Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Kubota Hiroyuki Last modified date:2021.07.01

Professor / Research Center for Transomics Medicine / Medical Institute of Bioregulation

1. Yuki Ito, Shinsuke Uda, Toshiya Kokaji, Akiyoshi Hirayama, Tomoyoshi Soga, Yutaka Suzuki, Shinya Kuroda and Hiroyuki Kubota, Comparison of hepatic responses to glucose perturbation between normal and obese mice using edge ontology., Fusion of Mathematics and Biology, 2020.10.
2. Hiroyuki Kubota, Selective regulation of the Insulin-AKT pathway by simultaneous processing of blood insulin pattern in the liver., Fusion of Mathematics and Biology, 2020.10.
3. Hiroyuki Kubota, Selective control of the Insulin-AKT pathway by simultaneous processing of blood insulin in the liver, 1st International Symposium on Interdisciplinary Approaches to Integrative Understanding of Biological Signaling Networks, 2019.02.
4. Fumiko Matsuzaki, Shinsuke Uda, Yukiyo Yamauchi, Masaki Matsumoto, Tomoyoshi Soga, Kazumitsu Maehara, Yasuyuki Ohkawa, Keiichi I. Nakayama, Shinya Kuroda, Hiroyuki Kubota, Time Series Transomics: Integrated analysis through multiple molecular layers, ICSB, 2019.11.
5. Hiroyuki Kubota , In vivo decoding mechanisms of the temporal patterns of blood insulin by the insulin-AKT pathway in the liver., 2019.02.
6. Kubota H., Uda S., Matsuzaki F., Kuroda S, Trans-Omic analysis of the acute insulin action in the liver =Toward in vivo trans-omic analysis, The 1st International Symposium for Trans-Omics , 2017.11.
7. 久保田 浩行, 宇田 新介, 松﨑 芙美子, 黒田 真也, Toward in vivo Trans-omic analysis, 日本プロテオーム学会, 2016.07, Cell system consists of a huge number of molecules across multiple “omic layers”, such as genome, transcriptome, proteome and metabolome layers. Therefore, in understanding the entire picture of the cell system, we need to integrate the multiple omic layers and reveal networks of the molecules across them. Recent advances in measurements of each omic layers enable us to extract information from multiple omic layers, so called “multiple omics analysis”. We now propose “trans-omic analysis” for reconstructing global comprehensive networks from multiple omics data [1]. Based on this concept, we reconstructed trans-omic networks from phospho-proteome and metabolome data using cultured cell line stimulated by insulin [2]. We are now developing a new method to integrate multiple omic layers including transcriptome and expression proteome in addition to phospho-proteome and metabolome data using mice administered with insulin. For “in vivo trans-omic analysis”, there are some problems to be solved. In this presentation, I want to talk and discuss about the problems and their solutions toward in vivo trans-omics analysis..
8. 久保田 浩行, 柚木 克之, 黒田 真也, Transomics analysis of acute insulin action: network reconstruction from multi-omics data, The 25th Hot Spring Harbor International Symposium, 2015.11.
9. 久保田 浩行, 柚木 克之, 黒田 真也, Reconstruction of insulin signal flow from phospho-proteome and metabolome data, International Symposium on Synthetic Systems Biology, 2015.09.
10. 久保田 浩行, 黒田 真也, 柚木 克之, Reconstruction of insulin signal flow from phosphoproteome and metabolome data, 2nd International Symposium on Protein Modification in Pathogenic Dysregulation of Signaling, 2015.01, Cellular responses are regulated by signals that are transmitted in multiple omic layers (trans-omic layers), such as epigenome, transcriptome, proteome and metabolome. Therefore, a new top-down approach combined with multiple omic layers will be necessary for understanding of the entire picture of the cellular responses. We call this strategy as “trans-omic analysis”. Since molecular interactions among trans-omic layers are mutually connected, it is almost impossible to reconstruct trans-omic network from the data with different conditions. Therefore, tans-omic analysis requires global, multiple omic measurements under the same experimental condition (1). In this study, we measured time course data from the metabolome and phosphoproteome of acute insulin action (<60 minutes) under the same experimental condition in Fao cells, which consisted of 304 metabolites and 7,277 phosphorylated residues on 3,458 proteins (2). We subsequently developed a new method to integrate multi omic data using multiple databases. We found that an insulin signal flows through a network that is consistent with 44 changed metabolites which are the targets of acute insulin action, 26 phosphorylated responsible metabolic enzymes which potentially regulate the amount of changed metabolites, and 13 protein kinases which potentially phosphorylate phosphorylated responsible metabolic enzymes (fig). Moreover, 35 metabolites out of 44 changed metabolites were identified as allosteric effectors which potentially regulate the activity of responsible metabolic enzymes. We then, focused on the conversion of fructose-1-phosphate to fructose-1,6-bisphospate which is one of the important steps of glycogenesis pathway, and constructed a kinetic model of it. As results of analysis, we found that a novel phosphorylation site and some allosteric effectors are important for the regulation. .
11. 久保田 浩行, 黒田 真也, Temporal Coding of Insulin Action through Multiplexing of AKT Pathway
, 蛋白質科学会, 2014.06.