1
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Ohkubo T, Soma Y, Sakumura Y, Hanai T, Kunida K. A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch. Sci Rep 2023; 13:13608. [PMID: 37666852 PMCID: PMC10477343 DOI: 10.1038/s41598-023-40469-y] [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: 05/18/2023] [Accepted: 08/10/2023] [Indexed: 09/06/2023] Open
Abstract
Bioprocess optimization using mathematical models is prevalent, yet the discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a significant challenge. This study proposes a novel hybrid control system called the hybrid in silico/in-cell controller (HISICC) to address PMM by combining model-based optimization (in silico feedforward controller) with feedback controllers utilizing synthetic genetic circuits integrated into cells (in-cell feedback controller). We demonstrated the efficacy of HISICC using two engineered Escherichia coli strains, TA1415 and TA2445, previously developed for isopropanol (IPA) production. TA1415 contains a metabolic toggle switch (MTS) to manage the competition between cell growth and IPA production for intracellular acetyl-CoA by responding to external input of isopropyl β-D-1-thiogalactopyranoside (IPTG). TA2445, in addition to the MTS, has a genetic circuit that detects cell density to autonomously activate MTS. The combination of TA2445 with an in silico controller exemplifies HISICC implementation. We constructed mathematical models to optimize IPTG input values for both strains based on the two-compartment model and validated these models using experimental data of the IPA production process. Using these models, we evaluated the robustness of HISICC against PMM by comparing IPA yields with two strains in simulations assuming various magnitudes of PMM in cell growth rates. The results indicate that the in-cell feedback controller in TA2445 effectively compensates for PMM by modifying MTS activation timing. In conclusion, the HISICC system presents a promising solution to the PMM problem in bioprocess engineering, paving the way for more efficient and reliable optimization of microbial bioprocesses.
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Affiliation(s)
- Tomoki Ohkubo
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan
| | - Yuki Soma
- Laboratory for Synthetic Biology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, W5-729, 744, Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Yuichi Sakumura
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan
- Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan
| | - Taizo Hanai
- Laboratory for Synthetic Biology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, W5-729, 744, Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Katsuyuki Kunida
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan.
- School of Medicine, Fujita Health University, Toyoake, Aichi, 470-1192, Japan.
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2
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Kunida K, Takagi N, Aoki K, Ikeda K, Nakamura T, Sakumura Y. Decoding cellular deformation from pseudo-simultaneously observed Rho GTPase activities. Cell Rep 2023; 42:112071. [PMID: 36764299 DOI: 10.1016/j.celrep.2023.112071] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 10/31/2022] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
Limitations in simultaneously observing the activity of multiple molecules in live cells prevent researchers from elucidating how these molecules coordinate the dynamic regulation of cellular functions. Here, we propose the motion-triggered average (MTA) algorithm to characterize pseudo-simultaneous dynamic changes in arbitrary cellular deformation and molecular activities. Using MTA, we successfully extract a pseudo-simultaneous time series from individually observed activities of three Rho GTPases: Cdc42, Rac1, and RhoA. To verify that this time series encoded information on cell-edge movement, we use a mathematical regression model to predict the edge velocity from the activities of the three molecules. The model accurately predicts the unknown edge velocity, providing numerical evidence that these Rho GTPases regulate edge movement. Data preprocessing using MTA combined with mathematical regression provides an effective strategy for reusing numerous individual observations of molecular activities.
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Affiliation(s)
- Katsuyuki Kunida
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara 8916-5, Japan; School of Medicine, Fujita Health University, Toyoake, Aichi 470-1192, Japan
| | - Nobuhiro Takagi
- Graduate School of Information Science and Technology, Aichi Prefectural University, Nagakute, Aichi 480-1342, Japan
| | - Kazuhiro Aoki
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Aichi 444-8787, Japan; Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi 444-8787, Japan; Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Aichi 444-8787, Japan
| | - Kazushi Ikeda
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara 8916-5, Japan; Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara 8916-5, Japan; RIKEN Center for Advanced Intelligence Project (RIKEN AIP), Kyoto 619-0288, Japan
| | - Takeshi Nakamura
- Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Chiba 278-0022, Japan
| | - Yuichi Sakumura
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara 8916-5, Japan; Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara 8916-5, Japan.
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3
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Wada T, Hironaka KI, Wataya M, Fujii M, Eto M, Uda S, Hoshino D, Kunida K, Inoue H, Kubota H, Takizawa T, Karasawa Y, Nakatomi H, Saito N, Hamaguchi H, Furuichi Y, Manabe Y, Fujii NL, Kuroda S. Single-Cell Information Analysis Reveals That Skeletal Muscles Incorporate Cell-to-Cell Variability as Information Not Noise. Cell Rep 2021; 32:108051. [PMID: 32877665 DOI: 10.1016/j.celrep.2020.108051] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 06/22/2020] [Accepted: 07/28/2020] [Indexed: 01/05/2023] Open
Abstract
Cell-to-cell variability in signal transduction in biological systems is often considered noise. However, intercellular variation (i.e., cell-to-cell variability) has the potential to enable individual cells to encode different information. Here, we show that intercellular variation increases information transmission of skeletal muscle. We analyze the responses of multiple cultured myotubes or isolated skeletal muscle fibers as a multiple-cell channel composed of single-cell channels. We find that the multiple-cell channel, which incorporates intercellular variation as information, not noise, transmitted more information in the presence of intercellular variation than in the absence according to the "response diversity effect," increasing in the gradualness of dose response by summing the cell-to-cell variable dose responses. We quantify the information transmission of human facial muscle contraction during intraoperative neurophysiological monitoring and find that information transmission of muscle contraction is comparable to that of a multiple-cell channel. Thus, our data indicate that intercellular variation can increase the information capacity of tissues.
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Affiliation(s)
- Takumi Wada
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Ken-Ichi Hironaka
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Mitsutaka Wataya
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Masashi Fujii
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; Molecular Genetics Research Laboratory, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Mathematical and Life Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan
| | - Miki Eto
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Shinsuke Uda
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Daisuke Hoshino
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Katsuyuki Kunida
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Haruki Inoue
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Hiroyuki Kubota
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Tsuguto Takizawa
- Department of Neurosurgery, Faculty of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yasuaki Karasawa
- Department of Neurosurgery, Faculty of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; Department of Rehabilitation, University of Tokyo Hospital, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hirofumi Nakatomi
- Department of Neurosurgery, Faculty of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Nobuhito Saito
- Department of Neurosurgery, Faculty of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hiroki Hamaguchi
- Department of Health Promotion Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan
| | - Yasuro Furuichi
- Department of Health Promotion Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan
| | - Yasuko Manabe
- Department of Health Promotion Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan
| | - Nobuharu L Fujii
- Department of Health Promotion Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan.
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4
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Hoshino D, Kawata K, Kunida K, Hatano A, Yugi K, Wada T, Fujii M, Sano T, Ito Y, Furuichi Y, Manabe Y, Suzuki Y, Fujii NL, Soga T, Kuroda S. Trans-omic Analysis Reveals ROS-Dependent Pentose Phosphate Pathway Activation after High-Frequency Electrical Stimulation in C2C12 Myotubes. iScience 2020; 23:101558. [PMID: 33083727 PMCID: PMC7522805 DOI: 10.1016/j.isci.2020.101558] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/06/2020] [Accepted: 09/10/2020] [Indexed: 12/21/2022] Open
Abstract
Skeletal muscle adaptation is mediated by cooperative regulation of metabolism, signal transduction, and gene expression. However, the global regulatory mechanism remains unclear. To address this issue, we performed electrical pulse stimulation (EPS) in differentiated C2C12 myotubes at low and high frequency, carried out metabolome and transcriptome analyses, and investigated phosphorylation status of signaling molecules. EPS triggered extensive and specific changes in metabolites, signaling phosphorylation, and gene expression during and after EPS in a frequency-dependent manner. We constructed trans-omic network by integrating these data and found selective activation of the pentose phosphate pathway including metabolites, upstream signaling molecules, and gene expression of metabolic enzymes after high-frequency EPS. We experimentally validated that activation of these molecules after high-frequency EPS was dependent on reactive oxygen species (ROS). Thus, the trans-omic analysis revealed ROS-dependent activation in signal transduction, metabolome, and transcriptome after high-frequency EPS in C2C12 myotubes, shedding light on possible mechanisms of muscle adaptation. We performed electrical pulse stimulation in differentiated C2C12 myotubes We constructed trans-omic network after high-frequency electrical pulse stimulation Trans-omic network integrates metabolome, transcriptome, and signaling molecules We identified ROS-dependent pentose phosphate pathway activation
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Affiliation(s)
- Daisuke Hoshino
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Bioscience and Technology Program, Department of Engineering Science, University of Electro-Communications, Tokyo 182-8585, Japan
| | - Kentaro Kawata
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Isotope Science Center, The University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
| | - Katsuyuki Kunida
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Graduate School of Biological Sciences, and Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Atsushi Hatano
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Department of Omics and Systems Biology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Katsuyuki Yugi
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan
- PRESTO, Japan Science and Technology Agency, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Takumi Wada
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Masashi Fujii
- Department of Mathematical and Life Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8526, Japan
| | - Takanori Sano
- Department of Mechanical and Biofunctional Systems, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Yuki Ito
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Yasuro Furuichi
- Department of Health Promotion Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan
| | - Yasuko Manabe
- Department of Health Promotion Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Nobuharu L. Fujii
- Department of Health Promotion Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
- Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan
- Corresponding author
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5
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Matsuda N, Hironaka KI, Fujii M, Wada T, Kunida K, Inoue H, Eto M, Hoshino D, Furuichi Y, Manabe Y, Fujii NL, Noji H, Imamura H, Kuroda S. Monitoring and mathematical modeling of mitochondrial ATP in myotubes at single-cell level reveals two distinct population with different kinetics. Quant Biol 2020. [DOI: 10.1007/s40484-020-0211-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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6
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Inoue H, Kunida K, Matsuda N, Hoshino D, Wada T, Imamura H, Noji H, Kuroda S. Automatic Quantitative Segmentation of Myotubes Reveals Single-cell Dynamics of S6 Kinase Activation. Cell Struct Funct 2018; 43:153-169. [PMID: 30047513 DOI: 10.1247/csf.18012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Automatic cell segmentation is a powerful method for quantifying signaling dynamics at single-cell resolution in live cell fluorescence imaging. Segmentation methods for mononuclear and round shape cells have been developed extensively. However, a segmentation method for elongated polynuclear cells, such as differentiated C2C12 myotubes, has yet to be developed. In addition, myotubes are surrounded by undifferentiated reserve cells, making it difficult to identify background regions and subsequent quantification. Here we developed an automatic quantitative segmentation method for myotubes using watershed segmentation of summed binary images and a two-component Gaussian mixture model. We used time-lapse fluorescence images of differentiated C2C12 cells stably expressing Eevee-S6K, a fluorescence resonance energy transfer (FRET) biosensor of S6 kinase (S6K). Summation of binary images enhanced the contrast between myotubes and reserve cells, permitting detection of a myotube and a myotube center. Using a myotube center instead of a nucleus, individual myotubes could be detected automatically by watershed segmentation. In addition, a background correction using the two-component Gaussian mixture model permitted automatic signal intensity quantification in individual myotubes. Thus, we provide an automatic quantitative segmentation method by combining automatic myotube detection and background correction. Furthermore, this method allowed us to quantify S6K activity in individual myotubes, demonstrating that some of the temporal properties of S6K activity such as peak time and half-life of adaptation show different dose-dependent changes of insulin between cell population and individuals.Key words: time lapse images, cell segmentation, fluorescence resonance energy transfer, C2C12, myotube.
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Affiliation(s)
- Haruki Inoue
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo
| | - Katsuyuki Kunida
- Laboratory of Computational Biology, Graduate School of Biological Sciences, Nara Institute of Science and Technology.,Department of Biological Sciences, Graduate School of Science, University of Tokyo
| | - Naoki Matsuda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo
| | - Daisuke Hoshino
- Department of Biological Sciences, Graduate School of Science, University of Tokyo.,Department of Engineering Science, Graduate School of Informatics and Engineering, University of Electro-Communications
| | - Takumi Wada
- Department of Biological Sciences, Graduate School of Science, University of Tokyo
| | - Hiromi Imamura
- Department of Functional Biology, Graduate School of Biostudies, Kyoto University
| | - Hiroyuki Noji
- Department of Applied Chemistry, Graduate School of Engineering, University of Tokyo
| | - Shinya Kuroda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo.,Department of Biological Sciences, Graduate School of Science, University of Tokyo.,CREST, Japan Science and Technology Corporation
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7
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Tsuchiya T, Fujii M, Matsuda N, Kunida K, Uda S, Kubota H, Konishi K, Kuroda S. System identification of signaling dependent gene expression with different time-scale data. PLoS Comput Biol 2017; 13:e1005913. [PMID: 29281625 PMCID: PMC5760096 DOI: 10.1371/journal.pcbi.1005913] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 01/09/2018] [Accepted: 12/01/2017] [Indexed: 01/11/2023] Open
Abstract
Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales.
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Affiliation(s)
- Takaho Tsuchiya
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
| | - Masashi Fujii
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
- Molecular Genetics Research Laboratory, Graduate School of Science, University of Tokyo, Tokyo, Japan
| | - Naoki Matsuda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
| | - Katsuyuki Kunida
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
- Laboratory of Computational Biology, Graduate School of Biological Sciences, Nara Institute of Science and Technology, Ikoma, Japan
| | - Shinsuke Uda
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Hiroyuki Kubota
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Katsumi Konishi
- Department of Computer Science, Faculty of Informatics, Kogakuin University, Tokyo, Japan
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
- CREST, Japan Science and Technology Corporation, Tokyo, Japan
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8
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Sano T, Kawata K, Ohno S, Yugi K, Kakuda H, Kubota H, Uda S, Fujii M, Kunida K, Hoshino D, Hatano A, Ito Y, Sato M, Suzuki Y, Kuroda S. Selective control of up-regulated and down-regulated genes by temporal patterns and doses of insulin. Sci Signal 2016; 9:ra112. [PMID: 27879394 DOI: 10.1126/scisignal.aaf3739] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Secretion of insulin transiently increases after eating, resulting in a high circulating concentration. Fasting limits insulin secretion, resulting in a low concentration of insulin in the circulation. We analyzed transcriptional responses to different temporal patterns and doses of insulin in the hepatoma FAO cells and identified 13 up-regulated and 16 down-regulated insulin-responsive genes (IRGs). The up-regulated IRGs responded more rapidly than did the down-regulated IRGs to transient stepwise or pulsatile increases in insulin concentration, whereas the down-regulated IRGs were repressed at lower concentrations of insulin than those required to stimulate the up-regulated IRGs. Mathematical modeling of the insulin response as two stages-(i) insulin signaling to transcription and (ii)transcription and mRNA stability-indicated that the first stage was the more rapid stage for the down-regulated IRGs, whereas the second stage of transcription was the more rapid stage for the up-regulated IRGs. A subset of the IRGs that were up-regulated or down-regulated in the FAO cells was similarly regulated in the livers of rats injected with a single dose of insulin. Thus, not only can cells respond to insulin but they can also interpret the intensity and pattern of signal to produce distinct transcriptional responses. These results provide insight that may be useful in treating obesity and type 2 diabetes associated with aberrant insulin production or tissue responsiveness.
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Affiliation(s)
- Takanori Sano
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Kentaro Kawata
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Satoshi Ohno
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Katsuyuki Yugi
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Hiroaki Kakuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Hiroyuki Kubota
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.,PRESTO, Japan Science and Technology Agency, Higashi-ku, Fukuoka 812-8582, Japan
| | - Shinsuke Uda
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Masashi Fujii
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Katsuyuki Kunida
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Daisuke Hoshino
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Atsushi Hatano
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yuki Ito
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Miharu Sato
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Shinya Kuroda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan. .,Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan
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Filippi S, Barnes CP, Kirk PDW, Kudo T, Kunida K, McMahon SS, Tsuchiya T, Wada T, Kuroda S, Stumpf MPH. Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling. Cell Rep 2016; 15:2524-35. [PMID: 27264188 PMCID: PMC4914773 DOI: 10.1016/j.celrep.2016.05.024] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 04/25/2016] [Accepted: 05/04/2016] [Indexed: 12/27/2022] Open
Abstract
Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphorylated MEK and ERK across cell populations and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modeling framework to show that extrinsic noise, particularly that from upstream MEK, is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. We furthermore show that without extrinsic noise in the core module, variable (including noisy) signals would be faithfully reproduced downstream, but the within-module extrinsic variability distorts these signals and leads to a drastic reduction in the mutual information between incoming signal and ERK activity. Active MEK and ERK levels differ profoundly among genetically identical cells A statistical framework is developed to identify the causes of this variability Analysis shows that extrinsic noise upstream MEK-ERK module causes cell variability Within-module extrinsic variability distorts signals
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Affiliation(s)
- Sarah Filippi
- Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK; Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Paul D W Kirk
- Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK
| | - Takamasa Kudo
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo 113-8654, Japan
| | - Katsuyuki Kunida
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo 113-8654, Japan
| | - Siobhan S McMahon
- Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK
| | - Takaho Tsuchiya
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo 113-8654, Japan
| | - Takumi Wada
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo 113-8654, Japan
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo 113-8654, Japan; CREST, Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Michael P H Stumpf
- Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK; Institute of Chemical Biology, Imperial College London, London SW7 2AZ, UK.
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10
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Yugi K, Kubota H, Toyoshima Y, Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y, Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K, Ikeda K, Soga T, Kuroda S. Reconstruction of insulin signal flow from phosphoproteome and metabolome data. Cell Rep 2014; 8:1171-83. [PMID: 25131207 DOI: 10.1016/j.celrep.2014.07.021] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2013] [Revised: 06/13/2014] [Accepted: 07/15/2014] [Indexed: 12/20/2022] Open
Abstract
Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.
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Affiliation(s)
- Katsuyuki Yugi
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Hiroyuki Kubota
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Division of integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan; PRESTO, Japan Science and Technology Corporation, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan
| | - Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Rei Noguchi
- Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Kentaro Kawata
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yasunori Komori
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Shinsuke Uda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Division of integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan
| | - Katsuyuki Kunida
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yoko Tomizawa
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yosuke Funato
- Department of Cellular Regulation, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka 565-0871, Japan
| | - Hiroaki Miki
- Department of Cellular Regulation, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka 565-0871, Japan
| | - Masaki Matsumoto
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan
| | - Keiichi I Nakayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan
| | - Kasumi Kashikura
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Keiko Endo
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Kazutaka Ikeda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo 113-0033, Japan.
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11
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Kunida K, Matsuda M, Aoki K. FRET imaging and statistical signal processing reveal positive and negative feedback loops regulating the morphology of randomly migrating HT-1080 cells. J Cell Sci 2012; 125:2381-92. [PMID: 22344265 DOI: 10.1242/jcs.096859] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Cell migration plays an important role in many physiological processes. Rho GTPases (Rac1, Cdc42, RhoA) and phosphatidylinositols have been extensively studied in directional cell migration. However, it remains unclear how Rho GTPases and phosphatidylinositols regulate random cell migration in space and time. We have attempted to address this issue using fluorescence resonance energy transfer (FRET) imaging and statistical signal processing. First, we acquired time-lapse images of random migration of HT-1080 fibrosarcoma cells expressing FRET biosensors of Rho GTPases and phosphatidyl inositols. We developed an image-processing algorithm to extract FRET values and velocities at the leading edge of migrating cells. Auto- and cross-correlation analysis suggested the involvement of feedback regulations among Rac1, phosphatidyl inositols and membrane protrusions. To verify the feedback regulations, we employed an acute inhibition of the signaling pathway with pharmaceutical inhibitors. The inhibition of actin polymerization decreased Rac1 activity, indicating the presence of positive feedback from actin polymerization to Rac1. Furthermore, treatment with PI3-kinase inhibitor induced an adaptation of Rac1 activity, i.e. a transient reduction of Rac1 activity followed by recovery to the basal level. In silico modeling that reproduced the adaptation predicted the existence of a negative feedback loop from Rac1 to actin polymerization. Finally, we identified MLCK as the probable controlling factor in the negative feedback. These findings quantitatively demonstrate positive and negative feedback loops that involve actin, Rac1 and MLCK, and account for the ordered patterns of membrane dynamics observed in randomly migrating cells.
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Affiliation(s)
- Katsuyuki Kunida
- Department of Pathology and Biology of Diseases, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan
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Parrini MC, Sadou-Dubourgnoux A, Aoki K, Kunida K, Biondini M, Hatzoglou A, Poullet P, Formstecher E, Yeaman C, Matsuda M, Rossé C, Camonis J. SH3BP1, an exocyst-associated RhoGAP, inactivates Rac1 at the front to drive cell motility. Mol Cell 2011; 42:650-61. [PMID: 21658605 DOI: 10.1016/j.molcel.2011.03.032] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2010] [Revised: 02/10/2011] [Accepted: 03/30/2011] [Indexed: 11/18/2022]
Abstract
The coordination of the several pathways involved in cell motility is poorly understood. Here, we identify SH3BP1, belonging to the RhoGAP family, as a partner of the exocyst complex and establish a physical and functional link between two motility-driving pathways, the Ral/exocyst and Rac signaling pathways. We show that SH3BP1 localizes together with the exocyst to the leading edge of motile cells and that SH3BP1 regulates cell migration via its GAP activity upon Rac1. SH3BP1 loss of function induces abnormally high Rac1 activity at the front, as visualized by in vivo biosensors, and disorganized and instable protrusions, as revealed by cell morphodynamics analysis. Consistently, constitutively active Rac1 mimics the phenotype of SH3BP1 depletion: slow migration and aberrant cell morphodynamics. Our finding that SH3BP1 downregulates Rac1 at the motile-cell front indicates that Rac1 inactivation in this location, as well as its activation by GEF proteins, is a fundamental requirement for cell motility.
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