Can Previous Associations of Single Nucleotide Polymorphisms in the TLR2, NOD1, CXCR5, and IL10 Genes in the Susceptibility to and Severity of Chlamydia trachomatis Infections Be Confirmed?
Jelmer B JukemaBernice Maria HoenderboomBirgit H B van BenthemMarianne Antonia Bernada van der SandeHenry J C de VriesChristian J P A HoebeNicole H T M Dukers-MuijrersCaroline J BaxServaas A MorréSander OuburgPublished in: Pathogens (Basel, Switzerland) (2021)
Clear inter-individual differences exist in the response to C. trachomatis (CT) infections and reproductive tract complications in women. Host genetic variation like single nucleotide polymorphisms (SNPs) have been associated with differences in response to CT infection, and SNPs might be used as a genetic component in a tubal-pathology predicting algorithm. Our aim was to confirm the role of four genes by investigating proven associated SNPs in the susceptibility and severity of a CT infection. A total of 1201 women from five cohorts were genotyped and analyzed for TLR2 + 2477 G > A, NOD1 + 32656 T -> GG, CXCR5 + 10950 T > C, and IL10 - 1082 A > G. Results confirmed that NOD1 + 32656 T ->GG was associated with an increased risk of a symptomatic CT infection (OR: 1.9, 95%CI: 1.1-3.4, p = 0.02), but we did not observe an association with late complications. IL10 - 1082 A > G appeared to increase the risk of late complications (i.e., ectopic pregnancy/tubal factor infertility) following a CT infection (OR = 2.8, 95%CI: 1.1-7.1, p = 0.02). Other associations were not found. Confirmatory studies are important, and large cohorts are warranted to further investigate SNPs' role in the susceptibility and severity of a CT infection.
Keyphrases
- image quality
- genome wide
- dual energy
- computed tomography
- contrast enhanced
- positron emission tomography
- polycystic ovary syndrome
- toll like receptor
- magnetic resonance imaging
- inflammatory response
- dna methylation
- immune response
- pregnant women
- gene expression
- risk factors
- machine learning
- metabolic syndrome
- pregnancy outcomes
- deep learning
- magnetic resonance
- adipose tissue
- skeletal muscle
- insulin resistance
- nuclear factor
- bioinformatics analysis
- genome wide association