RESUMO
Reporter gene assays are widely used in high-throughput screening (HTS) to identify compounds that modulate gene expression. Traditionally a reporter gene assay is built by cloning an endogenous promoter sequence or synthetic response elements in the regulatory region of a reporter gene to monitor transcriptional activity of a specific biological process (exogenous reporter assay). In contrast, an endogenous locus reporter has a reporter gene inserted in the endogenous gene locus that allows the reporter gene to be expressed under the control of the same regulatory elements as the endogenous gene, thus more accurately reflecting the changes seen in the regulation of the actual gene. In this chapter, we introduce some of the considerations behind building a reporter gene assay for high-throughput compound screening and describe the methods we have utilized to establish 1536-well format endogenous locus reporter and exogenous reporter assays for the screening of compounds that modulate Myc pathway activity.
Assuntos
Bioensaio/métodos , Genes Reporter/genética , Loci Gênicos/genética , Ensaios de Triagem em Larga Escala/métodos , Luciferases/genética , Bioensaio/instrumentação , Avaliação Pré-Clínica de Medicamentos/instrumentação , Avaliação Pré-Clínica de Medicamentos/métodos , Regulação da Expressão Gênica/efeitos dos fármacos , Vetores Genéticos/genética , Células HEK293 , Ensaios de Triagem em Larga Escala/instrumentação , Humanos , Proteínas Proto-Oncogênicas c-myc/antagonistas & inibidores , Proteínas Proto-Oncogênicas c-myc/genética , Proteínas Proto-Oncogênicas c-myc/metabolismo , Elementos de Resposta/genética , Transdução de Sinais/efeitos dos fármacosRESUMO
The term dark chemical matter (DCM) was recently introduced for those molecules in a screening collection that have never shown any substantial biological activity despite having been tested in hundreds of high-throughput assays. It was suggested that, if hits emerge from this compound pool in future screening campaigns, they should be prioritized due to their exquisite selectivity profile. In this article we define DCM at our company and describe on-going efforts to shed light on the bioactivity of these apparently silent compounds, with an emphasis on multi-parametric profiling methods. It is also demonstrated that compounds that are dark within one institution might be found active in another, but typically show the foretold selectivity.
Assuntos
Descoberta de Drogas , Avaliação Pré-Clínica de Medicamentos , Ensaios de Triagem em Larga Escala/métodosRESUMO
High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.
Assuntos
Fenômenos Fisiológicos Celulares/efeitos dos fármacos , Avaliação Pré-Clínica de Medicamentos/métodos , Perfilação da Expressão Gênica/métodos , Expressão Gênica/genética , Terapia de Alvo Molecular/métodos , Preparações Farmacêuticas/administração & dosagem , Animais , Expressão Gênica/efeitos dos fármacos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , HumanosRESUMO
Combination therapies that enhance efficacy or permit reduced dosages to be administered have seen great success in a variety of therapeutic applications. More fundamentally, the discovery of epistatic pathway interactions not only informs pharmacologic intervention but can be used to better understand the underlying biological system. There is, however, no systematic and efficient method to identify interacting activities as candidates for combination therapy and, in particular, to identify those with synergistic activities. We devised a pooled, self-deconvoluting screening paradigm for the efficient comprehensive interrogation of all pairs of compounds in 1000-compound libraries. We demonstrate the power of the method to recover established synergistic interactions between compounds. We then applied this approach to a cell-based screen for anti-inflammatory activities using an assay for lipopolysaccharide/interferon-induced acute phase response of a monocytic cell line. The described method, which is >20 times as efficient as a naïve approach, was used to test all pairs of 1027 bioactive compounds for interleukin-6 suppression, yielding 11 pairs of compounds that show synergy. These 11 pairs all represent the same two activities: ß-adrenergic receptor agonists and phosphodiesterase-4 inhibitors. These activities both act through cyclic AMP elevation and are known to be anti-inflammatory alone and to synergize in combination. Thus we show proof of concept for a robust, efficient technique for the identification of synergistic combinations. Such a tool can enable qualitatively new scales of pharmacological research and chemical genetics.