Christoph Freiberg

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.

Novel Whole-Cell Antibiotic Biosensors for Compound Discovery

ABSTRACT Cells containing reporters which are specifically induced via selected promoters are used in pharmaceutical drug discovery and in environmental biology. They are used in screening for novel drug candidates and in the detection of bioactive compounds in environmental samples. In this study, we generated and validated a set of five Bacillus subtilis promoters fused to the firefly luciferase reporter gene suitable for cell-based screening, enabling the as yet most-comprehensive high-throughput diagnosis of antibiotic interference in the major biosynthetic pathways of bacteria: the biosynthesis of DNA by the yorB promoter, of RNA by the yvgS promoter, of proteins by the yheI promoter, of the cell wall by the ypuA promoter, and of fatty acids by the fabHB promoter. The reporter cells mainly represent novel antibiotic biosensors compatible with high-throughput screening. We validated the strains by developing screens with a set of 14,000 pure natural products, representing a source of highly diverse chemical entities, many of them with antibiotic activity (6% with anti-Bacillus subtilis activity of ≤25 μg/ml]). Our screening approach is exemplified by the discovery of classical and novel DNA synthesis and translation inhibitors. For instance, we show that the mechanistically underexplored antibiotic ferrimycin A1 selectively inhibits protein biosynthesis.

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.

YkrB is the main peptide deformylase in Bacillus subtilis, a eubacterium containing two functional peptide deformylases

Peptide deformylation is an essential process in eubacteria. The peptide deformylase Def has been suggested to be an attractive target for antibacterial drug discovery. Some eubacteria including medically important pathogens possess two def-like genes. Until now, the functionality of both genes has been tested only in Staphylococcus aureus with the result that one gene copy was functional. Here, expression of two functional def-like gene products in Bacillus subtilis is demonstrated. Besides the def gene, which is chromosomally located close to the formyltransferase gene fmt and which was overexpressed and biochemically tested previously, B. subtilis possesses a second def-like gene, called ykrB. The encoded protein is 32% identical to the def gene product. It was shown that either def or ykrB had to be present for growth of B. subtilis in rich medium (each was individually dispensable). Studies with a def/ykrB double deletion strain with xylose-inducible ykrB copy demonstrated that, besides def, the gene ykrB is a second cellular target of deformylase inhibitors such as the antibiotic actinonin. The gene products exhibited similar enzymic properties, exemplified by similar inhibition efficacy of actinonin in biochemical assays. Antibiotic susceptibility tests with different deletion strains and Northern analyses indicated that YkrB is probably the predominant deformylase in B. subtilis. It was shown that duplication of the deformylase function does not lead to an increased actinonin-resistance frequency in comparison to B. subtilis mutants carrying only one deformylase gene.

Novel computational methods in anti-microbial target identification

Abstract The large number of microbial genome sequences currently available facilitates completely new methods of cross-genome analyses, which aid in target identification and prioritization for anti-microbial drug discovery. This article provides a review of novel computational methods in this context. Genome comparison methods are described that are suitable for the generation of a ‘periodic table of microbial functions’ that will help to detect novel cellular functions and metabolic networks. The methods discussed include novel approaches such as ortholog/paralog clustering, phylogenetic profiling, metabolic pathway mapping, gene-neighbor and domain-fusion analyses, differential genome analysis, and co-expression profiling.

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.