Biology subjects

Shimamura, T.

Publications and source records attributed to Shimamura, T..

33 records · Page 2Linked to original sources

bioRxiv2023

In vivo metabolomics identifies CD38 as an emergent vulnerability in LKB1-mutant lung cancer

LKB1/STK11 is a serine/threonine kinase that plays a major role in controlling cell metabolism, resulting in potential therapeutic vulnerabilities in LKB1-mutant cancers. Here, we identify the NAD+ degrading ectoenzyme, CD38, as a new target in LKB1-mutant NSCLC. Metabolic profiling of genetically engineered mouse models (GEMMs) revealed that LKB1 mutant lung cancers have a striking increase in ADP-ribose, a breakdown product of the critical redox co-factor, NAD+. Surprisingly, compared with other genetic subsets, murine and human LKB1-mutant NSCLC show marked overexpression of the NAD+-catabolizing ectoenzyme, CD38 on the surface of tumor cells. Loss of LKB1 or inactivation of Salt-Inducible Kinases (SIKs)--key downstream effectors of LKB1-- induces CD38 transcription induction via a CREB binding site in the CD38 promoter. Treatment with the FDA-approved anti-CD38 antibody, daratumumab, inhibited growth of LKB1-mutant NSCLC xenografts. Together, these results reveal CD38 as a promising therapeutic target in patients with LKB1 mutant lung cancer. SIGNIFICANCELoss-of-function mutations in the LKB1 tumor suppressor of lung adenocarcinoma patients and are associated with resistance to current treatments. Our study identified CD38 as a potential therapeutic target that is highly overexpressed in this specific subtype of cancer, associated with a shift in NAD homeostasis.

cancer biology↗

bioRxiv2022

Single-cell colocalization analysis using a deep generative model

1Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, cellular responses to external stimuli, and their biological functions in diseases and tissues. However, high-throughput methods for identifying spatial proximity at single-cell resolution are practically unavailable. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers inter-cellular colocalization networks with single cell resolution by the integration of single cell and spatial transcriptomes. It segregates cell populations defined by the colocalization relationships and predicts cell-cell interactions between colocalized single cells. DeepCOLOR could identify plausible cell-cell interaction candidates in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2 by reconstructing spatial colocalization maps at single-cell resolution. DeepCOLOR is typically applicable to studying cell-cell interactions in any spatial niche. Our newly developed computational framework could help uncover molecular pathways across single cells connected with colocalization networks.

bioinformatics↗

bioRxiv2022

Characterization of various remdesivir-resistant mutations of SARS-CoV-2 by mathematical modeling and molecular dynamics simulation.

Mutations continue to accumulate within the SARS-CoV-2 genome, and the ongoing epidemic has shown no signs of ending. It is critical to predict problematic mutations that may arise in clinical environments and assess their properties in advance to quickly implement countermeasures against future variant infections. In this study, we identified mutations resistant to remdesivir, which is widely administered to SARS-CoV-2-infected patients, and discuss the cause of resistance. First, we simultaneously constructed eight recombinant viruses carrying the mutations detected in in vitro serial passages of SARS-CoV-2 in the presence of remdesivir. Time course analyses of cellular virus infections showed significantly higher infectious titers and infection rates in mutant viruses than wild type virus under treatment with remdesivir. Next, we developed a mathematical model in consideration of the changing dynamic of cells infected with mutant viruses with distinct propagation properties and defined that mutations detected in in vitro passages canceled the antiviral activities of remdesivir without raising virus production capacity. Finally, molecular dynamics simulations of the NSP12 protein of SARS-CoV-2 revealed that the molecular vibration around the RNA-binding site was increased by the introduction of mutations on NSP12. Taken together, we identified multiple mutations that affected the flexibility of the RNA binding site and decreased the antiviral activity of remdesivir. Our new insights will contribute to developing further antiviral measures against SARS-CoV-2 infection. Significance StatementConsidering the emerging Omicron strain, quick characterization of SARS-CoV-2 mutations is important. However, owing to the difficulties in genetically modifying SARS-CoV-2, limited groups have produced multiple mutant viruses. Our cutting-edge reverse genetics technique enabled construction of eight reporter-carrying mutant SARS-CoV-2 in this study. We developed a mathematical model taking into account sequential changes and identified antiviral effects against mutant viruses with differing propagation capacities and lethal effects on cells. In addition to identifying the positions of mutations, we analyzed the structural changes in SARS-CoV-2 NSP12 by computer simulation to understand the mechanism of resistance. This multidisciplinary approach promotes the evaluation of future resistance mutations.

microbiology↗

bioRxiv2021

Structure and mechanism of oxalate transporter OxlT in an oxalate-degrading bacterium in the gut microbiota

Oxalobacter formigenes is an oxalate-degrading bacterium in the gut microbiota that absorbs food-derived oxalate to use this as a carbon and energy source and thereby helps reduce the risk of kidney stone formation of the host animals 1-4. The bacterial oxalate transporter OxlT uptakes oxalate from the gut to bacterial cells and excrete formate as a degradation product, with a strict discrimination from other carboxylates that serve as nutrients 5-7. Nevertheless, the underlying mechanism remains unclear. Here, we present crystal structures of oxalate-bound and ligand-free OxlT in two different conformations, occluded and outward-facing states. The oxalate binding site contains two basic residues that form salt bridges with a dicarboxylate substrate while preventing the conformational switch to the occluded state without an acidic substrate, a disallowed state for an antiporter 8, 9. The occluded ligand-binding pocket can accommodate oxalate but not larger dicarboxylates, such as metabolic intermediates. The permeation pathways from the binding pocket are completely blocked by extensive interdomain hydrophobic and ionic interactions. Nevertheless, a molecular dynamics simulation showed that a flip of a single side chain neighbouring the substrate is sufficient to trigger the gate opening. The OxlT structure indicates the underlying metabolic interactions enabling favourable symbiosis at a molecular level.

biochemistry↗

bioRxiv2021

scMM: Mixture-of-experts multimodal deep generative model for single-cell multiomics data analysis.

AO_SCPLOWBSTRACTC_SCPLOWThe recent development in single-cell multiomics analysis has enabled simultaneous detection of multiple traits at the single-cell level, thus providing deeper insights into the cellular phenotypes and functions in diverse tissues. However, currently, it is challenging to infer the joint representations and learn relationships among multiple modalities from complex multimodal single-cell data. Herein, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint representations and cross-modal generation. scMM addresses the complexity of data by leveraging a mixture-of-experts multimodal variational autoencoder. The pseudocell generation strategy of scMM compensates for the limited interpretability of deep learning models and discovered multimodal regulatory programs associated with latent dimensions. Analysis of recently produced datasets validated that scMM facilitates high-resolution clustering with rich interpretability. Furthermore, we show that cross-modal generation by scMM leads to more precise prediction and data integration compared with the state-of-the-art and conventional approaches.

bioinformatics↗

bioRxiv2019

Merger of Betula tatewakiana (Betulaceae) from northern Japan to northeast Asian B. ovalifolia based on ploidy level

It has been controversial whether Betula tatewakiana, a dwarf birch distributed in Hokkaido of northern Japan, is an endemic species or a synonym of B. ovalifolia broadly distributed in northeast Asia. The endemic hypothesis is based on the idea that B. tatewakiana is diploid while B. ovalifolia is tetraploid and that they are separated based on the ploidy level; however no chromosome data have actually been published before. Resolving the taxonomic problem is crucial also in judging the conservation priority of B. tatewakiana in a global perspective. Our chromosome observation revealed that B. tatewakiana is tetraploid as well as B. ovalifolia. Collaterally, we conducted morphological observation and clarified that B. tatewakiana is morphologically identical to B. ovalifolia in white hairs and dense resinous glands respectively on adaxial and abaxial leaf surfaces, based on which they are different from closely related species in the same section Fruticosae. We concluded that the hypothesis that B. tatewakiana is a Hokkaido endemic based on the ploidy level is not supported and that B. tatewakiana should be merged with B. ovalifolia.

plant biology↗

bioRxiv2019

Serpentine mountain uplift in northern Japan triggered the divergence of a narrow endemic from a widespread (sub)arctic Asia-Alaska species complex of Lagotis (Plantaginaceae)

In the circumboreal region, plants often have extremely-wide species ranges. Lagotis minor-glauca species complex widespread from (sub)arctic Asia to Alaska, however, have two allied narrow endemics in northern Japan: a serpentine plant L. takedana endemic to the Yubari Mountains (Mt. Yubari) and a non-serpentine plant L. yesoensis endemic to the Taisetsu Mountains (Mt. Taisetsu). Elucidating their origins sheds light on drivers for secondary-speciation of widespread circumboreal plants. To infer phylogenetic distinctiveness of two narrow endemics with those related taxa, which contained 25 out of all the 29 species of the genus, chloroplast DNA (cpDNA), nuclear ribosomal (nrITS), two low copy nuclear gene (LCN) markers and genome-wide single-nucleotide polymorphism genotyping (MIG-seq) were used. In the result of cpDNA analyses, the Lagotis minor-glauca species complex formed a clade. Within the clade, L. yesoensis and a portion of L. glauca samples formed a subclade. However, monophyly of each of the four species was not supported. In the results of nrITS and two LCN analyses, L. takedana was monophyletic, while monophyly was not recovered for each L. yesoensis, L. glauca, and L. minor. Based on a Bayesian dating analysis using nrITS data, the age of the most recent common ancestor of L. takedana was Ma (95% confidence interval: 0.05-1.75 Ma). Possible scenario is that an ancestral linage being adapted to serpentine soils migrated into the alpine habitat of Mt. Yubari, that was formed with mountain uplift by the early Pleistocene, and subsequently reproductively isolated from non-serpentine populations and speciated. The contrasting result of L. yesoensis, that was phylogenetically indistinct, is possibly explained by incorrect taxonomy, or alternatively, shallow history and incomplete lineage sorting. In Mt. Taisetsu, massive volcanic eruptions had occurred the Early Pleistocene and even after the last glacial period, suggesting that alpine plants have not migrated into and established populations in Mt. Taisetsu until very recently. To fully resolve the phylogeny of the three species L. yesoensis, L. glauca, and L. minor, further analyses using high resolution molecular markers are needed. The present study illustrated that two narrow endemics in northern Japan diverged from the widespread species include phylogenetically distinctive and indistinctive species, owing to historical orogeny and ecological factors.

ecology↗

bioRxiv2019

Two-way migration of Lychnis wilfordii caused by the circular landform of Japan-Korea-northeast China-Russian Far East region and its suggestion for conservation in northeast Asia

In northeast Asia, substantial portion of the floras, including endangered species, are shared among its component countries in the continental, peninsula, and island parts largely through Quaternary migration. To effectively conserve nationally endangered plants in Northeast Asia, transnational conservation studies are vitally needed. Lychnis wilfordii (Caryophyllaceae) has disjunct distribution in Russian Far East (Primorsky Krai), northeast China (Jilin), Korea (Gangwon-do) and Japan (Hokkaido, Aomori, Nagano), surrounding the sea, and this is designated as an endangered species in Japan and Korea. Population genetic and molecular dating analyses were conducted 1) to elucidate geographic genetic structure covering the species range, 2) to test possible scenarios of migration, and 3) to develop logical plans for effective conservation. Population genetic analyses indicated the continent and peninsula parts (north and south Primorsky Krai, Jilin, and Gangwon-do) had higher genetic diversity compared to those in the Japanese Archipelago (Hokkaido and Nagano). Five genetically distinct groups were recognized, namely, Nagano, Gangwon-do, Jilin, north and south Primorsky Krai plus Aomori, and Hokkaido. Genetic distance between Hokkaido and Nagano was larger than between Hokkaido and north Primorsky Krai, and between Nagano and Gangwon-do, crossing national borders and the natural barrier of the sea. Considering these results, L. wilfordii likely migrated from the Asian continent to the Japanese Archipelago using two routes: north route from Russian Far East to Hokkaido and Aomori, and south route from the Korean Peninsula to Nagano. Based on molecular dating, migration from the continent to the islands likely occurred from the middle Pleistocene to the Holocene. For effective conservation of L. wilfordii, Hokkaido and Nagano populations should be distinguished as different evolutionary significant units, although these two regions belong to the same country, because Hokkaido and Nagano populations are at the different ends of the two migratory routes based on the migration scenario.

evolutionary biology↗

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↗

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↗

bioRxiv2018

GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics

The regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator; these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R ([≥] 3.2.2) and is available from github (https://github.com/tshimam/GIMLET).

bioinformatics↗

bioRxiv2018

A Network of Networks Approach for Modeling Interconnected Brain Tissue-Specific Networks

MotivationRecent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits, and regions which are connected each other and affect behavior and cognitive processes.\n\nResultsWe introduce a novel and efficient framework, called a \"Network of Networks\" (NoN) approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning (GOSPEL), a new sparse structural learning algorithm for network graph data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that GOSPEL outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the NoN model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain.\n\nAvailabilityOur software is available from https://github.com/infinite-point/GOSPEL.\n\nContactkawakubo@med.nagoya-u.ac.jp, shimamura@med.nagoya-u.ac.jp\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2017

Tumor subclonal progression model for cancer hallmark acquisition

Recent advances in the methods for reconstruction of cancer evolutionary trajectories opened up the prospects of deciphering the subclonal populations and their evolutionary architectures within cancer ecosystems. An important challenge of the cancer evolution studies is how to connect genetic aberrations in subclones to a clinically interpretable and actionable target in the subclones for individual patients. In this study, our aim is to develop a novel method for constructing a model of tumor subclonal progression in terms of cancer hallmark acquisition using multiregional sequencing data. We prepare a subclonal evolutionary tree inferred from variant allele frequencies and estimate pathway alteration probabilities from large-scale cohort genomic data. We then construct an evolutionary tree of pathway alterations that takes into account selectivity of pathway alterations via selectivity score. We show the effectiveness of our method on a dataset of clear cell renal cell carcinomas.

bioinformatics↗