An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver

Summary: An effective combination of multi-omic datasets can enhance our understanding of complex biological phenomena. To build a context-dependent network with multiple omic layers, i.e., a trans-omic network, we perform phosphoproteomics, transcriptomics, proteomics, and metabolomics of murine li...

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Main Authors: Fumiko Matsuzaki, Shinsuke Uda, Yukiyo Yamauchi, Masaki Matsumoto, Tomoyoshi Soga, Kazumitsu Maehara, Yasuyuki Ohkawa, Keiichi I. Nakayama, Shinya Kuroda, Hiroyuki Kubota
Format: Article
Language:English
Published: Elsevier 2021-08-01
Series:Cell Reports
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2211124721010032
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spelling doaj-bda0cb6d08e645f0b537c16d4a675d842021-08-26T04:33:15ZengElsevierCell Reports2211-12472021-08-01368109569An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liverFumiko Matsuzaki0Shinsuke Uda1Yukiyo Yamauchi2Masaki Matsumoto3Tomoyoshi Soga4Kazumitsu Maehara5Yasuyuki Ohkawa6Keiichi I. Nakayama7Shinya Kuroda8Hiroyuki Kubota9Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, JapanResearch Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, JapanResearch Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, JapanDepartment of Omics and Systems Biology, Graduate School of Medical and Dental Sciences, Niigata University, 757 Ichibancho, Asahimachi-dori, Chuo-ku, Niigata 951-8510, JapanInstitute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, JapanResearch Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, JapanResearch Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, JapanDepartment of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, JapanDepartment of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, JapanResearch Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan; Corresponding authorSummary: An effective combination of multi-omic datasets can enhance our understanding of complex biological phenomena. To build a context-dependent network with multiple omic layers, i.e., a trans-omic network, we perform phosphoproteomics, transcriptomics, proteomics, and metabolomics of murine liver for 4 h after insulin administration and integrate the resulting time series. Structural characteristics and dynamic nature of the network are analyzed to elucidate the impact of insulin. Early and prominent changes in protein phosphorylation and persistent and asynchronous changes in mRNA and protein levels through non-transcriptional mechanisms indicate enhanced crosstalk between phosphorylation-mediated signaling and protein expression regulation. Metabolic response shows different temporal regulation with transient increases at early time points across categories and enhanced response in the amino acid and nucleotide categories at later time points as a result of process convergence. This extensive and dynamic view of the trans-omic network elucidates prominent regulatory mechanisms that drive insulin responses through intricate interlayer coordination.http://www.sciencedirect.com/science/article/pii/S2211124721010032trans-omicsmulti-omicstrans-omic networknetwork dynamicsinsulin
collection DOAJ
language English
format Article
sources DOAJ
author Fumiko Matsuzaki
Shinsuke Uda
Yukiyo Yamauchi
Masaki Matsumoto
Tomoyoshi Soga
Kazumitsu Maehara
Yasuyuki Ohkawa
Keiichi I. Nakayama
Shinya Kuroda
Hiroyuki Kubota
spellingShingle Fumiko Matsuzaki
Shinsuke Uda
Yukiyo Yamauchi
Masaki Matsumoto
Tomoyoshi Soga
Kazumitsu Maehara
Yasuyuki Ohkawa
Keiichi I. Nakayama
Shinya Kuroda
Hiroyuki Kubota
An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver
Cell Reports
trans-omics
multi-omics
trans-omic network
network dynamics
insulin
author_facet Fumiko Matsuzaki
Shinsuke Uda
Yukiyo Yamauchi
Masaki Matsumoto
Tomoyoshi Soga
Kazumitsu Maehara
Yasuyuki Ohkawa
Keiichi I. Nakayama
Shinya Kuroda
Hiroyuki Kubota
author_sort Fumiko Matsuzaki
title An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver
title_short An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver
title_full An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver
title_fullStr An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver
title_full_unstemmed An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver
title_sort extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver
publisher Elsevier
series Cell Reports
issn 2211-1247
publishDate 2021-08-01
description Summary: An effective combination of multi-omic datasets can enhance our understanding of complex biological phenomena. To build a context-dependent network with multiple omic layers, i.e., a trans-omic network, we perform phosphoproteomics, transcriptomics, proteomics, and metabolomics of murine liver for 4 h after insulin administration and integrate the resulting time series. Structural characteristics and dynamic nature of the network are analyzed to elucidate the impact of insulin. Early and prominent changes in protein phosphorylation and persistent and asynchronous changes in mRNA and protein levels through non-transcriptional mechanisms indicate enhanced crosstalk between phosphorylation-mediated signaling and protein expression regulation. Metabolic response shows different temporal regulation with transient increases at early time points across categories and enhanced response in the amino acid and nucleotide categories at later time points as a result of process convergence. This extensive and dynamic view of the trans-omic network elucidates prominent regulatory mechanisms that drive insulin responses through intricate interlayer coordination.
topic trans-omics
multi-omics
trans-omic network
network dynamics
insulin
url http://www.sciencedirect.com/science/article/pii/S2211124721010032
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