The genomics era has enabled the generation of numerous genetically modified bacteria which not only serve as valuable tools by which to functionally evaluate the pool of targets which might be addressed by novel antibiotics, but also offer novel cellular mechanism-based screening opportunities to complement the protein-based screening approaches in drug discovery. They have also become indispensable cellular tools for verifying the target-mediated antibiotic activities of screening hits and for mode-of-action characterization of unexplored agents that come from traditional whole-cell screening for antibacterial activities. The applications of genetically modified bacteria for antibiotic drug discovery are summarized below.
1. Resistance determination. A common way of defining the mode of action of antimicrobials is the generation and characterization of resistant mutants, since point mutations in a target gene can confer resistance. However, traditional methods of mutation mapping after selection of random mutants are too time-consuming for characterizing compound collections. Belanger et al. developed a rapid PCR-based method to map resistance genes in the naturally competent bacterium S. pneumoniae . They generated an ordered library of overlapping amplicons (4 kbp each) carrying random mutations introduced by error-prone PCR. Some of the amplicons contain mutations in drug-resistance genes including the drug target. Pools of defined amplicons are used to transform
S. pneumoniae in order to introduce mutations into defined chromosomal regions. In this way, individual amplicons that confer high frequencies of resistance can easily be identified. Using this method, not only possible target mutations, but also other resistance mechanisms such as efflux, altered gene regulation, and bypass mutations may be found. However, target-related resistance mutations against compounds that work on more than one target may not be identifiable, which could also be true for gene expression assay systems measuring hyper- or hyposusceptibility to antimicrobial compounds as described below.
2. Underexpression systems. Conditional mutants with reduced target gene expression are generally more sensitive to distinct target-specific antibiotics. Comparing the relative growth inhibition of such strains to the wild type provides a simple screen for identifying the target specificity of antimicrobials, such as published by DeVito et al., who used arabi-nose-regulable expression systems , and Forsyth et al. using antisense RNA . Instead of using individual mutant/wild-type strain pairs, conditional mutants with regulable antisense RNA constructs can also be pooled to conco-mitantly identify different drug targets of novel antimicrobials. A proof-of-concept study revealed that within a pool of 78 diverse antisense-RNA-expressing strains, the ones with increased susceptibility to known antibiotics could be selectively detected via DNA dot blot or microarray hybridization . Such sensitization assays on a miniaturized high-throughput scale remain challenging, since the degree of increased susceptibility is dependent on the extent of target gene repression and growth phase. These parameters might vary for different target genes. In addition, certain types of inhibition might not be detected by such assays, e.g., forma tion of toxic enzyme compound complexes (such as in case of the quinolones targeting topoisomerase II and IV).
3. Overexpression systems. In contrast to the hypersusceptibility tests described above, desensitization assays represent another tool for target identification. For instance, Huang et al. generated an expression library of 2300 unique open reading frames (ORFs) in S. aureus. Overexpression oftheseORFs led to reduced antibiotic susceptibility and enabled identification oftargets for antimicrobials and ofresistance mechanisms such as the multidrug resistance efflux protein MdeA . Such tests might be helpful in mechanistic analysis for novel antimicrobials. However, the low sensitivity of overexpression assays and high amounts of compound needed for testing do not make such tests suitable for screening approaches.
4. Promoter induction assays. Promoter induction assays represent an attractive complement for cell-based mechanistic characterization of antimicrobials. Some publications describe promoters coupled to reporter genes in order to measure their specific response to certain types of antibiotic stress [67-71]. Compendia of antibiotics-triggered expression profiles now make it possible to identify regulatory networks responsive to antibiotics and to select and functionally characterize the promoters most appropriate for such purposes. Coregulated genes and operons are generally controlled by the same transcriptional regulator, so it is likely that they share common regulatory elements. The combination of DNA sequence-motif detection algorithms and expression-based correlation analyses allows systematic prediction of promoters controlling specific bacterial stress responses. A genome-wide, systematic approach based on a transcriptome data compendium has recently been reported. Fischer et al. described the identification and high-throughput screening application of FapR regulator-dependent promoters that selectively and strongly respond to inhibitors of fatty acid biosynthesis in B. subtilis . Concomitantly, antibiotics-triggered expression profiles enabled identification of a panel of novel B. subtilis reporter strains indicative of various modes of action [73, 74]. Whole-cell-based reporter assays have limitations especially due to the limited concentration window in which compounds may be detected as inducing agents. In addition, some effort needs to be invested afterwards to identify the precise target of an inducing agent. Nevertheless, such assays are elegant tools for the detection of bioactive compounds that interfere with specific pathways. The application of diverse promoter induction assays represents one outcome of expression profiling. However, holistic genomics technologies such as transcriptome- and proteome-based expression analysis offer many more opportunities in antibiotic drug discovery.
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