A quantitative method for proteome reallocation using minimal regulatory interventions

Abstract

Engineering resource allocation in biological systems for synthetic biology applications is an ongoing challenge. Wild type organisms allocate abundant cellular resources for ensuring survival in changing environments, reducing the productivity of engineered functions. Here we present a novel approach for engineering the resource allocation of Escherichia coli by rationally modifying the transcriptional regulatory network of the bacterium. Our method (ReProMin) identifies the minimal set of genetic interventions that maximise the savings in cell resources that would normally be used to express non-essential genes. To this end we categorize Transcription Factors (TFs) according to the essentiality of the genes they regulate and we use available proteomic data to rank them based on its proteomic balance, defined as the net proteomic charge they release. Using a combinatorial approach, we design the removal of TFs that maximise the release of the proteomic charge and we validate the model predictions experimentally. Expression profiling of the resulting strain shows that our designed regulatory interventions are highly specific. We show that our resulting engineered strain containing only three mutations, theoretically releasing 0.5% of their proteome, has higher proteome budget and show increased production yield of a molecule of interest obtained from a recombinant metabolic pathway. This approach shows that combining whole-cell proteomic and regulatory data is an effective way of optimizing strains in a predictable way using conventional molecular methods.ImportanceBiological regulatory mechanisms are complex and occur in hierarchical layers such as transcription, translation and post-translational mechanisms. We foresee the use of regulatory mechanism as a control layer that will aid in the design of cellular phenotypes. Our ability to engineer biological systems will be dependent on the understanding of how cells sense and respond to their environment at a system level. Few studies have tackled this issue and none of them in a rational way. By developing a workflow of engineering resource allocation based on our current knowledge of E. coli’s regulatory network, we pursue the objective of minimizing cell proteome using a minimal genetic intervention principle. We developed a method to rationally design a set of genetic interventions that reduce the hedging proteome allocation. Using available datasets of a model bacterium we were able to reallocate parts of the unused proteome in laboratory conditions to the production of an engineered task. We show that we are able to reduce the unused proteome (theoretically 0.5%) with only three regulatory mutations designed in a rational way, which results in strains with increased capabilities for recombinant expression of pathways of interest.HighlightsProteome reduction with minimal genetic intervention as design principleRegulatory and proteomic data integration to identify transcription factor activated proteomeDeletion of the TF combination that reduces the greater proteomic loadRegulatory interventions are highly specificDesigned strains show less burden, improved protein and violacein production

Protein allocation and utilization in the versatile chemolithoautotrophCupriavidus necator

“…Its large genome and complex regulation of gene expression seems, however, highly adapted to environmental flexibility instead of one particular niche, resulting in inefficient gene expression. Engineering of its proteome budget for a particular substrate could result in strains with lower unutilized enzyme reserves and higher productivity [ Lastiri-Pancardo et al, 2020 ]. A second avenue is to optimize the balance of energy and carbon for formatotrophic or mixotrophic growth.…”

Resource reallocation in engineeredEscherichia colistrains with reduced genomes

“…Specifically, ReProMin utilizes transcription factor regulatory data to link highly expressed nonessential genes to their corresponding regulatory genes. By knocking out transcription factors that drive expression of highly-expressed, nonessential genes, they were able to free up over 0.5% of the proteome budget without inducing a significant growth defect (Lastiri-Pancardo et al, 2020).…”

Plasmid DNA Production in Proteome-Reduced Escherichia coli

“…E. coli K-12 derivatives BW25113 was used as wild type. The combinatorial mutations of the PFC strain (BW25113 Δ phoB , Δ flhC , Δ cueR ) developed by Lastiri and coworkers [ 6 ] were generated by sequential P1 phage transduction from the individual knockout using the Keio collection strain as donors. The recA gene was inactivated in both strains using the methodology proposed by Datsenko and Wanner [ 10 ].…”

“…This would generate an optimal resource allocation that maximizes the designed cell function [ 5 ]. Lastiri and co-workers [ 6 ] developed a method to engineer the resource allocation of Escherichia coli . This method identifies the minimum combinatorial set of genetic interventions that maximizes resource savings, which they applied by removing transcription factors that activate the expression of unused functions with the greater proteomic load.…”

“…This method identifies the minimum combinatorial set of genetic interventions that maximizes resource savings, which they applied by removing transcription factors that activate the expression of unused functions with the greater proteomic load. Namely, the deletion of phoB , phosphate scavenging system; flhC , flagella master regulator, and cueR , copper efflux system, resulted in a 0.5% reduction of the proteome compared to the wild type strain [ 6 ]. The mutant strain, named PFC, displayed an increased proteomic budget and improved performance when expressing a synthetic pathway for violacein production.…”