Transcriptomics-based analysis of sex-differentiated mechanisms of hepatotoxicity in zebrafish after long-term exposure to bisphenol AF

Bisphenol AF (BPAF) is an emerging endocrine-disrupting chemical (EDC) prevalent in the environment as one of the main substitutes for bisphenol A. Sex-specific effects of EDCs have been commonly reported and closely linked to sexually dimorphic patterns of hormone metabolism and related gene expression during different exposure windows, but our understanding of these mechanisms is still limited. Here, following 28-day exposure of adult zebrafish to an environmentally relevant concentration of BPAF at 10 μg/L, the global transcriptional networks applying RNA sequencing (RNA-seq) and Ingenuity Pathway Analysis (IPA) were respectively investigated in the male and female fish liver, connecting the sex-dependent toxicity of the long-term exposure of BPAF to molecular responses. As a result, more differentially expressed genes (DEGs) were detected in males (811) than in females (195), and spermatogenesis was the most enriched Gene Ontology (GO) functional classification in males, while circadian regulation of gene expression was the most enriched GO term in females. The expression levels of selected DEGs were routinely verified using qRT-PCR, which showed consistent alterations with the transcriptional changes in RNA-seq data. The causal network analysis by IPA suggested that the adverse outcomes of BPAF in males including liver damage, apoptosis, inflammation of organ, and liver carcinoma, associated with the regulation of several key DEGs detected in RNA-seq, could be linked to the activation of upstream regulatory molecules ifnα, yap1, and ptger2; while, the inhibition of upstream regulators hif1α, ifng, and igf1, leading to the down-regulated expression of several key DEGs, might be involved in BPAF's effects in females. Furthermore, BPAF exposure altered hepatic histological structure and inhibited antioxidant capability in both male and female livers. Overall, this study revealed different regulation networks involved in the sex-dependent effects of BPAF on the fish liver, and these detected DEGs upon BPAF exposure might be used as potential biomarkers for further assessing sex-specific hepatotoxicity following environmental EDC exposure.

Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure-activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.