(568c) Expecting the Unexpected: Synthesis Pathway-Host Incompatibility Due to Metabolic Network Disruption

Synthetic biology and metabolic engineering have been instrumental in transforming organisms such as Escherichia coli (E. coli) and yeast to microbial factories to produce a wide range of commercially useful biomolecules. This is achieved by introducing non-native synthesis pathways consisting of heterologous enzymes in these microbial hosts. While enzymes are generally known to be highly selective catalysts, growing evidence strongly suggests that enzymes are often promiscuous (i.e. can perform side reactions). We hypothesize that such promiscuity leads to formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high productivity and yield. Since the extent of disruption is a function of the host’s metabolic network topology, we posit that it can be used to quantify pathway-host incompatibility.

In this work, we developed a computational method to assess the disruptive impact of synthesis pathways in microbial hosts. The steps in this predictive method are as follows. First, we predict potential byproducts that could be synthesized due to promiscuous activities of the heterologous (i.e. non-native) enzymes on native metabolites. We also predict byproducts resulting from activity of native host enzymes on non-native heterologous metabolites. To predict byproducts, we use PROXIMAL, a technique to predict metabolic derivatives for a query molecule based on an input set of enzymatic reactions. Next, host (E. coli) network is augmented with generated products and their associated reactions. The yield of the target product is then calculated based on the augmented host and compared to the target product yield without assumptions about disruption. As a test case, we demonstrate disruption during 3-hydroxypropanoic acid (3-HP) synthesis in E. coli. We report on 3-HP yield with and without disruption.

This work is novel as it is the first to examine the adverse effect of heterologous enzyme promiscuity on host metabolism and its consequences on biocatalysts. Our method serves as the first computational tool that can assist synthetic biologists to: (1) identify sources of unexpected byproducts, (2) assess the consequences of metabolic engineering on the host, and (3) quantify pathway-host incompatibility using metabolic network disruption. Outcomes from this work will aid in future studies to design robust systems with more predictable behaviors.