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Goetz A, Akl H, Dixit P. The ability to sense the environment is heterogeneously distributed in cell populations. eLife 2024; 12:RP87747. [PMID: 38293960 PMCID: PMC10942581 DOI: 10.7554/elife.87747] [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] [Indexed: 02/01/2024] Open
Abstract
Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information-theoretic framework to quantify the distribution of sensing abilities from single-cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an 'average cell'. We verify these predictions using live-cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information-theoretic framework will significantly improve our understanding of how cells sense in their environment.
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Affiliation(s)
- Andrew Goetz
- Department of Biomedical Engineering, Yale UniversityNew HavenUnited States
| | - Hoda Akl
- Department of Physics, University of FloridaGainesvilleUnited States
| | - Purushottam Dixit
- Department of Biomedical Engineering, Yale UniversityNew HavenUnited States
- Systems Biology Institute, Yale UniversityWest HavenUnited States
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Goetz A, Akl H, Dixit P. The ability to sense the environment is heterogeneously distributed in cell populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.531554. [PMID: 36945613 PMCID: PMC10028875 DOI: 10.1101/2023.03.07.531554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information theoretic framework to quantify the distribution of sensing abilities from single cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an " average cell ". We verify these predictions using live cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information theoretic framework will significantly improve our understanding of how cells sense in their environment.
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Sarma U, Ripka L, Anyaegbunam UA, Legewie S. Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway. Methods Mol Biol 2023; 2634:215-251. [PMID: 37074581 DOI: 10.1007/978-1-0716-3008-2_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.
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Affiliation(s)
- Uddipan Sarma
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Lorenz Ripka
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Uchenna Alex Anyaegbunam
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Stefan Legewie
- Institute of Molecular Biology (IMB), Mainz, Germany.
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany.
- Stuttgart Research Center for Systems Biology, University of Stuttgart, Stuttgart, Germany.
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Kolbe N, Hexemer L, Bammert LM, Loewer A, Lukáčová-Medvid’ová M, Legewie S. Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signaling. PLoS Comput Biol 2022; 18:e1010266. [PMID: 35759468 PMCID: PMC9269928 DOI: 10.1371/journal.pcbi.1010266] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 07/08/2022] [Accepted: 05/30/2022] [Indexed: 11/30/2022] Open
Abstract
Cells sense their surrounding by employing intracellular signaling pathways that transmit hormonal signals from the cell membrane to the nucleus. TGF-β/SMAD signaling encodes various cell fates, controls tissue homeostasis and is deregulated in diseases such as cancer. The pathway shows strong heterogeneity at the single-cell level, but quantitative insights into mechanisms underlying fluctuations at various time scales are still missing, partly due to inefficiency in the calibration of stochastic models that mechanistically describe signaling processes. In this work we analyze single-cell TGF-β/SMAD signaling and show that it exhibits temporal stochastic bursts which are dose-dependent and whose number and magnitude correlate with cell migration. We propose a stochastic modeling approach to mechanistically describe these pathway fluctuations with high computational efficiency. Employing high-order numerical integration and fitting to burst statistics we enable efficient quantitative parameter estimation and discriminate models that assume noise in different reactions at the receptor level. This modeling approach suggests that stochasticity in the internalization of TGF-β receptors into endosomes plays a key role in the observed temporal bursting. Further, the model predicts the single-cell dynamics of TGF-β/SMAD signaling in untested conditions, e.g., successfully reflects memory effects of signaling noise and cellular sensitivity towards repeated stimulation. Taken together, our computational framework based on burst analysis, noise modeling and path computation scheme is a suitable tool for the data-based modeling of complex signaling pathways, capable of identifying the source of temporal noise.
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Affiliation(s)
- Niklas Kolbe
- Institute of Geometry and Practical Mathematics, RWTH Aachen University, Aachen, Germany
| | - Lorenz Hexemer
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics (IBMG), University of Stuttgart, Stuttgart, Germany
| | | | - Alexander Loewer
- Systems Biology of the Stress Response, Department of Biology, Technical University of Darmstadt, Darmstadt, Germany
| | | | - Stefan Legewie
- Department of Systems Biology, Institute for Biomedical Genetics (IBMG), University of Stuttgart, Stuttgart, Germany
- Stuttgart Research Center for Systems Biology (SRCSB), University of Stuttgart, Stuttgart, Germany
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Cho B, Yoo SJ, Kim SY, Lee CH, Lee YI, Lee SR, Moon C. Second-generation non-hematopoietic erythropoietin-derived peptide for neuroprotection. Redox Biol 2021; 49:102223. [PMID: 34953452 PMCID: PMC8715119 DOI: 10.1016/j.redox.2021.102223] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 12/20/2021] [Indexed: 12/25/2022] Open
Abstract
Erythropoietin (EPO) is a well-known erythropoietic cytokine having a tissue-protective effect in various tissues against hypoxic stress, including the brain. Thus, its recombinants may function as neuroprotective compounds. However, despite considerable neuroprotective effects, the EPO-based therapeutic approach has side effects, including hyper-erythropoietic and tumorigenic effects. Therefore, some modified forms and derivatives of EPO have been proposed to minimize the side effects. In this study, we generated divergently modified new peptide analogs derived from helix C of EPO, with several amino acid replacements that interact with erythropoietin receptors (EPORs). This modification resulted in unique binding potency to EPOR. Unlike recombinant EPO, among the peptides, ML1-h3 exhibited a potent neuroprotective effect against oxidative stress without additional induction of cell-proliferation, owing to a differential activating mode of EPOR signaling. Furthermore, it inhibited neuronal death and brain injury under hypoxic stress in vitro and in an in vivo ischemic brain injury model. Therefore, the divergent modification of EPO-derivatives for affinity to EPOR could provide a basis for a more advanced and optimal neuroprotective strategy. Short peptides derived from helix C of EPO have a neuroprotective effect. Divergent modification of EPO-derived peptides has a differential affinity to EPOR. ML1 and its analogs have differential cell protective and proliferative effects. ML1-h3 protects neurons by suppressing in vitro oxidative stress. ML1-h3 mitigates brain injury in the in vivo mouse ischemic model without hematopoietic effect.
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Affiliation(s)
- Bongki Cho
- Department of Brain & Cognitive Sciences, Graduate School, DGIST, Daegu, 42988, South Korea; Convergence Research Advanced Centre for Olfaction, DGIST, Daegu, 42988, South Korea; Division of Biotechnology, DGIST, Daegu, 42988, South Korea
| | - Seung-Jun Yoo
- Department of Brain & Cognitive Sciences, Graduate School, DGIST, Daegu, 42988, South Korea; Convergence Research Advanced Centre for Olfaction, DGIST, Daegu, 42988, South Korea
| | - So Yeon Kim
- Department of Brain & Cognitive Sciences, Graduate School, DGIST, Daegu, 42988, South Korea; Convergence Research Advanced Centre for Olfaction, DGIST, Daegu, 42988, South Korea
| | - Chang-Hun Lee
- Department of New Biology, DGIST, Daegu, 42988, South Korea; New Biology Research Center, DGIST, Daegu, 42988, South Korea
| | - Yun-Il Lee
- Division of Biotechnology, DGIST, Daegu, 42988, South Korea
| | - Seong-Ryong Lee
- Department of Pharmacology and ODR Center, Brain Research Institute, School of Medicine, Keimyung University, Daegu, 42601, South Korea.
| | - Cheil Moon
- Department of Brain & Cognitive Sciences, Graduate School, DGIST, Daegu, 42988, South Korea; Convergence Research Advanced Centre for Olfaction, DGIST, Daegu, 42988, South Korea.
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Identification of key genes in the tumor microenvironment of lung adenocarcinoma. Med Oncol 2021; 38:83. [PMID: 34117948 DOI: 10.1007/s12032-021-01529-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/04/2021] [Indexed: 01/12/2023]
Abstract
The tumor microenvironment plays an important role in tumor development and progression, but the role of immune and stromal cells in this environment has not been sufficiently studied. In this study, we aimed to identify key genes associated with the microenvironment of lung adenocarcinoma (LUAD). Raw data for stromal and immune cells in malignant tumors were downloaded from The Cancer Genome Atlas (TCGA). These expression data were used to identify the differentially expressed genes (DEGs) in tissue samples of LUAD with high and low immune scores. A protein-protein interaction (PPI) network based on genes with significant differential expression was constructed. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to functionally annotate putative hub genes. These genes were assessed via Kaplan Meier analysis to determine their correlation with overall survival. In total, we identified 216 DEGs which were correlated with immune and stromal scores, including 30 hub genes which were identified based on the PPI network. Further analysis suggested that the expression levels of 10 of these genes were significantly correlated with overall survival of LUAD patients. These key hub genes included CCR2, CCR5, CD53, CYBB, HCK, IRF8, LCP2, PLEK, PTPRC, and TLR7. Moreover, the expression level of CCR2 was found to have strong prognostic value for LUAD patients. Additionally, high expression of CYBB was also correlated with better survival of patients with LUAD. The results of this study open several new avenues to explore in the treatment of LUAD.
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Dixit PD, Lyashenko E, Niepel M, Vitkup D. Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks. Cell Syst 2020; 10:204-212.e8. [PMID: 31864963 PMCID: PMC7047530 DOI: 10.1016/j.cels.2019.11.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 07/30/2019] [Accepted: 11/25/2019] [Indexed: 12/13/2022]
Abstract
Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements.
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Affiliation(s)
- Purushottam D Dixit
- Department of Systems Biology, Columbia University, New York, NY, USA; Department of Physics, University of Florida, Gainesville, FL, USA.
| | - Eugenia Lyashenko
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Mario Niepel
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Dennis Vitkup
- Department of Systems Biology, Columbia University, New York, NY, USA; Department of Biomedical Informatics, Columbia University, New York, NY, USA; Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA.
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Correction: Correlated receptor transport processes buffer single-cell heterogeneity. PLoS Comput Biol 2018; 14:e1006037. [PMID: 29494615 PMCID: PMC5832185 DOI: 10.1371/journal.pcbi.1006037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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