Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Stress Response System01:21

Stress Response System

98
The stress response system, also known as the fight-or-flight response, is the body's automatic physiological reaction to perceived threats. Hans Selye introduced the concept of General Adaptation Syndrome (GAS) to describe the predictable pattern of changes that occur in response to stress. GAS consists of three sequential stages: alarm, resistance, and exhaustion. This model helps explain how chronic stress can contribute to health problems.
Alarm stage
In the alarm stage, the body's...
98
Other Stress Responses in Bacteria01:30

Other Stress Responses in Bacteria

20
Bacteria have global regulatory systems that control several types of stress mechanisms. These include Pho regulon and the heat shock response, which are essential systems for environmental adaptation, such as nutrient limitation and proteotoxic stress. The Pho regulon and the heat shock response exemplify bacterial resilience, enabling rapid adaptation to fluctuating environmental conditions.Pho RegulonBacteria require phosphorus for essential cellular processes, including nucleic acid...
20

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Decoding Cellular Stress States for Toxicology Using Single-Cell Transcriptomics.

bioRxiv : the preprint server for biology·2025
Same author

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data.

Chemical research in toxicology·2025
Same author

A Machine Learning Approach to Molecular Initiating Event Prediction Using High-Throughput Transcriptomic Chemical Screening Data.

Journal of chemical information and modeling·2025
Same author

Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks.

Chemical research in toxicology·2025
Same author

A Cheminformatics Workflow to Select Representative TSCA Chemicals for New Approach Methodology (NAM) Screening.

Chemical research in toxicology·2024
Same author

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping.

Toxicological sciences : an official journal of the Society of Toxicology·2024
Same journal

Building a compendium of expert driven read-across cases to facilitate an analysis of the contribution that different similarity contexts play in read-across performance.

Computational toxicology (Amsterdam, Netherlands)·2026
Same journal

Can graph similarity metrics be helpful for analogue identification as part of a read-across approach?

Computational toxicology (Amsterdam, Netherlands)·2026
Same journal

Development of the toxicity values database, ToxValDB: A curated resource for experimental and derived human health-relevant toxicity data.

Computational toxicology (Amsterdam, Netherlands)·2026
Same journal

The FAIR AOP roadmap for 2025: Advancing findability, accessibility, interoperability, and re-usability of adverse outcome pathways.

Computational toxicology (Amsterdam, Netherlands)·2025
Same journal

A comparative study of biostatistical pipelines for benchmark concentration modeling of <i>in vitro</i> screening assays.

Computational toxicology (Amsterdam, Netherlands)·2025
Same journal

Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment.

Computational toxicology (Amsterdam, Netherlands)·2025
查看所有相关文章

相关实验视频

Updated: Jul 13, 2025

Measurements of Physiological Stress Responses in C. Elegans
10:36

Measurements of Physiological Stress Responses in C. Elegans

Published on: May 21, 2020

14.0K

使用转录学评估适应性应激反应基因签名.

Bryant Chambers1, Imran Shah1

  • 1Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.

Computational toxicology (Amsterdam, Netherlands)
|October 13, 2023
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种使用基因组丰富分析 (GSEA) 的计算方法,从转录组数据评估细胞应激反应途径 (SRPs),改进化学安全评估.

关键词:
计算毒理学计算毒理学基因签名 基因签名 基因签名接收器的操作特征.应激反应路径 应激反应路径转录组学 转录组学是指转录组学.

更多相关视频

Author Spotlight: Polysome Profiling Protocol for Studying Translational Regulation in Arabidopsis Under Heat Stress
08:39

Author Spotlight: Polysome Profiling Protocol for Studying Translational Regulation in Arabidopsis Under Heat Stress

Published on: October 11, 2024

1.7K
A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure
09:06

A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure

Published on: October 24, 2018

10.8K

相关实验视频

Last Updated: Jul 13, 2025

Measurements of Physiological Stress Responses in C. Elegans
10:36

Measurements of Physiological Stress Responses in C. Elegans

Published on: May 21, 2020

14.0K
Author Spotlight: Polysome Profiling Protocol for Studying Translational Regulation in Arabidopsis Under Heat Stress
08:39

Author Spotlight: Polysome Profiling Protocol for Studying Translational Regulation in Arabidopsis Under Heat Stress

Published on: October 11, 2024

1.7K
A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure
09:06

A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure

Published on: October 24, 2018

10.8K

科学领域:

  • 计算生物学是一种计算生物学.
  • 毒素基因组学 毒素基因组学
  • 分子毒理学分子毒理学

背景情况:

  • 应激反应通路 (SRPs) 对于细胞防御化学攻击至关重要.
  • 过度的SRP激活可能导致细胞不良结果,需要强有力的评估方法.
  • 转录组数据为评估细胞对化学扰动的反应提供了一个强大的工具.

研究的目的:

  • 开发和评估一个计算方法来评估SRP活动使用转录数据.
  • 为了比较新的共识SRP签名与现有的已发布签名的性能.
  • 建立一种可靠的方法来描述SRP活动,以应对新的化学实体.

主要方法:

  • 从MSigDB中提取了六个关键SRP (DDR,UPR,HSR,HPX,MTL,OSR) 的已发表的基因签名.
  • 使用基因频率方法构建共识SRP签名.
  • 利用基因组丰富分析 (GSEA) 和在参考转录基因数据集上的受体运营者特征 (ROC) 分析.

主要成果:

  • 在6个SRP中,共识SRP签名在4个SRP中表现得与已公布的签名相当或优于公布的签名.
  • 在共识签名中实现了高的曲线下面面积 (AUC) 值,特别是DDR和HPX的1.00.
  • 通过转录基因配置匹配,成功地以高准确度分类扰素 (分别为78%和88%在第一和第二排).

结论:

  • 开发的计算方法有效地从转录基因数据中描述SRP活动.
  • 共识SRP签名为有毒性评估提供了对现有签名的有希望的替代方案或补充.
  • 这种方法具有评估新化学品SRP活性的潜力,有助于安全分析.