Details

Autor(en) / Beteiligte

• Aristizabal-Henao, Juan J.
• Bowden, John A.

Titel

Novel In Vitro Intestinal Microbiome Model to Study Lipidomic and Metabolomic Adaptations Due to Exposure to Glyphosate, Perfluorooctanoic Acid, and Docusate Sodium

Ist Teil von

• Chemical research in toxicology, 2023-07, Vol.36 (7), p.1121-1128

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• Cell and animal models have been used to provide insights with regard to physiological changes in intestinal flora due to exposure to drugs and environmental contaminants. Here, a novel in vitro model known as simulator of the human intestinal microbial ecosystem (SHIME) was used to assess the effects of three chemicals of emerging concern, namely glyphosate, perfluorooctanoic acid (PFOA), and docusate sodium (dioctyl sulfosuccinate, DOSS), on the lipidomic and metabolomic profiles of the gut microenvironment in both the proximal and distal colonic compartments. Nontargeted analyses by ultra-high performance liquid chromatography-tandem mass spectrometry and gas chromatography–electron ionization-mass spectrometry revealed minor differences in the lipidomic and metabolomic signatures of the proximal and distal colon following treatment with either glyphosate or PFOA at acceptable human daily intake levels or average daily exposures. However, global dysregulation of lipids and metabolites was observed due to DOSS treatment at conventional prescription doses when indicated as a stool softener. Our findings suggest that the current guidelines for glyphosate and PFOA exposure may be adequate at the level of the lower gut microbiome in healthy adults, but the probable yet uncharacterized off-target effects, safety, and efficacy of long-term DOSS treatment warrants further investigation. Indeed, we highlight the SHIME system as a novel in vitro approach which can be used as a screening tool to assess the impact of drugs and/or chemicals on the gut microbiome, while implementing state-of-the-art and data-driven mass spectrometric workflows to identify toxic lipidomic and metabolomic signatures.