RNA Polymerase II (Pol II) transcribes all protein-coding and many nonprotein-coding genes in the genome. Pol II transcription termination is crucial for RNA maturation and, when disrupted, can lead to altered RNA processing and RNA export. Termination involves two intertwined processes: RNA cleavage and Pol II release from the DNA (displacement). Despite its importance, the exact mechanisms underlying Pol II displacement from the DNA remain poorly understood. Moreover, under certain cellular stress conditions, there is a partial failure of cleavage, leading to a shift of the position of displacement further downstream, a phenomenon known as readthrough transcription. We present a systematic analysis of Pol II termination across cell types and species and provide novel insights into the mechanism of displacement. Using a probabilistic mixture model to quantify Pol II dynamics across entire gene bodies from nascent RNA sequencing data, we find that genes fall into two classes defined by conserved base composition patterns near the displacement site. Both classes share a T-rich region upstream of displacement, and one class additionally carries a GC-rich region surrounding displacement. We find that GC-rich predisplacement regions have more accessible chromatin and higher levels of phospho-threonine 4 on the CTD of Pol II. Additionally, we find that displacement at genes with only the upstream T-rich pattern are more likely to be affected by perturbations that alter displacement, including heat shock, viral infection, kinase inhibition, and arsenic treatment. Thus, our work identifies two types of Pol II displacement regions, which are differentially affected by perturbations of cellular homeostasis.
Structural variants (SVs) are major sources of human genetic diversity. Despite advances in SV discovery, most single-nucleotide polymorphism (SNP)-centric frameworks overlook sequence-level information and encode overlapping variants as independent features, potentially reducing power in association studies, biasing heritability estimation, and limiting prediction accuracy. Here, we present LAVrefiner, a scalable, sequence-aware method that decomposes overlapping sequence-resolved SVs and indels into nonoverlapping refined sequence components and generates genotype matrices compatible with standard SNP-based analyses. In tandem repeat (TR) regions, LAVrefiner further supports motif-aware allele comparison and repeat copy-number estimation. When applied to variants from the 1000 Genomes Project, LAVrefiner identifies 224,708 refined variants. Simulations show that LAVrefiner is fast and memory-efficient while maintaining robust genotype representations under TR-associated sequencing noise. In gene expression analyses, models combining LAVrefiner-derived copy-number estimates with refined variants yield the highest heritability estimates and prediction accuracy in short tandem repeat (STR) and variable number tandem repeat (VNTR) regions, outperforming models based on the original SV/indel representation and existing TR copy-number tools. Cis -expression quantitative trait locus ( cis -eQTL) mapping further shows that refined variants can localize regulatory signals to specific sequence components, including an independent VNTR-derived signal at the TFRC locus. Together, LAVrefiner provides an efficient, context-aware strategy for integrating sequence-resolved SVs and indels into existing genetic analysis frameworks.
Glucocorticoids (GCs) are widely used anti-inflammatory and immunosuppressive agents known to induce substantial changes in gene expression, yet their effects on noncoding RNAs such as long noncoding RNAs (lncRNAs) or microRNAs (miRNAs) remain poorly characterized. Here, we present the first comprehensive and systematic analysis of GC regulation of the noncoding genome across nine human primary cell types from both hematopoietic and nonhematopoietic lineages. In vitro GC treatment was applied to each cell type, with transcriptomic profiling (total RNA-seq and small RNA-seq) at 2 and 6 h posttreatment. We identify more than 2000 GC-responsive noncoding transcripts, including 654 annotated lncRNAs, 1376 novel lncRNAs, and 39 miRNAs. The noncoding RNA response to GCs is highly cell type dependent: 80% of GC-responsive lncRNAs and 97% of miRNAs are unique to a single cell type. Hematopoietic cells exhibit a greater magnitude of lncRNA induction compared with nonhematopoietic cells. Notably, GC treatment uncovers dozens of facultative, stimulus-dependent lncRNAs. GC-responsive transcripts span diverse lncRNA classes, with enrichment of host lncRNAs. In contrast, only a limited number of miRNAs are GC responsive. GC regulation of transcript abundance for miRNAs and their host lncRNAs appears to be independent. Our results, which are accessible through an interactive web application, establish a framework for studying how noncoding transcription contributes to physiological and clinical heterogeneity in GC responses. The newly identified GC-responsive noncoding transcripts could represent biomarkers of GC exposure, determinants of GC sensitivity or resistance, or candidate regulators of tissue-specific GC effects.
Sexual dimorphism is a widespread phenotypic innovation that plays a crucial role in species diversification, yet its genetic origins and evolutionary trajectories remain poorly understood in vertebrates. Leptobrachium provides a tractable system in which male moustache toads develop maxillary nuptial spines used in male–male combat, whereas closely related pseudo-moustache toads lack this trait. To investigate the evolutionary genomics of these sexually dimorphic nuptial spines, we construct a pangenome from chromosome-level assemblies of six Leptobrachium species and integrate trans -species transcriptomic analyses. We find that keratin gene expansion predated the origin of nuptial spines, potentially providing genetic material for keratinized skin innovation. Recently duplicated hair keratin genes acquired putative Hoxc13 binding sites, and promoter reporter assays confirm that Hoxc13 can activate these genes in both moustache toads and pseudo-moustache toads. Differential hoxc13 expression may therefore be more important for affecting whether duplicated hair keratin genes are recruited to develop nuptial spines. Transcriptomic comparisons further reveal maxillary skin–specific expression of genes involved in keratinocyte proliferation and differentiation, as well as melanogenesis during the formation of nuptial spines. In Leptobrachium tengchongense , which has undergone secondary loss of nuptial spines, igfbp2 , a conserved gene highly expressed in keratinized skin appendages across amphibians, shows positive selection and represents a candidate associated with this evolutionary transition. Our study provides amphibian pangenome resources and highlights how gene duplication coupled with regulatory recruitment may shape the evolution of novel sexually dimorphic traits in vertebrates.
Single-cell high-throughput chromosome conformation capture (scHi-C) enables the analysis of cell-to-cell variability in three-dimensional (3D) genome organization, but its utility is severely limited by pervasive technical dropouts, leading to extremely sparse contact maps. Accurately identifying dropout events and imputing the corresponding missing chromatin contacts remains a challenging problem, as technical dropouts are confounded with genuine absences of chromatin interactions. Existing imputation and enhancement methods often rely on smoothing strategies or restrictive parametric likelihood assumptions, which may obscure fine-scale chromatin structures and cell-specific features. Here, we present scHiC-Diff, a conditional diffusion-based framework for scHi-C data imputation and enhancement. scHiC-Diff employs a forward process that progressively perturbs contact maps and a reverse denoising process that reconstructs chromatin interactions from noisy observations. To better capture long-range dependencies in complex 3D genome structures, scHi-C contact matrices are treated as masked images and modeled using a Diffusion Transformer (DiT). Across two complementary simulated datasets and three real scHi-C datasets, scHiC-Diff improves the recovery of chromatin interaction patterns and enhances downstream analyses compared with existing methods. Application to a developmental scHi-C dataset further demonstrates improved reconstruction of chromatin organization and enhanced chromatin loop detection.
