Using Microfluidic Hepatic Spheroid Cultures to Assess Liver Toxicity of T-2 Mycotoxin.

The Fusarium fungi is found in cereals and feedstuffs and may produce mycotoxins, which are secondary metabolites, such as the T-2 toxin (T-2). In this work, we explored the hepatotoxicity of T-2 using microfluidic 3D hepatic cultures. The objectives were: (i) exploring the benefits of microfluidic 3D cultures compared to conventional 3D cultures available commercially (Aggrewell plates), (ii) establishing 3D co-cultures of hepatic cells (HepG2) and stellate cells (LX2) and assessing T-2 exposure in this model, (iii) characterizing the induction of metabolizing enzymes, and (iv) evaluating inflammatory markers upon T-2 exposure in microfluidic hepatic cultures. Our results demonstrated that, in comparison to commercial (large-volume) 3D cultures, spheroids formed faster and were more functional in microfluidic devices. The viability and hepatic function decreased with increasing T-2 concentrations in both monoculture and co-cultures. The RT-PCR analysis revealed that exposure to T-2 upregulates the expression of multiple Phase I and Phase II hepatic enzymes. In addition, several pro- and anti-inflammatory proteins were increased in co-cultures after exposure to T-2.

On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks.

One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high-quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine-containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides.

Theoretical and experimental study of polycyclic aromatic compounds as ß-tubulin inhibitors.

In this work, through a docking analysis of compounds from the ZINC chemical library on human ß-tubulin using high performance computer cluster, we report new polycyclic aromatic compounds that bind with high energy on the colchicine binding site of ß-tubulin, suggesting three new key amino acids. However, molecular dynamic analysis showed low stability in the interaction between ligand and receptor. Results were confirmed experimentally in in vitro and in vivo models that suggest that molecular dynamics simulation is the best option to find new potential ß-tubulin inhibitors. Graphical abstract Bennett's acceptance ratio (BAR) method.