Our team’s reading recommendation for this month goes to “Microbiome data enhances predictive models of lung function in people with cystic fibrosis”

by Conan Y Zhao et al. from @Georgia Tech[i], a very interesting paper just published in the journal of infectious diseases that supports the thesis that, in complement to the study of canonical human pathogens, airway microbiome sequencing provides highly valuable information to guide clinical practice in lung infections in people with Cystic Fibrosis.

Health information from a cohort of 77 children and adults suffering from Cystic Fibrosis (age, BMI and lung function) were collected during a period of clinical stability. Key pathogens were identified and 16S rDNA was sequenced for determining the airway microbiome composition. Clinical canonical results were correlated to sputum samples analyses.

Based on these data, 2 models, using Elastic Net regularization, were established to predict lung function: one based on pathogen quantitation alone and the other using the whole microbiome quantitation model. The latter outperformed the pathogen quantification alone and identified as negative predictive factors the presence of Pseudomonas and Achromobacter and as positive predictors the presence in the microbiota of non-pathogens like Fusobacterium and Rothia.  

This paper suggests a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice. In particular, Conan Y Zhao et al. finds that inclusion of non-pathogenic taxa significantly improves model prediction accuracy of patient health status.

We at 4bases support the scientific community for years in the genetic diagnostic of Cystic Fibrosis with our CFTR screen, available for RUO and we at the same time are working in developing new diagnostic approaches through the study of the microbiome.  


# Microbiome; #cystic fibrosis; #NGS ; #DNAsequencing


[i] Zhao CY, Hao Y, Wang Y, Varga JJ, Stecenko AA, Goldberg JB, Brown SP. Microbiome data enhances predictive models of lung function in people with cystic fibrosis. J Infect Dis. 2020 Dec 17:jiaa655. doi: 10.1093/infdis/jiaa655. Epub ahead of print. PMID: 33330902.