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KCL TEST: an open-source inspired asymptomatic SARS-CoV-2 surveillance programme in an academic institution. KCL TEST:一个学术机构的开源无症状 SARS-CoV-2 监测计划。
IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-20 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae046
Joana Reis de Andrade, Edward Scourfield, Shilpa Lekhraj Peswani-Sajnani, Kate Poulton, Thomas Ap Rees, Paniz Khooshemehri, George Doherty, Stephanie Ong, Iustina-Francisca Ivan, Negin Goudarzi, Isaac Gardiner, Estelle Caine, Thomas J A Maguire, Daniel Leightley, Luis Torrico, Alex Gasulla, Angel Menendez-Vazquez, Ana Maria Ortega-Prieto, Suzanne Pickering, Jose M Jimenez-Guardeño, Rahul Batra, Sona Rubinchik, Aaron V F Tan, Amy Griffin, David Sherrin, Stelios Papaioannou, Celine Trouillet, Hannah E Mischo, Victoriano Giralt, Samantha Wilson, Martin Kirk, Stuart J D Neil, Rui Pedro Galao, Jo Martindale, Charles Curtis, Mark Zuckerman, Reza Razavi, Michael H Malim, Rocio T Martinez-Nunez

Rapid and accessible testing was paramount in the management of the COVID-19 pandemic. Our university established KCL TEST: a SARS-CoV-2 asymptomatic testing programme that enabled sensitive and accessible PCR testing of SARS-CoV-2 RNA in saliva. Here, we describe our learnings and provide our blueprint for launching diagnostic laboratories, particularly in low-resource settings. Between December 2020 and July 2022, we performed 158277 PCRs for our staff, students, and their household contacts, free of charge. Our average turnaround time was 16 h and 37 min from user registration to result delivery. KCL TEST combined open-source automation and in-house non-commercial reagents, which allows for rapid implementation and repurposing. Importantly, our data parallel those of the UK Office for National Statistics, though we detected a lower positive rate and virtually no delta wave. Our observations strongly support regular asymptomatic community testing as an important measure for decreasing outbreaks and providing safe working spaces. Universities can therefore provide agile, resilient, and accurate testing that reflects the infection rate and trend of the general population. Our findings call for the early integration of academic institutions in pandemic preparedness, with capabilities to rapidly deploy highly skilled staff, as well as develop, test, and accommodate efficient low-cost pipelines.

在应对 COVID-19 大流行的过程中,快速、便捷的检测至关重要。我们大学建立了 KCL TEST:SARS-CoV-2 无症状检测项目,能够对唾液中的 SARS-CoV-2 RNA 进行灵敏、便捷的 PCR 检测。在此,我们将介绍我们的经验教训,并提供我们启动诊断实验室的蓝图,尤其是在资源匮乏的环境中。2020 年 12 月至 2022 年 7 月期间,我们免费为员工、学生及其家庭接触者进行了 158277 次 PCR 检测。从用户注册到结果送达,我们的平均周转时间为 16 小时 37 分钟。KCL TEST 将开源自动化与内部非商业试剂相结合,从而实现了快速实施和重复使用。重要的是,我们的数据与英国国家统计局的数据相似,但我们检测到的阳性率较低,而且几乎没有三角波。我们的观察结果有力地证明,定期进行无症状社区检测是减少疫情爆发和提供安全工作场所的重要措施。因此,大学可以提供敏捷、灵活和准确的检测,以反映普通人群的感染率和趋势。我们的研究结果呼吁尽早将学术机构纳入大流行病防备工作,使其具备快速部署高技能人员的能力,以及开发、测试和适应高效低成本管道的能力。
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引用次数: 0
Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns. 利用可解释的机器学习揭示癌症特异性 DNA 甲基化模式,实现癌症的早期检测和诊断。
IF 2.5 Q2 Agricultural and Biological Sciences Pub Date : 2024-06-20 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae028
Izzy Newsham, Marcin Sendera, Sri Ganesh Jammula, Shamith A Samarajiwa

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

癌症是两百多种不同疾病的集合体,仍然是全球发病率和死亡率的主要原因。转移性癌症通常在疾病晚期才被发现,占癌症相关死亡人数的 90%。因此,癌症的早期检测与当前的疗法相结合,将对各种癌症的生存和治疗产生重大影响。DNA 甲基化等表观遗传学变化是导致癌变的一些早期事件。在这里,我们报告了一种可解释的机器学习模型,它能仅利用 DNA 甲基化数据对 13 种癌症类型和非癌症组织样本进行分类,准确率高达 98.2%。我们利用该模型识别出的特征开发了 EMethylNET,这是一个由 XGBoost 模型组成的稳健模型,该模型可为深度神经网络提供信息,而深度神经网络可泛化到独立的数据集。我们还证明了分类器检测到的甲基化相关基因组位点与癌症相关的基因、通路和网络有关,为我们深入了解致癌的表观基因组调控提供了线索。
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引用次数: 0
A protocol to isolate, identify, and verify glucose- or carbohydrate-binding receptors. 分离、鉴定和验证葡萄糖或碳水化合物结合受体的方案。
IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-19 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae045
Nadia Rashid, Kavaljit H Chhabra

Sensing, transport, and utilization of glucose is pivotal to the maintenance of energy homeostasis in animals. Although transporters involved in mobilizing glucose across different cellular compartments are fairly well known, the receptors that bind glucose to mediate its effects independently of glucose metabolism remain largely unrecognized. Establishing precise and reproducible methods to identify glucose receptors in the brain or other peripheral organs will pave the way for comprehending the role of glucose signaling pathways in maintaining, regulating, and reprogramming cellular metabolic needs. Identification of such potential glucose receptors will also likely lead to development of effective therapeutics for treatment of diabetes and related metabolic disorders. Commercially available biotin or radiolabeled glucose conjugates have low molecular weight; therefore, they do not provide enough sensitivity and density to isolate glucose receptors. Here, we describe a protocol to isolate, identify, and verify glucose-binding receptor/s using high molecular weight glucose (or other carbohydrate) conjugates. We have produced 30 kDa glucose- (or other carbohydrate-) biotin-polyacrylamide (PAA) conjugates with mole fractions of 80:5:15% respectively. These conjugates are used with biotin-streptavidin biochemistry, In-cell ELISA, and surface plasmon resonance (SPR) methods to isolate, identify, and verify glucose- or carbohydrate-binding receptors. We first demonstrate how streptavidin-coated magnetic beads are employed to immobilize glucose-biotin-PAA conjugates. Then, these beads are used to enrich and isolate glucose-binding proteins from tissue homogenates or from single-cell suspensions. The enriched or isolated proteins are subjected to mass spectrometry/proteomics to reveal the identity of top candidate proteins as potential glucose receptors. We then describe how the In-cell ELISA method is used to verify the interaction of glucose with its potential receptor through stable expression of the receptor in-vitro. We further demonstrate how a highly sensitive SPR method can be used to measure the binding kinetics of glucose with its receptor. In summary, we describe a protocol to isolate, identify, and verify glucose- or carbohydrate-binding receptors using magnetic beads, In-cell ELISA, and SPR. This protocol will form the future basis of studying glucose or carbohydrate receptor signaling pathways in health and in disease.

葡萄糖的感知、运输和利用对于维持动物体内的能量平衡至关重要。尽管参与葡萄糖在不同细胞间隙中移动的转运体已相当为人所知,但与葡萄糖结合以独立于葡萄糖代谢介导其效应的受体在很大程度上仍未被认识。建立精确且可重复的方法来鉴定大脑或其他外周器官中的葡萄糖受体,将为理解葡萄糖信号通路在维持、调节和重塑细胞代谢需求中的作用铺平道路。鉴定这些潜在的葡萄糖受体还可能开发出治疗糖尿病和相关代谢紊乱的有效疗法。市售的生物素或放射性标记葡萄糖共轭物分子量较低,因此无法提供足够的灵敏度和密度来分离葡萄糖受体。在此,我们介绍一种利用高分子量葡萄糖(或其他碳水化合物)共轭物分离、鉴定和验证葡萄糖结合受体的方法。我们已制备出 30 kDa 葡萄糖(或其他碳水化合物)生物素-聚丙烯酰胺(PAA)共轭物,其摩尔分数分别为 80:5:15%。这些共轭物可与生物素-链霉亲和素生物化学、细胞内酶联免疫吸附和表面等离子体共振(SPR)方法一起用于分离、鉴定和验证葡萄糖或碳水化合物结合受体。我们首先展示了如何利用链霉亲和素涂层磁珠固定葡萄糖-生物素-PAA 共轭物。然后,利用这些磁珠从组织匀浆或单细胞悬浮液中富集和分离葡萄糖结合蛋白。对富集或分离的蛋白质进行质谱/蛋白质组学分析,以揭示作为潜在葡萄糖受体的顶级候选蛋白质的身份。然后,我们将介绍如何利用细胞内 ELISA 方法,通过受体在体外的稳定表达来验证葡萄糖与其潜在受体之间的相互作用。我们进一步展示了如何使用高灵敏度的 SPR 方法来测量葡萄糖与其受体的结合动力学。总之,我们介绍了一种利用磁珠、细胞内 ELISA 和 SPR 分离、鉴定和验证葡萄糖或碳水化合物结合受体的方法。该方案将成为未来研究健康和疾病中葡萄糖或碳水化合物受体信号通路的基础。
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引用次数: 0
Multimodal pretraining for unsupervised protein representation learning. 用于无监督蛋白质表征学习的多模式预训练。
IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-18 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae043
Viet Thanh Duy Nguyen, Truong Son Hy

Proteins are complex biomolecules essential for numerous biological processes, making them crucial targets for advancements in molecular biology, medical research, and drug design. Understanding their intricate, hierarchical structures, and functions is vital for progress in these fields. To capture this complexity, we introduce Multimodal Protein Representation Learning (MPRL), a novel framework for symmetry-preserving multimodal pretraining that learns unified, unsupervised protein representations by integrating primary and tertiary structures. MPRL employs Evolutionary Scale Modeling (ESM-2) for sequence analysis, Variational Graph Auto-Encoders (VGAE) for residue-level graphs, and PointNet Autoencoder (PAE) for 3D point clouds of atoms, each designed to capture the spatial and evolutionary intricacies of proteins while preserving critical symmetries. By leveraging Auto-Fusion to synthesize joint representations from these pretrained models, MPRL ensures robust and comprehensive protein representations. Our extensive evaluation demonstrates that MPRL significantly enhances performance in various tasks such as protein-ligand binding affinity prediction, protein fold classification, enzyme activity identification, and mutation stability prediction. This framework advances the understanding of protein dynamics and facilitates future research in the field. Our source code is publicly available at https://github.com/HySonLab/Protein_Pretrain.

蛋白质是复杂的生物大分子,对许多生物过程至关重要,因此成为分子生物学、医学研究和药物设计领域取得进展的重要目标。了解它们错综复杂的层次结构和功能对这些领域的研究进展至关重要。为了捕捉这种复杂性,我们引入了多模态蛋白质表征学习(MPRL),这是一种用于对称性保护多模态预训练的新型框架,它通过整合一级和三级结构来学习统一的、无监督的蛋白质表征。MPRL 采用进化尺度建模(ESM-2)进行序列分析,采用变异图自动编码器(VGAE)进行残基级图形分析,采用点网自动编码器(PAE)进行三维原子点云分析,每种方法都旨在捕捉蛋白质在空间和进化方面的复杂性,同时保留关键的对称性。通过利用自动融合(Auto-Fusion)技术从这些预训练模型中合成联合表征,MPRL 确保了稳健而全面的蛋白质表征。我们的广泛评估表明,MPRL 显著提高了蛋白质配体结合亲和力预测、蛋白质折叠分类、酶活性识别和突变稳定性预测等各种任务的性能。该框架促进了对蛋白质动力学的理解,并推动了该领域的未来研究。我们的源代码可在 https://github.com/HySonLab/Protein_Pretrain 公开获取。
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引用次数: 0
PROMER technology: A new real-time PCR tool enabling multiplex detection of point mutation with high specificity and sensitivity. PROMER 技术:一种新的实时 PCR 工具,能够以高特异性和高灵敏度对点突变进行多重检测。
IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-04 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae041
Hwanhee Nam, Esder Lee, Hichang Yang, Kyeyoon Lee, Taeho Kwak, Dain Kim, Hyemin Kim, Mihwa Yang, Younjoo Yang, Seungwan Son, Young-Hyean Nam, Il Minn

Real-time polymerase chain reaction (real-time PCR) is a powerful tool for the precise quantification of nucleic acids in various applications. In cancer management, the monitoring of circulating tumor DNA (ctDNA) from liquid biopsies can provide valuable information for precision care, including treatment selection and monitoring, prognosis, and early detection. However, the rare and heterogeneous nature of ctDNA has made its precise detection and quantification challenging, particularly for ctDNA containing hotspot mutations. We have developed a new real-time PCR tool, PROMER technology, which enables the precise and sensitive detection of ctDNA containing cancer-driven single-point mutations. The PROMER functions as both a PRObe and priMER, providing enhanced detection specificity. We validated PROMER technology using synthetic templates with known KRAS point mutations and demonstrated its sensitivity and linearity of quantification. Using genomic DNA from human cancer cells with mutant and wild-type KRAS, we confirmed that PROMER PCR can detect mutant DNA. Furthermore, we demonstrated the ability of PROMER technology to efficiently detect mutation-carrying ctDNA from the plasma of mice with human cancers. Our results suggest that PROMER technology represents a promising new tool for the precise detection and quantification of DNA containing point mutations in the presence of a large excess of wild-type counterpart.

实时聚合酶链反应(real-time PCR)是在各种应用中精确定量核酸的有力工具。在癌症治疗中,监测液体活检中的循环肿瘤 DNA(ctDNA)可为精准治疗提供有价值的信息,包括治疗选择和监测、预后和早期检测。然而,ctDNA 的稀有性和异质性使其精确检测和定量具有挑战性,尤其是对于含有热点突变的ctDNA。我们开发了一种新的实时 PCR 工具--PROMER 技术,它能精确灵敏地检测含有癌症单点突变的 ctDNA。PROMER 同时具有 PRObe 和 priMER 的功能,从而提高了检测的特异性。我们使用已知 KRAS 点突变的合成模板验证了 PROMER 技术,并证明了它的灵敏度和线性定量性。我们使用来自具有突变型和野生型 KRAS 的人类癌细胞的基因组 DNA,证实了 PROMER PCR 可以检测突变 DNA。此外,我们还证明了 PROMER 技术能从人类癌症小鼠的血浆中有效地检测出携带突变的 ctDNA。我们的研究结果表明,PROMER 技术是一种很有前途的新工具,它能在野生型DNA大量存在的情况下精确检测和量化含有点突变的 DNA。
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引用次数: 0
IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine. IntelliGenes:用于生物标记物发现和预测医学的交互式、用户友好型多模态人工智能/人工智能应用。
IF 3.6 Q2 Agricultural and Biological Sciences Pub Date : 2024-05-29 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae040
Rishabh Narayanan, William DeGroat, Dinesh Mendhe, Habiba Abdelhalim, Zeeshan Ahmed

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

人工智能(AI)和机器学习(ML)已在多个生活领域取得进展,但在多组学领域的进展却无法与其他领域相提并论。所面临的挑战包括但不限于处理和分析大量复杂的多组学数据,以及实施和执行人工智能/ML 方法所需的专业知识。在这篇文章中,我们介绍了 IntelliGenes,这是一种交互式、可定制、跨平台、用户友好的人工智能/ML 应用程序,用于多组学数据探索,以发现新型生物标记物,预测罕见、常见和复杂疾病。该方法基于传统统计技术和前沿 ML 算法的结合,优于单一算法并提高了准确性。IntelliGenes 的交互式跨平台图形用户界面分为三个主要部分:(i) 数据管理器,(ii) 人工智能/ML 分析,以及 (iii) 可视化。数据管理器支持用户加载和定制输入数据和现有生物标记物列表。AI/ML 分析允许用户应用默认的统计和 ML 算法组合,以及自定义和创建新的 AI/ML 管道。可视化功能提供了多种选项,用于解释各种生成结果,包括性能指标、疾病预测和各种图表。IntelliGenes 的性能已在不同的内部研究和同行评议研究中进行了成功测试,能够正确地将个体分类为患者,并高精度地预测疾病。它的与众不同之处主要在于其简单易用,适合非技术用户使用,并强调生成可解释的可视化结果。我们设计和实施 IntelliGenes 的方式是,无论用户是否具有计算背景,都可以应用人工智能/ML 方法来发现新的生物标记物和预测疾病。
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引用次数: 0
Mapping adipocyte interactome networks by HaloTag-enrichment-mass spectrometry. 利用 HaloTag 富集质谱法绘制脂肪细胞相互作用组网络图。
IF 3.6 Q2 Agricultural and Biological Sciences Pub Date : 2024-05-29 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae039
Junshi Yazaki, Takashi Yamanashi, Shino Nemoto, Atsuo Kobayashi, Yong-Woon Han, Tomoko Hasegawa, Akira Iwase, Masaki Ishikawa, Ryo Konno, Koshi Imami, Yusuke Kawashima, Jun Seita

Mapping protein interaction complexes in their natural state in vivo is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an in vitro pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.

绘制体内自然状态下的蛋白质相互作用复合物图谱可以说是蛋白质网络分析的圣杯。检测蛋白质相互作用的化学计量一直是一项重要的技术挑战,因为很少有研究关注这一问题。不过,人工智能(AI)和蛋白质组学可能会解决这个问题。在此,我们介绍了基于HaloTag的亲和纯化质谱(HaloMS)的开发情况,这是一种用于发现蛋白质相互作用的高通量HaloMS检测方法。这种方法能快速捕获新表达的蛋白质,省去了传统的逐一检测的繁琐过程。作为原理验证,我们使用 HaloMS 评估了人类脂肪细胞中 17 种调控蛋白的蛋白复合物相互作用。脂肪细胞相互作用组网络通过体外牵引试验和基于人工智能的预测工具得到了验证。应用HaloMS探测脂肪细胞分化有助于鉴定以前未知的转录因子(TF)-蛋白质复合物,揭示整个蛋白质组的人类脂肪细胞TF网络,并揭示不同通路是如何整合的。
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引用次数: 0
Northern blotting of endogenous full-length human-specific LINE-1 RNA. 内源性全长人类特异性 LINE-1 RNA 的 Northern 印迹。
IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-05-28 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae036
Maisa I Alkailani

LINE-1 belongs to a family of DNA elements that move to new locations in the genome in a process called "retrotransposition." This is achieved by a copy-and-paste mechanism with the aid of an RNA intermediate. The full-length LINE-1 is responsible for most retrotransposition activity in the human genome. Detecting the active LINE-1 RNA at the endogenous level is challenging due to its small percentage among inactive copies and its different forms of transcripts. Here, we describe a method of designing RNA probes to detect active LINE-1 by northern blotting and use optimized conditions and tools to make the detection practical. This method uses a classical long RNA probe and provides an alternative way to detect LINE-1 RNA using multiple short RNA probes.

LINE-1属于DNA元件家族的一员,它们通过一种叫做 "逆转录 "的过程转移到基因组中的新位置。这是通过一种复制粘贴机制,借助 RNA 中间体实现的。全长 LINE-1 负责人类基因组中的大部分逆转录活动。由于 LINE-1 RNA 在非活性拷贝中所占比例很小,而且其转录本形式各异,因此在内源性水平检测活性 LINE-1 RNA 是一项挑战。在这里,我们介绍了一种设计 RNA 探针的方法,通过北印迹法检测活性 LINE-1,并使用优化的条件和工具使检测切实可行。该方法使用经典的长 RNA 探针,并提供了一种使用多个短 RNA 探针检测 LINE-1 RNA 的替代方法。
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引用次数: 0
An optimized ligation-mediated PCR method for chromosome walking and fusion gene chromosomal breakpoints identification. 用于染色体走行和融合基因染色体断点鉴定的优化连接介导 PCR 方法。
IF 3.6 Q2 Agricultural and Biological Sciences Pub Date : 2024-05-25 eCollection Date: 2024-01-01 DOI: 10.1093/biomethods/bpae037
Jrhau Lung, Ming-Szu Hung, Chao-Yu Chen, Tsung-Ming Yang, Chin-Kuo Lin, Yu-Hung Fang, Yuan-Yuan Jiang, Hui-Fen Liao, Yu-Ching Lin

Molecular techniques that recover unknown sequences next to a known sequence region have been widely applied in various molecular studies, such as chromosome walking, identification of the insertion site of transposon mutagenesis, fusion gene partner, and chromosomal breakpoints, as well as targeted sequencing library preparation. Although various techniques have been introduced for efficiency enhancement, searching for relevant single molecular event present in a large-sized genome remains challenging. Here, the optimized ligation-mediated polymerase chain reaction (PCR) method was developed and successfully identified chromosomal breakpoints far away from the exon of the new exon junction without the need for nested PCR. In addition to recovering unknown sequences next to a known sequence region, the high efficiency of the method could also improve the performance of targeted  next-generation sequencing (NGS).

在已知序列区域旁恢复未知序列的分子技术已被广泛应用于各种分子研究中,如染色体走行、转座子诱变插入位点鉴定、融合基因伙伴和染色体断点,以及靶向测序文库制备等。尽管已经引入了各种技术来提高效率,但在大型基因组中搜索相关的单一分子事件仍然具有挑战性。在此,我们开发了优化的连接介导聚合酶链反应(PCR)方法,无需嵌套 PCR 即可成功鉴定出远离新外显子交界处外显子的染色体断点。除了恢复已知序列区域旁的未知序列外,该方法的高效率还能提高定向下一代测序(NGS)的性能。
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引用次数: 0
A One-Step Low-Cost molecular test for SARS-CoV-2 detection suitable for community testing using minimally processed saliva 一种适用于社区检测的一步式低成本 SARS-CoV-2 检测分子检验法,使用的唾液只需简单处理即可
IF 3.6 Q2 Agricultural and Biological Sciences Pub Date : 2024-05-22 DOI: 10.1093/biomethods/bpae035
Sofia M da Silva, C. Amaral, Cláudia Malta-Luís, D. Grilo, Américo G. Duarte, Inês Morais, G. Afonso, Nuno Faria, W. Antunes, I. Gomes, R. Sá-Leão, M. Miragaia, Mónica Serrano, C. Pimentel
The gold standard for COVID-19 diagnostic testing relies on RNA extraction from naso/oropharyngeal swab followed by amplification through RT-PCR with fluorogenic probes. While the test is extremely sensitive and specific, its high cost and the potential discomfort associated with specimen collection made it suboptimal for public health screening purposes. In this study, we developed an equally reliable, but cheaper and less invasive alternative test based on a one-step RT-PCR with the DNA-intercalating dye SYBR Green, which enables the detection of SARS-CoV-2 directly from saliva samples or RNA isolated from nasopharyngeal swabs. Importantly, we found that this type of testing can be fine-tuned to discriminate SARS-CoV-2 variants of concern. The saliva RT-PCR SYBR Green test was successfully used in a mass-screening initiative targeting nearly 4500 asymptomatic children under the age of 12. Testing was performed at a reasonable cost, and in some cases, the saliva test outperformed nasopharyngeal rapid antigen tests in identifying infected children. Whole genome sequencing revealed that the antigen testing failure could not be attributed to a specific lineage of SARS-CoV-2. Overall, this work strongly supports the view that RT-PCR saliva tests based on DNA-intercalating dyes represent a powerful strategy for community screening of SARS-CoV-2. The tests can be easily applied to other infectious agents and, therefore, constitute a powerful resource for an effective response to future pandemics.
COVID-19 诊断检测的黄金标准是从鼻/咽拭子中提取 RNA,然后通过含氟探针进行 RT-PCR 扩增。虽然该检测方法灵敏度和特异性极高,但由于其成本高昂,且标本采集过程中可能会引起不适,因此并不适合用于公共卫生筛查。在这项研究中,我们开发了一种同样可靠、但更便宜、侵入性更小的替代检测方法,该方法基于使用 DNA 交联染料 SYBR Green 的一步式 RT-PCR 检测,可直接从唾液样本或从鼻咽拭子中分离的 RNA 中检测 SARS-CoV-2 。重要的是,我们发现这种检测方法可以进行微调,以区分令人担忧的 SARS-CoV-2 变体。唾液 RT-PCR SYBR Green 检测法成功地应用于一项大规模筛查活动,对象是近 4500 名 12 岁以下无症状的儿童。检测成本合理,在某些情况下,唾液检测在识别受感染儿童方面的效果优于鼻咽快速抗原检测。全基因组测序显示,抗原检测失败不能归咎于 SARS-CoV-2 的特定血统。总之,这项工作有力地支持了这样一种观点,即基于 DNA 交联染料的 RT-PCR 唾液检测是社区筛查 SARS-CoV-2 的有力策略。这种检测方法可以很容易地应用于其他传染性病原体,因此是有效应对未来流行病的强大资源。
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Biology Methods and Protocols
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