Abstract: For almost two decades, pioglitazone has been prescribed primarily to prevent and treat insulin resistance in some type 2 diabetic patients. In this review, we trace the path to discovery of pioglitazone as a thiazolidinedione compound, the glitazone tracks through the regulatory agencies, the trek to molecular agonism in the nucleus and the binding of pioglitazone to the nuclear receptor PPAR gamma. Given the rise in consumption of pioglitazone in T2D patients worldwide and the increased number of clinical trials currently testing alternate medical uses for this drug, there is also merit to some reflection on the reported adverse effects. Going forward, it is imperative to continue investigations into the mechanisms of actions of pioglitazone, the potential of glitazone drugs to contribute to unmet needs in complex diseases associated with the dynamics of adaptive homeostasis, and also the routes to minimizing adverse effects in every-day patients throughout the world.

Abstract: Background: Osteoclast differentiation in the inflamed synovium of rheumatoid arthritis (RA) affected joints leads to the formation of bone lesions. Reconstruction and analysis of protein interaction networks underlying specific disease phenotypes are essential for designing therapeutic interventions. In this study, we have created a network that captures signal flow leading to osteoclast differentiation. Based on transcriptome analysis, we have indicated the potential mechanisms responsible for the phenotype in the RA affected synovium. Method: We collected information on gene expression, pathways and protein interactions related to RA from literature and databases namely Gene Expression Omnibus, Kyoto Encyclopedia of Genes and Genomes pathway and STRING. Based on these information, we created a network for the differentiation of osteoclasts. We identified the differentially regulated network genes and reported the signaling that are responsible for the process in the RA affected synovium. Result: Our network reveals the mechanisms underlying the activation of the neutrophil cytosolic factor complex in connection to osteoclastogenesis in RA. Additionally, the study reports the predominance of the canonical pathway of NF-kappaB activation in the diseased synovium. The network also confirms that the upregulation of T cell receptor signaling and downregulation of transforming growth factor beta signaling pathway favor osteoclastogenesis in RA. To the best of our knowledge, this is the first comprehensive protein-protein interaction network describing RA driven osteoclastogenesis in the synovium. Discussion: This study provides information that can be used to build models of the signal flow involved in the process of osteoclast differentiation. The models can further be used to design therapies to ameliorate bone destruction in the RA affected joints.

Abstract: Shen-Qi-Di-Huang decoction (SQDHD), a well-known herbal formula from China, has been widely used in the treatment of diabetic nephropathy (DN). However, the pharmacological mechanisms of SQDHD have not been entirely elucidated. At first, we conducted a comprehensive literature search to identify the active constituents of SQDHD, determined their corresponding targets, and obtained known DN targets from several databases. A protein-protein interaction network was then built to explore the complex relations between SQDHD targets and those known to treat DN. Following the topological feature screening of each node in the network, 400 major targets of SQDHD were obtained. The pathway enrichment analysis results acquired from DAVID showed that the significant bioprocesses and pathways include oxidative stress, response to glucose, regulation of blood pressure, regulation of cell proliferation, cytokine-mediated signaling pathway, and the apoptotic signaling pathway. More interestingly, five key targets of SQDHD, named AKT1, AR, CTNNB1, EGFR, and ESR1, were significant in the regulation of the above bioprocesses and pathways. This study partially verified and predicted the pharmacological and molecular mechanisms of SQDHD on DN from a holistic perspective. This has laid the foundation for further experimental research and has expanded the rational application of SQDHD in clinical practice.

Abstract: MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 +/- 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 +/- 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17beta-Estradiol and 5-Aza-2'-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA.

Abstract: Schistosomiasis is a neglected parasitosis caused by Schistosoma spp. Praziquantel is used for the chemoprophylaxis and treatment of this disease. Although this monotherapy is effective, the risk of resistance and its low efficiency against immature worms compromises its effectiveness. Therefore, it is necessary to develop new schistosomicide drugs. However, the development of new drugs is a long and expensive process. The repositioning of approved drugs has been proposed as a quick, cheap, and effective alternative to solve this problem. This study employs chemogenomic analysis with use of bioinformatics tools to search, identify, and analyze data on approved drugs with the potential to inhibit Schistosoma mansoni energy metabolism enzymes. The TDR Targets Database, Gene DB, Protein, DrugBank, Therapeutic Targets Database (TTD), Promiscuous, and PubMed databases were used. Fifty-nine target proteins were identified, of which 18 had one or more approved drugs. The results identified 20 potential drugs for schistosomiasis treatment; all approved for use in humans.

Abstract: Tumor-infiltrating lymphocytes are related to positive clinical prognoses in numerous cancer types. Programmed death ligand 1 (PD-L1), a mediator of the PD-1 receptor, plays an inhibitory role in cancer immune responses. PD-L1 upregulation can impede infiltrating T-cell functions in lung adenocarcinoma (LUAD), a lung cancer subtype. However, associations between the expression of PD-L1 and infiltration of B cells (a major immunoregulatory cell) remain unknown. Therefore, we investigated the role of infiltrating B cells in LUAD progression and its correlation with PD-L1 expression. The Cancer Genome Atlas (TCGA) LUAD data set was used to explore associations among B-cell infiltration, PD-L1 expression, clinical outcome, and gene landscape. Gene set enrichment analysis was used to explore putative signaling pathways and candidate genes. The drug enrichment analysis was used to identify candidate genes and the related drugs. We found that high B-cell infiltration was correlated with better prognoses; however, PD-L1 may interfere with the survival advantage in patients with high B-cell infiltration. The gene landscape was characterized comprehensively, with distinct PD-L1 levels in cell populations with high B-cell infiltration. We obtained five upregulated signaling pathways from the gene landscape: apoptosis, tumor necrosis factor (TNF)-alpha signaling via nuclear factor (NF)-kappaB, apical surface, interferon-alpha response, and KRAS signaling. Moreover, four candidate genes and their related target drugs were also identified, namely interleukin-2beta receptor (IL2RB), IL-2gamma receptor (IL2RG), Toll-like receptor 8 (TLR8), and TNF. These findings suggest that tumor-infiltrating B cells could act as a clinical factor in anti-PD-L1 immunotherapy for LUAD.

Abstract: Peptibodies represent a new class of biological therapeutics with combination of peptide activity and antibody-like properties. Previously, we discovered a novel peptide HRH that exhibited a dose-dependent angiogenesis-suppressing effect by targeting vascular endothelial growth factor receptors (VEGFRs). Here, we computationally designed an antiangiogenic peptibody, termed as PbHRH, by fusing the HRH peptide to human IgG1 Fc fragment using the first approved peptibody drug Romiplostim as template. The biologically active peptide of Romiplostim is similar with HRH peptide; both of them have close sequence lengths and can fold into a alpha-helical conformation in free state. Molecular dynamics simulations revealed that the HRH functional domain is highly flexible, which is functionally independent of Fc fragment in the designed PbHRH peptibody. Subsequently, the intermolecular interactions between VEGFR-1 domain 2 (D2) and PbHRH were predicted, clustered and refined into three representatives. Conformational analysis and energetic evaluation unraveled that the PbHRH can adopt multiple binding modes to block the native VEGF-A binding site of VEGFR-1 D2 with its HRH functional domain, although the binding effectiveness of HRH segments in peptibody context seems to be moderately decreased relative to that of free HRH peptide. Overall, it is suggested that integrating HRH peptide into PbHRH peptibody does not promote the direct intermolecular interaction between VEGFR-1 D2 and HRH. Instead, the peptibody may indirectly help to improve the pharmacokinetic profile and bioavailability of HRH.

Abstract: Tumor recurrence occurs in more than 70% of ovarian cancer patients, and the majority eventually becomes refractory to treatments. Ovarian Cancer Stem Cells (OCSCs) are believed to be responsible for the tumor relapse and drug resistance. Therefore, eliminating ovarian CSCs is important to improve the prognosis of ovarian cancer patients. However, there is a lack of effective drugs to eliminate OCSCs because the core signaling pathways regulating OCSCs remain unclear. Also it is often hard for biologists to identify a few testable targets and infer driver signaling pathways regulating CSCs from a large number of differentially expression genes in an unbiased manner. In this study, we propose a straightforward and integrative analysis to identify potential core signaling pathways of OCSCs by integrating transcriptome data of OCSCs isolated based on two distinctive markers, ALDH and side population, with regulatory network (Transcription Factor (TF) and Target Interactome) and signaling pathways. We first identify the common activated TFs in two OCSC populations integrating the gene expression and TF-target Interactome; and then uncover up-stream signaling cascades regulating the activated TFs. In specific, 22 activated TFs are identified. Through literature search validation, 15 of them have been reported in association with cancer stem cells. Additionally, 10 TFs are found in the KEGG signaling pathways, and their up-stream signaling cascades are extracted, which also provide potential treatment targets. Moreover, 40 FDA approved drugs are identified to target on the up-stream signaling cascades, and 15 of them have been reported in literatures in cancer stem cell treatment. In conclusion, the proposed approach can uncover the activated up-stream signaling, activated TFs and up-regulated target genes that constitute the potential core signaling pathways of ovarian CSC. Also drugs and drug combinations targeting on the core signaling pathways might be able to eliminate OCSCs. The proposed approach can also be applied for identifying potential activated signaling pathways of other types of cancers.

Abstract: Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disorder and the pathogenesis of AD is poorly understood. G protein-coupled receptors (GPCRs) are involved in numerous key AD pathways and play a key role in the pathology of AD. To fully understand the pathogenesis of AD and design novel drug therapeutics, analyzing the connection between AD and GPCRs is of great importance. In this paper, we firstly build and analyze the AD-related pathway by consulting the KEGG pathway of AD and a mass of literature and collect 25 AD-related GPCRs for drug discovery. Then the ILbind and AutoDock Vina tools are integrated to find out potential drugs related to AD. According to the analysis of DUD-E dataset, we select five drugs, that is, Acarbose (ACR), Carvedilol (CVD), Digoxin (DGX), NADH (NAI), and Telmisartan (TLS), by sorting the ILbind scores (>/=0.73). Then depending on their AutoDock Vina scores and pocket position information, the binding patterns of these five drugs are obtained. We analyze the regulation function of GPCRs in the metabolic network of AD based on the drug screen results, which may be helpful for the study of the off-target effect and the side effect of drugs.

Abstract: The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.