Precision multidimensional assay for high-throughput microRNA drug discovery.

Benjamin Haefliger,Bartolomeo Angelici,Laura Prochazka,Yaakov Benenson

doi:10.1038/ncomms10709

Abstract

Development of drug discovery assays that combine high content with throughput is challenging. Information-processing gene networks can address this challenge by integrating multiple potential targets of drug candidates' activities into a small number of informative readouts, reporting simultaneously on specific and non-specific effects. Here we show a family of networks implementing this concept in a cell-based drug discovery assay for miRNA drug targets. The networks comprise multiple modules reporting on specific effects towards an intended miRNA target, together with non-specific effects on gene expression, off-target miRNAs and RNA interference pathway. We validate the assays using known perturbations of on- and off-target miRNAs, and evaluate an ∼700 compound library in an automated screen with a follow-up on specific and non-specific hits. We further customize and validate assays for additional drug targets and non-specific inputs. Our study offers a novel framework for precision drug discovery assays applicable to diverse target families.

Highlights

Background correctionSignal integrationStatistical analysis t-test on mCitrine: P value < 0.1 → reject due to gross effects on gene expression t-test on mCerulean: P value < 0.1 → reject due to non-specific RNAi effects t-test on mCherry: P value < 0.01 → specific hit Fluorescence, a.u. mCerulean, rel.u. –log[10] (P value)Non-specific module log[2]0 log[2]Specific module cGene expression module ‘hits’ ×105 18Levothyroxine (1.7×) Tadalafil (1.6×)
Our study offers a novel framework for precision drug discovery assays applicable to diverse target families
In this report we describe a large-scale mammalian gene circuit serving as an assay for drug discovery against miRNA targets, enabling highly precise identification of specific target modulators with high throughput

Summary

Introduction

Statistical analysis t-test on mCitrine: P value < 0.1 → reject due to gross effects on gene expression t-test on mCerulean: P value < 0.1 → reject due to non-specific RNAi effects t-test on mCherry: P value < 0.01 → specific hit Fluorescence, a.u. mCerulean, rel.u. MCerulean) log[2] (fold change). 0 log[2] (fold change). Gene expression module ‘hits’ ×105 18 (mCitrine) Statistical analysis t-test on mCitrine: P value < 0.1 → reject due to gross effects on gene expression t-test on mCerulean: P value < 0.1 → reject due to non-specific RNAi effects t-test on mCherry: P value < 0.01 → specific hit Fluorescence, a.u. mCerulean, rel.u. –log[10] (P value)

Methods

Results

Conclusion