Transcriptomics integrated with metabolomics reveals the effect of Bisphenol F (BPF) exposure on intestinal inflammation

转录组 肿瘤坏死因子α 炎症 代谢组学 生物 脂多糖 白细胞介素 细胞因子 化学 细胞生物学 免疫学 生物化学 基因表达 生物信息学 基因
作者
Yongjie Liu,Weifeng Tang,Junjie Ao,Jun Zhang,Liping Feng
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:816: 151644-151644 被引量:14
标识
DOI:10.1016/j.scitotenv.2021.151644
摘要

As a viable alternative to Bisphenol A (BPA), Bisphenol F (BPF) has been detected in humans at comparable concentrations and detection frequencies. Emerging evidence reveals that BPF induces intestinal toxicity. However, less information is available concerning BPF and its potential effects on intestinal inflammation, which has been associated with numerous disorders. The results from the present study showed that BPF exposure triggered lipopolysaccharide (LPS)-induced explosion of pro-inflammatory cytokines interleukin-17A (IL-17A), tumor necrosis factor-α (TNF-α), and interferon-γ (IFN-γ) and impairment of the intestinal epithelial barrier by downregulating the expression of tight junction proteins Zonula Occludens-1 (ZO-1) and Claudin-1 (CLDN1) in normal colonic epithelial cells (NCM460). A multi-omics analysis integrating the transcriptomics with metabolomics revealed an altered transcripts and metabolites profile following BPF exposure. Correlation analysis indicated that RAS Guanyl Releasing Protein 2 (RASGRP2) and Phospholipase A2 Group IVE (PLA2G4E) were positively associated with the increased serotonin which was positively associated with the stimulated IFN-γ in BPF-treated NCM460 cells. Pyrogallol, pyridoxine, and N-acetylputrescine were positively associated with IL-17A levels. Collectively, the integrative analyses demonstrated an orchestrated coordination between the inflammatory response, transcriptomic, and metabolomics changes. Data presented herein provide evidence for the possible roles of BPF in the pathogenesis of intestinal inflammation. These results illustrate the advantages of using integrative analyses of high throughput datasets for characterizing the effects and mechanisms of toxicants.
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