Biology subjects

Azuma, I.

Publications and source records attributed to Azuma, I..

4 recordsLinked to original sources

bioRxiv2024

Establishment of an easy-to-construct liver injury mouse model for longitudinal analysis by drinking-water administration of 4,4'-methylenedianiline

Background & AimsLongitudinal animal models are essential for understanding the temporal dynamics of liver injury and recovery. While drinking water-based administration is ideal for sustained exposure in high feasibility, available compounds are limited, with thioacetamide (TAA) being the primary option. Here, we aimed to establish a novel drinking water-induced mouse model of cholestatic liver injury using 4,4-methylenedianiline (MDA), and to characterize its pathological trajectory in comparison to the TAA model. MethodsMice were administered MDA via drinking water for 28 Days. To elucidate the early events that give rise to chronic pathological divergence, we conducted a multi-layered analysis comprising plasma biochemical assays, immune cell profiling by flow cytometry, and hepatic transcriptomics at five time points during the early phase. The MDA model was evaluated against the established TAA model. ResultsMDA administration induced sustained ALT elevation, peribiliary fibrosis, and spatially irregular focal hepatocellular necrosis, distinguishing it from the centrilobular injury observed with TAA. Additionally, the MDA model showed significant elevations in ALP, TBIL, and TCHO, indicating cholestatic liver dysfunction. Early-phase analyses revealed model-specific differences in immunological and molecular responses, including increased CD8 T cell populations and enrichment of fibrinolysis-related gene expression in MDA-DW mice. ConclusionsWe present the MDA-DW model as a novel, longitudinally tractable liver injury model that complements existing systems by capturing alternative spatial, immunological, and transcriptional patterns of injury. This model offers a valuable platform for dissecting the temporal dynamics of liver disease progression in experimental settings. Significance StatementWe developed a cost-effective, non-invasive mouse model of cholestatic liver injury using drinking-water administration of 4,4-methylenedianiline (MDA). This model exhibits periportal-predominant damage, peribiliary fibrosis, and spatially irregular focal hepatocellular necrosis, distinct from conventional centrilobular models. Early-phase multi-omics analysis revealed immunological and transcriptomic differences, including increased CD8 T cells and activation of fibrinolysis-related pathways. The low mortality rate and ease of implementation enable long-term studies and cross-sectional comparisons across time points or interventions. This study provides not only a practical model for investigating chronic liver injury, but also a rich time-series, multi-view dataset, offering a valuable resource for advancing research on liver pathophysiology and toxicological mechanisms.

pharmacology and toxicology↗

bioRxiv2024

GLDADec: marker-gene guided LDA modelling for bulk gene expression deconvolution

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce GLDADec (Guided LDA Deconvolution), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to TCGA tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

bioinformatics↗

bioRxiv2023

Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases

Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could extend immune information. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats to retrieve information regarding immune cells from toxicogenomics databases. The rat-specific deconvolution showed high correlations for several types of immune cells between spleen and blood, and between liver treated with toxicants compared with those based on human and mouse data. Additionally, we found 4 clusters of compounds in Open TG-GATEs database based on estimated immune cell trafficking, which are different from those based on transcriptome data itself. The contributions of this work are three-fold. First, we obtained the gene expression profiles of 6 rat immune cells necessary for deconvolution. Second, we clarified the importance of species differences on deconvolution. Third, we retrieved immune cell trafficking from toxicogenomics databases. Accumulated and comparable immune cell profiles of massive data of immune cell trafficking in rats could deepen our understanding of enable us to clarify the relationship between the order and the contribution rate of immune cells, chemokines and cytokines, and pathologies. Ultimately, these findings will lead to the evaluation of organ responses in Adverse Outcome Pathway.

bioinformatics↗

bioRxiv2023

Investigation of the usefulness of liver-specific deconvolution method toward legacy data utilization

BackgroundImmune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. In this study, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data. ResultsWe prepared seven mouse liver injury models using small-molecule compounds with known hepatotoxicity and established a dataset with corresponding liver bulk RNA-Seq and immune cell proportions, covering various immune responses. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus (GEO) to provide a reference covering liver component cells. Here, we found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods. We established a liver tissue-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling focusing on reference cell sets is highly extrapolatable. ConclusionsWe provide an approach of liver-specific modeling when applying reference-based deconvolution to bulk RNA-Seq data and show its importance. It is expected to enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.

bioinformatics↗