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Front. Environ. Sci. Eng.    2023, Vol. 17 Issue (1) : 4    https://doi.org/10.1007/s11783-023-1604-5
RESEARCH ARTICLE
Time-dependent metabolomics uncover dynamic metabolic adaptions in MCF-7 cells exposed to bisphenol A
Haoduo Zhao1,2, Min Liu1,2, Junjie Yang1,2, Yuyang Chen3, Mingliang Fang1,4()
1. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
2. Nanyang Environment & Water Research Institute, Nanyang Technological University, Singapore 637141, Singapore
3. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
4. Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
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Abstract

● Metabolomic temporal profiling of cells exposed to xenobiotics.

● Global metabolome dysregulation patterns with time-resolved landscapes.

● Synchronized regulation behavior and specific dysregulation sensitivity.

● Temporal metabolic adaptions indicated cellular emphasis transition.

The biochemical consequences induced by xenobiotic stress are featured in dose-response and time-resolved landscapes. Understanding the dynamic process of cellular adaptations is crucial in conducting the risk assessment for chemical exposure. As one of the most phenotype-related omics, metabolome in response to environmental stress can vary from seconds to days. Up to now, very few dynamic metabolomics studies have been conducted to provide time-dependent mechanistic interpretations in understanding xenobiotics-induced cellular adaptations. This study aims to explore the time-resolved metabolite dysregulation manner and dynamically perturbed biological functions in MCF-7 cells exposed to bisphenol A (BPA), a well-known endocrine-disrupting chemical. By sampling at 11 time points from several minutes to hours, thirty seven significantly dysregulated metabolites were identified, ranging from amino acids, fatty acids, carboxylic acids and nucleoside phosphate compounds. The metabolites in different pathways basically showed distinct time-resolved changing patterns, while those within the common class or same pathways showed similar and synchronized dysregulation behaviors. The pathway enrichment analysis suggested that purine metabolism, pyrimidine metabolism, aminoacyl-tRNA biosynthesis as well as glutamine/glutamate (GABA) metabolism pathways were heavily disturbed. As exposure event continued, MCF-7 cells went through multiple sequential metabolic adaptations from cell proliferation to energy metabolism, which indicated an enhancing cellular requirement for elevated energy homeostasis, oxidative stress response and ER-α mediated cell growth. We further focused on the time-dependent metabolite dysregulation behavior in purine and pyrimidine metabolism, and identified the impaired glycolysis and oxidative phosphorylation by redox imbalance. Lastly, we established a restricted cubic spline-based model to fit and predict metabolite’s full range dysregulation cartography, with metabolite’ sensitivity comparisons retrieved and novel biomarkers suggested. Overall, the results indicated that 8 h BPA exposure leaded to global dynamic metabolome adaptions including amino acid, nucleoside and sugar metabolism disorders, and the dysregulated metabolites with interfered pathways at different stages are of significant temporal distinctions.
Keywords Metabolomics      BPA      MCF-7      Temporal profiling      Metabolic adaption      Dysregulation correlation     
Corresponding Author(s): Mingliang Fang   
Issue Date: 04 August 2022
 Cite this article:   
Haoduo Zhao,Min Liu,Junjie Yang, et al. Time-dependent metabolomics uncover dynamic metabolic adaptions in MCF-7 cells exposed to bisphenol A[J]. Front. Environ. Sci. Eng., 2023, 17(1): 4.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1604-5
https://academic.hep.com.cn/fese/EN/Y2023/V17/I1/4
Fig.1  Tiered approach of the time-resolved metabolomics study. (A) Experimental workflow to harvest MCF-7 cells exposed to Bisphenol A at 11 varying time points (0 h, 0.25 h, 0.5 h, 0.75 h, 1 h, 1.5 h, 2 h, 3 h, 4 h, 6 h, and 8 h), with intercellular metabolites extracted and analyzed by HPLC-MS. (B) Data processing and metabolite identification under untargeted metabolomics framework. The applied methodology included but not limited to: MS1 profiling, MS2 validation, RT shifted correction, in-house standard library alignment, one-way ANOVA, t-test, pathway enrichment analysis, etc. (C) Time-resolved metabolomics was implemented to uncover metabolite time-dependent dysregulation behavior, including metabolite dysregulation analysis, temporal pathway enrichment and restricted cubic spline-based prediction.
Fig.2  (A) Principal component analysis score plot of aligned features at 0 h, 2 h, 6 h, and 8 h (3 control samples and 1 mean point at 0 h; 4 treated samples and 1 mean point at 2 h, 6 h, and 8 h); (B) Principal component analysis scree plot of total ion features at 0 h, 2 h, 6 h, and 8 h; (C) Up and down-regulated significant features of 10 time point detected by global profiling (pairwise comparison with control samples); (D) Venn diagram summarizing the number of shared and distinct features of total metabolome features at 2 h, 4 h, 6 h, and 8 h (multigroup analysis).
Fig.3  Clustered heatmap of 37 significant dysregulated metabolites. Scales in colored key represent z-score scaled fold change value of metabolites. [Abbreviations: Nicotinamide adenine dinucleotide (NAD); 1,4-Dihydronicotinamide adenine dinucleotide (NADH); Adenosine 5'-diphosphate (ADP).]
Fig.4  (A) Metabolite enrichment analysis of 37 significant dysregulated metabolites (p ≤ 0.05); (B) Temporal diagram of the top 3 mostly enriched pathway (with minimum p-value) at each time point; (C) Rose diagrams which present the number of significantly changed metabolites and significantly disturbed pathway (p ≤ 0.05) at each time point; (D) Average abundance profiles of detected metabolites for four pathways (purine metabolism, pyrimidine metabolism, aminoacyl-tRNA biosynthesis, alanine, aspartate and glutamate metabolism). The pathway value was set as the mean of the involved metabolite within specified pathway. All abundance levels are normalized to their mean values at 0 h.
Fig.5  Metabolomic analysis of purine metabolism and pyrimidine metabolism. The time-response plot for detected metabolites were shown (fold change vs. time), with red dashed line stands for control (FC = 1).
Fig.6  (A) Pearson correlation matrix of 37 metabolites. Dots and figure in color stands for correlation coefficient from –1(red) to 1 (blue), with significance level presented (”*” for p < 0.05; ”**” for p < 0.01; ”***” for p < 0.001, respectively); (B) Schematic diagram of dynamically changing NAD:NADH ratio; (C) Schematic diagram of dynamically changing GSH:GSSG ratio.
Fig.7  Restricted cubic spline fitting curve of four metabolites, with confidence level indicated in grey (p < 0.05).
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