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Li D. Structure and Function of the Glycosylphosphatidylinositol Transamidase, a Transmembrane Complex Catalyzing GPI Anchoring of Proteins. Subcell Biochem 2024; 104:425-458. [PMID: 38963495 DOI: 10.1007/978-3-031-58843-3_16] [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] [Indexed: 07/05/2024]
Abstract
Glycosylphosphatidylinositol (GPI) anchoring of proteins is a ubiquitous posttranslational modification in eukaryotic cells. GPI-anchored proteins (GPI-APs) play critical roles in enzymatic, signaling, regulatory, and adhesion processes. Over 20 enzymes are involved in GPI synthesis, attachment to client proteins, and remodeling after attachment. The GPI transamidase (GPI-T), a large complex located in the endoplasmic reticulum membrane, catalyzes the attachment step by replacing a C-terminal signal peptide of proproteins with GPI. In the last three decades, extensive research has been conducted on the mechanism of the transamidation reaction, the components of the GPI-T complex, the role of each subunit, and the substrate specificity. Two recent studies have reported the three-dimensional architecture of GPI-T, which represent the first structures of the pathway. The structures provide detailed mechanisms for assembly that rationalizes previous biochemical results and subunit-dependent stability data. While the structural data confirm the catalytic role of PIGK, which likely uses a caspase-like mechanism to cleave the proproteins, they suggest that unlike previously proposed, GPAA1 is not a catalytic subunit. The structures also reveal a shared cavity for GPI binding. Somewhat unexpectedly, PIGT, a single-pass membrane protein, plays a crucial role in GPI recognition. Consistent with the assembly mechanisms and the active site architecture, most of the disease mutations occur near the active site or the subunit interfaces. Finally, the catalytic dyad is located ~22 Å away from the membrane interface of the GPI-binding site, and this architecture may confer substrate specificity through topological matching between the substrates and the elongated active site. The research conducted thus far sheds light on the intricate processes involved in GPI anchoring and paves the way for further mechanistic studies of GPI-T.
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Affiliation(s)
- Dianfan Li
- CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences (CAS), Shanghai, China.
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Kawaguchi K, Yamamoto-Hino M, Matsuyama N, Suzuki E, Goto S. Subunits of the GPI transamidase complex localize to the endoplasmic reticulum and nuclear envelope in Drosophila. FEBS Lett 2021; 595:960-968. [PMID: 33496978 DOI: 10.1002/1873-3468.14048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 12/25/2020] [Accepted: 01/09/2021] [Indexed: 11/07/2022]
Abstract
A total of 10-20% of plasma membrane proteins are anchored by glycosylphosphatidylinositol (GPI). GPI is attached to proteins by GPI transamidase (GPI-T), which contains five subunits named PIGK, PIGS, PIGT, PIGU, and GPAA1. We previously reported that PIGT localizes near the nucleus in Drosophila. However, localizations of the other four subunits remain unknown. Here, we show that a catalytic subunit of GPI-T, PIGK, mainly localizes to the endoplasmic reticulum (ER), while the other four subunits localize to the nuclear envelope (NE) and ER. The NE/ER localization ratio of PIGS differs between cell types and developmental stages. Our results suggest that GPI-T catalyzes GPI attachment in the ER and the other four subunits may have other unknown functions in the NE.
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Affiliation(s)
| | | | - Nina Matsuyama
- Department of Life Science, Rikkyo University, Tokyo, Japan
| | - Emiko Suzuki
- Department of Life Science, Rikkyo University, Tokyo, Japan
| | - Satoshi Goto
- Department of Life Science, Rikkyo University, Tokyo, Japan
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Ma Y, Jun GR, Zhang X, Chung J, Naj AC, Chen Y, Bellenguez C, Hamilton-Nelson K, Martin ER, Kunkle BW, Bis JC, Debette S, DeStefano AL, Fornage M, Nicolas G, van Duijn C, Bennett DA, De Jager PL, Mayeux R, Haines JL, Pericak-Vance MA, Seshadri S, Lambert JC, Schellenberg GD, Lunetta KL, Farrer LA. Analysis of Whole-Exome Sequencing Data for Alzheimer Disease Stratified by APOE Genotype. JAMA Neurol 2019; 76:1099-1108. [PMID: 31180460 PMCID: PMC6563544 DOI: 10.1001/jamaneurol.2019.1456] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Accepted: 03/22/2019] [Indexed: 12/13/2022]
Abstract
IMPORTANCE Previous genome-wide association studies of common variants identified associations for Alzheimer disease (AD) loci evident only among individuals with particular APOE alleles. OBJECTIVE To identify APOE genotype-dependent associations with infrequent and rare variants using whole-exome sequencing. DESIGN, SETTING, AND PARTICIPANTS The discovery stage included 10 441 non-Hispanic white participants in the Alzheimer Disease Sequencing Project. Replication was sought in 2 independent, whole-exome sequencing data sets (1766 patients with AD, 2906 without AD [controls]) and a chip-based genotype imputation data set (8728 patients with AD, 9808 controls). Bioinformatics and functional analyses were conducted using clinical, cognitive, neuropathologic, whole-exome sequencing, and gene expression data obtained from a longitudinal cohort sample including 402 patients with AD and 647 controls. Data were analyzed between March 2017 and September 2018. MAIN OUTCOMES AND MEASURES Score, Firth, and sequence kernel association tests were used to test the association of AD risk with individual variants and genes in subgroups of APOE ε4 carriers and noncarriers. Results with P ≤ 1 × 10-5 were further evaluated in the replication data sets and combined by meta-analysis. RESULTS Among 3145 patients with AD and 4213 controls lacking ε4 (mean [SD] age, 83.4 [7.6] years; 4363 [59.3.%] women), novel genome-wide significant associations were obtained in the discovery sample with rs536940594 in AC099552 (odds ratio [OR], 88.0; 95% CI, 9.08-852.0; P = 2.22 × 10-7) and rs138412600 in GPAA1 (OR, 1.78; 95% CI, 1.44-2.2; meta-P = 7.81 × 10-8). GPAA1 was also associated with expression in the brain of GPAA1 (β = -0.08; P = .03) and its repressive transcription factor, FOXG1 (β = 0.13; P = .003), and global cognition function (β = -0.53; P = .009). Significant gene-wide associations (threshold P ≤ 6.35 × 10-7) were observed for OR8G5 (P = 4.67 × 10-7), IGHV3-7 (P = 9.75 × 10-16), and SLC24A3 (P = 2.67 × 10-12) in 2377 patients with AD and 706 controls with ε4 (mean [SD] age, 75.2 [9.6] years; 1668 [54.1%] women). CONCLUSIONS AND RELEVANCE The study identified multiple possible novel associations for AD with individual and aggregated rare variants in groups of individuals with and without APOE ε4 alleles that reinforce known and suggest additional pathways leading to AD.
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Affiliation(s)
- Yiyi Ma
- Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Center for Translational & Computational Neuroimmunology, Multiple Sclerosis Clinical Care and Research Center, Division of Neuroimmunology, Columbia University Medical Center, New York, New York
- Department of Neurology, Columbia University Medical Center, New York, New York
| | - Gyungah R. Jun
- Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Department of Biostatistics, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
| | - Xiaoling Zhang
- Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, Massachusetts
| | - Jaeyoon Chung
- Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, Massachusetts
| | - Adam C. Naj
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Yuning Chen
- Department of Biostatistics, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
| | - Celine Bellenguez
- Universite de Lille, INSERM UMR1167, Institute Pasteur de Lille, Lille, France
| | - Kara Hamilton-Nelson
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida
| | - Eden R. Martin
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida
| | - Brian W. Kunkle
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, UMR1219, University Bordeaux, Inserm, Bordeaux, France
- Department of Neurology, Bordeaux University Hospital, Bordeaux, France
| | - Anita L. DeStefano
- Department of Biostatistics, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Department of Neurology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
| | - Myriam Fornage
- School of Public Health, University of Texas Health Science Center at Houston, Houston
| | - Gaël Nicolas
- UNIROUEN, Inserm U1245, Normandie University, Rouen, France
- Department of Genetics, Rouen University Hospital, Rouen, France
- Normandy Centre for Genomic and Personalized Medicine, Centre National de Référence pour les Malades Alzheimer Jeunes, Rouen, France
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Multiple Sclerosis Clinical Care and Research Center, Division of Neuroimmunology, Columbia University Medical Center, New York, New York
- Department of Neurology, Columbia University Medical Center, New York, New York
- Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Richard Mayeux
- Department of Neurology, Columbia University Medical Center, New York, New York
| | - Jonathan L Haines
- Institute for Computational Biology, Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Margaret A. Pericak-Vance
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida
| | - Sudha Seshadri
- Department of Neurology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio
| | | | | | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Department of Biostatistics, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Department of Neurology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Department of Ophthalmology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
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