从对转录组动态的可观测性分析中学习扰乱诱导的细胞状态
Aqib Hasnain1, Shara Balakrishnan2, Dennis M Joshy3
1Department of Mechanical Engineering, University of California Santa Barbara, Santa Barbara, CA, USA. aqib@ucsb.edu.
Nature communications
|May 30, 2023
概括
研究人员开发了一种机器学习工具,以发现用于检测环境毒素的基因生物标志物. 该系统识别了对分析剂有反应的促进剂,在现实条件下创建了一个用于检测马拉的活体传感器.
科学领域:
- 生物技术和合成生物学
- 环境科学 环境科学
- 基因组学和生物信息学
背景情况:
- 确定特定干扰和代谢物的可靠生物标志物是生物技术和生物制造中的一个重大障碍.
- 对基因表达动态的全转录组分析对于理解细胞对外部刺激的反应至关重要.
研究的目的:
- 开发一种以数据为导向的方法,使用转录组范围的时间序列RNA测序数据来发现分析物响应的促进体.
- 创建一个活生生的生物传感器,用于检测环境样本中的有机酸.
- 建立适用于各种宿主生物的机器学习框架,以发现可诱导扰乱的基因表达系统.
主要方法:
- 采用全转录组方法,从时间序列RNA测序数据中对干扰诱导基因进行排序.
- 建立了基因表达动态的低维模型,并使用可观察性分析对基因进行排名,以确定细胞状态捕获生物标志物.
- 合成遗传记者是从SBW25的Pseudomonas光体SBW25中发现的15个马拉反应性促进体中开发出来的.
主要成果:
- 这项研究成功地确定了Pseudomonas fluorescens SBW25.25中马拉提昂特异性的15种分析剂响应性促进体.
- 合成遗传记者表现出对马拉的可测量反应,使其能够检测到.
- 一种合成联盟方法增强了马拉报告,该系统被验证用于实验室以外的环境检测.
结论:
- 开发的机器学习工具和已识别的促进者使得能够创建一个用于检测马拉的活体传感器.
- 工程化Pseudomonas光体SBW25作为一个有前途的环境诊断平台.
- 该方法可适应在各种宿主生物中发现基因表达系统,用于各种应用.
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