite reversible association in membranes and solutions: From non-Markovian to Markovian kinetics. J Chem Phys 2020; 152:104101. [PMID: 32171220 DOI: 10.1063/1.5144282] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The role of diffusion on the kinetics of reversible association to a macromolecule with two inequivalent sites is studied. Previously, we found that, in the simplest possible description, it is not sufficient to just renormalize the rate constants of chemical kinetics, but one must introduce direct transitions between the bound states in the kinetic scheme. The physical reason for this is that a molecule that just dissociated from one site can directly rebind to the other rather than diffuse away into the bulk. Such a simple description is not valid in two dimensions because reactants can never diffuse away into the bulk. In this work, we consider a variety of more sophisticated implementations of our recent general theory that are valid in both two and three dimensions. We compare the predicted time dependence of the concentrations for a wide range of parameters and establish the range of validity of various levels of the general theory.
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Affiliation(s)
- Irina V Gopich
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
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12
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Abstract
MEK, a central component of the Ras/MAPK cascade, is mutated in human tumors and developmental disorders. Recent studies are beginning to dissect the mechanisms that make these MEK mutants hyperactive.
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Affiliation(s)
- Lee Bardwell
- Department of Developmental and Cell Biology, 2208 Natural Sciences I, University of California, Irvine, CA 92697-2300, USA.
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