Detecting erm-Mediated Inducible Macrolide-Lincosamide-Streptogramin B Resistance in Anaerobic Clinical Isolates

Background: Inducible macrolide-lincosamide-streptogramin B (iMLSB) resistance is well defined in Gram-positive aerobes but remains poorly characterized in anaerobes, where standardized detection strategies are lacking. Following withdrawal of EUCAST guidance to infer clindamycin resistance from erythromycin resistance in Peptostreptococcus and Bacteroides spp. because of inconsistent species-specific performance, a diagnostic gap persists. Methods: We therefore assessed the accuracy of the D-test for detecting iMLSB resistance in anaerobes by correlating phenotypic results with whole-genome sequencing data. Fifty clinical anaerobic isolates, including Finegoldia magna, Peptostreptococcus anaerobius, and Bacteroides spp., were included in the analysis. Antimicrobial susceptibility testing was performed using gradient diffusion to determine minimum inhibitory concentrations of erythromycin and clindamycin, complemented by D-test analysis for phenotypic detection of inducible resistance. Whole-genome sequencing was undertaken to identify erm genes encoding ribosomal methyltransferases associated with the iMLSB phenotype. Results: Among the 50 isolates, erm genes were detected in 16 strains (32.0%). The prevalence of erm positivity was highest among Gram-positive cocci (50%), followed by Gram-positive rods (35.3%) and Gram-negative rods (11.8%). Five erm-positive isolates exhibited a characteristic D-shaped growth pattern, with high erythromycin MICs (>256 mg/L) and low clindamycin MICs (≤2 mg/L), consistent with an inducible iMLSB phenotype, whereas the remaining eleven demonstrated constitutive resistance. Conclusions: The D-test accurately identified inducible iMLSB resistance among Gram-positive anaerobic cocci and, if confirmed in larger studies, could form the basis of an accessible and pragmatic screening strategy for this subgroup. Integration of molecular analyses seems essential for the evidence-based refinement of diagnostic algorithms, particularly in the absence of robust, species-specific guidance.