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全部共 43 篇
合成生物学基础研究Nature genetics

Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo.

1 次引用趋势数据不足

研究以小鼠胚胎为对象,旨在设计组织特异性哺乳动物增强子。方法上,利用精选染色质可及性数据训练紧凑卷积神经网络,并通过迁移学习在已验证的人和小鼠增强子上微调模型。据此设计心脏、肢体和中枢神经系统15个合成增强子,均能在预期靶组织中发挥作用。结果表明,哺乳动物增强子功能可仅由DNA序列可靠推断,借助适度训练集即可预测性从头设计组织特异性合成增强子,为体内哺乳动物基因表达的可编程控制提供通用框架。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Shenzhi Chen、Vincent Loubiere、Ethan W Hollingsworth、Ken Murakami、Nikolaus Mandlburger、Sandra H Jacinto、Atrin Dizehchi、Jacob Schreiber、Evgeny Z Kvon、Alexander Stark

作者机构

  • Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria.
  • Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria.
  • Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, USA.
  • Medical Scientist Training Program, University of California Irvine, Irvine, CA, USA.
  • Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, USA.
  • Department of Systems Biology, University of California Irvine, Irvine, CA, USA.
  • Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria. stark@starklab.org.
  • Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria. stark@starklab.org.

技术路线

数据/样本:整理染色质可及性数据及已验证的人和小鼠增强子;分析:训练紧凑卷积神经网络,并通过迁移学习微调;设计15个针对小鼠胚胎心脏、肢体和中枢神经系统的合成增强子;验证:在目标组织中检测到所有设计增强子均具有活性。

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 12:31
公共数据库临床研究Nature

An Icelandic pangenome reference.

0 次引用趋势数据不足

冰岛全基因组参考图谱研究:针对参考偏倚问题,研究人员开发了Emblask(用于泛基因组构建的混合长读长与短读长单倍型解析双组装流程)和Weaver(用于大规模泛基因组比对)两种新方法。利用Emblask对亲子三人数据进行组装,获得698个冰岛单倍型,并将其加入人类泛基因组参考联盟泛基因组,构建了包含5141万个小型变异的冰岛泛基因组参考(HPRC-ICE)。使用Weaver将57630名冰岛人的短读长序列比对至HPRC-ICE,共调用9896万个变异,较线性参考比对增加6.17%。在低可比对区域发现了新变异,包括与早发性帕金森病相关的GBA1致病性单核苷酸多态性(SNP)以及与同型胱氨酸尿症致病相关的CBS错义SNP。通过对429193名英国和爱尔兰参与者进行靶向重比对,在英国生物银行中重复验证了GBA1关联。

为什么值得关注

编辑短评

线性参考基因组带来的参考偏倚会丢失低可比对区的致病变异;该研究构建冰岛人群特异性泛基因组 HPRC‑ICE,配套 Emblask 和 Weaver 工具,在大样本短读数据中多检出约 6% 变异,成功找回被掩盖的 GBA1 帕金森风险变异,证明人群特异性泛基因组可以提升疾病遗传位点发现能力。

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完整作者

Guillaume Holley、Hannes P Eggertsson、Snaedis Kristmundsdottir、Doruk Beyter、Astros Th Skuladottir、Kristjan H S Moore、Pall I Olason、Arnaldur Gylfason、Olafur T Magnusson、Asmundur Oddsson、Hreinn Stefansson、Agnar Helgason、Gisli Masson、Patrick Sulem、Daniel F Gudbjartsson、Kari Stefansson、Bjarni V Halldorsson

作者机构

  • deCODE genetics/Amgen, Reykjavik, Iceland. gholley@amgen.com.
  • deCODE genetics/Amgen, Reykjavik, Iceland. heggerts@amgen.com.
  • deCODE genetics/Amgen, Reykjavik, Iceland.
  • Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
  • Department of Anthropology, University of Iceland, Reykjavik, Iceland.
  • School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland.
  • School of Technology, Reykjavik University, Reykjavik, Iceland.

技术路线

698个冰岛单倍型(亲代-子代三人组数据) → Emblask短读长读混合单倍型分型组装

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 03:55
公共数据库基础研究Nature

Human brain organoids record the passage of time over multiple years.

6 次引用趋势数据不足

人类大脑发育成熟历时近二十年,物种特异性发育过程难以在动物模型中研究,人脑类器官虽具潜力但此前仅能模拟早期发育。本研究将人脑类器官培养5年,优化生长条件使兴奋性神经元存活时间超越以往限制。利用源自内源性人脑的成熟相关模块,发现类器官在培养数年中以细胞类型特异性方式发生转录组衰老。全基因组甲基化谱显示,类器官预测的表观基因组年龄与体外培养时间精确相关,并与体内表观基因组衰老平行。值得注意的是,在混合不同年龄神经祖细胞形成的嵌合类器官中,年老祖细胞迅速产生晚期神经元命运,跳过早期神经元子代的生产,表明类器官中衰老的祖细胞保留了体外培养时间的记忆。数据表明,人脑类器官可在培养中持续成熟并记录多年时间流逝。

为什么值得关注

编辑短评

本文通过优化培养条件将人脑类器官维持5年,解决类器官长期培养中兴奋性神经元存活受限、难以模拟出生后成熟的问题。研究显示类器官转录组和表观基因组随培养时间成熟并记录体外时间,为研究人脑后期发育提供了长期追踪模型。

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完整作者

Irene Faravelli、Noelia Antón-Bolaños、Anqi Wei、Tyler Faits、Abhishek Sampath Kumar、Sophia Andreadis、Rahel Kastli、Marta Montero Crespo、Mara Steiger、Daniel Leible、Elizabeth Zhang、Bobae An、Yaron Meirovitch、Sayara Silwal、Sung Min Yang、Alexander Kovacsovics、Xian Adiconis、Helene Kretzmer、Joshua Z Levin、Edward S Boyden、Jeff Lichtman、Aviv Regev、Alexander Meissner、Paola Arlotta

作者机构

  • Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department for Clinical Sciences and Community Health (DISCCO), University of Milan, Milan, Italy.
  • Department of Translational Neuroscience, University Medical Center Utrecht (UMCU), Brain Center, Utrecht University, Utrecht, The Netherlands.
  • Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.
  • Digital Health Cluster, Hasso Plattner Institute for Digital Engineering, Digital Engineering Faculty, University of Potdsdam, Potsdam, Germany.
  • McGovern Institute, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, MA, USA.
  • Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Genentech, San Francisco, CA, USA.
  • Broad Institute of MIT and Harvard, Boston, MA, USA.
  • Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA. paola_arlotta@harvard.edu.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. paola_arlotta@harvard.edu.

技术路线

人脑类器官长期培养样本 → 转录组成熟模块分析

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 03:55
合成生物学基础研究Nature biotechnology

High-throughput synthesis of DNA fragments by molecular self-assembly of overlapping oligonucleotides.

2 次引用趋势数据不足

DNA合成技术的局限是合成生物学的基本瓶颈。本研究提出一种由杂交驱动的高通量基因合成方法,称为分子自组装诱导克隆。该方法通过将体外重叠DNA片段的正交自组装与体内宿主细胞的DNA修复机制相结合,克服了基因间的分子交叉干扰和寡核苷酸错配。组装后的目标片段被细胞摄取后作为模板用于回收和克隆。研究采用基于微芯片的寡核苷酸合成,在简单的一锅反应中可生产超过1000个不同的基因片段。近零错配是并行合成的不可或缺特征,寡核苷酸合成错误保持在恒定但可控的水平。研究还构建了工业酶PETase的大规模变体文库,并发现了比金标准酶效力更高的变体。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Zhiguang Wu、Zhengyang Sun、Shuangying Jiang、Yaqi Wang、Tanxi Bai、Cheng Zeng、Zelin Cai、Chenghao Su、Xiandi Zhang、Yunan Chen、Yue Shen、Chenyou Zhu、Ye Xiang、Fang Fang、Ninuo Xia、Dongsheng Liu、Junbiao Dai、Bryan Wei

作者机构

  • School of Life Sciences, Tsinghua University, Beijing, China.
  • Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China.
  • Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China.
  • BGI Research, Changzhou, China.
  • Engineering Research Center of Advanced Rare Earth Materials (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, China.
  • Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Center for Infectious Disease Research, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China.
  • Reproductive and Genetic Center, The First Affiliated Hospital of USTC, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
  • CodeR Therapeutics, Ltd, Hefei, China. nxia007@gmail.com.
  • Engineering Research Center of Advanced Rare Earth Materials (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, China. dongsheng.liu@polyu.edu.hk.
  • Department of Chemistry, The Hong Kong Polytechnic University, Hong Kong, China. dongsheng.liu@polyu.edu.hk.
  • Research Institute for Future Food, The Hong Kong Polytechnic University, Hong Kong, China. dongsheng.liu@polyu.edu.hk.
  • PolyU Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China. dongsheng.liu@polyu.edu.hk.
  • Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. daijunbiao@caas.cn.
  • Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China. daijunbiao@caas.cn.
  • School of Life Sciences, Tsinghua University, Beijing, China. bw@tsinghua.edu.cn.
  • Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China. bw@tsinghua.edu.cn.
  • Center for Life Sciences and Artificial Intelligence, School of Life Sciences, Tsinghua University, Beijing, China. bw@tsinghua.edu.cn.

技术路线

微芯片寡核苷酸合成 → 重叠DNA片段体外正交自组装

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:2026/10/30 05:59
公共数据库方法学Nature genetics

Rare variant effect estimation and polygenic risk prediction.

2 次引用趋势数据不足

由于罕见变异频率低,估计其效应具有挑战性。为此,研究提出RareEffect方法,先估计基于基因或区域的遗传力,再通过经验贝叶斯方法估计每个变异的效应大小。该方法采用罕见变异检验中常用的方差分量模型,可提供基因/区域水平和变异水平两个层次的效应大小,以便更好地解释。为校正表型中的病例对照不平衡,该方法采用Firth偏倚校正的快速实现。研究通过大量模拟及对UK Biobank全外显子组测序数据中100个性状的分析,验证了该方法的准确性和计算效率。此外,研究表明该模型获得的效应大小可用于改善多基因评分性能,从而优于近期开发的罕见变异多基因评分方法。

为什么值得关注

编辑短评

传统罕见变异检验只能得到基因水平 P 值,无法可靠估计单个变异效应;RareEffect 结合方差组分与经验贝叶斯,双层估计基因和单位点效应、校正病例对照偏倚,基于该效应构建的罕见变异 PRS 预测表现更好。

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完整作者

Kisung Nam、Minjung Kho、Wei Zhou、Bhramar Mukherjee、Seunggeun Lee

作者机构

  • Graduate School of Data Science, Seoul National University, Seoul, South Korea.
  • Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA.
  • Graduate School of Data Science, Seoul National University, Seoul, South Korea. lee7801@snu.ac.kr.

技术路线

数据/样本:UK Biobank全外显子组测序数据(100个性状)及模拟数据 → 分析:方差组分模型估计基因/区域水平遗传力,经验贝叶斯估计变异水平效应量,Firth偏倚校正处理病例对照不平衡

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:2026/11/01 03:55
公共数据库方法学Nature genetics

Correlations between causal effect sizes of proximal SNPs vary with functional annotations and implicate stabilizing selection.

2 次引用趋势数据不足

本研究提出连锁不平衡SNP对效应相关回归(LDSPEC)新方法,用于估计邻近SNP衍生等位基因因果疾病效应量之间的相关性,模拟显示其估计稳健。分析英国生物银行70种疾病和性状(平均N=305,646),检测到显著非零的SNP对效应相关性(如低频正连锁不平衡0-100 bp SNP对为-0.37±0.09),该相关性随距离衰减,并随等位基因频率及SNP间连锁不平衡变化。具有共享功能的SNP对效应相关性更强且跨越更远基因组距离。因此,SNP遗传力估计值小于SNP因果效应量方差总和,在某些功能注释中尤为明显。正向模拟涉及稳定选择,重现了上述发现,提示连锁掩蔽作用:包含对疾病效应相反的连锁SNP的单倍型对适合度影响降低,从而逃避负选择。

为什么值得关注

编辑短评

邻近 SNP 的因果效应并不独立,同一调控单元内倾向负相关;该模式由稳定选择下的连锁掩蔽产生,打破了遗传力分析里 SNP 效应独立的经典假设,会让高功能区域遗传力被高估。

查看扩展信息

完整作者

Martin Jinye Zhang、Arun Durvasula、Colby Chiang、Evan M Koch、Benjamin J Strober、Huwenbo Shi、Alison R Barton、Samuel S Kim、Omer Weissbrod、Po-Ru Loh、Steven Gazal、Shamil Sunyaev、Alkes L Price

作者机构

  • Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. martinzh@andrew.cmu.edu.
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. martinzh@andrew.cmu.edu.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. martinzh@andrew.cmu.edu.
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Arun.Durvasula@med.usc.edu.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Arun.Durvasula@med.usc.edu.
  • Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Arun.Durvasula@med.usc.edu.
  • Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA. Arun.Durvasula@med.usc.edu.
  • Department of Pediatrics, Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA. Colby.Chiang@childrens.harvard.edu.
  • Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
  • Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
  • Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
  • Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ssunyaev@hms.harvard.edu.
  • Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. ssunyaev@hms.harvard.edu.
  • Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. ssunyaev@hms.harvard.edu.
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. aprice@hsph.harvard.edu.
  • Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.

技术路线

UK Biobank 70种疾病/性状的基因型与表型数据(平均N=305,646) → LDSPEC估计近端SNP对因果效应量相关性

最近 5 个完整年度引用趋势

0120202102022020232202402025

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 03:55
合成生物学基础研究Nature methods

Decoding mechanoregulation in immunological synapses using biomimetic artificial cells.

0 次引用趋势数据不足

研究人员开发出一种基于生物材料的仿生人工细胞平台kpiCells,其采用仿生膜—内质网结构,可通过模块化的力学、化学和形貌输入,精细复制细胞状态。研究表明,kpiCells能够参与生理性细胞间相互作用并再现关键亚细胞特征。在T细胞系统中,该平台可整合研究传入的力学感知通路和传出的力学施加通路,支持在单个T细胞抗原受体上测量皮牛顿级力,并获得界定激活阈值的单细胞间力学指纹。该工作确立了kpiCells作为一种仿生模型,使合成材料设计的功能复杂性接近活细胞系统。

为什么值得关注

编辑短评

该文针对现有生化与合成材料方法难以动态调控细胞间相互作用力学的问题,构建kpiCells仿生人工细胞平台,模拟细胞状态并参与生理性相互作用。其价值在于将机械、化学与形貌输入模块化整合,为解码免疫突触机械调控提供可控实验体系。

查看扩展信息

完整作者

Xiaolei Yu、Vincent Mukwaya、Minxing Yue、Shuo Yang、Qian Li、Weili Zhao、Chunhai Fan、Lin Wang、Yingxi Zhao、Hongkai Yang、Jiacan Su、Li Wang、Hongjing Dou

作者机构

  • State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. mukvince@sjtu.edu.cn.
  • Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhang Jiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. liqian2018@sjtu.edu.cn.
  • State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhang Jiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Hebei Senlang Biotechnology, Shijiazhuang, China.
  • Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. dr_wangli@126.com.
  • State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. hjdou@sjtu.edu.cn.

技术路线

数据/样本:基于仿生膜-内质网架构构建的kpiCells人工细胞平台,作为可调机械、化学和形貌输入的仿生体系 → 分析:使kpiCells参与生理性细胞间相互作用,复现亚细胞特征;在T细胞系统中整合 interrogate 传入机械感知通路与传出力施加通路

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 03:55
合成生物学基础研究Nature methods

Design and optimization of a kinase-controlled allosteric switch.

4 次引用趋势数据不足

本研究以变构可控的Gal4转录因子为支架,将经典激酶FRET生物传感器结构作为磷酸化开关设计的起点,并对开关各要素进行优化,构建出受ERK控制的转录因子,其转录输出呈现20倍的磷酸化依赖性变化。该合成ERK响应转录因子对c-fos启动子的响应灵敏度相当,并揭示了哺乳动物发育类器官中ERK信号的空间模式。研究进一步表明,该开关架构可推广至其他输入激酶及变构控制靶标,为生物传感与合成生物学应用提供了新一代激酶响应工具的通用平台。

为什么值得关注

编辑短评

本文以变构Gal4为支架,将FRET生物传感器架构改造为激酶控制的转录开关,并经优化获得ERK依赖转录输出变化约20倍的响应元件。其价值在于提供可推广至其他激酶与靶标的磷酸化开关设计平台,为生物传感与合成生物学提供新工具。

查看扩展信息

完整作者

Qinhao Cao、Jared E Toettcher

作者机构

  • Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
  • Department of Molecular Biology, Princeton University, Princeton, NJ, USA. toettcher@princeton.edu.
  • Omenn-Darling Bioengineering Institute, Princeton University, Princeton, NJ, USA. toettcher@princeton.edu.

技术路线

已表征的变构Gal4转录因子与经典激酶FRET生物传感器架构(作为设计起点) → 构建并优化磷酸化控制开关(phospho-switch)各特征,获得ERK控制的转录因子

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
公共数据库方法学Nature genetics

Extending genome-wide association studies to admixed cohorts with high degrees of relatedness.

2 次引用趋势数据不足

混血人群占全球人口很大比例,但因分析挑战常被全基因组关联研究(GWAS)排除。研究团队此前开发了Tractor,一种基于局部 ancestry 的GWAS工具,专为混血样本设计,可产生准确的 ancestry 特异性效应量并提升发现 ancestry 富集位点的能力,但该工具假设样本无亲缘关系。为此,作者提出Tractor-Mix,允许在包含具有亲缘关系的混血样本的数据集中进行良好校准的关联研究。大量模拟显示,该方法与不产生 ancestry 特异性结果的其他先进方法相比具有竞争力。在英国生物银行、Yale-Penn队列和墨西哥城前瞻性研究的混血样本上的实证测试凸显了该方法的价值,识别出 ancestry 特异性关联。总之,Tractor-Mix扩展了现有模型的能力,使具有亲缘关系的混血样本能够进行良好校准的GWAS。

为什么值得关注

编辑短评

传统 Tractor 假设样本无亲缘,Tractor‑Mix 通过混合模型纳入亲缘关系,在同时有混合祖源 + 亲缘的队列中实现校准可靠的 GWAS,并保留估计祖先特异性遗传效应的能力,释放大型生物库中混合祖源亲缘样本的分析价值。

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完整作者

Taotao Tan、Alejandra Vergara-Lope、José Jaime Martínez-Magaña、Nirav N Shah、Yi-Sian Lin、Kai Yuan、Jaime Berumen、Jesus Alegre-Díaz、Pablo Kuri-Morales、Roberto Tapia-Conyer、Joel Gelenter、Janitza L Montalvo-Ortiz、Wei Zhou、Jason M Torres、Elizabeth G Atkinson

作者机构

  • Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
  • Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA.
  • Clinical Trial Service Unit and Epidemiological Studies Unit, Oxford Population Health, Big Data Institute, University of Oxford, Oxford, UK.
  • Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
  • VA Connecticut Healthcare System, West Haven, CT, USA.
  • Clinical Neurosciences Division, US Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, West Haven, CT, USA.
  • Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Experimental Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
  • Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
  • Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Mexico.
  • Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. Elizabeth.Atkinson@bcm.edu.
  • Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA. Elizabeth.Atkinson@bcm.edu.

技术路线

混合作战样本(UK Biobank、Yale-Penn队列、墨西哥城前瞻性研究) → Tractor-Mix关联分析(考虑亲缘关系与局部祖先)

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 06:10
合成生物学基础研究Nucleic acids research

Multi-level precise regulation of gene transcription in the yeast Saccharomyces cerevisiae based on light-sensitive CRISPR/Cas systems.

0 次引用趋势数据不足

基于CRISPR的基因转录调控是构建酿酒酵母合成基因电路的重要工具,但现有策略多局限于单一调控层面,灵敏度有待提高。本研究将翻译水平和翻译后水平的基因调控方法与光遗传学控制模式相结合,实现对dCas9蛋白的高灵敏多层级精准调控,从而灵活调节靶基因转录水平。利用该策略调控荧光蛋白转录水平,mCherry荧光强度较无调控时最高提升2.58倍。该基于CRISPR的多层级光遗传学系统有助于理解基因调控网络,并为合成生物学设计稳健的基因电路提供支持。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Yaokun Liang、Xianghui Qi、Song Gao、Yunjiao Wang、Guoshi Kan、Leonid N Valentovich、Jingjing Guo、Yingfeng An

作者机构

  • College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang 110866, Liaoning, China.
  • Shenyang Key Laboratory of Microbial Resource Mining and Molecular Breeding, Liaoning 110000, Shenyang, China.
  • School of Life Sciences, Guangzhou University, Guangzhou, Guangdong 510006, China.
  • Institute of Microbiology, National Academy of Sciences of Belarus, Minsk 220000, Belarus.
  • Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence, Macao Polytechnic University, Macau 999078, China.

技术路线

以荧光蛋白为报告基因→基于光敏CRISPR/dCas9系统,整合翻译与翻译后水平的调控模式,对dCas9蛋白实施多层级精准调控,分析目标基因转录水平变化(mCherry荧光强度较无调控提升最高2.58倍)

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:2026/10/30 06:00
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