Abstract: Post-menopausal osteoporosis (PMOP) is associated with estrogen deficiency and worldwide, is becoming increasingly more prevalent in aging women. Various anti-PMOP drugs have been developed to reduce the burden of PMOP; generally, these drugs are efficacious, but with some adverse side effects. Tubson-2 decoction (TBD), a popular traditional Mongolian medicine, has been used to treat PMOP for centuries. However, the precise mechanisms underlying the action of TBD on PMOP have yet to be fully elucidated. Herein, we combined network pharmacology with untargeted metabolomics to identify the key targets and metabolic pathways associated with the interventional effects of TBD on ovariectomized (OVX) rats. Furthermore, we investigated the bone histomorphometry of eight different groups of rats to evaluate the therapeutic effect of TBD. First, we established a TBD-target/PMOP network via network pharmacology; this network identified three key protein targets-vitamin D receptor (VDR), cytochrome P450 19A1 (CYP19A1), and 11beta-hydroxysteroid dehydrogenase type 1 (HSD11B1). Morphological analysis showed that severe impairment of the bone micro-architecture in OVX rats could be improved by TBD administration. The TBD-treated rats had a significantly lower bone surface-to-tissue volume (BS/TV) and a significantly smaller trabecular separation (Tb.Sp.) (P<0.05) than the OVX rats; in contrast, bone volume fraction (BVF), trabecular thickness (Tb.Th.), trabecular number (Tb.N.), and bone mineral density (BMD) were significantly higher in the TBD-treated rats (P<0.05). Multivariate and univariate analysis showed that OVX resulted in significant alterations in the concentrations of 105 metabolites and 11 metabolic pathways (P<0.05); in addition, 26 potential biomarkers were identified to investigate the progression of PMOP. Network pharmacology showed that major alterations in vitamin B6 metabolism were associated with the VDR target. Next, we validated the three crucial targets (VDR [P<0.01], HSD11B1 [P<0.01], and CYP19A1 [P<0.05]) by enzyme-linked immunosorbent assays (ELISAs) and demonstrated that the levels of these targets were elevated in the OVX group but reduced in the TBD-treatment group. Collectively, our results suggest that the interventional effects of TBD on OVX rats are likely to be associated with the down regulation of VDR. Our findings enhance our molecular understanding of the interventional effects of TBD on PMOP and will allow us to develop further TBD studies.

Abstract: Anastrozole is a selective non-steroidal aromatase inhibitor that blocks the conversion of androgens to estrogens in peripheral tissues. It is used as adjuvant therapy for early-stage hormone-sensitive breast cancer in postmenopausal women. Significant side effects of anastrozole include osteoporosis and increased levels of cholesterol. To date, seven case reports on anastrozole hepatotoxicity have been published. We report the case of an 81-year-old woman with a history of breast cancer, arterial hypertension, type 2 diabetes mellitus, hyperlipidemia, and chronic renal insufficiency. Four days after switching hormone therapy from tamoxifen to anastrozole, icterus developed along with a significant increase in liver enzymes (measured in the blood). The patient was admitted to hospital, where a differential diagnosis of jaundice was made and anastrozole was withdrawn. Subsequently, hepatic functions quickly normalized. The observed liver injury was attributed to anastrozole since other possible causes of jaundice were excluded. However, concomitant pharmacotherapy could have contributed to the development of jaundice and hepatotoxicity, after switching from tamoxifen to anastrozole since several the patient's medications were capable of inhibiting hepatobiliary transport of bilirubin, bile acids, and metabolized drugs through inhibition of ATP-binding cassette proteins. Telmisartan, tamoxifen, and metformin all block bile salt efflux pumps. The efflux function of multidrug resistance protein 2 is known to be reduced by telmisartan and tamoxifen and breast cancer resistance protein is known to be inhibited by telmisartan and amlodipine. Moreover, the activity of P-glycoprotein transporters are known to be decreased by telmisartan, amlodipine, gliquidone, as well as the previously administered tamoxifen. Finally, the role of genetic polymorphisms of cytochrome P450 enzymes and/or drug transporters cannot be ruled out since the patient was not tested for polymorphisms.

Abstract: Background: Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with high rates of mortality and morbidity. The traditional Chinese medicine Wenxin Keli (WXKL) can effectively improve clinical symptoms and is safe for the treatment of AF. However, the active substances in WXKL and the molecular mechanisms underlying its effects on AF remain unclear. In this study, the bioactive compounds in WXKL, as well as their molecular targets and associated pathways, were evaluated by systems pharmacology. Materials and Methods: Chemical constituents and potential targets of WXKL were obtained via the Traditional Chinese Medicine Systems Pharmacology (TCMSP). The TTD, DrugBank, DisGeNET, and GeneCards databases were used to collect AF-related target genes. Based on common targets related to both AF and WXKL, a protein interaction network was generated using the STRING database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) pathway enrichment analyses were performed. Network diagrams of the active component-target and protein-protein interactions (PPIs) were constructed using Cytoscape. Results: A total of 30 active ingredients in WXKL and 219 putative target genes were screened, including 83 genes identified as therapeutic targets in AF; these overlapping genes were considered candidate targets for subsequent analyses. The effect of treating AF was mainly correlated with the regulation of target proteins, such as IL-6, TNF, AKT1, VEGFA, CXCL8, TP53, CCL2, MMP9, CASP3, and NOS3. GO and KEGG analyses revealed that these targets are associated with the inflammatory response, oxidative stress reaction, immune regulation, cardiac energy metabolism, serotonergic synapse, and other pathways. Conclusions: This study demonstrated the multicomponent, multitarget, and multichannel characteristics of WXKL, providing a basis for further studies of the mechanism underlying the beneficial effects of WXKL in AF.

Abstract: Methods: The targets of antineoplastic drugs with cardiotoxicity were obtained from the National Center for Biotechnology Information (NCBI) database, China national knowledge infrastructure (CNKI) database, and Swiss Target Prediction platform. Then, the cardiotoxicity-related targets were derived from the Gene Cards, Disgenet, OMIM, and DrugBank databases, as well as the drug of current clinical guidelines. The targets both in these two sets were regarded as potential targets to alleviate ADIC. Then, candidate compounds and herbs were matched via Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform. Cytoscape3.7.1 was used to set up the target-compound-herb network. Molecular docking between core targets and compounds was performed with AutodockVina1.1.2. The rules of herbs were summarized by analyzing their property, flavor, and channel tropism. Results: Twenty-one potential targets, 332 candidate compounds, and 400 kinds of herbs were obtained. Five core targets including potassium voltage-gated channel subfamily H member 2 (KCNH2), cyclin-dependent kinase 1 (CDK1), matrix metalloproteinase 2 (MMP2), mitogen-activated protein kinase1 (MAPK1), and tumor protein p53 (TP53) and 29 core compounds (beta-sitosterol, quercetin, kaempferol, etc.) were collected. Five core herbs (Yanhusuo, Gouteng, Huangbai, Lianqiao, and Gancao) were identified. Also, the TCM against ADIC were mainly bitter and acrid in taste, warm in property, and distributed to the liver and lung meridians. Conclusion: TCM against ADIC has great potential. Our study provides a new method and ideas for clinical applications of integrated Chinese and western medicine in treating ADIC.

Abstract: Multidrug-resistant Vibrio parahaemolyticus has become a significant public health concern. The development of effective drugs and vaccines against Vibrio parahaemolyticus is the current research priority. Thus, we aimed to find out effective drug and vaccine targets using a comprehensive genome-based analysis. A total of 4822 proteins were screened from V. parahaemolyticus proteome. Among 16 novel cytoplasmic proteins, 'VIBPA Type II secretion system protein L' and 'VIBPA Putative fimbrial protein Z' were subjected to molecular docking with 350 human metabolites, which revealed that Eliglustat, Simvastatin and Hydroxocobalamin were the top drug molecules considering free binding energy. On the contrary, 'Sensor histidine protein kinase UhpB' and 'Flagellar hook-associated protein of 25 novel membrane proteins were subjected to T-cell and B-cell epitope prediction, antigenicity testing, transmembrane topology screening, allergenicity and toxicity assessment, population coverage analysis and molecular docking analysis to generate the most immunogenic epitopes. Three subunit vaccines were constructed by the combination of highly antigenic epitopes along with suitable adjuvant, PADRE sequence and linkers. The designed vaccine constructs (V1, V2, V3) were analyzed by their physiochemical properties and molecular docking with MHC molecules- results suggested that the V1 is superior. Besides, the binding affinity of human TLR-1/2 heterodimer and construct V1 could be biologically significant in the development of the vaccine repertoire. The vaccine-receptor complex exhibited deformability at a minimum level that also strengthened our prediction. The optimized codons of the designed construct was cloned into pET28a(+) vector of E. coli strain K12. However, the predicted drug molecules and vaccine constructs could be further studied using model animals to combat V. parahaemolyticus associated infections.

Abstract: Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.

Abstract: Identifying the drug-target interactions (DTIs) plays an essential role in new drug development. However, there still has the limited knowledge of DTIs and a significant number of unknown DTI pairs. Moreover, the traditional experimental methods have inevitable disadvantages such as high cost and time-consuming. Therefore, developing computational methods for predicting DTIs is attracting more and more attention. In this study, we report a novel computational approach for predicting DTI using GIST feature, position-specific scoring matrix (PSSM), and rotation forest (RF). Specifically, each target protein is first converted into a PSSM for retaining evolutionary information. Then, the GIST feature is extracted from PSSM and substructure fingerprint information is adopted to extract the feature of the drug. Finally, combining each protein and drug features to form a new drug-target pair, which is employed as input feature for RF classifier. In the experiment, the proposed method achieves high average accuracies of 89.25%, 85.93%, 82.36%, and 73.89% on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively. For further evaluating the prediction performance of the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the same golden standard dataset. These promising results illustrate that the proposed method is more effective and stable than other methods. We expect the proposed method to be a useful tool for predicting large-scale DTIs.

Abstract: SARS-CoV-2 rapidly spread from China until it was defined a pandemic by WHO in March 2020. Related scientific papers have rapidly extended information regarding the diagnosis, treatment and epidemiology of COVID-19 infection. To date, no vaccine or definitive treatment is available to defeat the virus and therapies are mainly based on existing drugs used to treat other conditions. Existing therapies used in several clinical trials work by affecting the biology of COVID-19 and/or counteracting the harmful host excessive immune response. Here, we have reviewed 526 ongoing clinical trials for COVID-19 to provide a perspective on the first 6 months of global efforts to identify an effective therapy. The drugs most actively tested in various centers include hydroxychloroquine, ritonavir, azithromycin, tocilizumab, lopinavir chloroquine and ivermectin. Our analysis shows that most clinical trials focus on a small number of candidate drugs (namely hydroxychloroquine and chloroquine representing 25% of total clinical trials) while underestimating the potential of other promising drugs. A global coordination in clinical trial management could avoid duplications and increase the effectiveness of the response to the global challenge.

Abstract: From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

Abstract: Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.