Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus
Sidarous L, Ibrahim M, Elhadidy M, Abouelfetouh A, Saif N, Aboelnaga N, Shehat M, Youssef M, Badr E
Published in
Interdisciplinary Sciences, Computational Life Sciences, Page 10.1007/s12539-026-00855-2
Abstract
Staphylococcus aureus (S. aureus) is a well-recognized pathogen known for its multi-drug resistance and diverse virulence mechanisms. Its ability to grow biofilms on implanted medical devices enhances its antimicrobial resistance (AMR) and virulence. Despite its clinical relevance, the underlying genetic basis of S. aureus biofilm formation remains insufficiently characterized, particularly regarding key biofilm-associated genes (BAGs) and their regulatory contributions. This study presents a two-part integrative approach to identify genetic determinants of biofilm formation in 178 Egyptian, clinical S. aureus isolates. The framework integrates a genome-wide association study (GWAS) module with a learning-based classification module. GWAS was conducted using a linear mixed model, while logistic regression was the best-performing model in binary and multiclass classification. Integrating both modules, we identified 20 BAGs as promising determinants of biofilm formation. Protein-protein interaction network and pathway enrichment analyses revealed their involvement in biofilm-related pathways. Of the identified BAGs, nine genes have direct links to biofilm formation in S. aureus or other bacteria, while the rest are linked to AMR, nutrient acquisition, and cell division. This study presents a robust framework for biofilm genomics research, uncovering 20 candidate BAGs that span diverse biological functions and capture the multi-faceted nature of biofilm formation in S. aureus.
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