Toxicity Assessment of Mixed Exposure of Nine Perfluoroalkyl Substances at Concentrations Relevant to Daily Intake.
Kazuki TakedaTaki SaitoSakura SasakiAkifumi EguchiMakoto SugiyamaSaeka EtoKio SuzukiRyo KamataPublished in: Toxics (2024)
Per- and poly-fluoroalkyl substances (PFAS) exhibit high persistence in the environment and accumulate within the human body, warranting a thorough assessment of their toxicity. In this study, we exposed mice (male C57BL/6J mice aged 8 weeks) to a composite of nine PFAS, encompassing both long-chain PFAS (e.g., perfluorooctanoic acid and perfluorooctanesulfonic acid) and short-chain PFAS (e.g., perfluorobutanoic acid and perfluorobutanesulfonic acid). The exposure concentrations of PFAS were equivalent to the estimated daily human intake in the composition reported (1 µg/L (sum of the nine compounds), representing the maximum reported exposure concentration). Histological examination revealed hepatocyte vacuolization and irregular hepatocyte cord arrangement, indicating that exposure to low levels of the PFAS mixture causes morphological changes in liver tissues. Transcriptome analysis revealed that PFAS exposure mainly altered a group of genes related to metabolism and chemical carcinogenesis. Machine learning analysis of the liver metabolome showed a typical concentration-independent alteration upon PFAS exposure, with the annotation of substances such as glutathione and 5-aminovaleric acid. This study demonstrates that daily exposure to PFAS leads to morphological changes in liver tissues and alters the expression of metabolism- and cancer-related genes as well as phospholipid metabolism.
Keyphrases
- machine learning
- endothelial cells
- gene expression
- physical activity
- drinking water
- poor prognosis
- induced pluripotent stem cells
- type diabetes
- single cell
- artificial intelligence
- dna methylation
- papillary thyroid
- squamous cell carcinoma
- genome wide
- body mass index
- long non coding rna
- metabolic syndrome
- transcription factor
- skeletal muscle
- weight loss
- deep learning