Biology subjects

Yasuzawa, K.

Publications and source records attributed to Yasuzawa, K..

11 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↗

bioRxiv2026

Co-regulatory changes in Homo sapiens prefrontal cortex shape RNA-chromatin interactions

Noncoding RNAs (ncRNAs) regulate gene expression through RNA-chromatin interactions, yet their role in human brain evolution remains unclear. To map these interactions, we integrated FANTOM6 neurogenic data with a comprehensive atlas of regulatory regions active during early cortical development. This integration effort linked ncRNAs and transcription factors (TFs) to their genomic targets, revealing functional interactions captured by RNA-DNA contact mapping, yet largely missed by chromatin conformation data. Overlaying these interactions with nearly fixed variants in Homo sapiens relative to extinct hominins uncovered widespread rewiring of TF binding, affecting genes implicated in progenitor proliferation and neuronal differentiation. Gene regulatory network reconstruction identified TEAD2 and ONECUT2 as key regulators of intermediate progenitors and migrating neurons. Functional perturbation followed by Cut&Tag and ATAC-seq linked the two TFs to disease- and evolutionarily- relevant pathways. Together, these results show how ncRNA-TF cooperation and selective cis-regulatory rewiring contributed to sapiens-specific features of cortical development.

molecular biology↗

bioRxiv2025

Extending cis-regulatory networks using chromatin-RNA interactions

Cis-regulatory networks are essential for determining gene transcriptional states, yet the mechanisms that mediate cellular signaling and enhancer-promoter communications are not yet well understood. Here, we integrate high-resolution enhancer-promoter interactions obtained using targeted chromosome conformation capture and RNA-DNA contacts from RADICL-seq to uncover the role of RNA in modulating enhancer-promoter interactions. Both datasets were generated from human induced pluripotent cells at different stages across neural differentiation. As expected, most enhancer-promoter interactions were dynamic with only 38% shared across all cell states and 68% of promoter interacting regions overlapped at least one annotated neural enhancer. Integration with RADICL-seq data revealed a 9.3-fold enrichment for cis-interacting DNA regions to associate with at least one RNA. Among 18,346 cis-interacting promoters, only 1,170 (6.3%) lacked any RNA association, whereas 3,702 (28%) showed stable RNA association and 13,579 (74%) displayed dynamic RNA association across differentiation. Promoters associated with RNA engaged with higher number of enhancers and exhibited higher expression levels, while enhancers with RNA associations showed higher levels of chromatin activation marks. Promoters with dynamic RNA association were also more likely to be differentially expressed and enriched for relevant biological processes, phenotypes and diseases. Importantly, RNA-DNA association dynamics correlated strongly with DNA-DNA interaction dynamics; gain of RNA association on enhancers was typically accompanied by enhancer-promoter interaction gain, whereas concurrent RNA association gain on both promoter and enhancers frequently led to interaction loss. The correlation of RNA association on cis-interaction dynamics across differentiation were also captured in the community structure of the DNA-RNA network. Together, our results reveal extensive coupling between RNA-DNA and DNA-DNA networks supporting a coordinating role of RNA in gene regulation.

genomics↗

bioRxiv2025

A genome-wide, machine learning-guided exploration of the cis-regulatory code involved in neuronal differentiation

Gene expression is controlled by proximal and distal cis-regulatory elements (CREs), containing DNA motifs bound by various transcription factors (TFs). Other sequence features, such as specific k-mers or low complexity regions, have also been implicated [1-3]. However, in a dynamic biological process such as cell differentiation, we lack an understanding of how the transcriptional activity of CREs progressively change and what sequence features underlie these transitions, which may reflect common and/or coordinated regulatory processes. Here, we use single-cell ATAC-seq and RNA-seq to follow, at a genome scale, CREs along differentiation of induced pluripotent stem cells into cortical neurons and develop a method to automatically identify the diversity of CRE profiles and their underlying sequence features. We propose a machine-learning guided clustering algorithm, STOIC (Statistical learning TO Inform Clustering), that jointly learns an unsupervised clustering of the CREs in the space of the activity profiles and a supervised predictor associated with each cluster in the DNA-sequence space. STOIC is specifically designed to provide readily interpretable results. We show that the method identifies CRE profiles associated with highly predictive sequence features and outperforms methods solely concerned with co-activity clustering on this task. Orthogonal data collected in the same settings link the inferred CRE clusters to specific enhancer or promoter signatures. Furthermore, we show that the DNA features unveiled by STOIC reflect biologically relevant regulators and offer a valuable basis to dissect elements of the cisregulatory grammar. Finally, we demonstrate the general applicability of STOIC by analyzing five bulk CAGE datasets of human cells responding to various treatments.

bioinformatics↗

bioRxiv2025

Cell type-specific intronic RNAs shape genome architecture during neuronal lineage specification

Cell differentiation towards neurons is accompanied by widespread changes in three-dimensional (3D) genome organization and gene expression. Chromatin-associated RNAs have been proposed to be important regulators of such changes; however, the type, abundance, and role of these RNAs during neuronal differentiation remain largely unexplored. Here, we integrate multi-omic data generated in the frame of the Functional ANnoTation Of the Mammalian genome (FANTOM6) to chart 3D genome, RNA-DNA contactome, and transcriptome changes occurring during in vitro differentiation of human induced pluripotent stem cells to neural stem cells and neurons. We reveal a previously unreported phenomenon, in which intronic RNAs engage in long-distance contacts with DNA loci distributed all over the genome. These trans-contacting intronic RNAs (TIRs) are produced from exceptionally long (mean length: 750 kilobases, kb) protein-coding genes that carry ultra-long introns and are selectively expressed in neurons. We show that TIRs do not undergo rapid co-transcriptional degradation but rather accumulate in the nucleus of neuronal cells, forming large dot clouds around their source loci and spreading across the nucleus, as visualized by single-molecule RNA fluorescence in situ hybridization. TIRs engage in contacts with a set of genomic regions (TIR-contacted regions or TIRCs) that carry much shorter (mean length: [~]30 kb) neuronally expressed genes forming high-connectivity hubs. We also show that the expression of genes within TIRCs contacted by the same set of TIRs is highly co-varied, and that TIR source genes, especially their introns, are enriched in genetic risk loci for neurodevelopmental and neuropsychiatric disorders. Our findings point to a functional role of the persistence of long intronic RNAs in the nucleus of neuronal cells and might contribute to explain why neurons uniquely express many ultra-long genes. We propose a model in which TIRs form pan-nuclear scaffolds--which we propose to name introsomes--that constitute a dynamically self-renewing regulatory layer, where transcription itself continuously regenerates the very scaffold that organizes genome function in neurons, and that might be involved in the pathogenesis of neurodevelopmental and neuropsychiatric disorders.

genomics↗

bioRxiv2024

CFC-seq: identification of full-length capped RNAs unveil enhancer-derived transcription

Long-read sequencing has transformed transcriptome profiling, yet capturing full-length, non-polyadenylated transcripts like enhancer RNAs (eRNAs) remains challenging. Here, we introduce CFC-seq, combining cap-trapping and in vitro poly(A)-tailing to sequence poly(A) and non-poly(A) RNAs with precise transcription start site. Paired with our assembler, SALA, we identified 39,425 novel transcriptional units, including [~]24,000 eRNAs. Our data reveal a distinct genomic code governing eRNA fate dictated by core promoter architecture. CpG-island enhancers show high chromatin connectivity but yield short, exosome-sensitive RNAs. Conversely, TATA-box enhancers systematically co-opt LTR retrotransposons to inherit structural motifs that produce long, stable, and spliced RNAs. Mechanistically, the pioneer factor NF-Y activates these viral elements to license transcription, balanced by TEAD4 activity across a dual-gear regulatory axis. Finally, non-poly(A) eRNAs terminate via exosome-associated processing at structural-depleted cleavage zones. This comprehensive annotation links enhancer sequence architecture to RNA fate, providing a new transformative framework for decoding the functional human genome. HighlightsO_LIExpanded genomic architecture: CFC-seq unmasks a hidden layer of human transcriptome, identifying 39,425 novel transcriptional units with high-confidence TSS support, including [~]24,000 eRNAs. C_LIO_LITSS-first assembler: We introduce SALA, a specialized long-read assembler that prioritizes authentic 5 Cap-trapped ends to accurately reconstruct the TSS-resolved transcript models. C_LIO_LIGenomic code of eRNA fate: CGI enhancers drive short and exosome-sensitive transcripts associated with repressive H3K27me3 mark and high chromatin connectivity. TATA-box enhancers produce cell-type-specific, long, stable, and frequently spliced eRNAs. C_LIO_LIEvolutionary co-option of retrotransposons: A major fraction of TATA-box eRNAs originate from LTR retrotransposons, providing a direct mechanism for integration of viral elements into the human regulatory landscape. C_LIO_LIA dual-gear pioneering axis: The pioneer factor NF-Y activates unprimed LTR-TATA enhancers to license transcription independent of histone acetylation cascades, operating in parallel with TEAD4-mediated activation. C_LIO_LIStructural determinants of eRNA termination: Non-poly(A) eRNA TES features a secondary structure depletion zone that coordinates pol II termination and calibrates exosome-mediated turnover. C_LI

genomics↗

bioRxiv2023

Abundant capped RNAs are derived from mRNA cleavage at 3'UTR G-Quadruplexes

The 3 untranslated region (3UTR) plays a crucial role in determining mRNA stability, localisation, translation and degradation. Cap analysis gene expression (CAGE), a method for the detection of capped 5 ends of mRNAs, additionally reveals a large number of apparently 5 capped RNAs derived from 3UTRs. Here we provide the first direct evidence that these 3UTR-derived RNAs are indeed capped and often more abundant than the corresponding full-length mRNAs. By using a combination of AGO2 enhanced individual nucleotide resolution UV crosslinking and immunoprecipitation (eiCLIP) and CAGE following siRNA knockdowns, we find that these 3UTR-derived RNAs likely originate from AGO2-mediated cleavage, and most often occur at locations with potential to form RNA-G-quadruplexes and are enriched by RNA-binding protein UPF1. High-resolution imaging and long-read sequencing analysis validates several 3UTR-derived RNAs, demonstrates their abundance and shows that they tend not to co-localise with the parental mRNAs. We also find that production of 3UTR-derived RNA could explain the previously reported role of a 3UTR G-quadruplex in regulating the production of APP protein. Taken together, we provide new insights into the origin and abundance of 3UTR-derived RNAs, show the utility of CAGE-seq for their quantitative detection, and provide a rich dataset for exploring new biology of a poorly understood new class of RNAs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/538568v3_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@3f2ca7org.highwire.dtl.DTLVardef@18ca1b6org.highwire.dtl.DTLVardef@1ccdd8dorg.highwire.dtl.DTLVardef@e58938_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2022

The high-throughput perturbation of long non-coding RNA reveals functional features in stem cells and across cell-types

Within the scope of FANTOM6 consortium, we perform a large-scale knockdown of 200 long non-coding RNAs (lncRNAs) in human induced pluripotent stem (iPS) cells, and systematically characterize their roles in self-renewal and pluripotency. We find 36 lncRNAs (18%) exhibiting cell growth inhibition From the knockdown of 123 lncRNAs with transcriptome profiling, 36 lncRNAs (29.3%) show molecular phenotypes. Integrating the molecular phenotypes with chromatin-interaction assays further reveals cis- and trans-interacting partners as potential primary targets. Additionally, cell type enrichment analysis identifies lncRNAs associated with pluripotency while the knockdown of LINC02595, CATG00000090305.1 and RP11-148B6.2 modulates colony formation of iPS cells. We compare our results with previously published fibroblasts phenotyping data and find that 2.9% of the lncRNAs exhibit consistent cell growth phenotype, whereas we observe 58.3% agreement in molecular phenotypes. This highlights molecular phenotyping is more comprehensive in revealing affected pathways.

genomics↗

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↗

bioRxiv2019

Functional Annotation of Human Long Non-Coding RNAs via Molecular Phenotyping

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

genomics↗

bioRxiv2019

Recounting the FANTOM Cage Associated Transcriptome

Long non-coding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly-defined human transcriptome, inclusive of over 109,000 coding and non-coding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue specific transcription profiles for distinct classes of coding and non-coding genes, (b) perform differential expression analyses across thirteen cancer types, providing new insights linking promoter and enhancer lncRNAs expression to tumor pathogenesis, and (c) confirm the prognostic value of several enhancers in cancer. Comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs. Most importantly, access to the FC-R2 atlas is available from https://jhubiostatistics.shinyapps.io/recount/, the recount Bioconductor package, and http://marchionnilab.org/fcr2.html.

genomics↗