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Koebbing, S.

Publications and source records attributed to Koebbing, S..

2 recordsLinked to original sources

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

Pseudomonas putida has become an increasingly important chassis for the production of valuable bioproducts. This development is not at least due to the ever-improving genetic toolbox, including gene and genome editing techniques. Here, we present a novel, one plasmid design of a key genetic tool, the pEMG/pSW system, guaranteeing one engineering cycle to be finalized in three days. The pEMG/pSW system proved in the last decade to be valuable for targeted gensome engineering in Pseudomonas, as it enables the deletion of large regions of the genome, the integration of heterologous gene clusters or 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 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 a msfGFP gene in three days. In addition, a large fragment of almost 16 kb was also integrated. In summary, an updated pEMG/pSW genome editing system is presented that allows efficient and rapid genome editing in P. putida. The pEMG-RIS will be available via the Addgene platform.

microbiology↗

Exp2Ipynb: A general machine-learning workflow for the analysis of promoter libraries

Strain engineering in biotechnology modifies metabolic pathways in microorganisms to overproduce target metabolites. To modify metabolic pathway activity in bacteria, gene expression is an effective and easy manipulated process, specifically the promoter sequence recognized by sigma factors. Promoter libraries are generated to scan the expression activity of different promoter sequences and to identify sequence positions that predict activity. To maximize information retrieval, a well-designed experimental setup is required. We present a computational workflow to analyse promoter libraries; by applying this workflow to seven libraries, we aim to identify critical design principles. The workflow is based on a Python Jupyter Notebook and covers the following steps: (i) statistical sequence analysis, (ii) sequence-input to expression-output predictions, (iii) estimator performance evaluation, and (iv) new sequence prediction with defined activity. The workflow can process multiple promoter libraries, across species or reporter proteins, and classify or regress expression activity. The strongest 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. A tradeoff between sample size and sequence diversity reduces prediction quality, and we present a relationship to estimate the minimum sample size. The workflow guides the user through analysis and machine-learning training, is open source and easily adaptable to include alternative machine-learning strategies and to process sequence libraries from other expression-related problems. The workflow is a contribution to increase insight to the growing application of high-throughput experiments and provides support for efficient strain engineering. AvailabilityFreely available on GitHub: https://qithub.com/iAMB-RWTH-Aachen/Exp2Ipynb and licensed under the terms of GPLv3. Contactulf.liebal@rwth-aachen.de Supplementary informationSupplementary data available in the Git folder.

bioinformatics↗