Proceedings of the National Academy of SciencesMolecular BiologyPeer-reviewed journal article
The role of a single non-coding nucleotide in the evolution of an epidemic African clade of Salmonella
Disa L. HammarlöfiD, Carsten KrögeriD, Siân V. OweniD, Rocı́o CanalsiD, Lizeth Lacharme‐Lora, Nicolas WenneriD, Anna E. Schager, Timothy J. WellsiD, Ian R. HendersoniD, Paul WigleyiD, Karsten HokampiD, Nicholas FeaseyiD, Melita A. GordoniD, Jay C. D. HintoniD
Abstract
Introductory ParagraphSalmonella enterica serovar Typhimurium ST313 is a relatively newly emerged sequence type that is causing a devastating epidemic of bloodstream infections across sub-Saharan Africa. Analysis of hundreds of Salmonella genomes has revealed that ST313 is closely-related to the ST19 group of S. Typhimurium that cause gastroenteritis across the world. The core genomes of ST313 and ST19 vary by just 1000 single-nucleotide polymorphisms (SNPs). We hypothesised that the phenotypic differences that distinguish African Salmonella from ST19 are caused by certain SNPs that directly modulate the transcription of virulence genes.\n\nHere we identified 3,597 transcriptional start sites (TSS) of the ST313 strain D23580, and searched for a gene expression signature linked to pathogenesis of Salmonella. We identified a SNP in the promoter of the pgtE gene that caused high expression of the PgtE virulence factor in African S. Typhimurium, increased the degradation of the factor B component of human complement, contributed to serum resistance and modulated virulence in the chicken infection model. The PgtE protease is known to mediate systemic infection in animal models. We propose that high levels of expression PgtE of by African S. Typhimurium ST313 promotes bacterial survival and bacterial dissemination during human infection.\n\nOur finding of a functional role for an extra-genic SNP shows that approaches used to deduce the evolution of virulence in bacterial pathogens should include a focus on non-coding regions of the genome.
- organism: bacteria (other)
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
- Citations
- 90
- FWCI
- 11.73
- Field percentile
- top 1%
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