Combined cytotoxic and genotoxic effects of ochratoxin A and fumonisin B1 in human kidney and liver cell models

• Autor(a) principal: Pinhão, Mariana
• Data de Publicação: 2020
• Outros Autores: Tavares, Ana Maria, Loureiro, Susana, Louro, Henriqueta, Alvito, Paula, Silva, Maria João
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.18/7358
• topic: Mycotoxins; Mixtures Toxicity; Fumonisin B(1); Genotoxicity; Environmental Genotoxicity; Interactive Effects; Food Safety; Liver and Kidney Toxicity; Ochratoxin A; Genotoxicidade Ambiental; Segurança Alimentar

Food products can be contaminated by several fungi species and each species may produce different mycotoxins, leading to human combined exposure. Although predictions about the joint toxic effects of mycotoxins can be made from their individual toxicities, experimental data is still limited to allow a reliable hazard assessment. Thus, this study aimed to characterize the combined cytotoxic and genotoxic effects of ochratoxin A (OTA) and fumonisin B1 (FB1) in cell lines representative of their target organs, kidney and liver. Interactions were ascertained using mathematical extensions to the reference models of concentration addition and independent action. Cytotoxicity (MTT assay) data modeling revealed a synergistic pattern for low doses of both FB1 and OTA shifting to antagonism at higher concentration levels, irrespectively of the reference model applied. Concerning genotoxicity assessment, neither OTA nor FB1, individually or in combination, induced a prominent increase in DNA damage (comet assay) or oxidative DNA damage (FPG-comet assay). In conclusion, this study revealed a synergistic cytotoxic effect for OTA and FB1 at low concentration levels. Given that human co-exposure to these two mycotoxins is probable to occur at low doses, these results raise concerns regarding their potential health outcomes that seem to differ from those predicted by an additive model.