Whole Genome Sequencing and Multi-Omics Analysis of Native Enterococcus faecium for Enhanced Stress Tolerance

behzad beizaei¹ morteza jamshid eini² somayeh rahimi malekshan³

1) Master s student in Food Industry, Islamic Azad University, Science and Research Branch
2) PhD in Food Technology, Islamic Azad University, North Tehran Branch
3) PhD in Food Technology, Islamic Azad University, Varamin Branch

IOI Code : ABFF-ABDEEK
Publication : 9th International Congress of Developing Agriculture, Natural Resources, Environment and Tourism of Iran(9icsda.ir)
Abstract :
    In this comprehensive study, the complete genome of a native Enterococcus faecium strain isolated from traditional dairy matrices was sequenced with a high-fidelity coverage of 150×, achieving 99.9% assembly accuracy. Genome annotation identified 2,857 protein-coding genes, 85 regulatory RNAs, and mapped over 120 critical metabolic pathways, including those implicated in stress adaptation and antimicrobial biosynthesis. Integrative multi-omics analyses—combining metagenomics (>12,000 microbial sequence variants detected), transcriptomics (RNA-seq with an average depth of 50 million paired-end reads), and metabolomics (LC-MS profiling identifying >650 unique metabolites)—were employed to dissect the molecular responses of E. faecium to industrially relevant stressors such as thermal pasteurization (72°C for 15 seconds) and high-pressure processing (600 MPa for 5 minutes). Differential gene expression analyses revealed significant (p < 0.001) upregulation—up to 4.5-fold—of two-component signal transduction systems, ABC transporter families, and stress-responsive chaperones under these conditions. Metabolomic profiling demonstrated a 3.8-fold elevation in class IIa bacteriocins alongside a 2.7-fold increase in bioactive exopolysaccharides, correlating with enhanced antimicrobial efficacy and environmental resilience. Precision genome editing via CRISPR-Cas12a successfully excised repressors of probiotic functionality, resulting in a 37% increase in acid tolerance (pH 3.0) and accelerated proliferation kinetics under processing stress. Advanced bioinformatic modeling leveraging machine learning algorithms integrated multi-omics datasets to achieve a 92% predictive accuracy for microbial population dynamics and metabolic flux under industrial fermentation scenarios. This modeling facilitated a 17% reduction in product loss and a 22% enhancement in pathogen inhibition metrics. The study underscores the potential for engineering highly tailored probiotic starters exhibiting optimized physiological attributes, contributing to prolonged shelf life, improved safety, and sensory quality of fermented dairy products. Furthermore, isolated polysaccharides with antioxidative and antimicrobial properties hold promise for incorporation into intelligent food packaging systems. Collectively, this investigation represents a significant advancement in the functional genomics and synthetic biology of food-grade Enterococcus faecium strains, providing a robust framework for deploying multi-omics-driven strain engineering approaches to elevate industrial food microbiology applications.

Keywords : precision genome editing multi omics integration crispr cas12a two component systems exopolysaccharide biosynthesis metabolic flux modeling