Abstract: Objective: Ameloblastoma is a benign odontogenic tumor that may lead to ameloblastic carcinoma. This study aimed to determine potential signaling pathways and biological processes, critical genes and their regulating transcription factors (TFs), and miRNAs, as well as protein kinases involved in the etiology of primary ameloblastoma. Methods: The dataset GSE132472 was obtained from the GEO database, and multivariate statistical analyses were applied to identify differentially expressed genes (DEGs) in primary ameloblastoma tissues compared to the corresponding normal gingiva samples. A protein-protein interaction (PPI) map was built using the STRING database. The Cytoscape software identified significant modules and the hub genes within the PPI network. Gene Ontology annotation and signaling pathway analyses were executed by employing the DAVID and Reactome databases, respectively. Significant TFs and miRNAs acting on the hub genes were identified using the iRegulon plugin and MiRWalk 2.0 database, respectively. A protein kinase enrichment analysis was conducted using the online Kinase Enrichment Analysis 2 (KEA2) web server. The approved drugs acting on the hub genes were also found. Results: A total of 1,629 genes were differentially expressed in primary ameloblastoma (P value <0.01 and |Log2FC| > 1). HRAS, CDK1, MAPK3, ERBB2, COL1A1, CYCS, and BRCA1 demonstrated high degree and betweenness centralities in the PPI network. E2F4 was the most significant TF acting on the hub genes. BTK was the protein kinase significantly enriched by the TFs. Cholesterol biosynthesis was considerably involved in primary ameloblastoma. Conclusions: This study provides an intuition into the potential mechanisms involved in the etiology of ameloblastoma.

Abstract: Background: Gout is a common crystal-related arthritis caused by the deposition of monosodium urates (MSU). Tibetan medicine Wuwei Shexiang Pills (WSP) has been demonstrated to exhibit anti-inflammatory, antihyperuricemia, and antigout activities. However, the underlying mechanism is unknown. Objectives: To explore the mechanisms of Wuwei Shexiang Pills on gouty arthritis via network pharmacology, molecule docking, and pharmacological verification. Methods: The ingredients and targets of WSP were obtained by searching and screening in BATMAN-TCM and SwissADME. The targets involving the gout were acquired from public databases. The shared targets were put onto STRING to construct a PPI network. Furthermore, Metascape was applied for the GO and KEGG enrichment analysis to predict the biological processes and signaling pathways. And molecular docking was performed to validate the binding association between the key ingredients and the relative proteins of TNF signaling. Based on the serum pharmacology, the predicted antigout mechanism of WSP was validated in MSU-induced THP-1 macrophages. The levels of inflammatory cytokines and mRNA were measured by ELISA and qRT-PCR, respectively, and MAPK, NF-kappaB, and NLRP3 signaling-associated proteins were determined by western blot and immunofluorescence staining. Results: 48 bioactive ingredients and 165 common targets were found in WSP. The data showed that 5-Cis-Cyclopentadecen-1-One, 5-Cis-Cyclotetradecen-1-One, (-)-isoshyobunone, etc. were potential active ingredients. TNF signaling, HIF-1 signaling, and Jak-STAT signaling were predicted to be the potential pathways against gout. The molecule docking analysis found that most ingredients had a high affinity for p65, NLRP3, IL-1beta, TNF-alpha, and p38. The data from in vitro experiment showed that WSP suppressed the production and gene expression of inflammatory cytokines. Furthermore, WSP could inhibit the activation of MAPK, NF-kappaB, and NLRP3 signaling pathways. Conclusion: Our finding suggested that the antigout effect of WSP could be achieved by inhibiting MAPK, NF-kappaB, and NLRP3 signaling pathways. WSP might be a candidate drug for gouty treatment.

Abstract: The main pathophysiological abnormalities in type 2 diabetes (T2D) include pancreatic beta-cell dysfunction and insulin resistance. Due to hyperglycemia, patients receive long-term treatment. However, side effects and drug tolerance usually lead to treatment failure. GuaLouQuMaiWan (GLQMW), a common traditional Chinese medicine (TCM) prescription, has positive effects on controlling blood sugar and improving quality of life, but the mechanism is still unclear. To decipher their molecular mechanisms, we used a novel computational systems pharmacology-based approach consisting of bioinformatics analysis, network pharmacology, and drug similarity comparison. We divided the participants into nondisease (ND), impaired glucose tolerance (IGT), and type 2 diabetes groups according to the WHO's recommendations for diabetes. By analyzing the gene expression profile of the ND-IGT-T2D (ND to IGT to T2D) process, we found that the function of downregulated genes in the whole process was mainly related to insulin secretion, while the upregulated genes were related to inflammation. Furthermore, other genes in the ND-IGT (ND to IGT) process are mainly related to inflammation and lipid metabolic disorders. We speculate that 17 genes with a consistent trend may play a key role in the process of ND-IGT-T2D. We further performed target prediction for 50 compounds in GLQMW that met the screening criteria and intersected the differentially expressed genes of the T2D process with the compounds of GLQMW; a total of 18 proteins proved potential targets for GLQMW. Among these, RBP4 is considerably related to insulin resistance. GO/KEGG enrichment analyses of the target genes of GLQMW showed enrichment in inflammation- and T2D therapy-related pathways. Based on the RDKit tool and the DrugBank database, we speculate that (-)-taxifolin, dialoside A_qt, spinasterol, isofucosterol, and 11,14-eicosadienoic acid can be used as potential drugs for T2D via molecular docking and drug similarity comparison.

Abstract: Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.

Abstract: The treatment of COVID-19 disease has been one of the most critical essential concerns of researchers in recent years. One of the most exciting and potential therapeutic targets for SARS-CoV-2 therapy progression is RNA-dependent RNA polymerase (RdRP), a viral enzyme for viral RNA replication throughout host cells. According to some research, Remdesivir suppresses RdRp. The nucleoside medication remdesivir has been authorized under an Emergency Use Authorization to treat COVID-19. Given the role of this enzyme in virus replication, our scientific question is whether Remdesivir is the most appropriate antiviral drug to inhibit this enzyme or not. Accordingly, this study aimed to repurpose antiviral drugs to inhibition of RdRp using virtual screening and Molecular Dynamics simulation methods. Five FDA-approved antiviral medications, including Elbasvir, Glecaprevir, Ledipasvir, Paritaprevir, and Simeprevir, had good interaction potential with RdRp. Also, the results show that the number of H-bonds and contacts and G interactions between the protein and ligand in the Remdesivir complex is less than those of other complexes. According to the given data which shows the tendency of binding with RdRp for Paritaprevir, Simeprevir, Glecaprevir, and Ledipasvir and Elbasvir is more than Remdesivir and due to the fact that these five drugs have a high tendency to bind to other targets in the SARS-CoV-2, the use of Remdesivir as an antiviral drug in the treatment of COVID-19 should be considered more sensitively.

Abstract: Purpose: Traditional Chinese medicine (TCM) sometimes plays a crucial role in advanced cancer treatment. Despite the significant therapeutic efficacy in hepatocellular carcinoma (HCC) that Actinidia chinensis Planch root extract (acRoots) has proven, its complex composition and underlying mechanism have not been fully elucidated. Therefore, this study analyzed the multiple chemical compounds in acRoots and their targets via network pharmacology and bioinformatics analysis, with the overarching goal of revealing the potential mechanisms of the anti-HCC effect. Methods: The main ingredients contained in acRoots were initially screened from the traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the candidate bioactive ingredient targets were identified using DrugBank and the UniProt public databases. Second, the biological processes of the targets of active molecules filtered from the ingredients of acRoots were evaluated using gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Third, weighted gene coexpression network analysis (WGCNA) was performed to identify gene coexpression modules associated with HCC. The hub genes of acRoots in HCC were defined via contrasting the above module eigengenes with candidate target genes of acRoots. Furthermore, the target-pathway network was analyzed to explore the mechanism for anti-HCC effect of hub genes. Kaplan-Meier plotter database analysis was performed to validate the hub genes of acRoots correlation with prognostic values in HCC. In order to verify the results of the network pharmacological analysis, we performed a molecular docking approach on the active ingredients and key targets using the Discovery Studio software. The viability of SMMC-7721 and HL-7702 cells was determined by Cell counting kit-8 (CCK-8) after being treated with different concentrations of (+)-catechin (0, 50, 100, 150, 200, and 250 g/ml) for 24, 48, and 72 hours, respectively. Finally, qRT-PCR and Western blot involving human hepatocarcinoma cells were utilized to verify the impact of (+)-catechin on the hub genes associated with prognosis. Results: 6 out of 26 active ingredients extracted from TCMSP were deemed as the core ingredients of acRoots. 175 bioactive-ingredient targets of acRoots were obtained and a bioactive-ingredient targets network was established correspondingly. The biological processes (BP) of target genes mainly involved processes, such as toxic substance and wounding. The results of KEGG pathways indicated that the target genes were mainly enriched in pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, and other pathways. Also, the two hub genes (i.e., ESR1 and CAT) were closely associated with the prognosis of HCC patients. As a consequence, we predicated a series of signaling pathways, including estrogen signaling pathway and longevity regulation pathway, through which acRoots could facilitate the treatment for HCC. The molecular docking experiment ascertained that ESR1 and CAT had an effective binding force with (+)-catechin, one of the core ingredients of acRoots. Furthermore, (+)-catechin inhibited SMMC-7721 cell growth in a dose-dependent manner and a time-dependent manner. Finally, we suggest that the expression level of ESR1 and CAT is positively related to the (+)-catechin concentrations in in-vitro experiments. Conclusion: The bioactive ingredients of acRoots, including quercetin, (+)-catechin, beta-sitosterol, and aloe-emodin, have synergistic interactions in reinforcing the anticancer effect in HCC. Evidently, acRoots took effect by regulating multitargets and multipathways through its active ingredients. Further, (+)-catechin, the possible paramount anti-HCC active ingredient in acRoots, helped improve the prognosis of HCC patients by increasing the expression of ESR1 and CAT. Additionally, the findings yielded provide a conceptual guidance for the clinical treatment of HCC and the methods adopted are potentially applicable in the future comprehensive analysis of the underlying mechanisms of TCMs.

Abstract: Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer's disease Association estimates that 6.2 million Americans are detected with Alzheimer's disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson's disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning-based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.

Abstract: Ankylosing spondylitis (AS) is a seronegative, chronic inflammatory arthritis with high genetic burden. A strong association with HLA-B27 has long been established, but to date its contribution to disease aetiology remains unresolved. Recent insights through genome wide studies reveal an increasing array of immunogenetic risk variants extraneous to the HLA complex in AS cohorts. These genetic traits build a complex profile of disease causality, highlighting several molecular pathways associated with the condition. This and other evidence strongly implicates T-cell-driven pathology, revolving around the T helper 17 cell subset as an important contributor to disease. This prominence of the T helper 17 cell subset has presented the opportunity for therapeutic intervention through inhibition of interleukins 17 and 23 which drive T helper 17 activity. While targeting of interleukin 17 has proven effective, this success has not been replicated with interleukin 23 inhibition in AS patients. Evidence points to significant genetic diversity between AS patients which may, in part, explain the observed refractoriness among a proportion of patients. In this review we discuss the impact of genetics on our understanding of AS and its relationship with closely linked pathologies. We further explore how genetics can be used in the development of therapeutics and as a tool to assist in the diagnosis and management of patients. This evidence indicates that genetic profiling should play a role in the clinician's choice of therapy as part of a precision medicine strategy towards disease management.

Abstract: Ulcerative colitis (UC) is a subtype of inflammatory bowel disease (IBD) and occurs mainly in the colon. The etiology of UC is rather complex and involves various pathological factors, including genetic susceptibility, dietary intakes, environment, and microbiota. In China, the Chang-Yan-Ning (CYN) formula has been utilized in the clinic to treat gastrointestinal disorders, but its pharmacological evidence remains elusive. The investigation was designed to explore the molecular and cellular mechanisms of CYN. Liquid Chromatography with tandem mass spectrometry (LC/MS) was performed to identify the key components in the formula; Network pharmacology analysis was executed to predict the potential targets of CYN; An experimental murine colitis model was established by utilizing 2% dextran sodium sulfate (DSS), and CYN was administered for 14 days. The pharmacological mechanism of the CYN formula was corroborated by in-vivo and in-vitro experiments, and high throughput techniques including metabolomics and 16S rRNA sequencing. Results: LC/MS identified the active components in the formula, and network pharmacology analysis predicted 37 hub genes that were involved in tumor necrosis factor (TNF), interleukin (IL)-17, hypoxia-inducible factor (HIF) signaling pathways. As evidenced by in-vivo experiments, DSS administration shortened the length of the colon and led to weight loss, with a compromised structure of epithelium, and the CYN formula reversed these pathological symptoms. Moreover, CYN suppressed the levels of pro-inflammatory cytokines, including IL-4, IL-1b, and TNFalphain the serum, inhibited the protein abundance of IL17 and HIF-1alphaand increased PPARgamma and CCL2 in the colon, and facilitated the alternative activation of peritoneal macrophages. While peritoneal macrophages of colitis mice enhanced reactive oxygen species (ROS) production in murine intestinal organoids, the ROS level remained stable co-cultured with the macrophages of CYN-treated mice. Furthermore, the decreased microbiota richness and diversity and the prevalence of pathogenic taxa in colitis mice were rescued after the CYN treatment. The altered metabolic profile during colitis was also restored after the therapy. We posit that the CYN therapy attenuates the development and progression of colitis by maintaining the homeostasis of immune responses and microbiota.

Abstract: Drug-target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN.