Trans-omic Analysis Reveals Selective Responses to Induced and Basal Insulin across Signaling, Transcriptional, and Metabolic Networks

Kawata, Kentaro; Hatano, Atsushi; Yugi, Katsuyuki; Konishi, Hiroyuki; Sano, Takanori; Fujii, Masashi; Tomizawa, Yasushi; Kokaji, Toshiya; Tanaka, Kaori; Uda, Shinsuke; Suzuki, Yutaka; Matsumoto, Masaki; Nakayama, Keiichi I.; Saitoh, Kaori; Kato, Keiko; Ueno, Ayano; Ohishi, Maki; Hirayama, Akiyoshi; Soga, Tomoyoshi; Kuroda, Shinya

doi:10.1016/j.isci.2018.07.022

Cited by 40 publications

(50 citation statements)

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“…We extracted pathways related to various cellular functions from KEGG (hereafter cellular functional pathways), except for those related to signaling and those that function specifically in tissues other than liver (see Section ). The analysis with KEGG pathways related to signaling was performed in our previous study (Kawata et al, ). The pathways that function specifically in tissues other than liver were excluded because the phosphoproteomic data are from rat Fao hepatoma cells, which are a liver cell line.…”

Section: Resultsmentioning

confidence: 99%

Reconstruction of global regulatory network from signaling to cellular functions using phosphoproteomic data

et al. 2018

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Cellular signaling regulates various cellular functions via protein phosphorylation.Phosphoproteomic data potentially include information for a global regulatory network from signaling to cellular functions, but a procedure to reconstruct this network using such data has yet to be established. In this paper, we provide a procedure to reconstruct a global regulatory network from signaling to cellular functions from phosphoproteomic data by integrating prior knowledge of cellular functions and inference of the kinase-substrate relationships (KSRs). We used phosphoproteomic data from insulin-stimulated Fao hepatoma cells and identified protein phosphorylation regulated by insulin specifically over-represented in cellular functions in the KEGG database. We inferred kinases for protein phosphorylation by KSRs, and connected the kinases in the insulin signaling layer to the phosphorylated proteins in the cellular functions, revealing that the insulin signal is selectively transmitted via the Pi3k-Akt and Erk signaling pathways to cellular adhesions and RNA maturation, respectively. Thus, we provide a method to reconstruct global regulatory network from signaling to cellular functions based on phosphoproteomic data. K E Y W O R D S cellular functions, insulin signaling, network integration, phosphoproteomic data, phosphorylation, systems biology, Trans-omics | 91Genes to Cells KAWATA eT Al. ACKNOWLEDGMENTSWe thank our laboratory members for critically reading this manuscript and for their technical assistance with the experiments. The computational analysis of this work was performed in part with support of the supercomputer system

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Section: Resultsmentioning

confidence: 99%

Reconstruction of global regulatory network from signaling to cellular functions using phosphoproteomic data

et al. 2018

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“…In particular, we could not identify transcription factors regulating most of the 40 glucose-responsive genes encoding metabolic enzymes in the liver of ob/ob mice. Proteomic data will provide large-scale information about the regulation of metabolic reactions by changes in the amount of transcription factors and metabolic enzymes (45,46), and phosphoproteomic data will identify the regulation of metabolic reactions by phosphorylation of transcription factors and metabolic enzymes (15,18,47). In addition, epigenomic data, as well as detailed information about the binding affinities of metabolic reactions for the metabolites that function as substrates, products, and allosteric regulators are required for constructions of a comprehensive regulatory trans-omic network for glucose-responsive metabolic reactions.…”

Section: Discussionmentioning

confidence: 99%

“…The numbers of each type of glucose-responsive node and edge are shown with the same colors in the network summary to the right. The Insulin signal layer is the insulin signaling pathway constructed in our previous phosphoproteomic study (18). The Enzyme, Reaction, and Metabolite layers are organized into global metabolic pathway (mmu01100) in the 20 KEGG database (24,25).…”

Section: Figure 2 Identification Of Glucose-responsive Metabolites mentioning

confidence: 99%

“…We have applied an approach that we call 'trans-omic' analysis for the construction of global biochemical network using simultaneously measured multi-omic data based on direct molecular interaction (15)(16)(17). We used this approach to construct an insulin-induced regulatory trans-omic network for metabolism of FAO hepatoma cells by integrating simultaneously measured multi-15 omic measurements (15,18). With this trans-omic network, we discovered selective regulation of the trans-omic network that depended on doses of insulin.…”

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Trans-omic analysis reveals allosteric and gene regulation-axes for altered glucose-responsive liver metabolism associated with obesity

Kokaji

Hatano

Ito

et al. 2019

Preprint

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Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in the healthy and obese states, we constructed and analyzed trans-omic glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, 5 and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomic time-course data from wild-type (WT) and leptin-deficient obese (ob/ob) mice after orally administered glucose. In WT mice, metabolic reactions were rapidly regulated (within 10 minutes of oral glucose administration) primarily by glucose-responsive metabolites, especially ATP and NADP+, which functioned as allosteric regulators and substrates of metabolic enzymes, 10 and by Akt-dependent glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, most rapid regulation by glucose-responsive metabolites was absent; instead, glucose administration produced slow changes in the expression of metabolic enzyme-encoding genes to alter metabolic reactions in a time scale of hours. Few common regulatory events occurred in both the healthy and obese mice. Thus, our trans-omic network analysis revealed regulation of 15 liver metabolism in response to glucose is mediated through different mechanisms in the healthy and obese states: Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, and slow transcriptional regulation dominates the obese state.One Sentence Summary: Rapid changes in regulatory metabolites and gene expression 20 dominate the healthy state, and slow transcriptional regulation dominates the obese state. Main Text:The ability to produce stable blood glucose is indispensable for human life and health (1-3). Although a large amount of glucose enters the body through meals, changes in organ metabolism 25 maintains glucose homeostasis (4-6). Impairment of the regulation of organ metabolism, commonly due to obesity and insulin resistance, results in hyperglycemia and development of type 2 diabetes mellitus (4-6). The liver, into which dietary glucose flows directly through the portal vein, has a primary function in maintaining glucose homeostasis (7,8). Indeed, the liver is both a glucose-producing organ, supplying glucose for extra-hepatic organs, and glucose-30 utilizing organ, metabolizing one third of orally administered glucose (8, 9). Oral intake of glucose produces drastic changes in the liver metabolism-not only glucose metabolism but also lipid and amino-acid metabolism, collectively glucose-responsive metabolism. The mechanisms regulating glucose-responsive metabolism in the liver and how these mechanisms are altered in obesity have yet to be identified. 35 Metabolism is a set of chemical reactions that convert one metabolite into another. Chemical reactions in metabolism, denoted here as metabolic reactions, involve metabolites, which function as substrates, products, and allosteri...

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“…Omics data have enabled the unbiased characterization of the molecular features of multiple human diseases, particularly in cancer [1][2][3] . It is becoming increasingly common to characterize multiple omics layers in parallel, with so-called "trans-omics analysis", to gain biological insights spanning multiple types of cellular processes [4][5][6] . Consequently, many tools are developed to analyze such data [7][8][9][10][11] , mainly by adapting and combining existing "single omics" methodologies to multiple parallel datasets.…”

Section: Introductionmentioning

confidence: 99%

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Dugourd

Kuppe

Sciacovelli

et al. 2020

Preprint

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Multi-omics datasets can provide molecular insights beyond the sum of individual omics.Diverse tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from nine renal cell carcinoma patients. We used COSMOS to generate novel hypotheses such as the impact of Androgen Receptor on nucleoside metabolism and the influence of the JAK-STAT pathway on propionyl coenzyme A production. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

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Trans-omic Analysis Reveals Selective Responses to Induced and Basal Insulin across Signaling, Transcriptional, and Metabolic Networks

Cited by 40 publications

References 58 publications

Reconstruction of global regulatory network from signaling to cellular functions using phosphoproteomic data

Reconstruction of global regulatory network from signaling to cellular functions using phosphoproteomic data

Trans-omic analysis reveals allosteric and gene regulation-axes for altered glucose-responsive liver metabolism associated with obesity

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

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