Detection of low-level parental somatic mosaicism for clinically relevant SNVs and indels identified in a large exome sequencing dataset.

BACKGROUND: Due to the limitations of the current routine diagnostic methods, low-level somatic mosaicism with variant allele fraction (VAF) < 10% is often undetected in clinical settings. To date, only a few studies have attempted to analyze tissue distribution of low-level parental mosaicism in a large clinical exome sequencing (ES) cohort. METHODS: Using a customized bioinformatics pipeline, we analyzed apparent de novo single-nucleotide variants or indels identified in the affected probands in ES trio data at Baylor Genetics clinical laboratories. Clinically relevant variants with VAFs between 30 and 70% in probands and lower than 10% in one parent were studied. DNA samples extracted from saliva, buccal cells, redrawn peripheral blood, urine, hair follicles, and nail, representing all three germ layers, were tested using PCR amplicon next-generation sequencing (amplicon NGS) and droplet digital PCR (ddPCR). RESULTS: In a cohort of 592 clinical ES trios, we found 61 trios, each with one parent suspected of low-level mosaicism. In 21 parents, the variants were validated using amplicon NGS and seven of them by ddPCR in peripheral blood DNA samples. The parental VAFs in blood samples varied between 0.08 and 9%. The distribution of VAFs in additional tissues ranged from 0.03% in hair follicles to 9% in re-drawn peripheral blood. CONCLUSIONS: Our study illustrates the importance of analyzing ES data using sensitive computational and molecular methods for low-level parental somatic mosaicism for clinically relevant variants previously diagnosed in routine clinical diagnostics as apparent de novo.

Urethral Microbiota in Men: Association of Haemophilus influenzae and Mycoplasma penetrans With Nongonococcal Urethritis.

BACKGROUND: Nongonococcal urethritis (NGU) is a common syndrome with no known etiology in ≤50% of cases. We estimated associations between urethral bacteria and NGU in men who have sex with men (MSM) and men who have sex with women (MSW). METHODS: Urine was collected from NGU cases (129 MSM, 121 MSW) and controls (70 MSM, 114 MSW) attending a Seattle STD clinic. Cases had ≥5 polymorphonuclear leukocytes on Gram stain plus symptoms or discharge; controls had <5 PMNs, no symptoms, no discharge. NGU was considered idiopathic when Neisseria gonorrhoeae, Chlamydia trachomatis, Mycoplasma genitalium, Trichomonas vaginalis, adenovirus, and herpes simplex virus were absent. The urethral microbiota was characterized using 16S rRNA gene sequencing. Compositional lasso analysis was conducted to identify associations between bacterial taxa and NGU and to select bacteria for targeted qPCR. RESULTS: Among NGU cases, 45.2% were idiopathic. Based on compositional lasso analysis, we selected Haemophilus influenzae (HI) and Mycoplasma penetrans (MP) for targeted qPCR. Compared with 182 men without NGU, the 249 men with NGU were more likely to have HI (14% vs 2%) and MP (21% vs 1%) (both P ≤ .001). In stratified analyses, detection of HI was associated with NGU among MSM (12% vs 3%, P = .036) and MSW (17% vs 1%, P < .001), but MP was associated with NGU only among MSM (13% vs 1%, P = .004). Associations were stronger in men with idiopathic NGU. CONCLUSIONS: HI and MP are potential causes of male urethritis. MP was more often detected among MSM than MSW with urethritis.

Temporal variability is a personalized feature of the human microbiome.

BACKGROUND: It is now apparent that the complex microbial communities found on and in the human body vary across individuals. What has largely been missing from previous studies is an understanding of how these communities vary over time within individuals. To the extent to which it has been considered, it is often assumed that temporal variability is negligible for healthy adults. Here we address this gap in understanding by profiling the forehead, gut (fecal), palm, and tongue microbial communities in 85 adults, weekly over 3 months. RESULTS: We found that skin (forehead and palm) varied most in the number of taxa present, whereas gut and tongue communities varied more in the relative abundances of taxa. Within each body habitat, there was a wide range of temporal variability across the study population, with some individuals harboring more variable communities than others. The best predictor of these differences in variability across individuals was microbial diversity; individuals with more diverse gut or tongue communities were more stable in composition than individuals with less diverse communities. CONCLUSIONS: Longitudinal sampling of a relatively large number of individuals allowed us to observe high levels of temporal variability in both diversity and community structure in all body habitats studied. These findings suggest that temporal dynamics may need to be considered when attempting to link changes in microbiome structure to changes in health status. Furthermore, our findings show that, not only is the composition of an individual's microbiome highly personalized, but their degree of temporal variability is also a personalized feature.