Biology subjects

Fujii, M.

Publications and source records attributed to Fujii, M..

22 records · Page 2Linked to original sources

bioRxiv2018

\"Small and many\" is the strategy for robust and efficient information transfer in dendritic spines

A dendritic spine is a small structure on the dendrites of a neuron that processes input timing information from other neurons. Tens of thousands of spines are present on a neuron. Why are spines so small and many? Because of the small number of molecules in the spine volume, biochemical reactions become stochastic. Therefore, we used the stochastic simulation model of N-methyl-D-aspartate receptor (NMDAR)-mediated Ca2+ increase to address this issue. NMDAR-mediated Ca2+ increase codes the input timing information between prespiking and postspiking. We examined how much the input timing information is encoded by Ca2+ increase against prespiking fluctuation. We found that the input timing information encoded in the spine volume (10-1 m3) is more robust against prespiking fluctuation than that in the cell volume (103 m3). We further examined the mechanism of the robust information transfer in the spine volume. We demonstrated that the necessary and sufficient condition for robustness is that the stochastic NMDAR-mediated Ca2+ increase (intrinsic noise) becomes much larger than the prespiking fluctuation (extrinsic noise). The condition is satisfied in the spine volume, but not in the cell volume. Moreover, we compared the information transfer in many small \"spine-volume\" spines with that in a single large \"cell-volume\" spine. We found that many small \"spine-volume\" spines is much more efficient for information transfer than a single large \"cell-volume\" spine when prespiking fluctuation is large. Thus, robustness and efficiency are two functional reasons why dendritic spines are so small and many.\n\nSignificanceA dendritic spine is a small platform for information processing in a neuron, and tens of thousands of spines are present on a neuron. Why are spines so small and many? Here we addressed this issue using stochastic simulation of NMDAR-mediated Ca2+ increase in a spine. We demonstrated that smallness of a spine enables the robust information transfer against input fluctuation, and that many small spines are much efficient for information transfer than a single large cell. This is the first demonstration that shows the advantage of the \"small and many\" of spines in information processing. The \"small and many\" strategy may be used not only in spines of a neuron, but also in other small and many intracellular organelles.

biophysics↗

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↗

bioRxiv2017

System Identification Using Compressed Sensing Reveals Signaling-Decoding System By Gene Expression

Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales.\n\nHighlightsO_LIWe developed a system identification method using compressed sensing.\nC_LIO_LIThis method allowed us to find a pathway using data of different time scales.\nC_LIO_LIWe identified a selective signaling-decoding system by gene expression.\nC_LIO_LIWe validated the identified system by pharmacological perturbation.\nC_LI\n\neTOC BlurbWe describe a system identification method of molecular networks with different time-scale data using a signal recovery technique in compressed sensing.

systems biology↗

bioRxiv2017

Bidirectional Changes Of Hepatic And Peripheral Insulin Clearance Characterize Abnormal Temporal Patterns Of Serum Insulin Concentration In Diabetic Subjects

Insulin plays a central role in glucose homeostasis, and impairment of insulin action causes glucose intolerance and leads to type 2 diabetes mellitus (T2DM). A decrease in the transient peak and sustained increase of circulating insulin by glucose infusion accompany T2DM pathogenesis. However, the mechanism underlying this abnormal temporal pattern of circulating insulin concentration remains unknown. Here we show that bidirectional changes of hepatic and peripheral insulin clearance characterize this abnormal temporal pattern of circulating insulin concentration during the progression of T2DM. We developed a mathematical model using a hyperglycemic and hyperinsulinemic-euglycemic clamp in 111 subjects, including healthy normoglycemic and diabetic subjects. The hepatic and peripheral insulin clearance significantly increase and decrease, respectively, during the progression of glucose intolerance. The increased hepatic insulin clearance reduces the amplitude of circulating insulin concentration, whereas the decreased peripheral insulin clearance changes the temporal patterns of circulating insulin concentration from transient to sustained. These results provide insight that may be useful in treating T2DM associated with aberrant insulin clearance.

systems biology↗