Biology subjects

Suzuki, K.

Publications and source records attributed to Suzuki, K..

113 records · Page 7Linked to original sources

bioRxiv2019

Sox17 expression in endocardium precursor cells regulates heart development in mice

The endocardium is the endothelial component of the vertebrate heart and plays a key role in heart development. Cardiac progenitor cells (CPCs) that express the homeobox gene Nkx2-5 give rise to the endocardium. Where, when, and how the endocardium segregates during embryogenesis have remained largely unknown, however. We now show that Nkx2-5+ CPCs that express the Sry-type HMG box gene Sox17 specifically differentiate into the endocardium in mouse embryos. Approximately 20% to 30% of Nkx2-5+CPCs transiently express Sox17 from embryonic day (E) 7.5 to E8.5.Although Sox17 is not essential or sufficient for endocardium fate, it can bias the fate of CPCs toward the endocardium. On the other hand, Sox17 expression in the endocardium is required for heart development. Deletion of Sox17 specifically in the mesoderm markedly impaired endocardium development with regard to cell proliferation and behavior. The proliferation of cardiomyocytes, ventricular trabeculation, and myocardium thickening were also impaired in a non-cell-autonomous manner in the Sox17 mutant, resulting in anomalous morphology of the heart, likely as a consequence of down-regulation of NOTCH signaling. Changes in gene expression profile in both the endocardium and myocardium preceded the reduction in NOTCH-related gene expression in the mutant embryos, suggesting that Sox17 expression in the endocardium regulates an unknown signal required for nurturing of the myocardium. Our results thus provide insight into differentiation of the endocardium and its role in heart development. SignificanceThe endocardium is vital for vertebrate heart development; however, the molecular mechanisms regulating fate determination and differentiation remain largely unknown. Here, we show that a part of the earliest cardiac progenitor cells (CPCs) transiently and exclusively express Sry-type HMG box gene Sox17 in the mouse embryo. Sox17-expressing CPCs specifically differentiate to the endocardium. Sox17 biases the fate of CPCs toward the endocardium, and regulates proliferation and cellular behavior cell autonomously. Conversely, Sox17 in the endocardium regulates the myocardium non-cell autonomously. Notably, Sox17 is required for the ventricular trabeculation via the NOTCH signal that is not directly induced but maintained by Sox17. This study, thus, sheds light on endocardium development.

developmental biology↗

bioRxiv2019

A glucose-dependent spatial patterning of exocytosis inhuman β-cells is disrupted in type 2 diabetes

Impaired insulin secretion in type 2 diabetes (T2D) is linked to reduced insulin granule docking, disorganization of the exocytotic site, and an impaired glucose-dependent facilitation of insulin exocytosis. We show in {beta}-cells from 80 human donors that the glucose-dependent amplification of exocytosis is disrupted in T2D. Spatial analyses of granule fusion events, visualized by total internal reflection fluorescence (TIRF) microscopy, demonstrate that these are non-random across the surface of {beta}-cells from donors with no diabetes (ND). The compartmentalization of events occurs within regions defined by concurrent or recent membrane-resident secretory granules. This organization, and the number of membrane-associated granules, is glucose-dependent and notably impaired in T2D {beta}-cells. Mechanistically, multi-channel Kv2.1 clusters contribute to maintaining the density of membrane-resident granules and the number of fusion hot spots, while SUMOylation sites at the channel N-(K145) and C-terminus (K470) determine the relative proportion of fusion events occurring within these regions. Thus, a glucose-dependent compartmentalization of fusion, regulated in part by a structural role for Kv2.1, is disrupted in {beta}-cells from donors with type 2 diabetes. HIGHLIGHTSO_LIExocytosis of secretory granules is non-random across the surface of human {beta}-cells, and this organization is disrupted in type 2 diabetes. C_LIO_LIIncreasing glucose facilitates the spatial compartmentalization of fusion, independent of an overall increase in event frequency. C_LIO_LICompartmentalized hot spots occur at sites marked by membrane-associated granules, the density of which is regulated in part by a clustered K+ channel (Kv2.1). C_LIO_LISUMOylation status of the channel controls the proportion of events that occur within these local regions. C_LI

cell biology↗

bioRxiv2019

GWAS identifies nine nephrolithiasis susceptibility loci related with metabolic and crystallization pathways

Nephrolithiasis is a common urological trait disorder with acute pain. Although previous studies have identified various genetic variations associated with nephrolithiasis, the host genetic factors remain largely unidentified. To identify novel nephrolithiasis loci in the Japanese population, we performed large-scale GWAS (Genome wide association study) using 11,130 cases and 187,639 controls, followed by a replication analysis using 2,289 cases and 3,817 controls. The analysis identified 14 significant loci, including 9 novel loci on 2p23.2-3, 6p21.2, 6p12.3, 6q23.2, 16p12.3, 16q12.2, 17q23.2, 19p13.12, and 20q13.2. Interestingly, 10 of the 14 regions showed a significant association with any of 16 quantitative traits, including metabolic, kidney-related, and electrolyte traits, suggesting a common genetic background among nephrolithiasis patients and these quantitative traits. Four novel loci are related to the metabolic pathway, while the remaining 10 loci are associated with the crystallization pathway. Our findings demonstrate the crucial roles of genetic variations in the development of nephrolithiasis.\n\nSIGNIFICANCE STATEMENTNephrolithiasis is a common urothelial disorders with frequent recurrence rate, but its genetic background is largely remained unidentified. Previous GWAS identified 6 genetic factors in total. Here we performed a GWAS using more than 200,000 samples in the Japanese populations, and identified 14 significant loci and nine of them are novel. We also found that 10 of the 14 loci showed a significant association with any of 16 quantitative traits, including metabolic, kidney-related, and electrolyte traits (BMI, eGFR, UA, Ca etc). All 14 significant loci are associate with either metabolic or crystallization pathways. Thus, our findings elucidated the underlying molecular pathogenesis of nephrolithiasis.

genetics↗

bioRxiv2017

The Hallucination Machine: A Deep-Dream VR platform for Studying the Phenomenology of Visual Hallucinations

Altered states of consciousness, such as psychotic or pharmacologically-induced hallucinations, provide a unique opportunity to examine the mechanisms underlying conscious perception. However, the phenomenological properties of these states are difficult to isolate experimentally from other, more general physiological and cognitive effects of psychoactive substances or psychopathological conditions. Thus, simulating phenomenological aspects of altered states in the absence of these other more general effects provides an important experimental tool for consciousness science and psychiatry. Here we describe such a tool, the Hallucination Machine. It comprises a novel combination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted display (panoramic VR). By doing this, we are able to simulate visual hallucinatory experiences in a biologically plausible and ecologically valid way. Two experiments illustrate potential applications of the Hallucination Machine. First, we show that the system induces visual phenomenology qualitatively similar to classical psychedelics. In a second experiment, we find that simulated hallucinations do not evoke the temporal distortion commonly associated with altered states. Overall, the Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology.

neuroscience↗

bioRxiv2016

Liberation from equations: An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

O_LIMapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.\nC_LIO_LIWe developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.\nC_LIO_LIWe showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.\nC_LIO_LIThe complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.\nC_LI

ecology↗