Metabolic Burden of CRISPR-Based Genetic Control Suggests Viability of Large Circuits for Control of Metabolic Pathway Expression
Synthetic Biology Engineering Evolution Design SEED
2021
2021 Synthetic Biology: Engineering, Evolution & Design (SEED)
Poster Session
Poster Presenters - Accepted
It is well understood that the timing and levels of gene expression constituting engineered pathways and circuits must be carefully controlled to balance the supply and demand of cellular resources. Since heterologous genetics share these resources with native processes, particular attention paid to the mechanisms of controlling heterologous gene expression could better optimize the balance between cost and production; however, relatively little is known about the material and energy costs of these control systems. Here, we investigate the metabolic burden of genetic control systems implemented with CRISPR transcriptional activation and T7 RNA polymerase in E. coli, compared to lac-controlled native transcription. After quantifying the costs of individual CRISPRa components, as well as separating the cost of the control systems from their outputs, we find that a single node of CRISPR-based control costs significantly more than a node of T7 control (29% of available expression capacity vs. 5%). However, because the protein components of CRISPRa are an overhead cost shared across nodes, and because they make up the bulk of CRISPR-based control cost, additional nodes can be added to CRISPR circuits relatively cheaply (<5%). This suggests an exciting expandability of CRISPR-based circuits that is limited more by the cost of the output proteins it controls than by the cost of the control circuit itself. Intriguing directions for this circuit expansion could include inducer-free delays of pathway expression or metabolite sensing for dynamic control of pathway enzymes. Finally, through a set of feedback and feedforward control circuits, we find that while output efficiency is generally unchanged compared to open-circuit designs, these closed circuits impose limits on output expression levels that have important implications for genetic stability and, in turn, production titer or circuit performance.