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The In Vitro Redundant Enzymes PurN and PurT Are Both Essential for Systemic Infection of Mice in Salmonella enterica Serovar Typhimurium.

Lotte JelsbakMie I B MortensenMogens KilstrupJohn Elmerdahl Olsen
Published in: Infection and immunity (2016)
Metabolic enzymes show a high degree of redundancy, and for that reason they are generally ignored in searches for novel targets for anti-infective substances. The enzymes PurN and PurT are redundant in vitro in Salmonella enterica serovar Typhimurium, in which they perform the third step of purine synthesis. Surprisingly, the results of the current study demonstrated that single-gene deletions of each of the genes encoding these enzymes caused attenuation (competitive infection indexes [CI] of <0.03) in mouse infections. While the ΔpurT mutant multiplied as fast as the wild-type strain in cultured J774A.1 macrophages, net multiplication of the ΔpurN mutant was reduced approximately 50% in 20 h. The attenuation of the ΔpurT mutant was abolished by simultaneous removal of the enzyme PurU, responsible for the formation of formate, indicating that the attenuation was related to formate accumulation or wasteful consumption of formyl tetrahydrofolate by PurU. In the process of further characterization, we disclosed that the glycine cleavage system (GCV) was the most important for formation of C1 units in vivo (CI = 0.03 ± 0.03). In contrast, GlyA was the only important enzyme for the formation of C1 units in vitro The results with the ΔgcvT mutant further revealed that formation of serine by SerA and further conversion of serine into C1 units and glycine by GlyA were not sufficient to ensure C1 formation in S Typhimurium in vivo The results of the present study call for reinvestigations of the concept of metabolic redundancy in S Typhimurium in vivo.
Keyphrases
  • wild type
  • listeria monocytogenes
  • genome wide
  • metabolic syndrome
  • type diabetes
  • endothelial cells
  • protein kinase
  • copy number
  • skeletal muscle
  • dna methylation
  • drinking water
  • insulin resistance
  • bioinformatics analysis