Quantifying cellular capacity identifies gene expression designs with reduced burden

Abstract

5Heterologous gene expression can be a significant burden to cells, consuming resources and 6 causing decreased growth and stability. We describe here an in vivo monitor that tracks E. 7 coli capacity changes in real-time and can be used to assay the burden synthetic constructs and 8 their parts impose. By measuring capacity, construct designs with reduced burden can be 9 identified and shown to predictably outperform less efficient designs, despite having equivalent 10 expression outputs. 11Robust expression of heterologous genes is necessary for many applications in biotechnology and is 12 central to synthetic biology where predictable fine-tuning of expression is typically desired 1-3 . 13However, for engineered bacteria all heterologous expression represents an unnatural load, 14 consuming cellular resources usually allocated to replication, repair and native gene expression 15 ( Figure 1A). Gene expression burden is a well-known phenomenon characterised by decreased 16 growth rates that can predispose synthetic constructs to evolutionary instability and can 17 unexpectedly alter their behaviour 4-10 . Burden presents a major barrier to predictable and stable 18 engineering of cells, yet it is largely an unquantified phenomenon, inferred in most cases by tracking 19 growth rate decline 5, 6, 11 . Recent research has begun to explore burden, demonstrating how its 20 impact varies between different E. coli strains 6, 11 and showing how expression load can be measured 21 in vitro using cell-free extracts 12 . However, an improved way of quantifying how heterologous gene 22 expression imposes burden in vivo has yet to be described, despite the arrival of new models of 23 bacterial growth that outline the importance of expression resources for the cell [13][14][15] . 24To advance in vivo quantification of burden we developed a fluorescence-based method to measure 25 in real-time the gene expression capacity of bacterial genomes. We built integration vectors to insert 26 a 'capacity monitor', a synthetic constitutive green fluorescent protein (GFP) expression cassette, 27 into defined genomic loci of commonly-used E. coli strains ( Figure S1); reasoning that because this 28 cassette lacks regulation, changes in GFP expression due to global expression changes will reflect 29 changes in resource availability 16 . To demonstrate how the capacity monitor improves on using 30 growth rates to assess burden, we measured GFP expression rates from the genome of DH10B E. coli 31 hosting an operon-expressing plasmid induced at different time-points during exponential growth 32 ( Figure 1B). Capacity (determined as GFP production rate per cell) decreases significantly compared 33 to uninduced cells within 30 minutes of construct induction, and this rapid change contrasts with the 34 smaller, slower decreases in growth observed when culture optical density is measured. The fact 35 that capacity changes precede growth rate changes supports the view that decreased expression 36 resources causes growth rate decline and underlin...

A GenoChemetic strategy for derivatization of the violacein natural product scaffold

“…Interestingly, this adjustment led to an increase in the proportion of chlorinated analogues from 28.6 ± 6.4% for weak strength promoters (annotated WWW strain in Figure 3D) to 56.5 ± 4.3% for medium strength promoters (annotated MMM strain in Figure 3D), although the total peak area of chlorinated analogues decreased by approximately 13.2% ( Figure 3D). The large error in peak area could be due to variations in pathway expression profile between different biological replicates, possibly as a result of the presence of medium-strength promoters causing metabolic burden to cells 35 . However, by expressing VioD using the weak promoter, but RebF and RebH with medium promoters (annotated MMW strain in Figure 3D), the proportion of chlorinated analogues further increased to 60.2 ± 7.7%, although the total peak area of chlorinated analogues decreased by 22.1% compared to the WWW strain.…”

Phage-mediated Delivery of Targeted sRNA Constructs to Knock Down Gene Expression in <em>E. coli</em>

“…Infection with M13 is not thought to trigger systemic stress responses in E. coli 20 , but may alter transcriptional patterns indirectly. Alternately, the GFP or ampicillin resistance markers included on the phagemid may compete for cellular resources, reducing mKate2 expression and growth 9 . Finally, the sRNA cassette itself may globally alter gene expression profiles by titrating the Hfq protein, or through off-target mRNA silencing.…”

A data model for biopart datasheets

“…† E-datasheets are not bound to a single application (since they link to all the data collected during a characterisation exercise and order them logically). For instance, the raw data can be used for systems biology exercises -such as the development of a minimal cell model [51,52] or an analysis of cell burden [69]. CAD software can use the extracted parameters, as is, or reanalyse the data according to the models they use.…”