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Abstract Enzyme specificity in lipid metabolic pathways often remains unresolved at the lipid species level, which is needed to link lipidomic molecular phenotypes with their protein counterparts to construct functional pathway maps. We created lipidomic profiles of 23 gene knockouts in a proof-of-concept study based on a CRISPR/Cas9 knockout screen in mammalian cells. This results in a lipidomic resource across 24 lipid classes. We highlight lipid species phenotypes of multiple knockout cell lines compared to a control, created by targeting the human safe-harbor locus AAVS1 using up to 1228 lipid species and subspecies, charting lipid metabolism at the molecular level. Lipid species changes are found in all knockout cell lines, however, some are most apparent on the lipid class level (e.g., SGMS1 and CEPT1), while others are most apparent on the fatty acid level (e.g., DECR2 and ACOT7). We find lipidomic phenotypes to be reproducible across different clones of the same knockout and we observed similar phenotypes when two enzymes that catalyze subsequent steps of the long-chain fatty acid elongation cycle were targeted.

Abstract Lipidomics has become an indispensable method for the quantitative assessment of lipid metabolism in basic, clinical, and pharmaceutical research. It allows for the generation of information-dense datasets in a large variety of experimental setups and model organisms. Previous studies, mostly conducted in mice (Mus musculus), have shown a remarkable specificity of the lipid compositions of different cell types, tissues, and organs. However, a systematic analysis of the overall variation of the mouse lipidome is lacking. To fill this gap, in the present study, the effect of diet, sex, and genotype on the lipidomes of mouse tissues, organs, and bodily fluids has been investigated. Baseline quantitative lipidomes consisting of 796 individual lipid molecules belonging to 24 lipid classes are provided for 10 different sample types. Furthermore, the susceptibility of lipidomes to the tested parameters is assessed, providing insights into the organ-specific lipidomic plasticity and flexibility. This dataset provides a valuable resource for basic and pharmaceutical researchers working with murine models and complements existing proteomic and transcriptomic datasets. It will inform experimental design and facilitate interpretation of lipidomic datasets.

Abstract Lipidomics have a great potential as clinical tool for monitoring metabolic changes in health and disease. Nevertheless hardly anything is known about the heritability of lipids. Therefore, it is necessary to clarify how and how much we can affect these progresses in individuals. In our interventional twin study (46 healthy, non-obese twin pairs) we investigated the lipid profile in plasma samples after switching from a low fat diet to an isocaloric high fat diet (HFD) to characterize the metabolic adaptation. Additionally we used the ACE model for Additive genetics, Common and unique Environment as well as linear mixed modelling to analyse the heritability of lipids. The heritability of lipids varied between 0–62% and applied to lipid species rather than to lipid classes. Phospholipids showed the highest inheritance. In addition, sex, body mass index (BMI) and age were important modifiers. The lipid profile changed already after one week of HFD and diverged further after 5 weeks of additional HFD. Basal concentrations of specific lipids within phospholipids are strongly inherited and are likely to be associated with heritable disease risks. BMI, sex and age were major modifiers. Nutrition strongly alters specific lipid classes, and has to be controlled in clinical association studies.