RESUMEN
The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.
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Antineoplásicos/uso terapéutico , Drogas en Investigación/uso terapéutico , Bases del Conocimiento , Terapia Molecular Dirigida/métodos , Neoplasias/tratamiento farmacológico , Programas Informáticos , Antineoplásicos/química , Bases de Datos Factuales , Conjuntos de Datos como Asunto , Descubrimiento de Drogas/métodos , Drogas en Investigación/química , Humanos , Internet , Neoplasias/clasificación , Neoplasias/genética , Neoplasias/patologíaRESUMEN
The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The associations are displayed in an intuitive user interface (https://www.targetvalidation.org), and are available through a REST-API (https://api.opentargets.io/v3/platform/docs/swagger-ui) and a bulk download (https://www.targetvalidation.org/downloads/data). In addition to target-disease associations, we also aggregate and display data at the target and disease levels to aid target prioritisation. Since our first publication two years ago, we have made eight releases, added new data sources for target-disease associations, started including causal genetic variants from non genome-wide targeted arrays, added new target and disease annotations, launched new visualisations and improved existing ones and released a new web tool for batch search of up to 200 targets. We have a new URL for the Open Targets Platform REST-API, new REST endpoints and also removed the need for authorisation for API fair use. Here, we present the latest developments of the Open Targets Platform, expanding the evidence and target-disease associations with new and improved data sources, refining data quality, enhancing website usability, and increasing our user base with our training workshops, user support, social media and bioinformatics forum engagement.
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Biología Computacional/métodos , Bases de Datos Genéticas , Genómica/métodos , Almacenamiento y Recuperación de la Información/métodos , Terapia Molecular Dirigida/métodos , Biología Computacional/tendencias , Perfilación de la Expresión Génica/métodos , Genómica/tendencias , Humanos , Almacenamiento y Recuperación de la Información/tendencias , Internet , Reproducibilidad de los Resultados , Programas InformáticosRESUMEN
Ribosomes are the protein synthesizing factories of the cell, polymerizing polypeptide chains from their constituent amino acids. However, distinct combinations of amino acids, such as polyproline stretches, cannot be efficiently polymerized by ribosomes, leading to translational stalling. The stalled ribosomes are rescued by the translational elongation factor P (EF-P), which by stimulating peptide-bond formation allows translation to resume. Using metabolic stable isotope labeling and mass spectrometry, we demonstrate in vivo that EF-P is important for expression of not only polyproline-containing proteins, but also for specific subsets of proteins containing diprolyl motifs (XPP/PPX). Together with a systematic in vitro and in vivo analysis, we provide a distinct hierarchy of stalling triplets, ranging from strong stallers, such as PPP, DPP, and PPN to weak stallers, such as CPP, PPR, and PPH, all of which are substrates for EF-P. These findings provide mechanistic insight into how the characteristics of the specific amino acid substrates influence the fundamentals of peptide bond formation.
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Escherichia coli K12/fisiología , Factores de Elongación de Péptidos/metabolismo , Prolina/metabolismo , Biosíntesis de Proteínas/fisiología , Ribosomas/metabolismo , Secuencias de Aminoácidos/genética , Cromatografía Liquida , Escherichia coli K12/metabolismo , Humanos , Proteómica , Espectrometría de Masas en Tándem , beta-GalactosidasaRESUMEN
Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off-patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species- or genus-specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole-resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.
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Antifúngicos/farmacología , Candida/efectos de los fármacos , Cryptococcus/efectos de los fármacos , Fluconazol/farmacología , Saccharomyces/efectos de los fármacos , Animales , Candida/crecimiento & desarrollo , Biología Computacional , Cryptococcus/crecimiento & desarrollo , Farmacorresistencia Fúngica/genética , Sinergismo Farmacológico , Ergosterol/antagonistas & inhibidores , Ergosterol/biosíntesis , Perfilación de la Expresión Génica/métodos , Insectos/efectos de los fármacos , Pruebas de Sensibilidad Microbiana , Saccharomyces/genética , Saccharomyces/crecimiento & desarrollo , Especificidad de la EspecieRESUMEN
Identifying cellular functions dysregulated by disease-associated variants could implicate novel pathways for drug targeting or modulation in cell therapies. However, follow-up studies can be challenging if disease-relevant cell types are difficult to sample. Variants associated with immune diseases point toward the role of CD4+ regulatory T cells (Treg cells). We mapped genetic regulation (quantitative trait loci [QTL]) of gene expression and chromatin activity in Treg cells, and we identified 133 colocalizing loci with immune disease variants. Colocalizations of immune disease genome-wide association study (GWAS) variants with expression QTLs (eQTLs) controlling the expression of CD28 and STAT5A, involved in Treg cell activation and interleukin-2 (IL-2) signaling, support the contribution of Treg cells to the pathobiology of immune diseases. Finally, we identified seven known drug targets suitable for drug repurposing and suggested 63 targets with drug tractability evidence among the GWAS signals that colocalized with Treg cell QTLs. Our study is the first in-depth characterization of immune disease variant effects on Treg cell gene expression modulation and dysregulation of Treg cell function.
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Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.
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Diseño de Fármacos , Descubrimiento de Drogas/métodos , Aprendizaje Automático , Animales , Humanos , Redes Neurales de la ComputaciónRESUMEN
The melanocyte-inducing transcription factor (MITF)-low melanoma transcriptional signature is predictive of poor outcomes for patients, but little is known about its biological significance, and animal models are lacking. Here, we used zebrafish genetic models with low activity of Mitfa (MITF-low) and established that the MITF-low state is causal of melanoma progression and a predictor of melanoma biological subtype. MITF-low zebrafish melanomas resembled human MITF-low melanomas and were enriched for stem and invasive (mesenchymal) gene signatures. MITF-low activity coupled with a p53 mutation was sufficient to promote superficial growth melanomas, whereas BRAFV600E accelerated MITF-low melanoma onset and further promoted the development of MITF-high nodular growth melanomas. Genetic inhibition of MITF activity led to rapid regression; recurrence occurred following reactivation of MITF. At the regression site, there was minimal residual disease that was resistant to loss of MITF activity (termed MITF-independent cells) with very low-to-no MITF activity or protein. Transcriptomic analysis of MITF-independent residual disease showed enrichment of mesenchymal and neural crest stem cell signatures similar to human therapy-resistant melanomas. Single-cell RNA sequencing revealed MITF-independent residual disease was heterogeneous depending on melanoma subtype. Further, there was a shared subpopulation of residual disease cells that was enriched for a neural crest G0-like state that preexisted in the primary tumor and remained present in recurring melanomas. These findings suggest that invasive and stem-like programs coupled with cellular heterogeneity contribute to poor outcomes for MITF-low melanoma patients and that MITF-independent subpopulations are an important therapeutic target to achieve long-term survival outcomes. SIGNIFICANCE: This study provides a useful model for MITF-low melanomas and MITF-independent cell populations that can be used to study the mechanisms that drive these tumors as well as identify potential therapeutic options.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/22/5769/F1.large.jpg.
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Melanoma/genética , Factor de Transcripción Asociado a Microftalmía/genética , Neoplasia Residual/genética , Transcripción Genética/genética , Pez Cebra/genética , Animales , Resistencia a Medicamentos/genética , Perfilación de la Expresión Génica/métodos , Regulación Neoplásica de la Expresión Génica/genética , Melanocitos/patología , Melanoma/patología , Recurrencia Local de Neoplasia/genética , Recurrencia Local de Neoplasia/patología , Neoplasia Residual/patología , Cresta Neural/patología , Proteínas Proto-Oncogénicas B-raf/genética , Células Madre/patologíaRESUMEN
Invasive fungal infections are an important cause of human mortality and morbidity, particularly for immunocompromised populations. However, there remains a paucity of antifungal drug treatments available to combat these fungal pathogens. Further, antifungal compounds are plagued with problems such as host toxicity, fungistatic activity, and the emergence of drug resistance in pathogen populations. A promising therapeutic strategy to increase drug effectiveness and mitigate the emergence of drug resistance is through the use of combination drug therapy. In this review we describe the current arsenal of antifungals in medicine and elaborate on the benefits of combination therapy to expand our current antifungal drug repertoire. We examine those antifungal combinations that have shown potential against fungal pathogens and discuss strategies being employed to discover novel combination therapeutics, in particular combining antifungal agents with non-antifungal bioactive compounds. The findings summarized in this review highlight the promise of combinatorial strategies in combatting invasive mycoses.
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Antifúngicos/uso terapéutico , Descubrimiento de Drogas/métodos , Sinergismo Farmacológico , Infecciones Fúngicas Invasoras/tratamiento farmacológico , Animales , Antifúngicos/efectos adversos , Antifúngicos/farmacología , Aspergilosis/tratamiento farmacológico , Aspergillus/efectos de los fármacos , Candida/efectos de los fármacos , Candidiasis/tratamiento farmacológico , Criptococosis/tratamiento farmacológico , Cryptococcus/efectos de los fármacos , Farmacorresistencia Fúngica , Quimioterapia Combinada , Humanos , Infecciones Fúngicas Invasoras/microbiología , Micosis/microbiologíaRESUMEN
The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.
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Antifúngicos , Genes Fúngicos , Saccharomyces cerevisiae , Relación Estructura-Actividad , Antifúngicos/química , Antifúngicos/farmacología , Biología Computacional , Descubrimiento de Drogas , Sinergismo Farmacológico , Saccharomyces cerevisiae/efectos de los fármacos , Saccharomyces cerevisiae/genéticaRESUMEN
Natural products are invaluable historic sources of drugs for infectious diseases; however, the discovery of novel antimicrobial chemical scaffolds has waned in recent years. Concurrently, there is a pressing need for improved therapeutics to treat fungal infections. We employed a co-culture screen to identify ibomycin, a large polyketide macrolactone that has preferential killing activity against Cryptococcus neoformans. Using chemical and genome methods, we determined the structure of ibomycin and identified the biosynthetic cluster responsible for its synthesis. Chemogenomic profiling coupled with cell biological assays link ibomycin bioactivity to membrane function. The preferential activity of ibomycin toward C. neoformans is due to the ability of the compound to selectively permeate its cell wall. These results delineate a novel antifungal agent that is produced by one of the largest documented biosynthetic clusters to date and underscore the fact that there remains significant untapped chemical diversity of natural products with application in antimicrobial research.
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Antifúngicos/química , Antifúngicos/farmacología , Criptococosis/tratamiento farmacológico , Cryptococcus neoformans/efectos de los fármacos , Lactonas/química , Lactonas/farmacología , Productos Biológicos/química , Productos Biológicos/farmacología , Pared Celular/efectos de los fármacos , Pared Celular/metabolismo , Técnicas de Cocultivo , Criptococosis/microbiología , Cryptococcus neoformans/crecimiento & desarrollo , Cryptococcus neoformans/metabolismo , Descubrimiento de Drogas , Hongos/efectos de los fármacos , Hongos/crecimiento & desarrollo , Hongos/metabolismo , Humanos , Pruebas de Sensibilidad Microbiana , Micosis/tratamiento farmacológico , Micosis/microbiologíaRESUMEN
The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.
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There is an urgent need to identify new treatments for fungal infections. By combining sub-lethal concentrations of the known antifungals fluconazole, caspofungin, amphotericin B, terbinafine, benomyl, and cyprodinil with â¼3,600 compounds in diverse fungal species, we generated a deep reservoir of chemical-chemical interactions termed the Antifungal Combinations Matrix (ACM). Follow-up susceptibility testing against a fluconazole-resistant isolate of C. albicans unveiled ACM combinations capable of potentiating fluconazole in this clinical strain. We used chemical genetics to elucidate the mode of action of the antimycobacterial drug clofazimine, a compound with unreported antifungal activity that synergized with several antifungals. Clofazimine induces a cell membrane stress for which the Pkc1 signaling pathway is required for tolerance. Additional tests against additional fungal pathogens, including Aspergillus fumigatus, highlighted that clofazimine exhibits efficacy as a combination agent against multiple fungi. Thus, the ACM is a rich reservoir of chemical combinations with therapeutic potential against diverse fungal pathogens.
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Antifúngicos/farmacología , Clofazimina/farmacología , Candida albicans/efectos de los fármacos , Cryptococcus neoformans/efectos de los fármacos , Combinación de Medicamentos , Sinergismo Farmacológico , Pruebas de Sensibilidad Microbiana , Saccharomyces cerevisiae/efectos de los fármacos , Schizosaccharomyces/efectos de los fármacosRESUMEN
Heterochromatin underpins gene repression, genome integrity, and chromosome segregation. In the fission yeast Schizosaccharomyces pombe, conserved protein complexes effect heterochromatin formation via RNA interference-mediated recruitment of a histone H3 lysine 9 methyltransferase to cognate chromatin regions. To identify small molecules that inhibit heterochromatin formation, we performed an in vivo screen for loss of silencing of a dominant selectable kanMX reporter gene embedded within fission yeast centromeric heterochromatin. Two structurally unrelated compounds, HMS-I1 and HMS-I2, alleviated kanMX silencing and decreased repressive H3K9 methylation levels at the transgene. The decrease in methylation caused by HMS-I1 and HMS-I2 was observed at all loci regulated by histone methylation, including centromeric repeats, telomeric regions, and the mating-type locus, consistent with inhibition of the histone deacetylases (HDACs) Clr3 and/or Sir2. Chemical-genetic epistasis and expression profiles revealed that both compounds affect the activity of the Clr3-containing Snf2/HDAC repressor complex (SHREC). In vitro HDAC assays revealed that HMS-I1 and HMS-I2 inhibit Clr3 HDAC activity. HMS-I1 also alleviated transgene reporter silencing by heterochromatin in Arabidopsis and a mouse cell line, suggesting a conserved mechanism of action. HMS-I1 and HMS-I2 bear no resemblance to known inhibitors of chromatin-based activities and thus represent novel chemical probes for heterochromatin formation and function.
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Dioxanos/farmacología , Regulación Fúngica de la Expresión Génica/efectos de los fármacos , Silenciador del Gen/efectos de los fármacos , Heterocromatina/efectos de los fármacos , Compuestos Heterocíclicos con 2 Anillos/farmacología , Piperazinas/farmacología , Piridinas/farmacología , Schizosaccharomyces/efectos de los fármacos , Tiofenos/farmacología , Animales , Arabidopsis/efectos de los fármacos , Arabidopsis/genética , Arabidopsis/metabolismo , Proteínas de Ciclo Celular/antagonistas & inhibidores , Proteínas de Ciclo Celular/genética , Proteínas de Ciclo Celular/metabolismo , Línea Celular Tumoral , Ensamble y Desensamble de Cromatina , Metilación de ADN , Dioxanos/síntesis química , Dioxanos/química , Heterocromatina/química , Compuestos Heterocíclicos con 2 Anillos/síntesis química , Compuestos Heterocíclicos con 2 Anillos/química , Histona Metiltransferasas , N-Metiltransferasa de Histona-Lisina/genética , N-Metiltransferasa de Histona-Lisina/metabolismo , Histonas/genética , Histonas/metabolismo , Ratones , Piperazinas/síntesis química , Piperazinas/química , Piridinas/síntesis química , Piridinas/química , Schizosaccharomyces/genética , Schizosaccharomyces/metabolismo , Proteínas de Schizosaccharomyces pombe/antagonistas & inhibidores , Proteínas de Schizosaccharomyces pombe/genética , Proteínas de Schizosaccharomyces pombe/metabolismo , Tiofenos/síntesis química , Tiofenos/químicaRESUMEN
Telomerase comprises a reverse transcriptase and an internal RNA template that maintains telomeres in many eukaryotes, and it is a well-validated cancer target. However, there is a dearth of small molecules with efficacy against human telomerase in vivo. We developed a surrogate yeast high-throughput assay to identify human telomerase inhibitors. The reversibility of growth arrest induced by active human telomerase was assessed against a library of 678 compounds preselected for bioactivity in S. cerevisiae. Four of eight compounds identified reproducibly restored growth to strains expressing active human telomerase, and three of these four compounds also specifically inhibited purified human telomerase in vitro. These compounds represent probes for human telomerase function, and potential entry points for development of lead compounds against telomerase-positive cancers.
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Evaluación Preclínica de Medicamentos , Inhibidores Enzimáticos/análisis , Inhibidores Enzimáticos/farmacología , Saccharomyces cerevisiae/genética , Telomerasa/antagonistas & inhibidores , Dominio Catalítico , Células HeLa , Ensayos Analíticos de Alto Rendimiento , Humanos , Fenotipo , Saccharomyces cerevisiae/efectos de los fármacos , Saccharomyces cerevisiae/crecimiento & desarrollo , Proteínas de Saccharomyces cerevisiae/genética , Telomerasa/química , Telomerasa/genética , Telomerasa/metabolismo , Proteínas de Unión a Telómeros/genéticaRESUMEN
Understanding how drugs work in vivo is critical for drug design and for maximizing the potential of currently available drugs. 5-nitrofurans are a class of prodrugs widely used to treat bacterial and trypanosome infections, but despite relative specificity, 5-nitrofurans often cause serious toxic side effects in people. Here, we use yeast and zebrafish, as well as human in vitro systems, to assess the biological activity of 5-nitrofurans, and we identify a conserved interaction between aldehyde dehydrogenase (ALDH) 2 and 5-nitrofurans across these species. In addition, we show that the activity of nifurtimox, a 5-nitrofuran anti-trypanosome prodrug, is dependent on zebrafish Aldh2 and is a substrate for human ALDH2. This study reveals a conserved and biologically relevant ALDH2-5-nitrofuran interaction that may have important implications for managing the toxicity of 5-nitrofuran treatment.
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Aldehído Deshidrogenasa/metabolismo , Melanocitos/efectos de los fármacos , Nitrofuranos/farmacología , Saccharomyces cerevisiae/efectos de los fármacos , Aldehído Deshidrogenasa Mitocondrial , Animales , Relación Dosis-Respuesta a Droga , Humanos , Modelos Moleculares , Estructura Molecular , Nitrofuranos/química , Proteínas Recombinantes/metabolismo , Saccharomyces cerevisiae/metabolismo , Especificidad de la Especie , Relación Estructura-Actividad , Pez Cebra/embriologíaRESUMEN
Hypopigmentation is a feature of copper deficiency in humans, as caused by mutation of the copper (Cu(2+)) transporter ATP7A in Menkes disease, or an inability to absorb copper after gastric surgery. However, many causes of copper deficiency are unknown, and genetic polymorphisms might underlie sensitivity to suboptimal environmental copper conditions. Here, we combined phenotypic screens in zebrafish for compounds that affect copper metabolism with yeast chemical-genetic profiles to identify pathways that are sensitive to copper depletion. Yeast chemical-genetic interactions revealed that defects in intracellular trafficking pathways cause sensitivity to low-copper conditions; partial knockdown of the analogous Ap3s1 and Ap1s1 trafficking components in zebrafish sensitized developing melanocytes to hypopigmentation in low-copper environmental conditions. Because trafficking pathways are essential for copper loading into cuproproteins, our results suggest that hypomorphic alleles of trafficking components might underlie sensitivity to reduced-copper nutrient conditions. In addition, we used zebrafish-yeast screening to identify a novel target pathway in copper metabolism for the small-molecule MEK kinase inhibitor U0126. The zebrafish-yeast screening method combines the power of zebrafish as a disease model with facile genome-scale identification of chemical-genetic interactions in yeast to enable the discovery and dissection of complex multigenic interactions in disease-gene networks.