Cholesterol modulates the physiological response to nanoparticles by changing the composition of protein corona.

Nanoparticles (NPs) in biological fluids form a layer of biomolecules known as the protein corona. The protein corona has been shown to determine the biological identity and in vivo fate of NPs, but whether and how metabolites, especially disease-related small molecules, regulate the protein corona and subsequently impact NP fate in vivo is relatively poorly understood. Here we report on the effects of cholesterol on the generation of protein corona and subsequent effects. We find that high levels of cholesterol, as in hypercholesterolemia, result in a protein corona with enriched apolipoproteins and reduced complement proteins by altering the binding affinity of the proteins to the NPs. The cholesterol-mediated protein corona can induce stronger inflammatory responses to NPs in macrophages and promote the cellular uptake of NPs in hepatocytes by enhancing the recognition of lipoprotein receptors when compared with normal protein corona. The result of in vivo biodistribution assays shows that, compared with healthy mice, NPs in hypercholesterolemic mice were more likely to be delivered to the liver, spleen and brain, and less likely to be delivered to the lungs. Our findings reveal that the metabolome profile is an unexploited factor impacting the target efficacy and safety of nanomedicines, providing a way to develop personalized nanomedicines by harnessing disease-related metabolites.

How eluents define proteomic fingerprinting of protein corona on nanoparticles.

Nanoparticles (NPs) have broad application prospects in the field of biomedicine due to their excellent physicochemical properties. When entering biological fluids, NPs inevitably encountered proteins and were subsequently surrounded by them, forming the termed protein corona (PC). As PC has been evidenced to have critical roles in deciding the biological fates of NPs, how to precisely characterize PC is vital to promote the clinical translation of nanomedicine by understanding and harnessing NPs' behaviors. During the centrifugation-based separation techniques for the PC preparation, direct elution has been most widely used to strip proteins from NPs due to its simpleness and robustness, but the roles of multifarious eluents have never been systematically declared. Herein, seven eluents composed of three denaturants, sodium dodecyl sulfate (SDS), dithiothreitol (DTT), and urea (Urea), were applied to detach PC from gold nanoparticles (AuNPs) and silica nanoparticles (SiNPs), and eluted proteins in PC have been carefully characterized by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and chromatography coupled tandem mass spectrometry (LC-MS/MS). Our results showed that SDS and DTT were the main contributors to the efficient desorption of PC on SiNPs and AuNPs, respectively. The molecular reactions between NPs and proteins were explored and verified by SDS-PAGE analysis of PC formed in the serums pretreated with protein denaturing or alkylating agents. The proteomic fingerprinting analysis indicated the difference of the eluted proteins brought by the seven eluents was the abundance rather than the species. The enrichment of some opsonins and dysopsonins in a special elution reminds us that the possibility of biased judgments on predicting NPs' biological behaviors under different elution conditions. The synergistic effects or antagonistic effects among denaturants for eluting PC were manifested in a nanoparticle-type dependent way by integrating the properties of the eluted proteins. Collectively, this study not only underlines the urgent need of choosing the appropriate eluents for identifying PC robustly and unbiasedly, but also provides an insight into the understanding of molecular interactions during PC formation.

Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis.

Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (R ia) for training samples is 99.8% and R ia by leave one out cross validation is 99.5%. Both R ia of 99.8% for training samples and R ia of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external R ia for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.