Chlamydia trachomatis intra-bacterial and total plasmid copy number in clinical urogenital samples.
Jeanne A M C DirksK JanssenC J P A HoebeT H B GeelenM LucchesiN H T M Dukers-MuijrersPetra F G WolffsPublished in: Scientific reports (2021)
Chlamydia trachomatis (CT) increases its plasmid numbers when stressed, as occurs in clinical trachoma samples. Most CT tests target the plasmid to increase the test sensitivity, but some only target the chromosome. We investigated clinical urogenital samples for total plasmid copy numbers to assess its diagnostic value and intra-bacterial plasmid copy numbers to assess its natural variation. Both plasmid and chromosome copies were quantified using qPCR, and the plasmid:chromosome ratio (PCr) calculated in two cohorts: (1) 383 urogenital samples for the total PCR (tPCr), and (2) 42 vaginal swabs, with one half treated with propium-monoazide (PMA) to prevent the quantification of extracellular DNA and the other half untreated to allow for both tPCr and intra-bacterial PCr (iPCr) quantification. Mann-Whitney U tests compared PCr between samples, in relation to age and gender. Cohort 1: tPCr varied greatly (1-677, median 16). Median tPCr was significantly higher in urines than vaginal swabs (32 vs. 11, p < 0.001). Cohort 2: iPCr was more stable than tPCr (range 0.1-3 vs. 1-11). To conclude, tPCr in urogenital samples was much more variable than previously described. Transport time and temperature influences DNA degradation, impacting chromosomal DNA more than plasmids and urine more than vaginal samples. Data supports a plasmid target in CT screening assays to increase clinical sensitivity.
Keyphrases
- escherichia coli
- copy number
- crispr cas
- mitochondrial dna
- computed tomography
- circulating tumor
- single molecule
- image quality
- genome wide
- contrast enhanced
- mental health
- cell free
- magnetic resonance imaging
- dna methylation
- magnetic resonance
- klebsiella pneumoniae
- positron emission tomography
- high resolution
- gene expression
- high throughput
- mass spectrometry
- deep learning
- pet ct
- circulating tumor cells
- single cell
- artificial intelligence