Biology subjects

Sato, K.

Publications and source records attributed to Sato, K..

7 recordsLinked to original sources

bioRxiv2018

cTAGE5 acts as a Sar1 GTPase regulator for collagen export

Secretory proteins synthesized within the endoplasmic reticulum (ER) are exported via coat protein complex II (COPII)-coated vesicles. The formation of the COPII-coated vesicles is initiated by activation of the small GTPase, Sar1. cTAGE5 directly interacts with a guanine-nucleotide exchange factor (GEF), Sec12, and a GTPase-activating protein (GAP) of Sar1, Sec23. We have previously shown that cTAGE5 recruits Sec12 to the ER exit sites for efficient production of activated Sar1 for collagen secretion. However, the functional significance of the interaction between cTAGE5 and Sec23 has not been fully elucidated. In this study, we showed that cTAGE5 enhances the GAP activity of Sec23 toward Sar1. In addition, the interaction of cTAGE5 with Sec23 is necessary for collagen exit from the ER. Our data suggests that cTAGE5 acts as a Sar1 GTPase regulator for collagen secretion.

cell biology↗

bioRxiv2018

HSF2BP Negatively Regulates Homologous Recombination in DNA Interstrand Crosslink Repair in Human Cells by Direct Interaction With BRCA2

The tumor suppressor BRCA2 is essential for homologous recombination, replication fork stability and DNA interstrand crosslink (ICL) repair in vertebrates. We show that a functionally uncharacterized protein, HSF2BP, is involved in a novel, direct and highly evolutionarily conserved interaction with BRCA2. Although HSF2BP was previously described as testis-specific, we find it is expressed in mouse ES cells, in human cancer cell lines, and in tumor samples. Elevated levels of HSF2BP sensitize human cells to ICL-inducing agents (mitomycin C and cisplatin) and PARP inhibitors, resulting in a phenotype characteristic of cells from Fanconi anemia (FA) patients. We biochemically recapitulate the suppression of ICL repair and establish that excess HSF2BP specifically compromises homologous recombination by preventing BRCA2 and RAD51 loading at the ICL. As increased ectopic expression of HSF2BP occurs naturally, we suggest that it can be considered as a causative agent in FA and a source of cancer-promoting genomic instability.

molecular biology↗

bioRxiv2018

CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing

Recent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1,600 cells per second.

bioinformatics↗

bioRxiv2018

Gorilla APOBEC3 restricts SIVcpz and influences lentiviral evolution in great ape cross-species transmissions

Restriction factors including APOBEC3 family proteins have the potential prevent cross-species lentivirus transmissions. Such events as well as ensuing pathogenesis require the viral Vif protein to overcome/neutralize/degrade the APOBEC3 enzymes of the new host species. Previous investigations have focused on the molecular interaction between human APOBEC3s and HIV-1 Vif. However, the evolutionary interplay between lentiviruses and great ape (including human, chimpanzee and gorilla) APOBEC3s has not been fully investigated. Here we demonstrate that gorilla APOBEC3G plays a pivotal role in restricting lentiviral transmission from chimpanzee to gorilla. We also reveal that a sole amino acid substitution in Vif is sufficient to overcome the gorilla APOBEC3G-mediated species barrier. Moreover, the antiviral effects of gorilla APOBEC3D and APOBEC3F are considerably weaker than those of human and chimpanzee counterparts, which can result in the skewed evolution of great ape lentiviruses leading to HIV-1.\n\nHighlightsO_LISIVcpz requires M16E mutation in Vif to counteract gorilla A3G\nC_LIO_LIAcidic residue at position 16 of Vif is crucial to counteract gorilla A3G\nC_LIO_LIGorilla A3D and A3F poorly suppress lentiviral infectivity\nC_LIO_LISIVgor and related HIV-1s counteract human A3D and A3F independently of DRMR motif\nC_LI

microbiology↗

bioRxiv2018

A new targeted capture method using bacterial artificial chromosome (BAC) libraries as baits for sequencing relatively large genes

To analyze a specific genome region using next-generation sequencing technologies, the enrichment of DNA libraries with targeted capture methods has been standardized. For enrichment of mitochondrial genome, a previous study developed an original targeted capture method that use baits constructed from long-range polymerase chain reaction (PCR) amplicons, common laboratory reagents, and equipment. In this study, a new targeted capture method is presented, that of bacterial artificial chromosome (BAC) double capture (BDC), modifying the previous method, but using BAC libraries as baits for sequencing a relatively large gene. We applied the BDC approach for the 214 kb autosomal region, ring finger protein 213, which is the susceptibility gene of moyamoya disease (MMD). To evaluate the reliability of BDC, cost and data quality were compared with those of a commercial kit. While the ratio of duplicate reads was higher, the cost was less than that of the commercial kit. The data quality was sufficiently the same as that of the kit. Thus, BDC can be an easy, low-cost, and useful method for analyzing individual genome region with substantial length.

genomics↗

bioRxiv2018

Direct inference of base-pairing probabilities with neural networks improves RNA secondary structure prediction with pseudoknots

MotivationExisting approaches for predicting RNA secondary structures depend on howto decompose a secondary structure into substructures, so-called the architecture, to define their parameter space. However, the architecture has not been sufficiently investigated especially for pseudoknotted secondary structures.\n\nResultsIn this paper, we propose a novel algorithm to directly infer base-pairing probabilities with neural networks that does not depend on the architecture of RNA secondary structures, followed by performing the maximum expected accuracy (MEA) based decoding algorithms; Nussinov-style decoding for pseudoknot-free structures, and IPknot-style decoding for pseudoknotted structures. To train the neural networks connected to each base-pair, we adopt a max-margin framework, called structured support vector machines (SSVM), as the output layer. Our benchmarks for predicting RNA secondary structures with and without pseudoknots show that our algorithm achieves the best prediction accuracy compared with existing methods.\n\nAvailabilityThe source code is available at https://github.com/keio-bioinformatics/neuralfold/.\n\nContactsatoken@bio.keio.ac.jp

bioinformatics↗

bioRxiv2017

A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

Motivation: A popular approach for predicting RNA secondary structure is the thermodynamic nearest neighbor model that finds a thermodynamically most stable secondary structure with the minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a machine learning based fine-grained model achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such model has been reported.\n\nResults: In this paper, we propose a novel algorithm for RNA secondary structure prediction that integrates the thermodynamic approach and the machine learning based weighted approach. Ourfine-grained model combines the experimentally determined thermodynamic parameters with a large number of scoring parameters for detailed contexts of features that are trained by the structured support vector machine (SSVM) with the{ell} 1 regularization to avoid overfitting. Our benchmark shows that our algorithm achieves the best prediction accuracy compared with existing methods, and heavy overfitting cannot be observed.\n\nAvailability: The implementation of our algorithm is available at https://github.com/keio-bioinformatics/mxfold.\n\nContact: satoken@bio.keio.ac.jp

bioinformatics↗