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Article Abstract

Introduction: This study has investigated the temporal disruptive effects of tributyltin (TBT) on lipid homeostasis in Daphnia magna. To achieve this, the study used Liquid Chromatography-Mass Spectrometry (LC-MS) analysis to analyze biological samples of Daphnia magna treated with TBT over time. The resulting data sets were multivariate and three-way, and were modeled using bilinear and trilinear non-negative factor decomposition chemometric methods. These methods allowed for the identification of specific patterns in the data and provided insight into the effects of TBT on lipid homeostasis in Daphnia magna.

Objectives: Investigation of how are the changes in the lipid concentrations of Daphnia magna pools when they were exposed with TBT and over time using non-targeted LC-MS and advanced chemometric analysis.

Methods: The simultaneous analysis of LC-MS data sets of Daphnia magna samples under different experimental conditions (TBT dose and time) were analyzed using the ROIMCR method, which allows the resolution of the elution and mass spectra profiles of a large number of endogenous lipids. Changes obtained in the peak areas of the elution profiles of these lipids caused by the dose of TBT treatment and the time after its exposure are analyzed by principal component analysis, multivariate curve resolution-alternative least square, two-way ANOVA and ANOVA-simultaneous component analysis.

Results: 87 lipids were identified. Some of these lipids are proposed as Daphnia magna lipidomic biomarkers of the effects produced by the two considered factors (time and dose) and by their interaction. A reproducible multiplicative effect between these two factors is confirmed and the optimal approach to model this dataset resulted to be the application of the trilinear factor decomposition model.

Conclusion: The proposed non-targeted LC-MS lipidomics approach resulted to be a powerful tool to investigate the effects of the two factors on the Daphnia magna lipidome using chemometric methods based on bilinear and trilinear factor decomposition models, according to the type of interaction between the design factors.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406683PMC
http://dx.doi.org/10.1007/s11306-023-02030-wDOI Listing

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