Sphingolipid metabolic flow controls phosphoinositide turnover at the trans Golgi network

Sphingolipids are membrane lipids, which are globally required for eukaryotic life. Sphingolipid composition varies among endomembranes with pre- and post-Golgi compartments being poor and rich in sphingolipids, respectively. Thanks to this different sphingolipid content, pre- and post-Golgi membranes serve different cellular functions. Nevertheless, how subcellular sphingolipid levels are maintained in spite of trafficking and metabolic fluxes is only partially understood. Here we describe a homeostatic control circuit that controls sphingolipid levels at the trans Golgi network. Specifically, we show that sphingomyelin production at the trans Golgi network triggers a signalling reaction leading to PtdIns(4)P dephosphorylation. Since PtdIns(4)P is required for cholesterol, and sphingolipid transport to the trans Golgi network, PtdIns(4)P consumption leads to the interruption of this transport in response to excessive sphingomyelin production. Based on this evidence we envisage a model where this homeostatic circuit maintains the lipid composition of trans Golgi network and thus of post-Golgi compartments constant, against instant fluctuations in the sphingolipid biosynthetic flow.Peer ReviewedPostprint (author's final draft

Cell type-specific assessment of cholesterol distribution in models of neurodevelopmental disorders

Most nervous system disorders manifest through alterations in neuronal signaling based on abnormalities in neuronal excitability, synaptic transmission, and cell survival. However, such neuronal phenotypes are frequently accompanied – or even caused – by metabolic dysfunctions in neuronal or non-neuronal cells. The tight packing and highly heterogenous properties of neural, glial and vascular cell types pose significant challenges to dissecting metabolic aspects of brain disorders. Perturbed cholesterol homeostasis has recently emerged as key parameter associated with sub-sets of neurodevelopmental disorders. However, approaches for tracking and visualizing endogenous cholesterol distribution in the brain have limited capability of resolving cell type-specific differences. We here develop tools for genetically-encoded sensors that report on cholesterol distribution in the mouse brain with cellular resolution. We apply these probes to examine sub-cellular cholesterol accumulation in two genetic mouse models of neurodevelopmental disorders, Npc1 and Ptchd1 knock-out mice. While both genes encode proteins with sterol-sensing domains that have been implicated in cholesterol transport, we uncover highly selective and cell type-specific phenotypes in cholesterol homeostasis. The tools established in this work should facilitate probing sub-cellular cholesterol distribution in complex tissues like the mammalian brain and enable capturing cell type-specific alterations in cholesterol flow between cells in models of brain disorders

The yeast P5 type ATPase, Spf1, regulates manganese transport into the endoplasmic reticulum

The endoplasmic reticulum (ER) is a large, multifunctional and essential organelle. Despite intense research, the function of more than a third of ER proteins remains unknown even in the well-studied model organism Saccharomyces cerevisiae. One such protein is Spf1, which is a highly conserved, ER localized, putative P-type ATPase. Deletion of SPF1 causes a wide variety of phenotypes including severe ER stress suggesting that this protein is essential for the normal function of the ER. The closest homologue of Spf1 is the vacuolar P-type ATPase Ypk9 that influences Mn2+ homeostasis. However in vitro reconstitution assays with Spf1 have not yielded insight into its transport specificity. Here we took an in vivo approach to detect the direct and indirect effects of deleting SPF1. We found a specific reduction in the luminal concentration of Mn2+ in ∆spf1 cells and an increase following it’s overexpression. In agreement with the observed loss of luminal Mn2+ we could observe concurrent reduction in many Mn2+-related process in the ER lumen. Conversely, cytosolic Mn2+-dependent processes were increased. Together, these data support a role for Spf1p in Mn2+ transport in the cell. We also demonstrate that the human sequence homologue, ATP13A1, is a functionally conserved orthologue. Since ATP13A1 is highly expressed in developing neuronal tissues and in the brain, this should help in the study of Mn2+-dependent neurological disorders

mTORC2 Promotes Tumorigenesis via Lipid Synthesis

Dysregulated mammalian target of rapamycin (mTOR) promotes cancer, but underlying mechanisms are poorly understood. We describe an mTOR-driven mouse model that displays hepatosteatosis progressing to hepatocellular carcinoma (HCC). Longitudinal proteomic, lipidomics, and metabolomic analyses revealed that hepatic mTORC2 promotes de novo fatty acid and lipid synthesis, leading to steatosis and tumor devel- opment. In particular, mTORC2 stimulated sphingolipid (glucosylceramide) and glycerophospholipid (cardi- olipin) synthesis. Inhibition of fatty acid or sphingolipid synthesis prevented tumor development, indicating a causal effect in tumorigenesis. Increased levels of cardiolipin were associated with tubular mitochondria and enhanced oxidative phosphorylation. Furthermore, increased lipogenesis correlated with elevated mTORC2 activity and HCC in human patients. Thus, mTORC2 promotes cancer via formation of lipids essential for growth and energy production

A Systems Biology Approach Reveals the Role of a Novel Methyltransferase in Response to Chemical Stress and Lipid Homeostasis

Using small molecule probes to understand gene function is an attractive approach that allows functional characterization of genes that are dispensable in standard laboratory conditions and provides insight into the mode of action of these compounds. Using chemogenomic assays we previously identified yeast Crg1, an uncharacterized SAM-dependent methyltransferase, as a novel interactor of the protein phosphatase inhibitor cantharidin. In this study we used a combinatorial approach that exploits contemporary high-throughput techniques available in Saccharomyces cerevisiae combined with rigorous biological follow-up to characterize the interaction of Crg1 with cantharidin. Biochemical analysis of this enzyme followed by a systematic analysis of the interactome and lipidome of CRG1 mutants revealed that Crg1, a stress-responsive SAM-dependent methyltransferase, methylates cantharidin in vitro. Chemogenomic assays uncovered that lipid-related processes are essential for cantharidin resistance in cells sensitized by deletion of the CRG1 gene. Lipidome-wide analysis of mutants further showed that cantharidin induces alterations in glycerophospholipid and sphingolipid abundance in a Crg1-dependent manner. We propose that Crg1 is a small molecule methyltransferase important for maintaining lipid homeostasis in response to drug perturbation. This approach demonstrates the value of combining chemical genomics with other systems-based methods for characterizing proteins and elucidating previously unknown mechanisms of action of small molecule inhibitors

Kinetic reconstruction and analysis of sphingolipid metabolism

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