1
|
Haga M, Iida K, Okada M. Positive and negative feedback regulation of the TGF-β1 explains two equilibrium states in skin aging. iScience 2024; 27:109708. [PMID: 38706856 PMCID: PMC11066433 DOI: 10.1016/j.isci.2024.109708] [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: 07/06/2023] [Revised: 02/05/2024] [Accepted: 04/06/2024] [Indexed: 05/07/2024] Open
Abstract
During aging, skin homeostasis is essential for maintaining appearance, as well as biological defense of the human body. In this study, we identified thrombospondin-1 (THBS1) and fibromodulin (FMOD) as positive and negative regulators, respectively, of the TGF-β1-SMAD4 axis in human skin aging, based on in vitro and in vivo omics analyses and mathematical modeling. Using transcriptomic and epigenetic analyses of senescent dermal fibroblasts, TGF-β1 was identified as the key upstream regulator. Bifurcation analysis revealed a binary high-/low-TGF-β1 switch, with THBS1 as the main controller. Computational simulation of the TGF-β1 signaling pathway indicated that THBS1 expression was sensitively regulated, whereas FMOD was regulated robustly. Results of sensitivity analysis and validation showed that inhibition of SMAD4 complex formation was a promising method to control THBS1 production and senescence. Therefore, this study demonstrated the potential of combining data-driven target discovery with mathematical approaches to determine the mechanisms underlying skin aging.
Collapse
Affiliation(s)
- Masatoshi Haga
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Basic Research Development Division, ROHTO Pharmaceutical Co., Ltd, Osaka 544-8666, Japan
| | - Keita Iida
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Mariko Okada
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Osaka 565-0871, Japan
| |
Collapse
|
2
|
Iida K, Okada M. Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome. Cancers (Basel) 2024; 16:1884. [PMID: 38791962 PMCID: PMC11119661 DOI: 10.3390/cancers16101884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 05/11/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.
Collapse
Affiliation(s)
- Keita Iida
- Institute for Protein Research, Osaka University, Suita 565-0871, Osaka, Japan;
| | | |
Collapse
|
3
|
Arakane K, Imoto H, Ormersbach F, Okada M. Extending BioMASS to construct mathematical models from external knowledge. BIOINFORMATICS ADVANCES 2024; 4:vbae042. [PMID: 38606187 PMCID: PMC11007111 DOI: 10.1093/bioadv/vbae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/13/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Motivation Mechanistic modeling based on ordinary differential equations has led to numerous findings in systems biology by integrating prior knowledge and experimental data. However, the manual curation of knowledge necessary when constructing models poses a bottleneck. As the speed of knowledge accumulation continues to grow, there is a demand for a scalable means of constructing executable models. Results We previously introduced BioMASS-an open-source, Python-based framework-to construct, simulate, and analyze mechanistic models of signaling networks. With one of its features, Text2Model, BioMASS allows users to define models in a natural language-like format, thereby facilitating the construction of large-scale models. We demonstrate that Text2Model can serve as a tool for integrating external knowledge for mathematical modeling by generating Text2Model files from a pathway database or through the use of a large language model, and simulating its dynamics through BioMASS. Our findings reveal the tool's capabilities to encourage exploration from prior knowledge and pave the way for a fully data-driven approach to constructing mathematical models. Availability and implementation The code and documentation for BioMASS are available at https://github.com/biomass-dev/biomass and https://biomass-core.readthedocs.io, respectively. The code used in this article are available at https://github.com/okadalabipr/text2model-from-knowledge.
Collapse
Affiliation(s)
- Kiwamu Arakane
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Hiroaki Imoto
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | | | - Mariko Okada
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka 565-0871, Japan
| |
Collapse
|
4
|
Hauber AL, Rosenblatt M, Timmer J. Uncovering specific mechanisms across cell types in dynamical models. PLoS Comput Biol 2023; 19:e1010867. [PMID: 37703301 PMCID: PMC10519600 DOI: 10.1371/journal.pcbi.1010867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 09/25/2023] [Accepted: 08/14/2023] [Indexed: 09/15/2023] Open
Abstract
Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results. To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements.
Collapse
Affiliation(s)
- Adrian L. Hauber
- Institute of Physics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Marcus Rosenblatt
- Institute of Physics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Jens Timmer
- Institute of Physics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| |
Collapse
|
5
|
Okada M. Systems biology of protein network. Biophys Rev 2022; 14:1231-1232. [PMID: 36659987 PMCID: PMC9842819 DOI: 10.1007/s12551-022-01023-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
The development of comprehensive measurement technology for biomolecules and the development of computer hardware and algorithms, enhanced by human genome research, have greatly contributed to the advancement of predictive biology. However, to make that happen, it was essential to have a common concept of sharing data in a standard format. In this article, I would like to briefly review how such concepts were developed in Japan for the foundation of today's biological sciences.
Collapse
Affiliation(s)
- Mariko Okada
- grid.136593.b0000 0004 0373 3971Laboratory for Cell Systems, Institute for Protein Research, Osaka University, Yamadaoka, Suita, Osaka Japan
| |
Collapse
|
6
|
Imoto H, Yamashiro S, Murakami K, Okada M. Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics. STAR Protoc 2022; 3:101619. [PMID: 35990741 PMCID: PMC9389415 DOI: 10.1016/j.xpro.2022.101619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling. For complete details on the use and execution of this protocol, please refer to Imoto et al. (2022). A computational framework for patient-specific modeling Integration of clinical data and cell line data for model calibration Building a mechanistic dynamic model from .txt file Stratification of patients with breast cancer based on in silico signaling dynamics
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Collapse
|
7
|
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.
Collapse
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.)
| |
Collapse
|