Biology subjects

Karasawa, Y.

Publications and source records attributed to Karasawa, Y..

3 recordsLinked to original sources

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

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↗

bioRxiv2018

Logical design of oral glucose ingestion pattern minimizing blood glucose in humans

Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose 2 level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in 3 healthy human subjects, used the model to predict an optimal glucose ingestion pattern, and showed that the optimal ingestion pattern minimized the peak value of blood glucose level. Subjects orally ingested 3 doses of glucose by bolus or over 2 hours, and blood glucose, insulin, C-peptide and incretins were measured for 4 hours. We constructed an ordinary differential equation model that reproduced the time course data of the blood glucose and blood hormone levels. Using the model, we predicted that intermittent ingestion 30 minutes apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern decreased the peak value of blood glucose level. This approach could be applied to design optimal dietary ingestion patterns.\n\nIn BriefAs a forward problem, we measured blood glucose and hormones in three human subjects after oral glucose ingestion and constructed a mathematical model of blood glucose control. As an inverse problem, we used the model to predict the optimal oral glucose ingestion pattern that minimized the peak value of blood glucose level, and validated the pattern with the subjects.\n\nHighlightsO_LIModeling blood glucose concentrations predicts an intermittent ingestion pattern is optimal\nC_LIO_LIHuman validation shows ingestion at 30-minute intervals limits peak blood glucose\nC_LIO_LIWe provide a strategy to design optimal dietary ingestion patterns\nC_LI

systems biology↗