Se pu = Chinese journal of chromatography最新文献

Emerging contaminants and their transformation products are widely distributed in the environment. These pollutants carry unknown risks owing to their persistence, migration, and toxicity. The wide variety and complex structures of these substances render them difficult to identify using only target analysis. Suspect screening analysis can identify more substances than target analysis in a single run. However, this analysis method is based on limited data and cannot meet the growing demand for compound identification, especially for emerging contaminants and their transformation products with unknown information. The development of high-resolution mass spectrometry technology has promoted the applications of nontarget analysis in the environmental field, especially for identifying unknown transformation products. At present, the challenges of nontarget analysis include the difficulty of finding compounds of interest and their transformation products from complex data. Molecular networking calculates the similarity between mass spectra based on an improved cosine similarity algorithm. This method can cluster molecular families with similar structures, achieve visualization and a collection of massive mass spectral datasets, and promote the annotation of pollutants through networks and communities. Molecular networking can globally organize and systematically interpret complex tandem mass spectral datasets, providing a new direction for nontarget analysis. This technology was first used in proteomics and gradually introduced into metabolomics for the discovery of new natural products. Recently, it has been introduced into the environmental field for the study of various man-made chemicals, particularly for the discovery of emerging contaminants and their transformation products. In this paper, we introduce a molecular networking analysis method based on high-resolution tandem mass spectrometry and describe its applications in the nontargeted screening of emerging contaminants, focusing on the technical principles, workflow, application status, and future development prospects. This paper discusses the applications of molecular networking technology in the detection of emerging contaminants and their transformation products such as drugs, perfluorinated compounds, and disinfection byproducts. Molecular networking technology is widely applicable to the screening of emerging contaminants in various environmental media, revealing the full range of pollutants in the environment and promoting studies on the environmental behavior and toxicological properties of these compounds.

While human exposure to perfluorooctanoic acid (PFOA) can lead to ulcerative colitis, the molecular mechanisms responsible for PFOA-induced intestinal toxicity are unclear. Herein, we examined the toxicity of PFOA toward human colorectal cancer cells (HCT116) from three dimensions: the cytotoxic phenotype, cell respiration, and transcription levels of metabolism-related genes. Formazan was used to assess how PFOA exposure affects HCT116-cell relative viability, after which the mitochondrial respiratory activities of these cells were determined by analyzing extracellular flux. The quantitative real-time polymerase chain reaction (qPCR) method was used to detect metabolism-related gene expression levels. The cytotoxicity assay revealed that the HCT116 showed significantly inhibited relative activities compared to those of the control when exposed to 300 μmol/L PFOA for 48 h (p<0.01), with most cells retained at the G0/G1 stage. In contrast, the mitochondrial respiratory activities of the HCT116 were promoted by concentrations of PFOA as low as 50 μmol/L. Two genes related to cellular metabolism (dipeptidase 1 (DPEP1) and sphingosine kinase 1 (SPHK1)) were found to be related to the PFOA-promoted formation of ulcerative colitis using our self-developed Metabolic Gene and Pathway Query software and Comparative Toxicogenomics Database (CTD). The qPCR studies revealed that DPEP1 and SPHK1 expression levels were enhanced by 8-10 times in HCT116 exposed to 300 μmol/L PFOA relative to the control, whereas this trend was not observed for HCT116 exposed to 50 μmol/L PFOA. Collectively, these results suggest that the respiratory activity of cellular mitochondria may serve as an index for determining the interference effects associated with PFOA and that metabolic pathways mediated by DPEP1 and SPHK1 may be involved in the development of PFOA-induced ulcerative colitis. Future studies should investigate the relationships between changes in metabolism-related genes (DPEP1 and SPHK1) and the mitochondrial respiratory activities of intestinal cells, and verify the roles played by the DPEP1 and SPHK1 genes in PFOA-induced intestinal inflammation using in-vivo models.

Arsenic is a ubiquitous environmental toxin that can affect normal physiological processes. Although the health impacts of arsenic have been investigated, its influence on hepatic metabolism in obese pregnant women and the underlying mechanisms remain unclear. Multi-omics analysis, including metabolomics and proteomics, can improve the understanding of arsenic-induced hepatotoxicity in obese pregnant women. This study aimed to investigate the adverse effects of gestational arsenic exposure on hepatic metabolism in high-fat-diet-induced obese pregnant mice. Following arsenic exposure during pregnancy, the liver tissue was evaluated comprehensively using metabolomics and proteomics techniques combined with pathological and biochemical analyses. Arsenic exposure not only significantly increased lipid accumulation in the livers of obese pregnant mice but also elevated inflammatory factors and oxidative stress markers. Specifically, histopathological examination revealed more steatosis, inflammatory cell infiltration, and hepatocyte ballooning in the livers of arsenic-exposed mice than in those of controls. These changes indicate that arsenic exposure exacerbates hepatic lipid accumulation and induces liver damage in the context of obesity. Metabolomic analysis provided further insight into the metabolic-level disruption caused by arsenic exposure. Significant changes were observed in lipid metabolism pathways, particularly the arachidonic acid metabolism pathway. As arachidonic acid and its metabolites play important roles in inflammation and oxidative stress, this pathway may be critical in arsenic-induced hepatotoxicity. Additionally, proteomic analysis showed differences in the expression levels of several key proteins involved in lipid synthesis, oxidative stress, and inflammatory response. Notably, oxidative-stress-related proteins, including glutathione peroxidase 4 (GPX4), were upregulated, suggesting an increased oxidative burden. In summary, there are complex interaction mechanisms among arsenic exposure, inflammatory response, and related lipid metabolism. The integration of metabolomics and proteomics aided in clarifying the molecular alterations induced by arsenic. The results show that arsenic exposure significantly affects hepatic lipid metabolism in obese pregnant mice through multiple metabolic pathways and protein regulatory mechanisms. In addition to providing new insights into the relationship between arsenic exposure and obesity as well as related metabolic diseases, this study can act as a reference for environmental health risk assessment and the formulation of public health policies. This enhanced understanding of the adverse effects of arsenic on hepatic metabolism will contribute to the development of strategies for mitigating the health risks associated with environmental toxins, particularly for vulnerable groups such as obese pregnant women.