Abstract: Background: Aloperine is an active component of Sophora alopecuroides Linn, which has been extensively applied for the treatment of cardiovascular disease (CVD). However, our current understanding of the molecular mechanisms supporting the effects of aloperine on CVD remains unclear. Methods: Systematic network pharmacology was conducted to provide testable hypotheses about pharmacological mechanisms of the protective effects of aloperine against CVD. Detailed structure was obtained from Traditional Chinese Medicines Integrated Database (TCMID). Target genes of aloperine against CVD were collected from SwissTargetPrediction, DrugBank database, and Online Mendelian Inheritance in Man (OMIM) database. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway performance, and network construction were adopted to explore common target genes. Results: Our findings showed that 25 candidate targets were the interacting genes between aloperine and CVD. GO analysis revealed biological process, cellular component, and molecular function of these target genes. More importantly, the majority of enrichment pathways was found to be highly associated with the nitrogen metabolism by KEGG analysis. Core genes particularly in nitrogen metabolism pathway including carbonic anhydrase (CA) III, CA IV, CA VA, CA VB, CA VI, CA VII, CA IX, CA XII, and CA XIV can be modulated by aloperine in the nitrogen metabolism. Conclusion: Our work revealed the pharmacological and molecular mechanisms of aloperine against CVD and provided a feasible tool to identify the pharmacological mechanisms of single active ingredient of traditional Chinese medicines.

Abstract: Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

Abstract: In developing countries, cervical cancer is still the major cause of cancer-related death among women. To better understand the correlation between tumor microenvironment (TME) and prognosis of cervical cancer, we screened 1367 differentially expressed genes (DEGs) of cervical cancer samples in The Cancer Genome Atlas (TCGA) database using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm-derived immune scores. Then, we extracted 401 tumor immune microenvironment (TIME)-related DEGs that related to patients' survival outcomes. Protein-protein interaction (PPI) network and functional enrichment analysis revealed that the prognostic genes mainly participated in myeloid leukocyte activation, adaptive immune response regulation, and receptor signaling pathways. A total of 79 key prognostic DEGs were obtained through PPI network. A TF-lncRNA-miRNA-mRNA regulatory network was constructed to explore the potential regulatory mechanism. 4 genes (CCR7, PD-1, ZAP70, and CD28) were validated in another independent cohort of cervical cancer from the Gene Expression Omnibus (GEO) database. Finally, potential drugs for key prognostics DEGs were predicted using DrugBank. In conclusion, we obtained a list of potential prognostic TIME-related genes and potential predicted drugs by integrative bioinformatics approaches. A comprehensive understanding of prognostic genes within the TIME may provide new strategies for cervical cancer treatment.

Abstract: Erzhi pill (EZP), a classical traditional Chinese medicine prescription, exerts a potent hepatoprotective effect against metabolic dysfunction-associated fatty liver disease (MAFLD), previously known as nonalcoholic fatty liver disease (NAFLD). However, the mechanism and bioactive compounds underlying the hepatoprotective effect of EZP have not been fully elucidated. In this study, a systematic analytical platform was built to explore the mechanism and bioactive compounds of EZP against MAFLD. This was carried out through target prediction, protein-protein interaction (PPI) network construction, gene ontology, KEGG pathway enrichment, and molecular docking. According to the topological parameters of the PPI network, compound-target-pathway network, 9 targets, and 11 bioactive compounds were identified as core targets and bioactive compounds for molecular docking. The results showed that EZP exerts anti-MAFLD effects through a multicomponent, multitarget, multipathway manner, and luteolin and linarin may be the bioactive compounds of EZP. This study provides further research insights and helps explore the hepatoprotective mechanism of EZP.

Abstract: Objective: To analyze the active compounds, potential targets, and diseases of JianPi Fu Recipe (JPFR) based on network pharmacology and bioinformatics and verify the potential biological function and mechanism of JPFR in vitro and in vivo. Methods: Network pharmacology databases including TCMSP, TCM-PTD, TCMID, and DrugBank were used to screen the active compounds and potential drug targets of JPFR. Cytoscape 3.7 software was applied to construct the interaction network between active compounds and potential targets. The DAVID online database analysis was performed to investigate the potential effective diseases and involved signaling pathways according to the results of the GO function and KEGG pathways enrichment analysis. To ensure standardization and maintain interbatch reliability of JPFR, High Performance Liquid Chromatography (HPLC) was used to establish a "chemical fingerprint." For biological function validation, the effect of JPFR on the proliferation and migration of CRC cells in vitro was investigated by CCK-8 and transwell and wound healing assay, and the effect of JPFR on the growth and metastasis of CRC cells in vivo was detected by building a lung metastasis model in nude mice and in vivo imaging. For the potential mechanism validation, the expressions of MALAT1, PTBP-2, and beta-catenin in CRC cells and transplanted CRC tumors were detected by real-time PCR, western blot, and immunohistochemical staining analysis. Results: According to the rules of oral bioavailability (OB) > 30% and drug-likeness (DL) > 0.18, 244 effective compounds in JPFR were screened out, as well as the corresponding 132 potential drug targets. By the analysis of DAVID database, all these key targets were associated closely with the cancer diseases such as prostate cancer, colorectal cancer, bladder cancer, small cell lung cancer, pancreatic cancer, and hepatocellular carcinoma. In addition, multiple signaling pathways were closely related to JPFR, including p53, Wnt, PI3K-Akt, IL-17, HIF-1, p38-MAPK, NF-kappaB, PD-L1 expression and PD-1 checkpoint pathway, VEGF, JAK-STAT, and Hippo. The systematical analysis showed that various active compounds of JPFR were closely connected with Wnt/beta-catenin, EGFR, HIF-1, TGFbeta/Smads, and IL6-STAT3 signaling pathway, including kaempferol, isorhamnetin, calycosin, quercetin, medicarpin, phaseol, spinasterol, hederagenin, beta-sitosterol, wighteone, luteolin, and isotrifoliol. For in vitro experiments, the migration and growth of human CRC cells were inhibited by the JPFR extract in a dose-dependent way, and the expression of MALAT1, PTBP-2, beta-catenin, MMP7, c-Myc, and Cyclin D1 in CRC cells were downregulated by the JPFR extract in a dose-dependent way. For in vivo metastasis experiments, the numbers of lung metastasis were found to be decreased by the JPFR extract in a dose-dependent manner, and the expressions of metastasis-associated genes including MALAT1, PTBP-2, beta-catenin, and MMP7 in the lung metastases were downregulated dose dependently by the JPFR extract. For the orthotopic transplanted tumor experiments, the JPFR extract could inhibit the growth of orthotopic transplanted tumors and downregulate the expression of c-Myc and Cyclin D1 in a dose-dependent manner. Moreover, the JPFR extract could prolong the survival time of tumor-bearing mice in a dose-dependent manner. Conclusions: Through effective network pharmacology analysis, we found that JPFR contains many effective compounds which may directly target cancer-associated signaling pathways. The in vitro and in vivo experiments further confirmed that JPFR could inhibit the growth and metastasis of CRC cells by regulating beta-catenin signaling-associated genes or proteins.

Abstract: Background: The emergence of COVID-19 as a pandemic has resulted in the need for urgent development of vaccines and drugs and the conduction of clinical trials to fight the outbreak. Because of the time constraints associated with the development of vaccines and effective drugs, drug repurposing and other alternative treatment methods have been used to treat patients that have been infected by the SARS-CoV-2 virus and have acquired COVID-19. Objective: The objective of this systematic scoping review is to provide an overview of the molecular mechanism of action of repurposed drugs or alternative treatment medicines used to attenuate COVID-19 disease. Method: The research articles or gray literature, including theses, government reports, and official news online, were identified from four databases and one search engine. The full content of a total of 160 articles that fulfilled our inclusion criteria was analyzed and information about six drugs (ritonavir, lopinavir, oseltamivir, remdesivir, favipiravir, and chloroquine) and four Traditional Chinese Medicines (Shuang Huang Lian Kou Fu Ye, TCM combination of Bu Huan Jin Zheng Qi San and Da Yuan Yin, Xue Bi Jing Injection, and Qing Fei Pai Du Tang) was extracted. Results: All of the repurposed drugs and complementary medicine that have been used for the treatment of COVID-19 depend on the ability of the drug to inhibit the proliferation of the SARS-CoV-2 virus by binding to enzyme active sites, viral chain termination, or triggering of the molecular pathway, whereas Traditional Chinese Medicine plays a pivotal role in triggering the inflammation pathway, such as the neuraminidase blocker, to fight the SARS-CoV-2 virus.

Abstract: Breast cancer is one of the most common cancers endangering women's health all over the world. Traditional Chinese medicine is increasingly recognized as a possible complementary and alternative therapy for breast cancer. Chaihu-Shugan-San is a traditional Chinese medicine prescription, which is extensively used in clinical practice. Its therapeutic effect on breast cancer has attracted extensive attention, but its mechanism of action is still unclear. In this study, we explored the molecular mechanism of Chaihu-Shugan-San in the treatment of breast cancer by network pharmacology. The results showed that 157 active ingredients and 8074 potential drug targets were obtained in the TCMSP database according to the screening conditions. 2384 disease targets were collected in the TTD, OMIM, DrugBank, GeneCards disease database. We applied the Bisogenet plug-in in Cytoscape 3.7.1 to obtain 451 core targets. The biological process of gene ontology (GO) involves the mRNA catabolic process, RNA catabolic process, telomere organization, nucleobase-containing compound catabolic process, heterocycle catabolic process, and so on. In cellular component, cytosolic part, focal adhesion, cell-substrate adherens junction, and cell-substrate junction are highly correlated with breast cancer. In the molecular function category, most proteins were addressed to ubiquitin-like protein ligase binding, protein domain specific binding, and Nop56p-associated pre-rRNA complex. Besides, the results of the KEGG pathway analysis showed that the pathways mainly involved in apoptosis, cell cycle, transcriptional dysregulation, endocrine resistance, and viral infection. In conclusion, the treatment of breast cancer by Chaihu-Shugan-San is the result of multicomponent, multitarget, and multipathway interaction. This study provides a certain theoretical basis for the treatment of breast cancer by Chaihu-Shugan-San and has certain reference value for the development and application of new drugs.

Abstract: Many Stramenopile species belonging to oomycetes from the genus Saprolegnia infect fish, amphibians, and crustaceans in aquaculture farms and natural ecosystems. Saprolegnia parasitica is one of the most severe fish pathogens, responsible for high losses in the aquaculture industry worldwide. Most of the molecules reported to date for the control of Saprolegnia infections either are inefficient or have negative impacts on the health of the fish hosts or the environment resulting in substantial economic losses. Until now, the whole proteome of S. parasitica has not been explored for a systematic screening of novel inhibitors against the pathogen. The present study was designed to develop a consensus computational framework for the identification of potential target proteins and their inhibitors and subsequent experimental validation of selected compounds. Comparative analysis between the proteomes of Saprolegnia, humans and fish species identified proteins that are specific and essential for the survival of the pathogen. The DrugBank database was exploited to select food and drug administration (FDA)-approved inhibitors whose high binding affinity to their respective protein targets was confirmed by computational modeling. At least six of the identified compounds significantly inhibited the growth of S. parasitica in vitro. Triclosan was found to be most effective with a minimum inhibitory concentration (MIC100) of 4 mug/ml. Optical microscopy showed that the inhibitors affect the morphology of hyphal cells, with hyper-branching being commonly observed. The inhibitory effects of the compounds identified in this study on Saprolegnia's mycelial growth indicate that they are potentially usable for disease control against this class of oomycete pathogens. Similar approaches can be easily adopted for the identification of potential inhibitors against other plant and animal pathogenic oomycete infections.

Abstract: BACKGROUND: Alzheimer's disease (AD) is considered a severe, irreversible and progressive neurodegenerative disorder. Currently, the pharmacological management of AD is based on a few clinically approved acethylcholinesterase (AChE) and N-methyl-D-aspartate (NMDA) receptor ligands, with unclear molecular mechanisms and severe side effects. METHODS: Here, we reviewed the most recent bioinformatics, cheminformatics (SAR, drug design, molecular docking, friendly databases, ADME-Tox) and experimental data on relevant structurebiological activity relationships and molecular mechanisms of some natural and synthetic compounds with possible anti-AD effects (inhibitors of AChE, NMDA receptors, beta-secretase, amyloid beta (Abeta), redox metals) or acting on multiple AD targets at once. We considered: (i) in silico supported by experimental studies regarding the pharmacological potential of natural compounds as resveratrol, natural alkaloids, flavonoids isolated from various plants and donepezil, galantamine, rivastagmine and memantine derivatives, (ii) the most important pharmacokinetic descriptors of natural compounds in comparison with donepezil, memantine and galantamine. RESULTS: In silico and experimental methods applied to synthetic compounds led to the identification of new AChE inhibitors, NMDA antagonists, multipotent hybrids targeting different AD processes and metal-organic compounds acting as Abeta inhibitors. Natural compounds appear as multipotent agents, acting on several AD pathways: cholinesterases, NMDA receptors, secretases or Abeta, but their efficiency in vivo and their correct dosage should be determined. CONCLUSION: Bioinformatics, cheminformatics and ADME-Tox methods can be very helpful in the quest for an effective anti-AD treatment, allowing the identification of novel drugs, enhancing the druggability of molecular targets and providing a deeper understanding of AD pathological mechanisms.

Abstract: Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughlyinvestigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequencedata, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models.