Antimicrobial resistance (AMR) is a critical global health threat, with ESKAPE pathogens — Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species —playing a central role due to their high adaptability and resistance to multiple drugs. Understanding the genomic and transcriptomic landscapes of these pathogens is crucial for identifying resistance mechanisms, virulence factors, and potential therapeutic targets. This research theme leverages bioinformatics tools and high-throughput sequencing technologies to dissect the molecular basis of AMR in ESKAPE microorganisms.

By analyzing whole-genome sequences and comparative genomics, researchers can identify resistance genes, mobile genetic elements (e.g., plasmids, integrons, transposons), and mutations in key regulatory pathways.
Pan-genomic studies help differentiate core from accessory genomes, offering insights into strain-specific resistance profiles and evolution. Additionally, transcriptomic analyses such as RNA-Seq enable the study of gene expression dynamics under antibiotic stress, revealing regulatory networks and pathways activated during resistance development.

Integrated omics approaches provide a comprehensive view of pathogen adaptation and survival strategies in hostile environments, including host interactions and biofilm formation. Advanced machine learning and data mining techniques further enhance the predictive modelling of resistance phenotypes and potential drug targets.

This research topic not only deepens our understanding of AMR mechanisms at the molecular level but also supports the development of novel diagnostics, vaccines, and antimicrobial agents. It contributes significantly to global efforts in AMR surveillance and stewardship programs, aiding in the design of more effective infection control policies and therapeutic interventions. The interdisciplinary nature of this field bridges microbiology, genomics, systems biology, and computational sciences to tackle one of the most pressing challenges of modern medicine.

To gather further insights into the cross-disciplinary implications, we welcome articles addressing, but not limited to, the following sub-themes:

o Resistance genes in ESKAPE pathogens
o Gene expression changes under antibiotic stress (RNA-Seq)
o Machine learning to predict AMR from genome data
o Resistome profiling of hospital-acquired infections
o Evolution of β-lactamase genes across regions
o CRISPR-Cas in controlling gene transfer in ESKAPE
o AMR genomic database for ESKAPE surveillance

We welcome the following article types: Hypothesis & Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Systematic Review, Technology and Code.

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Genomic and Transcriptomic Insights into ESKAPE Pathogens' Antimicrobial Resistance

Topic Coordinator