Microbial natural products have been an invaluable resource for providing clinically relevant therapeutics for almost a century, including most of the commonly used antibiotics that are still in medical use today. In more recent decades, the need for new biotherapeutics has begun to grow, as multi-drug resistant pathogens continue to emerge, putting into question the long-term efficacy of many drugs that we routinely depend on to combat infectious diseases. To affect this growing medical crisis, new efforts are being applied to computationally mine the genomes of microorganisms for biosynthetic gene clusters that code for molecules possessing anti-microbial activities that circumvent known resistance mechanisms. To this end, cutting-edge software platforms have been developed that can identify, with high predictive accuracy, microbial genomes that code for natural products of potential interest. However, with such analyses comes the need to thoroughly vet each predicted gene cluster, to identify those high-value candidate molecules that are not associated with known resistance mechanisms. In this work, a new strategy was developed that involved cataloguing all known ‘self-resistance’ mechanisms encoded by natural product producing microorganisms, which protect the producer from the highly toxic effects of their secreted anti-microbial agents. This collection of resistance data was leveraged and used to engineer an automated software-based pipeline that interrogates biosynthetic gene clusters and relates them to previously identified resistance mechanisms. Gene clusters that are revealed to be independent of known resistance mechanisms can then be flagged for further chemical and biological study in the laboratory. Such in-depth interrogations of microbial genomes aim to help reveal the full biological repertoire of antibiotics yet to be discovered from microorganisms, and will lead to the development of the next generation of biotherapeutics to quell the growing medical crisis of antibiotic-resistance among human pathogenic organisms. / Thesis / Master of Science (MSc) / It would be hard to imagine a world where we could no longer use the antibiotics we are routinely being prescribed for common bacterial infections. Currently, we are in an era where this thought could become a reality. Although we have been able to discover antibiotics in the past from soil dwelling microbes, this approach to discovery is being constantly challenged. At the same time, the bacteria are getting smarter in their ways to evade antibiotics, in the form of resistance, or self-protection mechanisms. As such is it essential to devise methods which can predict the potential for resistance to the antibiotics we use early in the discovery and isolation process. By using what we have learned in the past about how bacteria protect themselves for antibiotics, we can to stay one step ahead of them as we continue to search for new sources of antibiotics from bacteria.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/22190 |
| Date | January 2017 |
| Creators | Walker, Chelsea |
| Contributors | Magarvey, Nathan, Biochemistry and Biomedical Sciences |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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