Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database

Shu-Xie decoction alleviates oxidative stress and colon injury in acute sleep-deprived mice by suppressing p62/KEAP1/NRF2/HO1/NQO1 signaling

Introduction: Sleep disorders are common clinical psychosomatic disorders that can co-exist with a variety of conditions. In humans and animal models, sleep deprivation (SD) is closely related with gastrointestinal diseases. Shu-Xie Decoction (SX) is a traditional Chinese medicine (TCM) with anti-nociceptive, anti-inflammatory, and antidepressant properties. SX is effective in the clinic for treating patients with abnormal sleep and/or gastrointestinal disorders, but the underlying mechanisms are not known. This study investigated the mechanisms by which SX alleviates SD-induced colon injury in vivo. Methods: C57BL/6 mice were placed on an automated sleep deprivation system for 72 h to generate an acute sleep deprivation (ASD) model, and low-dose SX (SXL), high-dose SX (SXH), or S-zopiclone (S-z) as a positive control using the oral gavage were given during the whole ASD-induced period for one time each day. The colon length was measured and the colon morphology was visualized using hematoxylin and eosin (H&E) staining. ROS and the redox biomarkers include reduced glutathione (GSH), malondialdehyde (MDA), and superoxide dismutase (SOD) were detected. Quantitative real-time PCR (qRT-PCR), molecular docking, immunofluorescence and western blotting assays were performed to detect the antioxidant signaling pathways. Results: ASD significantly increased FBG levels, decreased colon length, moderately increased the infiltration of inflammatory cells in the colon mucosa, altered the colon mucosal structure, increased the levels of ROS, GSH, MDA, and SOD activity compared with the controls. These adverse effects were significantly alleviated by SX treatment. ASD induced nuclear translocation of NRF2 in the colon mucosal cells and increased the expression levels of p62, NQO1, and HO1 transcripts and proteins, but these effects were reversed by SX treatment. Conclusion: SX decoction ameliorated ASD-induced oxidative stress and colon injury by suppressing the p62/KEAP1/NRF2/HO1/NQO1 signaling pathway. In conclusion, combined clinical experience, SX may be a promising drug for sleep disorder combined with colitis.

Network Pharmacology Revealed the Mechanisms of Action of Lithospermum erythrorhizon Sieb on Atopic Dermatitis

Aim

The application of network analysis algorithms promoted the development of network pharmacology. This study aimed to combine network pharmacology and signed random walk with restart (SRWR) to reveal the mechanism by which Lithospermum erythrorhizon Sieb (LES) exerts effects on atopic dermatitis (AD).

Methods

The compounds and targets of LES were retrieved from Traditional Chinese Medicine Integrated Database (TCMID) and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and important compounds and targets were identified by intersection analysis and protein-protein interaction (PPI) network.

Results

We found that active LES-derived compounds such as caffeic acid, Isovaleric acid, Arnebinol, and Alannan may inhibit PTGS2, HSP90AA1 and MAPK14, which are key mediators involved in PI3K-Akt pathway, vascular endothelial growth factor signaling pathway, Fc epsilon RI signaling pathway, and calcium signaling pathway.

Conclusion

The application of SRWR could identify potential targets of LES with a low false-positive rate and help elucidate the mechanism of action of traditional Chinese medicine.

Mechanisms of Cardiorenal Protection With SGLT2 Inhibitors in Patients With T2DM Based on Network Pharmacology

Purpose

Sodium-glucose cotransporter 2 (SGLT2) inhibitors have cardiorenal protective effects regardless of whether they are combined with type 2 diabetes mellitus, but their specific pharmacological mechanisms remain undetermined.

Materials and Methods

We used databases to obtain information on the disease targets of “Chronic Kidney Disease,” “Heart Failure,” and “Type 2 Diabetes Mellitus” as well as the targets of SGLT2 inhibitors. After screening the common targets, we used Cytoscape 3.8.2 software to construct SGLT2 inhibitors' regulatory network and protein-protein interaction network. The clusterProfiler R package was used to perform gene ontology functional analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analyses on the target genes. Molecular docking was utilized to verify the relationship between SGLT2 inhibitors and core targets.

Results

Seven different SGLT2 inhibitors were found to have cardiorenal protective effects on 146 targets. The main mechanisms of action may be associated with lipid and atherosclerosis, MAPK signaling pathway, Rap1 signaling pathway, endocrine resistance, fluid shear stress, atherosclerosis, TNF signaling pathway, relaxin signaling pathway, neurotrophin signaling pathway, and AGEs-RAGE signaling pathway in diabetic complications were related. Docking of SGLT2 inhibitors with key targets such as GAPDH, MAPK3, MMP9, MAPK1, and NRAS revealed that these compounds bind to proteins spontaneously.

Conclusion

Based on pharmacological networks, this study elucidates the potential mechanisms of action of SGLT2 inhibitors from a systemic and holistic perspective. These key targets and pathways will provide new ideas for future studies on the pharmacological mechanisms of cardiorenal protection by SGLT2 inhibitors.

Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes

Background

Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4).

Results

The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo.

Conclusions

The proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04241-1.

A network pharmacology study on analgesic mechanism of Yuanhu-Baizhi herb pair

BACKGROUND

Millions of people are suffering from chronic pain conditions, such as headache, arthritis, cancer. Apart from western medicines, traditional Chinese medicines are also well accepted for pain management, especially in Asian countries. Yuanhu-Baizhi herb pair (YB) is a typical herb pair applied to the treatment of stomach pain, hypochondriac pain, headache, and dysmenorrhea, due to its effects on analgesia and sedation. This study is to identify potentially active compounds and the underlying mechanisms of YB in the treatment of pain.

METHODS

Compounds in YB were collected from 3 online databases and then screened by bioavailability and drug likeness parameters. Swiss target prediction was applied to obtain targets information of the active compounds. Pain-related genes were conducted for Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Protein-protein interaction (PPI) networks of the genes were constructed using Cytoscape software. In addition, the hub genes were screened using maximal clique centrality (MCC) algorithm.

RESULTS

In total, 31 compounds from Yuanhu were screened out with 35 putative target genes, while 26 compounds in Baizhi with 43 target genes were discovered. Hence, 78 potential target genes of YB were selected for further study. After overlap analysis of the 78 genes of YB and 2408 pain-associated genes, we finally achieved 34 YB-pain target genes, as well as 10 hub genes and 23 core compounds. Go enrichment and KEGG pathway analysis indicated that YB had a strong integration with neuro system, which might significantly contribute to antinociceptive effect.

CONCLUSION

Our data provide deep understanding of the pharmacological mechanisms of YB in attenuating pain. The discovery shed new light on the development of active compounds of YB for the treatment of pain.

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.

Oncolytic Adenoviruses: Strategies for Improved Targeting and Specificity

Cancer is a major health problem. Most of the treatments exhibit systemic toxicity, as they are not targeted or specific to cancerous cells and tumors. Adenoviruses are very promising gene delivery vectors and have immense potential to deliver targeted therapy. Here, we review a wide range of strategies that have been tried, tested, and demonstrated to enhance the specificity of oncolytic viruses towards specific cancer cells. A combination of these strategies and other conventional therapies may be more effective than any of those strategies alone.

Sputum and serum autoantibody profiles and their clinical correlation patterns in COPD patients with and without eosinophilic airway inflammation

Background

Autoimmunity plays a role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the autoantibody responses and their clinical correlation patterns in COPD patients with and without airway eosinophilic inflammation are unknown. The aim of this study was to compare the autoantibody profiles and their clinical associations in stable COPD patients, stratified by airway inflammatory phenotypes.

Methods

Matched sputum and serum, obtained from 62 stable COPD patients and 14 age-matched controls, were assayed for the presence of IgG and IgM antibodies against 13 autoantigens using protein array. A sputum eosinophil count ≥3% was used as cut-off value to stratify COPD patients into eosinophilic and non-eosinophilic groups. Correlation network analysis was used to evaluate the correlation patterns among autoantibody and clinical variables in each group.

Results

There were no significant differences of clinical parameters and autoantibody levels between the two COPD groups. In non-eosinophilic COPD, sputum anti-CytochromeC_IgG and anti-Aggrecan_IgM were significantly higher than those in healthy controls, and prior exacerbation was positively associated with lung function and sputum anti-Collagen-IV_IgG. While in eosinophilic COPD, sputum/serum anti-heat shock protein (HSP)47_IgG, serum anti-HSP70_IgG and serum anti-Amyloid-beta_IgG were significantly lower than those in healthy controls, and no significant correlation between prior exacerbations and lung function was found. Differences were also observed in network hubs, with the network for non-eosinophilic COPD possessing 9 hubs comprising two lung function parameters and seven autoantibodies, compared with eosinophilic COPD possessing 12 hubs all comprising autoantibodies.

Conclusions

Autoantibody responses were heterogeneous and differentially correlated with the exacerbation risk and other clinical parameters in COPD patients of different inflammatory phenotypes. These findings provide useful insight into the need for personalized management for preventing COPD exacerbations.

Chinese Medicine for Psoriasis Vulgaris Based on Syndrome Pattern: A Network Pharmacological Study

BACKGROUND

The long-term use of conventional therapy for psoriasis vulgaris remains a challenge due to limited or no patient response and severe side effects. Complementary and alternative treatments such as traditional Chinese medicine (TCM) are widely used in East Asia. TCM treatment is based on individual syndrome types. Three TCM formulae, Compound Qingdai Pills (F1), Yujin Yinxie Tablets (F2), and Xiaoyin Tablets (F3), are used for blood heat, blood stasis, and blood dryness type of psoriasis vulgaris, respectively.

OBJECTIVES

To explore the mechanism of three TCM formulae for three syndrome types of psoriasis vulgaris.

METHODS

The compounds of the three TCM formulae were retrieved from the Psoriasis Database of Traditional Chinese Medicine (PDTCM). Their molecular properties of absorption, distribution, metabolism, excretion and toxicity (ADME/T), and drug-likeness were compared by analyzing the distribution of compounds in the chemical space. The cellular targets of the compounds were predicted by molecular docking. By constructing the compound-target network and analyzing network centrality, key targets and compounds for each formula were screened. Three syndrome types of psoriasis vulgaris related pathways and biological processes (BPs) were enriched by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8.

RESULTS

The compounds of the three formulae exhibited structural diversity, good drug-like properties, and ADME/T properties. A total of 72, 97 and 85 targets were found to have interactions with compounds of F1, F2, and F3, respectively. The three formulae were all related to 53 targets, 8 pathways, 9 biological processes, and 10 molecular functions (MFs). In addition, each formula had unique targets and regulated different pathways and BPs.

CONCLUSION

The three TCM formulae exhibited common mechanisms to some extent. The differences at molecular and systems levels may contribute to their unique applications in individualized treatment.

A chronological pharmacovigilance network analytics approach for predicting adverse drug events

Objectives

This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs.

Materials and methods

large collection of annotated MEDLINE biomedical articles was used to construct a drug-ADE network, and the network was further equipped with information about drugs' target proteins. Several network metrics were extracted and used as predictors in ML algorithms to predict the existence of network edges (ie, associations or relationships).

Results

Gradient boosted trees (GBTs) as an ensemble ML algorithm outperformed other prediction methods in identifying the drug-ADE associations with an overall accuracy of 92.8% on the validation sample. The prediction model was able to predict drug-ADE associations, on average, 3.84 years earlier than they were actually mentioned in the biomedical literature.

Conclusion

While network analysis and ML techniques were used in separation in prior ADE studies, our results showed that they, in combination with each other, can boost the power of one another and predict better. Moreover, our results highlight the superior capability of ensemble-type ML methods in capturing drug-ADE patterns compared to the regular (ie, singular), ML algorithms.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey

Background

Preventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging, and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated.

Objective

This study aimed to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence.

Methods

We invited a convenience sample of 70 drug information experts, including compendia editors, knowledge-base vendors, and clinicians, via emails to complete a survey on identifying PDDI evidence. We created a Web-based survey that included questions regarding the (1) development and conduct of searches; (2) resources used, for example, databases, compendia, search engines, etc; (3) types of keywords used to search for the specific PDDI information; (4) study types included and excluded in searches; and (5) search terms used. Search strategy questions focused on 6 topics of the PDDI information—(1) that a PDDI exists; (2) seriousness; (3) clinical consequences; (4) management options; (5) mechanism; and (6) health outcomes.

Results

Twenty participants (response rate, 20/70, 29%) completed the survey. The majority (17/20, 85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists, with 60% (12/20) having >10 years’ experience. Over half (11/20, 55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants developed (18/20, 90%) and conducted (19/20, 95%) search strategies without librarian assistance. PubMed (20/20, 100%) and Google Scholar (11/20, 55%) were most commonly searched for papers, followed by Google Web Search (7/20, 35%) and EMBASE (3/20, 15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20, 45%), Micromedex (8/20, 40%), Drugs@FDA (17/20, 85%), and DailyMed (13/20, 65%). Facts and Comparisons was the most commonly used compendia (8/20, 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify the existence of PDDIs and determine their mechanism than when searching for the other 4 attributes (seriousness, consequences, management, and health outcomes). Regarding the types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not.

Conclusions

This study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the PDDI evidence they are seeking. Greater automation and standardization across search strategies could improve one’s ability to identify PDDI evidence. Hence, future research focused on enhancing the existing search tools and designing recommended standards is needed.

A systematic survey of centrality measures for protein-protein interaction networks

Background

Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.

Results

We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.

Conclusions

The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0598-2) contains supplementary material, which is available to authorized users.

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

Weak-binding molecules are not drugs?-toward a systematic strategy for finding effective weak-binding drugs

Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimer's disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them. Weak-binding drugs can be characterized by their high dissociation rates and transient interactions with their targets. In addition, network topologies and dynamics parameters involved in the targets of weak-binding drugs also influence the effects of the drugs. Here, we first performed a dynamics analysis for 33 elementary subgraphs to determine the desirable topology and dynamics parameters among targets. Then, by applying the elementary subgraphs to the mitogen-activated protein kinase (MAPK) pathway, several optimal target combinations were obtained. Combining drug-target interaction prediction with molecular dynamics simulation, we got two potential weak-binding drug candidates, luteolin and tanshinone IIA, acting on these targets. Further, the binding affinity of these two compounds to their targets and the anti-inflammatory effects of them were validated through in vitro experiments. In conclusion, weak-binding drugs have real opportunities for maximum efficiency and may show reduced adverse reactions, which can offer a bright and promising future for new drug discovery.

NiaoDuQing granules relieve chronic kidney disease symptoms by decreasing renal fibrosis and anemia

NiaoDuQing (NDQ) granules, a traditional Chinese medicine, has been clinically used in China for over fourteen years to treat chronic kidney disease (CKD). To elucidate the mechanisms underlying the therapeutic benefits of NDQ, we designed an approach incorporating chemoinformatics, bioinformatics, network biology methods, and cellular and molecular biology experiments. A total of 182 active compounds were identified in NDQ granules, and 397 putative targets associated with different diseases were derived through ADME modelling and target prediction tools. Protein-protein interaction networks of CKD-related and putative NDQ targets were constructed, and 219 candidate targets were identified based on topological features. Pathway enrichment analysis showed that the candidate targets were mostly related to the TGF-β, the p38MAPK, and the erythropoietin (EPO) receptor signaling pathways, which are known contributors to renal fibrosis and/or renal anemia. A rat model of CKD was established to validate the drug-target mechanisms predicted by the systems pharmacology analysis. Experimental results confirmed that NDQ granules exerted therapeutic effects on CKD and its comorbidities, including renal anemia, mainly by modulating the TGF-β and EPO signaling pathways. Thus, the pharmacological actions of NDQ on CKD symptoms correlated well with in silico predictions.

How Open Data Shapes In Silico Transporter Modeling

Chemical compound bioactivity and related data are nowadays easily available from open data sources and the open medicinal chemistry literature for many transmembrane proteins. Computational ligand-based modeling of transporters has therefore experienced a shift from local (quantitative) models to more global, qualitative, predictive models. As the size and heterogeneity of the data set rises, careful data curation becomes even more important. This includes, for example, not only a tailored cutoff setting for the generation of binary classes, but also the proper assessment of the applicability domain. Powerful machine learning algorithms (such as multi-label classification) now allow the simultaneous prediction of multiple related targets. However, the more complex, the less interpretable these models will get. We emphasize that transmembrane transporters are very peculiar, some of which act as off-targets rather than as real drug targets. Thus, careful selection of the right modeling technique is important, as well as cautious interpretation of results. We hope that, as more and more data will become available, we will be able to ameliorate and specify our models, coming closer towards function elucidation and the development of safer medicine.

Combination therapeutics in complex diseases

The biological redundancies in molecular networks of complex diseases limit the efficacy of many single drug therapies. Combination therapeutics, as a common therapeutic method, involve pharmacological intervention using several drugs that interact with multiple targets in the molecular networks of diseases and may achieve better efficacy and/or less toxicity than monotherapy in practice. The development of combination therapeutics is complicated by several critical issues, including identifying multiple targets, targeting strategies and the drug combination. This review summarizes the current achievements in combination therapeutics, with a particular emphasis on the efforts to develop combination therapeutics for complex diseases.

DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments

DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customized framework with a broad range of operation modes at all stages of the subpathway analysis, enabling so a case-specific approach. The operation modes include pathway network construction and processing, subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render DEsubs a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level drug targets and biomarkers for complex diseases.

AVAILABILITY AND IMPLEMENTATION

DEsubs is implemented as an R package following Bioconductor guidelines: http://bioconductor.org/packages/DEsubs/ CONTACT: tassos.bezerianos@nus.edu.sgSupplementary information: Supplementary data are available at Bioinformatics online.

Drug combination therapy increases successful drug repositioning

Repositioning of approved drugs has recently gained new momentum for rapid identification and development of new therapeutics for diseases that lack effective drug treatment. Reported repurposing screens have increased dramatically in number in the past five years. However, many newly identified compounds have low potency; this limits their immediate clinical applications because the known, tolerated plasma drug concentrations are lower than the required therapeutic drug concentrations. Drug combinations of two or more compounds with different mechanisms of action are an alternative approach to increase the success rate of drug repositioning.

Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure

Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.