Biology subjects

Koido, M.

Publications and source records attributed to Koido, M..

8 recordsLinked to original sources

bioRxiv2026

The human RNA-DNA interactome is cell type-specific and dynamic

More than twenty years ago, the FANTOM consortium uncovered that mammalian genomes are pervasively transcribed, revealing multitudes of RNAs with unknown functions. A subset of these transcripts has since then been linked to transcriptional control and to chromatin organization via their ability to interact with DNA, suggesting that chromatin-associated RNAs could be key players in genome regulation. Although recent technological advances now enable the mapping of genome-wide RNA-DNA contacts, a lack of analyses integrating these methods with other genomic features and across multiple cellular contexts hinders our comprehensive understanding of the principles underlying RNA-DNA interactions and of their biological importance. As part of the FANTOM6 project, we thus generated RNA-DNA interaction maps in 16 different human cell types, then combined these contacts with multiple layers of other genomic data to investigate how patterns of interaction between RNA and DNA relate to chromatin organization and function. We show that the RNA-DNA interactome is highly dynamic yet reproducibly organized in cell-type specific networks, constituted of a great diversity of interactions that vary in function of their distance, the nature of their sources and the chromatin state of their targets. In particular, we detected numerous regulatory elements that exhibit marked changes in activity when differentially bound by transcripts, implying that thousands of RNA-DNA interactions can play a mechanistic role in gene expression. This regulatory function correlates with RNA-protein interactions and significantly associates with cell type-relevant and disorder-related traits. In addition to providing essential resources for future research in RNA-mediated chromatin regulation, cellular biology and human diseases, our study thus establishes the RNA-DNA interactome as a new genome regulatory layer that defines and maintains cellular identity and behavior.

genomics↗

bioRxiv2024

Predicted constrained accessible regions mark regulatory elements and causal variants

Open chromatin regions (OCRs) define cell-type-specific regulatory elements across the genome, yet their functional significance varies, making it challenging to pinpoint biologically essential regions. Here, we introduce CAMBUS (Chromatin Accessibility Mutation Burden Score), a machine-learning framework that identifies active and evolutionarily constrained OCRs by leveraging surrounding DNA sequences. Applying CAMBUS to 29 immune cell types, we identified 66,043 constrained OCRs, which were substantially enriched in the known constraint genome (odds ratio=11.45 (95% confidence interval 9.33-14.05), P=4.7 x 10-68), while 90% of these OCRs were not prioritized by existing constraint metrics. These OCRs were highly enriched for known enhancers and super-enhancers, independent of known epigenetic markers and annotated regions, and overlapped with regulatory elements implicated in immune-mediated diseases and experimentally validated functional variants, including rare variants, particularly in leukocyte-related traits. Furthermore, CAMBUS revealed cell-type-specific transcriptional regulatory landscapes, linking genetic constraint with gene regulation in immune cells and identifying plausible connections between 1,533 causal variants and 70 complex traits. By defining biologically constrained regulatory elements at high resolution, CAMBUS provides a framework for understanding the selective pressures shaping the non-coding genome and its role in human health and disease.

genetics↗

bioRxiv2024

Self-Organization of Sinusoidal Vessels in Pluripotent Stem Cell-derived Human Liver Bud Organoids

The induction of tissue-specific vessels in in vitro living tissue systems remains challenging. Here, we directly differentiated human pluripotent stem cells into CD32b+ putative liver sinusoidal progenitors (iLSEP) by dictating developmental pathways. By devising an inverted multilayered air-liquid interface (IMALI) culture, hepatic endoderm, septum mesenchyme, arterial and sinusoidal quadruple progenitors self-organized to generate and sustain hepatocyte-like cells neighbored by divergent endothelial subsets composed of CD32blowCD31high, LYVE1+STAB1+CD32bhighCD31lowTHBD-vWF-, and LYVE1-THBD+vWF+ cells. Wnt2 mediated sinusoidal-to-hepatic intercellular crosstalk potentiates hepatocyte differentiation and branched endothelial network formation. Intravital imaging revealed iLSEP developed fully patent human vessels with functional sinusoid-like features. Organoid-derived hepatocyte- and sinusoid-derived coagulation factors enabled correction of in vitro clotting time with Factor V, VIII, IX, and XI deficient patients plasma and rescued the severe bleeding phenotype in hemophilia A mice upon transplantation. Advanced organoid vascularization technology allows for interrogating key insights governing organ-specific vessel development, paving the way for coagulation disorder therapeutics.

developmental biology↗

bioRxiv2023

Characterizing the quality metric in genotype imputation

Large-scale imputation reference panels are now available and have contributed to efficient genome-wide association studies through genotype imputation. However, it is still under debate whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations. We imputed genotypes of East Asian (EAS; 180k Japanese) subjects using the Trans-Omics for Precision Medicine (TOPMed) reference panel and found that the standard imputation quality metric (Rsq) substantially overestimated the dosage r2 (squared correlation between imputed dosage and true genotype). Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1, or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates ({theta} value) in the hidden Markov model, we uncovered that the lower {theta} value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the {theta} value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the {theta} value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could overestimate or underestimate dosage r2 for a subpopulation in the multi-ancestry panel and the deviation represents different imputed-dosage distributions. Finally, despite the impact of {theta} value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the {theta} value has a substantial impact on the imputed dosage and the imputation quality metric value.

bioinformatics↗

bioRxiv2021

Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expressions in unmeasured regions and tissues can enhance biologists histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region that is difficult to identify using raw data of spatial transcriptome alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

genomics↗

bioRxiv2021

SARS-CoV-2 ORF6 disturbs nucleocytoplasmic trafficking to advance the viral replication

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for the coronavirus disease 2019 pandemic. ORF6 is known to antagonize the interferon signaling by inhibiting the nuclear translocation of STAT1. Here we show that ORF6 acts as a virulence factor through two distinct strategies. First, ORF6 directly interacts with STAT1 in an IFN-independent manner to inhibit its nuclear translocation. Second, ORF6 directly binds to importin 1, which is a nuclear transport factor encoded by KPNA2, leading to a significant suppression of importin 1-mediated nuclear transport. Furthermore, we found that KPNA2 knockout enhances the viral replication, suggesting that importin 1 suppresses the viral propagation. Additionally, the analyses of gene expression data revealed that importin 1 levels decreased significantly in the lungs of older individuals. Taken together, SARS-CoV-2 ORF6 disrupts the nucleocytoplasmic trafficking to accelerate the viral replication, resulting in the disease progression, especially in older individuals.

microbiology↗

bioRxiv2021

Functional annotation of human long noncoding RNAs using chromatin conformation data

The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Studying lncRNAs is challenging due to their low expression level, cell type-specific occurrence, poor sequence conservation between orthologs, and lack of information about RNA domains. LncRNAs direct the regulatory factors in the locations that are in cis to their transcription sites. We designed a model to predict if an lncRNA acts in cis based on its features and trained it using RNA-chromatin interaction data. The trained model is cell type-independent and does not require RNA-chromatin data. Combining RNA-chromatin and Hi-C data, we showed that lncRNA-chromatin binding sites are determined by chromosome conformation. For each lncRNA, the spatially proximal genes were identified as their potential targets by combining Hi-C and Cap Analysis Gene Expression (CAGE) data in 18 human cell types. RNA-protein and RNA-chromatin interaction data suggested that lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. We provide the data through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.

genomics↗

bioRxiv2020

Predicting cell-type-specific non-coding RNA transcription from genome sequence

Transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). However, because transcription of ncRNAs, especially enhancer RNAs, is often low and cell type-specific, its dependency on genotype remains largely unexplored. Here, we developed mutation effect prediction on ncRNA transcription (MENTR), a quantitative machine learning framework reliably connecting genetic associations with expression of ncRNAs, resolved to the level of cell type. MENTR-predicted mutation effects on ncRNA transcription were concordant with estimates from previous genetic studies in a cell type-dependent manner. We inferred reliable causal variants from 41,223 GWAS variants, and proposed 7,775 enhancers and 3,548 long-ncRNAs as complex trait-associated ncRNAs in 348 major human primary cells and tissues, including plausible enhancer-mediated functional alterations in single-variant resolution in Crohns disease. In summary, we present new resources for discovering causal variants, the biological mechanisms driving complex traits, and the sequence-dependency of ncRNA regulation in relevant cell types.

genomics↗