Clarifying off-target effects for torcetrapib using network pharmacology and reverse docking approach

Repurposing α-Adrenoreceptor Blockers as Promising Anti-Virulence Agents in Gram-Negative Bacteria

Antimicrobial resistance is among the world’s most urgent public health problems. Diminishing of the virulence of bacteria is a promising approach to decrease the development of bacterial resistance. Quorum sensing (QS) systems orchestrate the bacterial virulence in inducer–receptors manner. Bacteria can spy on the cells of the host by sensing adrenergic hormones and other neurotransmitters, and in turn, these neurotransmitters can induce bacterial pathogenesis. In this direction, α-adrenergic blockers were proposed as an anti-virulence agents through inhibiting the bacterial espionage. The current study aimed to explore the α-blockers’ anti-QS activities. Within comprehensive in silico investigation, the binding affinities of seven α-adrenoreceptor blockers were evaluated towards structurally different QS receptors. From the best docked α-blockers into QS receptors, terazosin was nominated to be subjected for further in vivo and in vitro anti-QS and anti-virulence activities against Chromobacterium violaceum and Pseudomonas aeruginosa. Terazosin showed a significant ability to diminish the QS-controlled pigment production in C. violaceum. Moreover, Terazosin decreased the P. aeruginosa biofilm formation and down-regulated its QS-encoding genes. Terazosin protected mice from the P. aeruginosa pathogenesis. In conclusion, α-adrenergic blockers are proposed as promising anti-virulence agents as they hinder QS receptors and inhibit bacterial espionage.

Application of Reverse Docking in the Research of Small Molecule Drugs and Traditional Chinese Medicine

With the development of structural biology and data mining, computer-aided drug design (CADD) has been playing an important role in all aspects of new drug development. Reverse docking, a method of virtual screening based on molecular docking in CADD, is widely used in drug repositioning, drug rescue, and traditional Chinese medicine (TCM) research, for it can search for macromolecular targets that can bind to a given ligand molecule. This review revealed the principle of reverse docking, summarized common target protein databases and docking procedures, and enumerated the applications of reverse docking in drug repositioning, adverse drug reactions, traditional Chinese medicine, and COVID-19 treatment. Hope our work can give some inspiration to researchers engaged in drug development.

The latest automated docking technologies for novel drug discovery

INTRODUCTION

Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence.

AREAS COVERED

In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking.

EXPERT OPINION

There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.

Steroids-specific target library for steroids target prediction

Steroids exist universally and play critical roles in various biological processes. Identifying potential targets of steroids is of great significance in studying their physiological and biochemical activities, the side effects and for drug repurposing. Herein, aiming at more precise steroids targets prediction, a steroids-specific target library integrating 3325 PDB or homology modeling structures categorized into 196 proteins was built by considering chemical similarity from DrugBank and biological processes from KEGG. The main properties of this library include: (1) It was manually prepared and checked to eliminate mistakes. (2) The library enriched the possible steroids targets and could decrease the false positives of structure-based target screening for steroids. (3) The ranking by protein name instead of PDB ID could make the screening more efficiency and precise. (4) Protein flexibility was taken into account partially by the different active conformations through the structural redundancy of each category of protein, which leads to more accurate prediction. The case studies of glycocholic acid and 24-epibrassinolide proved its powerful predictive accuracy. In summary, our strategy to build the steroids-specific protein library for steroids target prediction is a promising approach and it provides a novel idea for the target prediction of small molecules.

Molecular pathogenesis involved in human idiopathic pulmonary fibrosis based on an integrated microRNA‑mRNA interaction network

Idiopathic pulmonary fibrosis (IPF) is considered to be an ailment of the lungs that cannot be cured, wherein the lung tissues are characterized by increased thickness and stiffness, and/or scars. Despite the fact that extensive success has been achieved regarding the molecular diagnostics and pathobiology, the basic pathogenesis associated with IPF has not yet been fully elucidated and requires further clarification. In the current research, the changes in microRNA (miRNA) and mRNA expression in IPF were investigated through an integrative network technique. The authentic miRNA and mRNA expression profiling datasets were downloaded from Gene Expression Omnibus, followed by identification of differentially expressed miRNAs and mRNAs with use of the Significance Analysis of Microarrays algorithm. Expansion of the molecular targets associated with miRNAs was performed with the use of CyTargetLinker in Cytoscape, which was succeeded by validation with the use of mRNA array expression profiling. The incorporated miRNA‑mRNA network covered 27 genes, in addition to 22 miRNAs that were associated with IPF development. As revealed by the functional enrichment analysis, the cytokine‑cytokine receptor interaction and glycine, serine and threonine metabolism signalling pathways were extensively associated with IPF development. Overall, the present incorporated network illustrated the key link between miRNA and genes in IPF; in particular, it was elucidated that miR‑409‑5p and has‑miR‑376c, together with their target genes (C‑C motif chemokine ligand 20 and oncostatin M), are likely candidates involved in the promotion of IPF initiation and progression.

Exploring the molecular mechanism associated with breast cancer bone metastasis using bioinformatic analysis and microarray genetic interaction network

BACKGROUND

Bone metastases are common in advanced breast cancer patients and frequently leading to skeletal-related morbidity and deterioration in the quality of life. Although chemotherapy and hormone therapy are able to control the symptoms caused by bone destruction, the underlying molecular mechanisms for the affinity of breast cancer cells towards skeletal bones are still not completely understood.

METHODS

In this study, bioinformatic analysis was performed on patients' microarray gene expression data to explore the molecular mechanism associated with breast cancer bone metastasis. Microarray gene expression profile regarding patients with breast cancer and disseminated tumor cells was downloaded from Gene Expression Omnibus (GEO) database (NCBI, NIH). Raw data were normalized and differently expressed genes were identified by using Significance Analysis of Microarrays (SAM) methods. Protein interaction networks were expanded using String. Moreover, molecular functions, biological processes and signaling pathway enrichment analysis were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).

RESULTS

We identified 66 differentially expressed genes. After submitting the set of genes to String, genetic interaction network was expanded, which consisted of 110 nodes and 869 edges. Pathway enrichment analysis suggested that adhesion kinase, ECM-receptor interaction, calcium signaling, Wnt pathways, and PI3K/AKT signaling pathway are highly associated with breast cancer bone metastasis.

CONCLUSION

In this study, we established a microarray genetic interaction network associated with breast cancer bone metastasis. This information provides some potential molecular therapeutic targets for breast cancer initiation and progression.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Bisdemethoxycurcumin exerts pro-apoptotic effects in human pancreatic adenocarcinoma cells through mitochondrial dysfunction and a GRP78-dependent pathway

Pancreatic cancer is a highly aggressive malignancy, which is intrinsically resistant to current chemotherapies. Herein, we investigate whether bisdemethoxycurcumin (BDMC), a derivative of curcumin, potentiates gemcitabine in human pancreatic cancer cells. The result suggests that BDMC sensitizes gemcitabine by inducing mitochondrial dysfunctions and apoptosis in PANC-1 and MiaPaCa-2 pancreatic cancer cells. Utilizing two-dimensional gel electrophoresis and mass spectrometry, we identify 13 essential proteins with significantly altered expressions in response to gemcitabine alone or combined with BDMC. Protein-protein interaction network analysis pinpoints glucose-regulated protein 78 (GRP78) as the key hub activated by BDMC. We then reveal that BDMC upregulates GRP78 and facilitates apoptosis through eIF2α/CHOP pathway. Moreover, DJ-1 and prohibitin, two identified markers of chemoresistance, are increased by gemcitabine in PANC-1 cells. This could be meaningfully reversed by BDMC, suggesting that BDMC partially offsets the chemoresistance induced by gemcitabine. In summary, these findings show that BDMC promotes apoptosis through a GRP78-dependent pathway and mitochondrial dysfunctions, and potentiates the antitumor effect of gemcitabine in human pancreatic cancer cells.

Predictive Systems Toxicology

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point of view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predicitive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e., equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.

SNP mutations occurring in thyroid hormone receptor influenced individual susceptibility to triiodothyronine: Molecular dynamics and site-directed mutagenesis approaches

The increasing evidences have suggested that expression of single nucleotide polymorphisms (SNP) coded thyroid hormone receptors (THR) generally are associated with individual susceptibility to chemicals. In the present research, multiple molecular dynamics simulations on four SNP mutants (G332R, T337Δ, G345R, and G347E) were performed to investigate the structural and dynamical altering, which could lead to a binding capability variation to triiodothyronine (T3). It proved the structures of two SNP mutants (G345R and T337Δ) occurring in the THR proteins had experienced conformational change to a great extend, which also led to a significant decreasing in binding ability with T3. In addition, two mutates (G345R and G347E) and wild type THR proteins were expressed and purified based on site-directed mutagenesis technology to test their binding abilities with T3 by fluorescence experiments. The fluorescence quenching efficiencies of two mutates displayed that the conjugation with T3 decreased with a significant rate in G345R system and a little rate in G347E system compared with its wild type. It was consistent with the molecular dynamic research that the SNP mutations did change structures of THR protein, and thereby decreased the binding behavior of T3 at different extent. The overall molecular-level look at the protein structure may provide the structural basis to explain how one amino acid change can create a ripple effect on the protein structures and eventually affect the binding affinity of the ligands, which maybe the first stage to understand how SNP mutation results in individual difference in susceptibility to variant chemicals.

Clarifying the molecular mechanism associated with carfilzomib resistance in human multiple myeloma using microarray gene expression profile and genetic interaction network

Carfilzomib is a Food and Drug Administration-approved selective proteasome inhibitor for patients with multiple myeloma (MM). However, recent studies indicate that MM cells still develop resistance to carfilzomib, and the molecular mechanisms associated with carfilzomib resistance have not been studied in detail. In this study, to better understand its potential resistant effect and its underlying mechanisms in MM, microarray gene expression profile associated with carfilzomib-resistant KMS-11 and its parental cell line was downloaded from Gene Expression Omnibus database. Raw fluorescent signals were normalized and differently expressed genes were identified using Significance Analysis of Microarrays method. Genetic interaction network was expanded using String, a biomolecular interaction network JAVA platform. Meanwhile, molecular function, biological process and signaling pathway enrichment analysis were performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Totally, 27 upregulated and 36 downregulated genes were identified and a genetic interaction network associated with the resistant effect was expanded basing on String, which consisted of 100 nodes and 249 edges. In addition, signaling pathway enrichment analysis indicated that cytokine–cytokine receptor interaction, autophagy, ErbB signaling pathway, microRNAs in cancer and fatty acid metabolism pathways were aberrant in carfilzomib-resistant KMS-11 cells. Thus, in this study, we demonstrated that carfilzomib potentially conferred drug resistance to KMS-11 cells by cytokine–cytokine receptor interaction, autophagy, ErbB signaling pathway, microRNAs in cancer and fatty acid metabolism pathways, which may provide some potential molecular therapeutic targets for drug combination therapy against carfilzomib resistance.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

In silico methods for drug repurposing and pharmacology

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

In silico assessment of adverse drug reactions and associated mechanisms

During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.

Development of broad-spectrum antibiofilm drugs: strategies and challenges

ABSTRACT 

The severity of many chronic bacterial infections is mainly due to the biofilm mode of life adapted by pathogenic bacteria. The bacteria in biofilm-stage exhibit high resistance to host immune responses and antimicrobials, which complicates the treatment process and results in life threatening conditions. Most of the chronic infections are polymicrobial in nature. In order to combat the polymicrobial biofilm infections and to increase the efficiency of antimicrobials, there is an urgent need for broad-spectrum antibiofilm drugs. This review discusses the clinical needs and current status of broad-spectrum antibiofilm drugs with special emphasis on prospective strategies and hurdles in the process of new drug discovery.

Nimbolide targets BCL2 and induces apoptosis in preclinical models of Waldenströms macroglobulinemia

Neem leaf extract (NLE) has medicinal properties, which have been attributed to its limonoid content. We identified the NLE tetranorterpenoid, nimbolide, as being the key limonoid responsible for the cytotoxicity of NLE in various preclinical models of human B-lymphocyte cancer. Of the models tested, Waldenströms macroglobulinemia (WM) cells were most sensitive to nimbolide, undergoing significant mitochondrial mediated apoptosis. Notably, nimbolide toxicity was also observed in drug-resistant (bortezomib or ibrutinib) WM cells. To identify putative targets of nimbolide, relevant in WM, we used chemoinformatics-based approaches comprised of virtual in silico screening, molecular modeling and target–ligand reverse docking. In silico analysis revealed the antiapoptotic protein BCL2 was the preferential binding partner of nimbolide. The significance of this finding was further tested in vitro in RS4;11 (BCL2-dependent) tumor cells, in which nimbolide induced significantly more apoptosis compared with BCL2 mutated (Jurkat BCL2^Ser70-Ala) cells. Lastly, intraperitoneal administration of nimbolide in WM tumor xenografted mice, significantly reduced tumor growth and IgM secretion in vivo, while modulating the expression of several proteins as seen on immunohistochemistry. Overall, our data demonstrate that nimbolide is highly active in WM cells, as well as other B-cell cancers, and engages BCL2 to exert its cytotoxic activity.

Reverse docking: a powerful tool for drug repositioning and drug rescue

Reverse or inverse docking is proving to be a powerful tool for drug repositioning and drug rescue. It involves docking a small-molecule drug/ligand in the potential binding cavities of a set of clinically relevant macromolecular targets. Detailed analyses of the binding characteristics lead to ranking of the targets according to the tightness of binding. This process can potentially identify novel molecular targets for the drug/ligand which may be relevant for its mechanism of action and/or side effect profile. Another potential application of reverse docking is during the lead discovery and optimization stages of the drug-discovery cycle. This review summarizes the state-of-the-art and future prospects of the reverse docking with particular emphasis on computational molecular design.

Looking into the Crystal Ball—Upcoming Drugs for Dyslipidemia

Dyslipidaemia is a critical risk factor for the development of cardiovascular complications such as ischemic heart disease and stroke. Although statins are effective anti-dyslipidemic drugs, their usage is fraught with issues such as failure of adequate lipid control in 30% of cases and intolerance in select patients. The limited potential of other alternatives such as fibrates, bile acid sequestrants and niacin has spurred the search for novel drug molecules with better efficacy and safety. CETP inhibitors such as evacetrapib and anacetrapib have shown promise in raising HDL besides LDL lowering property. Microsomal triglyceride transfer protein (MTP) inhibitors such as lomitapide and Apo CIII inhibitors such as mipomersen have recently been approved in Familial Hypercholesterolemia but experience in the non-familial setting is pretty much limited. One of the novel anti-dyslipidemic drugs which is greatly anticipated to make a mark in LDL-C control is the PCSK9 inhibitors. Some of the anti-dyslipidemic drugs which work by PCSK9 inhibition include evolocumab, alirocumab and ALN-PCS. Other approaches that are being given due consideration include farnesoid X receptor modulation and Lp-PLA2 inhibition. While it may not be an easy proposition to dismantle statins from their current position as a cholesterol reducing agent and as a drug to reduce coronary and cerebro-vascular atherosclerosis, our improved understanding of the disease and appropriate harnessing of resources using sound and robust technology could make rapid in-roads in our pursuit of the ideal anti-dyslipidemic drug.

Opposite angiogenic outcome of curcumin against ischemia and Lewis lung cancer models: in silico, in vitro and in vivo studies

The aim of this study was to investigate the angiogenic effects of curcumin on an ischemia and lung cancer model. To induce ischemia combined with lung cancer models, unilateral femoral arteries of C57BL/6 mice were disconnected on one side of the mouse and Lewis lung carcinoma (LLC) cells were xenografted on the opposite side. Angiogenic effects and underlying mechanisms associated with curcumin were investigated. Molecular target(s), signaling cascades and binding affinities were detected by Western blot, two-dimensional gel electrophoresis (2-DE), computer simulations and surface plasmon resonance (SPR) techniques. Curcumin promoted post-ischemic blood recirculation and suppressed lung cancer progression in inbred C57BL/6 mice via regulation of the HIF1α/mTOR/VEGF/VEGFR cascade oppositely. Inflammatory stimulation induced by neutrophil elastase (NE) promoted angiogenesis in lung cancer tissues, but these changes were reversed by curcumin through directly reducing NE secretion and stimulating α1-antitrypsin (α1-AT) and insulin receptor substrate-1 (IRS-1) production. Meanwhile, curcumin dose-dependently influenced endothelial cells (EC) tube formation and chicken embryo chorioallantoic membrane (CAM) neovascularization. Curcumin had opposite effects on blood vessel regeneration under physiological and pathological angiogenesis, which was effected through negative or positive regulation of the HIF1α/mTOR/VEGF/VEGFR cascade. Curcumin had the promise as a new treatment modality for both ischemic conditions and lung cancer simultaneously in the clinic.

Pharmacogenomics in the development and characterization of atheroprotective drugs

Atherosclerosis is the main cause of cardiovascular disease (CVD) and can lead to stroke, myocardial infarction, and death. The clinically available atheroprotective drugs aim mainly at reducing the levels of circulating low-density lipoprotein (LDL), increasing high-density lipoprotein (HDL), and attenuating inflammation. However, the cardiovascular risk remains high, along with morbidity, mortality, and incidence of adverse drug events. Pharmacogenomics is increasingly contributing towards the characterization of existing atheroprotective drugs, the evaluation of novel ones, and the identification of promising, unexplored therapeutic targets, at the global molecular pathway level. This chapter presents highlights of pharmacogenomics investigations and discoveries that have contributed towards the elucidation of pharmacological atheroprotection, while opening the way to new therapeutic approaches.

Computational methods for drug design and discovery: focus on China

In the past decades, China's computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of China's status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field.

Facilitating the use of large-scale biological data and tools in the era of translational bioinformatics

As both the amount of generated biological data and the processing compute power increase, computational experimentation is no longer the exclusivity of bioinformaticians, but it is moving across all biomedical domains. For bioinformatics to realize its translational potential, domain experts need access to user-friendly solutions to navigate, integrate and extract information out of biological databases, as well as to combine tools and data resources in bioinformatics workflows. In this review, we present services that assist biomedical scientists in incorporating bioinformatics tools into their research. We review recent applications of Cytoscape, BioGPS and DAVID for data visualization, integration and functional enrichment. Moreover, we illustrate the use of Taverna, Kepler, GenePattern, and Galaxy as open-access workbenches for bioinformatics workflows. Finally, we mention services that facilitate the integration of biomedical ontologies and bioinformatics tools in computational workflows.

Layered signaling regulatory networks analysis of gene expression involved in malignant tumorigenesis of non-resolving ulcerative colitis via integration of cross-study microarray profiles

Background

Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.

Methodology/Principal Findings

In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.

Results

Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.

Conclusions

The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.