1
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Connor MG, Hamon MA. Advances in regulation of homeostasis through chromatin modifications by airway commensals. Curr Opin Microbiol 2024; 80:102505. [PMID: 38936013 DOI: 10.1016/j.mib.2024.102505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 06/07/2024] [Accepted: 06/07/2024] [Indexed: 06/29/2024]
Abstract
Commensal bacteria are residents of the human airway where they interact with both colonizing pathogens and host respiratory epithelial cells of this mucosal surface. It is here that commensals exert their influence through host signaling cascades, host transcriptional responses and host immunity, all of which are rooted in chromatin remodeling and histone modifications. Recent studies show that airway commensals impact host chromatin, but compared the what is known for gut commensals, the field remains in its infancy. The mechanisms by which airway commensals regulate respiratory health and homeostasis through chromatin modifications is of increasing interest, specifically since their displacement precedes the increased potential for respiratory disease. Herein we will discuss recent advances and intriguing avenues of future work aimed at deciphering how airway commensals protect and influence respiratory health.
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Affiliation(s)
- Michael G Connor
- Institut Pasteur, Université de Paris Cité, Unité Chromatine et Infection, F-75015 Paris, France.
| | - Melanie A Hamon
- Institut Pasteur, Université de Paris Cité, Unité Chromatine et Infection, F-75015 Paris, France.
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2
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Konrath F, Willenbrock M, Busse D, Scheidereit C, Wolf J. A computational model of the DNA damage-induced IKK/ NF-κB pathway reveals a critical dependence on irradiation dose and PARP-1. iScience 2023; 26:107917. [PMID: 37817938 PMCID: PMC10561052 DOI: 10.1016/j.isci.2023.107917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/01/2023] [Accepted: 09/12/2023] [Indexed: 10/12/2023] Open
Abstract
The activation of IKK/NF-κB by genotoxic stress is a crucial process in the DNA damage response. Due to the anti-apoptotic impact of NF-κB, it can affect cell-fate decisions upon DNA damage and therefore interfere with tumor therapy-induced cell death. Here, we developed a dynamical model describing IKK/NF-κB signaling that faithfully reproduces quantitative time course data and enables a detailed analysis of pathway regulation. The approach elucidates a pathway topology with two hubs, where the first integrates signals from two DNA damage sensors and the second forms a coherent feedforward loop. The analyses reveal a critical role of the sensor protein PARP-1 in the pathway regulation. Introducing a method for calculating the impact of changes in individual components on pathway activity in a time-resolved manner, we show how irradiation dose influences pathway activation. Our results give a mechanistic understanding relevant for the interpretation of experimental and clinical studies.
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Affiliation(s)
- Fabian Konrath
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michael Willenbrock
- Laboratory for Signal Transduction in Tumor Cells, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Dorothea Busse
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Claus Scheidereit
- Laboratory for Signal Transduction in Tumor Cells, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Jana Wolf
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Department of Mathematics and Computer Science, Free University Berlin, Germany
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3
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Jaruszewicz-Błońska J, Kosiuk I, Prus W, Lipniacki T. A plausible identifiable model of the canonical NF-κB signaling pathway. PLoS One 2023; 18:e0286416. [PMID: 37267242 DOI: 10.1371/journal.pone.0286416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/15/2023] [Indexed: 06/04/2023] Open
Abstract
An overwhelming majority of mathematical models of regulatory pathways, including the intensively studied NF-κB pathway, remains non-identifiable, meaning that their parameters may not be determined by existing data. The existing NF-κB models that are capable of reproducing experimental data contain non-identifiable parameters, whereas simplified models with a smaller number of parameters exhibit dynamics that differs from that observed in experiments. Here, we reduced an existing model of the canonical NF-κB pathway by decreasing the number of equations from 15 to 6. The reduced model retains two negative feedback loops mediated by IκBα and A20, and in response to both tonic and pulsatile TNF stimulation exhibits dynamics that closely follow that of the original model. We carried out the sensitivity-based linear analysis and Monte Carlo-based analysis to demonstrate that the resulting model is both structurally and practically identifiable given measurements of 5 model variables from a simple TNF stimulation protocol. The reduced model is capable of reproducing different types of responses that are characteristic to regulatory motifs controlled by negative feedback loops: nearly-perfect adaptation as well as damped and sustained oscillations. It can serve as a building block of more comprehensive models of the immune response and cancer, where NF-κB plays a decisive role. Our approach, although may not be automatically generalized, suggests that models of other regulatory pathways can be transformed to identifiable, while retaining their dynamical features.
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Affiliation(s)
| | - Ilona Kosiuk
- Institute of Fundamental Technological Research of the Polish Academy of Sciences, Warsaw, Poland
| | - Wiktor Prus
- Institute of Fundamental Technological Research of the Polish Academy of Sciences, Warsaw, Poland
| | - Tomasz Lipniacki
- Institute of Fundamental Technological Research of the Polish Academy of Sciences, Warsaw, Poland
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4
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Nabuco Leva Ferreira de Freitas JA, Bischof O. Dynamic modeling of the cellular senescence gene regulatory network. Heliyon 2023; 9:e14007. [PMID: 36938415 PMCID: PMC10015196 DOI: 10.1016/j.heliyon.2023.e14007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 02/27/2023] Open
Abstract
Cellular senescence is a cell fate that prominently impacts physiological and pathophysiological processes. Diverse cellular stresses induce it, and dramatic gene expression changes accompany it. However, determining the interactions comprising the gene regulatory network (GRN) governing senescence remains challenging. Recent advances in signal processing techniques provide opportunities to reconstruct GRNs. Here, we describe a GRN for senescence integrating time-series transcriptome and transcription factor depletion datasets. Specifically, we infer a set of differential equations using the "Sparse Identification of Nonlinear Dynamics" (SINDy) algorithm, discriminate genes with potential hidden regulators, validate the inferred GRN for time-points not included in the training data, and comprehensively benchmark our approach. Our work is a proof of concept for a data-driven GRN reconstruction method, consolidating an iterative, powerful mathematical platform for senescence modeling that can be used to test hypotheses in silico and has the potential for future discoveries of clinical impact.
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Affiliation(s)
- José Américo Nabuco Leva Ferreira de Freitas
- IMRB, Mondor Institute for Biomedical Research, INSERM U955 – Université Paris Est Créteil, UPEC, Faculté de Médecine de Créteil 8, rue du Général Sarrail, 94010 Créteil
- Sorbonne Université, UMR 8256, Biological Adaptation and Ageing B2A–IBPS, F-75005, Paris, France
- INSERM U1164, F-75005, Paris, France
| | - Oliver Bischof
- IMRB, Mondor Institute for Biomedical Research, INSERM U955 – Université Paris Est Créteil, UPEC, Faculté de Médecine de Créteil 8, rue du Général Sarrail, 94010 Créteil
- Corresponding author.
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Amstein LK, Ackermann J, Hannig J, Đikić I, Fulda S, Koch I. Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis. PLoS Comput Biol 2022; 18:e1010383. [PMID: 35994517 PMCID: PMC9467317 DOI: 10.1371/journal.pcbi.1010383] [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: 12/22/2021] [Revised: 09/12/2022] [Accepted: 07/13/2022] [Indexed: 11/19/2022] Open
Abstract
The paper describes a mathematical model of the molecular switches of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the literature, we constructed a Petri net model based on detailed molecular reactions of the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 edges. We verified the model by evaluating invariant properties of the system at steady state and by in silico knockout analysis. Applying Petri net analysis techniques, we found 279 pathways, which describe signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic and led to multiple possible outcomes. We investigated the in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction. Despite not knowing enough kinetic data of sufficient quality, we estimated system’s dynamics using a discrete, semi-quantitative Petri net model. It is still a challenge to develop mechanistic models for big molecular systems without the knowledge of enough kinetic parameters of sufficient quality. At the same time, more qualitative and semi-quantitative data have been produced in increasing numbers, e.g., by new high-throughput technologies. This has generated demands for new concepts at appropriate abstraction levels. The Petri net formalism enables the integration of qualitative as well as quantitative data and provides algorithms and methods for model verification and model simulation. Moreover, Petri nets exhibit a clear and coherent visualization. Here, we modeled the molecular switches between cell survival, apoptosis, and necroptosis induced by tumor necrosis factor 1. We were interested not only in an exhaustive exploration of all possible signaling pathways, but also in finding the system’s checkpoints. Our Petri net model comprises 118 biochemical entities, 130 reactions, and 299 edges. We found 279 pathways that describe signal flows from receptor activation to cellular response.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. We applied in silico knockout analyses to the Petri net model and could identify important checkpoints of the tumor necrosis factor 1 signaling pathway.
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Affiliation(s)
- Leonie K. Amstein
- Goethe University Frankfurt, Institute of Computer Science, Department of Molecular Bioinformatics, Frankfurt am Main, Germany
| | - Jörg Ackermann
- Goethe University Frankfurt, Institute of Computer Science, Department of Molecular Bioinformatics, Frankfurt am Main, Germany
| | - Jennifer Hannig
- Cognitive Information Systems, Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen, Friedberg, Germany
| | - Ivan Đikić
- Goethe University Frankfurt, Institute of Biochemistry II, Medical Faculty, Frankfurt am Main, Germany
| | - Simone Fulda
- Goethe University Frankfurt, Institute of Biochemistry II, Medical Faculty, Frankfurt am Main, Germany
| | - Ina Koch
- Goethe University Frankfurt, Institute of Computer Science, Department of Molecular Bioinformatics, Frankfurt am Main, Germany
- * E-mail:
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Deciphering signal transduction networks in the liver by mechanistic mathematical modelling. Biochem J 2022; 479:1361-1374. [PMID: 35748700 PMCID: PMC9246346 DOI: 10.1042/bcj20210548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/10/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022]
Abstract
In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.
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Haga M, Okada M. Systems approaches to investigate the role of NF-κB signaling in aging. Biochem J 2022; 479:161-183. [PMID: 35098992 PMCID: PMC8883486 DOI: 10.1042/bcj20210547] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/06/2022] [Accepted: 01/10/2022] [Indexed: 12/14/2022]
Abstract
The nuclear factor-κB (NF-κB) signaling pathway is one of the most well-studied pathways related to inflammation, and its involvement in aging has attracted considerable attention. As aging is a complex phenomenon and is the result of a multi-step process, the involvement of the NF-κB pathway in aging remains unclear. To elucidate the role of NF-κB in the regulation of aging, different systems biology approaches have been employed. A multi-omics data-driven approach can be used to interpret and clarify unknown mechanisms but cannot generate mechanistic regulatory structures alone. In contrast, combining this approach with a mathematical modeling approach can identify the mechanistics of the phenomena of interest. The development of single-cell technologies has also helped clarify the heterogeneity of the NF-κB response and underlying mechanisms. Here, we review advances in the understanding of the regulation of aging by NF-κB by focusing on omics approaches, single-cell analysis, and mathematical modeling of the NF-κB network.
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Affiliation(s)
- Masatoshi Haga
- Laboratory for Cell Systems, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Basic Research Development Division, ROHTO Pharmaceutical Co., Ltd., Ikuno-ku, Osaka 544-8666, Japan
| | - Mariko Okada
- Laboratory for Cell Systems, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka 567-0085, Japan
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Thobe K, Konrath F, Chapuy B, Wolf J. Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma. Biomedicines 2021; 9:biomedicines9111655. [PMID: 34829885 PMCID: PMC8615565 DOI: 10.3390/biomedicines9111655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/30/2021] [Accepted: 11/05/2021] [Indexed: 11/16/2022] Open
Abstract
Personalized medicine aims to tailor treatment to patients based on their individual genetic or molecular background. Especially in diseases with a large molecular heterogeneity, such as diffuse large B-cell lymphoma (DLBCL), personalized medicine has the potential to improve outcome and/or to reduce resistance towards treatment. However, integration of patient-specific information into a computational model is challenging and has not been achieved for DLBCL. Here, we developed a computational model describing signaling pathways and expression of critical germinal center markers. The model integrates the regulatory mechanism of the signaling and gene expression network and covers more than 50 components, many carrying genetic lesions common in DLBCL. Using clinical and genomic data of 164 primary DLBCL patients, we implemented mutations, structural variants and copy number alterations as perturbations in the model using the CoLoMoTo notebook. Leveraging patient-specific genotypes and simulation of the expression of marker genes in specific germinal center conditions allows us to predict the consequence of the modeled pathways for each patient. Finally, besides modeling how genetic perturbations alter physiological signaling, we also predicted for each patient model the effect of rational inhibitors, such as Ibrutinib, that are currently discussed as possible DLBCL treatments, showing patient-dependent variations in effectiveness and synergies.
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Affiliation(s)
- Kirsten Thobe
- Mathematical Modelling of Cellular Processes, Max Delbrück Center for Molecular Medicine, 13125 Berlin-Buch, Germany; (K.T.); (F.K.)
| | - Fabian Konrath
- Mathematical Modelling of Cellular Processes, Max Delbrück Center for Molecular Medicine, 13125 Berlin-Buch, Germany; (K.T.); (F.K.)
| | - Björn Chapuy
- Department of Hematology and Medical Oncology, University of Göttingen, 37075 Göttingen, Germany;
- Department of Hematology, Oncology and Cancer Immunology, Berlin Medical Center Charité, 12203 Berlin, Germany
| | - Jana Wolf
- Mathematical Modelling of Cellular Processes, Max Delbrück Center for Molecular Medicine, 13125 Berlin-Buch, Germany; (K.T.); (F.K.)
- Department of Mathematics and Computer Science, Free University Berlin, Arnimallee 14, 14195 Berlin, Germany
- Correspondence:
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