International journal of molecular sciences. 2021-11-07; 22.21:

Multi-Omics Study of Keystone Species in a Cystic Fibrosis Microbiome

Silveira CB, Cobián-Güemes AG, Uranga C, Baker JL, Edlund A, Rohwer F, Conrad D

PMID: 34769481


Ecological networking and in vitro studies predict that anaerobic, mucus-degrading bacteria are keystone species in cystic fibrosis (CF) microbiomes. The metabolic byproducts from these bacteria facilitate the colonization and growth of CF pathogens like . Here, a multi-omics study informed the control of putative anaerobic keystone species during a transition in antibiotic therapy of a CF patient. A quantitative metagenomics approach combining sequence data with epifluorescence microscopy showed that during periods of rapid lung function loss, the patient's lung microbiome was dominated by the anaerobic, mucus-degrading bacteria belonging to , , and genera. Untargeted metabolomics and community cultures identified high rates of fermentation in these sputa, with the accumulation of lactic acid, citric acid, and acetic acid. utilized these fermentation products for growth, as indicated by quantitative transcriptomics data. Transcription levels of genes for the utilization of fermentation products were proportional to the abundance of anaerobic bacteria. Clindamycin therapy targeting Gram-positive anaerobes rapidly suppressed anaerobic bacteria and the accumulation of fermentation products. Clindamycin also lowered the abundance and transcription of even though this patient's strain was resistant to this antibiotic. The treatment stabilized the patient's lung function and improved respiratory health for two months, lengthening by a factor of four the between-hospitalization time for this patient. Killing anaerobes indirectly limited the growth of by disrupting the cross-feeding of fermentation products. This case study supports the hypothesis that facultative anaerobes operated as keystone species in this CF microbiome. Personalized multi-omics may become a viable approach for routine clinical diagnostics in the future, providing critical information to inform treatment decisions.