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.

Decoding the key compounds and mechanism of Shashen Maidong decoction in the treatment of lung cancer.

BACKGROUND: Lung cancer is a malignant tumour with the fastest increase in morbidity and mortality around the world. The clinical treatments available have significant side effects, thus it is desirable to identify alternative modalities to treat lung cancer. Shashen Maidong decoction (SMD) is a commonly used traditional Chinese medicine (TCM) formula for treating lung cancer in the clinic. While the key functional components (KFC) and the underlying mechanisms of SMD treating lung cancer are still unclear. METHODS: We propose a new integrated pharmacology model, which combines a novel node-importance calculation method and the contribution decision rate (CDR) model, to identify the KFC of SMD and to deduce their mechanisms in the treatment of lung cancer. RESULTS: The enriched effective Gene Ontology (GO) terms selected from our proposed node importance detection method could cover 97.66% of enriched GO terms of reference targets. After calculating CDR of active components in key functional network, the first 82 components covered 90.25% of the network information, which were defined as KFC. 82 KFC were subjected to functional analysis and experimental validation. 5-40 µM protocatechuic acid, 100-400 µM paeonol or caffeic acid exerted significant inhibitory activity on the proliferation of A549 cells. The results show that KFC play an important therapeutic role in the treatment of lung cancer by targeting Ras, AKT, IKK, Raf1, MEK, and NF-κB in the PI3K-Akt, MAPK, SCLC, and NSCLC signaling pathways active in lung cancer. CONCLUSIONS: This study provides a methodological reference for the optimization and secondary development of TCM formulas. The strategy proposed in this study can be used to identify key compounds in the complex network and provides an operable test range for subsequent experimental verification, which greatly reduces the experimental workload.

Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer.

BACKGROUND AND OBJECTIVE: Adjuvant chemotherapy is recommended as standard treatment for colorectal cancer (CRC) with stage III according to TNM stage. However, outcomes are varied even among patients receiving similar treatments. We aimed to develop a prognostic signature to stratify outcomes and benefit from different chemotherapy regimens by analyzing whole slide images (WSI) using deep learning. METHODS: We proposed an unsupervised deep learning network (variational autoencoder and generative adversarial network) in 180,819 image tiles from the training set (147 patients) to develop a WSI signature for predicting the disease-free survival (DFS) and overall survival (OS) of patients, and tested in validation set of 63 patients. An integrated nomogram was constructed to investigate the incremental value of deep learning signature (DLS) to TNM stage for individualized outcomes prediction. RESULTS: The DLS was associated with DFS and OS in both training and validation sets and proved to be an independent prognostic factor. Integrating the DLS and clinicopathologic factors showed better performance (C-index: DFS, 0.748; OS, 0.794; in the validation set) than TNM stage. In patients whose DLS and clinical risk levels were inconsistent, their risk of relapse was reclassified. In the subgroup of patients treated with 3 months, high-DLS was associated with worse DFS (hazard ratio: 3.622-7.728). CONCLUSIONS: The proposed based-WSI DLS improved risk stratification and could help identify patients with stage III CRC who may benefit from the prolonged duration of chemotherapy.

Computational Network Pharmacology-Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression.

Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.