High Genomic Identity between Clinical and Environmental Strains of Herbaspirillum frisingense Suggests Pre-Adaptation to Different Hosts and Intrinsic Resistance to Multiple Drugs

The genus Herbaspirillum is widely studied for its ability to associate with grasses and to perform biological nitrogen fixation. However, the bacteria of the Herbaspirillum genus have frequently been isolated from clinical samples. Understanding the genomic characteristics that allow these bacteria to switch environments and become able to colonize human hosts is essential for monitoring emerging pathogens and predicting outbreaks. In this work, we describe the sequencing, assembly, and annotation of the genome of H. frisingense AU14559 isolated from the sputum of patients with cystic fibrosis, and its comparison with the genomes of the uropathogenic strain VT-16-41 and the environmental strains GSF30, BH-1, IAC152, and SG826. The genes responsible for biological nitrogen fixation were absent from all strains except for GSF30. On the other hand, genes encoding virulence and host interaction factors were mostly shared with environmental strains. We also identified a large set of intrinsic antibiotic resistance genes that were shared across all strains. Unlike other strains, in addition to unique genomic islands, AU14559 has a mutation that renders the biosynthesis of rhamnose and its incorporation into the exopolysaccharide unfeasible. These data suggest that H. frisingense has characteristics that provide it with the metabolic diversity needed to infect and colonize human hosts.

Automated summary

The genus Herbaspirillum is known for its ability to associate with grasses and participate in biological nitrogen fixation, but can also be isolated from clinical samples. The genome analysis of H. frisingense AU14559 revealed unique metabolic characteristics that allow it to infect and colonize human hosts, along with shared antibiotic resistance genes with other strains. These findings suggest potential for monitoring emerging pathogens and predicting outbreaks.