Analysis of the potential and mechanism of Ginkgo biloba in the treatment of Alzheimer's disease based on network pharmacology

Molecular Network Mechanism Analysis of Urine Stem Cells Against Retinal Aging

To investigate the effect and potential mechanism of human-derived urine stem cells (hUSCs) in inhibiting retinal aging by using experimental and bioinformatics. Retinal pigment epithelial cells cultured in vitro, which were randomly divided into normal group, aging group and supernatant of hUSCs group. Cell counting kit-8 detection, senescence-related β-galactosidase, and Annexin V/PI staining were performed to detect cell viability, senescence, and apoptosis. Subsequently, bioinformatics methods were used to explore the underlying mechanisms, in which, targets both hUSCs and aging retina-related targets were obtained from GeneCards. Then, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and protein-protein interaction network were analysis, and the expressional level of hub gene was validated by q-PCR. Supernatant addition of hUSCs promoted markedly cellular proliferation, improved viability and inhibited senescence and apoptosis in vitro. A total of 1476 hUSCs-related targets (Relevance score > 20), 692 retinal disease-related targets, and 732 targets related to disease of aging were selected from GeneCards database, and 289 common targets of hUSCs against aging retina were confirmed through Venn analysis. Enrichment analysis demonstrated that hUSCs might exert its anti-apoptosis efficacy in multiple biological processes, including oxidative stress, inflammation and apoptosis, and core targets were associated with HIF-1, MAPK and PI3K-Akt signal. hUSCs inhibited retinal senescence by regulating multiply targets and signaling pathways, of these, HIF-1, MAPK, and PI3K may be important candidates.

The neuroprotective effects of Lutongkeli in traumatic brain injury rats by anti-apoptosis mechanism

Purpose:

To explore the neuroprotective effects of Lutongkeli (LTKL) in traumatic brain injury (TBI) and detect the related mechanism.

Methods:

TBI model was established with LTKL administration (2 and 4 g/kg/d, p.o.). Motor function of rats was examined by Rotarod test. Nissl staining was used to show neuron morphology. Furthermore, the disease-medicine common targets were obtained with the network pharmacology and analyzed with Kyoto Encyclopedia of Genes and Genomes. Lastly, the predicted targets were validated by real-time polymerase chain reaction.

Results:

After LTKL administration, neural behavior was significantly improved, and the number of spared neurons in brain was largely increased. Moreover, 68 bioactive compounds were identified, corresponding to 148 LTKL targets; 2,855 genes were closely associated with TBI, of which 87 overlapped with the LTKL targets and were considered to be therapeutically relevant. Functional enrichment analysis suggested LTKL exerted its pharmacological effects in TBI by modulating multiple pathways including apoptosis, inflammation, etc. Lastly, we found LTKL administration could increase the mRNA level of Bcl-2 and decrease the expression of Bax and caspase-3.

Conclusions:

This study reported the neuroprotective effect of LTKL against TBI is accompanied with anti-apoptosis mechanism, which provides a scientific explanation for the clinical application of LTKL in the treatment of TBI.

Alzheimer-Compound Identification Based on Data Fusion and forgeNet_SVM

Rapid screening and identification of potential candidate compounds are very important to understand the mechanism of drugs for the treatment of Alzheimer's disease (AD) and greatly promote the development of new drugs. In order to greatly improve the success rate of screening and reduce the cost and workload of research and development, this study proposes a novel Alzheimer-related compound identification algorithm namely forgeNet_SVM. First, Alzheimer related and unrelated compounds are collected using the data mining method from the literature databases. Three molecular descriptors (ECFP6, MACCS, and RDKit) are utilized to obtain the feature sets of compounds, which are fused into the all_feature set. The all_feature set is input to forgeNet_SVM, in which forgeNet is utilized to provide the importance of each feature and select the important features for feature extraction. The selected features are input to support vector machines (SVM) algorithm to identify the new compounds in Traditional Chinese Medicine (TCM) prescription. The experiment results show that the selected feature set performs better than the all_feature set and three single feature sets (ECFP6, MACCS, and RDKit). The performances of TPR, FPR, Precision, Specificity, F1, and AUC reveal that forgeNet_SVM could identify more accurately Alzheimer-related compounds than other classical classifiers.

Application value and challenge of traditional Chinese medicine carried by ZIF-8 in the therapy of ischemic stroke

Stroke is a group of major diseases that cause death or disability in adults, with high incidence and lack of available therapeutic strategies. Although traditional Chinese medicine (TCM) has continuously achieved good effects in the therapy of stroke while there is still not convincing due to the limitation of blood-brain permeability, as well as the individual differences in usage and dosage. With the improvement of nanotechnology, TCM nanopreparation has gradually become a research hotspot in various fields due to its advantages in permeating the blood-brain barrier, targeting delivery, enhancing sustained-release drug delivery, changing the distribution in the body, and improving bioavailability. Zeolitic imidazolate framework-8 (ZIF-8) is an ideal nano-drug delivery system for adsorption, catalysis, and drug loading, which is a biocompatible metal-organic framework framed by 2-methylimidazole and zinc ions. At present, ZIF-8 was wildly used in the treatment of ischemic stroke. However, challenges remain persists for its clinical application, such as preparation technology, detection technology in vivo, targeting specificity, safety and stability, and so forth. Therefore, more efforts need to overcome the above problems to develop the application of TCM nanopreparations in the therapy of ischemia/reperfusion in the future.

Single-cell RNA sequencing reveals B cell-related molecular biomarkers for Alzheimer's disease

In recent years, biomarkers have been integrated into the diagnostic process and have become increasingly indispensable for obtaining knowledge of the neurodegenerative processes in Alzheimer's disease (AD). Peripheral blood mononuclear cells (PBMCs) in human blood have been reported to participate in a variety of neurodegenerative activities. Here, a single-cell RNA sequencing analysis of PBMCs from 4 AD patients (2 in the early stage, 2 in the late stage) and 2 normal controls was performed to explore the differential cell subpopulations in PBMCs of AD patients. A significant decrease in B cells was detected in the blood of AD patients. Furthermore, we further examined PBMCs from 43 AD patients and 41 normal subjects by fluorescence activated cell sorting (FACS), and combined with correlation analysis, we found that the reduction in B cells was closely correlated with the patients' Clinical Dementia Rating (CDR) scores. To confirm the role of B cells in AD progression, functional experiments were performed in early-stage AD mice in which fibrous plaques were beginning to appear; the results demonstrated that B cell depletion in the early stage of AD markedly accelerated and aggravated cognitive dysfunction and augmented the Aβ burden in AD mice. Importantly, the experiments revealed 18 genes that were specifically upregulated and 7 genes that were specifically downregulated in B cells as the disease progressed, and several of these genes exhibited close correlation with AD. These findings identified possible B cell-based AD severity, which are anticipated to be conducive to the clinical identification of AD progression.