Antagonistic activity of native Streptomyces spp. against ESBL- and KPC- producing Enterobacteriaceae and MRSA
Actividad antagónica de Streptomyces spp. nativas frente a Enterobacteriaceae productoras de BLEE y KPC, y a MRSA

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Introduction. The search for microorganisms producing antimicrobial compounds represents a strategy to address the global crisis of antibiotic resistance. Identification of bacteria of the genus Streptomyces with antagonistic activity against resistant bacteria is the initial step for the subsequent recovery of effective antibacterial metabolites for the inhibition of these resistant organisms. Objectives. Recognize promising sources to isolate filamentous bacteria able to inhibit clinical isolates, including ESBL- and KPC- producing Enterobacteriaceae and MRSA. Methods. Actinobacteria were isolated from rhizospheres and a compost system. MRSA, Escherichia coli, and Klebsiella pneumoniae clinical isolates were chosen to address antagonism tests. Antibacterial activity was recorded by a cross-streak method. Streptomyces isolates were characterized according to International Streptomyces Project and the native isolates with antimicrobial activity were identified by molecular techniques. Results. Nine isolates of actinobacteria with activity against resistant-antibiotic bacteria were obtained, two from the avocado rhizosphere, four from a living fence and three from a composting system. Two of the isolates showed activity against all the tested antibiotic-resistant bacteria. Molecular taxonomic identification found S. jumonjinensis, S. bacillaris, S. prasinus, S. microflavus, and S. cadmiisoli as putative species for native isolates. Conclusions. Streptomyces with antibacterial activities against ESBL and KPC-producing Enterobacteria and MRSA have been isolated and the potential of rhizospheres and compost systems for obtaining antibiotic-producing bacteria was validated. Native isolates exhibited common traits for Streptomyces; although NCBI's Blast did not show a resolution to identification, EzBioCloud 16S-based identification was able to accurately detect the identity of the isolates down to the species level.
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