Biomedical subjects

Satoru Miyano

Publications and source records attributed to Satoru Miyano.

4 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↗