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Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation.

ABSTRACT: Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.

SUBMITTER: Johnson ECB

PROVIDER: S-EPMC7405761 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation.

Johnson Erik C B ECB Dammer Eric B EB Duong Duc M DM Ping Lingyan L Zhou Maotian M Yin Luming L Higginbotham Lenora A LA Guajardo Andrew A White Bartholomew B Troncoso Juan C JC Thambisetty Madhav M Montine Thomas J TJ Lee Edward B EB Trojanowski John Q JQ Beach Thomas G TG Reiman Eric M EM Haroutunian Vahram V Wang Minghui M Schadt Eric E Zhang Bin B Dickson Dennis W DW Ertekin-Taner Nilüfer N Golde Todd E TE Petyuk Vladislav A VA De Jager Philip L PL Bennett David A DA Wingo Thomas S TS Rangaraju Srikant S Hajjar Ihab I Shulman Joshua M JM Lah James J JJ Levey Allan I AI Seyfried Nicholas T NT

Nature medicine 20200413 5

Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflam ...[more]

PMID: 32284590

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Project description:IntroductionThe relevance of the cerebrospinal fluid (CSF) biomarkers for the diagnosis of Alzheimer's disease (AD) and related disorders is clearly established. However, the question remains on how to use these data, which are often heterogeneous (not all biomarkers being pathologic). The objective of this study is to propose to physicians in memory clinics a biologic scale of probabilities that the patient with cognitive impairments has an Alzheimer's disease (AD) pathologic process.MethodsFor that purpose, we took advantage of the multicenter data of our Paris-North, Lille, and Montpellier (PLM) study, which has emerged through the initial sharing of information from these memory centers. Different models combining the CSF levels of amyloid-β 42, tau, and p-tau(181) were tested to generate categories of patients with very low (<10%), low (<25%), high (>75%), and very high predictive values (>90%) for positive AD. In total, 1,273 patients (646 AD and 627 non-AD) from six independent memory-clinic cohorts were included.ResultsA prediction model based on logistic regressions achieved a very good stratification of the population but had the disadvantages of needing mathematical optimization and being difficult to use in daily clinical practice. Remarkably, a simple and intuitive model based on the number (from zero to three) of three pathologic CSF biomarkers resulted in a very efficient predictive scale for AD in patients seen in memory clinics. The scale's overall predictive value for AD for the different categories were as follows: class 0, 9.6% (95% confidence interval (CI), 6.0% to 13.2%); class 1, 24.7% (95% CI, 18.0% to 31.3%); class 2, 77.2% (95% CI, 67.8% to 86.5%); and class 3, 94.2% (95% CI, 90.7% to 97.7%). In addition, with this scale, significantly more patients were correctly classified than with the logistic regression. Its superiority in model performance was validated by the computation of the net reclassification index (NRI). The model was also validated in an independent multicenter dataset of 408 patients (213 AD and 195 non-AD).ConclusionsIn conclusion, we defined a new scale that could be used to facilitate the interpretation and routine use of multivariate CSF data, as well as helping the stratification of patients in clinical research trials.

Project description:Sphingolipids are important in many brain functions but their role in Alzheimer's disease (AD) is not completely defined. A major limit is availability of fresh brain tissue with defined AD pathology. The discovery that cerebrospinal fluid (CSF) contains abundant nanoparticles that include synaptic vesicles and large dense core vesicles offer an accessible sample to study these organelles, while the supernatant fluid allows study of brain interstitial metabolism. Our objective was to characterize sphingolipids in nanoparticles representative of membrane vesicle metabolism, and in supernatant fluid representative of interstitial metabolism from study participants with varying levels of cognitive dysfunction. We recently described the recruitment, diagnosis, and CSF collection from cognitively normal or impaired study participants. Using liquid chromatography tandem mass spectrometry, we report that cognitively normal participants had measureable levels of sphingomyelin, ceramide, and dihydroceramide species, but that their distribution differed between nanoparticles and supernatant fluid, and further differed in those with cognitive impairment. In CSF from AD compared with cognitively normal participants: a) total sphingomyelin levels were lower in nanoparticles and supernatant fluid; b) levels of ceramide species were lower in nanoparticles and higher in supernatant fluid; c) three sphingomyelin species were reduced in the nanoparticle fraction. Moreover, three sphingomyelin species in the nanoparticle fraction were lower in mild cognitive impairment compared with cognitively normal participants. The activity of acid, but not neutral sphingomyelinase was significantly reduced in the CSF from AD participants. The reduction in acid sphingomylinase in CSF from AD participants was independent of depression and psychotropic medications. Acid sphingomyelinase activity positively correlated with amyloid ?42 concentration in CSF from cognitively normal but not impaired participants. In dementia, altered sphingolipid metabolism, decreased acid sphingomyelinase activity and its lost association with CSF amyloid ?42 concentration, underscores the potential of sphingolipids as disease biomarkers, and acid sphingomyelinase as a target for AD diagnosis and/or treatment.

Dataset Information

Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation.

Publications

Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation.

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