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

Sebastian KöbbingThorsten LechtenbergBenedikt WynandsLars Mathias BlankNick Wierckx
Published in: ACS synthetic biology (2024)
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.
Keyphrases
  • gene expression
  • copy number
  • poor prognosis
  • dna methylation
  • rna seq
  • long non coding rna
  • oxidative stress
  • cystic fibrosis
  • machine learning
  • pseudomonas aeruginosa
  • risk assessment
  • cell free