Nina Brunner

Prediction of Mechanisms of Action of Antibacterial Compounds by Gene Expression Profiling

ABSTRACT We have generated a database of expression profiles carrying the transcriptional responses of the model organism Bacillus subtilis following treatment with 37 well-characterized antibacterial compounds of different classes. The database was used to build a predictor for the assignment of the mechanisms of action (MoAs) of antibacterial compounds by the use of support vector machines. This predictor was able to correctly classify the MoA class for most compounds tested. Furthermore, we provide evidence that the in vivo MoA of hexachlorophene does not match the MoA predicted from in vitro data, a situation frequently faced in drug discovery. A database of this kind may facilitate the prioritization of novel antibacterial entities in drug discovery programs. Potential applications and limitations are discussed.

Discovering the Mechanism of Action of Novel Antibacterial Agents through Transcriptional Profiling of Conditional Mutants

ABSTRACT We present a new strategy for predicting novel antibiotic mechanisms of action based on the analysis of whole-genome microarray data. We first built up a reference compendium of Bacillus subtilis expression profiles induced by 14 different antibiotics. This data set was expanded by adding expression profiles from mutants that showed downregulation of genes coding for proven or emerging antibacterial targets. Here, we investigate conditional mutants underexpressing ileS, pheST, fabF, and accDA, each of which is essential for growth. Our proof-of-principle analyses reveal that conditional mutants can be used to mimic chemical inhibition of the corresponding gene products. Moreover, we show that a statistical data analysis combined with thorough pathway and regulon analysis can pinpoint the molecular target of uncharacterized antibiotics. We apply this approach to two novel antibiotics: a recently published phenyl-thiazolylurea derivative and the natural product moiramide B. Our results support recent findings suggesting that the phenyl-thiazolylurea derivative is a novel phenylalanyl-tRNA synthetase inhibitor. Finally, we propose a completely novel antibiotic mechanism of action for moiramide B based on inhibition of the bacterial acetyl coenzyme A carboxylase.

Genome-wide mRNA profiling: impact on compound evaluation and target identification in anti-bacterial research

Abstract Array-based mRNA profiling offers a variety of opportunities to address different issues relevant to anti-bacterial research. The technique can be used to investigate the mode of action of antibiotics, bacterial resistance mechanisms and virulence factors, and to identify novel targets. This review discusses recent developments of this highly innovative field of technology with respect to technical requirements and experimental design. Several applications are described in which bacterial mRNA profiling has already been successfully performed, illustrating how the generation of large numbers of diverse datasets can be used as a powerful tool for evaluating anti-bacterial compounds and consequent counteracting mechanisms in the cell.

Discovering Antibiotic Efficacy Biomarkers TOWARD MECHANISM-SPECIFIC HIGH CONTENT COMPOUND SCREENING

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 (∼130) that were sufficient for identifying the mechanism of novel substances with reasonable accuracy (∼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.