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An Integrative Multi-Omics Workflow to Address Multifactorial Toxicology Experiments.

Víctor González-Ruiz1,2, Domitille Schvartz3,4, Jenny Sandström5,6

  • 1Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland. victor.gonzalez@unige.ch.

Metabolites
|April 27, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new workflow for analyzing multifactorial omics data in toxicology. It reveals that trimethyltin (TMT) exposure significantly impacts neural cell metabolism, affecting neuronal differentiation and signaling pathways.

Keywords:
AMOPLSmetabolomicsmultifactorial experimentsmultiplatform omicspathway analysisproteomicstoxicologytrimethyltin

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Area of Science:

  • Toxicology
  • Metabolomics
  • Neuroscience

Background:

  • Omics approaches are valuable in toxicology for understanding phenotypic alterations.
  • Analyzing multifactorial omics data requires specialized tools for factor deconvolution and data integration.

Purpose of the Study:

  • To develop and validate a novel workflow for analyzing complex omics data from toxicological studies.
  • To investigate the impact of trimethyltin (TMT) exposure on 3D neural cell cultures, considering maturation state, exposure duration, and concentration.

Main Methods:

  • Utilized a metabolomic approach combining four analytical techniques (RP-LC-MS/MS, HILIC-LC-MS/MS in positive and negative modes).
  • Applied the ANOVA multiblock OPLS (AMOPLS) method for factor deconvolution and quantification of experimental factor contributions.
  • Integrated proteomic data to enhance mechanistic understanding.

Main Results:

  • Culture maturation state and exposure duration were the primary drivers of metabolic variability.
  • Trimethyltin (TMT) exposure, while contributing the least to variability, had a statistically significant impact.
  • TMT exposure altered biosynthetic pathways, neuronal differentiation, and signaling, particularly affecting GABAergic and glutamatergic neurons.

Conclusions:

  • The developed workflow effectively deconvolutes and integrates multifactorial omics data.
  • Trimethyltin exerts significant toxic effects on neural cells, impacting key neuronal functions.
  • Combined omics and advanced data analysis provide robust mechanistic insights into toxicant-induced effects.