Collaco, J. M. | bioRxiv Science

bioRxiv2024

Tobacco Smoke Exposure is Characterized by a Distinct Nasal Rhinotype in a Pediatric Population

BackgroundSecondhand tobacco smoke exposure (TSE) increases susceptibility to respiratory diseases, but the mechanisms of action are poorly understood. ObjectiveTo study the effect of TSE in the nasal microbiome of children, and to evaluate whether such effect is dose-dependent with measured levels of cotinine in saliva and urine. MethodsThe study was performed at the Mount Sinai Kravis Childrens Hospital (New York, NY) and the Johns Hopkins Hospital (Baltimore, MD). We enrolled 236 children between 6 months and 10 years of age, both inpatients and outpatients. We collected swabs to characterize the diversity and composition of the nasal microbiome using 16S rRNA gene sequencing and measured cotinine levels in salivary and urinary samples to quantify TSE. We then determined the relationship between these measures and participant respiratory conditions, demographics and lifestyle factors. ResultsInfants with high cotinine levels had lower nasal microbiome alpha diversity and an enrichment in Moraxella, Dolosigranulum and Corynebacterium, which formed a distinct cluster in network analysis. A Dirichlet Multinomial Mixture model identified the existence of two distinct microbial rhinotypes, the first one characterized by significantly higher cotinine levels, lower alpha diversity, and enrichment of these taxa. ConclusionChildren with higher cotinine levels had reduced alpha diversity and a distinct nasal rhinotype. Our results suggest TSE is associated with alterations of the nasal microbiome and identify a rhinotype as a potential biomarker for TSE.

microbiology↗

bioRxiv2021

Accurate assignment of disease liability to genetic variants using only population data

PurposeThe growing size of public variant repositories prompted us to test the accuracy of predicting pathogenicity of DNA variants using population data alone. MethodsUnder the a priori assumption that the ratio of the prevalence of variants in healthy and affected populations form two distinct distributions (pathogenic and benign), we used a Bayesian method to assign probability of a variant belonging to either distribution. ResultsThe approach, termed BayPR, accurately parsed 300 of 313 expertly curated cystic fibrosis transmembrane conductance regulator (CFTR) variants: 284 of 296 pathogenic/likely pathogenic (P/LP) variants in one distribution and 16 of 17 benign/likely benign (B/LB) variants in another. BayPR produced an area under the receiver operating curve (AUC) of 0.99 for 103 functionally-confirmed missense CFTR variants, equal to or exceeding ten commonly used algorithms (AUC range: 0.54 to 0.99). Application of BayPR to expertly curated variants in eight genes associated with seven Mendelian conditions assigned [≥]80% disease-causing probability to 1,350 of 1,374 (98.3%) P/LP variants and [≤]20% to 22 of 23 (95.7%) B/LB variants. ConclusionAgnostic to variant type or functional effect, BayPR provides probabilities of pathogenicity for DNA variants responsible for Mendelian disorders using only variant counts in affected and unaffected population samples.

genetics↗