Abstract: The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein-protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.

Abstract: OBJECTIVE: To explore the molecular-level mechanism on the hematopoiesis effect of Angelicae sinensis Radix (ASR) with systems-based interactome analysis. METHODS: This systems-based interactome analysis was designed to enforce the workflow of "ASR (herb)-->compound-->target protein-->internal protein actions-->ending regulated protein for hematopoiesis". This workflow was deployed with restrictions on regulated proteins expresses in bone marrow and anemia disease and futher validated with experiments. RESULTS: The hematopoiesis mechanism of ASR might be accomplished through regulating pathways of cell proliferation towards hemopoiesis with cross-talking agents of spleen tyrosine kinase (SYK), Janus kinase 2 (JAK2), and interleukin-2-inducible T-cell kinase (ITK). The hematopoietic function of ASR was also validated by colony-forming assay performed on mice bone marrow cells. As a result, SYK, JAK2 and ITK were activated. CONCLUSION: This study provides a new approach to systematically study and predict the therapeutic mechanism for ASR based on interactome analysis towards biological process with experimental validations.

Abstract: Crz1p regulates Calcineurin, a serine-threonine-specific protein phosphatase, in Rhizoctonia solani. It has attracted consideration as a novel target of antifungal therapy based on studies in numerous pathogenic fungi, including, Cryptococcus neoformans, Candida albicans and Aspergillus fumigatus. To investigate whether Calcineurin can be a useful target for the treatment of Crz1 protein in R. solani causing wet root rot in Chickpea. The work presented here reports the in-silico studies of Crz1 protein against natural compounds. This study Comprises of quantitative structure-toxicity relationship (QSTR) and quantitative structure-activity relationship (QSAR). All compounds showed high binding energy for Crz1 protein through molecular docking. Further, a pharmacokinetic study revealed that these compounds had minimal side effects. Biological activity spectrum prediction of these compounds showed potential antifungal properties by showing significant interaction with Crz1. Hence, these compounds can be used for the prevention and treatment of wet root rot in Chickpea.

Abstract: As the structure and functions of proteins are correlated, investigating groups of proteins with the same gross structure may provide important insights about their functional roles. Trispanins, proteins that contain three alpha-helical transmembrane (3TM) regions, have not been previously studied considering their transmembrane features. Our comprehensive identification and classification using bioinformatic methods describe 152 3TM proteins. These proteins are frequently involved in membrane biosynthesis and lipid biogenesis, protein trafficking, catabolic processes, and in particular signal transduction due to the large ionotropic glutamate receptor family. Proteins that localize to intracellular compartments are overrepresented in the dataset in comparison to the entire human transmembrane proteome, and nearly 45% localize specifically to the endoplasmic reticulum (ER). Furthermore, nearly 20% of the trispanins function in lipid metabolic processes and transport, which are also overrepresented. Nearly one-third of trispanins are identified as being targeted by drugs and/or being associated with diseases. A high number of 3TMs have unknown functions and based on this analysis we speculate on the functional involvement of uncharacterized trispanins in relationship to disease or important cellular activities. This first overall study of trispanins provides a unique analysis of a diverse group of membrane proteins.

Abstract: Alterations in DNA damage response (DDR) is one of the several hallmarks of cancer. Genomic instability resulting from a disrupted DDR mechanism is known to contribute to cancer progression, and are subjected to radiation, cytotoxic, or more recently targeted therapies with limited success. Synthetic lethality (SL), which is a condition where simultaneous loss-of-function of the genes from complementary pathways result in loss of viability of cancer cells have been exploited to treat malignancies resulting from defects in certain DDR pathways. Albeit being a promising therapeutic strategy, number of SL based drugs currently in clinical trial is limited. In this work we performed a comprehensive pan-cancer analysis of alterations in 10 DDR pathways with different components of DNA repair. Using unsupervised clustering of single sample enrichment of these pathways in 7,272 tumor samples from 17 tumor types from TCGA, we identified three prominent clusters, each associated with specific DDR mechanisms. Somatic mutations in key DDR genes were found to be dominant in each of these three clusters with distinct DDR component. Using a machine-learning based algorithm we predicted SL partners specific to somatic mutations in key genes representing each of the three DDR clusters and identified potential druggable targets. We explored the potential FDA-approved drugs for targeting the predicted SL genes and tested the sensitivity using the drug screening data in cell lines with mutation in the primary DDR genes. We have shown clinical relevance, for selected targetable SL interactions using Kaplan-Meier analysis in terms of improved disease-free survival. Thus, our computational framework provides a basis for clinically relevant and actionable SL based drug targets specific to alterations in DDR pathways.

Abstract: The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions.

Abstract: Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein-protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates' therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.

Abstract: Prostate cancer (PCa) is the second leading cause of cancer-related death for men in the United States. Approximately 35% of PCa recurs and is often transformed to castration-resistant prostate cancer (CRPCa), the most deadly and aggressive form of PCa. However, the CRPCa standard-of-care treatment (enzalutamide with abiraterone) usually has limited efficacy. Herein, we report a novel molecule (PAWI-2) that inhibits cellular proliferation of androgen-sensitive and androgen-insensitive cells (LNCaP and PC-3, respectively). In vivo studies in a PC-3 xenograft model showed that PAWI-2 (20 mg/kg per day i.p., 21 days) inhibited tumor growth by 49% compared with vehicle-treated mice. PAWI-2 synergized currently clinically used enzalutamide in in vitro inhibition of PCa cell viability and resensitized inhibition of in vivo PC-3 tumor growth. Compared with vehicle-treated mice, PC-3 xenograft studies also showed that PAWI-2 (20 mg/kg per day i.p., 21 days) and enzalutamide (5 mg/kg per day i.p., 21 days) inhibited tumor growth by 63%. Synergism was mainly controlled by the imbalance of prosurvival factors (e.g., Bcl-2, Bcl-xL, Mcl-1) and antisurvival factors (e.g., Bax, Bak) induced by affecting mitochondrial membrane potential/mitochondria dynamics. Thus, PAWI-2 utilizes a distinct mechanism of action to inhibit PCa growth independently of androgen receptor signaling and overcomes enzalutamide-resistant CRPCa. SIGNIFICANCE STATEMENT: Castration-resistant prostate cancer (CRPCa) is the most aggressive human prostate cancer (PCa) but standard chemotherapies for CRPCa are largely ineffective. PAWI-2 potently inhibits PCa proliferation in vitro and in vivo regardless of androgen receptor status and uses a distinct mechanism of action. PAWI-2 has greater utility in treating CRPCa than standard-of-care therapy. PAWI-2 possesses promising therapeutic potency in low-dose combination therapy with a clinically used drug (e.g., enzalutamide). This study describes a new approach to address the overarching challenge in clinical treatment of CRPCa.

Abstract: Qingre Lidan Decoction (QRLDD), a classic precompounded prescription, is widely used as an effective treatment for cholelithiasis clinically. However, its chemical profile and mechanism have not been characterized and elucidated. In the present study, a rapid, sensitive, and reliable ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry method was established for comprehensively identifying the major constituents in QRLDD. Furthermore, a network pharmacology strategy based on the chemical profile was applied to clarify the synergetic mechanism. A total of 72 compounds containing flavonoids, terpenes, phenolic acid, anthraquinones, phenethylalchohol glycosides, and other miscellaneous compounds were identified, respectively. 410 disease genes, 432 compound targets, and 71 related pathways based on cholelithiasis-related and compound-related targets databases as well as related pathways predicted by the Kyoto Encyclopedia of Genes and Genomes database were achieved. Among these pathways and genes, pathway in cancer and MAPK signaling pathway may play an important role in the development of cholelithiasis. EGFR may be a crucial target in the conversion of gallstones to gallbladder carcinoma. Regulation of PRKCB/RAF1/MAP2K1/MAPK1 is associated with cell proliferation and differentiation. Thus, the fingerprint coupled with network pharmacology analysis could contribute to simplifying the complex system and providing directions for further research of QRLDD.

Abstract: Compound XiongShao Capsule (CXSC), a traditional herb mixture, has shown significant clinical efficacy against diabetic peripheral neuropathy (DPN). However, its multicomponent and multitarget features cause difficulty in deciphering its molecular mechanisms. Our study aimed to identify the key active ingredients and potential pharmacological mechanisms of CXSC in treating DPN by network pharmacology and provide scientific evidence of its clinical efficacy. CXSC active ingredients were identified from both the Traditional Chinese Medicine Systems Pharmacology database, with parameters of oral bioavailability >/= 30% and drug-likeness >/= 0.18, and the Herbal Ingredients' Targets (HIT) database. The targets of those active ingredients were identified using ChemMapper based on 3D-structure similarity and using HIT database. DPN-related genes were acquired from microarray dataset GSE95849 and five widely used databases (TTD, Drugbank, KEGG, DisGeNET, and OMIM). Next, we obtained candidate targets with therapeutic effects against DPN by mapping active ingredient targets and DPN-related genes and identifying the proteins interacting with those candidate targets using STITCH 5.0. We constructed an "active ingredients-candidate targets-proteins" network using Cytoscape 3.61 and identified key active ingredients and key targets in the network. We identified 172 active ingredients in CXSC, 898 targets of the active ingredients, 110 DPN-related genes, and 38 candidate targets with therapeutic effects against DPN. Three key active ingredients, namely, quercetin, kaempferol, and baicalein, and 25 key targets were identified. Next, we input all key targets into ClueGO plugin for KEGG enrichment and molecular function analyses. The AGE-RAGE signaling pathway in diabetic complications and MAP kinase activity were determined as the main KEGG pathway and molecular function involved, respectively. We determined quercetin, kaempferol, and baicalein as the key active ingredients of CXSC and the AGE-RAGE signaling pathway and MAP kinase activity as the main pharmacological mechanisms of CXSC against DPN, proving the clinical efficacy of CXSC against DPN.