Pub Date : 2024-06-17DOI: 10.1038/s41576-024-00740-y
Bram Prevo, William C. Earnshaw
Dense packaging of genomic DNA is crucial for organismal survival, as DNA length always far exceeds the dimensions of the cells that contain it. Organisms, therefore, use sophisticated machineries to package their genomes. These systems range across kingdoms from a single ultra-powerful rotary motor that spools the DNA into a bacteriophage head, to hundreds of thousands of relatively weak molecular motors that coordinate the compaction of mitotic chromosomes in eukaryotic cells. Recent technological advances, such as DNA proximity-based sequencing approaches, polymer modelling and in vitro reconstitution of DNA loop extrusion, have shed light on the biological mechanisms driving DNA organization in different systems. Here, we discuss DNA packaging in bacteriophage, bacteria and eukaryotic cells, which, despite their extreme variation in size, structure and genomic content, all rely on the action of molecular motors to package their genomes. In this Review, the authors summarize DNA packaging in bacteriophage, bacteria and eukaryotic cells. They describe the difficulties each system faces when packaging its DNA, outline the molecular motor components involved, and provide insights from new studies that reveal how DNA organization is achieved.
基因组 DNA 的密集包装对生物体的生存至关重要，因为 DNA 的长度总是远远超过含有 DNA 的细胞的尺寸。因此，生物体使用复杂的机器来包装它们的基因组。这些系统遍布各个领域，从将 DNA 装入噬菌体头部的单个超强旋转电机，到协调真核细胞有丝分裂染色体压缩的数十万个相对较弱的分子马达，不一而足。最近的技术进步，如基于DNA邻近性的测序方法、聚合物建模和DNA环挤压的体外重组，揭示了不同系统中驱动DNA组织的生物机制。在这里，我们将讨论噬菌体、细菌和真核细胞中的 DNA 包装，尽管它们在大小、结构和基因组内容上存在极大差异，但都依赖分子马达的作用来包装基因组。
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Pub Date : 2024-06-14DOI: 10.1038/s41576-024-00738-6
William Hemstrom, Jared A. Grummer, Gordon Luikart, Mark R. Christie
Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering — removing sequencing bases, reads, genetic variants and/or individuals from a dataset — to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy–Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima’s D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne). Filtering genomic data is a crucial step to ensure the quality and reliability of downstream analyses. The authors provide guidance on the choice of filtering strategies and thresholds, including filters that remove sequencing bases or reads, variants, loci, genotypes or individuals from genomic datasets to improve accuracy and reproducibility.
基因组数据在各学科中无处不在，从农业到生物多样性、生态学、进化论和人类健康。然而，这些数据集往往包含噪音或错误，而且缺少信息，会影响后续计算分析和结论的准确性和可靠性。基因组数据分析的一个关键步骤是过滤--从数据集中移除测序碱基、读数、基因变异和/或个体--以提高下游分析的数据质量。在过滤基因组数据时，研究人员面临着多种选择；他们必须选择应用哪些过滤器，并选择适当的阈值。为了帮助开创下一代基因组数据过滤技术，我们回顾并提出了最佳实践，以改进基因组数据集常用过滤类型和阈值的实施、可重复性和报告标准。我们主要关注小等位基因频率、每个个体或每个位点的缺失数据、连锁不平衡和哈代-温伯格偏差的过滤。我们利用模拟和经验数据集说明了不同过滤阈值对常见种群遗传学统计的巨大影响，如田岛 D 值、种群分化 (FST)、核苷酸多样性 (π) 和有效种群规模 (Ne)。
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Pub Date : 2024-06-12DOI: 10.1038/s41576-024-00753-7
Laura Ross
In this Journal Club article, Laura Ross discusses several seminal papers that describe the discovery of germline-specific chromosomes and paternal genome elimination, striking examples of non-Mendelian genetics.
{"title":"From Mendel’s laws to non-Mendelian inheritance","authors":"Laura Ross","doi":"10.1038/s41576-024-00753-7","DOIUrl":"10.1038/s41576-024-00753-7","url":null,"abstract":"In this Journal Club article, Laura Ross discusses several seminal papers that describe the discovery of germline-specific chromosomes and paternal genome elimination, striking examples of non-Mendelian genetics.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":null,"pages":null},"PeriodicalIF":39.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141309126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.1038/s41576-024-00752-8
L. Francisco Lorenzo-Martín, Matthias P. Lutolf
In this Tools of the Trade article, Francisco Lorenzo-Martín and Matthias Lutolf present mini-colons as a new ex vivo cancer model that incorporates microfabrication, tissue engineering and optogenetics.
{"title":"Mini-colons unlock tumour development outside the body","authors":"L. Francisco Lorenzo-Martín, Matthias P. Lutolf","doi":"10.1038/s41576-024-00752-8","DOIUrl":"10.1038/s41576-024-00752-8","url":null,"abstract":"In this Tools of the Trade article, Francisco Lorenzo-Martín and Matthias Lutolf present mini-colons as a new ex vivo cancer model that incorporates microfabrication, tissue engineering and optogenetics.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":null,"pages":null},"PeriodicalIF":39.1,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-30DOI: 10.1038/s41576-024-00747-5
Saori Sakaue
In this Tools of the Trade article, Saori Sakaue describes SCENT, a tool to generate cell-type-specific enhancer–gene maps using single-cell multi-omics data, which can help identify disease-causal, non-coding variants and genes from GWAS-defined loci.
在这篇 "贸易工具"（Tools of the Trade）文章中，Saori Sakaue 介绍了 SCENT，这是一种利用单细胞多组学数据生成细胞类型特异性增强子-基因图谱的工具，有助于从 GWAS 定义的位点中识别致病的非编码变异和基因。
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Pub Date : 2024-05-28DOI: 10.1038/s41576-024-00731-z
Esther Herrera-Luis, Kelly Benke, Heather Volk, Christine Ladd-Acosta, Genevieve L. Wojcik
Gene–environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions. Despite their impact on human complex traits and diseases, gene–environment interactions (G × E) remain challenging to assess statistically. The authors review considerations for the conceptualization, methodology, interpretation and reporting of G × E studies, and provide recommendations on how to avoid common pitfalls.
基因与环境的相互作用（G×E），即遗传变异与环境因素的相互作用，对人类复杂的性状和疾病有着举足轻重的影响。从统计学角度看，G × E 可通过确定预测模型的预期偏差来评估，而预测模型的预期偏差则完全基于遗传或环境暴露的表型效应。尽管 G × E 分析框架得到了前所未有的广泛和多样化应用，但其应用和报告的异质性阻碍了其在公共卫生领域的适用性。在本综述中，我们将讨论研究设计的注意事项以及 G × E 分析框架，以评估依赖于环境的多基因责任，确定表现出 G × E 的特定遗传变异，并描述这些动态的环境背景。最后，我们针对 G × E 研究的概念化、方法学和报告中最常见的挑战和误区提出了建议以及未来的发展方向。
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Pub Date : 2024-05-22DOI: 10.1038/s41576-024-00745-7
Jessica Tollkuhn
In this Journal Club, Jessica Tollkuhn discusses how a paper describing genome-wide application of chromatin immunoprecipitation (ChIP)-on-chip inspired her own research into oestrogen-based gene regulation in the brain.
{"title":"Nuclear receptors — studying genes to understand hormones","authors":"Jessica Tollkuhn","doi":"10.1038/s41576-024-00745-7","DOIUrl":"10.1038/s41576-024-00745-7","url":null,"abstract":"In this Journal Club, Jessica Tollkuhn discusses how a paper describing genome-wide application of chromatin immunoprecipitation (ChIP)-on-chip inspired her own research into oestrogen-based gene regulation in the brain.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":null,"pages":null},"PeriodicalIF":39.1,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.1038/s41576-024-00743-9
Vipul Singhal, Nigel Chou
In this Tools of the Trade article, Vipul Singhal and Nigel Chou describe BANKSY, a machine learning tool that harnesses gene expression gradients from the neighbourhood of a cell for cell typing and domain segmentation.
{"title":"BANKSY: scalable cell typing and domain segmentation for spatial omics","authors":"Vipul Singhal, Nigel Chou","doi":"10.1038/s41576-024-00743-9","DOIUrl":"10.1038/s41576-024-00743-9","url":null,"abstract":"In this Tools of the Trade article, Vipul Singhal and Nigel Chou describe BANKSY, a machine learning tool that harnesses gene expression gradients from the neighbourhood of a cell for cell typing and domain segmentation.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":null,"pages":null},"PeriodicalIF":39.1,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140944429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-13DOI: 10.1038/s41576-024-00742-w
Henry Ertl
A study in Nature finds that transient perturbation of the Polycomb complex and target epigenome can irreversibly induce cancer cell fates.
自然》杂志上的一项研究发现，对多聚酶复合体和目标表观基因组的短暂扰动可以不可逆地诱导癌细胞的命运。
{"title":"Dysregulation of epigenetically induced cancers","authors":"Henry Ertl","doi":"10.1038/s41576-024-00742-w","DOIUrl":"10.1038/s41576-024-00742-w","url":null,"abstract":"A study in Nature finds that transient perturbation of the Polycomb complex and target epigenome can irreversibly induce cancer cell fates.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":null,"pages":null},"PeriodicalIF":42.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140915129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-13DOI: 10.1038/s41576-024-00739-5
Mustafa Mir
Mustafa Mir reflects on a 1976 paper by McKnight and Miller, in which they developed a technique to directly visualize gene regulatory dynamics.
Mustafa Mir 回顾了 McKnight 和 Miller 1976 年的一篇论文，他们在这篇论文中开发了一种直接可视化基因调控动态的技术。
{"title":"Miller spreads and the power of observation","authors":"Mustafa Mir","doi":"10.1038/s41576-024-00739-5","DOIUrl":"10.1038/s41576-024-00739-5","url":null,"abstract":"Mustafa Mir reflects on a 1976 paper by McKnight and Miller, in which they developed a technique to directly visualize gene regulatory dynamics.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":null,"pages":null},"PeriodicalIF":39.1,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140915128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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