Abstract
The incorporation of multi-omics data methodologies facilitates the concurrent examination of proteins, metabolites, and genes associated with inflammation, thereby leveraging multi-dimensional biological data to achieve a comprehensive understanding of the complexities involved in the progression of inflammation. Inspired by ensemble learning principles, we implemented ID normalization preprocessing, categorical sampling homogenization, and pathway enrichment across each sample matrix derived from multi-omics datasets available in the literature, directing our focus on inflammation-related targets within lipopolysaccharide (LPS)-stimulated RAW264.7 cells towards β-alanine metabolism. Additionally, through the use of LPS-treated RAW264.7 cells, we tentatively validated the anti-inflammatory properties of the metabolite Ureidopropionic acid, originating from β-alanine metabolism, by evaluating cell viability, nitric oxide production levels, and mRNA expression of inflammatory biomarkers. In conclusion, our research represents the first instance of an integrated analysis of multi-omics datasets pertaining to LPS-stimulated RAW264.7 cells as documented in the literature, underscoring the pivotal role of β-alanine metabolism in cellular inflammation and successfully identifying Ureidopropionic acid as a novel anti-inflammatory compound. Moreover, the findings from database predictions and molecular docking studies indicated that the inflammatory-related pathways and proteins may serve as potential mechanistic targets for Ureidopropionic acid.