Submission
| Title: | Age-dependent changes in mouse brain and liver lipidomes |
| Presenter: | Panda Punyatoya |
| Institution: | Purdue University |
| Authors: | Punyatoya Panda, Department of Comparative Pathobiology, Purdue University, West Lafayette, IN 47907, USA Christina R. Ferreira, Bindley Bioscience Center, Purdue University,West Lafayette,IN 47907, USA Allison Schaser, Department of Speech, Language, and Hearing Sciences, Purdue University Uma Aryal, Department of Comparative Pathobiology, Purdue University, West Lafayette, IN 47907, USA |
Abstract
| Background/Significance/Rationale: | Aging is a major risk factor for various diseases such as cancer and neurological disorders including Alzheimer’s disease. However, the mechanisms of aging are complex and remain elusive. Like genes and proteins, lipids play key structural, regulatory, and signaling roles within the cells. Therefore, characterizing lipids in various organs provides useful information for understanding their functions under different physiological or disease states. However, relatively very little is known about the composition and age-dependent changes of lipids in the brain and liver, the two most lipid-rich organs after adipose tissues. In this work, we characterized the brain and liver lipidome using two complementary analytical approaches: targeted shotgun profiling using Multiple Reaction Monitoring and untargeted Liquid Chromatography-tandem mass spectrometry (LC-MS/MS). |
| Methods: | The brain and liver tissues collected from three age groups of mice-young adult(3–5-month-old), middle-aged (10–12-month-old)and old-aged mice(19–21-month-old) were homogenized and lipids were extracted by Bligh-Dyer method. For MRM-profiling, these were administered directly without any chromatographic separation into the ESI-source of an Agilent QqQ 6410 and samples were screened for specific ion transitions corresponding to different lipid classes and fatty acid composition based on LIPID MAPS database. For untargeted lipidomics, samples were analyzed using an Agilent 6545 Q-TOF MS coupled with Agilent 1290 Infinity II UPLC System. The untargeted LC-MS/MS-data were searched against MONA-database for lipid identification and relative quantitation. For DESI Imaging, the tissue sections were embedded in Carboxy-methyl-cellulose and subjected to ambient mass spectrometry in Waters Synapt XS. Statistical analysis of identified lipid species or lipid classes was performed by Perseus using ANOVA to identify the significantly changing lipids. |
| Results/Findings: | The MRM profiling analysis focused on specific ion transitions corresponding to different lipid classes and fatty acid composition based on LIPID MAPS database. Using MRM profiling without LC, the samples were screened for 3246 MRMs comprising of 24 different lipid classes and fatty acid composition including different phospholipid classes like phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), ceramides, di- and tri-acylglycerols, acylcarnitines. In the brain, phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and free fatty acids (FFAs) were among the most abundant classes of lipids, while in the liver, tri- and di-acylglycerols were among the most abundant ones, apart from PCs and FFAs. Statistical analysis revealed age-dependent changes in sphingomyelins, TGs and FFA in the brain, and TGs, DGs, and phospholipids classes in the liver. |
| Conclusions/Discussion: | Lipids detected in the MRM and LC-MS/MS data showed a high degree of overlap (100% in the brain and 98% in the liver)) indicating consistency and agreement between the two analytical methods. In conclusion, the shotgun profiling using MRM enables simpler, faster, sensitive, and cost-effective exploratory lipid analysis workflow for characterizing the lipidome in diverse biological samples. Understanding age-dependent changes in lipid composition using this MRM method can shed light on potential biomarkers and mechanisms associated with aging. |
| Translational/Human Health Impact: | Identification of lipid biomarkers of aging and their spatial mapping will help us understand the biology of aging and age-associated diseases better. |
