Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.     

Recent Advances in Drug Repositioning for the Discovery of New Anticancer Drugs

Drug repositioning (also referred to as drug repurposing), the process of finding new uses of existing drugs, has been gaining popularity in recent years. The availability of several established clinical drug libraries and rapid advances in disease biology, genomics and bioinformatics has accelerated the pace of both activity-based and in silico drug repositioning. Drug repositioning has attracted particular attention from the communities engaged in anticancer drug discovery due to the combination of great demand for new anticancer drugs and the availability of a wide variety of cell- and target-based screening assays. With the successful clinical introduction of a number of non-cancer drugs for cancer treatment, drug repositioning now became a powerful alternative strategy to discover and develop novel anticancer drug candidates from the existing drug space. In this review, recent successful examples of drug repositioning for anticancer drug discovery from non-cancer drugs will be discussed.     

Pharmacologically active metabolites,combination screening and target identification-driven drug repositioning in antituberculosis drug discovery

There has been renewed interest in alternative strategies to address bottlenecks in antibiotic development. These include the repurposing of approved drugs for use as novel anti-infective agents, or their exploitation as leads in drug repositioning. Such approaches are especially attractive for tuberculosis (TB), a disease which remains a leading cause of morbidity and mortality globally and, increasingly, is associated with the emergence of drug-resistance. In this review article, we introduce a refinement of traditional drug repositioning and repurposing strategies involving the development of drugs that are based on the active metabolite(s) of parental compounds with demonstrated efficacy. In addition, we describe an approach to repositioning the natural product antibiotic, fusidic acid, for use against Mycobacterium tuberculosis. Finally, we consider the potential to exploit the chemical matter arising from these activities in combination screens and permeation assays which are designed to confirm mechanism of action (MoA), elucidate potential synergies in polypharmacy, and to develop rules for drug permeability in an organism that poses a special challenge to new drug development.     

Repositioning drugs for inflammatory disease – fishing for new anti-inflammatory agents

Inflammation is an important and appropriate host response to infection or injury. However, dysregulation of this response, with resulting persistent or inappropriate inflammation, underlies a broad range of pathological processes, from inflammatory dermatoses to type 2 diabetes and cancer. As such, identifying new drugs to suppress inflammation is an area of intense interest. Despite notable successes, there still exists an unmet need for new effective therapeutic approaches to treat inflammation. Traditional drug discovery, including structure-based drug design, have largely fallen short of satisfying this unmet need. With faster development times and reduced safety and pharmacokinetic uncertainty, drug repositioning – the process of finding new uses for existing drugs – is emerging as an alternative strategy to traditional drug design that promises an improved risk-reward trade-off. Using a zebrafish in vivo neutrophil migration assay, we undertook a drug repositioning screen to identify unknown anti-inflammatory activities for known drugs. By interrogating a library of 1280 approved drugs for their ability to suppress the recruitment of neutrophils to tail fin injury, we identified a number of drugs with significant anti-inflammatory activity that have not previously been characterized as general anti-inflammatories. Importantly, we reveal that the ten most potent repositioned drugs from our zebrafish screen displayed conserved anti-inflammatory activity in a mouse model of skin inflammation (atopic dermatitis). This study provides compelling evidence that exploiting the zebrafish as an in vivo drug repositioning platform holds promise as a strategy to reveal new anti-inflammatory activities for existing drugs.KEY WORDS: Drug repositioning, Zebrafish, Inflammation, Neutrophil, Atopic dermatitis, Immunity     

An ultra-dense library resource for rapid deconvolution of mutations that cause phenotypes in Escherichia coli

With the wide availability of whole-genome sequencing (WGS), genetic mapping has become the rate-limiting step, inhibiting unbiased forward genetics in even the most tractable model organisms. We introduce a rapid deconvolution resource and method for untagged causative mutations after mutagenesis, screens, and WGS in Escherichia coli. We created Deconvoluter—ordered libraries with selectable insertions every 50 kb in the E. coli genome. The Deconvoluter method uses these for replacement of untagged mutations in the genome using a phage-P1-based gene-replacement strategy. We validate the Deconvoluter resource by deconvolution of 17 of 17 phenotype-altering mutations from a screen of N-ethyl-N-nitrosourea-induced mutants. The Deconvoluter resource permits rapid unbiased screens and gene/function identification and will enable exploration of functions of essential genes and undiscovered genes/sites/alleles not represented in existing deletion collections. This resource for unbiased forward-genetic screens with mapping-by-sequencing (‘forward genomics’) demonstrates a strategy that could similarly enable rapid screens in many other microbes.     

Two-hybrid reporter vectors for gap repair cloning

Yeast two-hybrid analysis is a valuable approach to the discovery and characterization of protein interactions. We have developed vectors that can indicate the presence of an insert when used in two-hybrid bait and prey construction by gap repair cloning. The strategy uses a recombination cloning site flanked by sequences encoding the GAL4 activation and binding domains. After gap repair cloning in standard hosts carrying an ADE2 reporter gene, disruption of GAL4 by an insert can be identified by the development of red colony color, while empty vector plasmids produce white colonies. Function in yeast two-hybrid applications was initially validated using known interacting proteins in pair-wise analyses, and subsequently, the bait vectors were used in library screens with the mouse Mad212 and human Mccd1 proteins, identifying a number of putative new interactions for these proteins. These vectors should facilitate high-throughput yeast two-hybrid screens in which large numbers of bait and prey constructs may be required.     

Chemogenomics and biotechnology

A robust knowledge of the interactions between small molecules and specific proteins aids the development of new biotechnological tools and the identification of new drug targets, and can lead to specific biological insights. Such knowledge can be obtained through chemogenomic screens. In these screens, each small molecule from a chemical library is applied to each cell type from a library of cells, and the resulting phenotypes are recorded. Chemogenomic screens have recently become very common and will continue to generate large amounts of data. The interpretation of this data will occupy biologists and chemists alike for some time to come. This review discusses methods for the acquisition and interpretation of chemogenomic data, in addition to possible applications of chemogenomics in biotechnology.     

Genetic screens for the control of influenza virus replication: from meta-analysis to drug discovery

Current anti-influenza virus drugs target two viral proteins and induce a selective pressure for the generation of drug resistant variants. This stresses the need for additional therapeutic strategies including drug targeting of cellular factors that are essential for viral replication. Reverse genetics approaches can be used to identify these factors and recently six independent genomic initiatives have led to the identification of 925 host factors that are essential for the replication of influenza viruses. Here we report a meta-analysis of this dataset, first revealing that these screens are poorly overlapping at the gene level. However, a strong convergence was observed at the level of biological processes which was further supported by an interactomic analysis showing a high interconnectivity of the essential host factors in the human protein network. Plugging virus-host protein interaction data on this dataset reveals a significant targeting of these factors by viral proteins, further validating the cellular targets. Combining this information, the first drug-influenza virus target network was constructed by retrieving from DrugBank 298 molecules interacting with 100 essential host factors. Of these, 204 are FDA-approved offering interesting potential for rapid drug repositioning in the treatment of flu.     

PIMD:An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion

The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development. However, the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge. Here, we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning. In PIMD, drug similarity networks (DSNs) based on chemical, pharmacological, and clinical data are fused into an integrated DSN (iDSN) composed of many clusters. Rather than simple fusion, PIMD offers a systematic way to annotate clusters. Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses. PIMD provides new insights into the universality, individuality, and complementarity of different drug properties by evaluating the contribution of each property data. To test the performance of PIMD, we use chemical, pharmacological, and clinical properties to generate an iDSN. Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs. Within the top 20 recommended drug pairs, 7 drugs have been reported to be repurposed. The source code for PIMD is available at https://github.com/Sepstar/PIMD/.     

Probabilistic analysis of recessive mutagenesis screen strategies

Random mutagenesis screens for recessive phenotypes require three generations of breeding, using either a backcross (BC) or intercross (IC) strategy. Hence, they are more costly and technically demanding than those for dominant phenotypes. Maximizing the return from these screens requires maximizing the number of mutations that are bred to homozyosity in the G₃ generation. Using a probabilistic approach, we compare different designs of screens for recessive phenotypes and the impact each one has on the number of mutations that can be effectively screened. We address the issue of BC versus IC strategies and consider genome-wide, region-specific screens and suppressor screens. We find that optimally designed BC and IC screens allow the screening of, on average, similar numbers of mutations but that interpedigree variation is more pronounced when the IC strategy is employed. By conducting a retrospective analysis of published mutagenesis screens, we show that, depending on the strategy, a threefold difference in the numbers of mutations screened per animal used could be expected. This method allows researchers to contrast, for a range of experimental designs, the cost per mutation screened and to maximize the number of mutations that one can expect to screen in a given experiment.     

Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.     

Genomewide two-generation screens for recessive mutations by ES cell mutagenesis

Forward genetic mutation screens in mice are typically begun by mutagenizing the germline of male mice with N-ethyl-N-nitrosourea (ENU). Genomewide recessive mutations transmitted by these males can be rendered homozygous after three generations of breeding, at which time phenotype screens can be performed. An alternative strategy for randomly mutagenizing the mouse genome is by chemical treatment of embryonic stem (ES) cells. Here we demonstrate the feasibility of performing genomewide mutation screens with only two generations of breeding. Mice potentially homozygous for mutations were obtained by crossing chimeras derived from ethylmethane sulfonate (EMS)–mutagenized ES cells to their daughters, or by intercrossing offspring of chimeras. This strategy was possible because chimeras transmit variations of the same mutagenized diploid genome, whereas ENU-treated males transmit numerous unrelated genomes. This also results in a doubling of screenable mutations in a pedigree compared to germline ENU mutagenesis. Coupled with the flexibility to treat ES cells with a variety of potent mutagens and the ease of producing distributable, quality-controlled, long-term supplies of cells in a single experiment, this strategy offers a number of advantages for conducting forward genetic screens in mice.     

Mining the cancer genome uncovers therapeutic activity of EphA7 against lymphoma

The functional annotation of the cancer genome can reveal new opportunities for cancer therapies. The wealth of genomic data on various cancers has not yet been mined for clinically and therapeutically useful information. We use cross-comparisons of genomic data with the results of unbiased genetic screens to prioritize genomic changes for further study. In this manner, we have identified a soluble variant of the ephrin receptor A7 (EPHA7^TR) as a tumor suppressor that is lost in lymphoma. We also developed antibody-based delivery to restore this tumor suppressor to the cancer cells in situ. We will discuss our strategy of screening genomic data, specific findings concerning EPHA7 and the potential for future discoveries.     

Unlocking the Bottleneck in Forward Genetics Using Whole-Genome Sequencing and Identity by Descent to Isolate Causative Mutations

Forward genetics screens with N-ethyl-N-nitrosourea (ENU) provide a powerful way to illuminate gene function and generate mouse models of human disease; however, the identification of causative mutations remains a limiting step. Current strategies depend on conventional mapping, so the propagation of affected mice requires non-lethal screens; accurate tracking of phenotypes through pedigrees is complex and uncertain; out-crossing can introduce unexpected modifiers; and Sanger sequencing of candidate genes is inefficient. Here we show how these problems can be efficiently overcome using whole-genome sequencing (WGS) to detect the ENU mutations and then identify regions that are identical by descent (IBD) in multiple affected mice. In this strategy, we use a modification of the Lander-Green algorithm to isolate causative recessive and dominant mutations, even at low coverage, on a pure strain background. Analysis of the IBD regions also allows us to calculate the ENU mutation rate (1.54 mutations per Mb) and to model future strategies for genetic screens in mice. The introduction of this approach will accelerate the discovery of causal variants, permit broader and more informative lethal screens to be used, reduce animal costs, and herald a new era for ENU mutagenesis.     

CRISPR screens in plants: approaches,guidelines, and future prospects

Clustered regularly interspaced short palindromic repeat (CRISPR)-associated systems have revolutionized genome engineering by facilitating a wide range of targeted DNA perturbations. These systems have resulted in the development of powerful new screens to test gene functions at the genomic scale. While there is tremendous potential to map and interrogate gene regulatory networks at unprecedented speed and scale using CRISPR screens, their implementation in plants remains in its infancy. Here we discuss the general concepts, tools, and workflows for establishing CRISPR screens in plants and analyze the handful of recent reports describing the use of this strategy to generate mutant knockout collections or to diversify DNA sequences. In addition, we provide insight into how to design CRISPR knockout screens in plants given the current challenges and limitations and examine multiple design options. Finally, we discuss the unique multiplexing capabilities of CRISPR screens to investigate redundant gene functions in highly duplicated plant genomes. Combinatorial mutant screens have the potential to routinely generate higher-order mutant collections and facilitate the characterization of gene networks. By integrating this approach with the numerous genomic profiles that have been generated over the past two decades, the implementation of CRISPR screens offers new opportunities to analyze plant genomes at deeper resolution and will lead to great advances in functional and synthetic biology.

Advances in CRISPR screening techniques in plants offer new opportunities to analyze plant genomes at higher resolution and scale and will greatly enhance functional and synthetic biology studies.     

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation

The advent of genome-wide RNA interference (RNAi)–based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function—mitotic chromosome condensation—and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.     

药物重定位的计算分析方法

全新结构药物的研发存在周期长、耗资大、风险高的问题.通过各种技术预测已有药物的新适应症,即药物重定位,可以缩短药物研发时间、降低研发成本和风险.由于疾病种类和已知药物的数量繁多,完全通过实验筛选已知药物的新用途仍然具有很高的成本.随着组学和药物信息学数据的积累,药物重定位进入到了理性设计和实验筛选相结合的阶段,药物重定位的计算预测已经成为计算生物学和系统生物学的重要研究方向.本文将目前药物重定位计算分析的策略归纳为药物-靶标关系分析、药物-药物关系分析和药物-疾病关系分析,对已报道的技术方法及其成功应用实例进行了综述.     

Using the noninformative families in family-based association tests: a powerful new testing strategy

For genetic association studies with multiple phenotypes, we propose a new strategy for multiple testing with family-based association tests (FBATs). The strategy increases the power by both using all available family data and reducing the number of hypotheses tested while being robust against population admixture and stratification. By use of conditional power calculations, the approach screens all possible null hypotheses without biasing the nominal significance level, and it identifies the subset of phenotypes that has optimal power when tested for association by either univariate or multivariate FBATs. An application of our strategy to an asthma study shows the practical relevance of the proposed methodology. In simulation studies, we compare our testing strategy with standard methodology for family studies. Furthermore, the proposed principle of using all data without biasing the nominal significance in an analysis prior to the computation of the test statistic has broad and powerful applications in many areas of family-based association studies.