Niacin: an old lipid drug in a new NAD(+) dress

Niacin, the first antidyslipidemic drug, has been at the center stage of lipid research for many decades before the discovery of statins. However, to date, despite its remarkable effects on lipid profiles, the clinical outcomes of niacin treatment on cardiac events is still debated. In addition to its historically well-defined interactions with central players of lipid metabolism, niacin can be processed by eukaryotic cells to synthesize a crucial cofactor, NAD(+). NAD(+) acts as a cofactor in key cellular processes, including oxidative phosphorylation, glycolysis, and DNA repair. More recently, evidence has emerged that NAD(+) also is an essential cosubstrate for the sirtuin family of protein deacylases and thereby has an impact on a wide range of cellular processes, most notably mitochondrial homeostasis, energy homeostasis, and lipid metabolism. NAD(+) achieves these remarkable effects through sirtuin-mediated deacetylation of key transcriptional regulators, such as peroxisome proliferator-activated receptor gamma coactivator 1-alpha, LXR, and SREBPs, that control these cellular processes. Here, we present an alternative point of view to explain niacin's mechanism of action, with a strong focus on the importance of how this old drug acts as a control switch of NAD(+)/sirtuin-mediated control of metabolism.

Integrating omics data into metabolic models of lipids: a platform for the identification of gene mutations from lipidomics data

Paolo Angelino, Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

Kinetic models of metabolic networks are mapping the biochemical reactions taking part in a cell to a mathematical representation. They are the blueprints of the biochemical network, connecting enzymatic activities and metabolic compounds. Kinetic models have been used in systems biology to produce estimates of response of an organism to stress and to reveal potentially efficient targets for cellular engineering. Lipids are major constituents of the cell membrane. They are responsible for major properties of the cellular membranes: hydrophobicity, selective permeability and being the scaffold of signaling proteins. Many diseases are associated with alterations in the lipid distribution in the cell and the composition of membrane domains. Metabolic syndrome, obesity, atherosclerosis, as well as Alzheimer’s, Huntington’s diseases and cancer, all originate from alterations in some stage of lipid biosynthesis. Therefore, advancement of knowledge in the field of lipid metabolism will provide novel insights for further biomedical research and potential strategies for drug development. In the current study we combine lipidomics data and metabolic models of lipid metabolism in yeast Saccharomyces cerevisiae. We use the models as platform for the application of sensitivity analysis and metabolic control analysis, which involves direct and inverse sensitivity analysis. In direct sensitivity analysis we quantify how the changes in enzymatic activities, either by gene deletion or RNAi, are mapped through the metabolic pathway model into variations in lipid profiles. This analysis allows us to identify the effects of the perturbations in enzyme activities (inhibition or overexpression) on the distribution of the lipids synthesized as the end products of the network. We further extend the analysis by casting the inverse problem. With the inverse sensitivity analysis framework, we consider as input an altered lipidomic profile and we are able to identify, the responsible perturbations in enzyme activities that account for such a change. The results of the analysis have been confirmed based on lipidomics analysis of mutant yeast cells. Although we demonstrate the application of the method in models of lipid metabolism it is possible to be applied to biochemical systems of various parts of metabolism and sizes creating a modular platform for identification of mutations.

2013

Kinetic reconstruction and analysis of sphingolipid metabolism

Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

Lipids are major constituents of the cell. They are responsible for major properties of the cellular membranes: hydrophobicity, selective permeability and being the scaffold of signaling proteins. Many diseases are associated with alterations in the lipid distribution in the cell and the composition of membrane domains. Metabolic syndrome, obesity, atherosclerosis, as well as Alzheimer’s, Huntington’s diseases and cancer, have an impact in the levels of lipids, with observed alterations in their concentrations compared to the healthy state. Applying computational techniques along with systematic modeling of lipid metabolism can provide insights that can guide biomedical research and develop potential strategies for prevention and cure. In the present study we developed a comprehensive model of the sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. Sphingolipids are one of the four major lipid categories, along with (glycero)phospholipids, sterols and fatty acids synthesized in the yeast S. cerevisiae. The importance of sphingolipids present in any higher eukaryote has been demonstrated in many recent studies. For this study, S. cerevisiae has been chosen as a model organism due to its high homology of cellular processes with mammalian cells. The developed model will be an essential part towards the construction of a detailed kinetic model of the whole lipidome of the cell. We first constructed a stoichiometric model that contains all the currently known reactions for the biosynthesis of ceramides and complex sphingolipids in the yeast. Additionally, we have accounted for all five reported hydroxylation states, along with the reactions synthesizing the necessary precursor metabolites from other lipid pathways (i.e. palmitate-CoA from fatty acid synthesis and phosphatidylinositol from the phospholipids metabolism). We next developed a kinetic model and we used a large number of lipidomic measurements of wild type yeast to consistently calibrate our model. Curation of the kinetic information of the model came from the comprehensive mining of references for operation of the enzymes as well as ranges of kinetic parameters from online databases. These datasets created the pool for a sampling technique that accounts for the uncertainty in the parameters of the model. This lead to a robust dynamic model, containing mass balances for all the components of the biochemical network as well as terms that accounted for the dillution of these molecules in the cell due to growth. We performed a thorough kinetic analysis of the system by examining the impact of different assumptions in enzyme operation on the levels of ceramides and complex sphingolipids (e.g. substrate competition, (un)competitive inhibition). By applying the principles of Metabolic Control Analysis (MCA) we were able to quantify the effect of enzyme activities on the lipid profiles and we identified enzymes of the biochemical reaction network as efficient targets for metabolic engineering towards a desired state. We also applied a reverse engineering method and we were able to identify enzyme perturbations responsible for an observed altered state compared to the wild type cellular lipidomic profile. Such an approach could lead to the identification of genetic mutations by imploring the information containd within metabolomic measurements. Although we demonstrate the application of the method in models of lipid metabolism it is possible to be applied to biochemical systems of various parts of metabolism and sizes creating a modular platform for kinetic analysis of cellular operations.

2014

Single-cell lipidomics reveals a role for cell-to-cell sphingolipid heterogeneity in fibroblast activation

Laura Capolupo

Cell-to-cell variability plays a key role in tissue patterning by setting initial asymmetry that primes cell fate decisions. Fluctuations in the activity of regulatory molecules can commit individual cells to divergent differentiation pathways, and cell-to-cell variability can diversify the response of an otherwise homogeneous cell community to its environment. Recent advances have provided scientists with tools to study the variability associated with individual cells. However, while single-cell data are available for genomes, transcriptomes and proteomes, they are scarce for metabolic products, such as lipids. Eukaryotic cells produce thousands of lipids â each potentially contributing to specific biological functions. Furthermore, a number of metabolic switches involving lipids have been described to occur in development and cell differentiation. Few studies have directly addressed cell-to-cell lipid variations in syngeneic cell populations suggesting that lipid heterogeneity contributes to the emergence of multicellular patterns. Nonetheless, lipid biologists have so far addressed lipidomes in bulk cell extracts or selected lipids at the single-cell level. Thus, how lipidomes vary from one cell to another and which cell-to-cell lipid variations have biological meaning remains to be defined. Recent developments have provided mass spectrometry with sufficient sensitivity to reveal molecules in few hundred copies, making single-cell lipidomics possible. A rapidly emerging technique with a potential use in single-cell lipidomics is imaging mass spectrometry. Here, we devised a high-resolution MALDI imaging mass spectrometry pipeline to study cell-to-cell variability of the lipidome of hundreds of primary dermal human fibroblasts. We found that sphingolipid metabolism shows high cell-to-cell heterogeneity and that specific sphingolipids mark distinct cell sub-populations: coined herein as âlipotypesâ. Furthermore, we found that fibroblast lipotypes correspond to cell states endowed with proliferative, inflammatory or fibrogenic properties. Finally, we asked whether lipotypes participate in the specification of cell states and find that specific glycosphingolipids modulate signalling pathways (i.e., the FGF and TGF-b) to shape the transcriptional cell landscape. In summary, through the use of single-cell omics techniques, we found that: (i) specific lipid metabolic segments (i.e., the sphingolipid pathways) have cell-to-cell variation; (ii) given lipid configurations mark discrete cell states; and (iii) sphingolipids modulate dermal fibroblast activation involved in wound healing and skin homeostasis. Altogether, this study is among the first to demonstrate the potential of MALDI-IMS for single-cell lipidomics and to reveal cell-to-cell lipidome heterogeneity. It also demonstrates a role for lipids in the determination of cell states and in tissue patterning and reveals a new regulatory component to the self-organization of multicellular systems.

EPFL2021