Biology subjects

Tsukamoto, M.

Publications and source records attributed to Tsukamoto, M..

5 recordsLinked to original sources

bioRxiv2022

Transcriptional lability of brain oxytocin receptor (Oxtr) generates diversity in brain OXTR distribution and social behaviors

Although oxytocin (OXT) exhibits a highly conserved neuroanatomical pattern among vertebrates, the distribution of OXT receptor (OXTR) in brain varies considerably across species and is associated with species-typical social behavior. To investigate the genomic basis of the phylogenetic plasticity in brain Oxtr expression and its social behavioral consequences, we generated transgenic mice carrying a bacteria artificial chromosome (BAC) harboring the entire prairie vole Oxtr locus and flanking intergenic regulatory regions. We established eight independent "volized" mouse lines expressing prairie vole Oxtr (pvOxtr). Strikingly, despite conserved Oxtr expression in mammary gland of all transgenic mouse lines, each line displayed a unique pattern of brain expression distinct from both mice and prairie voles. Together with topologically associating domain (TAD) structure analysis with mouse genome, our findings suggest that unlike Oxt, Oxtr expression patterns in brain, involve contributions of distal regulatory elements beyond the BAC insert. In contrast, Oxtr expression in peripheral tissues appears resistant to such distal influences. Moreover, the "volized" mouse lines with different brain Oxtr expression patterns showed differences in partner preference and maternal behaviors, providing direct functional evidence that variation in brain Oxtr expression can drive differences in social behaviors. We propose that brain Oxtr expression is transcriptionally sensitive to long-range interactions with distal genomic elements, rendering it more susceptible to diverse regulatory influences. This supports a model in which regulatory flexibility facilitates the evolutionary diversification of social behavior, while maintaining essential peripheral Oxtr expression.

neuroscience↗

bioRxiv2021

In vivo KRAS G12D/V Degradation Mediated by CANDDY Using a Modified Proteasome Inhibitor

"Undruggable" proteins, such as RAS proteins, remain problematic despite efforts to discover inhibitors against them. KRAS mutants are prevalent in human cancers. Recently, the KRAS G12C inhibitor have been clinically approved, but inhibitors for KRAS G12D/V are still under development. Here, we described the development of a novel chemical knockdown strategy, termed CANDDY (Chemical knockdown with Affinity aNd Degradation DYnamics). This strategy involves a CANDDY tag modified from a proteasome inhibitor inducing direct proteasomal degradation. We constructed TUS-007 as a multispecific small molecule tethered from a KRAS interactor and CANDDY tag to target KRAS G12D/V. We confirmed that the degradation by TUS-007 was independent of target ubiquitination. This allows to solve a laborious design of matchmaker in the current ubiquitination-dependent proteolysis technology. And TUS-007 successfully suppressed tumors due to in vivo degradation of KRAS G12D/V. The CANDDY technology could represent a simple and rational strategy to degrade currently "undruggable" proteins.

pharmacology and toxicology↗

bioRxiv2020

Limb-clasping, cognitive deficit and increased vulnerability to kainic acid -induced seizures in neuronal GPI anchor deficiency mouse models

Post-translational modification of a protein with glycosylphosphatidylinositol (GPI) is a conserved mechanism exists in all eukaryotes. Thus far, more than 150 human GPI anchored proteins have been discovered and about 30 enzymes have been reported to be involved in the biosynthesis and maturation of mammalian GPI. Phosphatidylinositol glycan biosynthesis class A protein (PIGA) catalyzes the very first step of GPI anchor biosynthesis. Patients carrying a mutation of the PIGA gene usually suffer from intractable epilepsy and intellectual developmental disorder. We generated three mouse models with PIGA deficits specifically in telencephalon excitatory neurons (Ex-M-cko), inhibitory neurons (In-M-cko), or thalamic neurons (Th-H-cko), respectively. Both Ex-M-cko and In-M-cko mice showed impaired long-term fear memory and were more susceptible to kainic acid (KA)-induced seizures. In addition, In-M-cko demonstrated a severe limb-clasping phenotype. Hippocampal synapse changes were observed in Ex-M-cko mice. Our Piga conditional knockout mouse models provide powerful tools to understand the cell-type specific mechanisms underlying inherited GPI deficiency and to test different therapeutic modalities.

neuroscience↗

bioRxiv2020

Collection of 2429 constrained headshots of 277 volunteers for deep learning

Deep learning has rapidly been filtrating many aspects of human lives. In particular, image recognition by convolutional neural networks has inspired numerous studies in this area. Hardware and software technologies as well as large quantities of data have contributed to the drastic development of the field. However, the application of deep learning is often hindered by the need for big data and the laborious manual annotation thereof. To experience deep learning using the data compiled by us, we collected 2429 constrained headshot images of 277 volunteers. The collection of face photographs is challenging in terms of protecting personal information; we established an online procedure in which both the informed consent and image data could be obtained. We did not collect personal information, but issued agreement numbers to deal with withdrawal requests. Gender and smile labels were manually and subjectively annotated only from the appearances, and final labels were determined by majority among our team members. Rotated, trimmed, resolution-reduced, decolorized, and matrix-formed data were allowed to be publicly released. Moreover, simplified feature vectors for data sciences were released. We performed gender recognition by building convolutional neural networks based on the Inception V3 model with pre-trained ImageNet data to demonstrate the usefulness of our dataset.

animal behavior and cognition↗

bioRxiv2018

Characterization of deep neural network features by decodability from human brain activity

Achievements of near human-level performances in object recognition by deep neural networks (DNNs) have triggered a flood of comparative studies between the brain and DNNs. Using a DNN as a proxy for hierarchical visual representations, our recent study found that human brain activity patterns measured by functional magnetic resonance imaging (fMRI) can be decoded (translated) into DNN feature values given the same inputs. However, not all DNN features are equally decoded, indicating a gap between the DNN and human vision. Here, we present a dataset derived through the DNN feature decoding analyses including fMRI signals of five human subjects during image viewing, decoded feature values of DNNs (AlexNet and VGG19), and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual features were highly correlated between subjects, suggesting the systematic differences between the brain and DNNs. We hope the present dataset will contribute to reveal the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications.

neuroscience↗