Gretchen E Bollar, Johnathan D Keith, Denise D Stanford, Ashley M Oden, S Vamsee Raju, T Spencer Poore, Susan E Birket
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引用次数: 0
Abstract
Cystic fibrosis (CF) respiratory outcomes are heavily influenced by complications of infection. Pseudomonas aeruginosa and Staphylococcus aureus are the most common colonizers of the cystic fibrosis lung, and frequently overlap to cause chronic and persistent coinfections associated with severe disease. However, the dynamics of P. aeruginosa and S. aureus coinfection and its impacts on the development of CF lung structural damage are poorly understood. Additionally, small colony variants (SCVs) of S. aureus have been associated with P. aeruginosa infections in people with CF, but their role in disease progression is largely unknown. In this work, the CF rat was used to model chronic lung coinfection with P. aeruginosa and S. aureus, using clinically and laboratory-derived normal colony and SCV strains of S. aureus to evaluate the impact of phenotype on clinical outcomes. Rats coinfected with clinically derived S. aureus of both phenotypes experienced increased inflammation in the lung, but only the combination of P. aeruginosa and clinically normal colony S. aureus led to lung structural decline, including mucus obstruction and bronchiectasis. In regression analyses, damage was associated with a higher burden of P. aeruginosa, indicating that chronic coinfection with normal colony S. aureus and P. aeruginosa may support the progression CF lung decline driven by P. aeruginosa, which might be avoided when coinfecting S. aureus exhibits the SCV phenotype.
期刊介绍:
The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.