bioRxivMicrobiologyPreprint
Bacterial Virulence Genes Detected by Metagenomic Sequencing in the Cystic Fibrosis Airway Microbiome
Medha L Valluri, Brennan T. HarmoniD, Aszia Burrell, Andrea HahniD
Abstract
BackgroundCystic fibrosis (CF) is an autosomal recessive genetic disorder that leads to chronic infection and mucus retention in the lungs, with lung function gradually deteriorating through recurrent pulmonary exacerbations (PEx). Virulence factors (VFs) of Pseudomonas aeruginosa and Staphylococcus aureus are thought to contribute to pulmonary exacerbations. Our study objective was to identify VF genes related to PEx, high Pseudomonas abundance, and high Staphylococcus abundance in persons with CF (pwCF). MethodsThis was an ancillary study of pwCF treated with IV antibiotics for PEx between 2016-2020 at Childrens National Hospital. Using shotgun metagenomics and ShortBRED, we identified bacterial VF genes and used DESeq2 to determine differential expression of VF genes across comparators. ResultsTwenty-two PwCF experienced 43 PEx. The study cohort had a mean age of 14.6 years, 41% female, 59% white, 36% Hispanic, and 45% had an F508del homozygous CFTR mutation. Minimal differences in VF gene abundance were identified across clinical state. The most differentially increased VF genes found in Pseudomonas high samples were associated with an aminotransferase (log2FC 25.9), flagellar biosynthesis (log2FC 8.3), and type VI secretion systems (log2FC 8.2). The most differentially increased VF genes found in Staphylococcus high samples were an exotoxin (log2FC 26.7), macrolide phosphotransferase (log2FC 25.8), pathogenicity island proteins (log2FC 25.2 and 24.7), and VOC family proteins (log2FC 24.8). ConclusionsThese findings demonstrate that specific VFs associated with immune modulation, motility secretion systems, bacterial motility, and antibiotic resistance are related to P. aeruginosa and S. aureus abundance, providing potential targets for more personalized antimicrobial interventions.
- organism: human
- assay: metagenomics
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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