​Cystic fibrosis (CF) or mucoviscidosis is a genetic disease that primarily affects the respiratory and digestive systems. Early morbidity and mortality in CF are due to progressive damage to the airways and lung parenchyma through a cycle of infection, inflammation and tissue damage. Although our knowledge of the CF respiratory microbiota has advanced through next-generation sequencing studies, host-pathogen interactions, microbiota dynamics and their impact on disease are only partially revealed. Beyond powerful metagenomics, metaproteomics is a promising opportunity to obtain unbiased information on the microbiota of clinical samples, regardless of their complexity.

CF sputum samples are among the most complex because of their heterogeneity, viscosity, and because of a microbial protein load that is much lower than the protein load from the host. In this study, Li2D researchers overcame these challenges by applying an efficient metaproteomics-based method for the analysis of such samples. This approach, which was used to establish the composition of the respiratory microbiota of 3 CF patients, provides a more complete and informative view of the diversity of microorganisms present in the microbiota, without the biases of conventional microbiological culture. The researchers were able to identify not only the components of the microbiota, but also its functions and host-specific biomarkers in this "all-in-one" approach.

In this "proof of concept," the authors provide an unbiased and expanded view of the CF microbiota that could be highly complementary to routine microbiological monitoring, and relevant to the clinical management of CF patients by improving our understanding of the host-pathogen dynamics and pathophysiology of CF.

Joliot contact: Lucia Grenga (lucia.grenga@cea.fr)