In House Rapid, Simple Multiple-Locus Variable-Number Tandem Repeat Analysis (MLVA): A Reliable Tool for Enterobacter hormaechei Genotyping.

Enterobacter hormaechei, a prominent species within the Enterobacter cloacae complex, is a significant cause of nosocomial infections and is frequently associated with multidrug resistance. Rapid genomic comparison helps guide timely infection control measures. This study aimed to develop a simple and rapid multiple-locus variable-number tandem-repeat analysis (MLVA) protocol for epidemiological surveillance of E. hormaechei. Eight variable-number tandem-repeat (VNTR) regions were selected for amplification using multiplex PCR, followed by gel electrophoresis. The method's discriminatory power was evaluated on 46 unrelated strains from 22 French hospitals. Then, suspected related strains from three potential outbreaks, including ESBL- and/or NDM-producing isolates were compared. An independent collection of 22 VIM-producing strains was also analyzed. Whole-genome sequencing (WGS) was used as the gold standard. Among 46 unrelated E. hormaechei strains, representing the five subspecies, MLVA and MLST showed similar discriminatory power (36 MLVA profiles vs. 33 STs, Hunter and Gaston diversity indices 0.9833 vs. 0.9824, respectively). Isolates with different ST typically had distinct MLVA profiles, except for three instances where different STs shared similar profiles. In STs represented by multiple strains, MLVA sometimes distinguished strains sharing the same ST. Among the three potential outbreak, epidemic strains exhibited unique MLVA profiles, with genetic distances of 0-11 SNPs using WGS, while unrelated isolates had different MLVA profiles, indicating this technique's potential as an effective screening tool for clonal groups. Similar results were observed for VIM-producing E. hormaechei, with consistent MLVA profiles within the same STs. The MLVA protocol developed is a rapid, cost-effective method for E. hormaechei epidemiological investigations that can quickly rule out unrelated strains. However, highly discriminatory techniques like WGS remain necessary when profiles are similar.