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全部共 43 篇
合成生物学基础研究Nucleic acids research

A self-replicating artificial module-genome that generates bacterial chromosome replication system in vitro.

1 次引用趋势数据不足

本研究旨在构建可自复制的人工基因组模块。研究者在体外利用重组元件蛋白合成系统(PURE)表达一个53 kb环状DNA(RCR模块基因组),该基因组编码全部26种复制循环反应(RCR)蛋白,可在一锅反应中实现自复制。研究先构建原型,再优化反应条件并解决表达瓶颈,从而实现稳健自复制。该人工模块基因组支持超过28次递归自复制。这一系统称为PRIMES,为构建自复制人工细胞迈出重要一步。

为什么值得关注

编辑短评

该研究构建了编码全部26个RCR蛋白的53 kb人工模块基因组,在PURE体系中实现一锅自我复制并支持逾28次递归倍增,解决了基因组规模复制系统无法由自身遗传信息生成的问题。其价值在于为自复制人工细胞提供了可操作的模块化复制平台。

查看扩展信息

完整作者

Yuta Yamagishi、Yoshiki Sonoyama、Naoki Kawakami、Tomonori Hasebe、Masayuki Su'etsugu

作者机构

  • Department of Life Science, College of Science, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171-8501, Japan.

技术路线

53 kb环状DNA模块基因组(编码26个RCR蛋白)→在PURE无细胞体系中一锅表达并自我复制→优化反应条件与解决表达瓶颈后实现递归自我复制(>28次倍增)

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:50
AI 靶点发现综述Nature

Towards the construction of a virtual yeast.

5 次引用趋势数据不足

为推进细胞生命的计算模拟,研究者提出“虚拟酵母”——一种由人工智能驱动的代理,以酿酒酵母为遗传可操作、数据丰富的模式系统,通过整合多模态生物学数据、机制推理与主动实验来模拟真核细胞行为。该系统将细胞复杂性分解为遗传、代谢和结构系统等八个以功能为中心的模块,每个模块由领域专用AI工具实现,并通过基于大语言模型的编排层协调。系统以机制知识、亚细胞结构和动态状态三大数据支柱为基础,在闭环学习流程中整合表征学习与生成建模,自主设计和执行实验。虚拟酵母可作为优化生物合成通路、支持多种细胞过程假设生成与优先级排序以及加速靶点发现的概念与操作平台,为构建虚拟真核细胞和推进合成生物学提供可推广的蓝图。

为什么值得关注

暂未整理

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完整作者

Liujia Qian、Zizhuo Zhou、Peijie Zhou、Zhen Dong、Xudong Zhang、Zhenwu Dai、Zhangyang Gao、Siqi Sun、Kevin R Roy、Shuaiyao Wang、Nicola Zamboni、Charles Boone、Michael Costanzo、Jianzhe Li、Gianni Liti、Jia-Xing Yue、Markus Ralser、Evan Williams、Mattia Zampieri、Heng Jiang、Tailin Wu、Yalin Wang、Feiran Li、Joseph Schacherer、Rui Sun、Zhaoxing Li、Yaming Deng、Yi Chen、Zhiping Xie、Huiqiang Lou、Xiaowen Wang、Linhai Xie、Han Wen、Liangyi Chen、Kai Lei、George Rosenberger、Xue Cai、Yingrui Wang、Qi Xiao、Huaizong Shen、Gaowen Liu、Lei Ma、Brenda Andrews、Hui Lu、Kiryl Piatkevich、Yi Zhu、Lei Bai、Yizhi Cai、Yuping Chen、Weinan E、Ge Gao、Fuchu He、Luonan Chen、Stan Z Li、Hongwu Ma、Liang Qiao、Lars M Steinmetz、Leihan Tang、Tang Tang、Xiaofan Zhang、Jing Yang、Yifan Yang、Kaicheng Yu、Jianyang Zeng、Yefeng Zheng、Bowen Zhou、Tiannan Guo

作者机构

  • Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, School of Future Biomedicine, Westlake University, Hangzhou, China.
  • Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China.
  • Center for Machine Learning Research, Peking University, Beijing, China.
  • AI for Science Institute, Beijing, China.
  • National Engineering Laboratory for Big Data Analysis and Applications, Beijing, China.
  • Center for Data Science, Peking University, Beijing, China.
  • Shanghai Artificial Intelligence Laboratory, Shanghai, China.
  • Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
  • Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.
  • Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
  • The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
  • Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
  • Institute of Research on Cancer and Ageing of Nice (IRCAN), Faculté de Médecine, Nice, France.
  • State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Department of Biochemistry, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité, Berlin, Germany.
  • Center for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Max Planck Institute for Molecular Genetics, Berlin, Germany.
  • Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
  • Department of Biomedicine, University of Basel, Basel, Switzerland.
  • Department of Artificial Intelligence, School of Engineering, Westlake University, Hangzhou, China.
  • Biomedical Research Core Facilities, Westlake University, Hangzhou, China.
  • Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
  • Université de Strasbourg, CNRS, Inserm, IGBMC UMR 7104-UMR-S 1258, Illkirch, France.
  • State Key Laboratory of Microbial Metabolism and Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • South China Hospital, Health Science Center, Guangdong Key Laboratory of Genome Instability and Disease Prevention, Shenzhen University School of Medicine, Shenzhen, China.
  • State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics Driven Cancer Precision Medicine (Chinese Academy of Medical Sciences), Beijing, China.
  • International Academy of Phronesis Medicine (Guangdong), Guangzhou, China.
  • DP Technology Co. Ltd., Beijing, China.
  • Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing, China.
  • State Key Laboratory of Medical Proteomics, Beijing, China.
  • State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.
  • Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China.
  • Bruker Switzerland AG, Faellanden, Switzerland.
  • Zhejiang Key Laboratory of Structural Biology, School of Life Sciences, Westlake University, Hangzhou, China.
  • Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • National Biomedical Imaging Center, College of Future Technology, Peking University, Beijing, China.
  • State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, China.
  • Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, China.
  • School of Life Sciences, Westlake University, Hangzhou, China.
  • Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
  • Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
  • Generative and Synthetic Genomics, Wellcome Sanger Institute, Cambridge, UK.
  • Shenzhen Institute of Synthetic Biology, Chinese Academy of Sciences, Shenzhen, China.
  • Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • School of Mathematical Sciences, Peking University, Beijing, China.
  • State Key Laboratory of Gene Function and Modulation Research, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Center for Bioinformatics (CBI), Peking University, Beijing, China.
  • School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University, Shanghai, China.
  • AI Laboratory, Research Center for Industries of the Future, Westlake University, Hangzhou, China.
  • Biodesign Center, State Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
  • National Center of Technology Innovation for Synthetic Biology, Tianjin, China.
  • Department of Chemistry, Fudan University, Shanghai, China.
  • Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
  • DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany.
  • Stanford Genome Technology Center, Palo Alto, CA, USA.
  • Center for Interdisciplinary Studies, Westlake University, Hangzhou, China.
  • Wuhan Metware Biotechnology Co. Ltd., Wuhan, China.
  • Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China.
  • State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing, China.
  • School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China.
  • Autolab, Westlake University, Hangzhou, China.
  • School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, China.
  • Medical Artificial Intelligence Laboratory, Westlake University, Hangzhou, China.
  • Department of Electronic Engineering, Tsinghua University, Beijing, China.
  • Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, School of Future Biomedicine, Westlake University, Hangzhou, China. guotiannan@westlake.edu.cn.
  • Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China. guotiannan@westlake.edu.cn.
  • Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China. guotiannan@westlake.edu.cn.

技术路线

多模态生物数据与机理知识、亚细胞结构、动态状态三类数据支柱 → 细胞复杂性分解为八个功能模块(遗传、代谢、结构系统),各模块为领域AI工具并由大语言模型调度层协调

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:32
AI 靶点发现基础研究Cell

AI-driven discovery of GPNMB CAR T cells as a multi-cancer therapy.

8 次引用趋势数据不足

研究开发了一种人工智能驱动的CAR-T细胞靶点发现方法,通过整合人皮肤癌与健康组织的单细胞RNA测序数据集筛选候选靶点,并利用公共数据集优化肿瘤组成、组织特异性及临床可行性,再以大语言模型优先排序和提名治疗潜力靶点。糖蛋白非转移性黑色素瘤蛋白B(GPNMB)为最常被提名的靶点,其在血液肿瘤和实体瘤中的表达得到验证。研究构建了靶向人GPNMB的CAR-T细胞,在小鼠单核母细胞白血病、黑色素瘤和结直肠腺癌模型中显示出强效抗肿瘤活性。研究建立了可扩展的CAR-T细胞靶点发现流程,并支持将GPNMB靶向CAR-T细胞作为多癌种治疗手段的转化。

为什么值得关注

暂未整理

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完整作者

Daniel J Baker、Leon M Frommer、Ugur Uslu、Kisha K Patel、Daniel Zhu、Nils W Engel、James M George、Wencao Zhao、Samuel I Kim、Lisa Sun、Christopher Roselle、Philipp C Rommel、Regina M Young、Jonathan A Epstein、Sikander Hayat、Zoltan Arany、Carl H June

作者机构

  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: bakerdj@upenn.edu.
  • Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, Germany.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, 97080 Würzburg, Germany.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
  • Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Institute for Regenerative Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
  • Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, Germany; Cardiovascular Research Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: shayat@ukaachen.de.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: zarany@upenn.edu.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: cjune@upenn.edu.

技术路线

单细胞RNA测序数据(人类皮肤癌与健康组织)整合→AI驱动候选靶点筛选,结合公共数据集优化肿瘤组成、组织特异性与临床可行性→大语言模型优先排序与提名→GPNMB跨血液与实体瘤表达验证→构建人源GPNMB CAR T细胞→单核细胞白血病、黑色素瘤和结直肠腺癌小鼠模型抗肿瘤活性验证

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:32
合成生物学基础研究Nature biotechnology

Efficient site-specific gene addition using R2 retrotransposons in tobacco and rice.

5 次引用趋势数据不足

在本氏烟草与水稻中利用R2逆转录转座子实现高效定点基因整合。多千碱基DNA片段的精确整合仍是植物中的主要技术障碍。研究人员将非长末端重复R2逆转录转座子引入植物,在本氏烟草中重建R2活性,并利用基于烟草花叶病毒的表游离报告系统评估插入效率与保真度。结果显示,GFP(2.2 kb)和重组酶兼容着陆垫(0.6 kb)可定点整合至28S rDNA阵列,完整盒插入频率分别高达75%和53%。为在时间上限制供体可用性并避免DNA中间体,研究将植物内效应器表达与重组RNA病毒介导的供体递送相结合。在水稻愈伤组织rDNA中靶向插入抗性盒,整合效率最高达17%。结果表明,R2逆转录转座子是一种无DNA双链断裂、基于RNA模板的系统,可在rDNA位点插入多千碱基基因盒,用于植物安全港性状叠加,并有望应用于作物改良和合成生物学。

为什么值得关注

编辑短评

该研究以R2逆转录转座子实现植物多千碱基DNA的无双链断裂、RNA模板定点整合,在烟草28S rDNA达75%与53%完整插入率,水稻愈伤达17%。其价值在于为作物安全港性状堆叠与合成生物学提供新工具,但效率与物种适用性仍需拓展。

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完整作者

Zahir Ali、Haroon Butt、Raghad Alghamdi、Jose Luis Moreno Ramirez、Magdy Mahfouz

作者机构

  • Laboratory for Genome Engineering and Synthetic Biology, Division of Biomedical Sciences, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
  • Laboratory for Genome Engineering and Synthetic Biology, Division of Biomedical Sciences, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. magdy.mahfouz@kaust.edu.sa.

技术路线

样本:本氏烟草与水稻愈伤组织;分析:基于TMV游离型报告系统重建R2活性,检测28S rDNA阵列定点整合效率与保真度,并统计水稻rDNA抗性盒整合效率

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:50
合成生物学基础研究Nucleic acids research

Cascading recombinase memory switch for programmable and stable gene expression in Pseudomonas putida.

2 次引用趋势数据不足

本研究针对模式细菌基因线路在非模式宿主中表现不佳的问题,在恶臭假单胞菌中构建了基于整合酶的级联翻转开关,以实现一致且可编程的基因表达。该系统将驱动Bxb1丝氨酸整合酶的鼠李糖诱导触发模块与紧凑绝缘的倒位模块耦合,提供二元开关状态;通过控制正交T7 RNA聚合酶基因表达激活可替换的PT7驱动目标模块。翻译水平调控使关闭状态严密、诱导强烈,切换高效且去除诱导剂后表型稳定,可作为遗传记忆元件。该系统进一步调控超活性双鸟苷酸环化酶以触发生物膜形成,并在2-氟顺,顺-粘康酸生产菌株中实现连续模式下由开关介导的催化生物膜稳定生产。

为什么值得关注

AI 辅助短评,基于标题和摘要整理

该研究针对遗传电路在非模式宿主恶臭假单胞菌中表现不佳的问题,构建整合酶级联开关,实现可编程、稳定的基因表达与遗传记忆。其价值在于将记忆元件与生物膜表型及连续生产耦合,拓展了假单胞菌合成生物学工具,但性能仍限于该宿主与特定产物场景。

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完整作者

Javier M Hernández-Sancho、Daniel C Volke、Pablo I Nikel

作者机构

  • BRIGHT, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.

技术路线

数据/样本:以Pseudomonas putida为宿主,构建基于Bxb1丝氨酸整合酶的级联双稳态开关,由鼠李糖诱导模块驱动,连接绝缘倒位模块产生二元OFF/ON转录状态;开关控制正交T7 RNA聚合酶基因以激活可替换的PT7靶模块;对整合酶与T7 RNA聚合酶基因进行翻译水平调谐。分析:评估开关效率、OFF态紧密性、诱导强度及去除诱导剂后的表型稳定性(遗传记忆)。验证:通过调控超活跃二鸟苷酸环化酶触发生物膜形成,并在2-氟顺,顺-黏康酸生产菌株中实现连续模式下开关介导的催化生物膜稳定产化。

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:51
合成生物学基础研究Nucleic acids research

Engineered suppressor tRNAs enable precise translational control of genetic circuits in E. coli.

1 次引用趋势数据不足

本研究针对合成生物学中基因回路翻译水平精准调控工具匮乏的问题,在大肠杆菌中工程化改造携带天然氨基酸的抑制性tRNA(sup-tRNA),以实现可编程的无义突变通读。对20种变体的筛选揭示了明确的设计规则:通读效率取决于天然tRNA反密码子与琥珀密码子(CUA)的相似性,因其可保留与天然氨酰-tRNA合成酶的相互作用。在基于LacI的生物传感器中,sup-tRNA调控使背景泄漏降低逾77%,诱导动态范围提高4.3倍(由6.67增至28.68)。通过靶向调控pykA与pykF动态平衡糖酵解通量,N-乙酰神经氨酸产量提高66%(由5.33增至8.82 g/l),且不影响细胞生长。研究表明,工程化携带天然氨基酸的sup-tRNA可作为基因回路中精准翻译控制的多功能、高效且经济的平台,为生物传感器优化与代谢工程提供新途径。

为什么值得关注

编辑短评

合成生物学缺少易用的翻译调控工具,转录诱导系统普遍泄漏;该研究系统构建天然氨基酸加载的琥珀抑制 tRNA,明确通读效率的设计规律,可在翻译水平可控开关基因表达,显著降低传感器本底、动态平衡代谢通量,为遗传回路提供新的正交调控维度。

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完整作者

Xiaotong Wang、Jianping Xu、Yipeng Wang、Qingsheng Qi、Zhiguo Wang、Qian Wang

作者机构

  • National Glycoengineering Research Center, Shandong University, Qingdao 266237, P. R. China.
  • State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, P. R. China.
  • Zhejiang Key Laboratory of Medical Epigenetics, Institute of Aging Research, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, P. R. China.

技术路线

数据/样本:20种sup-tRNA变体;分析:比较变体读通效率,归纳设计规则(反密码子与CUA相似性);应用:LacI生物传感器、pykA/pykF调控的N-乙酰神经氨酸合成;验证:原文未报告独立正交性或脱靶验证

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:50
合成生物学基础研究Nature biotechnology

Programmable, multiplexed and orthogonal gene control in bacteria with attenuated Cas13d systems.

0 次引用趋势数据不足

基于Cas13的RNA效应器可实现细菌中动态、多重且可逆的基因调控,但其应用受固有细胞毒性和旁切活性限制。研究提出理性蛋白质工程策略,通过靶向截短柔性区域,生成具有可调RNase活性的减毒Cas13d变体,在有效敲低转录本的同时显著降低毒性,生长光密度提高2.2倍。通过在CRISPR RNA间隔序列5'端引入近端错配,该系统可在翻译抑制、多顺反子mRNA降解和基于IF3融合的翻译水平CRISPR激活之间切换。研究展示了对多顺反子mRNA及合成回路中单个基因的可编程、正交和多重组调控。应用于番茄红素生物合成优化时,该系统在大肠杆菌中实现稳健的途径重构和产量提升,并可精细调节必需及竞争途径。

为什么值得关注

编辑短评

野生型 Cas13d 在细菌中因旁切毒性难以使用;该研究构建减活 Cas13d(SONAR),通过 crRNA 设计在同一蛋白上实现 RNA 降解、翻译抑制、翻译激活,支持多顺反子内多路正交调控,用于代谢通路精细优化。

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完整作者

Shengkun Tong、Yuxi Qin、Yaqian Sun、Yaojie Gao、Muyi Song、Tao Fang、Yaojun Tong

作者机构

  • State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. yaojun.tong@sjtu.edu.cn.

技术路线

理性蛋白质工程(截短柔性区)构建减毒Cas13d变体 → 生长密度与转录本敲低评估

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:51
公共数据库基础研究Nature immunology

Thymic alveolar type 2 epithelial mimetic cells revealed by RUNX1 deficiency.

2 次引用趋势数据不足

利用胸腺特异性敲除Runt相关转录因子1(Runx1)的小鼠模型,研究发现一种类似肺泡2型(AT2)肺上皮细胞的新型胸腺模拟细胞。AT2模拟性髓质胸腺上皮细胞(mTECs)表达表面活性物质基因;Runx1缺失后AT2 mTECs扩增,导致AIRE+ mTECs丢失及T细胞选择缺陷。RUNX1通过抑制表皮生长因子受体信号通路抑制AT2 mTECs。结果揭示了一种模拟肺AT2上皮细胞的胸腺模拟细胞类型,进一步展现了胸腺中此前未被认识的上皮细胞多样性。(约180字)

为什么值得关注

编辑短评

该研究在 RUNX1 缺失小鼠中发现一类全新、转录上模拟肺 AT2 肺泡上皮的胸腺髓质上皮模拟细胞;RUNX1 通过抑制 EGFR 信号限制该细胞扩增,RUNX1 缺失会造成该细胞大量扩增、Aire 阳性 mTEC 减少、T 细胞选择异常;揭示了胸腺通过肺泡样模拟细胞建立肺相关中枢耐受的机制,拓展了胸腺上皮细胞多样性与自身免疫耐受的认识

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完整作者

Jun Hyung Sin、Christopher J Bowman、Joe Germino、Sloan H Phillips、Juliana Sucharov、Xian Liu、Yi Wang、Corey N Miller、Audrey V Parent、Aaron Bodansky、Mark S Anderson、Michael R Waterfield

作者机构

  • Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
  • Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
  • Department of Pathology, University of California, San Francisco, San Francisco, CA, USA.
  • Diabetes Center, University of California, San Francisco, San Francisco, CA, USA.
  • 10x Genomics, Pleasanton, CA, USA.
  • Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
  • Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA. michael.waterfield@ucsf.edu.
  • Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA. michael.waterfield@ucsf.edu.

技术路线

胸腺特异性Runx1敲除小鼠模型 → 胸腺mTEC群体转录/表型分析(鉴定表达表面活性剂基因的AT2样mTEC,并评估AIRE+ mTEC及T细胞选择)

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/02 01:41
AI 靶点发现基础研究Nature

Accelerating scientific discovery with Co-Scientist.

157 次引用引用上升

研究旨在加速科学发现,研究人员开发了基于Gemini的多智能体人工智能系统Co-Scientist,用于结构化科学思维与假说生成。该系统根据研究目标和已有科学证据提出可验证的新颖研究假说,其设计包括多智能体架构与异步任务执行框架,以及通过锦标赛式进化过程实现假说生成的自我改进。自动评估显示,测试时计算扩展可持续提升假说质量。研究在药物重定位、新靶点发现和抗菌药物耐药机制解释三个生物医学应用中开展验证;Co-Scientist识别出急性髓系白血病的新药物重定位候选物和协同联合疗法,并经体外实验验证。真实世界验证表明,Co-Scientist有望加速科学发现。

为什么值得关注

暂未整理

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完整作者

Juraj Gottweis、Wei-Hung Weng、Alexander Daryin、Tao Tu、Petar Sirkovic、Artiom Myaskovsky、Grzegorz Glowaty、Felix Weissenberger、Alessio Orlandi、Dan Popovici、Anil Palepu、Keran Rong、Ryutaro Tanno、Khaled Saab、Fan Zhang、Jacob Blum、Andrew Carroll、Kavita Kulkarni、Nenad Tomašev、Dina Zverinski、Ivor Rendulic、Elahe Vedadi、Florian Hasler、Luka Rimanic、Marina Boia、Ivan Budiselic、Ben Feinstein、Mathias Bellaiche、Tom Sheffer、Jan Freyberg、Jeremy Ratcliff、Ottavia Bertolli、Katherine Chou、Avinatan Hassidim、Burak Gokturk、Amin Vahdat、Yuan Guan、Vikram Dhillon、Eeshit Dhaval Vaishnav、Byron Lee、Tiago R D Costa、José R Penadés、Gary Peltz、Yossi Matias、James Manyika、Demis Hassabis、Yunhan Xu、Pushmeet Kohli、Annalisa Pawlosky、Alan Karthikesalingam、Vivek Natarajan

作者机构

  • Google Cloud AI Research, Zurich, Switzerland. juro@google.com.
  • Google DeepMind, Mountain View, CA, USA. ckbjimmy@google.com.
  • Google Cloud AI Research, Zurich, Switzerland.
  • Google DeepMind, Mountain View, CA, USA.
  • Google Research, Mountain View, CA, USA.
  • Stanford University School of Medicine, Palo Alto, CA, USA.
  • Houston Methodist Hospital, Houston, TX, USA.
  • Sequome, San Francisco, CA, USA.
  • Fleming Initiative and Imperial College London, London, UK.
  • Google DeepMind, Mountain View, CA, USA. pushmeet@google.com.
  • Google Research, Mountain View, CA, USA. apawlosky@google.com.
  • Google DeepMind, Mountain View, CA, USA. alankarthi@google.com.
  • Google DeepMind, Mountain View, CA, USA. natviv@google.com.

技术路线

研究目标与科学证据输入→多智能体(基于Gemini)异步任务执行与锦标赛演化生成/批判/优化假设→自动化评估(测试时算力扩展)→三个生物医学应用(药物重定位、新靶点发现、抗菌素耐药机制解释)→急性髓系白血病候选药物与协同组合的体外实验验证

最近 5 个完整年度引用趋势

0153002021020220202302024302025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:32
AI 靶点发现基础研究Nucleic acids research

ARKbase: Antimicrobial Resistance Knowledgebase1.0.

4 次引用趋势数据不足

抗菌药物耐药(AMR)被视为“静默大流行”,是紧迫的全球健康挑战之一。为此,研究者开发了ARKbase,一个面向世界卫生组织细菌重点病原体的整合型、经人工整理并增值的AMR知识库。该知识库包含数据库模块、洞察模块和比较分析模块三个核心部分,提供经整理的数据以及利用定制流程分析所得数据。数据库模块收录经整理的参考基因组、泛基因组及抗菌药物敏感性试验(AST)谱;洞察模块含14个子模块,提供耐药基因、毒力因子、结构注释、宿主—病原互作、生物合成基因簇、已知抗生素、蛋白—蛋白互作、药物靶点、共靶点、药物—靶点互作、机器学习模型及抗生素暴露转录组数据集等深度注释;比较分析模块提供简易界面,用于比较不同重点病原体的耐药基因、毒力因子和药物靶点。数据经质量核查,AST按CLSI标准、基因组序列按EUCAST指南整理,并遵循FAIR数据原则。ARKbase是首个同时聚焦WHO重点病原体和AWaRe分类的综合知识库,有助于以综合证据方法整体理解AMR。

为什么值得关注

AI 辅助短评,基于标题和摘要整理

ARKbase整合了WHO优先病原体的基因组、药敏及多组学注释,模块化设计便于比较ARG、毒力因子和药物靶点。其遵循CLSI、EUCAST和FAIR原则进行数据质控,但摘要未说明任何实验验证或独立基准测试。作为知识库,其实际效用取决于数据覆盖度、更新机制及用户评估,需进一步验证其分析流程的准确性。

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完整作者

Simran Gambhir、Shweta Pandey、Harsh Bajetha、Jasleen Kaur、Ankita Das、Gnanasekar Pranavathiyani、Rupali Aggarwal、Upasana Maity、Suhani Dange、Vipin Singh、Mayur Zarkar、Raghav Sankhdher、Bhupender Singh、Shivani Seth、Anshu Bhardwaj

作者机构

  • Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh 160036, India.
  • Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
  • UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai Kalina Campus, Mumbai 400098, India.
  • Life Sciences Department, J.C. Bose University of Science and Technology, YMCA, Sector 6, Mathura road, Faridabad Haryana 121006, India.
  • Independent Researcher, IMTECH Housing, Sector 39A, Chandigarh 160036, India.

技术路线

数据/样本:聚焦WHO细菌优先病原体的参考基因组、泛基因组、AST谱、转录组数据集及多类注释资源(ARG、毒力因子、结构、宿主-病原互作、BGC、药物靶点等) → 分析:使用自建流程进行数据分析和注释,并按CLSI AST标准、EUCAST基因组序列指南及FAIR原则进行质量检查与人工整理

最近 5 个完整年度引用趋势

0110202102022020230202412025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:32
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