Genome research最新文献

UV light induces cyclobutane pyrimidine dimers (CPDs) and other mutagenic lesions in cellular DNA. Cytosine-containing CPDs can subsequently undergo rapid deamination to uracil, a process that has been linked to UV mutagenesis. However, the impact of genomic context and chromatin architecture on CPD deamination rates in cells remains poorly understood. Here, we develop a method known as dCPD-seq to map deaminated CPDs (dCPDs) across the genome of repair-deficient yeast cells at single-nucleotide resolution. Our dCPD-seq data reveal that sequence context significantly modulates CPD deamination rates in UV-irradiated yeast cells, with CPDs in TCG contexts showing particularly rapid deamination rates. Our analysis indicates that rapid CPD deamination can explain why UV-induced mutations are specifically enriched at TCG sequences, both in UV-irradiated yeast cells and in human skin cancers. CPD deamination is suppressed near the transcription start and end sites of yeast genes, which may in part by mediated by DNA-bound transcription factors. Finally, we show that the wrapping of DNA in nucleosomes modulates CPD deamination in yeast cells. Our data indicate that CPD deamination is elevated at minor-in rotational positions where the DNA minor groove faces the histone octamer, likely owing to increased solvent accessibility of the C4 position of the cytosine base. Moreover, we also observe strand-specific enrichment of CPD deamination at rotational positions where the DNA backbone faces out toward the solvent. Taken together, these findings reveal how DNA sequence context and chromatin architecture modulates CPD deamination rates across a eukaryotic genome.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to study the genomic localization of DNA-associated proteins. However, conventional protocols include multiple manual steps that can introduce inconsistency and limit scalability, thereby restricting the inclusion of appropriate replicates and controls. Although the introduction of liquid handling platforms has improved reproducibility, most existing efforts have automated only a subset of the workflow, and extending automation to efficiently map nonhistone proteins, such as chromatin regulators, remains challenging. Here, we present a fully automated implementation of our previously developed single-pot ChIP-seq protocol, named spa-ChIP-seq, which enables scalable processing of eight to 96 ChIP-seq samples from cross-linked cells to a sequencing-ready library in approximately 3 days with an estimated cost of $70 per sample. Benchmarking spa-ChIP-seq against manual ChIP-seq performed in parallel demonstrates a comparable signal-to-noise ratio between the two workflows. Using spa-ChIP-seq, we systematically evaluate multiple parameters including shearing and cross-linking conditions, buffer compositions, and the ratio of antibody to cell number. We find, for the first time to our knowledge, that weaker genomic localization signals are sensitive to changing the antibody-to-cell-number ratio, whereas the stronger signals remain unaffected. This finding underscores the importance of maintaining consistent antibody-to-cell-number ratio for comparative studies, such as treatment responses or chromatin-QTL mapping. The spa-ChIP-seq protocol is publicly available, including deck setups, operational parameters, and scripts. We envision that this robust, cost-efficient protocol will facilitate high-throughput, reproducible ChIP-seq analyses, supporting large-scale studies of antibody validation, compound screening, population genomics, and diagnostic frameworks.

Epigenetic mechanisms contribute to gene regulation by altering chromatin accessibility through changes in transcription factor (TF) and nucleosome occupancy across the genome. Despite numerous studies focusing on changes in gene expression, the intricate chromatin-mediated regulatory code remains largely uncharted on a comprehensive scale. We address this by employing a factor-agnostic, reverse-genetics approach that uses MNase-seq to capture genome-wide TF and nucleosome occupancies in response to the individual deletion of 201 transcriptional regulators in Saccharomyces cerevisiae, thereby assaying nearly 1 million mutant-gene interactions. We develop a principled new approach to identify and quantify chromatin changes genome-wide, allowing us to observe differences in TF and nucleosome occupancy that recapitulate well-established pathways identified by gene expression data. We also discover distinct chromatin signatures associated with the up- and downregulation of genes and use these signatures to reveal regulatory mechanisms previously unexplored in expression-based studies. Finally, we demonstrate that chromatin features are predictive of transcriptional activity, and we leverage these features to reconstruct chromatin-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes genome-wide, providing a deeper, more mechanistic understanding of the complex relationship between chromatin organization and transcription.

Epitranscriptomics, a rapidly evolving field mainly driven by massive parallel sequencing technologies, explores post-transcriptional RNA modifications. N ⁶-methyladenosine (m⁶A) has emerged as the most prominent and dynamically regulated modification in human mRNAs, being implicated in the regulation of diverse biological processes, including spermatogenesis, heat shock response, ultraviolet-induced DNA damage response and maternal mRNA clearance. Despite the recognized significance of m⁶A in mRNA regulation, limited studies have focused on the targeted and efficient manipulation of this modification in mRNAs. Here, we present Dem6A-Vec, an "all-in-one" plasmid vector designed for site-specific m⁶A demethylation in human mRNAs. Dem6A-Vec integrates the expression of a catalytically inactive RfxCas13d fused to the m⁶A demethylase ALKBH5 and a U6-driven customizable guide RNA in a single construct, simplifying experimental workflows and enhancing targeting efficiency. Using nanopore direct RNA sequencing, we identify high-confident m⁶A sites in HeLa cells, which serve as targets for Dem6A-Vec. We validate the targeted demethylation of m⁶A sites in the EEF2 and RRAGA genes using the established SELECT-qPCR method, confirming the impacts on mRNA stability and highlighting the tool's precision and versatility. The presented approach is implemented in multiple mRNA sites with diverse methylation stoichiometries, underscoring its adaptability to various transcriptomic contexts. This study provides a robust and scalable method for investigating the functional roles of m⁶A modifications, offering a transformative platform for advancing epitranscriptomic research and potential therapeutic applications.

Stem cells have the unique ability to self-renew and differentiate into specialized cell types. Epigenetic mechanisms, including histones and their post-translational modifications, play a crucial role in regulating programs integral to a cell's identity, like gene expression and DNA replication. However, the transcriptional, chromatin, and replication timing profiles of adult stem cells in vivo remain poorly understood. Containing germline stem cells (GSCs) and somatic cyst stem cells (CySCs), the Drosophila testis provides an excellent in vivo model for studying adult stem cells. However, the small number of stem cells and the cellular heterogeneity of this tissue have limited comprehensive genomic studies. In this study, we develop cell-type-specific genomic techniques to analyze the transcriptome, histone modification patterns, and replication timing of germline stem cell (GSC)-like and somatic cyst stem cell (CySC)-like cells. Single-cell RNA sequencing validates previous findings on GSC-CySC intercellular communication and reveals a high expression of chromatin regulators in GSC-like cells. To characterize chromatin landscapes, we develop a cell-type-specific chromatin profiling assay to map H3K4me3-, H3K27me3-, and H3K9me3-enriched regions, corresponding to the euchromatic, facultative heterochromatic, and constitutive heterochromatic domains, respectively. Finally, we determine cell-type-specific replication timing profiles, integrating our in vivo data sets with published data using cultured cell lines. Our results reveal that GSC-like cells display a distinct replication program, compared with somatic lineages, that aligns with chromatin state differences. Collectively, our integrated transcriptomic, chromatin, and replication data sets provide a comprehensive framework for understanding genome regulation differences between these in vivo stem-cell populations, demonstrating the power of multiomics in uncovering cell-type-specific regulatory features.

The rice genome underpins fundamental research and breeding, but the Nipponbare (japonica) reference does not fully encompass the genetic diversity of Asian rice. To address this gap, the Rice Population Reference Panel (RPRP) was developed, comprising high-quality assemblies of 16 rice cultivars to represent the japonica, indica, aus, and aromatic varietal groups. The RPRP has been consistently annotated and supported by extensive experimental data, and here, we report the computational assignment, characterization, and dissemination of stably identified pangenes, collectively called the PanOryza data set. We identify 25,178 core pangenes shared across all cultivars, alongside cultivar-specific and family-enriched genes. Core genes exhibit higher gene expression and proteomic evidence, higher confidence protein domains, and AlphaFold structures, whereas cultivar-specific genes are enriched for domains under selective breeding pressure, such as for disease resistance. We identify more than 5000 genes absent in the IRGSP rice reference genome and present in at least two other Oryza cultivars. We demonstrate the utility of this resource through various examples of pangenes and their protein domains. This resource, integrated into public databases, enables researchers to explore genetic and functional diversity via a population-aware "reference guide" across rice genomes, advancing both basic and applied research.

Many African great ape chromosomes possess large subterminal heterochromatic caps at their telomeres that are conspicuously absent from the human lineage. Leveraging the complete sequences of great ape genomes, we characterize the organization of subterminal caps and reconstruct the evolutionary history of these regions in chimpanzees and gorillas. Detailed analyses of the composition of the associated terminal 32 bp satellite array from chimpanzee (termed pCht) and intervening segmental duplication (SD) spacers confirm two independent origins in the Pan and gorilla lineages. In chimpanzee and bonobo, we estimate these structures emerged ∼7.7 million years ago (MYA) in contrast to gorilla, in which they expanded more recently, ∼5.0 MYA, and now make up 8.5% of the total gorilla genome. In both lineages, the SD spacers punctuating the pCht heterochromatic satellite arrays correspond to pockets of decreased methylation, although in gorilla such regions are significantly less methylated (P < 2.2 × 10^-16) than in chimpanzee or bonobo. Allelic pairs of subterminal caps show a higher degree of sequence divergence than euchromatic sequences, with bonobo showing less divergent haplotypes and less differentially methylated spacers. In contrast, we identify virtually identical subterminal caps mapping to nonhomologous chromosomes within a species, suggesting ectopic recombination potentially mediated by SD spacers. We find that the transition regions from heterochromatic subterminal caps to euchromatin are enriched for structural variant insertions and lineage-specific duplicated genes. Our findings suggest independent evolution of subterminal caps converging on a common genetic and epigenetic structure that promoted ectopic exchange as well as the emergence of novel genes at transition regions between euchromatin and heterochromatin.

Genomic imprinting is a specialized mechanism of transcriptional regulation whereby approximately 200 mammalian genes are expressed monoallelically according to their parental origin. This crucial developmental process is primarily controlled by discrete cis-regulatory elements known as imprinting control regions (ICRs), which play essential roles in directing allele-specific gene expression across large imprinted domains. In this review, we highlight the features that define ICRs as a distinct class of cis-regulatory regions, from their ability to maintain germline-inherited DNA methylation to their multifunctional roles in transcriptional control. For each imprinted domain, we examine the diverse mechanisms by which individual ICRs integrate multiple regulatory functions to coordinate both proximal and distal imprinted gene expression. By uncovering the multifaceted roles of ICRs, this review provides a compelling framework for understanding, more broadly, the molecular basis of finely controlled gene expression.

As sequencing techniques advance in precision, affordability, and diversity, an abundance of heterogeneous sequencing data has become available, encompassing a wide range of phenotypic features and biological perturbations. Unfortunately, increased resolution comes with the cost of increased complexity of the biological search space, even at the individual study level, as perturbations are now often examined across many dimensions simultaneously, including different donor phenotypes, anatomical regions and cell types, and time points. Furthermore, broad integration across studies promises a unique opportunity to explore the molecular underpinnings of distinct healthy and disease states, larger than the original scope of the individual study. To fully realize the promise of both individual higher resolution studies and large cross-study integrations, we need a robust methodology that can disentangle the influence of technical and nonrelevant phenotypic factors, isolating relevant condition-specific signals from shared biological information while also providing interpretable insights into the genetic effects of these conditions. Current methods typically excel in only one of these areas. To address this gap, we have developed ALPINE, a supervised nonnegative matrix factorization (NMF) framework that effectively separates both technical and nontechnical factors while simultaneously offering direct interpretability of condition-associated genes. Through simulations across four different scenarios, we demonstrate that ALPINE outperforms existing methods in both isolating the effect of different phenotypic conditions and prioritizing condition-associated genes. Furthermore, ALPINE has favorable performance in batch effect removal compared with state-of-the-art integration methods. When applied to real-world case studies, we showcase how ALPINE can be used to extract insights into the biological mechanisms that underlie differences between phenotypic conditions.

Gene-level rare variant association tests (RVATs) are essential for uncovering disease mechanisms and identifying therapeutic targets. Advances in sequence-based machine learning have generated diverse variant pathogenicity scores, creating opportunities to improve RVATs. However, existing methods often rely on rigid models or single annotations, limiting their ability to leverage these advances. Here, we introduce BayesRVAT, a Bayesian rare variant association test that jointly models multiple annotations. By specifying priors on annotation effects and estimating gene- and trait-specific posterior burden scores, BayesRVAT flexibly captures diverse rare-variant architectures. In simulations, BayesRVAT improves power while maintaining calibration. In UK Biobank analyses, it detects 10.2% more blood-trait associations and reveals novel gene-disease links, including PRPH2 with retinal disease. Integrating BayesRVAT within omnibus frameworks further increases discoveries, demonstrating that flexible annotation modeling captures complementary signals beyond existing burden and variance-component tests.