Quercetin conjugated with superparamagnetic iron oxide nanoparticles improves learning and memory better than free quercetin via interacting with proteins involved in LTP.

Biomedical application of quercetin (QT) as an effective flavonoid has limitations due to its low bioavailability. Superparamagnetic iron oxide nanoparticle (SPION) is a novel drug delivery system that enhances the bioavailability of quercetin. The effect of short time usage of quercetin on learning and memory function and its signaling pathways in the healthy rat is not well understood. The aim of this study was to investigate the effect of free quercetin and in conjugation with SPION on learning and memory in healthy rats and to find quercetin target proteins involved in learning and memory using Morris water maze (MWM) and computational methods respectively. Results of MWM show an improvement in learning and memory of rats treated with either quercetin or QT-SPION. Better learning and memory functions using QT-SPION reveal increased bioavailability of quercetin. Comparative molecular docking studies show the better binding affinity of quercetin to RSK2, MSK1, CytC, Cdc42, Apaf1, FADD, CRK proteins. Quercetin in comparison to specific inhibitors of each protein also demonstrates a better QT binding affinity. This suggests that quercetin binds to proteins leading to prevent neural cell apoptosis and improves learning and memory. Therefore, SPIONs could increase the bioavailability of quercetin and by this way improve learning and memory.

Application of quercetin in neurological disorders: from nutrition to nanomedicine.

Quercetin is a polyphenolic flavonoid, which is frequently found in fruits and vegetables. The antioxidant potential of quercetin has been studied from subcellular compartments, that is, mitochondria to tissue levels in the brain. The neurodegeneration process initiates alongside aging of the neurons. It appears in different parts of the brain as Aß plaques, neurofibrillary tangles, Lewy bodies, Pick bodies, and others, which leads to Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, and other diseases. So far, no specific treatment has been identified for these diseases. Despite common treatments that help to prevent the development of disease, the condition of patients with progressive neurodegenerative diseases usually do not completely improve. Currently, the use of flavonoids, especially quercetin for the treatment of neurodegenerative diseases, has been expanded in animal models. It has also been used to treat animal models of neurodegenerative diseases. In addition, improvements in behavioral levels, as well as in cellular and molecular levels, decreased activity of antioxidant and apoptotic proteins, and increased levels of antiapoptotic proteins have been observed. Low bioavailability of quercetin has also led researchers to construct various quercetin-involved nanoparticles. The treatment of animal models of neurodegeneration using quercetin-involved nanoparticles has shown that improvements are observed in shorter periods and with use of lower concentrations. Indeed, intranasal administration of quercetin-involved nanoparticles, constructing superparamagnetic nanoparticles, and combinational treatment using nanoparticles such as quercetin and other drugs are suggested for future studies.

Classification of DNA minor and major grooves binding proteins according to the NLSs by data analysis methods.

High-mobility group proteins are a superfamily of DNA-binding proteins that bind to the DNA minor groove and bend it, whereas most of the transcription factors such as centromere protein B (CENP-B), octamer (Oct)-1, growth factor independence 1 (Gfi-1), and WRKY bind to the major groove of DNA. Classification of proteins using their DNA-binding features is the aim of this study. Nuclear localization signals play more important roles in entering DNA-binding proteins to nucleus and doing their functions; therefore, they have been considered as a feature which is important for DNA-binding manner in proteins. Nuclear localization signals (NLSs) were predicted by two prediction web servers, and then, their sequence ordered features were extracted by Chou's pseudo amino acid composition (PseAAC) and ProtParam. Multilayer perceptron was used as an artificial neural network for analyzing the features by calculating the correlation coefficient and 30-fold cross-validation. Another used data-analyzing program was principal component analysis of the Minitab software. By calculating the eigenvalues and considering five principal components, the sequence length of NLSs was known as the best feature for classifying DNA-binding proteins. Minimum mean squared error (MSE) (0.1098) and the highest R (2) (0.963) mean that there is a significant difference between the NLS length of the DNA major groove and minor groove binder proteins. Results showed that it is possible to classify DNA major groove and minor groove binder proteins by their NLS sequences as a feature.