Discovering Antibiotic Efficacy Biomarkers

Christoph Freiberg,Nina Brunner,Hans Peter Fischer,Ludwig Macko

doi:10.1074/mcp.m600127-mcp200

Christoph Freiberg, Nina Brunner + Show 2 more

Open Access

https://doi.org/10.1074/mcp.m600127-mcp200

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Abstract

As current antibiotic therapy is increasingly challenged by emerging drug-resistant bacteria, new technologies are required to identify and develop novel classes of antibiotics. A major bottleneck in today's discovery efforts, however, is a lack of an efficient and standardized method for assaying the efficacy of a drug candidate. We propose a new high content screening approach for identifying efficacious molecules suitable for development of antibiotics. Key to our approach is a new microarray-based efficacy biomarker discovery strategy. We first produced a large dataset of transcriptional responses of Bacillus subtilis to numerous structurally diverse antibacterial drugs. Second we evaluated different protocols to optimize drug concentration and exposure time selection for profiling compounds of unknown mechanism. Finally we identified a surprisingly low number of gene transcripts (approximately 130) that were sufficient for identifying the mechanism of novel substances with reasonable accuracy (approximately 90%). We show that the statistics-based approach reveals a physiologically meaningful set of biomarkers that can be related to major bacterial defense mechanisms against antibiotics. We provide statistical evidence that a parallel measurement of the expression of the biomarkers guarantees optimal performance when using expression systems for screening libraries of novel substances. The general approach is also applicable to drug discovery for medical indications other than infectious diseases.

Highlights

As current antibiotic therapy is increasingly challenged by emerging drug-resistant bacteria, new technologies are required to identify and develop novel classes of antibiotics
To define objective decision rules applicable to the problem of predicting the mechanism of action (MOA)1 of novel substances in compound library screens, we used supervised classification algorithms. These algorithms require a training set of mRNA profiles to define a separation function (“to train the classifier”) to assign a compound to its antibiotic MOA category solely based on the mRNA profile it triggers in B. subtilis
Generation of a Compendium of Antibiotics-induced Expression Profiles—A whole-genome, two-channel microarray technology was used to monitor the transcriptional response of virtually all ϳ4,100 B. subtilis genes (10)

Summary

Introduction

As current antibiotic therapy is increasingly challenged by emerging drug-resistant bacteria, new technologies are required to identify and develop novel classes of antibiotics. We included a number of developmental compounds as well as unspecifically acting substances with antibacterial activity such as DNA intercalators For each of these compounds, we monitored the expression response of B. subtilis to different drug concentrations and exposure times. To define objective decision rules applicable to the problem of predicting the mechanism of action (MOA) of novel substances in compound library screens, we used supervised classification algorithms. These algorithms require a training set of mRNA profiles to define a separation function (“to train the classifier”) to assign a compound to its antibiotic MOA category solely based on the mRNA profile it triggers in B. subtilis.

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