RESUMO
BACKGROUND: Fluoranthene (FR) is a common environmental pollutant that exists in a complex mixture with other polycyclic aromatic hydrocarbons (PAHs). We identified biomarkers for monitoring FR exposure and investigated the rescue effect of FR-induced cellular toxicity via aryl hydrocarbon receptor (AHR) antagonist activity in bone marrow derived mesenchymal stem cells (BM-MSCs). METHODS: Morphological changes, viability, and rescue effects of an AHR antagonist (CH223191) were examined in BM-MSCs after exposure to FR. Cytotoxic effects were assayed using the tetrazolium-based colorimetric assay. Apoptosis was measured by annexin V and propidium iodide dye-based flowcytometry assay, mitochondrial membrane potential assay, and nuclear DNA fragmentation assay. Molecular signaling pathways of apoptosis and autophagy were investigated using immunoblotting. Proteomics were performed in order to reveal the spectra of cellular damage and identify biomarkers for FR exposure. RESULTS: Exposing BM-MSCs to FR (IC50=50 µM) induced cell death and morphological changes, while the AHR antagonist showed rescue effects. Autophagy was activated and mitochondrial membrane potential was decreased. Proteomic analysis identified 48 deregulated proteins (26 upregulated and 22 downregulated). Among them, annexin A6, pyruvate kinase, UDP-glucose dehydrogenase, and phospholipase A2 could be potential biomarkers for FR exposure. CONCLUSION: The exposure of BM-MSCs to FR induced remarkable alterations in cellular biology and the proteome, allowing for identification of novel biomarkers for FR exposure. Furthermore, AHR antagonists might be able to prevent cellular damage due to FR exposure.
RESUMO
BACKGROUND: Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard. METHODS: In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively. RESULTS: In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions. CONCLUSION: The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer.
