Biology subjects

Yugi, K.

Publications and source records attributed to Yugi, K..

5 recordsLinked to original sources

bioRxiv2023

Trans-omic analysis reveals opposite metabolic dysregulation between feeding and fasting in liver associated with obesity

Dysregulation of liver metabolism associated with obesity during feeding and fasting leads to the breakdown of metabolic homeostasis. However, the underlying mechanism remains unknown. Here, we measured multi-omics data in the liver of wild-type and leptin-deficient obese (ob/ob) mice at ad libitum feeding, and constructed a differential regulatory trans-omic network of metabolic reactions. We compared the trans-omic network at feeding with that at 16 h-fasting constructed in our previous study. Intermediate metabolites in glycolytic and nucleotide metabolism decreased in ob/ob mice at feeding but increased at fasting. Allosteric regulation reversely shifted between feeding and fasting, generally showing activation at feeding while inhibition at fasting in ob/ob mice. Transcriptional regulation was similar between feeding and fasting, generally showing inhibiting transcription factor regulations, activating enzyme protein regulations in ob/ob mice. The opposite metabolic dysregulation between feeding and fasting characterizes breakdown of metabolic homeostasis associated with obesity.

systems biology↗

bioRxiv2023

transomics2cytoscape: An automated software for interpretable 2.5-dimensional visualization of trans-omic networks

Biochemical network visualization is one of the essential technologies for mechanistic interpretation of omics data. In particular, recent advances in multi-omics measurement and analysis require the development of visualization methods that encompass multiple omics data. Visualization in 2.5 dimension (2.5D visualization), which is an isometric view of stacked X-Y planes, is a convenient way to interpret multi-omics/trans-omics data in the context of the conventional layouts of biochemical networks drawn on each of the stacked omics layers. However, 2.5D visualization of trans-omics networks is a state-of-the-art method that primarily relies on time-consuming human efforts involving manual drawing. Here, we present an R Bioconductor package transomics2cytoscape for automated visualization of 2.5D trans-omics networks. We confirmed that transomics2cytoscape could be used for rapid visualization of trans-omics networks presented in published papers within a few minutes. Transomics2cytoscape allows for frequent update/redrawing of trans-omics networks in line with the progress in multi-omics/trans-omics data analysis, thereby enabling network-based interpretation of multi-omics data at each research step. The transomics2cytoscape source code is available at https://github.com/ecell/transomics2cytoscape.

systems biology↗

bioRxiv2022

In vivo transomic analyses of glucose-responsive metabolism in skeletal muscle reveal core differences between the healthy and obese states

Metabolic regulation in skeletal muscle is essential for blood glucose homeostasis. Obesity causes insulin resistance in skeletal muscle, leading to hyperglycemia and type 2 diabetes. In this study, we performed multiomic analysis of the skeletal muscle of wild-type (WT) and genetically obese (ob/ob) mice, and constructed regulatory transomic networks for metabolism after oral glucose administration. Our network revealed that metabolic regulation by glucose-responsive metabolites had a major effect on WT mice, especially carbohydrate metabolic pathways. By contrast, in ob/ob mice, much of the metabolic regulation by glucose-responsive metabolites was lost and metabolic regulation by glucose-responsive genes was largely increased, especially in carbohydrate and lipid metabolic pathways. We present some characteristic metabolic regulatory pathways found in central carbon, branched amino acids, and ketone body metabolism. Our transomic analysis will provide insights into how skeletal muscle responds to changes in blood glucose and how it fails to respond in obesity.

systems biology↗

bioRxiv2019

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

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, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multi-omic 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, 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 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.\n\nOne Sentence SummaryRapid changes in regulatory metabolites and gene expression dominate the healthy state, and slow transcriptional regulation dominates the obese state.

systems biology↗

bioRxiv2017

Trans-omic analysis reveals fed and fasting insulin signal across phosphoproteome, transcriptome, and metabolome

The concentration and temporal pattern of insulin selectively regulate multiple cellular functions. To understand how insulin dynamics are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells from three networks--a phosphorylation-dependent cellular functions regulatory network using phosphoproteomic data, a transcriptional regulatory network using phosphoproteomic and transcriptomic data, and a metabolism regulatory network using phosphoproteomic and metabolomic data. With the trans-omic regulatory network, we identified selective regulatory networks that mediate differential responses to insulin. Akt and Erk, hub molecules of insulin signaling, encode information of a wide dynamic range of dose and time of insulin. Down-regulated genes and metabolites in glycolysis had high sensitivity to insulin (fasting insulin signal); up-regulated genes and dicarboxylic acids in the TCA cycle had low sensitivity (fed insulin signal). This integrated analysis enables molecular insight into how cells interpret physiologically fed and fasting insulin signals.\n\nHighlightsO_LIWe constructed a trans-omic network of insulin action using multi-omic data.\nC_LIO_LIThe trans-omic network integrates phosphorylation, transcription, and metabolism.\nC_LIO_LIWe classified signaling, transcriptome, and metabolome by sensitivity to insulin.\nC_LIO_LIWe identified fed and fasting insulin signal flow across the trans-omic network.\nC_LI

systems biology↗