Abstract: Pythium insidiosum is an oomycete microorganism that causes a life-threatening infectious disease, called pythiosis, in humans and animals. The disease has been increasingly reported worldwide. Conventional antifungal drugs are ineffective against P. insidiosum Treatment of pythiosis requires the extensive removal of infected tissue (i.e., eye and leg), but inadequate surgery and recurrent infection often occur. A more effective treatment is needed for pythiosis patients. Drug repurposing is a promising strategy for the identification of a U.S. Food and Drug Administration-approved drug for the control of P. insidiosum Disulfiram has been approved to treat alcoholism, but it exhibits antimicrobial activity against various pathogens. In this study, we explored whether disulfiram possesses an anti-P. insidiosum activity. A total of 27 P. insidiosum strains, isolated from various hosts and geographic areas, were susceptible to disulfiram in a dose-dependent manner. The MIC range of disulfiram against P. insidiosum (8 to 32 mg/liter) was in line with that of other pathogens. Proteogenomic analysis indicated that several potential targets of disulfiram (i.e., aldehyde dehydrogenase and urease) were present in P. insidiosum By homology modeling and molecular docking, disulfiram can bind the putative aldehyde dehydrogenase and urease of P. insidiosum at low energies (i.e., -6.1 and -4.0 Kcal/mol, respectively). Disulfiram diminished the biochemical activities of these enzymes. In conclusion, disulfiram can inhibit the growth of many pathogenic microorganisms, including P. insidiosum The drug can bind and inactivate multiple proteins of P. insidiosum, which may contribute to its broad antimicrobial property. Drug repurposing of disulfiram could be a new treatment option for pythiosis.

Abstract: Tumor mutational burden (TMB) and mutational signatures reflect the process of mutation accumulation in cancer. However, the significance of these emerging characteristics remains unclear. In the present study, we used whole-exome sequencing to analyze the TMB and mutational signature in solid tumors of 4046 Japanese patients. Eight predominant signatures-microsatellite instability, smoking, POLE, APOBEC, UV, mismatch repair, double-strand break repair, and Signature 16-were observed in tumors with TMB higher than 1.0 mutation/Mb, whereas POLE and UV signatures only showed moderate correlation with TMB, suggesting the extensive accumulation of mutations due to defective POLE and UV exposure. The contribution ratio of Signature 16, which is associated with hepatocellular carcinoma in drinkers, was increased in hypopharynx cancer. Tumors with predominant microsatellite instability signature were potential candidates for treatment with immune checkpoint inhibitors such as pembrolizumab and were found in 2.8% of cases. Furthermore, based on microarray analysis, tumors with predominant signatures were classified into 2 subgroups depending on the expression of immune-related genes reflecting differences in the immune context of the tumor microenvironment. Tumor subpopulations differing in the content of infiltrating immune cells might respond differently to immunotherapeutics. An understanding of cancer characteristics based on TMB and mutational signatures could provide new insights into mutation-driven tumorigenesis.

Abstract: Combination chemotherapy has been a mainstay in cancer treatment for the last 60 years. Although the mechanisms of action and signaling pathways affected by most treatments with single antineoplastic agents might be relatively well understood, most combinations remain poorly understood. This review presents the most common alterations of signaling pathways in response to cytotoxic and targeted anticancer drug treatments, with a discussion of how the knowledge of signaling pathways might support and orient the development of innovative strategies for anticancer combination therapy. The ultimate goal is to highlight possible strategies of chemotherapy combinations based on the signaling pathways associated with the resistance mechanisms against anticancer drugs to maximize the selective induction of cancer cell death. We consider this review an extensive compilation of updated known information on chemotherapy resistance mechanisms to promote new combination therapies to be to discussed and tested.

Abstract: Breast cancer has become the most common cancer that leads to women's death. Breast cancer is a complex, highly heterogeneous disease classified into various subtypes based on histological features, which determines the therapeutic options. System identification of effective drugs for each subtype remains challenging. In this work, we present a computational network biology approach to screen precision drugs for different breast cancer subtypes by considering the impact intensity of candidate drugs on the pathway crosstalk mediated by miRNAs. Firstly, we constructed and analyzed the subtype-specific risk pathway crosstalk networks mediated by miRNAs. Then, we evaluated 36 Food and Drug Administration (FDA)-approved anticancer drugs by quantifying their effects on these subtype-specific pathway crosstalk networks and combining with survival analysis. Finally, some first-line treatments of breast cancer, such as Paclitaxel and Vincristine, were optimized for each subtype. In particular, we performed precision screening of subtype-specific therapeutic drugs and also confirmed some novel drugs suitable for breast cancer treatment. For example, Sorafenib was applicable for the basal subtype treatment, Irinotecan was optimum for Her2 subtype treatment, Vemurafenib was suitable for the LumA subtype treatment, and Vorinostat could apply to LumB subtype treatment. In addition, the mechanism of these optimal therapeutic drugs in each subtype of breast cancer was further dissected. In summary, our study offers an effective way to screen precision drugs for various breast cancer subtype treatments. We also dissected the mechanism of optimal therapeutic drugs, which may provide novel insight into the precise treatment of cancer and promote researches on the mechanisms of action of drugs.

Abstract: Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we generate a small but significant improvement in the accuracy of transcriptome prediction and increase the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge on biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify compounds that mimic, or reverse, trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.

Abstract: Lactams are a class of compounds important for drug design, due to their great variety of potential therapeutic applications, spanning cancer, diabetes, and infectious diseases. So far, the biological profile and chemical diversity of lactams have not been characterized in a systematic and detailed manner. In this work, we report the chemoinformatic analysis of beta-, gamma-, delta- and epsilon-lactams present in databases of approved drugs, natural products, and bioactive compounds from the large public database ChEMBL. We identified the main biological targets in which the lactams have been evaluated according to their chemical classification. We also identified the most frequent scaffolds and those that can be prioritized in chemical synthesis, since they are scaffolds with potential biological activity but with few reported analogs. Results of the biological and chemoinformatic analysis of lactams indicate that spiro- and bridged-lactams belong to classes with the lowest number of compounds and unique scaffolds, and some showing activity against specific targets. Information obtained from this analysis allows focusing the design of new chemical structures in less explored spaces and with increased possibilities of success.

Abstract: The identification of drug-target interactions (DTIs) is a critical step in drug development. Experimental methods that are based on clinical trials to discover DTIs are time-consuming, expensive, and challenging. Therefore, as complementary to it, developing new computational methods for predicting novel DTI is of great significance with regards to saving cost and shortening the development period. In this paper, we present a novel computational model for predicting DTIs, which uses the sequence information of proteins and a rotation forest classifier. Specifically, all of the target protein sequences are first converted to a position-specific scoring matrix (PSSM) to retain evolutionary information. We then use local phase quantization (LPQ) descriptors to extract evolutionary information in the PSSM. On the other hand, substructure fingerprint information is utilized to extract the features of the drug. We finally combine the features of drugs and protein together to represent features of each drug-target pair and use a rotation forest classifier to calculate the scores of interaction possibility, for a global DTI prediction. The experimental results indicate that the proposed model is effective, achieving average accuracies of 89.15%, 86.01%, 82.20%, and 71.67% on four datasets (i.e., enzyme, ion channel, G protein-coupled receptors (GPCR), and nuclear receptor), respectively. In addition, we compared the prediction performance of the rotation forest classifier with another popular classifier, support vector machine, on the same dataset. Several types of methods previously proposed are also implemented on the same datasets for performance comparison. The comparison results demonstrate the superiority of the proposed method to the others. We anticipate that the proposed method can be used as an effective tool for predicting drug-target interactions on a large scale, given the information of protein sequences and drug fingerprints.

Abstract: In this research, we present a preliminary computational study of four Dermaseptin-related peptides from the skin exudate of the gliding tree frog Agalychnis spurrelli. Experimentally, the amino acid sequence of these peptides was elucidated through molecular cloning and tandem mass spectrometry and synthetic peptides were assayed against E. coli, S. aureus, and C. albicans to determine their antimicrobial properties. With the sequences on hand, a computational study of the structures was carried out, obtaining their physicochemical properties, secondary structure, and their similarity to other known peptides. A molecular docking study of these peptides was also performed against cell membrane and several enzymes are known to be vital for the organisms. Results showed that Dermaseptin-related peptides are alpha-helical cationic peptides with an isoelectric point above 9.70 and a positive charge of physiological pH. Introducing theses peptides in a database, it was determined that their identity compared with known peptides range from 36 to 82% meaning these four Dermaseptins are novel peptides. This preliminary study of molecular docking suggests the mechanism of action of this peptide is not given by the inhibition of essential enzymatic pathways, but by cell lysis. Graphical abstract.

Abstract: BACKGROUND: Nowadays, the use of social media is part of daily life, with more and more people, including governments and health organizations, using at least one platform regularly. Social media enables users to interact among large groups of people that share the same interests and suffer the same afflictions. Notably, these channels promote the ability to find and share information about health and medical conditions. OBJECTIVE: This study aimed to characterize the bowel disease (BD) community on Twitter, in particular how patients understand, discuss, feel, and react to the condition. The main questions were as follows: Which are the main communities and most influential users?; Where are the main content providers from?; What are the key biomedical and scientific topics under discussion? How are topics interrelated in patient communications?; How do external events influence user activity?; What kind of external sources of information are being promoted? METHODS: To answer these questions, a dataset of tweets containing terms related to BD conditions was collected from February to August 2018, accounting for a total of 24,634 tweets from 13,295 different users. Tweet preprocessing entailed the extraction of textual contents, hyperlinks, hashtags, time, location, and user information. Missing and incomplete information about the user profiles was completed using different analysis techniques. Semantic tweet topic analysis was supported by a lexicon-based entity recognizer. Furthermore, sentiment analysis enabled a closer look into the opinions expressed in the tweets, namely, gaining a deeper understanding of patients' feelings and experiences. RESULTS: Health organizations received most of the communication, whereas BD patients and experts in bowel conditions and nutrition were among those tweeting the most. In general, the BD community was mainly discussing symptoms, BD-related diseases, and diet-based treatments. Diarrhea and constipation were the most commonly mentioned symptoms, and cancer, anxiety disorder, depression, and chronic inflammations were frequently part of BD-related tweets. Most patient tweets discussed the bad side of BD conditions and other related conditions, namely, depression, diarrhea, and fibromyalgia. In turn, gluten-free diets and probiotic supplements were often mentioned in patient tweets expressing positive emotions. However, for the most part, tweets containing mentions to foods and diets showed a similar distribution of negative and positive sentiments because the effects of certain food components (eg, fiber, iron, and magnesium) were perceived differently, depending on the state of the disease and other personal conditions of the patients. The benefits of medical cannabis for the treatment of different chronic diseases were also highlighted. CONCLUSIONS: This study evidences that Twitter is becoming an influential space for conversation about bowel conditions, namely, patient opinions about associated symptoms and treatments. So, further qualitative and quantitative content analyses hold the potential to support decision making among health-related stakeholders, including the planning of awareness campaigns.

Abstract: BACKGROUND Osteoarthritis (OA) affects the health and wellbeing of the elderly. Shaoyao Gancao decoction (SGD) is used in traditional Chinese medicine (TCM) for the treatment of OA and has two active components, shaoyao (SY) and gancao (GC). This study aimed to undertake a network pharmacology analysis of the mechanism of the effects of SGD in OA. MATERIAL AND METHODS The active compounds and candidates of SGD were obtained from the Traditional Chinese Medicine (TCM) Databases@Taiwan, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the STITCH database, the ChEMBL database, and PubChem. The network pharmacology approach involved network construction, target prediction, and module analysis. Significant signaling pathways of the cluster networks for SGD and OA were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS Twenty-three bioactive compounds were identified, corresponding to 226 targets for SGD. Also, 187 genes were closely associated with OA, of which 161 overlapped with the targets of SGD and were considered to be therapeutically relevant. Functional enrichment analysis suggested that SGD exerted its pharmacological effects in OA by modulating multiple pathways, including cell cycle, cell apoptosis, drug metabolism, inflammation, and immune modulation. CONCLUSIONS A novel approach was developed to systematically identify the mechanisms of the TCM, SGD in OA using network pharmacology analysis.