Discussion on the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation technology.

To explore the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation. The active ingredients and potential targets were screened by the Systematic Pharmacological Analysis Platform of Traditional Chinese Medicine (TCMSP). Hypertension-related targets were obtained from OMIM and GeneCards databases. Common targets between drug and hypertension were screened in the Venny platform. A protein-protein interaction (PPI) network was constructed in the STRING database using intersection targets. Key targets in PPI network were analyzed by Cytoscape. R language program was used for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, the binding abilities of the main active ingredients to critical targets were verified by molecular simulation. Naringenin, quercetin, kaempferol, and ß-sitosterol in Lingguizhugan Decoction, and potential targets such as STAT3, AKT1, TNF, IL6, JUN, PTGS2, MMP9, CASP3, TP53, and MAPK3, were screened out. KEGG Enrichment analysis revealed that the common targets of Lingguizhugan Decoction and hypertension are mainly involved in the lipid and atherosclerosis signaling pathway, AGE-RAGE signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, and IL17 signaling pathway. The molecular simulation results showed that naringenin-MAPK3, quercetin-MMP9, quercetin-PTGS2, and quercetin-TP53 were the top four in the docking scores. Naringenin-MAPK3 and quercetin-MMP9 were stable, with binding free energies of -27.97 ± 1.41 kcal/mol and -21.15 ± 3.17 kcal/mol, respectively. The possible mechanism of Lingguizhugan Decoction in treating hypertension is characterized of multi-component, multi-target, and multi-pathway.Communicated by Ramaswamy H. Sarma.

Single cell landscape of parietal epithelial cells in healthy and diseased states.

The biology and diversity of glomerular parietal epithelial cells (PECs) are important for understanding podocyte regeneration and crescent formation. Although protein markers have revealed the morphological heterogeneity of PECs, the molecular characteristics of PEC subpopulations remain largely unknown. Here, we performed a comprehensive analysis of PECs using single-cell RNA sequencing (scRNA-seq) data. Our analysis identified five distinct PEC subpopulations: PEC-A1, PEC-A2, PEC-A3, PEC-A4 and PEC-B. Among these subpopulations, PEC- A1 and PEC-A2 were characterized as podocyte progenitors while PEC-A4 represented tubular progenitors. Further dynamic signaling network analysis indicated that activation of PEC-A4 and the proliferation of PEC-A3 played pivotal roles in crescent formation. Analyses suggested that upstream signals released by podocytes, immune cells, endothelial cells and mesangial cells serve as pathogenic signals and may be promising intervention targets in crescentic glomerulonephritis. Pharmacological blockade of two such pathogenic signaling targets, proteins Mif and Csf1r, reduced hyperplasia of the PECs and crescent formation in anti-glomerular basement membrane glomerulonephritis murine models. Thus, our study demonstrates that scRNA-seq-based analysis provided valuable insights into the pathology and therapeutic strategies for crescentic glomerulonephritis.

Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning.

The novel coronavirus pneumonia (COVID-19) has created great demands for medical resources. Determining these demands timely and accurately is critically important for the prevention and control of the pandemic. However, even if the infection rate has been estimated, the demands of many medical materials are still difficult to estimate due to their complex relationships with the infection rate and insufficient historical data. To alleviate the difficulties, we propose a co-evolutionary transfer learning (CETL) method for predicting the demands of a set of medical materials, which is important in COVID-19 prevention and control. CETL reuses material demand knowledge not only from other epidemics, such as severe acute respiratory syndrome (SARS) and bird flu but also from natural and manmade disasters. The knowledge or data of these related tasks can also be relatively few and imbalanced. In CETL, each prediction task is implemented by a fuzzy deep contractive autoencoder (CAE), and all prediction networks are cooperatively evolved, simultaneously using intrapopulation evolution to learn task-specific knowledge in each domain and using interpopulation evolution to learn common knowledge shared across the domains. Experimental results show that CETL achieves high prediction accuracies compared to selected state-of-the-art transfer learning and multitask learning models on datasets during two stages of COVID-19 spreading in China.