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Satoru Miyano

Publications and source records attributed to Satoru Miyano.

At least 19 recordsLinked to original sources
PubMed2026

Polycomb repressive complex 2 insufficiency underlies myeloid leukemia in Down syndrome.

Children with Down syndrome (DS) have an elevated risk of developing myeloid leukemia (ML; ML in DS [ML-DS]). In addition to mutations in GATA1, which generate the truncated isoform GATA1-short (GATA1s), ML-DS requires additional somatic gene mutations, most frequently in cohesion and Polycomb repressive complex 2 (PRC2) genes. Here, we show that PRC2 insufficiency underlies ML-DS pathogenesis. Transplantation of Gata1s fetal liver cells followed by deletion of the cohesion subunit Stag2 and/or the PRC2 component Ezh2 induced megakaryocyte-biased differentiation and expansion of megakaryocytic progenitors, culminating in lethal myelofibrosis. Mechanistically, loss of Stag2 or Ezh2 reinforced Gata1s-driven reduced chromatin accessibility at erythroid transcription factor target loci in premegakaryocyte/erythroid progenitors (pre-MegEs), thereby promoting megakaryocytic skewing. Ezh2 loss attenuated the Gata1s-mediated global elevation of H3K27 trimethylation in pre-MegEs, resulting in derepression of a broad set of PRC2 target genes and establishing a functionally PRC2-insufficient state. Similarly, Stag2 loss induced a moderate but significant degree of PRC2-insufficient state in Gata1s progenitors. Furthermore, chromosome 21-encoded miR-125b blocked megakaryocytic differentiation of Gata1s progenitors lacking either Stag2 or Ezh2 alone but drove full transformation and expansion of CD150+Sca-1+c-Kit+ leukemic stem cell-like populations only upon concurrent loss of both Stag2 and Ezh2, leading to acute megakaryoblastic leukemia in mice. These findings reveal that cohesin and PRC2 insufficiencies converge on PRC2 dysfunction while exerting distinct epigenetic effects and synergize with trisomy 21 and GATA1s to remodel the epigenetic landscape, driving progression from a preleukemic state to overt leukemia.

Animals↗
PubMed2026

MetaCCI: meta cell-cell interaction inference and its application to CCIs characteristics of MDS.

MOTIVATION: Cell-cell interactions (CCIs) are fundamental to multicellular organisms and play crucial roles in diverse biological processes and disease mechanisms. Understanding CCIs is vital for deciphering disease pathogenesis and developing therapeutic strategies. Although numerous computational methods have been developed to infer CCIs from complex biological data, most existing approaches rely primarily on single-gene expression levels and ligand-receptor databases, often failing to capture the nuanced network-wide changes characteristic of disease states. RESULT: We propose MetaCCI, a novel computational strategy that integrates meta-information into CCI inference by extending the traditional gene expression-based analysis to a gene regulatory network framework. MetaCCI meticulously combines established ligand-receptor pairs with quantitative insights into gene behavior within complex gene networks, enabling the precise extraction of relevant targets for CCI inference. Subsequently, CCI inference was performed using an eigen cell co-expression network, providing a more holistic view of cell-cell communication. Monte Carlo simulations demonstrated that MetaCCI consistently outperforms existing methods in CCI inference. We applied MetaCCI to characterize cell-cell communication in Myelodysplastic Syndromes (MDS). Our results identified distinct interaction patterns in MDS compared with normal cell populations, specifically highlighting the loss of CCIs between "Dendritic cells and Hematopoietic precursor cells" and between "Dendritic cells and Hematopoietic multipotent progenitor cells" as characteristic features of MDS. Furthermore, FABP5, CD63, and HMGB1 were identified as MDS-specific markers. These findings suggest that diminished CCIs involving dendritic cells, hematopoietic precursor cells, and multipotent progenitor cells are pivotal to MDS pathogenesis. AVAILABILITY AND IMPLEMENTATION: The MetaCCI software is freely available at https://github.com/HeewonGitHub/MetaCCI. An archived version of the software and example datasets used in this study is available at Zenodo: https://doi.org/10.5281/zenodo.20101527.

Myelodysplastic Syndromes↗
PubMed2025

Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA.

COVID-19, severe acute respiratory syndrome coronavirus 2, rapidly spread worldwide. Severe and critical patients are expected to rapidly deteriorate. Although several studies have attempted to uncover the mechanisms underlying COVID-19 severity, most have focused on the perturbations of single genes. However, the complex mechanism of COVID-19 involves numerous perturbed genes in a molecular network rather than a single abnormal gene. Thus, we aimed to identify COVID-19 severity-specific markers in the Japanese population using gene network analysis. In order to reveal the severity-specific molecular interplays, we developed a novel computational network biology strategy that measures dissimilarity between networks based on the comprehensive information of gene network (i.e., expression levels of genes and network structure) by using Kullback-Leibler divergence. Monte Carlo simulations demonstrated the effectiveness of our strategy for differential gene network analysis. We applied this method to publicly available whole blood RNA-seq data from the Japan coronavirus disease 2019 Task Force and identified differentially regulated molecular interplays between 368 severe and 105 non-severe samples. Our analysis suggests the gene network between HLA class II, CIITA, and CD74 as a COVID-19 severity specific molecular marker. Although the association between HLA class II and COVID-19 has been demonstrated, our data analysis revealed that the molecular interplay of HLA class II with its target and/or regulator is a crucial marker for COVID-19 severity. Our findings from computational network biology analysis suggest that suppression and activation of the molecular interplay between HLA class II, CIITA, and CD74 provide crucial clues to uncover the mechanisms of COVID-19 severity.

Humans↗
PubMed2024

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Stomach Neoplasms↗
PubMed2007

Analysis of gene networks for drug target discovery and validation.

Understanding responses of the cellular system for a dosing molecule is one of the most important problems in pharmacogenomics. In this chapter, we describe computational methods for identifying and validating drug target genes based on the gene networks estimated from microarray gene expression data. We use two types of microarray gene expression data: gene disruptant microarray data and time-course drug response microarray data. For this purpose, the information of gene networks plays an essential role and is unattainable from clustering methods, which are the standard for gene expression analysis. The gene network is estimated from disruptant microarray data by the Bayesian network model, and then the proposed method automatically identifies sets of genes or gene regulatory pathways affected by the drug. We use an actual example from analysis of Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information toward drug target discovery.

Antifungal Agents↗
PubMed2006

Contribution of comparative fish studies to general endocrinology: structure and function of some osmoregulatory hormones.

Fish endocrinologists are commonly motivated to pursue their research driven by their own interests in these aquatic animals. However, the data obtained in fish studies not only satisfy their own interests but often contribute more generally to the studies of other vertebrates, including mammals. The life of fishes is characterized by the aquatic habitat, which demands many physiological adjustments distinct from the terrestrial life. Among them, body fluid regulation is of particular importance as the body fluids are exposed to media of varying salinities only across the thin respiratory epithelia of the gills. Endocrine systems play pivotal roles in the homeostatic control of body fluid balance. Judging from the habitat-dependent control mechanisms, some osmoregulatory hormones of fish should have undergone functional and molecular evolution during the ecological transition to the terrestrial life. In fact, water-regulating hormones such as vasopressin are essential for survival on the land, whereas ion-regulating hormones such as natriuretic peptides, guanylins and adrenomedullins are diversified and exhibit more critical functions in aquatic species. In this short review, we introduce some examples illustrating how comparative fish studies contribute to general endocrinology by taking advantage of such differences between fishes and tetrapods. In a functional context, fish studies often afford a deeper understanding of the essential actions of a hormone across vertebrate taxa. Using the natriuretic peptide family as an example, we suggest that more functional studies on fishes will bring similar rewards of understanding. At the molecular level, recent establishment of genome databases in fishes and mammals brings clues to the evolutionary history of hormone molecules via a comparative genomic approach. Because of the functional and molecular diversification of ion-regulating hormones in fishes, this approach sometimes leads to the discovery of new hormones in tetrapods as exemplified by adrenomedullin 2.

Adrenomedullin↗
PubMed2006

ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles.

SUMMARY: One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail owing to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.

Bayes Theorem↗
PubMed2006

A new regulatory interaction suggested by simulations for circadian genetic control mechanism in mammals.

Knowledge of molecular biological systems is increasing at an amazing pace. It is becoming harder to intuitively evaluate the significance of each interaction between the molecules of the complex biological systems. Hence, we need to develop an efficient computational method to explore the biological mechanisms. In this study, we employed a hybrid functional Petri net in order to analyze mammalian circadian genetic control mechanisms, which consists of feedback loops of clock genes and generates endogenous near 24 h rhythms in mammals. We constructed a computational model based on the available biological data, and by using Genomic Object Net, we performed computer simulations of the time courses of clock gene transcription and translation. Although the original model successfully reproduced most of the circadian genetic control mechanisms, two discrepancies remained despite a wide selection of the parameters. We found that addition of a hypothetical path into the original model result in successful simulation of time courses and phase relationships among clock genes. This also demonstrates the usefulness of the hybrid functional Petri net approach to biological systems.

Animals↗
PubMed2006

Structural modeling and analysis of signaling pathways based on Petri nets.

The purpose of this paper is to discuss how to model and analyze signaling pathways by using Petri net. Firstly, we propose a modeling method based on Petri net by paying attention to the molecular interactions and mechanisms. Then, we introduce a new notion "activation transduction component" in order to describe an enzymic activation process of reactions in signaling pathways and shows its correspondence to a so-called elementary T-invariant in the Petri net models. Further, we design an algorithm to effectively find basic enzymic activation processes by obtaining a series of elementary T-invariants in the Petri net models. Finally, we demonstrate how our method is practically used in modeling and analyzing signaling pathway mediated by thrombopoietin as an example.

Algorithms↗
PubMed2006

Simulation-based validation of the p53 transcriptional activity with hybrid functional petri net.

MDM2 and p19ARF are essential proteins in cancer pathways forming a complex with protein p53 to control the transcriptional activity of protein p53. It is confirmed that protein p53 loses its transcriptional activity by forming the functional dimer with protein MDM2. However, it is still unclear that protein p53 keeps its transcriptional activity when it forms the trimer with proteins MDM2 and p19ARF. We have observed mutual behaviors among genes p53, MDM2, p19ARF and their products on a computational model with hybrid functional Petri net (HFPN) which is constructed based on information described in the literature. The simulation results suggested that protein p53 should have the transcriptional activity in the forms of the trimer of proteins p53, MDM2, and p19ARE This paper also discusses the advantages of HFPN based modeling method in terms of pathway description for simulations.

Animals↗
PubMed2006

Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles.

We propose a computational strategy for discovering gene networks affected by a chemical compound. Two kinds of DNA microarray data are assumed to be used: One dataset is short time-course data that measure responses of genes following an experimental treatment. The other dataset is obtained by several hundred single gene knock-downs. These two datasets provide three kinds of information; (i) A gene network is estimated from time-course data by the dynamic Bayesian network model, (ii) Relationships between the knocked-down genes and their regulatees are estimated directly from knock-down microarrays and (iii) A gene network can be estimated by gene knock-down data alone using the Bayesian network model. We propose a method that combines these three kinds of information to provide an accurate gene network that most strongly relates to the mode-of-action of the chemical compound in cells. This information plays an essential role in pharmacogenomics. We illustrate this method with an actual example where human endothelial cell gene networks were generated from a novel time course of gene expression following treatment with the drug fenofibrate, and from 270 novel gene knock-downs. Finally, we succeeded in inferring the gene network related to PPAR-alpha, which is a known target of fenofibrate.

Bayes Theorem↗
PubMed2005

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data.

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.

Algorithms↗
PubMed2005

Prediction of transcriptional terminators in Bacillus subtilis and related species.

In prokaryotes, genes belonging to the same operon are transcribed in a single mRNA molecule. Transcription starts as the RNA polymerase binds to the promoter and continues until it reaches a transcriptional terminator. Some terminators rely on the presence of the Rho protein, whereas others function independently of Rho. Such Rho-independent terminators consist of an inverted repeat followed by a stretch of thymine residues, allowing us to predict their presence directly from the DNA sequence. Unlike in Escherichia coli, the Rho protein is dispensable in Bacillus subtilis, suggesting a limited role for Rho-dependent termination in this organism and possibly in other Firmicutes. We analyzed 463 experimentally known terminating sequences in B. subtilis and found a decision rule to distinguish Rho-independent transcriptional terminators from non-terminating sequences. The decision rule allowed us to find the boundaries of operons in B. subtilis with a sensitivity and specificity of about 94%. Using the same decision rule, we found an average sensitivity of 94% for 57 bacteria belonging to the Firmicutes phylum, and a considerably lower sensitivity for other bacteria. Our analysis shows that Rho-independent termination is dominant for Firmicutes in general, and that the properties of the transcriptional terminators are conserved. Terminator prediction can be used to reliably predict the operon structure in these organisms, even in the absence of experimentally known operons. Genome-wide predictions of Rho-independent terminators for the 57 Firmicutes are available in the Supporting Information section.

Bacillus subtilis↗
PubMed2005

Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.

Bayes Theorem↗
PubMed2005

Identifying drug active pathways from gene networks estimated by gene expression data.

We present a computational method for identifying genes and their regulatory pathways influenced by a drug, using microarray gene expression data collected by single gene disruptions and drug responses. The automatic identification of such genes and pathways in organisms' cells is an important problem for pharmacogenomics and the tailor-made medication. Our method estimates regulatory relationships between genes as a gene network from microarray data of gene disruptions with a Bayesian network model, then identifies the drug affected genes and their regulatory pathways on the estimated network with time course drug response microarray data. Compared to the existing method, our proposed method can identify not only the drug affected genes and the druggable genes, but also the drug responses of the pathways. For evaluating the proposed method, we conducted simulated examples based on artificial networks and expression data. Our method succeeded in identifying the pseudo drug affected genes and pathways with the high coverage greater than 80 %. We also applied our method to Saccharomyces cerevisiae drug response microarray data. In this real example, we identified the genes and the pathways that are potentially influenced by a drug. These computational experiments indicate that our method successfully identifies the drug-activated genes and pathways, and is capable of predicting undesirable side effects of the drug, identifying novel drug target genes, and understanding the unknown mechanisms of the drug.

Bayes Theorem↗
PubMed2005

Prediction of debacle points for robustness of biological pathways by using recurrent neural networks.

Living organisms have ingenious control mechanisms in which many molecular interactions work for keeping their normal activities against disturbances inside and outside of them. However, at the same time, the control mechanism has debacle points at which the stability can be broken easily. This paper proposes a new method which uses recurrent neural network for predicting debacle points in an hybrid functional Petri net model of a biological pathway. Evaluation on an apoptosis signaling pathway indicates that the rates of 96.5 % of debacle points and 65.5 % of non-debacle points can be predicted by the proposed method.

Apoptosis↗
PubMed2005

Automatic drawing of biological networks using cross cost and subcomponent data.

Automatic graph drawing function for biopathways is indispensable for biopathway databases and softwares. This paper proposes a new grid-based algorithm for biopathway layout that considers (a) edge-edge crossing, (b) node-edge crossing, (c) distance measures between nodes, as its costs, and (d) subcellular localization information from Gene Ontology, as its constraints. For this algorithm, we newly define cost functions, devise an efficient method for computing the costs (a)-(c) by employing a matrix representing the difference between two layouts, and take a steepest descent method for searching locally optimal solutions and multi-step layout method for finding better solutions. We implemented this algorithm on Cell Illustrator which is a biopathway modeling and simulation software. The algorithm is applied to a signal transduction pathway of apoptosis induced by fas ligand. We compare our layout with that of the grid-based algorithm by Li and Kurata (Bioinformatics 21 (9):2036-2042, 2005). The result shows that our algorithm reduces edge-edge crossings and node-edge crossings, and solves the ''isolated island problem'', that is, despite the intension, some groups of nodes are apart from other nodes in the layout. As a result, the biological understandability of the layout is fairly improved.

Algorithms↗