Biology subjects

Abe, K.

Publications and source records attributed to Abe, K..

64 records · Page 4Linked to original sources

bioRxiv2019

Crystal structure of a human plasma membrane phospholipid flippase

ATP11C, a member of P4-ATPase flippase, exclusively translocates phosphatidylserine from the outer to the inner leaflets of the plasma membrane, and maintains the asymmetric distribution of phosphatidylserine in the living cell. However, the mechanisms by which ATP11C translocates phosphatidylserine remain elusive. Here we show the crystal structures of a human plasma membrane flippase, ATP11C-CDC50A complex, in an outward-open E2P conformation. Two phosphatidylserine molecules are in a conduit that continues from the cell surface to the occlusion site in the middle of the membrane. Mutations in either of the phosphotidylserine binding sites or along the pathway between significantly impairs specific ATPase and transport activities. We propose a model for phosphatidylserine translocation from the outer to the inner leaflet of the plasma membrane.

biochemistry↗

bioRxiv2019

Field evaluation of a quantitative, and rapid malaria diagnostic system using a fluorescent Blue-ray optical device

We improved a previously developed quantitative malaria diagnostic system based on fluorescent Blue-ray optical device. Here, we first improved the diagnostic system to enable fully automated operation and the field application was evaluated in Kenya. We detected Plasmodium falciparum in blood samples collected from 288 individuals aged 1-16 years using nested polymerase chain reaction (nPCR), rapid diagnostic test (RDT), and automated system. Compared to RDT, the automated system exhibited a higher sensitivity (100%; 95% confidence interval [CI], 93.3-100%) and specificity (92.8%; 95%CI, 88.5-95.8%). The limit of detection was 0.0061%. Linear regression analysis revealed a correlation between the automated system and microscopic examination for detecting parasitemia (adjusted R2 value=0.63, P=1.13x10-12). The automated system exhibited a stable quantification of parasitemia and a higher diagnostic accuracy for parasitemia than RDT. This indicates the potential of this system as a valid alternative to conventional methods used at local health facilities, which lack basic infrastructure.

physiology↗

bioRxiv2019

Model-based cell clustering and population tracking for time-series flow cytometry data

MotivationModern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on \"manual gating\" by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis.\n\nTherefore, there is a great need for tools to systematically analyze time-series flow cytometry data.\n\nResultsWe propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior.\n\nConclusionsOur results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.

systems biology↗

bioRxiv2019

Hierarchical non-negative matrix factorization using clinical information for microbial communities.

BackgroundThe human microbiome forms very complex communities that consist of hundreds to thousands of different microorganisms that not only affect the host, but also participate in disease processes. Several state-of-the-art methods have been proposed for learning the structure of microbial communities and to investigate the relationship between microorganisms and host environmental factors. However, these methods were mainly designed to model and analyze single microbial communities that do not interact with or depend on other communities. Such methods therefore cannot comprehend the properties between interdependent systems in communities that affect host behavior and disease processes.\n\nResultsWe introduce a novel hierarchical Bayesian framework, called BALSAMICO (BAyesian Latent Semantic Analysis of MIcrobial COmmunities), which uses microbial metagenome data to discover the underlying microbial community structures and the associations between microbiota and their environmental factors. BALSAMICO models mixtures of communities in the framework of nonnegative matrix factorization, taking into account environmental factors. This method first proposes an efficient procedure for estimating parameters. A simulation then evaluates the accuracy of the estimated parameters. Finally, the method is used to analyze clinical data. In this analysis, we successfully detected bacteria related to colorectal cancer. These results show that the method not only accurately estimates the parameters needed to analyze the connections between communities of microbiota and their environments, but also allows for the effective detection of these communities in real-world circumstances.

bioinformatics↗

bioRxiv2019

Epigenetic Effects of Exposure to Insecticide on Early Differentiation of Mouse Embryonic Stem Cells

Increasing evidence indicates that insecticides induce various diseases via DNA methylation. DNA methylation plays an important role during cell differentiation and exhibits its greatest vulnerability to environmental factors during embryogenesis. Therefore, it is important to evaluate the effects on DNA methylation at the early stage of cell differentiation to understand developmental toxicity. However, DNA methylation induced by insecticides and the associated effects on cell differentiation are unclear. In this research, we introduced a high-content approach utilizing mouse embryonic stem cells harboring enhanced green fluorescent protein fused with methyl CpG-binding protein to evaluate global DNA methylation induced by various insecticides. DNA methylation was assessed in 22 genes after pesticide exposure to investigate the relationships with biological processes such as cell cycle, cell apoptosis, and cell differentiation. Exposure to acetamiprid, imidacloprid, carbaryl, and o,p'-DDT increased the granular intensity, indicating their global DNA-methylating effects. Exposure to imidacloprid decreased DNA methylation in genes such as Cdkn2a, Dapk1, Cdh1, Mlh1, Timp3, and Rarb, indicating the potential influence of the DNA methylation pattern on cell differentiation. We developed a promising approach for evaluating global DNA methylation, and our findings suggested that imidacloprid might exhibit developmental effects through DNA methylation pattern.

pharmacology and toxicology↗

bioRxiv2019

A single K+-binding site in the crystal structure of the gastric proton pump

The gastric proton pump (H+,K+-ATPase), a P-type ATPase responsible for gastric acidification, mediates electro-neutral exchange of H+ and K+ coupled with ATP hydrolysis, but with an as yet undetermined transport stoichiometry. Here we show crystal structures at a resolution of 2.5 [A] of the pump in the E2-P transition state, in which the counter-transporting cation is occluded. We found a single K+ bound to the cation-binding site of H+,K+-ATPase, indicating an exchange of 1H+/1K+ per hydrolysis of one ATP molecule. This fulfils the energy requirement for the generation of a six pH unit gradient across the membrane. The structural basis of K+recognition is resolved, supported by molecular dynamics simulations, and this establishes how H+,K+-ATPase overcomes the energetic challenge to generate an H+ gradient of more than a million-fold - the highest cation gradient known in any mammalian tissue - across the membrane.

biochemistry↗

bioRxiv2019

Both galactosaminogalactan and α-1,3-glucan contribute to aggregation of Aspergillusoryzae hyphae in liquid culture

Filamentous fungi generally form aggregated hyphal pellets in liquid culture. We previously reported that -1,3-glucan-deficient mutants of Aspergillus nidulans did not form hyphal pellets and their hyphae were fully dispersed, and we suggested that -1,3-glucan functions in hyphal aggregation. Yet, Aspergillus oryzae -1,3-glucan-deficient (AG{Delta}) mutants still form small pellets; therefore, we hypothesized that another factor responsible for forming hyphal pellets remains in these mutants. Here, we identified an extracellular matrix polysaccharide galactosaminogalactan (GAG) as such a factor. To produce a double mutant of A. oryzae (AG-GAG{Delta}), we disrupted the genes required for GAG biosynthesis in an AG{Delta} mutant. Hyphae of the double mutant were fully dispersed in liquid culture, suggesting that GAG is involved in hyphal aggregation in A. oryzae. Addition of partially purified GAG fraction to the hyphae of the AG-GAG{Delta} strain resulted in formation of mycelial pellets. Acetylation of the amino group in galactosamine of GAG weakened GAG aggregation, suggesting that hydrogen bond formation by this group is important for aggregation. Genome sequences suggest that -1,3-glucan, GAG, or both are present in many filamentous fungi and thus may function in hyphal aggregation in these fungi. We also demonstrated that production of a recombinant polyesterase, CutL1, was higher in the AG-GAG{Delta} strain than in the wild-type and AG{Delta} strains. Thus, controlling hyphal aggregation factors of filamentous fungi may increase productivity in the fermentation industry.\n\nImportanceProduction using filamentous fungi is an important part of the fermentation industry, but hyphal aggregation in these fungi in liquid culture limits productivity compared with that of yeast or bacterial cells. We found that galactosaminogalactan and -1,3-glucan both function in hyphal aggregation in Aspergillus oryzae, and that the hyphae of a double mutant deficient in both polysaccharides become fully dispersed in liquid culture. We also revealed the relative contribution of -1,3-glucan and galactosaminogalactan to hyphal aggregation. Recombinant protein production was higher in the double mutant than in the wild-type strain. Our research provides a potential technical innovation for the fermentation industry that uses filamentous fungi, as regulation of the growth characteristics of A. oryzae in liquid culture may increase productivity.

cell biology↗

bioRxiv2018

A Latent Allocation Model for the Analysis of Microbial Composition and Disease

BackgroundEstablishing the relationship between microbiota and specific disease is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data.\n\nResultsThe purpose of this article is to develop a new probabilistic model, called BERMUDA (BERnoulli and MUltinomial Distribution-based latent Allocation), to address these problems. BERMUDA enables us to describe the differences in bacteria composition and a certain disease among samples. We also provide a simple and efficient learning procedure for the proposed model using an annealing EM algorithm.\n\nConclusionWe illustrate the performance of the proposed method both through both the simulation and real data analysis. BERMUDA is implemented with R and is available from GitHub (https://github.com/abikoushi/Bermuda).

bioinformatics↗

bioRxiv2018

ENIGMA: An Enterotype-Like Unigram Mixture Model for Microbial Association Analysis

BackgroundOne of the major challenges in microbial studies is to discover associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities have clusters reffered as enterotype characterized by differences in specific bacterial taxa, which makes it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish dysbiosis of interest with the individual differences.\n\nResultsWe propose a new probabilistic model, called ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), to address these problems. ENIGMA enables us to simultaneously estimate enterotype-like clusters characterized by the abundances of signature bacterial genera and environmental effects associated with the disease.\n\nConclusionWe illustrate the performance of the proposed method both through the simulation and clinical data analysis. ENIGMA is implemented with R and is available from GitHub (https://github.com/abikoushi/enigma).

bioinformatics↗

bioRxiv2017

Cell Type Specific Survey of Epigenetic Modifications by Tandem Chromatin Immunoprecipitation Sequencing

BackgroundThe nervous system of higher eukaryotes is composed of numerous types of neurons and glia that together orchestrate complex neuronal responses. However, this complex pool of cells typically poses analytical challenges in investigating gene expression profiles and their epigenetic basis for specific cell types. Here, we developed a novel method that enables cell type-specific analyses of epigenetic modifications using tandem chromatin immunoprecipitation sequencing (tChIP-Seq).\n\nResultsFLAG-tagged histone H2B, a constitutive chromatin component, was first expressed in Camk2a-positive pyramidal cortical neurons and used to purify chromatin in a cell type-specific manner. Subsequent chromatin immunoprecipitation using antibodies against H3K4me3--an active promoter mark--allowed us to survey neuron-specific coding and non-coding transcripts. Indeed, tChIP-Seq identified hundreds of genes associated with neuronal functions and genes with unknown functions expressed in cortical neurons.\n\nConclusionstChIP-Seq thus provides a versatile approach to investigating the epigenetic modifications of particular cell types in vivo.

molecular biology↗