Characterization of hypothetical proteins reveals function modules and virulence factors in Pseudomonas aeruginosa

Hypothetical proteins (HPs), known as biological dark matter, are genetic sequences of which the biological functions and regulatory processes remain largely unclear. In Pseudomonas aeruginosa, HPs account for 36.4% of all coding genes, representing a significant gap in understanding their functions and contribution to the virulence network. To address these challenges, we present a deletion repertoire comprising 938 strains targeting 1165 HPs in P. aeruginosa. Starting with transcriptome-wide analysis, we inferred putative functional predictions for HP mutants through systematic profiling of differential expression and candidate transcriptional pathways. We integrated our expression profiles with public datasets (5707 genes × 2312 conditions). This allowed us to distinguish constitutively expressed genes from variable genes. Using independent component analysis (ICA), we identified 56 independent regulatory Modules, providing a comprehensive transcriptional-association framework. These contributed to the discovery and experimental validation of 20 virulence-related hypothetical proteins (VRHPs) and 3 antibiotic resistance-related hypothetical proteins (ARHPs). Evolutionary analysis across bacterial taxa provided additional insights into HP sequence conservation and evolutionary dynamics. In summary, our study presents a large-scale HP mutant library combined with integrated transcriptomic and machine learning approaches, establishing both a valuable resource and a systematic strategy for predicting and prioritizing candidate functions of HPs across bacterial species. This study presents a deletion library of 938 hypothetical protein mutants in P. aeruginosa. Integrating transcriptomics and machine learning, it reveals comprehensive regulatory modules and discovers virulence and resistance factors.