Improved bacterial recombineering by parallelized protein discovery

Abstract

Exploiting bacteriophage-derived homologous recombination processes has enabled precise, multiplex editing of microbial genomes and the construction of billions of customized genetic variants in a single day. The techniques that enable this, multiplex automated genome engineering (MAGE) and directed evolution with random genomic mutations (DIvERGE), are however, currently limited to a handful of microorganisms for which single-stranded DNA-annealing proteins (SSAPs) that promote efficient recombineering have been identified. Thus, to enable genome-scale engineering in new hosts, efficient SSAPs must first be found. Here we introduce a high-throughput method for SSAP discovery that we call "serial enrichment for efficient recombineering" (SEER). By performing SEER in Escherichia coli to screen hundreds of putative SSAPs, we identify highly active variants PapRecT and CspRecT. CspRecT increases the efficiency of single-locus editing to as high as 50% and improves multiplex editing by 5- to 10-fold in E. coli, while PapRecT enables efficient recombineering in Pseudomonas aeruginosa, a concerning human pathogen. CspRecT and PapRecT are also active in other, clinically and biotechnologically relevant enterobacteria. We envision that the deployment of SEER in new species will pave the way toward pooled interrogation of genotype-to-phenotype relationships in previously intractable bacteria.

Keywords: MAGE; RecT; genome engineering; recombineering; synthetic biology.

Fig. 1.

SEER workflow. The SEER workflow is depicted across the top from left to right, with libraries being first assembled, then moved into a chassis organism, enriched over 3 to 10 cycles for efficient recombineering proteins, and finally analyzed by deep sequencing. This process can then be iterated on by learning from results and making improvements to the library design. The specific selective enrichment strategy that we designed for E. coli is shown in a gray callout bubble. Five successive antibiotic selections were applied to a library of E. coli cells expressing SSAP variants, and after the selective handles were exhausted the plasmid library was extracted and retransformed into the naïve SEER chassis for five further cycles of selection.

Fig. 2.

The Broad SSAP Library. (A) Circles of various sizes represent the number of variants present in the Broad SSAP Library from different protein families, as categorized by the Pfam database. The seven principle families of SSAPS are grouped into three clusters based on structural and phylogenetic information as proposed by Lopes et al. (18). (B) The phylogenetic distribution of the Broad SSAP Library is represented as vertical bar charts. For each phylogenetic level, any group that represents more than 5% of the total library is called out to the right of the bar. (C) The enrichment of each Broad SSAP Library member is plotted over 10 successive rounds of selection. Enrichment of each library member was calculated by dividing the average frequency across selective replicates by the frequency of the nonselective control. Frequency data are measured by amplification of the barcoded region of the plasmid library and NGS. (D) Total enrichment is plotted for each protein family over the 10 rounds of SEER. (E) Frequency is plotted against enrichment for each Broad SSAP Library member after the 10th round of selection. The bold line is a linear least-squares fit and the dashed lines represent the 95% confidence bounds of the fit. Five candidate proteins, which are shaded in a yellow box, were selected for further characterization, including SR011 and SR016, which are specifically called out. (F) Five top candidates were tested for their efficiency at incorporating a single base pair silent mutation at a nonessential gene, ynfF. Efficiency was read out by NGS. Significance values are indicated for a parametric two-tailed t test between two groups, where ns indicates *P < 0.05; **P < 0.01; ***P < 0.001; and ****P < 0.0001; ns, not significant.

Fig. 3.

Broad RecT Library and CspRecT. (A) Frequency is plotted against enrichment for each Broad RecT Library member after the 10th round of selection. One candidate protein, CspRecT (shaded box), was the standout winner. In all subsequent panels, Redβ, PapRecT, and CspRecT are compared when expressed from a pORTMAGE-based construct (SI Appendix, Fig. S1) in wild-type MG1655 E. coli. Significance values are indicated for a grouped parametric two-tailed t test, where not significant (ns) indicates P > 0.05 and ****P < 0.0001. Editing efficiency was measured by blue/white screening at the LacZ locus for (B) eight different single-base mismatches (n = 3) and (C) 18-base and 30-base mismatches (n = 3). (D) MAGE editing targeting 1, 5, 10, 15, or 20 genomic loci at once in triplicate, was read out by NGS. The solid lines represent the mean editing efficiency across all targeted loci, while the dashed lines represent the sum of all single-locus efficiencies, which we refer to as aggregate efficiency. (E) A 130-oligo DIvERGE experiment using oligos that were designed to tile four different genomic loci that encode the drug targets of fluoroquinolone antibiotics and are known hotspots for CIP resistance. The oligos contained 1.5% degeneracy at each nucleotide position along their entire length. All 130 oligos were mixed and transformed together into cells (n = 3). CFUs were measured at three different CIP concentrations after plating 1/100th of the final recovery volume, and “nd” is “none detected.”

Fig. 4.

Recombineering in Gammaproteobacteria. (A) Recombineering experiments were run with Redβ, PapRecT, and CspRecT expressed off of the pORTMAGE311B backbone, or with a pBBR1 origin in the case of P. aeruginosa. Editing efficiency was measured by colony counts on selective vs. nonselective plates (n = 3) (see Materials and Methods). Vector optimization resulted in improved efficiency of PapRecT in P. aeruginosa (SI Appendix, Fig. S7). (B) Diagram of a simple multidrug resistance experiment in P. aeruginosa harboring an optimized PapRecT plasmid expression system, pORTMAGE-Pa1. In a single round of MAGE, a pool of five oligos was used to incorporate genetic modifications that would provide resistance to STR, RIF, and CIP (n = 3). These populations were then selected by plating on all combinations of one-, two-, or three-antibiotic agarose plates and compared with a nonselective control. (C) Observed efficiencies were calculated by comparing colony counts on selective vs. nonselective plates. Expected efficiencies for multilocus events were calculated as the product of all relevant single-locus efficiencies.