438433 Metabolic Effects of Chronic Exposure to Endocrine Disruptors in Prostate Cancer: A Multi-Omic Data Integration Approach Via Constraint-Based Modeling

Wednesday, September 16, 2015
Crowne Plaza Heidelberg City Centre
Igor Marín de Mas Sr.1, Carmen Bedia1, Marta Cascante2,3 and Romà Tauler Sr.1, (1)IDAEA, CSIC, Barcelona, Spain, (2)Departamento de Bioquimica I Biologia Molecular, University of Barcelona, Barcelona, Spain, (3)Department of Biochemistry and Molecular Biology, Universitat de Barcelona, Barcelona, Spain

Endocrine disruptors (EDs) are environmental toxic molecules that can interfere with the hormone metabolism in mammals. It is widely accepted a dose-effect relation between these molecules and tumor progression and malignancy. However our understanding of the effects of chronic exposure to non-lethal concentrations of EDs in cancer metabolism still remains limited.

Here, we study the metabolic profile of DU145 prostate cancer cells before and after a chronic exposure to different EDs (aldrin, aroclor 1254 and chlorpyrifos (CPF)), that is reported to induce changes in the lipidic profile and to enhance the malignant phenotype of DU145 cells. To increase our understanding of the metabolic processes underlying these changes, we use constraint-based modeling approaches to integrate the transcriptomic and lipidomic data of DU145 cells in the different conditions into a genome-scale metabolic network reconstruction analysis.

First, we perform a thorough reconstruction and expansion of the lipid-associated pathways of one of the most recent reconstructions of the human metabolism by using different publicly available databases. The aim is to generate a model that enables the analysis of all the changes observed in the lipidomic profile of DU145 cells.

Next, we integrate transcriptomic and lipidomic data into the expanded reconstruction of human metabolism to: i) infer the activity state of the metabolic reactions and ii) define a space of experimentally supported metabolic flux solutions. Briefly, these constraints-basemethods aim to maximize the similarity between gene expression and the activity state of the metabolic network involving those experimentally measured metabolites while satisfies the stoichiometric and thermodynamic constraints imposed by the model.
Our findings will permit to decipher the metabolic mechanisms underlying malignant changes associated to long-term exposure to endocrine disruptors in prostate cancer with potential clinical and environmental applications.


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