Toll-like receptor 1 polymorphisms increase susceptibility to candidemia.
BACKGROUND: Candidemia is a severe invasive fungal infection with high mortality. Recognition of Candida species is mediated through pattern recognition receptors such as Toll-like receptors (TLRs). This study assessed whether genetic variation in TLR signaling influences susceptibility to candidemia. METHODS: Thirteen mostly nonsynonymous single nucleotide polymorphisms (SNPs) in genes encoding TLRs and signaling adaptors MyD88 and Mal/TIRAP were genotyped in 338 patients (237 white, 93 African American, 8 other race) with candidemia and 351 noninfected controls (263 white, 88 African American). The SNPs significant in univariate analysis were further analyzed with multivariable logistic regression to determine association with clinical outcomes. Functional consequences of these polymorphisms were assessed via in vitro stimulation assays. RESULTS: Analyses of TLR SNPs revealed that 3 TLR1 SNPs (R80T, S248N, I602S) were significantly associated with candidemia susceptibility in whites. This association was not found in African Americans, likely due to lower power in this smaller study population. Furthermore, these TLR1 polymorphisms displayed impaired cytokine release by primary monocytes. No associations with susceptibility to candidemia were observed for SNPs in TLR2, TLR4, TLR6, TLR9, MyD88, or TIRAP. CONCLUSIONS: Nonsynonymous SNPs in TLR1 are associated with impaired TLR1 function, decreased cytokine responses, and predisposition to candidemia in whites.
Duke Scholars
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- White People
- Toll-Like Receptor 1
- Signal Transduction
- Prospective Studies
- Polymorphism, Single Nucleotide
- Multivariate Analysis
- Middle Aged
- Microbiology
- Male
- Logistic Models
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- White People
- Toll-Like Receptor 1
- Signal Transduction
- Prospective Studies
- Polymorphism, Single Nucleotide
- Multivariate Analysis
- Middle Aged
- Microbiology
- Male
- Logistic Models