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Horvath, E. R. B.

Publications and source records attributed to Horvath, E. R. B..

2 recordsLinked to original sources

Global Footprint of the Multidrug Resistance Island Ec17R and Resistance Gene Co-Occurrence in Pathogenic Escherichia coli Isolates

Multidrug resistant (MDR) bacterial pathogens are a major threat to global health, limiting treatment options for common infections. Extraintestinal Pathogenic Escherichia coli (ExPEC), a leading cause of bloodstream and urinary tract infections (UTIs), are often resistant to one or more antibiotic classes. Previously, we identified a clonal group of ExPEC strains (P1A) that persisted over a 5-year period from 2012 to 2016 within a female patient who suffered from frequent recurrent UTIs. A subset of these isolates carried a plasmid-borne MDR island (Ec17R) harboring 17 resistance genes. Sampling of fecal and urine samples in 2019 indicated that the patient remained colonized with the P1A lineage, including Ec17R-positive strains, over 7 years after collection of the first P1A isolates in 2012. Highlighting the public health relevance and mobility of Ec17R, we found that Ec17R-like islands are globally distributed across diverse bacterial species from various environmental, agricultural, and clinical sources, including multiple ExPEC isolates from local pediatric patients. By applying clustering approaches and Bayesian network modeling to 267 pediatric ExPEC isolates, we found that functionally distinct classes of resistance genes (including several heavy metal resistance genes) have a high probability of co-occurrence, possibly reflecting carriage within MDR islands like Ec17R. Finally, we observed that strains within the P1A lineage are recalcitrant to antibiotics for which they have no known resistance mechanisms, suggesting that these pathogens have means beyond their formidable array of resistance genes to survive within a host for years despite the administration of numerous, robust antimicrobial treatments. SIGNIFICANCEMultidrug resistance (MDR) in bacteria is a growing global health crisis that compromises our ability to treat routine infections. In this study, we investigated a clonal lineage of pathogenic Escherichia coli that persisted for many years in a patient with recurrent urinary tract infections. A subset of the E. coli strains carried by this patient possessed a large genomic island encoding resistance to multiple antibiotic classes. This MDR island is globally distributed across diverse bacterial species and niches, from clinical samples to agricultural and environmental reservoirs. Using probabilistic modeling of nearly 300 clinical isolates, we identified networks of resistance gene co-occurrence that link antibiotic and heavy metal resistance, suggesting the potential for environmental pollutants to contribute to MDR dissemination. Notably, patient-derived isolates also survived certain clinically relevant antibiotic treatments despite lacking known resistance mechanisms, highlighting tolerance and persistence as important, often overlooked drivers of therapeutic failure.

microbiology↗

SeqForge: A scalable platform for alignment-based searches, motif detection, and sequence curation across meta/genomic datasets

BackgroundThe rapid increase in publicly available microbial and metagenomic data has created a growing demand for tools that can efficiently perform custom large-scale comparative searches and functional annotation. While BLAST+ remains the standard for sequence similarity searches, population-level studies often require custom scripting and manual curation of results, which can present barriers for many researchers. ResultsWe developed SeqForge, a scalable, modular command-line toolkit that streamlines alignment-based searches and motif mining across large genomic datasets. SeqForge automates BLAST+ database creation and querying, integrates amino acid motif discovery, enables sequence and contig extraction, and curates results into structured, easily parsed formats. The platform supports diverse input formats, parallelized execution for high-performance computing environments, and built-in visualization tools. Benchmarking demonstrates that SeqForge achieves near-linear runtime scaling for computationally intensive modules while maintaining modest memory usage. ConclusionsSeqForge lowers the computational barrier for large-scale meta/genomic exploration, enabling researchers to perform population-scale BLAST searches, motif detection, and sequence curation without custom scripting. The toolkit is freely available and platform-independent, making it suitable for both personal workstations and high-performance computing environments.

bioinformatics↗