A novel multi-omics data analysis of dose-dependent and temporal changes in regulatory pathways due to chemical perturbation: a case study on caffeine.

IF 3.2 4区医学 Q1 Pharmacology, Toxicology and Pharmaceutics Toxicology Mechanisms and Methods Pub Date : 2024-02-01 Epub Date: 2024-01-29 DOI:10.1080/15376516.2023.2265462

Yufan Liu, Guoping Lian, Tao Chen

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Abstract

Comprehensive analysis of multi-omics data can reveal alterations in regulatory pathways induced by cellular exposure to chemicals by characterizing biological processes at the molecular level. Data-driven omics analysis, conducted in a dose-dependent or dynamic manner, can facilitate comprehending toxicity mechanisms. This study introduces a novel multi-omics data analysis designed to concurrently examine dose-dependent and temporal patterns of cellular responses to chemical perturbations. This analysis, encompassing preliminary exploration, pattern deconstruction, and network reconstruction of multi-omics data, provides a comprehensive perspective on the dynamic behaviors of cells exposed to varying levels of chemical stimuli. Importantly, this analysis is adaptable to any number of omics layers, including site-specific phosphoproteomics. We implemented this analysis on multi-omics data obtained from HepG2 cells exposed to a range of caffeine doses over varying durations and identified six response patterns, along with their associated biomolecules and pathways. Our study demonstrates the effectiveness of the proposed multi-omics data analysis in capturing multidimensional patterns of cellular response to chemical perturbation, enhancing understanding of pathway regulation for chemical risk assessment.

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一项关于化学扰动引起的调节途径的剂量依赖性和时间变化的新的多组学数据分析：一项关于咖啡因的案例研究。

对多组学数据的综合分析可以通过在分子水平上表征生物过程来揭示细胞暴露于化学物质引起的调节途径的改变。以剂量依赖性或动态方式进行的数据驱动的组学分析有助于理解毒性机制。这项研究引入了一种新的多组学数据分析，旨在同时检测细胞对化学扰动反应的剂量依赖性和时间模式。该分析包括对多组学数据的初步探索、模式解构和网络重建，为暴露于不同水平化学刺激的细胞的动态行为提供了一个全面的视角。重要的是，这种分析适用于任何数量的组学层，包括位点特异性磷酸蛋白质组学。我们对在不同时间内暴露于一系列咖啡因剂量的HepG2细胞获得的多组学数据进行了分析，并确定了六种反应模式及其相关的生物分子和途径。我们的研究证明了所提出的多组学数据分析在捕捉细胞对化学扰动的多维反应模式方面的有效性，增强了对化学风险评估途径调控的理解。

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来源期刊

Toxicology Mechanisms and Methods 医学-毒理学

CiteScore

6.60

自引率

3.10%

发文量

审稿时长

6-12 weeks

期刊介绍： Toxicology Mechanisms and Methods is a peer-reviewed journal whose aim is twofold. Firstly, the journal contains original research on subjects dealing with the mechanisms by which foreign chemicals cause toxic tissue injury. Chemical substances of interest include industrial compounds, environmental pollutants, hazardous wastes, drugs, pesticides, and chemical warfare agents. The scope of the journal spans from molecular and cellular mechanisms of action to the consideration of mechanistic evidence in establishing regulatory policy. Secondly, the journal addresses aspects of the development, validation, and application of new and existing laboratory methods, techniques, and equipment. A variety of research methods are discussed, including: In vivo studies with standard and alternative species In vitro studies and alternative methodologies Molecular, biochemical, and cellular techniques Pharmacokinetics and pharmacodynamics Mathematical modeling and computer programs Forensic analyses Risk assessment Data collection and analysis.