Team:British Columbia/Description

Distributing metabolic pathways between microbial community members has shown significant potential for the large-scale production of complex, biologically-derived chemical products. Our goal is to address the challenge of regulating population dynamics in a synthetic microbial consortium, by improving the rate of production of naringenin and its pharmaceutically significant derivative, kaempferol, which has anti-cancer properties. This is done by distributing the synthesis of kaempferol between two E. coli strains and optimizing their relative proportions in co-culture. To optimize population dynamics for the production of kaempferol, we regulated the ratio of the two strains using GP2, a transcriptional inhibitor, under the control of a biosensor responsive to the pathway intermediate naringenin. This couples cell growth with the concentration of naringenin, allowing the co-culture to self-optimize based on pathway intermediate abundance. Using our system, we have demonstrated a novel way to optimize microbial polycultures for the synthesis of metabolically complex compounds.

  1. Zhang, H. and X. Wang, Modular co-culture engineering, a new approach for metabolic engineering. Metabolic Engineering, 2016. 37: p. 114-121.
  2. Ganesan, V., et al., Heterologous biosynthesis of natural product naringenin by co-culture engineering. Synthetic and Systems Biotechnology, 2017. 2(3): p. 236-242.
  3. Jones, J.A., et al., Experimental and computational optimization of an Escherichia coli co-culture for the efficient production of flavonoids. Metabolic Engineering, 2016. 35: p. 55-63.
  4. Cámara, B., et al., T7 phage protein Gp2 inhibits the Escherichia coli RNA polymerase by antagonizing stable DNA strand separation near the transcription start site. Proceedings of the National Academy of Sciences of the United States of America, 2010. 107(5): p. 2247-2252.
  5. Siedler, S., et al., Novel biosensors based on flavonoid-responsive transcriptional regulators introduced into Escherichia coli. Metabolic Engineering, 2014. 21: p. 2-8.
  6. Raman, S., et al., Evolution-guided optimization of biosynthetic pathways. Proceedings of the National Academy of Sciences of the United States of America, 2014. 111(50): p. 17803-17808.