Beijing Institute of Genomics

ABSTRACTBalancing growth and stress resilience remains a central challenge in breeding stress‐resistant crops. Here, we identify TOGR3 (THERMOTOLERANT GROWTH REQUIRED 3), which encodes the rice 26S proteasome β4 subunit, as a key regulator coordinating these two processes. Ubiquitylome profiling reveals that the TOGR3‐dependent ubiquitin‐proteasome system (UPS) maintains sugar homeostasis by selectively controlling the turnover of sugar‐metabolizing enzymes. This regulation promotes leaf sugar accumulation, which serves dual roles by supplying carbon to sustain growth and by optimizing stomatal development and aperture dynamics to enhance leaf cooling capacity. Together, these mechanisms synergistically promote thermal‐adaptive growth and thermotolerance in rice. Notably, combined overexpression of TOGR3 with the α2 subunit gene TT1 further enhances heat resistance, underscoring the potential of coordinated proteasome subunit engineering for crop improvement. Our findings uncover a regulatory module that fine‐tunes the trade‐off between plant growth and abiotic stress resilience, providing promising molecular targets for breeding climate‐resilient crops.

Large-scale population cohorts have become a cornerstone of modern biomedical research, enabling systematic discovery of disease mechanisms, refining risk stratification, and advancing precision medicine into reality.By integrating multi-omics and deep phenotypic data, these resources provide a powerful foundation for uncovering biological drivers of health and disease, guiding prevention strategies, and improving population health outcomes.In the current era marked by the rapid emergence of artificial intelligence (AI), data from large-scale population cohorts are becoming increasingly indispensable.Despite remarkable progress, population-scale studies still face fundamental challenges, including limited diversity, inequitable representation, incomplete coverage of certain omics layers, and persistent barriers to

Abstract We report a method for synthesizing site‐specifically modified long single‐stranded DNA (ssDNA) with high purity and fidelity. With the successful synthesis of long ssDNA carrying different 5‐methylcytosine (5mC) patterns, single‐base‐resolution analysis of 5mC epigenetic dynamics at specific genomic loci were enabled in mammalian cells. The results revealed that the density of 5mC sites synergistically correlated with their maintenance. This study opens new avenues for precise epigenetic manipulation and therapeutic applications.

Muscle-invasive (MI) urothelial carcinoma (UC) is a clinically challenging malignancy with a poor prognosis. Understanding the cellular dynamics that drive UC progression is critical for the development of optimized therapeutic strategies. Through integrative analysis of large-scale single-cell transcriptomic datasets from non-muscle-invasive (NMI) and MI tumours and validation with spatial transcriptomic datasets, we systematically characterized immune cell dynamics and cancer cell plasticity during UC progression. Our analysis revealed an immunosuppressive tumour microenvironment and a subset of cancer cells with upregulated major histocompatibility complex II (MHC-II) expression in MI tumours. Notably, MHC-II⁺ cancer cells were induced by interferon-γ signalling, as confirmed through in vitro experiments, and exhibited phenotypic alterations characterized by enhanced proliferative and migratory capacities. Furthermore, MHC-II⁺ cancer cells spatially colocalized with CD8⁺ T cells, regulatory T cells, and SPP1⁺ macrophages, where they engaged with inhibitory receptors on these immune cells, promoted CD8⁺ T cell exhaustion and facilitated immune evasion.

Tumor heterogeneity represents a critical determinant affecting both cancer diagnosis and therapeutic efficacy. Traditional bulk sequencing approaches are limited in their ability to resolve genomic alterations at the resolution of individual cells. In this study, we present scVar, an integrated analytical framework designed for mutation profiling using single-cell transcriptomic data. scVar enables sensitive and robust detection of single-nucleotide variants, particularly facilitating the identification of low-frequency variants. The framework incorporates customizable filtering parameters including variant allele frequency, and provides comprehensive functional and clinical annotations for identified mutations. Furthermore, scVar includes downstream analytical modules that facilitate the joint investigation of transcriptomic and mutational profiles within single cells. Benchmarking on simulated datasets and matched tumor samples demonstrates that scVar consistently outperforms existing methods, especially in detecting variants of low abundance, and shows strong concordance with whole-exome sequencing data. Application of scVar to non-small cell lung cancer samples effectively characterized ITH across spatially distinct tumor regions and diverse cellular populations. Collectively, scVar offers an integrated platform for concurrent analysis of somatic mutations and transcriptomic data derived from single-cell RNA sequencing, providing a powerful tool for elucidating tumor evolution and the complex interplay between genomic heterogeneity and the tumor microenvironment.

Abstract Extracting structured information from documents or scientific papers is crucial for data sharing and retrieval. Recently, Large Language Model (LLM) has shown its impressive ability in text understanding and several tools based on LLM has been developed. However, it’s still difficult to find a universal and user-friendly tool for various practical extraction tasks. To address this challenge, we propose OmniExtract, an automatic data extraction tool with user-friendly configuration files which can adapt to various data extraction tasks. OmniExtract uses a prompt optimized engineering to improve prompt and obtain high performance, and it can support a comprehensive data extraction including text and tables. Evaluation results show that OmniExtract obtains a high accuracy over 80% for 3 datasets. Furthermore, two additional data extraction applications using OmniExtract have been provided, achieving an accuracy of 92.21% and an average F1 score of 0.83 respectively. The data reliability performance shows that OmniExtract is a valuable tool for database updating.

The measurement of three-dimensional genome folding in the nucleus, mostly through Hi-C methods, is expressed as contact frequencies between genomic segments, without anchoring to physical axes of the spherical nucleus. Here, we mapped the chromatin contacts along nuclear radial axis and built radial score by factoring in contact frequencies. The chromatin high-order structures exhibit rich diversity along radial axis. Furthermore, the proximal trans contacts retrieved by radial score reveal conserved active/inactive chromatin segregation across intra- and interchromosomal interactions. Ablation of CTCF proteins disrupts chromatin loops with mild changes to chromatin radial positioning. By acutely perturbing multiple transcription factor (TF) occupancy, chromatin loop dissolutions are often accompanied by radial dissociations between two anchors. Our work provides a genome architecture reference map adhering to nuclear physical axis and suggests that multiple architectural TFs collectively shape nuclear positioning of chromatin and their contacts, with contacts serving as forces on chromatin positioning as well.