1
|
Johnson ECB, Bian S, Haque RU, Carter EK, Watson CM, Gordon BA, Ping L, Duong DM, Epstein MP, McDade E, Barthélemy NR, Karch CM, Xiong C, Cruchaga C, Perrin RJ, Wingo AP, Wingo TS, Chhatwal JP, Day GS, Noble JM, Berman SB, Martins R, Graff-Radford NR, Schofield PR, Ikeuchi T, Mori H, Levin J, Farlow M, Lah JJ, Haass C, Jucker M, Morris JC, Benzinger TLS, Roberts BR, Bateman RJ, Fagan AM, Seyfried NT, Levey AI. Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer's disease. Nat Med 2023; 29:1979-1988. [PMID: 37550416 PMCID: PMC10427428 DOI: 10.1038/s41591-023-02476-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/27/2023] [Indexed: 08/09/2023]
Abstract
Alzheimer's disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes-aggregation of the amyloid-β (Aβ) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)-are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of Aβ plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with Aβ plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than Aβ and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with Aβ and tau.
Collapse
Affiliation(s)
- Erik C B Johnson
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA.
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Rafi U Haque
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
| | - E Kathleen Carter
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Caroline M Watson
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Lingyan Ping
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Duc M Duong
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Michael P Epstein
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | | | - Celeste M Karch
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | - Chengjie Xiong
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
- Division of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
- Department of Pathology and Immunology, Washington University in St Louis, St Louis, MO, USA
| | - Aliza P Wingo
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Psychiatry, Emory University School of Medicine, Atlanta, GA, USA
- Division of Mental Health, Atlanta VA Medical Center, Atlanta, GA, USA
| | - Thomas S Wingo
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Jasmeer P Chhatwal
- Massachusetts General and Brigham & Women's Hospitals, Harvard Medical School, Boston, MA, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - James M Noble
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, and GH Sergievsky Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Sarah B Berman
- Departments of Neurology and Clinical and Translational Science, Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ralph Martins
- Edith Cowan University, Perth, Western Australia, Australia
| | | | - Peter R Schofield
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Hiroshi Mori
- Osaka Metropolitan University Medical School, Nagaoka Sutoku University, Nagaoka, Japan
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | | | - James J Lah
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Christian Haass
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Metabolic Biochemistry, Biomedical Center (BMC), Ludwig-Maximilians University, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Mathias Jucker
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Blaine R Roberts
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Nicholas T Seyfried
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Allan I Levey
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| |
Collapse
|
2
|
Yan M, Li W, Wei R, Li S, Liu Y, Huang Y, Zhang Y, Lu Z, Lu Q. Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy. J Transl Med 2023; 21:490. [PMID: 37480090 PMCID: PMC10360355 DOI: 10.1186/s12967-023-04350-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/11/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, which has the particularity of natural immune inflammation. The inhibition of inflammatory cytokine expression and regulation of pathways related to pyroptosis may be a novel strategy for DN treatment. The aim of this study is to identify pyroptosis-related genes and potential drugs for DN. METHODS DN differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE96804. Dataset GSE30528 and GSE142025 were downloaded to verify pyroptosis-related differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a pyroptosis-related gene predictive model. A consensus clustering analysis was performed to identify pyroptosis-related DN subtypes. Subsequently, Gene Set Variation Analysis (GSVA), Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the differences between DN clusters. A protein-protein interaction (PPI) network was used to select hub genes and DGIdb database was utilized to screen potential therapeutic drugs/compounds targeting hub genes. RESULTS A total of 24 differentially expressed pyroptosis-related genes were identified in DN. A 16 gene predictive model was conducted via LASSO regression analysis. According to the expression level of these 16 genes, DN cases were divided into two subtypes, and the subtypes are mainly associated with inflammation, activation of immune response and cell metabolism. In addition, we identified 10 hub genes among these subtypes, and predicted 65 potential DN therapeutics that target key genes. CONCLUSION We identified two pyroptosis-related DN clusters and 65 potential therapeutical agents/compounds for DN, which might shed a light on the treatment of DN.
Collapse
Affiliation(s)
- Meng Yan
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China.
- Department of Clinical Pharmacology, School of Pharmacy, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, 221004, Jiangsu, China.
| | - Wenwen Li
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Rui Wei
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Shuwen Li
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Yan Liu
- Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China
- Key Laboratory of Genetic Foundation and Clinical Application, Department of Genetics, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, China
| | - Yuqian Huang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Yunye Zhang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Zihao Lu
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Qian Lu
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China.
- Department of Clinical Pharmacology, School of Pharmacy, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, 221004, Jiangsu, China.
| |
Collapse
|
3
|
Liu X, Abudukeremu A, Jiang Y, Cao Z, Wu M, Sun R, Chen Z, Chen Y, Zhang Y, Wang J. Fine or Gross Motor Index as a Simple Tool for Predicting Cognitive Impairment in Elderly People: Findings from The Irish Longitudinal Study on Ageing (TILDA). J Alzheimers Dis 2021; 83:889-896. [PMID: 34366357 DOI: 10.3233/jad-210704] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Several kinds of motor dysfunction can predict future cognitive impairment in elderly individuals. However, the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of cognitive impairment has not been assessed. OBJECTIVE We investigated the associations between FINEA/GROSSA and cognitive impairment. METHODS The data of 4,745 participants from The Irish Longitudinal Study on Ageing (TILDA) were analyzed. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). We first assessed the correlation between the FINEA/GROSSA and MMSE in a cross-sectional study. Then, we further investigated the predictive role of the incidence of cognitive impairment in a prospective cohort study. RESULTS We found that both FINEA and GROSSA were negatively correlated with MMSE in both the unadjusted (FINEA: B = -1.00, 95%confidence intervals (CI): -1.17, -0.83, t = -11.53, p < 0.001; GROSSA: B = -0.85, 95%CI: -0.94, -0.76, t = -18.29, p < 0.001) and adjusted (FINEA: B = -0.63, 95%CI: -0.79, -0.47, t = -7.77, p < 0.001; GROSSA: B = -0.57, 95%CI: -0.66, -0.48, t = -12.61, p < 0.001) analyses in a cross-sectional study. In a prospective cohort study, both high FINEA and high GROSSA were associated with an increased incidence of cognitive function impairment (FINEA: adjusted odds ratios (OR) = 2.35, 95%CI: 1.05, 5.23, p = 0.036; GROSSA adjusted OR = 3.00, 95%CI: 1.49, 6.03, p = 0.002) after 2 years of follow-up. CONCLUSION Higher FINEA and GROSSA scores were both associated with an increased incidence of cognitive impairment. FINEA or GROSSA might be a simple tool for identifying patients with cognitive impairment.
Collapse
Affiliation(s)
- Xiao Liu
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangzhou Key Laboratory of Molecular Mechanism and Translation in Major Cardiovascular Disease, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ayiguli Abudukeremu
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuan Jiang
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhengyu Cao
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Maoxiong Wu
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Runlu Sun
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhiteng Chen
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yangxin Chen
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangzhou Key Laboratory of Molecular Mechanism and Translation in Major Cardiovascular Disease, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuling Zhang
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangzhou Key Laboratory of Molecular Mechanism and Translation in Major Cardiovascular Disease, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jingfeng Wang
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangzhou Key Laboratory of Molecular Mechanism and Translation in Major Cardiovascular Disease, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|