bioRxivMicrobiology
On-person adaptive evolution of Staphylococcus aureus during treatment for atopic dermatitis
F. M. Key, V. D. Khadka, C. Romo-Gonzalez, K. J. Blake, L. Deng, T. C. Lynn, J. C. M. Lee, I. Chiu, M. T. Garcia, T. D. Lieberman
Abstract
Genetic variation among bacterial strains can contribute to heterogeneity in the severity of chronic inflammatory diseases 1,2, but the degree of variation created by de novo mutation during colonization is not well understood. The inflamed skin of people with atopic dermatitis (AD) is frequently colonized with Staphylococcus aureus, an opportunistic pathogen associated with both asymptomatic colonization of nasal passages and invasive disease 3-6. While genetic risk and barrier disruption are critical to AD initiation 7,8, S. aureus colonization is thought to worsen disease severity by promoting skin damage9 1,4,5,10. Here we show, from tracking 23 children treated for AD over 9 months, that S. aureus adapts via de novo mutations during colonization. Patients S. aureus populations are typically dominated by a single lineage, with infrequent invasion by distant lineages. Variants emerge within each lineage with mutation accumulation rates similar to S. aureus in other contexts. Some of these variants replace their ancestors across the body within months, with signatures of adaptive, rather than neutral, forces. Most strikingly, the capsule synthesis gene capD obtained four parallel mutations within one patient and was involved in mutational sweeps in multiple patients. We confirm that selection for capD negativity is common in AD, but not in other contexts, via reanalysis of public S. aureus genomes from 276 people. Our finding of disease-specific selection raises the possibility that adaptation of pathobionts during colonization prolongs the positive feedback cycle of inflammation.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
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