Biomedical subjects

Seiya Imoto

Publications and source records attributed to Seiya Imoto.

7 recordsLinked to original sources

PubMed2026

Causal assessment of Bayesian gene regulatory networks from single-cell transcriptomics.

Gene regulatory network (GRN) inference is an essential tool for revealing dysregulated relationships between genes in different cell types from single-cell transcriptomic (SCT) data. GRNs based on Bayesian networks (BNs) learned from SCT data can elucidate directed regulatory relationships representing complex disease mechanisms and their interplay through graphical modeling. However, software for learning BNs from SCT data is not widely available, nor is software for evaluating the BNs' structural accuracy in representing causal relationships between genes. Here, we describe the scstruc R package. This package provides a suite of BN structure learning algorithms specifically designed to handle SCT data, to evaluate the resulting networks based on the causal relationships they represent regardless of the availability of established molecular interaction networks, and to compare regulatory relationships between conditions. We demonstrated that scstruc can identify biologically relevant differential regulatory relationships between groups on a per-cell basis.

Bayesian networks↗

PubMed2026

Binding capacity of Intravenous immunoglobulin G to BK polyomavirus determines its anti-BK polyomavirus activity in infected cultures.

BK polyomavirus (BKPyV) causes disease in immunocompromised individuals. This study tested the hypothesis that the antiviral efficacy of intravenous immunoglobulin (IVIG) against BKPyV depends on the relationship between its virus-binding capacity and the viral burden. We first quantified the BKPyV-binding capacity of IVIG and then characterized BKPyV replication in kidney-derived HK-2 and HEK293 cells and in HEL cells under IVIG treatment. Subsequently, we analyzed the efficacy of IVIG at 0.03-10 mg/mL against low-multiplicity of infection (MOI) and high-MOI infection in relation to the BKPyV-binding capacity of IVIG. BKPyV productively infected all three cell lines, with 14-day replication cycles of 2.2, 4.0, and 7.0 in HK-2, HEK293, and HEL cells, respectively, indicating that HEL cells were the most permissive. IVIG bound approximately 108 copies of BKPyV DNA per milligram. In the low-MOI infection model, where the total viral load remained within this estimated binding capacity, IVIG showed clear neutralizing activity, significantly reducing viral spread, viral DNA levels, and the number of VP1-positive cells in a dose-dependent manner (p < 0.001). In contrast, in the high-MOI infection model, the viral load appeared to be high relative to the estimated IVIG binding capacity even at 10 mg/mL, and IVIG showed little or no neutralizing effect on viral production or spread of infected cells. These findings identify an experimental relationship between IVIG binding capacity, viral burden, and antiviral efficacy and suggest that the antiviral effect of IVIG is greatest when administered early, thereby supporting further clinical evaluation of early or preemptive IVIG administration.

Humans↗

PubMed2026

BCL11B enhancer hijacking by t(14;16)(q32;q24) translocation defines a novel high-risk subtype of T-ALL.

The molecular classification of T-cell acute lymphoblastic leukemia (T-ALL) remains incomplete, limiting risk stratification and the development of targeted therapies. Enhancer hijacking is a critical oncogenic mechanism that deregulates proto-oncogenes by repositioning cisregulatory regions via structural variants. Here, we performed an integrated analysis of pediatric and adult T-ALL and mixed-phenotype acute leukemias (MPALs), using whole-genome and whole-transcriptome sequencing. This analysis identified a group of 14 patients with predominantly T-lineage neoplasms driven by a t(14;16)(q32;q24) translocation, harboring universal GATA3 mutations and CDKN2A/B deletions. Mechanistically, this translocation repositions the ThymoD locus downstream of BCL11B, causing monoallelic, ectopic overexpression of FENDRR and mesenchymal transcription factor genes FOXF1 and FOXC2 and activating epithelial-mesenchymal transition transcription signatures. Immunophenotypic and single-cell RNA sequencing analyses revealed marked lineage ambiguity with myeloid and B-cell differentiation potentials specific to this subtype. Furthermore, functional analyses in CD34+ cord blood cells demonstrated that FOXF1 overexpression promotes myeloid differentiation while suppressing T-cell differentiation, serving as a key factor for lineage specification. Clinically, this subtype was detected in 0.15% to 4.0% of T-ALL/MPAL cases depending on the cohort, showing a median age of 15 years and enrichment in adolescents and young adults. Importantly, patients with t(14;16)(q32;q24) have an extremely poor prognosis, showing a trend toward worse outcomes than high-risk groups such as KMT2A-rearranged early T-cell progenitor-like, SPI1-rearranged, and LMO2 γδ-like T-ALLs. The unique molecular landscape and poor prognosis of patients with the t(14;16)(q32;q24) translocation underscore the need for the development of novel subtype-specific therapeutic approaches.

Humans↗

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

High-grade gliomas derived from an ovarian mature teratoma: clonal dynamics and genetic insights.

UNLABELLED: High-grade glioma (HGG) arising from a mature ovarian teratoma is extremely rare and its genetic alterations remain largely unknown. We report a case of WHO Grade 4 HGG (HGG-G4) developing 3 years after cystectomy for ovarian mature teratoma, where a WHO Grade 3 HGG (HGG-G3) was identified upon pathological reevaluation. Whole-exome sequencing confirmed the clonal relationship between HGG-G3 and HGG-G4, revealing genome-wide copy-neutral loss of heterozygosity, copy-number alterations, and whole-genome doubling in both HGGs. Genomic and epigenetic analyses have suggested multistep tumorigenesis and clonal alteration during the clinical course, particularly in response to chemotherapy, in HGGs arising from ovarian teratomas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13691-025-00790-x.

High-grade glioma↗

PubMed2025

Gene behaviors-based network enrichment analysis and its application to reveal immune disease pathways enriched with COVID-19 severity-specific gene networks.

MOTIVATION: Gene network analysis is essential for understanding the complex mechanisms underlying diseases, which often involve disruptions in molecular networks rather than individual genes. Despite the availability of large-scale omics datasets and computational tools for gene network analysis, interpretation of the biological relevance of these extensive networks remains challenging. RESULTS: We propose a novel computational strategy, gene behaviors-based network enrichment analysis, which systematically identifies functional pathways enriched in phenotype-specific gene networks. Our novel method incorporates comprehensive network characteristics, i.e. gene expression levels, edge strengths, and structural patterns of edges, to rank genes based on activity and assess pathway enrichment, effectively identifying functional pathways enriched within these networks. Through simulation studies, our strategy demonstrated superior performance compared with that of existing methods in identifying enriched pathways. We applied this strategy to whole-blood RNA-seq data from 1102 COVID-19 samples provided by the Japan COVID-19 Task Force. The analysis revealed immune disease pathways enriched with COVID-19 severity-specific gene networks, including "Systemic lupus erythematosus" in asymptomatic and severe samples and "Inflammatory bowel disease," "Primary immunodeficiency," and "Rheumatoid arthritis" in mild samples. Key biomarkers of COVID-19, such as CXCL8, S100A9, and HLA class I genes, have been identified as critical hub genes and the main players within these networks. AVAILABILITY AND IMPLEMENTATION: Code is available in Figshare (https://doi.org/10.6084/m9.figshare.29093648.v3).

COVID-19↗

PubMed2025

PharaCon: a new framework for identifying bacteriophages via conditional representation learning.

MOTIVATION: Identifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. However, these models are typically pre-trained with self-supervised tasks that do not consider label variance in the pre-training data. This presents a challenge for phage identification as pre-training on mixed bacterial and phage data may lead to information bias due to the imbalance between bacterial and phage samples. RESULTS: To overcome this limitation, we proposed a novel conditional BERT framework that incorporates label classes as special tokens during pre-training. Specifically, our conditional BERT model attaches labels directly during tokenization, introducing label constraints into the model's input. Additionally, we introduced a new fine-tuning scheme that enables the conditional BERT to be effectively utilized for classification tasks. This framework allows the BERT model to acquire label-specific contextual representations from mixed sequence data during pre-training and applies the conditional BERT as a classifier during fine-tuning, and we named the fine-tuned model as PharaCon. We evaluated PharaCon against several existing methods on both simulated sequence datasets and real metagenomic contig datasets. The results demonstrate PharaCon's effectiveness and efficiency in phage identification, highlighting the advantages of incorporating label information during both pre-training and fine-tuning. AVAILABILITY AND IMPLEMENTATION: The source code and associated data can be accessed at https://github.com/Celestial-Bai/PharaCon.

Bacteriophages↗