Immune Recognition of the Epidemic Cystic Fibrosis Pathogen Burkholderia dolosa.
Damien RouxMolly WeatherholtBradley ClarkMihaela GadjevaDiane RenaudDavid ScottDavid SkurnikGregory P PriebeGerald PierCraig GerardDeborah R Yoder-HimesPublished in: Infection and immunity (2017)
Burkholderia dolosa caused an outbreak in the cystic fibrosis (CF) clinic at Boston Children's Hospital from 1998 to 2005 and led to the infection of over 40 patients, many of whom died due to complications from infection by this organism. To assess whether B. dolosa significantly contributes to disease or is recognized by the host immune response, mice were infected with a sequenced outbreak B. dolosa strain, AU0158, and responses were compared to those to the well-studied CF pathogen Pseudomonas aeruginosa In parallel, mice were also infected with a polar flagellin mutant of B. dolosa to examine the role of flagella in B. dolosa lung colonization. The results showed a higher persistence in the host by B. dolosa strains, and yet, neutrophil recruitment and cytokine production were lower than those with P. aeruginosa The ability of host immune cells to recognize B. dolosa was then assessed, B. dolosa induced a robust cytokine response in cultured cells, and this effect was dependent on the flagella only when bacteria were dead. Together, these results suggest that B. dolosa can be recognized by host cells in vitro but may avoid or suppress the host immune response in vivo through unknown mechanisms. B. dolosa was then compared to other Burkholderia species and found to induce similar levels of cytokine production despite being internalized by macrophages more than Burkholderia cenocepacia strains. These data suggest that B. dolosa AU0158 may act differently with host cells and is recognized differently by immune systems than are other Burkholderia strains or species.
Keyphrases
- cystic fibrosis
- pseudomonas aeruginosa
- immune response
- induced apoptosis
- escherichia coli
- primary care
- emergency department
- healthcare
- young adults
- end stage renal disease
- oxidative stress
- type diabetes
- ejection fraction
- risk factors
- biofilm formation
- endoplasmic reticulum stress
- metabolic syndrome
- sensitive detection
- artificial intelligence
- prognostic factors
- toll like receptor
- electronic health record
- big data
- peritoneal dialysis
- machine learning
- drug induced
- reduced graphene oxide
- stress induced
- patient reported outcomes