Unraveling the Mechanisms of Ch-SeNP Cytotoxicity against Cancer Cells: Insights from Targeted and Untargeted Metabolomics.

Although chitosan-stabilized selenium nanoparticles (Ch-SeNPs) have emerged as a promising chemical form of selenium for anticancer purposes, gathering more profound knowledge related to molecular dysfunctions contributes significantly to the promotion of their evolution as a chemotherapeutic drug. In this sense, metabolites are the end products in the flow of gene expression and, thus, the most sensitive to changes in the physiological state of a biological system. Therefore, metabolomics provides a functional readout of the biochemical activity and cell state. In the present study, we evaluated alterations in the metabolomes of HepG2 cells after the exposure to Ch-SeNPs to elucidate the biomolecular mechanisms involved in their therapeutic effect. A targeted metabolomic approach was conducted to evaluate the levels of four of the main energy-related metabolites (adenosine triphosphate (ATP); adenosine diphosphate (ADP); nicotinamide adenine dinucleotide (NAD+); and 1,4-dihydronicotinamide adenine dinucleotide (NADH)), revealing alterations as a result of exposure to Ch-SeNPs related to a shortage in the energy supply system in the cell. In addition, an untargeted metabolomic experiment was performed, which allowed for the study of alterations in the global metabolic profile as a consequence of Ch-SeNP exposure. The results indicate that the TCA cycle and glycolytic pathways were impaired, while alternative pathways such as glutaminolysis and cysteine metabolism were upregulated. Additionally, increased fructose levels suggested the induction of hypoxia-like conditions. These findings highlight the potential of Ch-SeNPs to disrupt cancer cell metabolism and provide insights into the mechanisms underlying their antitumor effects.

A Multi-Omics Approach to Evaluate the Toxicity Mechanisms Associated with Silver Nanoparticles Exposure.

Silver nanoparticles (AgNPs) are currently used in many different industrial, commercial and health fields, mainly due to their antibacterial properties. Due to this widespread use, humans and the environment are increasingly exposed to these types of nanoparticles, which is the reason why the evaluation of the potential toxicity associated with AgNPs is of great importance. Although some of the toxic effects induced by AgNPs have already been shown, the elucidation of more complete mechanisms is yet to be achieved. In this sense, and since the integration of metabolomics and transcriptomics approaches constitutes a very useful strategy, in the present study targeted and untargeted metabolomics and DNA microarrays assays have been combined to evaluate the molecular mechanisms involved in the toxicity induced by 10 nm AgNPs. The results have shown that AgNPs induce the synthesis of glutathione as a cellular defense mechanism to face the oxidative environment, while inducing the depletion of relevant molecules implicated in the synthesis of important antioxidants. In addition, it has been observed that AgNPs completely impair the intracellular energetic metabolism, especially affecting the production of adenosine triphosphate (ATP) and disrupting the tricarboxylic acids cycle. It has been demonstrated that AgNPs exposure also affects the glycolysis pathway. The effect on such pathway differs depending on the step of the cycle, which a significant increase in the levels of glucose as way to counterbalance the depleted levels of ATP.

Quantification of phenolic compounds in olive oil mill wastewater by artificial neural network/laccase biosensor.

In this paper is considered a new computerized approach to the determination of concentrations of phenolic compounds (caffeic acid and catechol). An integrated artificial neural network (ANN)/laccase biosensor is designed. The data collected (current signals) from amperometric detection of the laccase biosensor were transferred into an ANN trained computer for modeling and prediction of output. Such an integrated ANN/laccase biosensor system is capable of the prediction of caffeic acid and catechol concentrations of olive oil mill wastewater, based on the created models and patterns, without any previous knowledge of this phenomenon. The predicted results using the ANN were compared with the amperometric detection of phenolic compounds obtained at a laccase biosensor in olive oil wastewater of the 2004-2005 harvest season. The difference between the real and the predicted values was <0.5%. biosensor; olive oil mill wastewater; chemical analysis; phenolic compounds.