Biology subjects

Hironaka, K.-i.

Publications and source records attributed to Hironaka, K.-i..

8 recordsLinked to original sources

bioRxiv2023

In vitro pigmentation of human iPSC-derived retinal pigment epithelium cells does not indicate their quality for cell transplantation

Retinal pigment epithelium (RPE) cells show heterogeneous level of pigmentation when cultured in vitro. To know whether their color in appearance is correlated with the function of the RPE, we analyzed the color intensities of human-induced pluripotent stem cell-derived RPE cells (iPSC-RPE) together with the gene expression profile at the single cell level. For this purpose, we utilized our recent invention, Automated Live imaging and cell Picking System (ALPS), which enabled photographing each cell before RNA-sequencing analysis to profile the gene expression of each cell. While our iPSC-RPE were categorized in 4 clusters by gene expression, the color intensity of iPSC-RPE did not project any specific gene expression profiles. We reasoned this by less correlation between the actual color and the gene expressions that directly define the level of pigmentation, from which we hypothesized the color of RPE cells may be a temporal condition not strongly indicating the functional characteristics of the RPE.

cell biology↗

bioRxiv2023

Model selection reveals selective regulation of blood amino acid and lipid metabolism by insulin in humans

Insulin plays a crucial role in regulating the metabolism of blood glucose, amino acids, and lipids in humans. However, the mechanisms by which insulin selectively controls these different metabolites are not yet fully understood. To address this question, we used mathematical modeling to identify the selective regulatory mechanisms of insulin on blood amino acids and lipids. Our study revealed that insulin negatively regulates the influx and positively regulates the efflux of lipids, which is consistent with previous findings. In contrast, we did not observe the previously reported negative regulation of BCAA influx by insulin; instead, we found that insulin positively regulates BCAA efflux. We also observed that the earlier peak time of lipids compared to BCAAs is dependent on insulins negative regulation of their influx. Overall, our findings shed new light on how insulin selectively regulates the levels of different metabolites in human blood, providing valuable insights into the pathogenesis of metabolic disorders and potential therapeutic interventions.

systems biology↗

bioRxiv2022

Insulin Effectiveness, a Measure Consisting of Insulin Sensitivity, Secretion, and Clearance, Captures Differences Between Diabetic and Nondiabetic States

ContextInsulin clearance is implicated in regulation of glucose homeostasis independently of insulin sensitivity and insulin secretion. ObjectiveTo understand the relation between blood glucose and insulin sensitivity, secretion, and clearance. MethodsWe performed a hyperglycemic clamp, a hyperinsulinemic-euglycemic clamp, and an oral glucose tolerance test (OGTT) in 47, 16, and 49 subjects with normal glucose tolerance (NGT), impaired glucose tolerance (IGT), and type 2 diabetes mellitus (T2DM), respectively. Mathematical analyses were retrospectively performed on this dataset. ResultsThe disposition index (DI), defined as the product of insulin sensitivity and secretion, showed a weak correlation with blood glucose levels, especially in IGT (r = 0.04; 95% CI, -0.63- 0.44). However, an equation relating DI, insulin clearance, and blood glucose levels was well conserved regardless of the extent of glucose intolerance. As a measure of the effect of insulin, we developed an index, designated "DI/cle," that is based on this equation and corresponds to DI divided by the square of insulin clearance. DI/cle was not impaired in IGT compared with NGT, possibly as a result of a decrease in insulin clearance in response to a reduction in DI, whereas it was impaired in T2DM relative to IGT. Moreover, DI/cle estimated from a hyperinsulinemic-euglycemic clamp, OGTT, or a fasting blood test were significantly correlated with that estimated from two clamp tests (r = 0.52; 95% CI, 0.37- 0.64, r = 0.43; 95% CI, 0.24- 0.58, and r = 0.54; 95% CI, 0.38- 0.68, respectively). ConclusionsDI/cle can serve as a new indicator for the trajectory of changes in glucose tolerance.

systems biology↗

bioRxiv2022

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant/variable) and time dependence (sustained/transient). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation. Author SummaryFor biological interpretation of lage-scale omics data, two analyses, hypothesis-driven analysis and data-driven analysis including tensor decomposition, have been used. These two analyses have their own advantages and disadvantages, and are mutually complementary. However, the direct comparison between these two analyses for omic data is poorly examined. In this study, we applied tensor decomposition to a dataset representing temporal changes in the human 562 blood molecules as data-driven analysis and extracted three features. We have previously analyzed the same data by hypothesis-driven analysis (Fujita et al., 2022). The three features extracted by the tensor decomposition are the same features extracted by the hypothesis-driven analysis, indicating that the tensor decomposition can extract the features in an unbiased manner. Although the same features can be extracted by the tensor decomposition and hypothesis-driven analysis, hypothesis-driven analysis in our earlier study took years (Fujita et al., 2022), while feature extraction by tensor decomposition took only days in this study. Thus, tensor decomposition can extract biological features in a non-biased manner without time-consuming process of hypothesis generation. We propose that tensor decomposition can be the first choice for analysis of omic data.

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↗

bioRxiv2021

Omics-based label-free metabolic flux inference reveals dysregulation of glucose metabolism in liver associated with obesity

Glucose homeostasis is maintained by modulation of metabolic flux. Enzymes and metabolites regulate the involved metabolic pathways. Dysregulation of glucose homeostasis is a pathological event in obesity. Analyzing metabolic pathways and the mechanisms contributing to obesity-associated dysregulation in vivo is challenging. Here, we introduce OMELET: Omics-Based Metabolic Flux Estimation without Labeling for Extended Trans-omic Analysis. OMELET uses metabolomic, proteomic, and transcriptomic data to identify changes in metabolic flux, and to quantify contributions of metabolites, enzymes, and transcripts to the changes in metabolic flux. By evaluating the livers of fasting ob/ob mice, we found that increased metabolic flux through gluconeogenesis resulted primarily from increased transcripts, whereas that through the pyruvate cycle resulted from both increased transcripts and changes in substrates of metabolic enzymes. With OMELET, we identified mechanisms underlying the obesity-associated dysregulation of metabolic flux in liver. HighlightsO_LIWe created OMELET to infer metabolic flux and its regulation from multi-omic data. C_LIO_LIGluconeogenic and pyruvate cycle fluxes increased in fasting ob/ob mice. C_LIO_LITranscripts increases mediated the increase in gluconeogenic fluxes in ob/ob mice. C_LIO_LIIncreases in transcripts and substrates enhanced pyruvate cycle flux in ob/ob mice. C_LI

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↗

bioRxiv2019

Single-cell information analysis reveals small intra- and large intercellular variations increase cellular information capacity

Cells transmit information about extracellular stimulation through signaling pathways to control cellular function. A signaling pathway can be regarded as a communication channel. In the analysis of channels of cell populations, intercellular variation is considered noise. However, intercellular variation enables individual cells to encode different information. Therefore, at the single-cell level, each cell can be regarded as an independent channel. Thus, we propose that responses of cells of the same type in tissues, such as the fibers in a skeletal muscle, should be regarded as a multiple-cell channel composed of single-cell channels, in which intercellular variation contains information. Here, we applied electrical pulses to individual myotubes from cultured C2C12 cells or dissociated skeletal muscle fibers and measured Ca2+ responses or contraction, respectively, to estimate information capacity in a biological system. For each muscle cell system, we found that a single-cell channel transmitted more information than did a cell-population channel, indicating that the cellular response is consistent with each cell (low intracellular variation) but different among individual cells (high intercellular variation). As cell number and thus the number of single-cell channels increased, a multiple-cell channel transmitted more information by incorporating the differences among individual cells. Thus, a tissue with small intracellular and large intercellular variations has the capacity to distinguish differences in stimulation intensity to precisely control physiological function.\n\nOne Sentence SummarySmall intracellular and large intercellular variations increase information transmission for precise control of tissue function.

systems biology↗