Investigation of the metabolomic crosstalk between liver sinusoidal endothelial cells and hepatocytes exposed to paracetamol using organ-on-chip technology

代谢组学 代谢物 对乙酰氨基酚 药物代谢 药理学 异型生物质的 生物 药品 化学 生物化学 生物信息学
作者
Taha Messelmani,Anne Le Goff,Fabrice Soncin,Françoise Gilard,Zied Souguir,Nathalie Maubon,Bertrand Gakière,Cécile Legallais,Éric Leclerc,Rachid Jellali
出处
期刊:Toxicology [Elsevier]
卷期号:492: 153550-153550 被引量:2
标识
DOI:10.1016/j.tox.2023.153550
摘要

Organ-on-chip technology is a promising in vitro approach recapitulating human physiology for the study of responses to drug exposure. Organ-on-chip cell cultures have paved new grounds for testing and understanding metabolic dose-responses when evaluating pharmaceutical and environmental toxicity. Here, we present a metabolomic investigation of a coculture of liver sinusoidal endothelial cells (LSECs, SK-HEP-1) with hepatocytes (HepG2/C3a) using advanced organ-on-chip technology. To reproduce the physiology of the sinusoidal barrier, LSECs were separated from hepatocytes by a membrane (culture insert integrated organ-on-chip platform). The tissues were exposed to acetaminophen (APAP), an analgesic drug widely used as a xenobiotic model in liver and HepG2/C3a studies. The differences between the SK-HEP-1, HepG2/C3a monocultures and SK-HEP-1/HepG2/C3a cocultures, treated or not with APAP, were identified from metabolomic profiles using supervised multivariate analysis. The pathway enrichment coupled with metabolite analysis of the corresponding metabolic fingerprints contributed to extracting the specificity of each type of culture and condition. In addition, we analysed the responses to APAP treatment by mapping the signatures with significant modulation of the biological processes of the SK-HEP-1 APAP, HepG2/C3a APAP and SK-HEP-1/HepG2/C3a APAP conditions. Furthermore, our model shows how the presence of the LSECs barrier and APAP first pass can modify the metabolism of HepG2/C3a. Altogether, this study demonstrates the potential of a "metabolomic-on-chip" strategy for pharmaco-metabolomic applications predicting individual response to drugs.
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