Antibiotic treatment of infectious diseases has become increasingly challenging as pathogenic bacteria have acquired a broad spectrum of resistance mechanisms. In particular, the emergence and spread of multi-resistant staphylococci has progressed to a global health threat [1
]. They are not only resistant to almost all treatments, but also adapt very well to different conditions in the host, including persistence [2
]. In the face of increasing resistance against antibiotics as well as persistence of staphylococci in the patient, an intensive search of new antibacterial lead compounds addressing new targets is urgently required.
Currently, several '-omics' techniques are available, but they are expensive and, in general, only limited information is available for each type of data [5
]. We will show how different data sets for studying the metabolic effects of a xenobiotic can be efficiently combined to derive a maximum of information utilizing pathway modeling [6
] while validating the latter by experimental data.
A new emerging paradigm for investigating drug effects and toxicity is followed here: instead of considering the body of the studied organism as a black box and just identifying toxic or antibiotic concentrations, genomics and post-genomics strategies are used to reveal affected pathways. This combination enables a more rapid understanding of metabolic effects and at the same time also reveals side effects in unprecedented detail, leading to a network paradigm: a substance is not just toxic or nontoxic but has, in general, stronger or weaker and concentration-dependent network effects.
In our studies we observed a drastic change in metabolic activity after administration of the isoquinolinium salt IQ-143 (Figure ) and show for staphylococci that this compound is a xenobiotic with antibiotic properties. IQ-143 constitutes a structurally simplified analogue of a new subclass of bioactive natural products, the N
-coupled naphthylisoquinoline alkaloids, which were first isolated from tropical lianas belonging to the Ancistrocladaceae plant family. Representatives of these alkaloids, such as ancistrocladinium A and B, exhibit excellent antiinfective activities - for example, against the pathogen Leishmania major
- and thus serve as promising lead structures for the treatment of severe infectious diseases [9
]. This class of compounds comprises complex natural products and newly developed synthetic analogues thereof [14
] and provides a rich repertoire of representatives with a large potential against a number of infectious diseases, but potentially also bears the risk of toxic effects in humans.
Figure 1 Structure of IQ-143. Shown is the structure of the environmental challenge and xenobiotic chosen, isoquinolinium salt IQ-143, a structurally simplified analogue of a new subclass of bioactive natural products, the N,C-coupled naphthyl-isoquinolines alkaloids. (more ...)
Starting from publicly available genome sequences [17
], genome annotation in the staphylococci strains was completed by sequence and domain analysis [19
] to identify several previously unidentified metabolic enzymes of their central metabolism. The respective bioinformatic results obtained were validated by PCR analysis. The obtained gene expression data helped to monitor in detail the effect of different concentrations of the isoquinoline on staphylococci. Also, the combination with metabolic modeling allowed us to fill in missing information on all central metabolic enzymes, including those not affected by significant gene expression changes, and to obtain a complete view of the resulting metabolic adaptations of the staphylococci. These genome-scale predictions were further validated by direct metabolite measurements on specific nucleotides.
In general, the pathway modeling allows one to consider network effects besides target effects (for instance, on glycolysis, which decreases with increasing IQ-143 concentrations but is not a direct target of IQ-143) and to find areas that are comparatively resistant (for example, the pentose phosphate pathway). Gene expression data are complemented by the network modeling and from these counter regulation by higher gene expression can be identified. Only a few metabolite measurements are sufficient to validate the predictions regarding the involved pathways - for example, here regarding nucleotides as well as nucleotide-containing cofactors. We tested the independence of the data sets carefully and used them to also cross-validate the modeled pathway fluxes - for example, whether the network predictions from gene expression data fit measured nucleotide concentrations.
Metabolic responses in human cells were modeled considering measurements on cytochrome P450 (CYP) detoxification data. We extrapolated again for all effects on central pathways and compared the resulting predictions to cytotoxicity data on human cells.