Escherichia coli limits Salmonella Typhimurium infections after diet shifts and fat-mediated microbiota perturbation in mice.
Sandra Y WotzkaMarkus KreuzerLisa MaierMarkus ArnoldiniBidong D NguyenAlexander O BrachmannDorothée Lisa BertholdMirjam ZündAnnika HausmannErik BakkerenDaniel HocesErsin GülMarkus BeutlerTamas DolowschiakMichael ZimmermannTobias FuhrerKathrin MoorUwe SauerAthanasios TypasJörn PielMédéric DiardAndrew J MacphersonBärbel StecherShinichi SunagawaEmma SlackWolf-Dietrich HardtPublished in: Nature microbiology (2019)
The microbiota confers colonization resistance, which blocks Salmonella gut colonization1. As diet affects microbiota composition, we studied whether food composition shifts enhance susceptibility to infection. Shifting mice to diets with reduced fibre or elevated fat content for 24 h boosted Salmonella Typhimurium or Escherichia coli gut colonization and plasmid transfer. Here, we studied the effect of dietary fat. Colonization resistance was restored within 48 h of return to maintenance diet. Salmonella gut colonization was also boosted by two oral doses of oleic acid or bile salts. These pathogen blooms required Salmonella's AcrAB/TolC-dependent bile resistance. Our data indicate that fat-elicited bile promoted Salmonella gut colonization. Both E. coli and Salmonella show much higher bile resistance than the microbiota. Correspondingly, competitive E. coli can be protective in the fat-challenged gut. Diet shifts and fat-elicited bile promote S. Typhimurium gut infections in mice lacking E. coli in their microbiota. This mouse model may be useful for studying pathogen-microbiota-host interactions, the protective effect of E. coli, to analyse the spread of resistance plasmids and assess the impact of food components on the infection process.
Keyphrases
- escherichia coli
- listeria monocytogenes
- adipose tissue
- weight loss
- physical activity
- biofilm formation
- klebsiella pneumoniae
- fatty acid
- mouse model
- high fat diet induced
- insulin resistance
- type diabetes
- risk assessment
- metabolic syndrome
- pseudomonas aeruginosa
- skeletal muscle
- staphylococcus aureus
- cystic fibrosis
- climate change
- candida albicans
- machine learning
- ionic liquid