Reliable Genomic Integration Sites in Pseudomonas putida Identified by Two-Dimensional Transcriptome Analysis.

Genomic integration is commonly used to engineer stable production hosts. However, so far, for many microbial workhorses, only a few integration sites have been characterized, thereby restraining advanced strain engineering that requires multiple insertions. Here, we report on the identification of novel genomic integration sites, so-called landing pads, for Pseudomonas putida KT2440. We identified genomic regions with constant expression patterns under diverse experimental conditions by using RNA-Seq data. Homologous recombination constructs were designed to insert heterologous genes into intergenic sites in these regions, allowing condition-independent gene expression. Ten potential landing pads were characterized using four different msfGFP expression cassettes. An insulated probe sensor was used to study locus-dependent effects on recombinant gene expression, excluding genomic read-through of flanking promoters under changing cultivation conditions. While the reproducibility of expression in the landing pads was very high, the msfGFP signals varied strongly between the different landing pads, confirming a strong influence of the genomic context. To showcase that the identified landing pads are also suitable candidates for heterologous gene expression in other Pseudomonads, four equivalent landing pads were identified and characterized in Pseudomonas taiwanensis VLB120. This study shows that genomic "hot" and "cold" spots exist, causing strong promoter-independent variations in gene expression. This highlights that the genomic context is an additional parameter to consider when designing integrable genomic cassettes for tailored heterologous expression. The set of characterized genomic landing pads presented here further increases the genetic toolbox for deep metabolic engineering in Pseudomonads.

Establishing a straightforward I-SceI-mediated recombination one-plasmid system for efficient genome editing in P. putida KT2440.

Pseudomonas putida has become an increasingly important chassis for producing valuable bioproducts. This development is not least due to the ever-improving genetic toolbox, including gene and genome editing techniques. Here, we present a novel, one-plasmid design of a critical genetic tool, the pEMG/pSW system, guaranteeing one engineering cycle to be finalized in 3 days. The pEMG/pSW system proved in the last decade to be valuable for targeted genome engineering in Pseudomonas, as it enables the deletion of large regions of the genome, the integration of heterologous gene clusters or the targeted generation of point mutations. Here, to expedite genetic engineering, two alternative plasmids were constructed: (1) The sacB gene from Bacillus subtilis was integrated into the I-SceI expressing plasmid pSW-2 as a counterselection marker to accelerated plasmid curing; (2) double-strand break introducing gene I-sceI and sacB counterselection marker were integrated into the backbone of the original pEMG vector, named pEMG-RIS. The single plasmid of pEMG-RIS allows rapid genome editing despite the low transcriptional activity of a single copy of the I-SceI encoding gene. Here, the usability of the pEMG-RIS is shown in P. putida KT2440 by integrating an expression cassette including an msfGFP gene in 3 days. In addition, a large fragment of 12.1 kb was also integrated. In summary, we present an updated pEMG/pSW genome editing system that allows efficient and rapid genome editing in P. putida. All plasmids designed in this study will be available via the Addgene platform.

Engineering adipic acid metabolism in Pseudomonas putida.

Bio-upcycling of plastics is an upcoming alternative approach for the valorization of diverse polymer waste streams that are too contaminated for traditional recycling technologies. Adipic acid and other medium-chain-length dicarboxylates are key components of many plastics including polyamides, polyesters, and polyurethanes. This study endows Pseudomonas putida KT2440 with efficient metabolism of these dicarboxylates. The dcaAKIJP genes from Acinetobacter baylyi, encoding initial uptake and activation steps for dicarboxylates, were heterologously expressed. Genomic integration of these dca genes proved to be a key factor in efficient and reliable expression. In spite of this, adaptive laboratory evolution was needed to connect these initial steps to the native metabolism of P. putida, thereby enabling growth on adipate as sole carbon source. Genome sequencing of evolved strains revealed a central role of a paa gene cluster, which encodes parts of the phenylacetate metabolic degradation pathway with parallels to adipate metabolism. Fast growth required the additional disruption of the regulator-encoding psrA, which upregulates redundant ß-oxidation genes. This knowledge enabled the rational reverse engineering of a strain that can not only use adipate, but also other medium-chain-length dicarboxylates like suberate and sebacate. The reverse engineered strain grows on adipate with a rate of 0.35 ± 0.01 h-1, reaching a final biomass yield of 0.27 ± 0.00 gCDW gadipate-1. In a nitrogen-limited medium this strain produced polyhydroxyalkanoates from adipate up to 25% of its CDW. This proves its applicability for the upcycling of mixtures of polymers made from fossile resources into biodegradable counterparts.

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb.

Metabolic engineering relies on modifying gene expression to regulate protein concentrations and reaction activities. The gene expression is controlled by the promoter sequence, and sequence libraries are used to scan expression activities and to identify correlations between sequence and activity. We introduce a computational workflow called Exp2Ipynb to analyze promoter libraries maximizing information retrieval and promoter design with desired activity. We applied Exp2Ipynb to seven prokaryotic expression libraries to identify optimal experimental design principles. The workflow is open source, available as Jupyter Notebooks and covers the steps to 1) generate a statistical overview to sequence and activity, 2) train machine-learning algorithms, such as random forest, gradient boosting trees and support vector machines, for prediction and extraction of feature importance, 3) evaluate the performance of the estimator, and 4) to design new sequences with a desired activity using numerical optimization. The workflow can perform regression or classification on multiple promoter libraries, across species or reporter proteins. The most accurate predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. The prediction confidence mostly depends on sample size and sequence diversity, and we present a relationship to estimate their respective effects. The workflow can be adapted to process sequence libraries from other expression-related problems and increase insight to the growing application of high-throughput experiments, providing support for efficient strain engineering.

Characterization of Context-Dependent Effects on Synthetic Promoters.

Understanding the composability of genetic elements is central to synthetic biology. Even for seemingly well-known elements such as a sigma 70 promoter the genetic context-dependent variability of promoter activity remains poorly understood. The lack of understanding of sequence to function results in highly limited de novo design of novel genetic element combinations. To address this issue, we characterized in detail concatenated "stacked" synthetic promoters including varying spacer sequence lengths and compared the transcription strength to the output of the individual promoters. The proxy for promoter activity, the msfGFP synthesis from stacked promoters was consistently lower than expected from the sum of the activities of the single promoters. While the spacer sequence itself had no activity, it drastically affected promoter activities when placed up- or downstream of a promoter. Single promoter-spacer combinations revealed a bivalent effect on msfGFP synthesis. By systematic analysis of promoter and spacer combinations, a semi-empirical correlation was developed to determine the combined activity of stacked promoters.