Research Highlight
Foundation Model: A New Era for Plant Single-cell Genomics
Yuansong Zeng (曾远松), Yuedong Yang (杨跃东)
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Research Highlight
Haplotype-based Pangenomics: A Blueprint for Climate Adaptation in Plants
Wanfei Liu (刘万飞), Peng Cui (崔鹏)
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Review Article
CRISPR Technology and Its Emerging Applications
Xuejing Zhang , Dongyuan Ma , Feng Liu
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abstract
The discovery and iteration of clustered regularly interspaced short palindromic repeats (CRISPR) systems have revolutionized genome editing due to their remarkable efficiency and easy programmability, enabling precise manipulation of genomic elements. Owing to these unique advantages, CRISPR technology has the transformative potential to elucidate biological mechanisms and develop clinical treatments. This review provides a comprehensive overview of the development and applications of CRISPR technology. After describing the three primary CRISPR-Cas systems — CRISPR-associated protein 9 (Cas9) and Cas12a targeting DNA, and Cas13 targeting RNA — which serve as the cornerstone for technological advancements, we describe a series of novel CRISPR-Cas systems that offer new avenues for research, and then explore the applications of CRISPR technology in large-scale genetic screening, lineage tracing, genetic diagnosis, and gene therapy. As this technology evolves, it holds significant promise for studying gene functions and treating human diseases in the near future.
CRISPR技术作为一种革命性的基因编辑工具，凭借高效性和多功能性的特点，实现了对基因组序列的精确操控。本文概述了CRISPR技术及其应用，强调技术的改进及其在大规模遗传筛选、谱系追踪、遗传诊断和基因治疗等新兴领域的应用前景。随着技术的不断进步，CRISPR有望在深入解析生物机制和临床应用等方面展现出更大的潜力。
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Review Article
Characterization of Tumor Antigens from Multi-omics Data: Computational Approaches and Resources
Yunzhe Wang (王韫哲) , James Wengler , Yuzhu Fang (房钰竹), Joseph Zhou, Hang Ruan, Zhao Zhang, Leng Han
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abstract
Tumor-specific antigens, also known as neoantigens, have potential utility in anti-cancer immunotherapy, including immune checkpoint blockade (ICB), neoantigen-specific T cell receptor-engineered T (TCR-T), chimeric antigen receptor T (CAR-T), and therapeutic cancer vaccines (TCVs). After recognizing presented neoantigens, the immune system becomes activated and triggers the death of tumor cells. Neoantigens may be derived from multiple origins, including somatic mutations (single nucleotide variants, insertions/deletions, and gene fusions), circular RNAs, alternative splicing, RNA editing, and polymorphic microbiomes. An increasing amount of bioinformatics tools and algorithms are being developed to predict tumor neoantigens derived from different sources, which may require inputs from different multi-omics data. In addition, calculating the peptide–major histocompatibility complex (MHC) affinity can aid in selecting putative neoantigens, as high binding affinities facilitate antigen presentation. Based on these approaches and previous experiments, many resources have been developed to reveal the landscape of tumor neoantigens across multiple cancer types. Herein, we summarize these tools, algorithms, and resources to provide an overview of computational analysis for neoantigen discovery and prioritization, as well as the future development of potential clinical utilities in this field.
肿瘤特异性抗原（又称新抗原）在抗癌免疫治疗中具有重要的应用潜力，包括免疫检查点阻断疗法（immune checkpoint blockade, ICB）、新抗原特异性T细胞受体工程化T细胞（T cell receptor engineered T cell, TCR-T）、嵌合抗原受体T细胞（chimeric antigen receptor engineered T cell，CAR-T）和治疗性的癌症疫苗（therapeutic cancer vaccines, TCVs）。当免疫系统识别到呈递的新抗原后，会被激活并引发肿瘤细胞死亡。新抗原可能存在多种来源，包括体细胞突变（单核苷酸变异、插入缺失和基因融合）、环状RNA、选择性剪接、RNA编辑以及多态性的微生物组。目前有越来越多的生物信息学工具和算法用于预测不同来源的肿瘤新抗原，这些工具可能会需要不同的多组学数据作为输入。此外，计算肽段与主要组织相容性复合体（major histocompatibility complex, MHC）或人类中的人白细胞抗原（human leukocyte antigen, HLA）的亲和力可以帮助筛选候选新抗原（高结合亲和力有助于抗原呈递）。本综述总结了这些工具、算法和资源，提供了新抗原发现和优先级排序的分析概述，以及该领域潜在临床应用的未来发展方向。
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Original Research
LCORL and STC2 Variants Increase Body Size and Growth Rate in Cattle and Other Animals
Fengting Bai (白凤庭) , Yudong Cai (蔡钰东) , Min Qiu (邱敏) , Chen Liang (梁晨) , Linqian Pan (潘麟茜) , Yayi Liu (刘雅怡) , Yanshuai Feng (冯衍帅) , Xuesha Cao (曹雪莎) , Qimeng Yang (杨启蒙) , Gang Ren (任刚) , Shaohua Jiao (焦少华) , Siqi Gao (高思祺) , Meixuan Lu (卢美轩) , Xihong Wang (王喜宏) , Rasmus Heller , Johannes A Lenstra , Yu Jiang (姜雨)
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abstract
Natural variants can significantly improve growth traits in livestock and serve as safe targets for gene editing, thus being applied in animal molecular design breeding. However, such safe and large-effect mutations are severely lacking. Using ancestral recombination graphs, we investigated recent selection signatures in beef cattle breeds, pinpointing sweep-driving variants in the LCORL and STC2 loci with notable effects on body size and growth rate. The ACT-to-A frameshift mutation in LCORL occurs mainly in central-European cattle, and stimulates growth. Remarkably, convergent truncating mutations were also found in commercial breeds of sheep, goats, pigs, horses, dogs, rabbits, and chickens. In the STC2 gene, we identified a missense mutation (A60P) located within the conserved region across vertebrates. We validated the two natural mutations in gene-edited mouse models, where both variants in homozygous carriers significantly increase the average weight by 11%. Our findings provide insights into a seemingly recurring gene target of body size enhancing truncating mutations across domesticated species, and offer valuable targets for gene editing-based breeding in animals.
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Method
clusIBD: Robust Detection of Identity-by-descent Segments Using Unphased Genetic Data from Poor-quality Samples
Ran Li (李燃) , Yu Zang (臧钰) , Zhentang Liu (刘震棠) , Jingyi Yang (杨静怡) , Nana Wang (汪娜娜) , Jiajun Liu (刘佳俊) , Enlin Wu (吴恩霖) , Riga Wu (乌日嘎) , Hongyu Sun (孙宏钰)
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abstract
The detection of identity-by-descent (IBD) segments is widely used to infer relatedness in many fields, including forensics and ancient DNA analysis. However, existing methods are often ineffective for poor-quality DNA samples. Here, we propose a method, clusIBD, which can robustly detect IBD segments using unphased genetic data with a high rate of genotyping error. We evaluated and compared the performance of clusIBD with that of IBIS, TRUFFLE, and IBDseq using simulated data, artificial poor-quality materials, and ancient DNA samples. The results show that clusIBD outperforms these existing tools and could be used for kinship inference in fields such as ancient DNA analysis and criminal investigation. clusIBD is publicly available at GitHub (https://github.com/Ryan620/clusIBD/) and BioCode (https://ngdc.cncb.ac.cn/biocode/tool/BT007882).
研究问题：
基于同源相同（Identity-by-descent, IBD）片段推断亲缘关系，已广泛应用于生物分类、物种保护、疾病机制和人类演化等研究。然而，现有方法在面对法医及古生物学等应用场景中的低质量DNA样本（如犯罪现场样本、古代人类遗骸）时，难以准确识别IBD片段。因此，亟需开发适用于微量、降解、受污染等低质量DNA分型数据的IBD识别算法，以充分挖这类样本中蕴含的遗传信息。
研究方法：
本研究提出了一种新IBD片段识别算法——clusIBD，通过识别样本间以低频率的异纯合基因型（即不一致的纯合基因型分型）为特征的DNA区域簇（cluster），实现对IBD片段的检测。研究结合模拟数据、人工制备的低质量样本和古DNA样本，对clusIBD的性能进行了系统评估，并与现有主流工具（如IBIS、TRUFFLE、IBDseq）进行了对比分析。
主要结果：
结果表明，与现有算法（软件）相比，clusIBD能够更准确地识别和检测微量、降解等低质量DNA样本的IBD片段，提示该算法在法医学和古生物学等领域的亲缘关系鉴定和系谱分析具有广阔的应用前景。
软件代码：
GitHub仓库（https://github.com/Ryan620/clusIBD/）和BioCode平台（https://ngdc.cncb.ac.cn/biocode/tool/BT007882）
分析代码：
https://github.com/Ryan620/clusIBD/tree/main/source_code
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