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Identification of conditionally essential genes for growth of Pseudomonas putida KT2440 on minimal medium through the screening of a genome-wide mutant library
In silico models for Pseudomonas putida KT2440 metabolism predict 68 genes to be essential for growth on minimal medium. In this study a genome-wide collection of single-gene P. putida KT2440 knockouts was generated by mini-Tn5 transposon mutagenesis and used to identify genes essential for growth in minimal medium with glucose. Our screening of the knockout library allowed us to rescue mutants for 48 different knockouts that were conditionally essential for growth on minimal medium. The in vivo screening showed that 24 of these mutants had a insertion in genes proposed to be conditionally essential based on in silico models, whereas another 24 newly implicated conditionally essential genes have been found. For 10 of the in silico proposed conditionally essential genes not found in the screening, knockout mutants were available at the Pseudomonas Reference Culture Collection. These mutants were tested for conditional growth on minimal medium, but none of them was shown to be essential, suggesting that the in silico proposal was inaccurate. Among the set of identified conditionally essential genes were a number of genes involved in the biosynthesis of certain amino acids and vitamins. Auxotrophs for all amino acids predicted by the in silico models were found and, in addition, we also found auxotrophs for proline, serine, threonine and methionine, as well as auxotrophs for biotin, nicotinate and vitamin B12 that were not predicted in silico. Metabolic tests were performed to validate the mutants' phenotypes. Auxotrophies for l-Arg, l-Leu, l-Pro and l-Cys were bypassed by external addition of the corresponding d-amino acids, suggesting the existence of number of d- to l-amino acid racemases encoded by the KT2440 genome. Therefore, the in vivo high-throughput analysis presented here provides relevant insights into the metabolic cross-road of biosynthetic pathways in this microorganism, as well as valuable information for the fine tuning of current in silico metabolic models.