Genome research最新文献

Species adapting to a similar lifestyle may undergo convergent changes in organ structure and cellular function, themselves relying or not on convergent genetic changes. The extent of genomic convergence is thus debated and may further depend on the interplay between temporal factors, such as species relatedness or the age of the transition. Rodents have repeatedly adapted to life in arid conditions, notably with altered renal morphology and physiology. By analyzing kidney transcriptomes from 33 species, we find convergence at all examined biological levels, from the whole kidney transcriptome down to the coding sequences and expression level of individual genes. Transcriptome-level signatures reflect convergent changes in cell proportions, suggesting convergent structural adaptations of the kidney. A large proportion of genes shows convergent substitutions, but those happened in small subsets of species, showing that there are multiple genetic paths repeatedly taken in a mosaic manner. A similar mosaic signal of convergence is found comparing gene expression in species spanning the Rodentia order, but convergence is more widely shared at the lower level of the Murinae family. Therefore we test more directly the influence of temporal factors. We observe more convergent changes when we select species independently adapted from more closely than more distantly related ancestors, and when we select older transitions rather than recent transitions. Our study shows that there are many different, yet repeatedly selected, ways to adapt to aridity, and that the degree of convergent evolution increases with both the age of the transitions and species relatedness.

Variation in short tandem repeats (STRs) is implicated in Mendelian disease and complex traits, but can be difficult to resolve with short-read genome sequencing. We present STRkit, a software package for genotyping STRs using long read sequencing (LRS) that uses proximate single-nucleotide variants to improve genotyping accuracy without a priori haplotype information. We show that STRkit has unique strengths versus other methods: it can use data from both major LRS technologies (Pacific Biosciences HiFi [PB] and Oxford Nanopore [ONT]) to output both allele- and read-level copy number and sequence, performs best in benchmarking with F1 scores of 0.9631 and 0.9544 with PB and ONT data respectively, achieves higher rates of Mendelian consistency than other genotyping tools, and is open source software. STRkit's features open up new possibilities for association testing, assessing patterns of STR inheritance, and better understanding the functional effects of these notable repeat elements.

Chromosomal rearrangements on the short arm of Chromosome 8 cause 8p syndrome, a rare developmental disorder characterized by neurodevelopmental delays, epilepsy, and cardiac abnormalities. While significant progress has been made in managing the symptoms of 8p syndrome and other conditions caused by large-scale chromosomal aneuploidies, no therapeutic approach has yet been demonstrated to target the underlying disease-causing chromosome. Here, we establish a two-step approach to eliminate the abnormal copy of Chromosome 8 and restore euploidy in cells derived from an individual with a complex rearrangement of Chromosome 8p. Transcriptomic analysis revealed 361 differentially expressed genes between the proband and the euploid revertant, highlighting genes both within and outside the 8p region that may contribute to 8p syndrome pathology. Furthermore, we demonstrate that the proband exhibits a significant defect in neural differentiation that could be partially rescued by treatment with small-molecule inhibitors of cell death. Our work demonstrates the feasibility of using chromosome engineering to correct complex aneuploidies in vitro and establishes a platform to further dissect the pathophysiology of 8p syndrome and other conditions caused by chromosomal rearrangements.

Fine-grained prediction of chromatin accessibility from DNA sequence is a foundational step in modeling gene expression changes resulting from sequence variants. Yet, few methods operate at the resolution necessary to capture subtle effects of single-nucleotide changes. Furthermore, it remains unclear which architectural components, such as residual connections, normalization strategies, or attention mechanisms, drive performance in these high-resolution predictions. To address these knowledge gaps, we systematically evaluate classic architectural choices and introduce ConvNeXt V2 blocks, originally developed for computer vision, as high-resolution feature extractors in deep learning models for genomic data. Integrated into diverse architectures such as CNNs, LSTMs, dilated CNNs, and transformers, ConvNeXt V2 blocks consistently improve performance, leading to similar prediction accuracy across these different model types. This reveals that early feature extraction, rather than downstream architecture, is the primary determinant of prediction accuracy. A comprehensive evaluation of these models on ATAC-seq signal prediction at 4 bp resolution in a cell type-specific manner identifies the ConvNeXt-based dilated CNN as the most robust performer, better preserving the signal's shape. Our codebase and benchmarks provide practical tools for high-resolution chromatin modeling.

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.

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.

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.