Genomics, proteomics & bioinformatics最新文献

X chromosome inactivation is a process that compensates X-linked gene dosage in mammalian female cells. The silencing of a randomly selected chromosome is accompanied by dramatic three-dimensional reorganization across the entire chromosome. To investigate the four-dimensional chromatin dynamics during early inactivation stages, we applied the multi-omics sequencing technique HiRES (Hi-C and RNA-seq employed simultaneously), which simultaneously detects the three-dimensional genome and transcriptome in single cells, in a mouse embryonic stem cell line with induced random inactivation. This three-dimensional genome and transcriptome dual-omics data allowed us to identify random inactivation trajectories at single-cell resolution. We characterized multiple layers of X-chromosome reorganization and discovered a transient structural state shared by both X chromosomes, associated with biallelic X-inactive specific transcript (Xist) expression. By constructing single-cell inactivation trajectories, we found that most chromatin remodeling either accompanied or followed gene silencing. Further analysis of interaction decay kinetics revealed that topologically associating domain (TAD) attenuation began from loss of interactions on TAD anchors. This study thus provides a detailed depiction of fine-scale chromatin reorganization during the initiation of random X chromosome inactivation.

Intratumoral heterogeneity (ITH) is a major driver of mortality in breast cancer (BC) patients and a critical factor in the variable therapeutic outcomes observed in BC treatment. Understanding the mechanisms underlying ITH is essential for advancing both clinical and basic BC research. Chromatin accessibility is critical for regulation of gene expression and cellular identity and plays a central role in shaping ITH and tumor evolution. However, studying chromatin accessibility in situ has been challenging due to the availability of technical platforms. Here, we leveraged the spatial ATAC-seq platform to profile the chromatin accessibility landscape of tumors from six BC patients. Our analyses revealed prominent heterogeneity within tumor regulatory modules and spatial variations in immune cell composition and stromal structures, offering a framework for investigation of the molecular architecture underlying ITH. Moreover, we identified two tumor subclones with potential common origin but distinct immune infiltration conferred by regulatory cascades, suggesting that epigenetic regulation may further contribute to the divergent tumor microenvironments and phenotypic diversity of these subclones. Our study provides novel insights into the molecular mechanisms driving ITH and opens up potential avenues for therapeutic intervention.

Recent breakthroughs in single-cell multi-omics technologies have enabled simultaneous measurement of diverse cellular modalities, offering unprecedented biological insights. However, integrating such multimodal data faces dual challenges: Small-scale paired-modality studies (hundreds of cells) risk overfitting, while large-scale reference atlases often struggle to generalize effectively to new datasets. To overcome these challenges, we present multimodal integration with self-supervised learning (MINERVA), a unified deep learning framework employing self-supervised strategies for single-cell multimodal integration. MINERVA outperforms six state-of-the-art methods in dimensionality reduction, missing feature imputation, and batch effect correction, even with limited training cells. For large-scale applications, MINERVA constructs scalable multi-tissue references that support zero-shot knowledge transfer to unseen datasets, instant cell type annotation, novel cell states identification, and comprehensive downstream analyses, all without model retraining. Uniquely bridging small-scale precision with atlas-level generalization, MINERVA serves as a versatile tool for both de novo integration and cost-effective atlas reuse in single-cell research.

Age-related hearing loss (ARHL) is the most common type of hearing loss. Genetic factors are considered to play important roles in the development of ARHL. To identify novel susceptibility genes and cell types relevant to ARHL, we performed a two-stage single-cell transcriptome-wide association study (scTWAS) on ARHL in 96,372 cases and 141,590 controls of European descent. In the discovery stage, we identified 1034 gene-cell pairs that showed suggestive associations with ARHL (P < 1.0 × 10-5), representing 450 genes across various cell types. These genes were enriched in multiple pathways, including the immune-related, estrogen signaling, and oxidative damage response pathways. Besides, we provided prominent genetic evidence for putative drug repurposing, highlighting several genes as potential targets, including NR3C2, CHRM4 and SHBG. Further, we validated the significant association of 41 genes with ARHL in the replication stage of scTWAS, including previously reported genes such as HLA-DRA, as well as novel candidates such as TNF, ZC3HAV1, and SLC44A4. Among these novel candidates, several are highly biologically plausible in the development of ARHL. In conclusion, this scTWAS broadens our understanding of the genetic susceptibility to ARHL, which might be helpful in developing new strategies for the treatment and prevention of ARHL.

The integration of genotype with envirotype is essential for achieving precise phenotypic prediction. However, there is currently a scarcity of datasets derived from variety approval testing trials that encompass a broad spectrum of trial locations, conform to standardized management protocols, and which incorporate comprehensive records of environmental variables. This study introduces MaizeGEP, a dataset consisting of 260 hybrid maize varieties from national maize variety regional trials. This dataset includes 12,233 selected tag single nucleotide polymorphisms for each variety, phenotypic survey data on 11 traits across 2382 year-county locations, and daily meteorological records. We utilized this dataset to conduct analyses on the clustering of 2382 year-county locations, the population structure of the 260 varieties, and genome-wide association studies. Furthermore, a novel mixture of experts (MoE) framework incorporating genotype-envirotype to phenotype (GE2P) algorithms was employed for best linear unbiased estimator (BLUE) value prediction and phenotypic prediction. Additionally, several machine learning and deep learning algorithms, including Bayesian methods, support vector machines (SVM), LightGBM, multilayer perceptron (MLP), DeepGS, DEM, and Cropformer, were utilized to validate the effectiveness of the dataset and improve phenotypic prediction accuracy. The findings suggest that the MaizeGEP dataset serves as a valuable resource for investigating the relationship among genotype, envirotype, and phenotype, as well as predicting cross-environmental performance. This underscores the importance of encouraging researchers to utilize this dataset in developing sophisticated GE2P models. Such models can aid plant breeders in selecting new varieties and facilitating their deployment across diverse regions. MaizeGEP is publicly accessible at http://user.ebreed.cn:9992/scb/.

Somatic structural variations (somatic SVs) are hallmarks of tumors, but their comprehensive detection remains technically challenging. Long-read sequencing (LRS) technology, which generates reads spanning large-scale SVs and their flanking sequences, enables a wide range of prospects for somatic SV detection. However, existing LRS-based somatic SV detection algorithms and pipelines exhibit variable performance that has not been systematically characterized. In this study, we conducted a rigorous evaluation of 51 LRS-based somatic SV detection strategies, integrating 3 reference genomes, 2 aligners, 5 SV callers, and 5 processing methods tailored for SV callers. We use both simulated datasets and empirical data from HCC1395/HCC1395BL cell lines sequenced on Oxford Nanopore (ONT) and Pacific Biosciences (PacBio) platforms for technical assessment. Our findings highlight the need for further refinement of specialized somatic SV detection tools, as no single strategy consistently outperforms across all scenarios. Workflows based on germline SV callers exhibit a high false-positive rate, which cannot be mitigated by increasing sequencing depth or tumor purity. Furthermore, challenges persist in detecting insertions, genomic tandem repeat regions, and ultra-long SVs. We delineate technical bottlenecks in current somatic SV detection approaches and provide recommendations for their further advancement. Additionally, we offer suggestions for selecting specific tools in different application scenarios. This work offers a comprehensive benchmark for somatic SV detection and valuable insights for future LRS-based tools development and methodological improvements.

Cohesin plays critical roles in chromatin organization and transcription regulation. REC8 is a meiosis-specific cohesin subunit and is essential for homologous chromosome synapsis, recombination, and segregation. However, little is known about the relationship between the dynamic genome-wide distribution of cohesin and transcription regulation during meiotic initiation. In this study, we report that REC8-cohesin is preferentially localized to open promoter regions of genes involved in spermatogonial differentiation and meiosis at early meiosis from preleptonema to zygonema. Genomic localization of REC8-cohesin is changed by the gene knockout of the transcriptional suppressor BEND2. We also find that REC8 is able to interact with mitotic cyclin CCNA2, that the CCNA2 expression is extended to leptonema in Bend2 knockout mice, and that the meiotic cells of Bend2 knockout mice do not exit the mitotic cell cycle completely. We further found that a large number of genes are commonly bound by BEND2, STRA8, MEIOSIN, and REC8-cohesin. Our study has therefore revealed that genes with open promoters are bound by meiotic cohesin and transcription factors coordinately to facilitate chromatin reorganization and transcription regulation leading to the switch from a mitotic cell cycle to a meiotic one at the initiation stage of meiosis.

Genetic interactions play a crucial role in elucidating the susceptibility and etiology of complex multifactorial diseases. Despite significant efforts to identify disease-associated nonlinear effects in genome-wide association studies, efficient methods for detecting the epistatic impact of rare variants remain lacking. In this study, we proposed iRUNNER, a novel and powerful mutation burden test focused on analyzing the interaction effects of rare variants on a binary trait. Different from conventional association tests comparing cases with controls, iRUNNER evaluates the relative enrichment of rare variant interaction burden of pairwise genes in patients against its baseline, estimated by a recursive truncated negative-binomial regression model that leverages multiple genomic features from public databases. Extensive simulations demonstrated that iRUNNER outperforms existing epistasis tests in statistical power and maintains reasonable type I error rates even when population stratification exists in control samples. Applied to real datasets of five complex diseases, iRUNNER yielded substantial gains in gene-gene interaction detections. Notably, the majority of these signals were missed by alternative methods, especially in small to medium-sized samples. Furthermore, we found that these identified gene pairs of each trait can form interconnected networks, which may provide valuable insights into the underlying molecular mechanisms. We have implemented iRUNNER as a module in our integrative platform KGGSeq (http://pmglab.top/kggseq/) that enables rapid testing of pairwise interactions among all possible non-synonymous rare coding variants within hours.

Previous genomic studies have predominantly analyzed oral squamous cell carcinoma (OSCC) in conjunction with other head and neck squamous cell carcinomas (HNSCC), constraining our comprehension of OSCC-specific structural variants (SVs). Here, we performed long-read whole-genome sequencing on 16 paired OSCC tumor and blood samples to elucidate the biological functions of somatic SVs. We identified a total of 5775 high-confidence somatic SVs, including five recurrent simple repeat expansions (SREs). Notably, one SRE located within the promoter region of the OBI1 gene is present in 45% of OSCC samples. Knocking out this SRE in the HSC4 cell line significantly reduces the expression of OBI1, resulting in decreased proliferative and migratory capacities compared to wild-type cells. Furthermore, we found that the frequently amplified region 11q13 in HNSCC is prone to large-scale somatic SVs, affecting the expression of ANO1, FADD, and CTTN, thereby confirming the association of SVs in this region with OSCC development. Our study provides novel insights into the role of somatic SVs in OSCC, especially with respect to SREs and large-scale SVs in critical genomic regions, thereby enhancing our comprehension of the molecular pathogenesis of OSCC.