Engineered microbes are particularly effective due to a versatile metabolism housed in a relatively simple cell structure. One of the challenges, however, is that when microbes are engineered for making a new product, it is difficult to pin-point the best performer in a pool of microbial cells with variable product formation efficiencies. Furthermore, testing the efficiency of microbial cells has always been a low throughput process and ultimately a bottleneck in the Design-Build-Test-Learn (DBTL) cycle for bio-based products.

 

In order to overcome this challenge of identifying and isolating top performing cells, LANL scientists have leveraged longstanding capabilities in protein design and computational modeling of ligand binding pockets in proteins. This knowledge base allows researchers at LANL to engineer custom biosensors that detect intracellular concentrations of a specific precursor or building block as a target. These protein-based biosensors for small molecules detect the presence of the desired target and respond by accumulating another molecule that physically glows/lights up, enabling researchers to actually visualize the productivity. Furthermore, by coupling this “smart microbial cell” to the high throughput efficiency of a single-cell sorting technique called flow cytometry, the team can successfully evaluate a large number of metabolic designs formulated for improved production of whatever precursor or building block is desired.

With enhanced throughput in testing the designs, this approach can accelerate the DBTL cycle for biomanufacturing. Specifically, this approach is crucial for relieving the bottleneck in the Test step of the DBTL cycle. Not only does the increased throughput in the “test” step help match the “design” and “build” steps, but the capability to produce enormous amounts of data by high throughput evaluation contributes to enhanced learning for subsequent DBTL cycles.

 

Overall, LANL’s smart microbial cell technology is an advanced platform for high throughput screening for enzyme discovery, design, and evolution. The approach can be translated to screening of metagenomic samples, rational enzyme design, or directed evolution of known enzymes. The technology is adaptable to a single enzyme, or a pathway, or global optimization of an industrial strain.

 

The microbial biosensor team includes LANL scientists Ramesh Jha, Niju Narayanan, and Taraka Dale (lead). The work was performed under the Agile BioFoundry, a multi-national laboratory effort to expedite biomanufacturing processes (https://agilebiofoundry.org).