Abstract: Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug-protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

Abstract: Computational prediction of xenobiotic metabolism can provide valuable information to guide the development of drugs, cosmetics, agrochemicals, and other chemical entities. We have previously developed FAME 2, an effective tool for predicting sites of metabolism (SoMs). In this work, we focus on the prediction of the chemical structures of metabolites, in particular metabolites of xenobiotics. To this end, we have developed a new tool, GLORY, which combines SoM prediction with FAME 2 and a new collection of rules for metabolic reactions mediated by the cytochrome P450 enzyme family. GLORY has two modes: MaxEfficiency and MaxCoverage. For MaxEfficiency mode, the use of predicted SoMs to restrict the locations in the molecule at which the reaction rules could be applied was explored. For MaxCoverage mode, the predicted SoM probabilities were instead used to develop a new scoring approach for the predicted metabolites. With this scoring approach, GLORY achieves a recall of 0.83 and can predict at least one known metabolite within the top three ranked positions for 76% of the molecules of a new, manually curated test set. GLORY is freely available as a web server at https://acm.zbh.uni-hamburg.de/glory/, and the datasets and reaction rules are provided in the Supplementary Material.

Abstract: Naringenin, extracted from grapefruits and citrus fruits, is a bioactive flavonoid with antioxidative, anti-inflammatory, antifibrogenic, and anticancer properties. In the past two decades, the growth of publications of naringenin in PubMed suggests that naringenin is quickly gaining interest. However, systematically regarding its biological functions connected to its direct and indirect target proteins remains difficult but necessary. Herein, we employed a set of bioinformatic platforms to integrate and dissect available published data of naringenin. Analysis based on DrugBank and the Search Tool for the Retrieval of Interacting Genes/Proteins revealed seven direct protein targets and 102 indirect protein targets. The protein-protein interaction (PPI) network of total 109 naringenin-mediated proteins was next visualized using Cytoscape. What's more, all naringenin-mediated proteins were subject to Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery, which resulted in three ESR1-related signaling pathways and prostate cancer pathway. Refined analysis of PPI network and KEGG pathway identified four genes (ESR1, PIK3CA, AKT1, and MAPK1). Further genomic analysis of four genes using cBioPortal indicated that naringenin might exert biological effects via ESR1 signaling axis. In general, this work scrutinized naringenin-relevant knowledge and provided an insight into the regulation and mediation of naringenin on prostate cancer.

Abstract: The ability of microorganisms to generate resistance outcompetes with the generation of new and efficient antibiotics; therefore, it is critical to develop novel antibiotic agents and treatments to control bacterial infections. An alternative to this worldwide problem is the use of nanomaterials with antimicrobial properties. Silver nanoparticles (AgNPs) have been extensively studied due to their antimicrobial effect in different organisms. In this work, the synergistic antimicrobial effect of AgNPs and conventional antibiotics was assessed in Gram-positive and Gram-negative bacteria. AgNPs minimal inhibitory concentration was 10-12 mug mL-1 in all bacterial strains tested, regardless of their different susceptibility against antibiotics. Interestingly, a synergistic antimicrobial effect was observed when combining AgNPs and kanamycin according to the fractional inhibitory concentration index, FICI: <0.5), an additive effect by combining AgNPs and chloramphenicol (FICI: 0.5 to 1), whereas no effect was found with AgNPs and beta-lactam antibiotics combinations. Flow cytometry and TEM analysis showed that sublethal concentrations of AgNPs (6-7 mug mL-1) altered the bacterial membrane potential and caused ultrastructural damage, increasing the cell membrane permeability. No chemical interactions between AgNPs and antibiotics were detected. We propose an experimental supported mechanism of action by which combinatorial effect of antimicrobials drives synergy depending on their specific target, facilitated by membrane alterations generated by AgNPs. Our results provide a deeper understanding about the synergistic mechanism of AgNPs and antibiotics, aiming to combat antimicrobial infections efficiently, especially those by multi-drug resistant microorganisms, in order to mitigate the current crisis due to antibiotic resistance.

Abstract: Background: It is becoming increasingly clear that cancers can rarely be ascribed to just one or a few genomic variations. Genes generally do not function alone, but in groups that function as "networks". This study aimed to develop a competing endogenous RNA (ceRNA) network to elucidate the role of long non-coding RNA H19 in colorectal cancer. Methods: Large-scale RNA-seq data was obtained from The Cancer Genome Atlas database. Differentially expressed RNAs were identified by bioinformatics analysis, and a competing endogenous RNA network was constructed. Functional enrichment analysis and correlation analysis between competing endogenous RNAs and clinical features were performed to reveal their roles in the tumorigenesis of colorectal cancer. To verify the conclusions derived from bioinformatics analysis, we investigated the effect of lncRNA H19 knockdown in human colorectal cancer cell lines HT-29 and HCT116. Results: The present study successfully identify various cancer-specific lncRNAs and pseudogenes in CRC. The lncRNA/pseudogene-miRNA-mRNA ceRNA network was constructed using 10 lncRNAs, 5 pseudogenes, 122 mRNAs and 39 miRNAs. In the ceRNA network of CRC, H19 up-regulates various cancer-related mRNA by competitively sponging various miRNA, and participates in PI3K-Akt signaling pathway in this manner. Cox regression and correlation analysis showed that H19 and some other competing endogenous RNAs in the network are associated with poor prognosis and clinical parameters such as tumor grade and metastasis. Knockdown of H19 reduces the protein level of MET, ZEB1, and COL1A1 in vitro. Conclusions: H19 regulates PI3K-Akt signal pathway through a competing endogenous RNA network and predicts poor prognosis in colorectal cancer. The pseudogene RPLP0P2 may be an important oncogene like H19 and needs to be studied further.

Abstract: Backgrounds and Aims: Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI. Materials and Methods: In this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM. Results: 16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity. Conclusion: Overall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs.

Abstract: Transcriptomics technologies such as next-generation sequencing and microarray platforms provide exciting opportunities for improving diagnosis and treatment of complex diseases. Transcriptomics studies often share similar hypotheses, but are carried out on different platforms, in different conditions, and with different analysis approaches. These factors, in addition to small sample sizes, can result in a lack of reproducibility. A clear understanding and unified picture of many complex diseases are still elusive, highlighting an urgent need to effectively integrate multiple transcriptomic studies for disease signatures. We have integrated more than 3,000 high-quality transcriptomic datasets in oncology, immunology, neuroscience, cardiovascular and metabolic disease, and from both public and internal sources (DiseaseLand database). We established a systematic data integration and meta-analysis approach, which can be applied in multiple disease areas to create a unified picture of the disease signature and prioritize drug targets, pathways, and compounds. In this bipolar case study, we provided an illustrative example using our approach to combine a total of 30 genome-wide gene expression studies using postmortem human brain samples. First, the studies were integrated by extracting raw FASTQ or CEL files, then undergoing the same procedures for preprocessing, normalization, and statistical inference. Second, both p-value and effect size based meta-analysis algorithms were used to identify a total of 204 differentially expressed (DE) genes (FDR < 0.05) genes in the prefrontal cortex. Among these were BDNF, VGF, WFS1, DUSP6, CRHBP, MAOA, and RELN, which have previously been implicated in bipolar disorder. Finally, pathway enrichment analysis revealed a role for GPCR, MAPK, immune, and Reelin pathways. Compound profiling analysis revealed MAPK and other inhibitors may modulate the DE genes. The ability to robustly combine and synthesize the information from multiple studies enables a more powerful understanding of this complex disease.

Abstract: Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Abstract: A subtractive genomic approach has been utilized for the identification of potential drug targets and vaccine candidates in Clostridium botulinum, the causative agent of flaccid paralysis in humans. The emergence of drug-resistant pathogenic strains has become a significant global public health threat. Treatment with antitoxin can target the neurotoxin at the extracellular level, however, can't converse the paralysis caused by botulism. Therefore, identification of drug targets and vaccine candidates in C. botulinum would be crucial to overcome drug resistance to existing antibiotic therapy. A total of 1729 crucial proteins, including chokepoint, virulence, plasmid and resistance proteins were mined and used for subtractive channel of analysis. This analysis disclosed 15 potential targets, which were non-similar to human, gut micro flora, and anti-targets in the host. The cellular localization of 6 targets was observed in the cytoplasm and might be used as a drug target, whereas 9 targets were localized in extracellular and membrane bound proteins and can be used as vaccine candidates. Furthermore, 4 targets were observed to be homologous to more than 75 pathogens and hence are considered as broad-spectrum antibiotic targets. The identified drug and vaccine targets in this study would be useful in the design and discovery of novel therapeutic compounds against botulism.

Abstract: BACKGROUND: In the literature-based discovery, considerable research has been done based on the ABC model developed by Swanson. ABC model hypothesizes that there is a meaningful relation between entity A extracted from document set 1 and entity C extracted from document set 2 through B entities that appear commonly in both document sets. The results of ABC model are relations among entity A, B, and C, which is referred as paths. A path allows for hypothesizing the relationship between entity A and entity C, or helps discover entity B as a new evidence for the relationship between entity A and entity C. The co-occurrence based approach of ABC model is a well-known approach to automatic hypothesis generation by creating various paths. However, the co-occurrence based ABC model has a limitation, in that biological context is not considered. It focuses only on matching of B entity which commonly appears in relation between two entities. Therefore, the paths extracted by the co-occurrence based ABC model tend to include a lot of irrelevant paths, meaning that expert verification is essential. METHODS: In order to overcome this limitation of the co-occurrence based ABC model, we propose a context-based approach to connecting one entity relation to another, modifying the ABC model using biological contexts. In this study, we defined four biological context elements: cell, drug, disease, and organism. Based on these biological context, we propose two extended ABC models: a context-based ABC model and a context-assignment-based ABC model. In order to measure the performance of the both proposed models, we examined the relevance of the B entities between the well-known relations "APOE-MAPT" as well as "FUS-TARDBP". Each relation means interaction between neurodegenerative disease associated with proteins. The interaction between APOE and MAPT is known to play a crucial role in Alzheimer's disease as APOE affects tau-mediated neurodegeneration. It has been shown that mutation in FUS and TARDBP are associated with amyotrophic lateral sclerosis(ALS), a motor neuron disease by leading to neuronal cell death. Using these two relations, we compared both of proposed models to co-occurrence based ABC model. RESULTS: The precision of B entities by co-occurrence based ABC model was 27.1% for "APOE-MAPT" and 22.1% for "FUS-TARDBP", respectively. In context-based ABC model, precision of extracted B entities was 71.4% for "APOE-MAPT", and 77.9% for "FUS-TARDBP". Context-assignment based ABC model achieved 89% and 97.5% precision for the two relations, respectively. Both proposed models achieved a higher precision than co-occurrence-based ABC model.