Antibiotic drug discovery, microbiology, natural product drug discovery, experimental evolution

This project will force Pseudomonas aeruginosa and Streptomyces spp. to compete for the same resources to see if this can activate previously cryptic antibiotic biosynthesis. Genome sequencing has revealed that Streptomyces genomes have more antibiotic biosynthetic gene clusters than are normally produced under laboratory conditions. This is unsurprising when we consider the heterogeneity of the rhizosphere compared with rich culture in the lab. We hypothesise that by forcing Streptomyces to compete with multi-drug resistant Pseudomonas aeruginosa that we will be able to select for mutants that make novel antibiotics. 

To the best of our knowledge, this has never been tried before. This novel approach can be summarised as a Shark vs Tiger experiment. Both organisms are capable of producing toxic compounds to kill competitors so can be considered predators in their own right, even though they have very different lifestyles. Question is, which one will win and why?

This approach can be applied to a wide variety of strain combinations. There may be the opportunity to perform mass spectrometry imaging on promising strain combinations.

Project aim
Activate cryptic biosynthetic gene clusters by co-culture in a segregated culture dish. The two organisms are separated by a permeable membrane so can be sampled and analysed separately. Samples will be taken every two days and analysed for morphological differences and enhanced antibiotic production. Any colonies that are found to be producing antibiotics or exhibit morphological changes will selected for genome sequencing.

Microbiology, molecular biology, whole genome sequencing, computational biology, mass spectrometry

1. Pennisi, E. (2013, November 15). The man who bottled evolution. Science (New York, NY), pp. 790-793. American Association for the Advancement of Science. http://doi.org/10.1126/science.342.6160.790 
2. Purves, K., Macintyre, L., Brennan, D., Hreggviðsson, G. Ó., Kuttner, E., Ásgeirsdóttir, M. E., et al. (2016). Using Molecular Networking for Microbial Secondary Metabolite Bioprospecting. Metabolites, 6(1), 2. http://doi.org/10.3390/metabo6010002