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Burbano de Lara S, Kemmer S, Biermayer I, Feiler S, Vlasov A, D'Alessandro LA, Helm B, Mölders C, Dieter Y, Ghallab A, Hengstler JG, Körner C, Matz-Soja M, Götz C, Damm G, Hoffmann K, Seehofer D, Berg T, Schilling M, Timmer J, Klingmüller U. Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease. Mol Syst Biol 2024; 20:187-216. [PMID: 38216754 PMCID: PMC10912216 DOI: 10.1038/s44320-023-00007-4] [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: 07/04/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 01/14/2024] Open
Abstract
Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.
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Affiliation(s)
- Sebastian Burbano de Lara
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
| | - Svenja Kemmer
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
- FDM - Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Ina Biermayer
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
| | - Svenja Feiler
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of General, Visceral and Transplant Surgery, Heidelberg University, Heidelberg, Germany
| | - Artyom Vlasov
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lorenza A D'Alessandro
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Barbara Helm
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christina Mölders
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
| | - Yannik Dieter
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ahmed Ghallab
- Systems Toxicology, Leibniz Research Center for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany
- Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena, 83523, Egypt
| | - Jan G Hengstler
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Systems Toxicology, Leibniz Research Center for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany
| | - Christiane Körner
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Division of Hepatology, Clinic of Oncology, Gastroenterology, Hepatology, and Pneumology, University Hospital Leipzig, 04103, Leipzig, Germany
| | - Madlen Matz-Soja
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Division of Hepatology, Clinic of Oncology, Gastroenterology, Hepatology, and Pneumology, University Hospital Leipzig, 04103, Leipzig, Germany
| | - Christina Götz
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Department of Hepatobiliary Surgery and Visceral Transplantation, University Hospital Leipzig, Leipzig University, 04103, Leipzig, Germany
| | - Georg Damm
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Department of Hepatobiliary Surgery and Visceral Transplantation, University Hospital Leipzig, Leipzig University, 04103, Leipzig, Germany
| | - Katrin Hoffmann
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Department of General, Visceral and Transplant Surgery, Heidelberg University, Heidelberg, Germany
| | - Daniel Seehofer
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Department of Hepatobiliary Surgery and Visceral Transplantation, University Hospital Leipzig, Leipzig University, 04103, Leipzig, Germany
| | - Thomas Berg
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany
- Division of Hepatology, Clinic of Oncology, Gastroenterology, Hepatology, and Pneumology, University Hospital Leipzig, 04103, Leipzig, Germany
| | - Marcel Schilling
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Jens Timmer
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany.
- Institute of Physics, University of Freiburg, Freiburg, Germany.
- FDM - Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany.
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany.
| | - Ursula Klingmüller
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Liver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany.
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2
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Riesle AJ, Gao M, Rosenblatt M, Hermes J, Hass H, Gebhard A, Veil M, Grüning B, Timmer J, Onichtchouk D. Activator-blocker model of transcriptional regulation by pioneer-like factors. Nat Commun 2023; 14:5677. [PMID: 37709752 PMCID: PMC10502082 DOI: 10.1038/s41467-023-41507-z] [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: 02/10/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023] Open
Abstract
Zygotic genome activation (ZGA) in the development of flies, fish, frogs and mammals depends on pioneer-like transcription factors (TFs). Those TFs create open chromatin regions, promote histone acetylation on enhancers, and activate transcription. Here, we use the panel of single, double and triple mutants for zebrafish genome activators Pou5f3, Sox19b and Nanog, multi-omics and mathematical modeling to investigate the combinatorial mechanisms of genome activation. We show that Pou5f3 and Nanog act differently on synergistic and antagonistic enhancer types. Pou5f3 and Nanog both bind as pioneer-like TFs on synergistic enhancers, promote histone acetylation and activate transcription. Antagonistic enhancers are activated by binding of one of these factors. The other TF binds as non-pioneer-like TF, competes with the activator and blocks all its effects, partially or completely. This activator-blocker mechanism mutually restricts widespread transcriptional activation by Pou5f3 and Nanog and prevents premature expression of late developmental regulators in the early embryo.
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Affiliation(s)
- Aileen Julia Riesle
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Epigenetics and Neurobiology Unit, European Molecular Biology Laboratory, EMBL Rome, Adriano Buzzati-Traverso Campus, Via Ramarini 32, 00015, Monterotondo, RM, Italy
| | - Meijiang Gao
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Signalling Research centers BIOSS and CIBSS, 79104, Freiburg, Germany
| | - Marcus Rosenblatt
- Institute of Physics, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Freiburg Center for Data Analysis and Modelling (FDM), 79104, Freiburg, Germany
| | - Jacques Hermes
- Institute of Physics, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Freiburg Center for Data Analysis and Modelling (FDM), 79104, Freiburg, Germany
| | - Helge Hass
- Institute of Physics, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Freiburg Center for Data Analysis and Modelling (FDM), 79104, Freiburg, Germany
| | - Anna Gebhard
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
| | - Marina Veil
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
| | - Björn Grüning
- Department of Computer Science, University of Freiburg, 79110, Freiburg, Germany
- Center for Biological Systems Analysis (ZBSA), University of Freiburg, 79104, Freiburg, Germany
| | - Jens Timmer
- Signalling Research centers BIOSS and CIBSS, 79104, Freiburg, Germany.
- Institute of Physics, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany.
- Freiburg Center for Data Analysis and Modelling (FDM), 79104, Freiburg, Germany.
| | - Daria Onichtchouk
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany.
- Signalling Research centers BIOSS and CIBSS, 79104, Freiburg, Germany.
- Institute of Developmental Biology RAS, 119991, Moscow, Russia.
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3
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Lefaudeux D, Sen S, Jiang K, Hoffmann A. Kinetics of mRNA nuclear export regulate innate immune response gene expression. Nat Commun 2022; 13:7197. [PMID: 36424375 PMCID: PMC9691726 DOI: 10.1038/s41467-022-34635-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 11/01/2022] [Indexed: 11/25/2022] Open
Abstract
The abundance and stimulus-responsiveness of mature mRNA is thought to be determined by nuclear synthesis, processing, and cytoplasmic decay. However, the rate and efficiency of moving mRNA to the cytoplasm almost certainly contributes, but has rarely been measured. Here, we investigated mRNA export rates for innate immune genes. We generated high spatio-temporal resolution RNA-seq data from endotoxin-stimulated macrophages and parameterized a mathematical model to infer kinetic parameters with confidence intervals. We find that the effective chromatin-to-cytoplasm export rate is gene-specific, varying 100-fold: for some genes, less than 5% of synthesized transcripts arrive in the cytoplasm as mature mRNAs, while others show high export efficiency. Interestingly, effective export rates do not determine temporal gene responsiveness, but complement the wide range of mRNA decay rates; this ensures similar abundances of short- and long-lived mRNAs, which form successive innate immune response expression waves.
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Affiliation(s)
- Diane Lefaudeux
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, 90095, USA
| | - Supriya Sen
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, CA, 90095, USA
| | - Kevin Jiang
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, 90095, USA
| | - Alexander Hoffmann
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA.
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, 90095, USA.
- Molecular Biology Institute, University of California, Los Angeles, CA, 90095, USA.
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4
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Kemmer S, Berdiel-Acer M, Reinz E, Sonntag J, Tarade N, Bernhardt S, Fehling-Kaschek M, Hasmann M, Korf U, Wiemann S, Timmer J. Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis. Cancers (Basel) 2022; 14:cancers14102379. [PMID: 35625984 PMCID: PMC9139462 DOI: 10.3390/cancers14102379] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/06/2022] [Accepted: 05/10/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Breast cancer subtypes are characterized by the expression and activity of estrogen-, progesterone- and HER2-receptors and differ by the treatment as well as patient prognosis. Tumors of the HER2-subtype overexpress this receptor and are successfully targeted with anti-HER2 therapies. We wanted to know if the HER2-receptor and the downstream signaling network act similarly also in the other subtypes and if this network could potentially be a therapeutic target beyond the HER2-positive subtype. To this end, we quantitatively assessed the wiring of signaling events in the individual subtypes to unravel the characteristics of HER-signaling. Our data along with a model-based analysis suggest that major parts of the intracellular signal transduction network are unchanged between the different breast cancer subtypes and that the clinical differences mostly come from the different levels at which these receptors are present in tumor cells as well as from the particular mutations that are present in individual tumors. Abstract Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand–drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L1 regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.
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Affiliation(s)
- Svenja Kemmer
- Institute of Physics, University of Freiburg, 79104 Freiburg, Germany; (S.K.); (M.F.-K.)
- FDM—Freiburg Center for Data Analysis and Modeling, University of Freiburg, 79104 Freiburg, Germany
| | - Mireia Berdiel-Acer
- Division of Molecular Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany; (M.B.-A.); (E.R.); (J.S.); (N.T.); (S.B.); (U.K.)
| | - Eileen Reinz
- Division of Molecular Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany; (M.B.-A.); (E.R.); (J.S.); (N.T.); (S.B.); (U.K.)
| | - Johanna Sonntag
- Division of Molecular Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany; (M.B.-A.); (E.R.); (J.S.); (N.T.); (S.B.); (U.K.)
| | - Nooraldeen Tarade
- Division of Molecular Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany; (M.B.-A.); (E.R.); (J.S.); (N.T.); (S.B.); (U.K.)
- Faculty of Biosciences, University of Heidelberg, 69117 Heidelberg, Germany
| | - Stephan Bernhardt
- Division of Molecular Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany; (M.B.-A.); (E.R.); (J.S.); (N.T.); (S.B.); (U.K.)
| | - Mirjam Fehling-Kaschek
- Institute of Physics, University of Freiburg, 79104 Freiburg, Germany; (S.K.); (M.F.-K.)
- FDM—Freiburg Center for Data Analysis and Modeling, University of Freiburg, 79104 Freiburg, Germany
| | | | - Ulrike Korf
- Division of Molecular Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany; (M.B.-A.); (E.R.); (J.S.); (N.T.); (S.B.); (U.K.)
| | - Stefan Wiemann
- Division of Molecular Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany; (M.B.-A.); (E.R.); (J.S.); (N.T.); (S.B.); (U.K.)
- Correspondence: (S.W.); (J.T.)
| | - Jens Timmer
- Institute of Physics, University of Freiburg, 79104 Freiburg, Germany; (S.K.); (M.F.-K.)
- FDM—Freiburg Center for Data Analysis and Modeling, University of Freiburg, 79104 Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, 79104 Freiburg, Germany
- Correspondence: (S.W.); (J.T.)
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5
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Dynamic modeling of Nrf2 pathway activation in liver cells after toxicant exposure. Sci Rep 2022; 12:7336. [PMID: 35513409 PMCID: PMC9072554 DOI: 10.1038/s41598-022-10857-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 04/13/2022] [Indexed: 12/15/2022] Open
Abstract
Cells are exposed to oxidative stress and reactive metabolites every day. The Nrf2 signaling pathway responds to oxidative stress by upregulation of antioxidants like glutathione (GSH) to compensate the stress insult and re-establish homeostasis. Although mechanisms describing the interaction between the key pathway constituents Nrf2, Keap1 and p62 are widely reviewed and discussed in literature, quantitative dynamic models bringing together these mechanisms with time-resolved data are limited. Here, we present an ordinary differential equation (ODE) based dynamic model to describe the dynamic response of Nrf2, Keap1, Srxn1 and GSH to oxidative stress caused by the soft-electrophile diethyl maleate (DEM). The time-resolved data obtained by single-cell confocal microscopy of green fluorescent protein (GFP) reporters and qPCR of the Nrf2 pathway components complemented with siRNA knock down experiments, is accurately described by the calibrated mathematical model. We show that the quantitative model can describe the activation of the Nrf2 pathway by compounds with a different mechanism of activation, including drugs which are known for their ability to cause drug induced liver-injury (DILI) i.e., diclofenac (DCF) and omeprazole (OMZ). Finally, we show that our model can reveal differences in the processes leading to altered activation dynamics amongst DILI inducing drugs.
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6
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Ghosh D, Mandal C. Clustering Based Parameter Estimation of Thyroid Hormone Pathway. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:343-354. [PMID: 32750849 DOI: 10.1109/tcbb.2020.2995589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
An ordinary differential equation (ODE) model of the working of the thyroid system for euthyroidism has been presented. As clinical data for thyroid hormones is relatively scarce, such modelling offers potential benefits over wet lab procedures. Genetic algorithms developed for determining of parameters of the ODE system using the available data have been presented and evaluated. This approach enables subject specific parameter estimation towards characterisation of individual thyroid operation. Initially, a simple steady state model was used. Later a cosinor model for the circadian variation of thyroid hormones was used to obtain more reliable results, as indicated through sensitivity analysis in conjunction with other statistical methods. Our parameter determination method has been tested on groups of patients with similar observed values of thyroid stimulating hormone (TSH), free T 3 and free T 4 (identified through clustering) to determine their parameter values jointly. This approach appears to produce parameter sets with lower variation than parameters determined independently, thus leading to better parameter determination.
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7
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Braakman S, Pathmanathan P, Moore H. Evaluation framework for systems models. CPT Pharmacometrics Syst Pharmacol 2021; 11:264-289. [PMID: 34921743 PMCID: PMC8923730 DOI: 10.1002/psp4.12755] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 12/16/2022] Open
Abstract
As decisions in drug development increasingly rely on predictions from mechanistic systems models, assessing the predictive capability of such models is becoming more important. Several frameworks for the development of quantitative systems pharmacology (QSP) models have been proposed. In this paper, we add to this body of work with a framework that focuses on the appropriate use of qualitative and quantitative model evaluation methods. We provide details and references for those wishing to apply these methods, which include sensitivity and identifiability analyses, as well as concepts such as validation and uncertainty quantification. Many of these methods have been used successfully in other fields, but are not as common in QSP modeling. We illustrate how to apply these methods to evaluate QSP models, and propose methods to use in two case studies. We also share examples of misleading results when inappropriate analyses are used.
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Affiliation(s)
- Sietse Braakman
- Application Engineering, MathWorks Inc, Natick, Massachusetts, USA
| | - Pras Pathmanathan
- Office of Science and Engineering Laboratories (OSEL), Center for Devices and Radiological Health (CDRH), US Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Helen Moore
- Laboratory for Systems Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Florida, Gainesville, Florida, USA
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8
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Akimov V, Fehling-Kaschek M, Barrio-Hernandez I, Puglia M, Bunkenborg J, Nielsen MM, Timmer J, Dengjel J, Blagoev B. Magnitude of Ubiquitination Determines the Fate of Epidermal Growth Factor Receptor Upon Ligand Stimulation. J Mol Biol 2021; 433:167240. [PMID: 34508725 DOI: 10.1016/j.jmb.2021.167240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/17/2021] [Accepted: 09/01/2021] [Indexed: 12/23/2022]
Abstract
Receptor tyrosine kinases (RTK) bind growth factors and are critical for cell proliferation and differentiation. Their dysregulation leads to a loss of growth control, often resulting in cancer. Epidermal growth factor receptor (EGFR) is the prototypic RTK and can bind several ligands exhibiting distinct mitogenic potentials. Whereas the phosphorylation on individual EGFR sites and their roles for downstream signaling have been extensively studied, less is known about ligand-specific ubiquitination events on EGFR, which are crucial for signal attenuation and termination. We used a proteomics-based workflow for absolute quantitation combined with mathematical modeling to unveil potentially decisive ubiquitination events on EGFR from the first 30 seconds to 15 minutes of stimulation. Four ligands were used for stimulation: epidermal growth factor (EGF), heparin-binding-EGF like growth factor, transforming growth factor-α and epiregulin. Whereas only little differences in the order of individual ubiquitination sites were observed, the overall amount of modified receptor differed depending on the used ligand, indicating that absolute magnitude of EGFR ubiquitination, and not distinctly regulated ubiquitination sites, is a major determinant for signal attenuation and the subsequent cellular outcomes.
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Affiliation(s)
- Vyacheslav Akimov
- Center for Experimental BioInformatics, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Mirjam Fehling-Kaschek
- Institut of Physics, University of Freiburg, Hermann-Herder-Str. 3, 79104 Freiburg, Germany
| | - Inigo Barrio-Hernandez
- Center for Experimental BioInformatics, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Michele Puglia
- Center for Experimental BioInformatics, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Jakob Bunkenborg
- Center for Experimental BioInformatics, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Mogens M Nielsen
- Center for Experimental BioInformatics, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Jens Timmer
- Institut of Physics, University of Freiburg, Hermann-Herder-Str. 3, 79104 Freiburg, Germany; Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schänzlestr. 18, 79104 Freiburg, Germany
| | - Jörn Dengjel
- Department of Biology, University of Fribourg, Chemin du Musée 10, 1700 Fribourg, Switzerland.
| | - Blagoy Blagoev
- Center for Experimental BioInformatics, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.
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9
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Schmiester L, Schälte Y, Bergmann FT, Camba T, Dudkin E, Egert J, Fröhlich F, Fuhrmann L, Hauber AL, Kemmer S, Lakrisenko P, Loos C, Merkt S, Müller W, Pathirana D, Raimúndez E, Refisch L, Rosenblatt M, Stapor PL, Städter P, Wang D, Wieland FG, Banga JR, Timmer J, Villaverde AF, Sahle S, Kreutz C, Hasenauer J, Weindl D. PEtab-Interoperable specification of parameter estimation problems in systems biology. PLoS Comput Biol 2021; 17:e1008646. [PMID: 33497393 PMCID: PMC7864467 DOI: 10.1371/journal.pcbi.1008646] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 02/05/2021] [Accepted: 12/18/2020] [Indexed: 01/24/2023] Open
Abstract
Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been-so far-no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.
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Affiliation(s)
- Leonard Schmiester
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | | | - Tacio Camba
- Department of Applied Mathematics II, University of Vigo, Vigo, Galicia, Spain
- BioProcess Engineering Group, IIM-CSIC, Vigo, Galicia, Spain
| | - Erika Dudkin
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Janine Egert
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
| | - Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lara Fuhrmann
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Adrian L. Hauber
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Svenja Kemmer
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Polina Lakrisenko
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Carolin Loos
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Simon Merkt
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Elba Raimúndez
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Lukas Refisch
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
| | - Marcus Rosenblatt
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Paul L. Stapor
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Philipp Städter
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Dantong Wang
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Franz-Georg Wieland
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Julio R. Banga
- BioProcess Engineering Group, IIM-CSIC, Vigo, Galicia, Spain
| | - Jens Timmer
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | | | - Sven Sahle
- BioQUANT/COS, Heidelberg University, Heidelberg, Germany
| | - Clemens Kreutz
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
- * E-mail:
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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Borisov I, Metelkin E. Confidence intervals by constrained optimization-An algorithm and software package for practical identifiability analysis in systems biology. PLoS Comput Biol 2020; 16:e1008495. [PMID: 33347435 PMCID: PMC7785248 DOI: 10.1371/journal.pcbi.1008495] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 01/05/2021] [Accepted: 11/06/2020] [Indexed: 11/30/2022] Open
Abstract
Practical identifiability of Systems Biology models has received a lot of attention in recent scientific research. It addresses the crucial question for models’ predictability: how accurately can the models’ parameters be recovered from available experimental data. The methods based on profile likelihood are among the most reliable methods of practical identification. However, these methods are often computationally demanding or lead to inaccurate estimations of parameters’ confidence intervals. Development of methods, which can accurately produce parameters’ confidence intervals in reasonable computational time, is of utmost importance for Systems Biology and QSP modeling. We propose an algorithm Confidence Intervals by Constraint Optimization (CICO) based on profile likelihood, designed to speed-up confidence intervals estimation and reduce computational cost. The numerical implementation of the algorithm includes settings to control the accuracy of confidence intervals estimates. The algorithm was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model, discussed in the current article. The CICO algorithm is implemented in a software package freely available in Julia (https://github.com/insysbio/LikelihoodProfiler.jl) and Python (https://github.com/insysbio/LikelihoodProfiler.py). Differential equations-based models are widely used in Systems Biology and Quantitative Systems Pharmacology and play a significant role in the discovery of new disease-directed drugs. Complexity of models is a trade off from their employment to crucial fields of biology and medicine. These areas of application require large non-linear models with many unknown parameters. How accurately can the parameters of a model be recovered from experimental data? What is the identifiable subset of parameters? Can the model be reduced or reparameterized to become identifiable? All those questions of identifiability analysis are essential for model’s predictability and reliability. That explains why the topic of identifiability of Systems Biology models has received a lot of attention in recent scientific research. However, existing numerical methods of identifiability analysis are computationally demanding or often lead to inaccurate estimations. Development of methods, which can accurately produce parameters’ confidence intervals in reasonable computational time, is of utmost importance for Systems Biology and QSP modeling. We propose an algorithm and a software package to test identifiability of Systems Biology models, designed to speed-up confidence intervals estimation and reduce computational cost. The software package was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model, discussed in the current article.
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11
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Should a viral genome stay in the host cell or leave? A quantitative dynamics study of how hepatitis C virus deals with this dilemma. PLoS Biol 2020; 18:e3000562. [PMID: 32730280 PMCID: PMC7392214 DOI: 10.1371/journal.pbio.3000562] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 06/26/2020] [Indexed: 12/13/2022] Open
Abstract
Virus proliferation involves gene replication inside infected cells and transmission to new target cells. Once positive-strand RNA virus has infected a cell, the viral genome serves as a template for copying (“stay-strategy”) or is packaged into a progeny virion that will be released extracellularly (“leave-strategy”). The balance between genome replication and virion release determines virus production and transmission efficacy. The ensuing trade-off has not yet been well characterized. In this study, we use hepatitis C virus (HCV) as a model system to study the balance of the two strategies. Combining viral infection cell culture assays with mathematical modeling, we characterize the dynamics of two different HCV strains (JFH-1, a clinical isolate, and Jc1-n, a laboratory strain), which have different viral release characteristics. We found that 0.63% and 1.70% of JFH-1 and Jc1-n intracellular viral RNAs, respectively, are used for producing and releasing progeny virions. Analysis of the Malthusian parameter of the HCV genome (i.e., initial proliferation rate) and the number of de novo infections (i.e., initial transmissibility) suggests that the leave-strategy provides a higher level of initial transmission for Jc1-n, whereas, in contrast, the stay-strategy provides a higher initial proliferation rate for JFH-1. Thus, theoretical-experimental analysis of viral dynamics enables us to better understand the proliferation strategies of viruses, which contributes to the efficient control of virus transmission. Ours is the first study to analyze the stay-leave trade-off during the viral life cycle and the significance of the replication-release switching mechanism for viral proliferation. A theoretical-experimental analysis of viral dynamics reveals a stay/leave trade-off during the viral life cycle and demonstrates the significance for viral proliferation of a replication-release switching mechanism.
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12
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Kok F, Rosenblatt M, Teusel M, Nizharadze T, Gonçalves Magalhães V, Dächert C, Maiwald T, Vlasov A, Wäsch M, Tyufekchieva S, Hoffmann K, Damm G, Seehofer D, Boettler T, Binder M, Timmer J, Schilling M, Klingmüller U. Disentangling molecular mechanisms regulating sensitization of interferon alpha signal transduction. Mol Syst Biol 2020; 16:e8955. [PMID: 32696599 PMCID: PMC7373899 DOI: 10.15252/msb.20198955] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 05/29/2020] [Accepted: 06/16/2020] [Indexed: 12/20/2022] Open
Abstract
Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNα) orchestrates antiviral responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNα doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNα signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNα dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNα leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFNα signal response.
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Affiliation(s)
- Frédérique Kok
- Division Systems Biology of Signal TransductionGerman Cancer Research Center (DKFZ)HeidelbergGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Marcus Rosenblatt
- Institute of PhysicsUniversity of FreiburgFreiburgGermany
- FDM ‐ Freiburg Center for Data Analysis and ModelingUniversity of FreiburgFreiburgGermany
| | - Melissa Teusel
- Division Systems Biology of Signal TransductionGerman Cancer Research Center (DKFZ)HeidelbergGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Tamar Nizharadze
- Division Systems Biology of Signal TransductionGerman Cancer Research Center (DKFZ)HeidelbergGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Vladimir Gonçalves Magalhães
- Research Group “Dynamics of Early Viral Infection and the Innate Antiviral Response”Division Virus‐Associated CarcinogenesisGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Christopher Dächert
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
- Research Group “Dynamics of Early Viral Infection and the Innate Antiviral Response”Division Virus‐Associated CarcinogenesisGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Tim Maiwald
- Institute of PhysicsUniversity of FreiburgFreiburgGermany
| | - Artyom Vlasov
- Division Systems Biology of Signal TransductionGerman Cancer Research Center (DKFZ)HeidelbergGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Marvin Wäsch
- Division Systems Biology of Signal TransductionGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Silvana Tyufekchieva
- Department of General, Visceral and Transplantation SurgeryRuprecht Karls University HeidelbergHeidelbergGermany
| | - Katrin Hoffmann
- Department of General, Visceral and Transplantation SurgeryRuprecht Karls University HeidelbergHeidelbergGermany
| | - Georg Damm
- Department of Hepatobiliary Surgery and Visceral TransplantationUniversity of LeipzigLeipzigGermany
| | - Daniel Seehofer
- Department of Hepatobiliary Surgery and Visceral TransplantationUniversity of LeipzigLeipzigGermany
| | - Tobias Boettler
- Department of Medicine IIUniversity Hospital Freiburg—Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Marco Binder
- Research Group “Dynamics of Early Viral Infection and the Innate Antiviral Response”Division Virus‐Associated CarcinogenesisGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Jens Timmer
- Institute of PhysicsUniversity of FreiburgFreiburgGermany
- FDM ‐ Freiburg Center for Data Analysis and ModelingUniversity of FreiburgFreiburgGermany
- Signalling Research Centres BIOSS and CIBSSUniversity of FreiburgFreiburgGermany
- Center for Biological Systems Analysis (ZBSA)University of FreiburgFreiburgGermany
| | - Marcel Schilling
- Division Systems Biology of Signal TransductionGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Ursula Klingmüller
- Division Systems Biology of Signal TransductionGerman Cancer Research Center (DKFZ)HeidelbergGermany
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13
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Sokolov V, Helmlinger G, Nilsson C, Zhudenkov K, Skrtic S, Hamrén B, Peskov K, Hurt-Camejo E, Jansson-Löfmark R. Comparative quantitative systems pharmacology modeling of anti-PCSK9 therapeutic modalities in hypercholesterolemia. J Lipid Res 2019; 60:1610-1621. [PMID: 31292220 PMCID: PMC6718444 DOI: 10.1194/jlr.m092486] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 06/27/2019] [Indexed: 12/21/2022] Open
Abstract
Since the discovery of proprotein convertase subtilisin/kexin type 9 (PCSK9) as an attractive target in the treatment of hypercholesterolemia, multiple anti-PCSK9 therapeutic modalities have been pursued in drug development. The objective of this research is to set the stage for the quantitative benchmarking of two anti-PCSK9 pharmacological modality classes, monoclonal antibodies (mAbs) and small interfering RNA (siRNA). To this end, we developed an integrative mathematical model of lipoprotein homeostasis describing the dynamic interplay between PCSK9, LDL-cholesterol (LDL-C), VLDL-cholesterol, HDL-cholesterol (HDL-C), apoB, lipoprotein a [Lp(a)], and triglycerides (TGs). We demonstrate that LDL-C decreased proportionally to PCSK9 reduction for both mAb and siRNA modalities. At marketed doses, however, treatment with mAbs resulted in an additional ∼20% LDL-C reduction compared with siRNA. We further used the model as an evaluation tool and determined that no quantitative differences were observed in HDL-C, Lp(a), TG, or apoB responses, suggesting that the disruption of PCSK9 synthesis would provide no additional effects on lipoprotein-related biomarkers in the patient segment investigated. Predictive model simulations further indicate that siRNA therapies may reach reductions in LDL-C levels comparable to those achieved with mAbs if the current threshold of 80% PCSK9 inhibition via siRNA could be overcome.
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Affiliation(s)
| | - Gabriel Helmlinger
- Clinical Pharmacology & Safety Sciences R&D BioPharmaceuticals, AstraZeneca, Boston, MA
| | - Catarina Nilsson
- Clinical Pharmacology & Safety SciencesCardiovascular, R&D BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | | | - Stanko Skrtic
- Clinical Pharmacology & Safety SciencesCardiovascular, R&D BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden; Institute of Medicine at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Bengt Hamrén
- Clinical Pharmacology & Safety SciencesCardiovascular, R&D BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | - Kirill Peskov
- M&S Decisions, Moscow, Russia; I. M. Sechenov First Moscow State Medical University of the Russian Ministry of Health Moscow, Russia
| | - Eva Hurt-Camejo
- Renal and Metabolism R&D BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden
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