Intracellular protein GBF1 displays significant associations with amyloid pathology in Alzheimer's disease.

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Learn more: PMC Disclaimer | PMC Copyright Notice Alzheimers Dement . 2026 Apr 16;22(4):e71271. doi: 10.1002/alz.71271 Search in PMC Search in PubMed View in NLM Catalog Add to search Intracellular protein GBF1 displays significant associations with amyloid pathology in Alzheimer's disease Sean J Miller Sean J Miller 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA 2 Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, Connecticut, USA Find articles by Sean J Miller 1, 2 , Dmitry Prokopenko Dmitry Prokopenko 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Dmitry Prokopenko 1 , Ping Bai Ping Bai 3 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Ping Bai 3 , Prasenjit Mondal Prasenjit Mondal 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Prasenjit Mondal 1 , Abigael Scott Abigael Scott 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Abigael Scott 1 , Wei Zhang Wei Zhang 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Wei Zhang 1 , Ashley Gomm Ashley Gomm 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Ashley Gomm 1 , Siyi Zhang Siyi Zhang 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Siyi Zhang 1 , Daniel D Child Daniel D Child 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Daniel D Child 1 , Nolan Shen Nolan Shen 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Nolan Shen 1 , Joseph Ward Joseph Ward 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Joseph Ward 1 , Scott Schulte Scott Schulte 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Scott Schulte 1 , Dan Lei Dan Lei 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Dan Lei 1 , Brian P Hafler Brian P Hafler 2 Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, Connecticut, USA 4 Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA 5 The Broad Institute, MIT and Harvard, Cambridge, Massachusetts, USA Find articles by Brian P Hafler 2, 4, 5 , Changning Wang Changning Wang 3 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Changning Wang 3 , Rudolph E Tanzi Rudolph E Tanzi 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Rudolph E Tanzi 1 , Can Zhang Can Zhang 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA Find articles by Can Zhang 1, ✉ Author information Article notes Copyright and License information 1 Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA 2 Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, Connecticut, USA 3 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA 4 Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA 5 The Broad Institute, MIT and Harvard, Cambridge, Massachusetts, USA * Correspondence , Can Zhang, Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA. Email: [email protected] ✉ Corresponding author. Revised 2025 Nov 18; Received 2025 Aug 25; Accepted 2025 Dec 30; Collection date 2026 Apr. © 2026 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMC Copyright notice PMCID: PMC13084705  PMID: 41988936 Abstract INTRODUCTION Despite the identification of familial Alzheimer's disease (FAD) genes and neuropathological alterations, AD displays complex genetic heterogeneity and molecular pathogenesis that warrant further investigation. GBF1 (Golgi brefeldin A resistant guanine nucleotide exchange factor 1) regulates protein trafficking, and genetic variants of GBF1 are associated with axonal neuropathy, intelligence, and cognitive function. METHODS We sought to identify GBF1 neuropathological and molecular alterations using human post mortem brains, 5XFAD mouse brains, and FAD cells, as well as two family‐based datasets (total sample size of 2522) to explore candidate GBF1 variants associated with AD. RESULTS GBF1 revealed neuropathological alterations in association with amyloid plaques. Genetic analysis identified GBF1 suggestive variants associated with AD. Downregulation of GBF1 retarded amyloid beta (Aβ) protein precursor maturation and reduced levels of Aβ proteins. DISCUSSION Collectively, GBF1 reveals neuropathological alterations in AD, and may lead to AD by a pathogenic mechanism altering Aβ levels and amyloid deposition in the brain. Keywords: Alzheimer's disease, amyloid beta, amyloid precursor protein, Golgi brefeldin A‐resistant guanine nucleotide exchange factor‐1, golgicide A, neuropathology Highlights Golgi brefeldin A resistant guanine nucleotide exchange factor 1 (GBF1) displays neuropathological changes in Alzheimer's disease (AD) brains. Genetics associate GBF1 with AD. GBF1 regulates amyloid precursor protein maturation. GBF1 inhibitors reduce amyloid beta levels. GBF1 may serve as a therapeutic candidate for the treatment of AD. 1. BACKGROUND Alzheimer's disease (AD) is a leading cause of dementia in the elderly population and is characterized by a complex and strong genetic etiology. 1 Four disease genes have been identified that either cause early‐onset familial AD (FAD) with complete penetrance (amyloid precursor protein [ APP ], presenilin 1 [ PSEN1 ], and presenilin 2 [ PSEN2 ]) or increase susceptibility for late‐onset AD (LOAD) with partial penetrance (apolipoprotein E [ APOE ]). 2 , 3 , 4 In addition, >75 genes with smaller effects play an important role for LOAD risk. 5 Whole genome sequencing (WGS) datasets can identify additional rare variants contributing to AD heritability. 6 Pathologically, AD displays two hallmarks: extracellular amyloid plaques comprised primarily of the small peptide amyloid beta (Aβ), and intracellular neurofibrillary tangles composed of hyperphosphorylated tau. 2 , 7 , 8 Mounting genetic, biochemical, and molecular biological evidence shows that there is a strong association of Aβ accumulation in AD and that Aβ deposition may be a critical pathogenetic event leading to AD. 3 , 7 , 8 Aβ is a small peptide that is produced by a sequential proteolytic cleavage of the type I transmembrane protein, APP. 9 Initially, APP cleavage may be mediated by either α‐ or β‐secretase (BACE1). α‐secretase cleavage generates secreted amyloid precursor protein alpha (sAPPα) and C‐terminal fragment alpha (CTFα), whereas BACE1 cleavage releases sAPPβ and CTFβ. CTFα and CTFβ can then be cleaved by γ‐secretase to produce P3 or Aβ peptides, respectively. APP proteolytic processing is a highly complex process and usually occurs on its mature form. Disruption of APP processing underlies AD pathogenesis and is related to changes in cerebral Aβ levels. 10 , 11 , 12 After translation, immature APP is N‐glycosylated in the endoplasmic reticulum (ER), and a fraction translocates to the Golgi complex where it undergoes O‐glycosylation and other post‐translational modifications to become mature APP. 11 The mature APP is subsequently sorted, and a portion may traffic to the plasma membrane, the primary site of α‐secretase cleavage. Additionally, plasma membrane APP can undergo further endocytosis via clathrin‐coated pits. Genetic 13 , 14 , 15 and pharmacological factors 12 , 16 that affect APP maturation have been identified, and these hits collectively establish the importance of the Golgi apparatus for this process. Newly synthesized proteins and lipids from the ER are transported to the cis Golgi network and then migrate through the stack to the trans Golgi network, where the proteins are packaged and transported to the cell surface, secretory vesicles, or the late endosomal compartments. 17 , 18 Adenosine diphosphate ribosylation factors (ARFs) are small GTPases of the Ras superfamily that play a major role in regulating Golgi‐associated trafficking. The cellular activity of ARFs is stimulated by the Sec7 family of guanine nucleotide exchange factors (GEFs), which regulate the exchange of inactive guanosine diphosphate (GDP)‐bound forms to active guanosine triphosphate (GTP)‐bound forms. 19 ARFGEFs play critical roles in pathophysiology. 20 , 21 GEF mutations have been associated with various neurological disorders. 20 , 21 RESEARCH IN CONTEXT Systematic review : Extracellular amyloid plaque formation is a pathological hallmark in the brain of Alzheimer's disease (AD). Amyloid plaques are primarily composed of the amyloid beta (Aβ) proteins, which are generated through the amyloid precursor protein (APP). APP undergoes an extensive amount of intracellular trafficking and maturation before the subsequential production of Aβ proteins, followed by accumulation and deposition in the extracellular space. Recent anti‐Aβ antibodies have provided valuable disease‐modifying therapeutics. To interfere with APP maturation prior to its proteolytic cleavage may reduce Aβ generation and potentially serve as a feasible therapeutic strategy to treat AD. Here, we aimed to focus on an intracellular protein involved in APP trafficking. Interpretation : We hypothesized that blocking APP trafficking may be specifically targeted to reduce amyloidogenic processing through the intracellular protein GBF1 (Golgi brefeldin A resistant guanine nucleotide exchange factor 1), which is involved in the endoplasmic reticulum–Golgi trafficking of APP. Our functional analysis of GBF1 revealed pathological alterations of GBF1 in association with amyloid plaques not only in AD human post mortem brains but also in the brain of 5XFAD transgenic amyloid model animals. Our whole genome sequencing cohorts of AD discovered GBF1 variants associated with AD. Moreover, we showed in multiple AD models that GBF1 inhibitors dramatically reduced Aβ levels. Our work suggested that GBF1 is a critical player of AD pathogenesis, which may lead to AD through changing Aβ levels and amyloid deposition in the brain. Future directions : Future directions should focus on identification and characterization of amyloid‐specific intracellular modulators that affect APP trafficking as well as new GBF1 genetic variants associated with AD. These studies will be useful to extend our findings that highlights the role of GBF1 in the trafficking of APP and its manipulation as a therapeutic strategy to reduce Aβ generation. Golgi brefeldin A‐resistant guanine nucleotide exchange factor 1 (GBF1), is a GEF protein localized to the cis Golgi that mediates protein trafficking between the ER and the cis Golgi. 22 , 23 , 24 , 25 Pharmacological inhibitors of GBF1 have been generated that occupy the binding site of Sec7 domain of GBF1 and reveal roles for GBF1 in Golgi assembly and protein trafficking. 22 , 26 , 27 Large genome‐wide association studies have identified single nucleotide variants (SNVs) in GBF1 associated with intelligence, 28 , 29 education attainment and cognitive function, 30 and Parkinson's disease. 31 A recent cell‐based analysis showed that silencing GBF1 led to an increase in intracellular Aβ levels. 32 Presently, the recent advancement of AD genetics provided a well‐posed opportunity to identify novel candidate variants in association with AD, which can be further functionally characterized for their molecular mechanisms underlying AD. These collective findings prompted us to investigate whether GBF1 contributes to AD‐associated amyloid pathology by regulating Golgi‐mediated trafficking and maturation of APP, thereby influencing Aβ generation. 2. METHODS 2.1. Ethics statement We complied with all relevant ethical regulations for work with human participants. All human tissue samples were obtained with informed consent prior to tissue collection from participants if enrolled ante mortem or legal guardians if post mortem . Paraffin embedded and formalin fixed human brain sections were from the Massachusetts Alzheimer's Disease Research Center (MADRC), and were used for immunohistochemistry (IHC) analysis following previously documented standard methods. 33 2.2. Animal studies All animal study procedures were approved by the Massachusetts General Hospital institutional animal use and care committee and conformed to the animal welfare guidelines. Animals were housed with ad libitum access to food and water with a 12‐hour light and dark cycle in the animal facility. We used the previously reported 5XFAD mouse model because it expresses APP Swedish/Florida/London and PSEN1 M146L/ L286V mutations 34 , 35 and primarily recapitulates the features of Aβ pathology in the brain. 34 5XFAD mice and their littermate non‐transgenic control mice (8‐month‐old male and 15‐month‐old female; n = 2 per group) were investigated. They were transcardially perfused under isoflurane anesthesia. After brain dissection, tissue was processed for post mortem IHC analysis. 2.3. Genetic discovery Sequencing in the National Institute of Mental Health (NIMH) cohort was performed using the HiSeq 2000 System at Illumina Inc. Variants were jointly called for each family within the bcbio‐nextgen workflow ( https://github.com/chapmanb/bcbio‐nextgen ). Additionally, we performed sample quality control and variant quality control based on estimated identity by descent, genotyping rate, inbreeding coefficient, Mendel errors, and Hardy–Weinberg equilibrium. Variant calls in VCF format for the families and unrelated subjects from the National Institute on Aging (NIA) Alzheimer's Disease Sequencing Project (ADSP) cohort were obtained from the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) under accession number: NG00067.v9. GEMINI and SNPEff tools were used to perform functional annotation of the variants. In family‐based datasets, we used the family‐based association test (FBAT), 36 which is a generalization of the transmission disequilibrium test (TDT; https://sites.google.com/view/fbat‐web‐page ) to perform association analysis. FBATs have been specifically designed to analyze datasets in which a family‐based sampling design has been implemented. Based solely on Mendelian transmissions, they have been shown to be robust against population structure and deviations from the Hardy–Weinberg equilibrium. Such tests condition on the phenotype and are robust against potential misspecification of the phenotype distribution or disease model. 37 , 38 We used an offset of 0.15 as an approximation of the disease prevalence when modeling the phenotype in FBAT. Both family‐based datasets (NIMH and NIA ADSP family‐based portion) were analyzed together (total sample size of 2522 and 644 nuclear families). We used PBAT to perform power calculations. 39 Approximate power to detect a variant in a family‐based association study of 644 nuclear families depends critically on the variant's minor allele frequency (MAF), effect size, and the number of affected offspring per family. For common variants with MAF = 0.2 and odds ratio (OR) = 2, moderate to high power (>67%) can be achieved with 644 families with two discordant offspring at an alpha level of 0.0001. Variants with MAF = 0.05 and OR = 2.5 to 3.5 provide power ranging from ≈ 25% to 73% at an alpha level of 0.0001, depending on the specific family‐based design and test used. The case–control portion of the NIA ADSP was divided into four subpopulations based on self‐assignment: non‐Hispanic White (NHW), African American (AA), Hispanic ethnicity (HISP), and Asian (ASIA). We have verified the population assignment based on principal component analysis using the Jaccard matrix and removed outliers that were >5 standard deviations away from the mean based on each of the first 10 principal components. Principal components were calculated based on a subset of linkage disequilibrium (LD)‐pruned subset of rare variants using the Jaccard index. 40 For case–control datasets, we used PLINK to perform a logistic regression in each of the subpopulations with covariates (age, sex, sequencing center, sample set, and 5 principal components to adjust for population structure). 41 Age was not included as a covariate in the NHW subpopulation due to the study design in this group: controls were much older than cases, which introduced confounding between age and affection status. For meta‐analysis, we have used the previously reported meta software 42 and performed a sample size–weighted fixed‐effects meta‐analysis. R statistical software was used for any additional analyses. Unadjusted p values are reported. 2.4. Cell culture Culturing methods for cell models of AD have been reported, specifically using the Chinese hamster ovary (CHO) cell model stably expressing the Indiana mutation in APP (7PA2 cells). 43 , 44 , 45 , 46 , 47 In addition, we cultured CHO or human neuroglioma H4 cells that stably express APP751 or the APP‐GFP construct. 48 , 49 , 50 These cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum, 2 mM L‐glutamine, 100 units/mL penicillin, 100 µg/mL streptomycin, and 200 µg/mL G418. 2.5. Chemicals and antibodies Golgicide A (GCA; Sigma, catalog #: G0923), AG1478 (Sigma, catalog #: T4182), and erlotinib (Sigma, catalog # SML3621) were dissolved in dimethyl sulfoxide (DMSO) prior to use. OptiPrep solution was provided as a 60% solution of iodixanol (from Alex Shield). The APP polyclonal antibody A8717 (Sigma; targeting the 19 c‐terminal residues of APP; 1:1000) and the G12A antibody 51 , 52 were used to detect full‐length APP and APP‐CTFs. The 3D6 and DE2B4 antibodies have been previously reported to detect amyloid deposition of AD. 53 The monoclonal 6E10 antibody (Covance or BioLegend; 1:1000 for western blotting and 1:500 for immunofluorescence imaging) was reactive to amino acid residues 1 to 16 of Aβ from the N‐terminal sequence and was used to detect sAPPα in the medium and APP in the lysates. GBF1 antibodies were from Bethyl (catalog #: A301‐962A) and Abcam (catalog #: ab86071; 1:1000 for Western blotting and 1:200 for immunofluorescence imaging). The BiP antibody was from Sigma (G8918; 1:1000), the EEA1 antibody was from Cell Signaling (catalog # 2411; 1:1000), and the TGN46 antibody was from Abcam (ab50595; 1:1000 in WB and 1:250 in ICC). The antibodies for β‐actin (Sigma; catalog #A5441; 1:10,000) and GADPH (Cell Signaling; catalog # 2118S; 1:1000) were used as control proteins. The horseradish peroxidase (HRP)‐conjugated secondary antibodies (anti‐mouse and anti‐rabbit; 1:10,000) were from Pierce. 2.6. Aβ measurement Aβ measurement was performed following protocols that were previously described. 54 , 55 In brief, Aβ triplex levels (Aβ40, Aβ38, and Aβ42) in the medium were measured on an electrochemiluminescence‐based multi‐array platform from Meso Scale Discovery (MSD). Aβ levels from treated samples were compared and normalized to levels from appropriate control samples. 2.7. Lactate dehydrogenase assay We performed the lactate dehydrogenase (LDH) assay to investigate cell viability following previously reported methods. 53 Briefly, the CytoTox‐ONE kit was used according to the manufacturer's guidelines (Promega). Fifty microliters of cell culture media were removed, and a 1:1 dilution of substrate was added. The plate was incubated for 30 minutes in a 37°C incubator. In the presence of the assay's substrate, resazurin was converted to resorufin, its fluorescent form, due to LDH. The fluorescence signal was measured using a fluorometer (excitation: 560 nm, emission: 590 nm) and the fluorometric results were used to compare the dose‐dependent GCA or AG1478‐related group to the control group. 2.8. Cell lysis and protein amount quantification Cells were lysed in M‐PER lysis buffer (Mammalian Protein Extraction Reagent, Thermo Scientific) with 1X Halt protease inhibitor cocktail (Thermo Scientific). Next, the lysates were collected, centrifuged at 16,200 × g for 20 minutes, the pellets were discarded, and the supernatants were transferred to a new Eppendorf tube. Total protein concentrations were identified using the bicinchoninic acid (BCA) protein assay kit (Pierce) that was previously reported. 13 2.9. Western blotting analysis Western blotting (WB) analysis was carried out via methods that have been well reported. 13 , 14 Briefly, after protein concentration measurements, an equal amount of protein was applied to polyacrylamide electrophoresis, followed by blot transfer, antibody incubation, and signal development. The β‐actin and GADPH proteins were used as controls for loading differences. We used the VersaDoc imaging system (Bio‐Rad) and the Touch Imager (E‐Blot) to develop the blots and the Quantity One software (Bio‐Rad) and ImageJ to quantify the proteins of interest by densitometry, following the previously described protocols. 13 , 14 2.10. Arf1 pull‐down assay Cells were seeded overnight in 10 cm dishes to ≈ 90% confluency and treated with various concentrations of GCA (5, 12.5, and 25 µM) and AG1478 (25 and 50 µM) in serum‐free medium for 1 hour before collection and processing following the manufacturer‐provided Arf1 pull‐down kit protocol (Thermo Scientific, catalog # 16121), including lysates with GTPγS and GDP positive and negative controls. We performed WB analysis of Arf1 pull‐down lysates as well as total lysates to reveal Arf1 levels using an Arf1 antibody (1:2000) provided by the Arf1 pull‐down kit. 2.11. GBF1 small interfering RNA silencing and WB analysis For GBF1 knockdown, 7PA2 cells were transfected in serum‐free DMEM, using 1:1000 polyethylenimine (PEI) transfection reagent (MedChemExpress, catalog # HY‐K2014) and 1 µM GBF1 small interfering RNA (siRNA; Horizon; catalog # L‐057154‐00‐0020) SMARTPool containing the following target sequences: GCAGCAAGAGUCAUAAGUA, UCAGUGAGUUAUUGAGAAA, GGGAAGCACCCGUUAUUCA, and GAUGAGAUGUGCCGACACU. In brief, cells were transfected with mixed siRNA and PEI or the PEI alone control, incubated at 37°C, changed into regular growth medium and allowed to grow for an additional 2 days and then applied to collection and WB analysis. 2.12. GBF1 overexpression and silencing experiments followed by immunocytochemistry imaging analysis For GBF1 overexpression or silencing, 7PA2 cells were respectively transfected with 1 µg GBF1 cDNA (OriGene, catalog # RC220987) or 1 µM GBF1 siRNA described above (Horizon), which were then mixed with 3 µL Turbofectin 8.0 (OriGene; catalog # TF81001) in serum‐free medium on 8‐well glass chambers and incubated overnight. We then performed immunocytochemistry (ICC) analysis on the cells using the methods previously reported. 53 , 56 , 57 In brief, the medium was discarded, and the cells were incubated with 4% paraformaldehyde (PFA) for 20 minutes for fixation. Subsequently, fixed cells were washed four times with phosphate‐buffered saline (PBS) and incubated in blocking solution containing 5% donkey serum (with 0.1% TritonX‐100) for 1 hour. Samples were then incubated in primary antibodies in blocking solution overnight at 4°C, followed by washing four times with washing buffer containing 0.1% Triton X‐100 and 0.05% Tween‐20 in PBS, and then incubated in secondary antibodies (1:500) for 2 hours at room temperature. After washing, slides were mounted with ProLong gold antifade mountant with DAPI (Thermo Scientific; P36931 ), or incubated with Hoechst solution (20 nM) for 10 minutes and mounted with ProLong gold antifade reagent (Invitrogen; P36934 ), followed by imaging under the Nikon C2 confocal microscope. 2.13. Immunofluorescence microscope analysis IHC study was performed using the formalin fixed paraffin embedded (8 µm thick) human post mortem brain tissues, including healthy ( n = 6; 3 males and 3 females) and AD ( n = 6; 3 males and 3 females) to investigate the potential association of GBF1 in AD following previously documented work. 33 , 58 In brief, human male and female control (Braak stage 0–IV and Thal stage 0–3) and AD (Braak stage V–VI and Thal stage 4–5), n = 6 per control or AD group, post mortem brain sections from the hippocampal formation with lateral geniculate body and tail of the caudate nucleus region were deparaffinized by washing three times with xylene (3 minutes each), two times with 100% EtOH (2 minutes each), 95% EtOH (2 minutes), 70% EtOH (2 minutes), and 5 minutes of washing in running water. Next, these sections were immersed in a glass jar containing sodium citrate buffer for 30 minutes in a water bath (95°C) for antigen retrieval. Sections were cooled down, gently wiped to remove excess buffer, and encircled using a PAP pen. Endogenous peroxidase activities were blocked by incubating the sections in 3% H 2 O 2 solution for 10 minutes followed by washing with Tris‐buffered saline two times. In addition to formalin‐fixed human post mortem brain tissues, we performed IHC analysis on free‐floating mouse brain sections, following previous reported methods. 58 Prepared brain sections were blocked with blocking buffer (5% donkey serum, 0.3% triton X‐100 in PBS) for 1 hour, followed by overnight incubation with primary antibodies GBF1 and 6E10, at 4°C. Then, primary antibodies were removed, and glass sections were washed with PBS (four times) and incubated with appropriate mouse and rabbit secondary antibodies for 2 hours at room temperature. Thereafter, slides were washed with PBS (four times) and covered with cover slips after adding Prolong Gold antifade mounting reagent ( P36934 , Invitrogen), and then analyzed under the Nikon C2 confocal microscope. Confocal images were analyzed using Imaris v10.0 (Oxford Instruments). Regions of interest were selected, and background subtraction was applied uniformly across all samples. Co‐localization between APP and TGN46 signals was quantified using the Coloc module. Automatic thresholding based on the Costes method was used to minimize bias, and the correlation coefficient ( R ) was calculated to evaluate the linear correlation of proteins of interest voxel intensities within each region, followed by R values (ranging from –1 to +1) averaged across independent biological replicates for statistical analysis. For fluorescence normalization measurements, fluorescence surfaces were generated using the Surfaces module with fixed smoothing (0.3–0.5 µm) and background subtraction set to the estimated object diameter with Imaris 10.0. Thresholds were determined from representative images and applied uniformly across samples; objects <0.5 µm 3 or touching borders were excluded. For each surface, mean fluorescence intensity (µm 2 ) was extracted from the Statistics tab. Local background was measured in a 1 to 2 µm perisurface shell and subtracted to yield corrected mean intensity. The same parameters were batch‐applied to all datasets, and data were exported to CSV for averaging per biological replicate. Next, intensities were normalized to reference channels. 2.14. OptiPrep subcellular fractionation analysis OptiPrep subcellular fractionation analysis was performed following methods that have been previously published. 59 , 60 In brief, cells grown in 15 cm diameter plates were homogenized in 1 mL homogenization buffer (10 mM Hepes, pH 7.4, 1 mM EDTA, 0.25 M sucrose with protease and phosphatase inhibitors) using a Dounce homogenizer. The homogenates were then centrifuged at 900 × g for 15 minutes to collect the post‐nuclear supernatant, which was then resuspended in 1 mL homogenization buffer and placed at the bottom of an ultracentrifuge tube (14 × 89 mm). One milliliter each of the 10 different gradient OptiPrep solutions in cold homogenization buffer was then overlaid successively over the supernatant (30%–7.5% gradient; 2.5% intervals). The gradients were centrifuged at 125, 000 × g for 18 hours at 4°C in a SW 41 rotor (Beckman Instruments). Fractions (900 µL) were sequentially collected from the top of the ultracentrifuge tubes (F1–F12) and analyzed by WB analysis through probing with antibodies for APP and different subcellular biomarker proteins. 2.15. Molecular docking The X‐ray crystal structure of the Sec7 domain of GBF1 (PDB code: 1RE0) was downloaded from the Protein Data Bank ( www.rcsb.org ). 61 PyMOL was used to remove the original ligand and the water molecules. The AUTODOCK 4.2 program was used for the docking study of two GBF1 inhibitors, GCA and AG1478. Each docked system was performed by 200 runs of the Autodock search by the Lamarckian genetic algorithm (LGA). A cluster analysis was performed on the docking results using a root mean square (RMS) tolerance of 1.0, and the lowest energy conformation of the highest populated cluster was selected for analysis. Graphic manipulations and visualizations were done by AutoDock Tools (ADT; version 1.5.6) or Discovery Studio 2.5. 2.16. Data analysis Our statistical analysis was performed based on 80% power at a type I error 5% comparing differences of protein levels from treatment groups to control. p values ≤ 0.05 were considered statistically significant. To assess the statistical significance, two‐tailed Student t tests were performed, as appropriate, to reveal differences comparing one treatment group to control, and one‐way analysis of variance (ANOVA) with Dunnett multiple comparison test was used for more than two treatment groups compared to control. 3. RESULTS 3.1. Neuropathological and genetic alterations of GBF1 in AD To investigate the potential association of GBF1 in AD, we first used human post mortem brain tissues of AD, which were applied to an IHC study using previously reported methods. 33 We assessed potential pathological changes of GBF1 related to AD hallmark amyloid neuropathology compared to non‐AD control brain samples ( n = 6 per group), with neuropathological information listed in Table S1 in supporting information. Particularly, we used Aβ antibody, 6E10, and Thioflavin‐S (ThS), a compound responsive to β‐sheet structured protein aggregates, with the GBF1 antibody. Our data revealed Aβ deposits and ThS‐positive aggregation in AD brains, as expected, and an increase in GBF1 signal intensity in AD brains (Figure 1A,B ). Interestingly, we found pathological alterations of GBF1 characterized by close interaction with Aβ and ThS‐positive aggregates (Figure 1C,D ). These data suggested a possible association of GBF1 and protein aggregation in AD neuropathology. FIGURE 1. Open in a new tab Pathological alterations of GBF1 in AD brains. A, GBF1 immunofluorescence in human AD and control brain sections. B, Average intensities of GBF1 were measured. GBF1 and 6E10 antibody immunofluorescence in human AD and control brain sections. C and D, Human paraffin‐embedded brain sections of AD and non‐AD were probed with 6E10, GBF1, and Thioflavin‐S (ThS) and images were captured in different laser channels. Results shown as mean ± standard error of the mean. Statistical analysis was performed using two‐tailed Student t test (* p ≤ 0.05, ** p ≤ 0.01, ns = non‐significant). AD, Alzheimer's disease; GBF1, Golgi brefeldin A resistant guanine nucleotide exchange factor 1. To elucidate the molecular association of GBF1 in AD, we used the well‐characterized and previously reported 5XFAD mouse model, which expresses FAD genes with APP Swedish/Florida/London and PSEN1 M146L/L286V mutations. 34 , 35 The primary neuropathology of these animals is well characterized with Aβ pathology, including both intraneuronal amyloid deposition and extracellular amyloid deposition. 34 5XFAD mice and their littermate non‐transgenic wild type (WT) mice (8‐month‐old male and 15‐month‐old female; n = 2 per group) were investigated. After transcardial perfusion under isoflurane anesthesia, brains were dissected and applied to post mortem IHC analysis focusing on GBF1 and Aβ pathology. Our IHC analysis of 8‐month‐old male 5XFAD and WT mice showed GBF1 with characteristic cytosolic, peri‐nucleus features, representing the physiological condition of GBF1, which was as expected and consistent with previous reports (Figure 2A ). Additionally, 5XFAD showed 3D6 antibody‐responsive amyloid plaques in the brain, but not in WT animals (Figure 2B ). Interestingly, 5XFAD animals showed dense GBF1 features, which form amyloid plaque‐surrounding deposition, presenting an AD‐related pathological condition closely associated with amyloid deposition in the brain. Furthermore, we conducted z‐stack orthogonal analysis, which showed the characteristic features of GBF1 in intact and physiological conditions of WT animals and GBF1 deposition with amyloid plaques in 5XFAD animals (Figure 2C,D ). Moreover, we validated our findings by z‐stack frame‐by‐frame analysis of GBF1 in association with amyloid deposition in brain sections in 8‐month‐old male 5XFAD, which showed GBF1 pathological alterations in association with hallmark amyloid neuropathology (Figure S1A in supporting information). Given that aging and sex are major risk factors of AD, we further validated our findings in 8‐month‐old male 5XFAD mice and performed immunofluorescence z‐stack frame‐by‐frame analysis of 15‐month‐old female 5xFAD mice, which indeed showed GBF1 pathological changes in association with amyloid plaques (Figure S1B ). Furthermore, while we found GBF1 pathological alterations in association with hallmark amyloid neuropathology in brain sections of 15‐month‐old female 5XFAD mice (Figure S2A in supporting information), we did not find these pathological changes in 15‐month‐old female WT mice (Figure S2B ). Collectively, our neuropathological studies in both human and mouse brains showed a strong association of GBF1 with AD, linked to amyloid pathology. FIGURE 2. Open in a new tab Pathological alterations of GBF1 in close association with hallmark amyloid neuropathology in 5xFAD mouse brains revealed by immunohistochemistry. A–D, Animal brain sections of 8‐month‐old male 5XFAD or WT mice were applied to fluorescent immunohistochemistry analysis (A, B) and z‐stack analysis (C, D) using confocal microscopy. 5XFAD animals expressing amyloid pathology in the brain were used to study the pathological changes of GBF1 compared to WT animals by IHC through 3D6 (for amyloid pathology), GBF1, and Hoechst (nucleus). AD, Alzheimer's disease; GBF1, Golgi brefeldin A resistant guanine nucleotide exchange factor 1; IHC, immunohistochemistry; WT, wild type. Additionally, we analyzed two family‐based and one multi‐ethnic case–control WGS datasets from AD cohorts (Table S2 in supporting information). We performed deep (>40×) WGS of the large family‐based AD sample from the NIMH, 62 consisting of 446 families, each with at least two affected as well as unaffected siblings. In addition, we gained access to the WGS data from the ADSP. 63 The combined family‐based sample consisted of 1559 affected and 881 unaffected subjects (82 subjects had a missing phenotype). The case–control sample consisted of 10,281 affected and 14,600 unaffected unrelated individuals of different ancestral background from the ADSP. This dataset was split into four subsets based on population structure: NHW, AA, admixed HISP, and ASIA. We then sought to identify SNVs in GBF1 that are associated with AD. By using the family‐based dataset as a discovery dataset, we identified 11 SNVs that showed nominal significance ( p ≤ 0.05, Table 1 ). Out of those, the most significant rare variant associated with AD was an intronic variant rs72845655 (minor allele G, z = –2.930, p = 0.003) identified in the family‐based sample, which predominantly consisted of individuals of European ancestry. This variant was carried in 38 families in NIMH and 28 families in NIA. In the independent case–control dataset, we did not observe a significant association ( p = 0.5 in NHW, p = 0.731 in AA, p = 0.762 in HISP, and p = 0.99 in ASIA). When combining families with the NHW case–control dataset in a meta‐analysis, rs72845655 achieved a p value of 0.18. Thus, our genetic findings warrant future validation in larger cohorts as well as the assessment of candidate variants linked with amyloid pathology to further validate the effects of GBF1 on the amyloid pathology of AD. Collectively, our exploratory genetic analysis identified GBF1 suggestive variants associated with AD. TABLE 1. AD association analysis of GBF1 . Family‐based dataset (NIMH + NIA) NHW ADSP case‐control dataset AA ADSP case‐control dataset HISP ADSP case‐control dataset ASIA ADSP case‐control dataset Meta analysis, order: fams, nhw, aa, hisp, asia Meta analysis, order: fams, nhw rsid ea eaf. fams z. fams p. fams eaf. nhw z. nhw p. nhw eaf. aa z. aa p. aa eaf. hisp z. hisp p. hisp eaf. asia z. asia p. asia z ‐score p ‐value Direction z‐ score p ‐value Direction Annotation Rs 72845655 G 0.026 −2.93 0.003 0.026 −0.666 0.505 0.003 0.344 0.731 0.013 −0.303 0.762 0.001 −0.013 0.989 −0.904 0.366 −−+−− −1.341 0.180 −− intron_ variant Rs 567365905 G 0.002 −2.414 0.016 0.003 −0.164 0.870 NA NA NA 0.001 −0.337 0.736 NA NA NA −0.772 0.440 −−?−? −0.731 0.465 −− downstream gene variant Rs 191587776 T 0.008 −2.402 0.016 0.006 0.925 0.355 0.001 1.310 0.190 0.003 0.950 0.342 0.001 −0.014 0.989 1.275 0.202 −+++− 0.33 0.741 −+ intron_ variant Rs 187303832 T 0.006 2.371 0.018 0.005 −0.825 0.410 0.001 1.088 0.277 0.002 −0.207 0.836 0.003 −0.408 0.683 0.039 0.969 +−+−− −0.24 0.811 +− intron_ variant Rs 936216796 T 0.001 2.219 0.027 0.001 0.652 0.514 NA NA NA NA NA NA NA NA NA 1.16 0.246 ++??? 1.16 0.246 ++ intron_ variant Rs 2479550 G 0.101 −2.155 0.031 0.109 0.943 0.346 0.017 1.160 0.246 0.057 1.013 0.311 0.025 −0.256 0.798 1.223 0.222 −+++− 0.405 0.685 −+ intron_ variant Rs 571242867 A 0.006 −2.118 0.034 0.004 2.717 0.007 NA NA NA NA NA NA 0.009 0.379 0.704 2.072 0.038 ‐+??+ 2.125 0.034 ‐+ intron_ variant Rs 180677952 T 0.006 −2.118 0.034 0.004 2.714 0.007 0.001 1.443 0.149 0.001 −0.547 0.585 0.009 0.538 0.591 1.826 0.068 −++−+ 2.135 0.033 −+ intron_ variant Rs 183235135 G 0.006 −2.118 0.034 0.004 2.721 0.007 0.001 2.019 0.044 0.002 −1.299 0.194 0.009 0.379 0.704 1.588 0.112 −++−+ 2.142 0.032 −+ intron_ variant Rs 138505789 C 0.018 2.108 0.035 0.020 −0.197 0.844 0.005 0.645 0.519 0.010 −0.835 0.403 0.018 −0.069 0.945 −0.034 0.973 +−+−− 0.308 0.758 +− intron_ variant Rs 181476741 T 0.018 2.108 0.035 0.020 −0.245 0.806 0.005 0.641 0.522 0.010 −0.835 0.404 0.018 −0.065 0.948 −0.064 0.949 +−+−− 0.261 0.794 +− intron_ variant Open in a new tab Note : Single nucleotide variants in GBF1, which showed a significant ( p <0.05, in bold) association with AD in the family‐based dataset. Abbreviations: AA, African Americans; AD, Alzheimer's disease; ADSP, Alzheimer's Disease Sequencing Project; ASIA, Asian subset; ea, effect allele; eaf, effect allele frequency; fams, NIMH + NIA family‐based dataset; HISP, Hispanic subset; NA, not available; NHW, non‐Hispanic Whites; NIA, National Institute on Aging; NIMH, National Institute of Mental Health. 3.2. Downregulation of GBF1 decreases Aβ levels and impairs APP maturation After our neuropathological and genetic identification of GBF1 in association with AD and amyloid pathology, we next asked if modulation of GBF1 may change Aβ levels. First, we used GCA, a previously reported GBF1 inhibitor, which was well reported, and analyzed its effects on Aβ levels and/or APP processing using cell models of AD. We initially used 7PA2 cells, a CHO cell line stably expressing APP V717F , the Indiana FAD mutation 43 with high Aβ expression. 64 We conducted the LDH assay to detect cell viability, in which 7PA2 cells were treated with different doses of GCA or control for 18 hours. We did not find significant changes comparing GCA groups and control (Figure S3 in supporting information; n = 3; p >0.05). Cells were then treated with GCA at concentrations of 0, 3, 6, 12, 25, and 50 µM overnight, and the conditioned medium was subsequently analyzed for Aβ‐triplex concentration—that is, Aβ40, Aβ38, and Aβ42—which showed that GCA dose‐dependently and robustly decreased the levels of all Aβ species (Figure 3A ). Notably, the two highest GCA doses, 25 and 50 µM, almost completely abolished Aβ peptide production compared to the control treatment (0 µM; Figure 3A ). GCA‐related Aβ reduction was determined with statistical significance analyzed by one‐way ANOVA with Dunnett multiple comparison test (*, P ≤ 0.05; **, P ≤ 0.01; n = 3). We analyzed IC50s of Aβ species using GraphPad by a non‐linear fit of data mechanism (log[inhibitor] versus response – Variable slope [four parameters]), showing close to 5 µM of each Aβ species (Aβ40/5.4 µM, Aβ38/5.6 µM, and Aβ42/5.2 µM; Figure S4A in supporting information). FIGURE 3. Open in a new tab GCA decreases Aβ levels by affecting APP maturation. A, B, APP Indiana ‐expressing CHO cells (7PA2) were treated with different concentrations of GCA, harvested after 24 hours and analyzed by Aβ‐MSD assay (A) and WB analysis (B). A, Conditioned medium was analyzed by Aβ‐MSD study to detect the Aβ40, Aβ38, and Aβ42 levels. N = 3; mean ± SE; *, p ≤ 0.05; **, p ≤ 0.01. p values were determined by one‐way ANOVA with Dunnett multiple comparison test. B, Cell lysates were applied to WB analysis using the A8717 primary antibody to detect APP levels. β‐actin was used as a control protein. Cell medium was applied to WB using the 6E10 antibody to detect the sAPPα protein. Quantification and statistical analysis are shown below. N = 4; mean ± SE; *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001. p values were determined by one‐way ANOVA with Dunnett multiple comparison test. C, Immunofluorescence microscopy analysis of the impacts of GCA on APP trafficking in APP‐GFP expressing H4 cells. Cells were treated with control (top) or 12.5 µM GCA (bottom) for 1 hour and applied to immunostaining to examine APP expression by the immunofluorescence microscopy analysis. Aβ, amyloid beta; ANOVA, analysis of variance; APP, amyloid precursor protein; CHO, Chinese hamster ovary; GCA, Golgicide A; GFP, green fluorescent protein; MSD, Meso Scale Discovery; sAPPα, secreted amyloid precursor protein alpha; SE, standard error; WB, western blot. Given its profound effects on all three Aβ analytes, we then studied whether GCA affects APP maturation in 7PA2 cells. Cell lysates were collected from the above dose‐response experiment and analyzed by WB using the APP8717 antibody, which targets the C‐terminus of APP. β‐actin was used as a control for variation in loading between gel lanes. GCA treatment led to a dose‐dependent decrease of the mature APP, an increase in the intermediate APP proteins, and an increase of a protein at the 188 kDa molecular weight marker that may be the previously reported APP dimers, 65 , 66 , 67 , 68 here labeled as APP* (Figure 3B ). Two additional proteins were also identified above and below the APP* at GCA concentrations 25 and 50 µM, which may represent APP proteins with post‐translational modifications. Strikingly, at GCA treatment concentrations 12 µM, the expected double band representing mature and immature APP was replaced with a single band with intermediate (inter) molecular weight, thus described as intermediate APP proteins. The effect of GCA promoting intermediate APP moieties was similar to previously reported effects of compounds that affect APP trafficking, including curcumin 16 and brefeldin A (BFA). 69 We confirmed altered APP maturation by measuring sAPPα levels in cell culture medium by WB and probing with the 6E10 antibody. We showed that GCA dose‐dependently decreased sAPPα levels (Figure 3B,C ). Similar to Aβ concentration, 25 and 50 µM GCA almost completely abolished sAPPα proteins, supporting a downregulation of mature APP proteins and the generation of intermediate APP proteins that are not cleaved by α‐secretase. Protein quantification and statistical analyses were performed to further investigate these GCA‐related changes, which showed statistical significance on both mature APP (APPma), immature (APPim), and sAPPα (*, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001; n = 4). We also used lower doses to analyze dose‐dependent effects of GCA. 7PA2 cells were treated with GCA of different doses (0, 0.625, 1.25, 2.5, 5, and 10 µM) for 24 hours. Cell lysates were collected and applied to WB to visualize APP, with β‐actin used as a control protein. It showed that GCA started to affect APP maturation from a concentration close to 5 to 10 µM (Figure S4B ). Overall, these data suggested that GCA decreases Aβ levels by retarding APP maturation. Next, we validated the effects of GCA on APP maturation using different molecular methods and various cell models. Our ICC analysis was performed to visually analyze APP expression and localization in human neuroglioma H4 cells stably expressing APP‐GFP, which were treated with control or 12.5 µM GCA for 1 hour (Figure 3D ). The concentration and treatment time were similar to previous reports. 22 , 26 We found that the APP expression pattern in cells treated with control showed the punctate patterns characteristic of Golgi resident proteins, as anticipated. Conversely, the APP expression in cells treated with GCA was a smeared pattern circling the nucleus, suggesting a disruption of the Golgi architecture and localization of APP to the ER. We analyzed the number of colocalization (Figure S5A in supporting information) and Manders coefficients (Figure S5B ) for APP colocalizing with GBF1, comparing cells treated with control or GCA, which revealed significant increases in both colocalization numbers and Manders coefficients as a function of GCA. Moreover, these data support that GCA leads to redistributions of Golgi‐associated proteins and APP to the ER. To further validate the effects of GCA on disrupting the Golgi architecture and relocating APP, we performed immunofluorescence microscopy analysis of APP‐GFP expressing H4 cells treated with 12.5 µM GCA or control overnight using the trans‐Golgi network marker protein TGN46 (Figure S6A in supporting information). Indeed, in the presence of GCA, we found higher numbers of colocalized voxels (Figure S6B ) and Manders coefficient (Figure S6C ) as a function of GCA, which confirmed the effects of GCA on influencing the Golgi architecture. Collectively, our ICC findings on GCA support the results from WB analyses. We next performed the previously reported subcellular fractionation analysis 59 , 60 to investigate the effects of GCA on APP levels in the ER‐related subcellular compartment. Human neuroglioma H4 cells stably expressing APP were treated with control or 25 µM GCA for 24 hours and applied to the OptiPrep analysis to examine mature and immature APP levels in 12 different fractions. β‐actin was used as a control protein (Figure S7A in supporting information). Cells treated with GCA showed increased APP levels in fractions 5 to 11, reflecting primary ER residence, which was confirmed by BiP, an ER marker protein (Figure S7B ). Quantification and statistical analysis showed that GCA increased ER‐resident APP levels by 99.4% compared to control ( p ≤ 0.05; n = 3; two‐sided Student t test), consistent with APP expression to ER‐like patterns from our ICC results. Subsequently, we studied whether downregulation of GBF1 using a different GBF1 inhibitor may block APP maturation. AG1478 is a reported GBF1 inhibitor that is a functional and structural analog of GCA. 26 , 27 The structures of GCA and AG1478 have been demonstrated (Figure 4A ). A molecular docking study of GCA and AG1478 (Figure 4B ) showed that the two compounds can occupy the binding site of the Sec7 domain of GBF1. The polycyclic skeleton of compounds interacted with amino acid Phe51 and Ser198 via π–π interaction. Hydrophobic interactions could be observed between the ligands and Val65, Asn52, Glu54, Trp78, Tyr334, and Asp67. The pyridine ring or the benzene ring was involved in an interaction with Val53 and Glu54. The halogen or the methoxy group interact with residues of Ser198 and Gly196 simultaneously. Human neuroblastoma H4 cells stably expressing APP751 were treated with GCA or AG1478 at different doses (0, 10, and 100 µM) for 3 hours and harvested. Cell lysates were assessed by WB and probed with G12A for APP proteins, and the cell medium was probed with 6E10 antibody for sAPPα protein. We showed that GCA (10 and 100 µM) and AG1478 at 100 µM led to downregulation of mature APP and upregulation of intermediate or immature form of APP (Figure 4C ; ***, p ≤ 0.001; n = 3). Furthermore, GCA (10 and 100 µM) and AG1478 at 100 µM significantly downregulated sAPPα levels in the medium (Figure 4C ; *, p ≤ 0.05; ***, p ≤ 0.001; n = 3). GCA more potently blocked APP maturation, with 10 µM sufficient to generate an intermediate form of APP compared to control, while 100 µM AG1478 was required before the intermediate APP proteins appeared. These data recapitulate the effects of GCA treatment on Aβ changes and APP trafficking in several different cell types. FIGURE 4. Open in a new tab Both GCA and a different GBF1 inhibitor, AG1478, impair APP maturation. A, Structure of GCA (left) and AG1478 (right). B, GCA (left) and AG1478 (right) interact with residues in the binding site of the Sec7 domain of GBF1 (PDB code: 1RE0). C, H4‐APP751 cells were treated with different doses of GCA (left) or AG1478 (right) for 3 hours and then harvested. Cell lysates and medium were applied to WB analysis and probed with G12A or 6E10 to detect APP or sAPPα, respectively, followed by quantification and statistical analysis. N = 3 for each treatment group; Mean ± standard error; *, p ≤ 0.05; ***, p ≤ 0.001; p values were determined by one‐way analysis of variance with Dunnett multiple comparison test. APP, amyloid precursor protein; GBF1, Golgi brefeldin A resistant guanine nucleotide exchange factor 1; GCA, Golgicide A; sAPPα, secreted amyloid precursor protein alpha; WB, western blot. As a GEF of the small GTPase ARF1, GBF1 mediates protein trafficking between the ER and the Golgi. It has been previously reported that GCA specifically target the Sec7 domain of GBF1. 22 Here, we investigated if GCA or AG1478 may inhibit Arf1 activity through the Sec7 domain of GBF1 using the active Arf1 pull‐down and detection kit. We used the kit to analyze the effects of GCA or AG1478 on pull‐downing active Arf1 from 7PA2 cells, which were first treated with GCA or AG1478 at different doses for 1 hour. Then, one portion of lysates was applied to Arf1 pull‐down analysis, including GTPγS and GDP, respectively, used as the positive and negative control of Arf1 activation, and the other portion lysates were used to reveal total Arf1. Lysates were applied to WB analysis using the Arf1 antibody to reveal active and total Arf1 proteins. Regarding active Arf1 proteins, while AG1478 at 25 and 50 µM did not strongly reduce Arf1 activation, GCA of high doses at 25 µM reduced active Arf1 proteins from the pull‐down samples (Figure S8A in supporting information). Furthermore, to test the potential toxicity, conditioned medium was applied to the LDH assay, which showed that GCA and AG1478 of the tested doses showed reduced LDH readout compared to control, supporting high cellular tolerance to these molecules (Figure S8B ). Thus, GCA displayed Arf1 pull‐down effects stronger than AG1478, and our data were consistent with previous findings suggesting the effects of GCA on specifically targeting the Sec7 domain of GBF1. 22 To further investigate the impact of GBF1 pharmacological inhibition on APP maturation, 7PA2 cells were transfected with GBF1 siRNA (1 µM) using the PEI transfection reagent. Cell lysates were then collected and analyzed by WB to assess GBF1 and APP proteins. Lysates from cells transfected with GBF1 siRNA showed a significant decrease in GBF1 levels (Figure S9A in supporting information) as well as in mature and immature APP levels compared to the PEI alone control samples (Figure S9B ; *, p ≤ 0.05; n = 3). Moreover, we assessed the effects of GBF1 silencing by the ICC analysis on 7PA2 cells, which were transfected and applied to immunostaining using antibodies for 6E10 and GBF1, in combination with Hoechst (Figure S9C ). Our ICC analysis showed significant reductions of GBF1 and APP as a function of GBF1 knockdown (Figure S9D ), which were consistent with WB results. Collectively, the effects of genetic knockdown of GBF1 on APP were reflective of the effects of pharmacological inhibition by GCA and AG1478. In addition to the analysis of silencing GBF1 , we investigated the effects of GBF1 overexpression on APP metabolism by ICC analysis. Particularly, 7PA2 cells were transfected in the presence or absence of GBF1 cDNA overnight, which were applied to the ICC analysis, probed with antibodies of 6E10 and GBF1, in combination with Hoechst (Figure S10A in supporting information). We found that GBF1 cDNA not only significantly increased the signal of GBF1, as expected, but also significantly increased the signal of APP probed by 6E10 (Figure S10B ). Thus, the effects of GBF1 genetic overexpression were in contrast with those of silencing GBF1 , which further support the effects of pharmacological inhibition of GBF1. Next, we tested whether the effects of AG1478 on APP metabolism are associated with GBF1, but not epidermal growth factor receptor (EGFR), considering previous findings showing inhibitory effects of AG1478 on EGFR, 70 in addition to its effects on GBF1. Particularly, we analyzed erlotinib, a reported EGFR‐specific inhibitor. 71 7PA2 cells were treated with erlotinib of different doses (0, 5, 10, 50, and 100 µM) or AG1478 (10 and 100 µM) for 3 hours and then cell lysates were collected and applied to WB analysis. While AG1478 reduced mature APP levels at the high dose 100 µM, as expected, erlotinib at all tested low and high doses did not display effects reducing mature APP levels (Figure S11 in supporting information). Collectively, our data suggest that AG1478 reduced APP maturation through its effects on GBF1, but not EGFR. We further characterized cell responses during pharmacological inhibition of GBF1 through a shorter time treatment and a reversibility experiment. Particularly, we first showed that 10 µM GCA significantly reduced both Aβ40 and Aβ42 levels after 3 hours of treatment in H4‐APP751 cells (Figure S12 in supporting information; *, p ≤ 0.05; ***, p ≤ 0.001; n = 3). We next performed a reversibility experiment to ask if the inhibitory effects of GCA and AG1478 on APP maturation can be reversed. H4‐APP751 cells were first treated with medium containing 100 µM GCA or AG1478 for 3 hours and then replaced with regular medium and grown for another 3 hours. Our quantitative WB on cell lysates using the G12A antibody showed that GCA or AG1478‐related inhibition of APP maturation was fully reversed (Figure S13 in supporting information). These data not only suggested high viability of treated cells and quick responses of cells related to pharmacological inhibition of GBF1, but also further validated GBF1 as a key regulator of Aβ metabolism and APP trafficking. 4. DISCUSSION In this study, we show that GBF1 inhibition leads to decreased Aβ levels through restricting APP maturation and trafficking. We found that GBF1 inhibitors led to robust increases of intermediate APP and APP dimers, further supporting the hypothesis that blocking APP maturation precludes the generation of Aβ peptide. Our results highlight a novel mechanism underlying AD pathogenesis mediated by GBF1 through regulation of Aβ levels, which could serve as a potential target to better understand AD. ARF proteins are small GTPases in the Ras superfamily that play a key role in regulating Golgi membrane trafficking. GBF1 is an ARF‐associated GEF that mediates protein trafficking between the ER and the Golgi. Despite the discovery of small molecule inhibitor of GBF1, their functions on APP trafficking and Aβ have only begun to be elucidated. Here, we investigated the effects of GBF1 pharmacological inhibitors, including the GCA and AG1478 (a tyrosine kinase inhibitor), on APP maturation using cell models of AD. GCA is related to a significant downregulation of Aβ in the medium and attenuation of intracellular APP maturation as assessed by multiple molecular approaches, including WB, immunofluorescence analysis, and subcellular protein fractionation. Both GCA and AG1478 are well‐reported GBF‐1 inhibitors with different levels of specificity. GCA is a more specific inhibitor and has been reported with high specificity and high potency as well as reversible activity in inhibition of GBF1. 22 , 26 Different from GCA, AG1478 not only inhibit GBF1 in a Sec7 domain‐dependent manner, 27 but also is a highly potent inhibitor of the EGFR tyrosine kinase 72 and Arf1‐binding property. 26 Comparing both molecules, we observed stronger effects of GCA in blocking APP maturation. The differences of these two molecules were also reported by a study that showed differential effects of these compounds in enterovirus replication; the effects of AG1478, but not those of GCA, could be normalized by overexpression of Arf1. 26 Because our study is focused on biochemical validation of our genetic findings of GBF1, the effects of GCA on specific engagement of GBF1 toward AD‐related Aβ/APP changes support our results and highlight GBF1‐related changes in AD. Our results suggest that GBF1 inhibitors provide a unique and powerful tool to elucidate the mechanisms underlying assembly and transport through the Golgi apparatus. We showed that inhibition of GBF1 via GCA abolished the generation of Aβ proteins, which was in line with a recent study showing that silencing GBF1 led to an increase in intracellular Aβ levels. 32 Additionally, the GBF1 inhibitors in this study used mechanisms different from BFA. 69 Specifically, BFA binds the ARF1–GDP–Sec7 complex, which may inhibit the Sec7 guanine nucleotide exchange activity and prevent ARF activation. 61 , 73 BFA thus displays pleiotropic effects on intracellular organelles other than the Golgi. 27 Different from BFA, the GBF1 inhibitors GCA and AG1478 target the cis Golgi without affecting the endosomal compartment. 27 Importantly, the mechanism of GBF1 in reducing Aβ levels is different from conventional inhibitors of β‐ and γ‐secretases. Thus, the GBF1 inhibitors in our current study will be useful molecules to dissect and characterize mechanisms related to changes in Aβ levels and APP trafficking. Furthermore, our findings may be used to elucidate the mechanisms of other GEF‐related disorders. Modulatory roles of GEFs with regard to cell survival suggest that GEFs may mediate a critical balance between neurotoxicity and neuroprotection, 74 and dysregulated activity of GEF proteins has been associated with neuronal loss in neurodegenerative and neurodevelopmental disorders. 74 Specific GEF proteins that have been implicated in neurological pathology include Rho guanine nucleotide exchange factor (RGNEF/p190RhoGEF), 74 brefeldin A‐inhibited GEF2 protein (BIG2), IQSEC family members, 75 , 76 among others. These GEF proteins may play critical functions in synaptic scaffolds, 75 , 77 cell survival, 78 neurogenesis, and neurodevelopment. 74 RGNEF is a 190 kDa GEF and an RNA‐binding protein involved in the pathogenesis of amyotrophic lateral sclerosis (ALS), 74 colon carcinoma, and fibroblast cell motility. 78 BIG2, encoded by the gene ARFGEF2 , affects vesicle and membrane trafficking from the trans‐Golgi network to the plasma membrane. Inhibition of BIG2 reduces transport of essential structural proteins—including E‐cadherin and β‐catenin—from the Golgi apparatus. Additionally, mutations in ARFGEF2 result in microcephaly and periventricular heterotopia by affecting vesicle trafficking in neural progenitors, which subsequently disrupts their proliferation and migration in the human cerebral cortex. 20 , 79 An IQSEC2 mutation is associated with intellectual disability and autism by downregulation of surface AMPA receptors. 77 Collectively, GBF1 functions as a complementary protein member with the other previously reported GEFs that are critical in the pathogenic events of neurological disorders. Our study has several limitations. First, our sample size in the WGS analysis of GBF1 was limited, and we only observed nominally significant associations in the family‐based dataset. While 1 out of 11 variants showed a lower p value ( p = 0.003), we could not replicate it in the ADSP case–control dataset. Larger sample sizes are needed to confirm these findings. Second, our candidate rare variants are intronic. This could indicate that it is not the functional variant, which alters GBF1 , but rather a correlated variant in LD. Additional genetic and functional studies are required in the future. Although previous findings 22 and our Arf1 pull‐down results support GCA‐associated specific inhibition, which has provided valuable insight into GBF1 as a significant regulator of APP processing and Aβ generation, GCA displays relatively low potency in retarding APP trafficking and reducing Aβ levels, starting to be effective from 5 to 10 µM. High doses may cause potential off‐target effects on other ARF‐GEFs and Golgi function, which remain unexplored and require future studies. Given GBF1's fundamental role in Golgi assembly and vesicular trafficking, therapeutic interventions may be cautiously approached, with careful evaluation of potential neuropathological consequences of GBF1 disruption. We present neuropathological and molecular evidence of GBF1 underlying AD, which may provide potential aspects for clinical development. While our family‐based genetic analysis identified 11 GBF1 variants showing nominal significance ( p ≤ 0.05) with AD, including the intronic variant rs72845655 ( p = 0.003), these associations did not survive correction for multiple testing and require validation in larger cohorts to establish statistical significance beyond the nominal threshold. We speculate that GBF1 ‐related genetic variants in AD may associate with amyloid pathology changes in the brains of subjects carrying these variants. In future studies, analysis of humans carrying these variants and animals expressing these variants or with GBF1 deficiency, linked to Aβ/APP changes, should be warranted. Additionally, our data support studies of synthesizing GCA analogs and characterizing recently reported new Arf1 inhibitors 80 for APP trafficking and Aβ levels. Our results also warrant studies combining pharmacological and genetic approaches—such as GBF1 knockdown or knockout cell or animal models—which will be essential to further extend GBF1‐associated effects on amyloid pathology. When GBF1 ‐related genetic variants and their links to AD neuropathology are validated, GBF1 could emerge as a therapeutic target amenable to either genetic correction or pharmacological modulation with GBF1‐specific molecules like GCA or its analogs. Additionally, to assess GBF1 expression in relation to amyloid pathology in AD brains, it may be useful to use positron emission tomography (PET) to detect GBF1 expression in the brain, using GCA analogs as molecular imaging markers. The molecular imaging probes will first be developed in animal models of AD to assess safety and dosing, before they can be tested through a human trial. Furthermore, given the identification of mutations in GEF family protein‐encoding genes related to human neurological disorders, 74 , 75 , 76 there should be an effort to identify potential mutations in these genes related to AD. In summary, our study provided mechanistic evidence supporting the association between GBF1 and the pathogenesis of AD. GBF1 functions as a key molecule that regulates Aβ levels and APP trafficking; furthermore, inhibitors of GBF1 can be used as resourceful molecules to characterize Aβ levels and APP trafficking underlying the pathophysiology of AD. Collectively, our results support that GBF1 is a molecular target that may not only provide useful knowledge to increase our understanding of APP trafficking, but may also elucidate the pathogenesis of AD. CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. Author disclosures are available in the supporting information . CONSENT STATEMENT All human tissue samples were obtained with informed consent prior to tissue collection from participants if enrolled ante mortem or legal guardians if post mortem . Supporting information Supporting Information ALZ-22-e71271-s002.pdf (1.1MB, pdf) Supporting Information ALZ-22-e71271-s001.docx (4.5MB, docx) ACKNOWLEDGMENTS The authors wish to thank Dr. Wilma Wasco for providing advice on the paper. The authors would like to thank the staff from the National Institute of Mental Health (NIMH) Divisions of Clinical and Treatment Research (DCTR) and Epidemiology and Services Research (DESR), including David Shore, MD; Mary Farmer, MD, MPH; Debra Wynne, MSW; Steven O. Moldin, PhD; Darrell G. Kirch, MD (1989–1994); Nancy E. Maestri, PhD (1992–1994); William Huber (1989–1995); Pamela Wexler (1995–); and Darrel A. Regier, MD, MPH. They would also like to thank the study staff at all three sites and the data management staff at SRA Technologies, Inc., particularly Cheryl McDonnell, PhD, for the care and attention that they paid to all aspects of the study. The authors are also extremely grateful to the families whose participation made this work possible. Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24‐AG041689), funded by the National Institute on Aging. The Alzheimer's Disease Sequencing Project (ADSP) is comprised of two Alzheimer's Disease (AD) genetics consortia and three National Human Genome Research Institute (NHGRI) funded Large Scale Sequencing and Analysis Centers (LSAC). The two AD genetics consortia are the Alzheimer's Disease Genetics Consortium (ADGC) funded by NIA (U01 AG032984), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) funded by NIA (R01 AG033193), the National Heart, Lung, and Blood Institute (NHLBI), other National Institute of Health (NIH) institutes, and other foreign governmental and non‐governmental organizations. The Discovery Phase analysis of sequence data are supported through UF1AG047133 (to Drs. Schellenberg, Farrer, Pericak‐Vance, Mayeux, and Haines); U01AG049505 to Dr. Seshadri; U01AG049506 to Dr. Boerwinkle; U01AG049507 to Dr. Wijsman; and U01AG049508 to Dr. Goate and the Discovery Extension Phase analysis is supported through U01AG052411 to Dr. Goate, U01AG052410 to Dr. Pericak‐Vance, and U01 AG052409 to Drs. Seshadri and Fornage. Sequencing for the Follow Up Study (FUS) is supported through U01AG057659 (to Drs. Pericak‐Vance, Mayeux, and Vardarajan) and U01AG062943 (to Drs. Pericak‐Vance and Mayeux). Data generation and harmonization in the Follow‐up Phase is supported by U54AG052427 (to Drs. Schellenberg and Wang). The FUS Phase analysis of sequence data are supported through U01AG058589 (to Drs. Destefano, Boerwinkle, De Jager, Fornage, Seshadri, and Wijsman), U01AG058654 (to Drs. Haines, Bush, Farrer, Martin, and Pericak‐Vance), U01AG058635 (to Dr. Goate), RF1AG058066 (to Drs. Haines, Pericak‐Vance, and Scott), RF1AG057519 (to Drs. Farrer and Jun), R01AG048927 (to Dr. Farrer), and RF1AG054074 (to Drs. Pericak‐Vance and Beecham). The ADGC cohorts include: Adult Changes in Thought (ACT; U01 AG006781, U19 AG066567), the Alzheimer's Disease Research Centers (ADRC; P30 AG062429, P30 AG066468, P30 AG062421, P30 AG066509, P30 AG066514, P30 AG066530, P30 AG066507, P30 AG066444, P30 AG066518, P30 AG066512, P30 AG066462, P30 AG072979, P30 AG072972, P30 AG072976, P30 AG072975, P30 AG072978, P30 AG072977, P30 AG066519, P30 AG062677, P30 AG079280, P30 AG062422, P30 AG066511, P30 AG072946, P30 AG062715, P30 AG072973, P30 AG066506, P30 AG066508, P30 AG066515, P30 AG072947, P30 AG072931, P30 AG066546, P20 AG068024, P20 AG068053, P20 AG068077, P20 AG068082, P30 AG072958, P30 AG072959), the Chicago Health and Aging Project (CHAP; R01 AG11101, RC4 AG039085, K23 AG030944), Indiana Memory and Aging Study (IMAS; R01 AG019771), Indianapolis Ibadan (R01 AG009956, P30 AG010133), the Memory and Aging Project (MAP; R01 AG17917), Mayo Clinic (MAYO; R01 AG032990, U01 AG046139, R01 NS080820, RF1 AG051504, P50 AG016574), Mayo Parkinson's Disease controls (NS039764, NS071674, 5RC2HG005605), University of Miami (R01 AG027944, R01 AG028786, R01 AG019085, IIRG09133827, A2011048), the Multi‐Institutional Research in Alzheimer's Genetic Epidemiology Study (MIRAGE; R01 AG09029, R01 AG025259), the National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD; U24 AG021886), the National Institute on Aging Late Onset Alzheimer's Disease Family Study (NIA‐ LOAD; U24 AG056270), the Religious Orders Study (ROS; P30 AG10161, R01 AG15819), the Texas Alzheimer's Research and Care Consortium (TARCC; funded by the Darrell K. Royal Texas Alzheimer's Initiative), Vanderbilt University/Case Western Reserve University (VAN/CWRU; R01 AG019757, R01 AG021547, R01 AG027944, R01 AG028786, P01 NS026630, and Alzheimer's Association), the Washington Heights‐Inwood Columbia Aging Project (WHICAP; RF1 AG054023), the University of Washington Families (VA Research Merit Grant, NIA: P50AG005136, R01AG041797, NINDS: R01NS069719), the Columbia University Hispanic Estudio Familiar de Influencia Genetica de Alzheimer (EFIGA; RF1 AG015473), the University of Toronto (UT; funded by Wellcome Trust, Medical Research Council, Canadian Institutes of Health Research), and Genetic Differences (GD; R01 AG007584). The CHARGE cohorts are supported in part by National Heart, Lung, and Blood Institute (NHLBI) infrastructure grant HL105756 (Psaty), RC2HL102419 (Boerwinkle) and the neurology working group is supported by the National Institute on Aging (NIA) R01 grant AG033193. The CHARGE cohorts participating in the ADSP include the following: Austrian Stroke Prevention Study (ASPS), ASPS‐Family study, and the Prospective Dementia Registry‐Austria (ASPS/PRODEM‐Aus), the Atherosclerosis Risk in Communities (ARIC) Study, the Cardiovascular Health Study (CHS), the Erasmus Rucphen Family Study (ERF), the Framingham Heart Study (FHS), and the Rotterdam Study (RS). ASPS is funded by the Austrian Science Fond (FWF) grant numbers P20545‐P05 and P13180 and the Medical University of Graz. The ASPS‐Fam is funded by the Austrian Science Fund (FWF) project I904), the EU Joint Programme – Neurodegenerative Disease Research (JPND) in the frame of the BRIDGET project (Austria, Ministry of Science) and the Medical University of Graz and the Steiermärkische Krankenanstalten Gesellschaft. PRODEM‐Austria is supported by the Austrian Research Promotion agency (FFG; Project No. 827462) and by the Austrian National Bank (Anniversary Fund, project 15435). ARIC research is carried out as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Neurocognitive data in ARIC are collected by U01 2U01HL096812, 2U01HL096814, 2U01HL096899, 2U01HL096902, 2U01HL096917 from the NIH (NHLBI, NINDS, NIA and NIDCD), and with previous brain MRI examinations funded by R01‐HL70825 from the NHLBI. CHS research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and grants U01HL080295 and U01HL130114 from the NHLBI with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG023629, R01AG15928, and R01AG20098 from the NIA. FHS research is supported by NHLBI contracts N01‐HC‐25195 and HHSN268201500001I. This study was also supported by additional grants from the NIA (R01s AG054076, AG049607 and AG033040 and NINDS (R01 NS017950). The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG‐CT‐2006‐01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007‐2013)/grant agreement HEALTH‐F4‐ 2007‐201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no. QLG2‐CT‐2002‐ 01254). High‐throughput analysis of the ERF data was supported by a joint grant from the Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO‐RFBR 047.017.043). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the municipality of Rotterdam. Genetic data sets are also supported by the Netherlands Organization of Scientific Research NWO Investments (175.010.2005.011, 911‐03‐012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014‐93‐015; RIDE2), and the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project 050‐060‐810. All studies are grateful to their participants, faculty and staff. The content of these manuscripts is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the U.S. Department of Health and Human Services. The FUS cohorts include: the Alzheimer's Disease Research Centers (ADRC) (P30 AG062429, P30 AG066468, P30 AG062421, P30 AG066509, P30 AG066514, P30 AG066530, P30 AG066507, P30 AG066444, P30 AG066518, P30 AG066512, P30 AG066462, P30 AG072979, P30 AG072972, P30 AG072976, P30 AG072975, P30 AG072978, P30 AG072977, P30 AG066519, P30 AG062677, P30 AG079280, P30 AG062422, P30 AG066511, P30 AG072946, P30 AG062715, P30 AG072973, P30 AG066506, P30 AG066508, P30 AG066515, P30 AG072947, P30 AG072931, P30 AG066546, P20 AG068024, P20 AG068053, P20 AG068077, P20 AG068082, P30 AG072958, P30 AG072959), Alzheimer's Disease Neuroimaging Initiative (ADNI; U19AG024904), Amish Protective Variant Study (RF1AG058066), Cache County Study (R01AG11380, R01AG031272, R01AG21136, RF1AG054052), Case Western Reserve University Brain Bank (CWRUBB; P50AG008012), Case Western Reserve University Rapid Decline (CWRURD; RF1AG058267, NU38CK000480), CubanAmerican Alzheimer's Disease Initiative (CuAADI; 3U01AG052410), Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA; 5R37AG015473, RF1AG015473, R56AG051876), Genetic and Environmental Risk Factors for Alzheimer Disease Among African Americans Study (GenerAAtions; 2R01AG09029, R01AG025259, 2R01AG048927), Gwangju Alzheimer and Related Dementias Study (GARD; U01AG062602), Hillblom Aging Network (2014‐A‐004‐NET, R01AG032289, R01AG048234), Hussman Institute for Human Genomics Brain Bank (HIHGBB; R01AG027944, Alzheimer's Association “Identification of Rare Variants in Alzheimer Disease”), Ibadan Study of Aging (IBADAN; 5R01AG009956), Longevity Genes Project (LGP) and LonGenity (R01AG042188, R01AG044829, R01AG046949, R01AG057909, R01AG061155, P30AG038072), Mexican Health and Aging Study (MHAS; R01AG018016), Multi‐Institutional Research in Alzheimer's Genetic Epidemiology (MIRAGE; 2R01AG09029, R01AG025259, 2R01AG048927), Northern Manhattan Study (NOMAS; R01NS29993), Peru Alzheimer's Disease Initiative (PeADI; RF1AG054074), Puerto Rican 1066 (PR1066; Wellcome Trust (GR066133/GR080002), European Research Council (340755), Puerto Rican Alzheimer Disease Initiative (PRADI; RF1AG054074), Reasons for Geographic and Racial Differences in Stroke (REGARDS; U01NS041588), Research in African American Alzheimer Disease Initiative (REAAADI; U01AG052410), the Religious Orders Study (ROS; P30 AG10161, P30 AG72975, R01 AG15819, R01 AG42210), the RUSH Memory and Aging Project (MAP; R01 AG017917, R01 AG42210), Stanford Extreme Phenotypes in AD (R01AG060747), University of Miami Brain Endowment Bank (MBB), University of Miami/Case Western/North Carolina A&T African American (UM/CASE/NCAT; U01AG052410, R01AG028786), and Wisconsin Registry for Alzheimer's Prevention (WRAP; R01AG027161 and R01AG054047). The four LSACs are: the Human Genome Sequencing Center at the Baylor College of Medicine (U54 HG003273), the Broad Institute Genome Center (U54HG003067), The American Genome Center at the Uniformed Services University of the Health Sciences (U01AG057659), and the Washington University Genome Institute (U54HG003079). Genotyping and sequencing for the ADSP FUS is also conducted at John P. Hussman Institute for Human Genomics (HIHG) Center for Genome Technology (CGT). Biological samples and associated phenotypic data used in primary data analyses were stored at Study Investigators institutions, and at the National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD, U24AG021886) at Indiana University funded by NIA. Associated Phenotypic Data used in primary and secondary data analyses were provided by Study Investigators, the NIA funded Alzheimer's Disease Centers (ADCs), and the National Alzheimer's Coordinating Center (NACC, U24AG072122) and the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA. Harmonized phenotypes were provided by the ADSP Phenotype Harmonization Consortium (ADSP‐PHC), funded by NIA (U24 AG074855, U01 AG068057, and R01 AG059716) and Ultrascale Machine Learning to Empower Discovery in Alzheimer's Disease Biobanks (AI4AD, U01 AG068057). This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Library of Medicine. Contributors to the Genetic Analysis Data included Study Investigators on projects that were individually funded by NIA, and other NIH institutes, and by private US organizations, or foreign governmental or non‐governmental organizations. The ADSP Phenotype Harmonization Consortium (ADSP‐PHC) is funded by NIA (U24 AG074855, U01 AG068057 and R01 AG059716). The harmonized cohorts within the ADSP‐PHC include: the Anti‐Amyloid Treatment in Asymptomatic Alzheimer's study (A4 Study), a secondary prevention trial in preclinical Alzheimer's disease, aiming to slow cognitive decline associated with brain amyloid accumulation in clinically normal older individuals. The A4 Study is funded by a public–private–philanthropic partnership, including funding from the National Institutes of Health‐National Institute on Aging, Eli Lilly and Company, Alzheimer's Association, Accelerating Medicines Partnership, GHR Foundation, an anonymous foundation and additional private donors, with in‐kind support from Avid and Cogstate. The companion observational Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study is funded by the Alzheimer's Association and GHR Foundation. The A4 and LEARN Studies are led by Dr. Reisa Sperling at Brigham and Women's Hospital, Harvard Medical School and Dr. Paul Aisen at the Alzheimer's Therapeutic Research Institute (ATRI), University of Southern California. The A4 and LEARN Studies are coordinated by ATRI at the University of Southern California, and the data are made available through the Laboratory for Neuro Imaging at the University of Southern California. The participants screening for the A4 Study provided permission to share their de‐identified data in order to advance the quest to find a successful treatment for Alzheimer's disease. We would like to acknowledge the dedication of all the participants, the site personnel, and all of the partnership team members who continue to make the A4 and LEARN Studies possible. The complete A4 Study Team list is available on: a4study.org/a4‐study‐team ; the Adult Changes in Thought study (ACT), U01 AG006781, U19 AG066567; Alzheimer's Disease Neuroimaging Initiative (ADNI): Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol‐Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann‐La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California; Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA): 5R37AG015473, RF1AG015473, R56AG051876; the Health & Aging Brain Study – Health Disparities (HABS‐HD), supported by the National Institute on Aging of the National Institutes of Health under Award Numbers R01AG054073, R01AG058533, R01AG070862, P41EB015922, and U19AG078109; the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE), which was supported by a grant from Ministry of Science, ICT and Future Planning (Grant No: NRF‐2014M3C7A1046042); Memory & Aging Project at Knight Alzheimer's Disease Research Center (MAP at Knight ADRC): The Memory and Aging Project at the Knight‐ADRC (Knight‐ADRC). This work was supported by the National Institutes of Health (NIH) grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614, and U01AG052410. We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/ ), and the Departments of Neurology and Psychiatry at Washington University School of Medicine; National Alzheimer's Coordinating Center (NACC): The NACC database is funded by NIA/NIH Grant U24 AG072122. SCAN is a multi‐institutional project that was funded as a U24 grant (AG067418) by the National Institute on Aging in May 2020. Data collected by SCAN and shared by NACC are contributed by the NIA‐funded ADRCs as follows: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD); National Institute on Aging Alzheimer's Disease Family Based Study (NIA‐AD FBS): U24 AG056270; Religious Orders Study (ROS): P30AG10161, R01AG15819, R01AG42210; Memory and Aging Project (MAP—Rush): R01AG017917, R01AG42210; Minority Aging Research Study (MARS): R01AG22018, R01AG42210; the Texas Alzheimer's Research and Care Consortium (TARCC), funded by the Darrell K Royal Texas Alzheimer's Initiative, directed by the Texas Council on Alzheimer's Disease and Related Disorders; Washington Heights/Inwood Columbia Aging Project (WHICAP): RF1 AG054023; and Wisconsin Registry for Alzheimer's Prevention (WRAP): R01AG027161 and R01AG054047. Additional acknowledgments include the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA. sa000002 ‐ Alzheimer's Disease Neuroimaging Initiative: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol‐Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann‐La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development, LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Additional information to include in an acknowledgement statement can be found on the LONI site: https://adni.loni.usc.edu/wp‐content/uploads/how_to_apply/ADNI_Data_Use_Agreement.pdf . sa000011 ‐ Accelerating Medicines Partnership‐Alzheimer's Disease (AMP‐AD): Mayo RNAseq Study‐ Study data were provided by the following sources: The Mayo Clinic Alzheimer's Disease Genetic Studies, led by Dr. Nilufer Ertekin‐Taner and Dr. Steven G. Younkin, Mayo Clinic, Jacksonville, FL, using samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer's Disease Research Center, and the Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation, and support from Mayo Foundation. Study data includes samples collected through the Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona. The Brain and Body Donation Program is supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson's Disease and Related Disorders), the National Institute on Aging (P30 AG19610 Arizona Alzheimer's Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer's Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05‐901, and 1001 to the Arizona Parkinson's Disease Consortium) and the Michael J. Fox Foundation for Parkinson's Research. ROSMAP: We are grateful to the participants in the Religious Order Study, the Memory and Aging Project. This work is supported by the US National Institutes of Health (U01 AG046152, R01 AG043617, R01 AG042210, R01 AG036042, R01 AG036836, R01 AG032990, R01 AG18023, RC2 AG036547, P50 AG016574, U01 ES017155, KL2 RR024151, K25 AG041906‐01, R01 AG30146, P30 AG10161, R01 AG17917, R01 AG15819, K08 AG034290, P30 AG10161 and R01 AG11101). Mount Sinai Brain Bank (MSBB): This work was supported by the grants R01AG046170, RF1AG054014, RF1AG057440 and R01AG057907 from the NIH/National Institute on Aging (NIA). R01AG046170 is a component of the AMP‐AD Target Discovery and Preclinical Validation Project. Brain tissue collection and characterization was supported by NIH HHSN271201300031C. sa000019 ‐ The Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI‐DAD): The Longitudinal Aging Study in India, Diagnostic Assessment of Dementia data are sponsored by the National Institute on Aging (grant numbers R01AG051125 and U01AG065958) and is conducted by the University of Southern California. sa000023 ‐ Dissecting the Genomic Etiology of non‐Mendelian Early‐Onset Alzheimer Disease (EOAD) and Related Phenotypes: This work was supported by the National Institutes of Health (NIH) grant R01AG064614. The ADSP‐FUS is supported by U01AG057659. The National Institutes of Health, National Institute on Aging (NIH‐NIA) supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; samples from the National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. Sequencing data generation and harmonization is supported by the Genome Center for Alzheimer's Disease, U54AG052427, and data sharing is supported by NIAGADS, U24AG041689. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. NIH grants supported enrollment and data collection for the individual studies including the Alzheimer's Disease Centers (ADC, P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD). The Miami ascertainment and research were supported in part through: RF1AG054080, R01AG027944, R01AG019085, R01AG028786‐02, RC2AG036528. The Columbia ascertainment and research were supported in part through: R37AG015473 and U24AG056270. The University of Washington ascertainment and research were supported in part through R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877. sa000003 ‐ Alzheimer's Disease Genetics Consortium: The Alzheimer's Disease Genetics Consortium (ADGC) supported sample preparation, sequencing and data processing through NIA grant U01AG032984. Sequencing data generation and harmonization is supported by the Genome Center for Alzheimer's Disease, U54AG052427, and data sharing is supported by NIAGADS, U24AG041689. Samples from the National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG021886) awarded by the National Institute on Aging (NIA), were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This study was made possible by the Texas Alzheimer's Research and Care Consortium (TARCC) funded by the state of Texas through the Texas Council on Alzheimer's Disease and Related Disorders and the Darrell K. Royal Texas Alzheimer's Initiative. This work was supported by the Cure Alzheimer's Fund and MADRC grant (1P30AG062421‐01). B.P.H. receives research funding from the National Eye Institute (NEI) (R01‐EY034234), the H. Eric Cushing Foundation, the Nancy Lurie Marks Family Foundation, and the C.J.L. charitable Foundation. REFERENCES 1. Cummings JL. 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