Abstract

Lipids were once thought to only be the building blocks of cell membranes and to serve as energy reserves. With time however, it became increasingly clear that they are actually involved in many more roles. Not surprisingly, the comprehensive characterisation of lipids in cells and tissues has experienced a growing interest worldwide, to the point that the term "lipidomics" was coined. This field is a subset of metabolomics, and the interesting point about these two sciences is that they are closest to the phenotype as compared to their "omics" counterparts (genomics, trascriptomics, ...), because metabolites and lipids are the end products of the –omics cascade. We have investigated mass spectrometry-based lipidomics from different perspectives: first of all, we have devised a targeted approach in which we have focused on sphingolipids and their perturbations. We started by working on neuronal cell cultures where we inhibited GBA, a key enzyme of the sphingolipid metabolism known to be one of the risk factors for Parkinson's disease. We found a significant sphingolipid unbalance characterised by an accumulation of glycosyl-ceramides. We then moved on by investigating the effects that LRRK2, an important and complex protein known to be related to autosomal-dominant forms of the disease, has on sphingolipids. We worked on mouse models, and we compared the sphingolipid profiles of wild-type (Lrrk2+/+) and knock-out (Lrrk2–/–) mice, finding a marked increase in ceramide levels and, more in general, in all lipids downstream of GBA. Such results hint to a possible interaction between LRRK2 and GBA, with LRRK2 playing a role in GBA regulation. In a second lipidomics investigation, we tried to understand whether or not anti-cancer treatments affect the lipid composition of tumours. Specifically, we concentrated on a common anti-angiogenic drug, whose aim is to starve cancer cells by inhibiting angiogenesis, a process required by the tumours to grow. We considered four different adenocarcinoma cell lines, which were subcutaneously inoculated into mice; the "control" animals received no treatment, whereas the "treated" ones were periodically given the drug. Interestingly, we found the treatment to have significant effects on the cancer lipidome, although the different lines responded unequally to the drug. Such results may reflect the huge heterogeneity of cancers and of individual responses to the treatment. Finally, we developed an informatics algorithm that deals with labelling experiments. The key point is that mass spectrometry measures isotopic patterns of analytes, which depend on the isotopic distribution of the elements; consequently, if an analyte incorporates the stable isotope employed in a labelling experiment, it will show a modified isotopic pattern. Our algorithm analyses such pattern, estimating the abundance of the incorporated label; we first tested it over carefully planned samples, and then we used it in a biochemical application where we wished to establish whether the rate of de novo lipogenesis is influenced by diet. This was accomplished by designing an experiment where mice were given partially deuterated water, while being fed different diets; we were able to ascertain that diet does indeed affect de novo lipogenesis, with the lowest rates occurring on fat-rich diets. We are confident that our tool may find useful applications, considering that stable isotope-based labelling experiments are becoming more and more popular.