Loss of robustness as the key to drug repositioning

The Recycling of Antibiotics

There is a dire need for new classes of antimicrobials. Drug repositioning is the finding of new uses for known drugs. Our lab has developed a new bioinformatics strategy, which enabled the repositioning of a known drug as a potential new class of antibiotic. The underlying scientific question was: what are the conditions necessary for the development of robust biological systems? Theoretical work suggests that robustness should only emerge in unpredictable environments but it remained untested. One source of biological robustness is genetic redundancy, where a given biochemical function is encoded by two or more genes. Our lab showed that bacterial intracellular parasites, which live in a predictable environment, have substantially more singletons than could be expected under any model of gene loss (singletons are genes without duplicates), and that this corresponds to an effective loss of genetic redundancy.
We have refined the previous work by compiling and analyzing an extensive list of sequenced parasites along with other information such as taxonomic classification, facultative vs. obligate nature, specific intracellular replication niche. This research led to the hypothesis that intracellular parasites will adapt to one of four different intracellular replication niches. To test this, we built a bioinformatics pipeline that revealed a core set of protein families/domains that characterize bacterial parasites that inhabit in phagolysosomes. These proteins represent fragile functions for a wide range of pathogens. Furthermore, we developed a predictor (80% accuracy) that is able to identify parasites capable of replication in phagolysosomes.
Furthermore, using a bioinformatics approach, we were able to identify three known drugs that should act upon these fragile functions. These drugs could have an extensive range: from Staphylococcus aureus to Mycobacterium tuberculosis. We are currently submitting our theoretical results to experimental validation.

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