Data Mining of Lung Microbiota in Cystic Fibrosis PatientsReportar como inadecuado

Data Mining of Lung Microbiota in Cystic Fibrosis Patients - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

The major therapeutic strategy used to treat exacerbated cystic fibrosis CF is antibiotic treatment. As this approach easily generates antibiotic-resistant strains of opportunistic bacteria, optimized antibiotic therapies are required to effectively control chronic and recurrent bacterial infections in CF patients. A promising future for the proper use of antibiotics is the management of lung microbiota. However, the impact of antibiotic treatments on CF microbiota and vice versa is not fully understood. This study analyzed 718 sputum samples from 18 previous studies to identify differences between CF and uninfected lung microbiota and to evaluate the effects of antibiotic treatments on exacerbated CF microbiota. A reference-based OTU operational taxonomic unit picking method was used to combine analyses of data generated using different protocols and platforms. Findings show that CF microbiota had greater richness and lower diversity in the community structure than uninfected control NIC microbiota. Specifically, CF microbiota showed higher levels of opportunistic bacteria and dramatically lower levels of commensal bacteria. Antibiotic treatment affected exacerbated CF microbiota notably but only transiently during the treatment period. Limited decrease of the dominant opportunistic bacteria and a dramatic decrease of commensal bacteria were observed during the antibiotic treatment for CF exacerbation. Simultaneously, low abundance opportunistic bacteria were thriving after the antibiotic treatment. The inefficiency of the current antibiotic treatment against major opportunistic bacteria and the detrimental effects on commensal bacteria indicate that the current empiric antibiotic treatment on CF exacerbation should be reevaluated and optimized.

Autor: Jianguo Li , Chunyan Hao , Lili Ren , Yan Xiao, Jianwei Wang, Xuemei Qin



Documentos relacionados