A consensus proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation: Molecular and cell biology/others. (7th December 2020)

• Record Type: Journal Article
• Authors: Johnson, Erik C.B.; Dammer, Eric B.; Duong, Duc; Ping, Lingyan; Zhou, Maotian; Yin, Luming; Higginbotham, Lenora A.; Guajardo, Andrew; White, Bartholomew; Troncoso, Juan C.; Thambisetty, Madhav; Montine, Thomas J.; Lee, Eddie B.; Trojanowski, John Q.; Beach, Thomas G.; Reiman, Eric M.; Haroutunian, Vahram; Wang, Minghui; Schadt, Eric; Zhang, Bin; Dickson, Dennis W.; Ertekin‐Taner, Nilufer; Golde, Todd E.; Petyuk, Vladislav A.; Jager, Philip L.; Bennett, David A.; Wingo, Thomas S.; Rangaraju, Srikant; Hajjar, Ihab; Shulman, Joshua M.; Lah, James J.; Levey, Allan I.; Seyfried, Nicholas T.;
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 2
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.039504
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Our understanding of the biological changes in the brain associated with Alzheimer's disease (AD) pathology and cognitive impairment remains incomplete. Unbiased discovery proteomic analysis of AD brain can help to increase our understanding of these changes. Method: We analyzed dorsolateral prefrontal cortex, precuneus, and temporal cortex of control, asymptomatic AD, and AD brains from multiple different centers by label‐free quantitative mass spectrometry and weighted protein co‐expression analysis to obtain a consensus protein co‐expression network of AD brain. We also analyzed cerebrospinal fluid to investigate potential biomarkers related to this network. Result: The network consisted of 13 protein co‐expression modules. Six of these modules correlated with amyloid‐β plaque burden, tau neurofibrillary tangle burden, cognitive function, and clinical functional status, and were altered in asymptomatic AD, AD, or in both disease states. These six modules reflected synaptic, mitochondrial, sugar metabolism, extracellular matrix, cytoskeletal, and RNA binding/splicing biological functions. The identified protein network modules were preserved in a community‐based cohort analyzed by a different quantitative mass spectrometry approach. They were also preserved in temporal lobe and precuneus brain regions. Some of the modules were influenced by aging, and showed changes in other neurodegenerative diseases such as frontotemporal dementia and corticobasal degeneration. The module most strongly associated with AD pathology and cognitive impairment was the sugar metabolism module. This module was enriched in AD genetic risk factors, and was also highly enriched in microglia and astrocyte protein markers associated with an anti‐inflammatory state, suggesting that the biological functions it represents may serve a protective role in AD. Proteins from the sugar metabolism module were increased in cerebrospinal fluid from asymptomatic AD and AD cases, highlighting their potential as biomarkers of the altered brain network. Conclusion: In this study of >2000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brain that may serve as therapeutic targets and fluid biomarkers for the disease.