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Nourisa J, Passemiers A, Shakeri F, Omidi M, Helmholz H, Raimondi D, Moreau Y, Tomforde S, Schlüter H, Luthringer-Feyerabend B, Cyron CJ, Aydin RC, Willumeit-Römer R, Zeller-Plumhoff B. Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg 2+ ions. Comput Struct Biotechnol J 2024; 23:1773-1785. [PMID: 38689715 PMCID: PMC11058716 DOI: 10.1016/j.csbj.2024.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 05/02/2024] Open
Abstract
Magnesium (Mg)-based implants have emerged as a promising alternative for orthopedic applications, owing to their bioactive properties and biodegradability. As the implants degrade, Mg2+ ions are released, influencing all surrounding cell types, especially mesenchymal stem cells (MSCs). MSCs are vital for bone tissue regeneration, therefore, it is essential to understand their molecular response to Mg2+ ions in order to maximize the potential of Mg-based biomaterials. In this study, we conducted a gene regulatory network (GRN) analysis to examine the molecular responses of MSCs to Mg2+ ions. We used time-series proteomics data collected at 11 time points across a 21-day period for the GRN construction. We studied the impact of Mg2+ ions on the resulting networks and identified the key proteins and protein interactions affected by the application of Mg2+ ions. Our analysis highlights MYL1, MDH2, GLS, and TRIM28 as the primary targets of Mg2+ ions in the response of MSCs during 1-21 days phase. Our results also identify MDH2-MYL1, MDH2-RPS26, TRIM28-AK1, TRIM28-SOD2, and GLS-AK1 as the critical protein relationships affected by Mg2+ ions. By offering a comprehensive understanding of the regulatory role of Mg2+ ions on MSCs, our study contributes valuable insights into the molecular response of MSCs to Mg-based materials, thereby facilitating the development of innovative therapeutic strategies for orthopedic applications.
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Affiliation(s)
- Jalil Nourisa
- Institute of Material Systems Modeling, Helmholtz Zentrum Hereon, Geesthacht, Germany
| | | | - Farhad Shakeri
- Institute of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Maryam Omidi
- Institute of Clinical Chemistry/Central Laboratories, University Medical Center Hamburg, Hamburg, Germany
| | - Heike Helmholz
- Institute of Metallic Biomaterials, Helmholtz Zentrum Hereon, Geesthacht, Germany
| | | | | | - Sven Tomforde
- Department of Computer Science, Intelligent Systems, University of Kiel, Kiel, Germany
| | - Hartmuth Schlüter
- Institute of Clinical Chemistry and Laboratory Medicine Diagnostic Center, University of Hamburg, Hamburg, Germany
| | | | - Christian J. Cyron
- Institute of Material Systems Modeling, Helmholtz Zentrum Hereon, Geesthacht, Germany
- Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany
| | - Roland C. Aydin
- Institute of Material Systems Modeling, Helmholtz Zentrum Hereon, Geesthacht, Germany
- Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany
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Kholodenko BN, Kolch W, Rukhlenko OS. Reversing pathological cell states: the road less travelled can extend the therapeutic horizon. Trends Cell Biol 2023; 33:913-923. [PMID: 37263821 PMCID: PMC10593090 DOI: 10.1016/j.tcb.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 06/03/2023]
Abstract
Acquisition of omics data advances at a formidable pace. Yet, our ability to utilize these data to control cell phenotypes and design interventions that reverse pathological states lags behind. Here, we posit that cell states are determined by core networks that control cell-wide networks. To steer cell fate decisions, core networks connecting genotype to phenotype must be reconstructed and understood. A recent method, cell state transition assessment and regulation (cSTAR), applies perturbation biology to quantify causal connections and mechanistically models how core networks influence cell phenotypes. cSTAR models are akin to digital cell twins enabling us to purposefully convert pathological states back to physiologically normal states. While this capability has a range of applications, here we discuss reverting oncogenic transformation.
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Affiliation(s)
- Boris N Kholodenko
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland; Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Ireland; Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland; Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Ireland
| | - Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
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Wang B. Random periodic sequence of globally mean-square exponentially stable discrete-time stochastic genetic regulatory networks with discrete spatial diffusions. era 2023; 31:3097-3122. [DOI: 10.3934/era.2023157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
<abstract><p>This paper regards the dual effects of discrete-space and discrete-time in stochastic genetic regulatory networks via exponential Euler difference and central finite difference. Firstly, the global exponential stability of such discrete networks is investigated by using discrete constant variation formulation. In particular, the optimal exponential convergence rate is explored by solving a nonlinear optimization problem under nonlinear constraints, and an implementable computer algorithm for computing the optimal exponential convergence rate is given. Secondly, random periodic sequence for such discrete networks is investigated based on the theory of semi-flow and metric dynamical systems. The researching findings show that the spatial diffusions with nonnegative intensive coefficients have no influence on global mean square boundedness and stability, random periodicity of the networks. This paper is pioneering in considering discrete spatial diffusions, which provides a research basis for future research on genetic regulatory networks.</p></abstract>
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Seçilmiş D, Hillerton T, Sonnhammer ELL. GRNbenchmark - a web server for benchmarking directed gene regulatory network inference methods. Nucleic Acids Res 2022; 50:W398-W404. [PMID: 35609981 PMCID: PMC9252735 DOI: 10.1093/nar/gkac377] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/20/2022] [Accepted: 05/19/2022] [Indexed: 11/30/2022] Open
Abstract
Accurate inference of gene regulatory networks (GRN) is an essential component of systems biology, and there is a constant development of new inference methods. The most common approach to assess accuracy for publications is to benchmark the new method against a selection of existing algorithms. This often leads to a very limited comparison, potentially biasing the results, which may stem from tuning the benchmark's properties or incorrect application of other methods. These issues can be avoided by a web server with a broad range of data properties and inference algorithms, that makes it easy to perform comprehensive benchmarking of new methods, and provides a more objective assessment. Here we present https://GRNbenchmark.org/ - a new web server for benchmarking GRN inference methods, which provides the user with a set of benchmarks with several datasets, each spanning a range of properties including multiple noise levels. As soon as the web server has performed the benchmarking, the accuracy results are made privately available to the user via interactive summary plots and underlying curves. The user can then download these results for any purpose, and decide whether or not to make them public to share with the community.
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Affiliation(s)
- Deniz Seçilmiş
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Thomas Hillerton
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
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