Microbial biosensors exploit genetically engineered microorganisms to detect the presence of target chemicals. Usually, they are based on the screening of a large library of enzyme or pathway variants in order to identify those that can efficiently generate or detect the desired compound. Such high-throughput screening can be challenging, especially if the tested library is very large.
To address this limitation, the FutureAgriculture team at the MPI-MP proposes the use of metabolic sensor strains which link the bacterial growth to the presence of the relevant compound, thus replacing screening with direct selection. Their method is based on strains with a series of gene-deletions; each strain displays a different dependency on the relevant compound to support growth. In this way, the growth phenotype of the different strains can be used to accurately estimate the availability of the target compound.
In order to design the most suitable metabolic sensor strains, the team at MPI-MP has developed a computational platform that, based on flux coupling, identifies the growth requirement of several combinations of gene deletions.
As a first application, they designed a set of E. coli sensor strains for glycerate – as this compound is one of the key expected metabolic products of the FutureAgriculture pathways.
They constructed several strains, in which glycerate serves as a carbon source for a different fraction of cellular biomass: in each strain, a different set of enzymes is disrupted such that central metabolism is effectively dissected into multiple segments.
The experimentally measured glycerate dependencies on the strains were found to almost perfectly match predicted values, thus confirming the validity of the computational platform. Moreover, they showed that this method can be used to accurately detect a compound concentration across two orders of magnitude. The glycerate biosensors also reveal key phenomena in central metabolism, such as the spontaneous degradation of central metabolites and the importance of metabolic sinks for balancing small metabolic networks. Such valuable information is now used to optimize the pathway design in FutureAgriculture.