Abundance-weighted analysis and deep functional metagenomic selection reveals unrecognized diversity of soil-derived streptothricin acetyltransferases

Streptothricins (STCs) are broad-spectrum antibiotics under investigation for their therapeutic potential against antibiotic-resistant Gram-negative pathogens. However, the extent and diversity of genes that confer resistance to STCs, or their environmental resistome, remains largely unknown and is essentially limited to a handful of STC acetyltransferases. Here, we used a deep functional metagenomic approach to uncover previously unrecognized STC resistance determinants. A functional metagenomic library containing ca. 700 Gigabases of soil metagenomic DNA was selected for resistance to nourseothricin (NTC), a mixture of STC congeners, resulting in collection and sequencing of nearly 30,000 resistant clones. An abundance-weighted sequence analysis identified 265 acetyltransferase-like candidates spanning broad phylogenetic, taxonomic, and genomic diversity. Re-cloning and experimental testing of 18 candidates chosen to cover acetyltransferase sequence space confirmed that 10 conferred significant NTC resistance in Escherichia coli. Phylogenetic analyses identified three previously uncharacterized STC acetyltransferase lineages, designated as SatC, SatD, and SatE. Despite substantial sequence divergence from characterized streptothricin acetyltransferases (maximum amino acid identities of approximately 31% for SatC and SatD and up to 47.5% for SatE), these enzymes largely retained key residues associated with streptothricin binding and catalysis. In addition to strongly increasing NTC resistance, expression of the SatC construct in E. coli also significantly decreased susceptibility to the aminoglycoside antibiotics amikacin and kanamycin. Together, these findings reveal a substantially broader than previously known environmental repertoire of STC acetyltransferases and demonstrate how deep functional metagenomic selections, combined with abundance-weighted hit calling, phylogenetic analysis, and structural prediction, can uncover novel resistance functions.