Biology subjects

Tsuchida, A.

Publications and source records attributed to Tsuchida, A..

8 recordsLinked to original sources

bioRxiv2026

Gluconeogenesis and glycogen metabolism in the epidermis and endoderm of Xenopus tropicalis embryos and larvae.

In many oviparous animals, egg yolk is the sole source of nutrition until feeding begins, and carbohydrates are present in only small amounts in the yolk. Glucose plays an important role in the developmental processes of various animals. In addition, gluconeogenesis has been reported to occur in the yolk syncytial layer (YSL) of cartilaginous fish and teleosts. In contrast, the role of gluconeogenesis in tetrapods remains unclear. In this study, we used Xenopus tropicalis, an anuran amphibian, which lacks YSL, and therefore provide an opportunity to examine the evolutionary conservation of gluconeogenic mechanisms among vertebrates. In X. tropicalis, liquid chromatography/mass spectrometry revealed that glucose levels increased before liver formation. Subsequent tracer experiments using 13C-labeled metabolic substrates detected gluconeogenesis activity from glycerol and lactate. Expression analyses showed that gluconeogenic genes are expressed in the epidermis and endoderm. Consistently, generation 0 (G0) knockout of fbp1, a key gluconeogenic gene, resulted in a significant reduction in glucose levels, affecting brain development. These findings first demonstrate that gluconeogenesis supports development of X. tropicalis. To the best of our knowledge, gluconeogenesis in developing epidermis has not been reported, highlighting previously unrecognized diversity in tissue-specific metabolism during vertebrate development. Comparative analyses across species will provide further insights into the evolution and functional significance of embryonic gluconeogenesis and nutrient metabolism.

developmental biology↗

bioRxiv2024

Branched-chain amino acid metabolism is a crucial modulator of cellular senescence

Cellular senescence is a complex stress response that results in the permanent arrest of cell proliferation. The accumulation of senescent cells occurs during aging in living organisms, and contributes to tissue dysfunction. Although there are growing lines of evidence that various metabolic changes occur in senescent cells, the link between cellular metabolism and senescence is not yet fully understood. In this study, we demonstrate that alterations in the metabolism of branched-chain amino acids (BCAAs) play a crucial role in establishing cellular senescence. Furthermore, we identified mitochondrial BCAA transamination as a crucial step in this process. Our findings show that various types of cellular stress lead to a reduction in the expression of BCAA aminotransferase 2 (BCAT2), one of the BCAA catabolic enzymes, resulting in decreased catabolism of BCAAs and reduced synthesis of glutamate. The reduction of BCAA catabolites, together with the consequent limitation in glutathione production from glutamate, triggers cellular senescence. Furthermore, we demonstrate that a reduction in BCAT2 levels alone is sufficient to induce cellular senescence, both in cultured cells and in mice. Additionally, our results demonstrate that aging alters BCAA metabolism in both mice and humans. Our findings provide new insights into the metabolic mechanisms underlying cellular senescence, with a particular focus on the role of BCAAs.

cell biology↗

bioRxiv2023

Early detection of white matter hyperintensities using SHIVA-WMH detector

White matter hyperintensities (WMH) are well-established markers of cerebral small vessel disease (cSVD), and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e. with confluency) in evaluation datasets from three distinct databases: MRi-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LST-LPA: Schmidt, 2017), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al, 2021), and HyperMapper tool (HPM: Mojiri Forooshani et al., 2022), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH. HighlightsO_LIWe propose a novel 3D Unet-based model, SHIVA-WMH detector, with much improved detection of small WMH across subjects with a wide range of WMH burden compared to existing methods C_LIO_LIWe characterize microstructural properties of small white matter hyperintensities in young adults from MRi-Share study C_LI

neuroscience↗

bioRxiv2021

Changes in regional white matter volumetry and microstructure during the post-adolescence period: a cross-sectional study of a cohort of 1,713 university students

Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterizations of the microstructural architecture of white matter, and they are increasingly utilised to study white matter changes during development and ageing. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18 to 26. We assessed the age and sex dependence, as well as hemispheric asymmetry of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), in the white matter regions as defined in the John Hopkins University (JHU) white matter labels atlas. We demonstrate that while regional white matter volume is relatively stable over the age range of our sample, the white matter microstructural properties show clear age-related variations. Globally, it is characterised by a robust increase in neurite density index (NDI), and to a lesser extent, orientation dispersion index (ODI). These changes are accompanied by a decrease in diffusivity. In contrast, there is minimal age-related variation in fractional anisotropy. There are regional variations in these microstructural changes: some tracts, most notably cingulum bundles, show a strong age-related increase in NDI coupled with decreases in radial and mean diffusivity, while others, mainly cortico-spinal projection tracts, primarily show an ODI increase and axial diffusivity decrease. These age-related variations are not different between males and females, but males show higher NDI and ODI and lower diffusivity than females across many tracts. We also report a robust hemispheric asymmetry in both the volume and microstructural properties in many regions. These findings emphasise the complexity of changes in white matter structure occurring in this critical period of late maturation in early adulthood.

neuroscience↗

bioRxiv2020

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

neuroscience↗

bioRxiv2020

Genetic variants for head size share genes and pathways with cancer

The size of the human head is determined by growth in the first years of life, while the rest of the body typically grows until early adulthood1. Such complex developmental processes are regulated by various genes and growth pathways2. Rare genetic syndromes have revealed genes that affect head size3, but the genetic drivers of variation in head size within the general population remain largely unknown. To elucidate biological pathways underlying the growth of the human head, we performed the largest genome-wide association study on human head size to date (N = 79,107). We identified 67 genetic loci, 50 of which are novel, and found that these loci are preferentially associated with head size and mostly independent from height. In subsequent neuroimaging analyses, the majority of genetic variants demonstrated widespread effects on the brain, whereas the effects of 17 variants could be localized to one or two specific brain regions. Through hypothesis-free approaches, we find a strong overlap of head size variants with both cancer pathways and cancer genes. Gene set analyses showed enrichment for different types of cancer and the p53, Wnt and ErbB signalling pathway. Genes overlapping or close to lead variants - such as TP53, PTEN and APC - were enriched for genes involved in macrocephaly syndromes (up to 37-fold) and high-fidelity cancer genes (up to 9-fold), whereas this enrichment was not seen for human height variants. This indicates that genes regulating early brain and cranial growth are associated with a propensity to neoplasia later in life, irrespective of height. Our results warrant further investigations of the link between head size and cancer, as well as its clinical implications in the general population.

genomics↗

bioRxiv2020

The MRi-Share database: brain imaging in a cross-sectional cohort of 1,870 university students

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging.

neuroscience↗

bioRxiv2020

Age-related changes of Peak width Skeletonized Mean Diffusivity (PSMD) across the adult life span: a multi-cohort study

Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and meta-analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing DTI metrics. For each individual MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40 and 50, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process.

neuroscience↗