Deep Mining of Novel Acylated Polyamines by Integrated Prior-Knowledge-Guided Prediction and Chemical Isotope Labeling-Based Metabolomics

Acylated polyamines (acyl-PAs) are gaining significant attention due to their involvement in various diseases. However, their annotation and quantification remain challenging, as robust analytical methods are lacking, with only a few acyl-PAs characterized to date. In this study, we integrated prior-knowledge-guided prediction with chemical isotope labeling-based metabolomics to identify novel acyl-PAs. An in silico library of 267 predicted acyl-PAs was constructed. Using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) and paired labeling reagents (dansyl chloride and d₆-dansyl chloride), we successfully annotated 41 acyl-PAs across diverse biological samples, 38 of which were novel. Representative acyl-PAs were synthesized for the validation of annotation. Furthermore, we developed a pseudotargeted metabolomic approach for the semiquantification of acyl-PAs in a mouse model of ulcerative colitis (UC), revealing significant changes in 13 acyl-PAs in the feces or colon samples from UC mice. Notably, an antibiotic-treated mouse model revealed that gut microbiota significantly influenced the abundance of acyl-PAs. This study introduces a comprehensive workflow for discovering novel metabolites and provides valuable insights into the roles of acyl-PAs in health and disease.