Biology subjects

Koga, Y.

Publications and source records attributed to Koga, Y..

10 recordsLinked to original sources

bioRxiv2026

eIF3 interactions with miRNAs involved in translational control of early T cell activation

Early T cell activation induces extensive remodeling of the cellular transcriptome and proteome. We previously showed using a transcriptome-wide crosslinking approach that human translation initiation factor eIF3 directly interacts with a select set of immune-related mRNAs shortly after T cell activation. We also found that eIF3 binding to the 3'-untranslated regions (3'-UTRs) of the TCRA and TCRB mRNAs encoding the T cell receptor alpha and beta subunits dynamically regulates a burst in their translation. MicroRNAs (miRNAs) add an additional layer of regulation by fine-tuning both mRNA and protein expression. Although miRNA expression is dynamically regulated during T cell activation, how miRNAs interact with core regulatory pathways in primary T cells remains poorly understood. Here, we reexamined the eIF3-RNA crosslinking experiments in Jurkat cells and probed eIF3 function to investigate miRNA-mediated translational regulation during T cell activation. We found that eIF3 interacts with multiple mature miRNAs in activated Jurkat cells, including members of the miR-17[~]92 cluster. These interactions also occur in primary T cells, as shown by RNA immunoprecipitation followed by qPCR (RIP-qPCR). Knocking out the miR-17[~]92 cluster led to a delay in ILR2A (CD25) cell surface expression in Jurkat cells and reduced activation-associated cell size increase in primary T cells during early T cell activation. In parallel, we used mass spectrometry to identify eIF3-interacting factors in activated primary T cells, and surprisingly found no evidence of Argonaute binding. Together, these findings provide evidence that eIF3-miRNA interactions may play an unappreciated role in translational control during early T cell activation.

Molecular Biology↗

bioRxiv2026

Personalized reference genome-based pipeline reveals comprehensive haplotype-resolved views of cancer genomes

Cancer genome analysis relies on standard human reference genomes, but detecting somatic alterations in highly repetitive or individual-specific regions remains challenging. We developed the Personalized Reference genome-based Cancer Genome Analysis Pipeline (PRCGAP, https://github.com/yos-sk/PRCGAP), to our knowledge, the first comprehensive pipeline integrating haplotype-resolved analyses of somatic point mutations, structural variants, copy number, and DNA methylation on personalized diploid reference genomes, with each variant phased and annotated by genomic features. We applied PRCGAP to eight tumor-normal cell line pairs and three newly collected pediatric B-cell acute lymphoblastic leukemia (B-ALL) clinical samples. PRCGAP recovered most standard reference-based variants (86.7-96.4% across variant types), validated by orthogonal short-read sequencing, while additionally detecting variants that standard references missed (23.4% of SNVs and 32.6% of SVs). Notably, PRCGAP uncovered variants in centromeric and telomeric regions, and somatic single-nucleotide variants were enriched in centromere dip regions, the putative kinetochore formation sites. Using PRCGAP outputs, we identified L1 retrotransposition source sites absent from standard references and showed that a B-ALL IGH::DUX4 fusion arose from a DUX4 pseudogene within an internal D4Z4 repeat unit, rather than the canonical full-length DUX4 gene. Within a single haplotype-resolved framework, PRCGAP opens regions of the cancer genome that are inaccessible to standard references.

bioinformatics↗

bioRxiv2024

Diptera-specific Daedalus controls Zucchini endonucleolysis in piRNA biogenesis independent of exonucleases

piRNAs protect germline genomes and maintain fertility by repressing transposons. Daedalus and Gasz act together as a mitochondrial scaffold for Armitage, a necessary factor for Zucchini-dependent piRNA processing. However, the mechanism underlying this function remains unclear. Here, we find that the roles of Daedalus and Gasz in this process are distinct, although both are necessary: Daedalus physically interacts with Armitage, whereas Gasz supports Daedalus to maintain its function. Daedalus binds to Armitage through two distinct regions, an extended coiled-coil identified in this study, and a SAM. The former tethers Armitage to mitochondria, while the latter controls Zucchini endonucleolysis to define the length of piRNAs in an exonuclease-independent manner. piRNAs produced in the absence of Daedalus SAM do not exhibit full transposon silencing functionality. Daedalus is Diptera specific. Unlike Drosophila and mosquitoes, evolutionarily more complex species, such as mice, rely on exonucleases after Zucchini processing to specify the length of piRNAs.

biochemistry↗

bioRxiv2023

TCF7L2: a potential key regulator of antidepressant effects on hippocampal astrocytes in depression model mice

AimClinical and preclinical studies suggest that hippocampal astrocyte dysfunction is involved in the pathophysiology of depression; however, the underlying molecular mechanisms remain unclear. Here, we attempted to identify the hippocampal astrocytic transcripts associated with antidepressant effects in a mouse model of depression. MethodsWe used a chronic corticosterone-induced mouse model of depression to assess the behavioral effects of amitriptyline, a tricyclic antidepressant. Hippocampal astrocytes were isolated using fluorescence-activated cell sorting, and RNA sequencing was performed to evaluate the transcriptional profiles associated with depressive effects and antidepressant responses. ResultsDepression model mice exhibited typical depression-like behaviors that improved after amitriptyline treatment; the depression group mice also had significantly reduced GFAP-positive astrocyte numbers in hippocampal subfields. Comprehensive transcriptome analysis of hippocampal astrocytes showed opposing responses to amitriptyline in depression group and control mice, suggesting the importance of using the depression model. Transcription factor 7 like 2 (TCF7L2) was the only upstream regulator gene altered in depression model mice and restored in amitriptyline-treated depression model mice. In fact, TCF7L2 expression was significantly decreased in the depression group. The level of TCF7L2 long non-coding RNA, which controls mRNA expression of the TCF7L2 gene, was also significantly decreased in this group and recovered after amitriptyline treatment. The Gene Ontology biological processes associated with astrocytic TCF7L2 included proliferation, differentiation, and cytokine production. ConclusionWe identified TCF7L2 as a gene associated with depression- and antidepressant-like behaviors in response to amitriptyline in hippocampal astrocytes. Our findings could provide valuable insights into the mechanism of astrocyte-mediated antidepressant effects.

neuroscience↗

bioRxiv2022

Interactive analysis of single-cell data using flexible workflows with SCTK2.0

Analysis of single-cell RNA-seq (scRNA-seq) data can reveal novel insights into heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analysis. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single Cell Toolkit 2.0 (SCTK2.0), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/499900v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@2a3964org.highwire.dtl.DTLVardef@1debe9org.highwire.dtl.DTLVardef@6b3a2aorg.highwire.dtl.DTLVardef@1b7bf84_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIntuitive graphical user interface for interactive analysis of scRNA-seq data C_LIO_LIAllows non-computational users to analyze scRNA-seq data with end-to-end workflows C_LIO_LIProvides interoperability between tools across different programming environments C_LIO_LIProduces HTML reports for reproducibility and easy sharing of results C_LI

bioinformatics↗

bioRxiv2020

Celda: A Bayesian model to perform bi-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform simultaneous co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population, and each cell population to each sample. We used Celda to identify transcriptional modules and cell subpopulations in a publicly available peripheral blood mononuclear cell (PBMC) dataset. Celda identified a population of proliferating T cells and a single plasma cell which were missed by two other clustering methods. Celda identified transcriptional modules that highlighted unique and shared biological programs across cell types. Celda also outperformed a PCA-based approach for gene clustering on simulated data. Overall, Celda presents a novel statistically principled approach towards characterizing transcriptional programs and cellular heterogeneity in single-cell RNA-seq data.

bioinformatics↗

bioRxiv2020

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, a variety of technical artifacts can be present in scRNA-seq data and need to be assessed before downstream analyses can be performed. While several algorithms and tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Comprehensive pipelines to streamline the process of generating and visualizing QC metrics are lacking. To address this need, we built the SCTK-QC pipeline within the singleCellTK R package (https://github.com/compbiomed/singleCellTK). Features in this pipeline include the ability to import data from 11 different preprocessing tools or file formats, perform empty droplet detection with 2 different algorithms, generate standard quality control metrics such as number of UMIs per cell or the percentage of mitochondrial counts, predict doublets using 6 different algorithms, and estimate ambient RNA. QC data can be exported to R and/or Python objects used in popular down-stream workflows. Results are visualized in an easy-to-read HTML report. This pipeline can also be used by non-computational users with an interactive graphical user interface developed with R/Shiny. Overall, the SCTK-QC pipeline will streamline and standardize QC analysis for scRNA-seq data across a variety of different single-cell transcriptomic platforms and preprocessing tools.

bioinformatics↗

bioRxiv2020

Whole genome doubling confers unique genetic vulnerabilities on tumor cells

Whole genome doubling (WGD) occurs early in tumorigenesis and generates genetically unstable tetraploid cells that fuel tumor development. Cells that undergo WGD (WGD+) must adapt to accommodate their abnormal tetraploid state; however, the nature of these adaptations, and whether they confer vulnerabilities that can subsequently be exploited therapeutically, is unclear. Using sequencing data from [~]10,000 primary human cancer samples and essentiality data from [~]600 cancer cell lines, we show that WGD gives rise to common genetic traits that are accompanied by unique vulnerabilities. We reveal that WGD+ cells are more dependent on spindle assembly checkpoint signaling, DNA replication factors, and proteasome function than WGD- cells. We also identify KIF18A, which encodes for a mitotic kinesin, as being specifically required for the viability of WGD+ cells. While loss of KIF18A is largely dispensable for accurate chromosome segregation during mitosis in WGD- cells, its loss induces dramatic mitotic errors in WGD+ cells, ultimately impairing cell viability. Collectively, our results reveal new strategies to specifically target WGD+ cancer cells while sparing the normal, non-transformed WGD- cells that comprise human tissue.

cancer biology↗

bioRxiv2019

Decontamination of ambient RNA in single-cell RNA-seq with DecontX

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq) and discover novel cellular heterogeneity in complex biological systems. However, ambient RNA present in the cell suspension can be incorporated into these droplets and aberrantly counted along with a cells native mRNA. This results in cross-contamination of transcripts between different cell populations and can potentially decrease the precision of downstream analyses. We developed a novel hierarchical Bayesian method called DecontX to estimate and remove contamination in individual cells from scRNA-seq data. DecontX accurately predicted the proportion of contaminated counts in a mixture of mouse and human cells. Decontamination of PBMC datasets removed aberrant expression of cell type specific marker genes from other cell types and improved overall separation of cell clusters. In general, DecontX can be incorporated into scRNA-seq workflows to assess quality of dissociation protocols and improve downstream analyses.

bioinformatics↗

bioRxiv2018

Pipeliner: A Nextflow-based framework for the definition of sequencing data processing pipelines

The advent of high-throughput sequencing technologies has led to the need for flexible and user-friendly data pre-processing platforms. The Pipeliner framework provides an out-of-the-box solution for processing various types of sequencing data. It combines the Nextflow scripting language and Anaconda package manager to generate modular computational workflows. We have used Pipeliner to create several pipelines for sequencing data processing including bulk RNA-seq, single-cell RNA-seq (scRNA-seq), as well as Digital Gene Expression (DGE) data. This report highlights the design methodology behind Pipeliner which enables the development of highly flexible and reproducible pipelines that are easy to extend and maintain on multiple computing environments. We also provide a quick start user guide demonstrating how to setup and execute available pipelines with toy datasets.

bioinformatics↗