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Montefusco D, Jamil M, Canals D, Saligrama S, Yue Y, Allegood J, Cowart LA. SPTLC3 regulates plasma membrane sphingolipid composition to facilitate hepatic gluconeogenesis. Cell Rep 2024; 43:115054. [PMID: 39661520 DOI: 10.1016/j.celrep.2024.115054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 10/25/2024] [Accepted: 11/20/2024] [Indexed: 12/13/2024] Open
Abstract
SPTLC3, an inducible subunit of the serine palmitoyltransferase (SPT) complex, causes production of alternative sphingoid bases, including a 16-carbon dihydrosphingosine, whose biological function is only beginning to emerge. High-fat feeding induced SPTLC3 in the liver, prompting us to produce a liver-specific knockout mouse line. Following high-fat feeding, knockout mice showed decreased fasting blood glucose, and knockout primary hepatocytes showed suppressed glucose production, a core function of hepatocytes. Stable isotope tracing revealed suppression of the gluconeogenic pathway, finding that SPTLC3 was required to maintain expression of key gluconeogenic genes via adenylate cyclase/cyclic AMP (cAMP)/cAMP response element binding protein (CREB) signaling. Additionally, by employing a combination of a recently developed lipidomics methodology, exogenous C14/C16 fatty acid treatment, and in situ adenylate cyclase activity, we implicated a functional interaction between sphingomyelin with a d16 backbone and adenylate cyclase at the plasma membrane. This work pinpoints a specific sphingolipid-protein functional interaction with broad implications for understanding sphingolipid signaling and metabolic disease.
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Affiliation(s)
- David Montefusco
- Department of Biochemistry, Virginia Commonwealth University, Richmond, VA 23298, USA.
| | - Maryam Jamil
- Department of Biochemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Daniel Canals
- Department of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Siri Saligrama
- Department of Biochemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Yang Yue
- Department of Biochemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Jeremy Allegood
- Department of Biochemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - L Ashley Cowart
- Department of Biochemistry, Virginia Commonwealth University, Richmond, VA 23298, USA.
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Carreras-Torres R, Galván-Femenía I, Farré X, Cortés B, Díez-Obrero V, Carreras A, Moratalla-Navarro F, Iraola-Guzmán S, Blay N, Obón-Santacana M, Moreno V, de Cid R. Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk. Genome Med 2024; 16:122. [PMID: 39449064 PMCID: PMC11515386 DOI: 10.1186/s13073-024-01397-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 10/17/2024] [Indexed: 10/26/2024] Open
Abstract
BACKGROUND Understanding genetic-metabolite associations has translational implications for informing cardiovascular risk assessment. Interrogating functional genetic variants enhances our understanding of disease pathogenesis and the development and optimization of targeted interventions. METHODS In this study, a total of 187 plasma metabolite levels were profiled in 4974 individuals of European ancestry of the GCAT| Genomes for Life cohort. Results of genetic analyses were meta-analysed with additional datasets, resulting in up to approximately 40,000 European individuals. Results of meta-analyses were integrated with reference gene expression panels from 58 tissues and cell types to identify predicted gene expression associated with metabolite levels. This approach was also performed for cardiovascular outcomes in three independent large European studies (N = 700,000) to identify predicted gene expression additionally associated with cardiovascular risk. Finally, genetically informed mediation analysis was performed to infer causal mediation in the relationship between gene expression, metabolite levels and cardiovascular risk. RESULTS A total of 44 genetic loci were associated with 124 metabolites. Lead genetic variants included 11 non-synonymous variants. Predicted expression of 53 fine-mapped genes was associated with 108 metabolite levels; while predicted expression of 6 of these genes was also associated with cardiovascular outcomes, highlighting a new role for regulatory gene HCG27. Additionally, we found that atherogenic metabolite levels mediate the associations between gene expression and cardiovascular risk. Some of these genes showed stronger associations in immune tissues, providing further evidence of the role of immune cells in increasing cardiovascular risk. CONCLUSIONS These findings propose new gene targets that could be potential candidates for drug development aimed at lowering the risk of cardiovascular events through the modulation of blood atherogenic metabolite levels.
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Affiliation(s)
- Robert Carreras-Torres
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), 17190, Salt, Girona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
| | - Iván Galván-Femenía
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
| | - Xavier Farré
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
- Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP), Badalona, Spain
| | - Beatriz Cortés
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
| | - Virginia Díez-Obrero
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, 08908, Barcelona, Spain
| | - Anna Carreras
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
| | - Ferran Moratalla-Navarro
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, 08908, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Susana Iraola-Guzmán
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
- Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP), Badalona, Spain
| | - Natalia Blay
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
- Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP), Badalona, Spain
| | - Mireia Obón-Santacana
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, 08908, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain
| | - Víctor Moreno
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain.
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, 08908, Barcelona, Spain.
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain.
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain.
| | - Rafael de Cid
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain.
- Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP), Badalona, Spain.
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Sakkers TR, Mokry M, Civelek M, Erdmann J, Pasterkamp G, Diez Benavente E, den Ruijter HM. Sex differences in the genetic and molecular mechanisms of coronary artery disease. Atherosclerosis 2023; 384:117279. [PMID: 37805337 DOI: 10.1016/j.atherosclerosis.2023.117279] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/09/2023] [Accepted: 09/01/2023] [Indexed: 10/09/2023]
Abstract
Sex differences in coronary artery disease (CAD) presentation, risk factors and prognosis have been widely studied. Similarly, studies on atherosclerosis have shown prominent sex differences in plaque biology. Our understanding of the underlying genetic and molecular mechanisms that drive these differences remains fragmented and largely understudied. Through reviewing genetic and epigenetic studies, we identified more than 40 sex-differential candidate genes (13 within known CAD loci) that may explain, at least in part, sex differences in vascular remodeling, lipid metabolism and endothelial dysfunction. Studies with transcriptomic and single-cell RNA sequencing data from atherosclerotic plaques highlight potential sex differences in smooth muscle cell and endothelial cell biology. Especially, phenotypic switching of smooth muscle cells seems to play a crucial role in female atherosclerosis. This matches the known sex differences in atherosclerotic phenotypes, with men being more prone to lipid-rich plaques, while women are more likely to develop fibrous plaques with endothelial dysfunction. To unravel the complex mechanisms that drive sex differences in CAD, increased statistical power and adjustments to study designs and analysis strategies are required. This entails increasing inclusion rates of women, performing well-defined sex-stratified analyses and the integration of multi-omics data.
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Affiliation(s)
- Tim R Sakkers
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Michal Mokry
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Mete Civelek
- Center for Public Health Genomics, University of Virginia, 1335 Lee St, Charlottesville, VA, 22908, USA; Department of Biomedical Engineering, University of Virginia, 351 McCormick Road, Charlottesville, VA, 22904, USA
| | - Jeanette Erdmann
- Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Gerard Pasterkamp
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Ernest Diez Benavente
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Hester M den Ruijter
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands.
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López Rodríguez M, Arasu UT, Kaikkonen MU. Exploring the genetic basis of coronary artery disease using functional genomics. Atherosclerosis 2023; 374:87-98. [PMID: 36801133 DOI: 10.1016/j.atherosclerosis.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/05/2023]
Abstract
Genome-wide Association Studies (GWAS) have identified more than 300 loci associated with coronary artery disease (CAD), defining the genetic risk map of the disease. However, the translation of the association signals into biological-pathophysiological mechanisms constitute a major challenge. Through a group of examples of studies focused on CAD, we discuss the rationale, basic principles and outcomes of the main methodologies implemented to prioritize and characterize causal variants and their target genes. Additionally, we highlight the strategies as well as the current methods that integrate association and functional genomics data to dissect the cellular specificity underlying the complexity of disease mechanisms. Despite the limitations of existing approaches, the increasing knowledge generated through functional studies helps interpret GWAS maps and opens novel avenues for the clinical usability of association data.
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Affiliation(s)
- Maykel López Rodríguez
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland; Department of Pathology and Laboratory Medicine, University of California, UCLA, Los Angeles, USA.
| | - Uma Thanigai Arasu
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland
| | - Minna U Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland.
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Mitok KA, Keller MP, Attie AD. Sorting through the extensive and confusing roles of sortilin in metabolic disease. J Lipid Res 2022; 63:100243. [PMID: 35724703 PMCID: PMC9356209 DOI: 10.1016/j.jlr.2022.100243] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 01/06/2023] Open
Abstract
Sortilin is a post-Golgi trafficking receptor homologous to the yeast vacuolar protein sorting receptor 10 (VPS10). The VPS10 motif on sortilin is a 10-bladed β-propeller structure capable of binding more than 50 proteins, covering a wide range of biological functions including lipid and lipoprotein metabolism, neuronal growth and death, inflammation, and lysosomal degradation. Sortilin has a complex cellular trafficking itinerary, where it functions as a receptor in the trans-Golgi network, endosomes, secretory vesicles, multivesicular bodies, and at the cell surface. In addition, sortilin is associated with hypercholesterolemia, Alzheimer's disease, prion diseases, Parkinson's disease, and inflammation syndromes. The 1p13.3 locus containing SORT1, the gene encoding sortilin, carries the strongest association with LDL-C of all loci in human genome-wide association studies. However, the mechanism by which sortilin influences LDL-C is unclear. Here, we review the role sortilin plays in cardiovascular and metabolic diseases and describe in detail the large and often contradictory literature on the role of sortilin in the regulation of LDL-C levels.
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Affiliation(s)
- Kelly A Mitok
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Alan D Attie
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
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