Genomic Epidemiology and Machine Learning-Based Drug Discovery for Antimicrobial Resistant Diarrheagenic Escherichia...

Area of Science:

• Microbiology and Infectious Diseases
• Genomics and Bioinformatics
• Drug Discovery

Background:

• Diarrheagenic Escherichia coli (DEC) is a major cause of pediatric diarrhea globally.
• Antimicrobial resistance (AMR) in E. coli complicates treatment, necessitating novel therapeutic strategies.

Purpose of the Study:

• To characterize molecular pathotypes and AMR patterns of DEC and non-DEC E. coli strains.
• To identify potential therapeutic targets and novel drug compounds against DEC using bioinformatics and machine learning.

Main Methods:

• Analysis of 350 E. coli isolates (175 DEC, 175 non-DEC) using PCR and 16S rRNA sequencing.
• Antimicrobial susceptibility testing, phylogenetic analysis, and PICRUSt for functional insights.
• Machine learning, molecular docking, and molecular dynamics simulations for drug discovery.

Main Results:

• Enteropathogenic E. coli was the most prevalent DEC pathotype (35%).
• High resistance observed for ampicillin, trimethoprim-sulfamethoxazole, and erythromycin; carbapenems and colistin remained effective.
• Alatamide and Isosativan identified as potential therapeutic compounds against DEC virulence targets.

Conclusions:

• DEC strains exhibit distinct genetic and functional profiles compared to non-DEC strains.
• Bioinformatics and machine learning accelerate the discovery of alternative treatments for AMR pathogens.
• Further validation of identified drug candidates is crucial for combating pediatric E. coli diarrhea.