Evaluation of Inflammatory Cellular Model by Advanced Bioanalytic and Artificial Intelligence Analyses of Lipids: Lipidomic Landscape of Inflammaging

Lipids are emerging as important potential targets for the early diagnosis and prognosis of several inflammatory diseases. Studying the lipid profiles is important for understanding cellular events such as low-grade inflammation, a condition common to many human diseases, including cancer, neurodegenerative diseases, diabetes, and obesity. This work aimed to explore lipid signatures in an inflammation cellular model using an advanced bioanalytical approach complemented by Machine Learning techniques. Analyses based on the high-resolution mass spectrometry of extracted lipids in TNF-α inflamed cells (R3/1 NF-κB reporter cells) versus lipids in control cells resulted in 469 quantified lipids, of which 20% were phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs), 10% were sphingomyelins (SMs), 6% were phosphatidylinositols (PIs), 7% were ceramides (Cer), 6% were phosphatidylglycerols (PGs), and 5% were phosphatidylserines (PSs). TNF-α induced a significant alteration compared to the control, with a fold change higher than 1.5; of the 88 lipids, 71 were upregulated and 17 were downregulated, impacting various pathways as revealed by network analyses. To validate the inflammation model, the TNF-α induced cells were treated with polyphenols from thinned young apples (TAPs), which are known to have anti-inflammatory properties. The dysregulation of ceramides (Cer(d18:1/23:0), Cer(d18:1/23:0), and Cer(d18:1/22:0)) observed in TNF-α inflamed cells was completely reverted after TAP treatment. Network analyses showed the alteration of arachidonic acid and TNF signaling, which were modulated by polyphenols from thinned young apples. The results highlighted the potentiality of the inflammatory model and the bioanalytical approach to describe lipid profiles in complex biological matrices and different states. In addition, the quantified lipids were interpreted by an Artificial Intelligence approach to identify relevant signatures and clusters of lipids that can impact cellular states. Lastly, this study underlines both the potential applications of lipidomics combined with Machine Learning and how to build and validate Machine Learning models to predict inflammation based on lipid-related pattern signatures.