Analysis and Interpretation of Complex Lipidomic Data Using Bioinformatic Approaches
The field of lipidomics has rapidly progressed since its inception only a decade ago. Technological revolutions in mass spectrometry, chromatography, and computational biology now enables high-throughput high-accuracy quantification of the cellular lipidome. One significant improvement of these technologies is that lipids can now be identified and quantified as individual molecular species. Lipidomics provides an additional layer of information to genomics and proteomics and opens a new opportunity for furthering our understanding of cellular signaling networks and physiology, which have broad therapeutic values. As with other 'omics sciences, these new technologies are producing vast amounts of lipidomic data, which require sophisticated statistical and computational approaches for analysis and interpretation. However, computational tools for utilizing such data are sparse. The complexity of lipid metabolic systems and the fact that lipid enzymes remain poorly understood also present challenges to computational lipidomics. The focus of my dissertation has been the development of novel computational methods for systematic study of lipid metabolism in cellular function and human diseases using lipidomic data. In this dissertation, I first present a mathematical model describing cardiolipin molecular species distribution in steady state and its relationship with fatty acid chain compositions. Knowledge of this relationship facilitates determination of isomeric species for complex lipids, providing more detailed information beyond current limits of mass spectrometry technology. I also correlate lipid species profiles with diseases and predict potential therapeutics. Second, I present statistical studies of mechanisms influencing phosphatidylcholine and phosphatidylethanolamine molecular architectures, respectively. I describe a statistical approach to examine dependence of sn1 and sn2 acyl chain regulatory mechanisms. Third, I describe a novel network inference approach and illustrate a dynamic model of ethanolamine glycerophospholipid acyl chain remodeling. The model is the first that accurately and robustly describes lipid species changes in pulse-chase experiments. A key outcome is that the deacylation and reacylation rates of individual acyl chains can be determined, and the resulting rates explain the well-known prevalence of sn1 saturated chains and sn2 unsaturated chains. Lastly, I summarize and remark on future studies for lipidomics.