Biology subjects

Eto, M.

Publications and source records attributed to Eto, M..

3 recordsLinked to original sources

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↗

bioRxiv2019

Thick perirenal fat predicts the growth pattern of renal cell carcinoma

ObjectiveTo examine the relationship between the direction of renal cell carcinoma growth and the visceral/perirenal fat volume.\n\nPatients and Methods: We retrospectively reviewed computed tomography scans of 153 patients with stage 1 renal cell carcinoma who underwent radical or partial nephrectomy in our hospital between January 2013 and July 2016. We calculated the visceral/subcutaneous/perirenal fat volumes using SYNAPSE VINCENT(R). Of the 60 patients, the perirenal fat was immunohistochemically stained for leptin, adiponectin, COX-2 and UCP-1, and the association with outward tumor protrusion was evaluated.\n\nResultsOf the 153 cases, 88 had confirmed outward expansion (57.5%), 110 were classed as pT1a (52 and 58 with outer and inner expansion, respectively), 43 were classed as pT1b (36 and 7 with outer and inner expansion, respectively; P<0.0001). Multivariate logistic regression model showed a trend toward significance in pT1b (vs pT1a, [OR] 6.033, 95%CI=2.409-15.108, P=0.0001), perirenal fat percentage >1.0 (vs [≤]1.0, [OR] 2.596, 95%CI=1.205-5.591, P=0.014). as independent predictors for outer protrusion. Immunohistochemical staining was positive for UCP-1 expression in 31 out of 41 outgrowth types (75.6%), and all 19 endogenous types (100%; P=0.003).\n\nConclusionsRenal cell carcinoma with thick perirenal fat correlates with an increased likelihood of developing outward tumor protrusion; therefore, fat distribution may affect the development of renal cell carcinoma.

cancer biology↗