Systematic Part Transfer by Extending a Modular Toolkit to Diverse Bacteria

Abstract

It is impractical to develop a new parts collection for every potential host organism. It is well-established that gene expression parts, like genes, are qualitatively transferable, but there is little quantitative information defining transferability. Here, we systematically quantified the behavior of a parts set across multiple hosts. To do this, we developed a broad host range (BHR) plasmid system compatible with the large, modular CIDAR parts collection for E. coli, which we named openCIDAR. This enabled testing of a library of DNA constructs across the Pseudomonadota─Escherichia coli, Pseudomonas putida, Cupriavidus necator, and Komagataeibacter nataicola. Part performance was evaluated with a standardized characterization procedure that quantified expression in terms of molecules of equivalent fluorescein (MEFL), an objective unit of measure. The results showed that the CIDAR parts enable graded gene expression across all organisms─meaning that the same parts can be used to program E. coli, P. putida, C. necator, and K. nataicola. Most parts had a similar expression trend across hosts, although each organism had a different average gene expression level. The variability is enough that to achieve the same MEFL in a different organism, a lookup table is required to translate a design from one host to another. To identify truly divergent parts, we applied linear regression to a combinatorial set of promoters and ribosome binding sites, finding that the promoter J23100 behaves very differently in K. nataicola than in the other hosts. Thus, it is now possible to evaluate any CIDAR compatible part in three other hosts of interest, and the diversity of these hosts implies that the collection will also be compatible with many other Proteobacteria (Pseudomonadota). Furthermore, this work defines an approach to generalize modular synthetic biology parts sets beyond a single host, implying that only a few parts sets may be needed to span the tree of life. This will accelerate current efforts to engineer diverse species for environmental, biotechnological, and health applications.

Keywords: automated genetic assembly; genetic design; nonconventional chassis; synthetic biology.

Figure 1

Enabling parts expression across hosts. (A) Known pBBR1 compatible bacteria, presented in a reconstructed phylogenetic tree. All major clades of Pseudomonadota are represented in this group. Hosts used in this study are highlighted. (B) Diagram of the native architecture of pBBR1, as isolated from Bordatella bronchiseptica. Modern derivatives do not include the Mob transcription unit, but are otherwise similar.

Figure 2

OpenCIDAR design and function. (A) Design of the new destination vectors containing KanR, the pBBR1 origin and rep gene, and a CIDAR cloning site where transcription units may be assembled. Although this is over 100 parts, only the parts in this study are shown. (B) Molecules of Equivalent Fluorescein (MEFL) expression values for the combinations of parts included in the original CIDAR paper. These were interpolated from CIDAR Figure S1. Expression from the original vector series in E. coli matches closely to the expression reported in this study. (C) Violin plots of the expression values in MEFL for E. coli populations expressing each construct, along with the average copy number assessed by ddPCR. The violin plots for three replicates are overlaid, showing little variation between replicates. (D) The same plots in as in C for the same constructs expressed in P. putida. (E) The same plots in as in C for the same constructs expressed in C. necator. (F) The same plots in as in C for the same constructs expressed in K. nataicola.

Figure 3

Comparing and correlating part function across hosts. (A) Rank order comparison of MEFL for each construct in each host. This shows a rough global correlation for expression in all species. Divergent values are largely found only in K. nataicola. (B) Species–species correlations. The species is in the diagonal, with the MEFL values in each species plotted in the top right boxes and the correlation coefficients in the bottom left boxes.

Figure 4

MEFL lookup table (LUT) for openCIDAR. By matching MEFL across organisms, researchers may design genetic constructs that achieve the same expression values across different organisms.

Figure 5

Linear models for part performance across species. (A) General form of the linear models. Expression is the sum of an intercept term plus estimates computed for each promoter, RBS, and the interactions between them. Due to high correlation between variables (i.e., very similar expression between parts), some parameters were not estimated (see Methods). (B) Bar chart of the intercept values, interpreted as background fluorescence, were similar between species. (C) Bar chart of the promoter coefficients. The contribution of each promoter to expression had conserved rank order in each species, however the magnitudes varied. Notably, the expected very low expression of the J23103 promoter in E. coli was not conserved. (D) Bar chart of the RBS coefficients. Rank order of the RBS coefficients is not conserved between species. (E) Bar chart of the interaction terms between promoters and RBSs. Interactions between promoter and RBS terms were very high in K. nataicola relative to other species, especially for those terms including the J23100 promoter.