Predicting selective drug targets in cancer through metabolic networks

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome‐scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled.     

Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major

Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systems‐based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof‐of‐concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage‐specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.     

In silico prediction of drug targets in <Emphasis Type="Italic">Vibrio cholerae</Emphasis>

Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations.     

创伤弧菌毒力相关因子的全基因组关联分析研究

【目的】创伤弧菌是致死率最高的弧菌物种,但目前尚无在全基因组层面挖掘毒力相关因子的研究。本研究以创伤弧菌分离来源(临床和环境)作为不同表型,通过与260株基因组序列进行关联分析,挖掘毒力相关因子,从而进一步了解创伤弧菌致病因素。【方法】对139株创伤弧菌分离株进行高通量测序,获取其全基因组序列;与公共数据库已公开发表的121株基因组整合,使用pyseer软件进行全基因组关联分析,对与不同分离来源显著相关的基因进行注释和解读。【结果】共发现11个基因与临床分离株显著相关,其中9个是本研究新发现的创伤弧菌潜在毒力相关因子。【结论】本研究使用群体基因组学和统计遗传学方法,在全基因组范围扫描挖掘了创伤弧菌毒力相关因子,为深入揭示该物种致病机制、设计新的疫苗和治疗靶点提供了重要依据。     

In silico identification of common putative drug targets in <Emphasis Type="Italic">Leptospira interrogans</Emphasis>

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.     

A recombinant metalloprotease antigen of Vibrio vulnificus elicits protective antibodies in a murine model

Aims: To investigate whether Vibrio vulnificus metalloprotease (VvpE) can induce the production of specific anti‐VvpE antibody to confer effective protection against Vibrio vulnificus infection and to evaluate the possibility of VvpE as a potential vaccine candidate against disease caused by V. vulnificus. Methods and Results: The gene encoding the 65‐kDa VvpE of V. vulnificus was amplified by PCR and cloned into the expression vector pET21(b). The recombinant VvpE of V. vulnificus was expressed in Escherichia coli BL21(DE3). This His₆‐tagged VvpE was purified and injected intramuscularly into mice to evaluate its ability to stimulate immune response. Specific antibody levels were measured by ELISA. The 75% protective efficacy of recombinant VvpE was evaluated by active immunization and intraperitoneal challenge with V. vulnificus in mice. Conclusions: The recombinant His₆‐tagged VvpE of V. vulnificus is capable of inducing high antibody response in mice to confer effective protection against lethal challenge with V. vulnificus. VvpE might be a potential vaccine candidate to against V. vulnificus infection. Significance and Impact of the Study: This study uses His₆‐tagged VvpE to act as vaccine that successfully induces effective and specific anti‐VvpE antibody and offers an option for the potential vaccine candidate against V. vulnificus infection.     

“Amyloid‐beta accumulation cycle” as a prevention and/or therapy target for Alzheimer's disease

The cell cycle and its regulators are validated targets for cancer drugs. Reagents that target cells in a specific cell cycle phase (e.g., antimitotics or DNA synthesis inhibitors/replication stress inducers) have demonstrated success as broad‐spectrum anticancer drugs. Cyclin‐dependent kinases (CDKs) are drivers of cell cycle transitions. A CDK inhibitor, flavopiridol/alvocidib, is an FDA‐approved drug for acute myeloid leukemia. Alzheimer's disease (AD) is another serious issue in contemporary medicine. The cause of AD remains elusive, although a critical role of latent amyloid‐beta accumulation has emerged. Existing AD drug research and development targets include amyloid, amyloid metabolism/catabolism, tau, inflammation, cholesterol, the cholinergic system, and other neurotransmitters. However, none have been validated as therapeutically effective targets. Recent reports from AD‐omics and preclinical animal models provided data supporting the long‐standing notion that cell cycle progression and/or mitosis may be a valid target for AD prevention and/or therapy. This review will summarize the recent developments in AD research: (a) Mitotic re‐entry, leading to the “amyloid‐beta accumulation cycle,” may be a prerequisite for amyloid‐beta accumulation and AD pathology development; (b) AD‐associated pathogens can cause cell cycle errors; (c) thirteen among 37 human AD genetic risk genes may be functionally involved in the cell cycle and/or mitosis; and (d) preclinical AD mouse models treated with CDK inhibitor showed improvements in cognitive/behavioral symptoms. If the “amyloid‐beta accumulation cycle is an AD drug target” concept is proven, repurposing of cancer drugs may emerge as a new, fast‐track approach for AD management in the clinic setting.     

Assessing the essentiality of the decaprenyl‐phospho‐d‐arabinofuranose pathway in Mycobacterium tuberculosis using conditional mutants

In Mycobacterium tuberculosis the decaprenyl‐phospho‐d ‐arabinofuranose (DPA) pathway is a validated target for the drugs ethambutol and benzothiazinones. To identify other potential drug targets in the pathway, we generated conditional knock‐down mutants of each gene involved using the TET‐PIP OFF system. dprE1, dprE2, ubiA, prsA, rv2361c, tkt and rpiB were confirmed to be essential under non‐permissive conditions, whereas rv3807c was not required for survival. In the most vulnerable group, DprE1‐depleted cells died faster in vitro and intracellularly than those lacking UbiA and PrsA. Downregulation of DprE1 and UbiA resulted in similar phenotypes, namely swelling of the bacteria, cell wall damage and lysis as observed at the single cell level, by real time microscopy and electron microscopy. By contrast, depletion of PrsA led to cell elongation and implosion, which was suggestive of a more pleiotropic effect. Drug sensitivity assays with known DPA‐inhibitors supported the use of conditional knock‐down strains for target‐based whole‐cell screens. Together, our work provides strong evidence for the vulnerability of all but one of the enzymes in the DPA pathway and generates valuable tools for the identification of lead compounds targeting the different biosynthetic steps. PrsA, phosphoribosyl‐pyrophosphate synthetase, appears to be a particularly attractive new target for drug discovery.     

THE ROLE OF ANTISENSE OLIGONUCLEOTIDES IN THE WAVE OF GENOMIC INFORMATION

Technologies which efficiently dissect gene function and validate therapeutic targets are of great value in the post-sequencing era of the human genome project. The antisense oligonucleotide approach can directly use genomic sequence information, in a relatively time and cost effective manner, to define a gene's function and/or validate it as a potential therapeutic target. Antisense oligonucleotide inhibitors of gene expression may be applied to cellular assays (in vitro) or animal models of disease (in vivo). Information generated by this approach may then direct or supplement traditional drug discovery programs, or support development of the antisense oligonucleotide inhibitor, used to validate the target, as a drug.     

Additive Function of Vibrio vulnificus MARTXVv and VvhA Cytolysins Promotes Rapid Growth and Epithelial Tissue Necrosis During Intestinal Infection

Vibrio vulnificus is a pathogen that causes both severe necrotizing wound infections and life-threatening food-borne infections. Food-borne infection is particularly lethal as the infection can progress rapidly to primary septicemia resulting in death from septic shock and multiorgan failure. In this study, we use both bioluminescence whole animal imaging and V. vulnificus bacterial colonization of orally infected mice to demonstrate that the secreted multifunctional-autoprocessing RTX toxin (MARTX_Vv) and the cytolysin/hemolysin VvhA of clinical isolate CMCP6 have an important function in the gut to promote early in vivo growth and dissemination of this pathogen from the small intestine to other organs. Using histopathology, we find that both cytotoxins can cause villi disruption, epithelial necrosis, and inflammation in the mouse small intestine. A double mutant deleted of genes for both cytotoxins was essentially avirulent, did not cause intestinal epithelial tissue damage, and was cleared from infected mice by 36 hours by an effective immune response. Therefore, MARTX_Vv and VvhA seem to play an additive role for pathogenesis of CMCP6 causing intestinal tissue damage and inflammation that then promotes dissemination of the infecting bacteria to the bloodstream and other organs. In the absence of these two secreted factors, we propose that this bacterium is unable to cause intestinal infection in humans.     

A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

Comparative Genomics Analysis of Mycobacterium ulcerans for the Identification of Putative Essential Genes and Therapeutic Candidates

Mycobacterium ulcerans, the causative agent of Buruli ulcer, is the third most common mycobacterial disease after tuberculosis and leprosy. The present treatment options are limited and emergence of treatment resistant isolates represents a serious concern and a need for better therapeutics. Conventional drug discovery methods are time consuming and labor-intensive. Unfortunately, the slow growing nature of M. ulcerans in experimental conditions is also a barrier for drug discovery and development. In contrast, recent advancements in complete genome sequencing, in combination with cheminformatics and computational biology, represent an attractive alternative approach for the identification of therapeutic candidates worthy of experimental research. A computational, comparative genomics workflow was defined for the identification of novel therapeutic candidates against M. ulcerans, with the aim that a selected target should be essential to the pathogen, and have no homology in the human host. Initially, a total of 424 genes were predicted as essential from the M. ulcerans genome, via homology searching of essential genome content from 20 different bacteria. Metabolic pathway analysis showed that the most essential genes are associated with carbohydrate and amino acid metabolism. Among these, 236 proteins were identified as non-host and essential, and could serve as potential drug and vaccine candidates. Several drug target prioritization parameters including druggability were also calculated. Enzymes from several pathways are discussed as potential drug targets, including those from cell wall synthesis, thiamine biosynthesis, protein biosynthesis, and histidine biosynthesis. It is expected that our data will facilitate selection of M. ulcerans proteins for successful entry into drug design pipelines.     

T-iDT : tool for identification of drug target in bacteria and validation by Mycobacterium tuberculosis

With the completion of the Human Genome Project in 2003, many new projects to sequence bacterial genomes were started and soon many complete bacterial genome sequences were available. The sequenced genomes of pathogenic bacteria provide useful information for understanding host-pathogen interactions. These data prove to be a new weapon in fighting against pathogenic bacteria by providing information about potential drug targets. But the limitation of computational tools for finding potential drug targets has hindered the process and further experimental analysis. There are many in silico approaches proposed for finding drug targets but only few have been automated. One such approach finds essential genes in bacterial genomes with no human homologue and predicts these as potential drug targets. The same approach is used in our tool. T-iDT, a tool for the identification of drug targets, finds essential genes by comparing a bacterial gene set against DEG (Database of Essential Genes) and excludes homologue genes by comparing against a human protein database. The tool predicts both the set of essential genes as well as potential target genes for the given genome. The tool was tested with Mycobacterium tuberculosis and results were validated. With default parameters, the tool predicted 236 essential genes and 52 genes to encode potential drug targets. A pathway-based approach was used to validate these potential drug target genes. The pathway in which the products of these genes are involved was determined. Our analysis shows that almost all these pathways are very essential for the bacterial survival and hence these genes encode possible drug targets. Our tool provides a fast method for finding possible drug targets in bacterial genomes with varying stringency level. The tool will be helpful in finding possible drug targets in various pathogenic organisms and can be used for further analysis in novel therapeutic drug development. The tool can be downloaded from http://www.milser.co.in/research.htm and http://www.srmbioinformatics.edu.in/ forum.htm.     

N‐glycoprotein surfaceome of human induced pluripotent stem cell derived hepatic endoderm

Using cell surface capture technology, the cell surface N‐glycoproteome of human‐induced pluripotent stem cell derived hepatic endoderm cells was assessed. Altogether, 395 cell surface N‐glycoproteins were identified, represented by 1273 N‐glycopeptides. This study identified N‐glycoproteins that are not predicted to be localized to the cell surface and provides experimental data that assist in resolving ambiguous or incorrectly annotated transmembrane topology annotations. In a proof‐of‐concept analysis, combining these data with other cell surface proteome datasets is useful for identifying potentially cell type and lineage restricted markers and drug targets to advance the use of stem cell technologies for mechanistic developmental studies, disease modeling, drug discovery, and regenerative medicine.