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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

Nature Medicine volume 26, pages 769–780 (2020)Cite this article

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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.

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Fig. 1: Protein network analysis of asymptomatic and symptomatic AD brain.
Fig. 2: AD protein network is preserved in different brain regions.
Fig. 3: Effects of aging on AD protein network modules.
Fig. 4: AD protein network module changes in other neurodegenerative diseases.
Fig. 5: The M4 astrocyte/microglial metabolism module is enriched in AD genetic risk factors and markers of anti-inflammatory disease-associated microglia.
Fig. 6: M4 astrocyte/microglial metabolism module protein levels are elevated in AsymAD and AD CSF.

Data availability

All raw data, case traits and analyses (differential and coexpression) related to this manuscript are available at https://www.synapse.org/consensus. The results published here are in whole or in part based on data obtained from the AMP-AD Knowledge Portal (https://adknowledgeportal.synapse.org). The AMP-AD Knowledge Portal is a platform for accessing data, analyses and tools generated by the AMP-AD Target Discovery Program and other programs supported by the National Institute on Aging to enable open-science practices and accelerate translational learning. The data, analyses and tools are shared early in the research cycle without a publication embargo on secondary use. Data are available for general research use according to the following requirements for data access and data attribution (https://adknowledgeportal.synapse.org/#/DataAccess/Instructions). ROS/MAP resources can be requested at www.radc.rush.edu.

Code availability

The algorithm used for batch correction is fully documented and available as an R function, which can be downloaded from https://github.com/edammer/TAMPOR.

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Acknowledgements

We are grateful to those who agreed to donate their brains and CSF for research and who participated in the described observational studies. This study was supported by the following National Institutes of Health funding mechanisms: R01AG053960, R01AG057911, R01AG061800, RF1AG057471, RF1AG057470, R01AG061800, R01AG057911, R01AG057339, U01AG061357, P50AG025688, RF1AG057470, RF1AG051633, P30AG10161, R01AG15819, R01AG17917, U01AG61356, R01AG056533, K08NS099474, U01AG046170, RF1AG054014, RF1AG057440, R01AG057907, U01AG052411, P30AG10124, U01AG046161, R01AG050631, R01AG053960, R01AG057339, U01AG061357, P50AG005146, U24NS072026 and P30AG19610. The study was also supported in part by the intramural program of the National Institute on Aging (NIA).

Author information

Author notes

  1. These authors contributed equally: Erik C.B. Johnson, Eric B. Dammer.

Authors and Affiliations

  1. Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA

    Erik C. B. Johnson, Eric B. Dammer, Duc M. Duong, Lingyan Ping, Maotian Zhou, Thomas S. Wingo, James J. Lah, Allan I. Levey & Nicholas T. Seyfried

  2. Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA

    Erik C. B. Johnson, Lingyan Ping, Maotian Zhou, Lenora A. Higginbotham, Thomas S. Wingo, Srikant Rangaraju, Ihab Hajjar, James J. Lah, Allan I. Levey & Nicholas T. Seyfried

  3. Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA

    Eric B. Dammer, Duc M. Duong, Lingyan Ping, Maotian Zhou, Luming Yin & Nicholas T. Seyfried

  4. Johns Hopkins School of Medicine, Baltimore, MD, USA

    Andrew Guajardo, Bartholomew White & Juan C. Troncoso

  5. Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA

    Madhav Thambisetty

  6. Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA

    Thomas J. Montine

  7. Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Edward B. Lee & John Q. Trojanowski

  8. Department of Pathology, Banner Sun Health Research Institute, Sun City, AZ, USA

    Thomas G. Beach

  9. Banner Alzheimer’s Institute, Arizona State University and University of Arizona, Phoenix, AZ, USA

    Eric M. Reiman

  10. Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Vahram Haroutunian

  11. JJ Peters VA Medical Center MIRECC, Bronx, NY, USA

    Vahram Haroutunian

  12. Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Minghui Wang, Eric Schadt & Bin Zhang

  13. Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA

    Dennis W. Dickson & Nilüfer Ertekin-Taner

  14. Department of Neurology, Mayo Clinic, Jacksonville, FL, USA

    Nilüfer Ertekin-Taner

  15. Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL, USA

    Todd E. Golde

  16. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Vladislav A. Petyuk

  17. Center for Translational & Computational Neuroimmunology, Department of Neurology, Taub Institute, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY, USA

    Philip L. De Jager

  18. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    David A. Bennett

  19. Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA

    Thomas S. Wingo

  20. Departments of Neurology, Neuroscience and Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Joshua M. Shulman

  21. Jan and Dan Duncan Neurologic Research Institute, Texas Children’s Hospital, Houston, TX, USA

    Joshua M. Shulman

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  1. Erik C. B. Johnson
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Contributions

E.C.B.J., E.B.D., D.M.D., L.P., M.Z., A.G., V.A.P. and N.T.S. designed experiments; D.M.D., L.P., M.Z., L.Y., A.G. and V.A.P. carried out experiments; E.C.B.J., E.B.D., M.Z., V.A.P. and N.T.S. analyzed data; D.M.D., L.P., L.A.H., B.W., J.C.T., M.T., T.J.M., E.B.L., J.Q.T., T.G.B., E.M.R., V.H., M.W., E.S., B.Z., D.W.D., N.E.-T., T.E.G., V.A.P., P.L.D.J., D.A.B., T.S.W., S.R., I.H. and J.M.S. provided advice on the interpretation of data; E.C.B.J. wrote the manuscript with input from co-authors; J.C.T., T.J.M., J.Q.T., T.G.B., V.H., M.W., D.W.D., D.A.B. and I.H. provided tissue samples; A.I.L., J.J.L. and N.T.S. supervised the study. All authors approved the final manuscript.

Corresponding authors

Correspondence to Erik C. B. Johnson, Allan I. Levey or Nicholas T. Seyfried.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Kate Gao was the primary editor on this article, and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Analysis of Missing Protein Quantitative Measurements and their Effect on the AD Network.

a–d, The percentage of quantified proteins with a given level of missing quantitative measurements was analyzed for both the consensus LFQ and ROS/MAP TMT networks (a). Each bar represents a bin of 2%. The red line indicates the 50% missing measurement threshold used in this study. The total number of quantified proteins for each dataset, and the percentage of quantified proteins removed due to ≥50% missing measurements prior to construction of the respective protein networks, is provided in the legend. b, The effect of missing value threshold on AD network modules. The AD network was constructed using different allowed levels of missing protein measurements. Preservation of AD network modules (50% missingness threshold) in each network generated using a more stringent threshold (10-40% missingness) was assessed by Zsummary score. Module preservation Zsummary was calculated as described by Langfelder et al.74 The dashed blue line indicates a zsummary score of 1.96, or FDR q value <0.05, above which module preservation was considered statistically significant. The dashed red line indicates a zsummary score of 10, or FDR q value ~ 1e-23, above which module preservation was considered highly statistically significant. Each module is color coded as shown in Fig. 1. Module memberships are provided in Supplementary Table 2. c, Percentage of total quantified proteins with ≥50% missing measurements in each cohort used for the AD consensus network. The total number of quantified proteins, and the percentage removed by applying the ≥50% missingness threshold, is provided in the legend for each cohort. d, Percentage of total quantified proteins with ≥50% missing measurements in each cohort used in this study. The total number of quantified proteins, and the percentage removed by applying the ≥50% missingness threshold, is provided in the legend for each cohort. For the consensus LFQ cohort, the dotted line indicates the percent removed (41.3%) when missingness is controlled separately in each cohort prior to combination for construction of the AD network, as was done in this study. The solid bar is provided for direct method comparison to other cohorts used in the study. LFQ, label-free quantitation; TMT, tandem-mass tag; BLSA, Baltimore Longitudinal Study of Aging, Banner, Banner Sun Health Research Institute; MSSB, Mount Sinai School of Medicine Brain Bank; ACT, Adult Changes in Thought Study; ROS/MAP, Religious Orders Study and Memory and Aging Project; PC, precuneus; TC, temporal cortex.

Extended Data Fig. 2 Covariate Effects on AD Network Protein Quantitative Values and Modules.

a,b, Principal component analysis was performed on AD network protein quantitative values after batch correction but prior to regression for age, sex, and post-mortem interval (PMI) covariates (n=418 case samples after network connectivity outlier removal) (a), Correlation values between case status (control, AsymAD, or AD), age, sex, PMI, and the first five principal components of the data are shown. The covariate most strongly correlated to each principal component is highlighted in bold. The percentage of variance in the data explained by each principal component is given in parentheses. (b), Effects of sex on AD network modules shown in Fig. 1c. The AD network was built without regression for sex, and module eigenprotein levels were compared between male and female sex for each case group (n=123 AD, 54 AsymAD, 44 control females; n=103 AD, 45 AsymAD, 49 control males). Statistically significant differences are highlighted in red. Correlations were performed using Spearman’s rank correlation. Differences in protein levels were assessed by Kruskal-Wallis one-way ANOVA. Boxplots represent the median, 25th, and 75th percentiles, and whiskers represent measurements to the 5th and 95th percentiles. PC, principal component; PMI, post-mortem interval; Cntl, control; AsymAD, asymptomatic Alzheimer’s disease; AD, Alzheimer’s disease.

Extended Data Fig. 3 Relationship of AD Network Proteins by t-SNE Analysis.

Dimensionality reduction and visualization by t-distributed stochastic neighbor embedding (t-SNE) was applied to proteins that were in the top 25% by kME value within each AD network module. Proteins are color coded as shown in Fig. 1b according to the network module in which they reside. Network module ontologies and cell type enrichments are provided as shown in Fig. 1b. Ontologies are highlighted based on the most robust AD trait correlations as shown in Fig. 1b.

Extended Data Fig. 4 AD Protein Network Module Trait and Pathology Correlations.

a–c, The eigenprotein of each protein network module was correlated with neuropathological, molecular, and cognitive/functional traits (n=419 independent case sample traits after network connectivity outlier removal except for cognitive measures, where n=167 MMSE, n=159 CDR, and n=56 CASI) (a), Protein modules are bolded as in Fig. 1b using CERAD, Braak, MMSE, and CDR correlations. Strength of positive (red) or negative (blue) correlation is shown by two-color heatmap, with p values provided for all correlations with p < 0.05. (b), Correlation between CERAD plaque score and Aβ levels measured by label free quantification (LFQ) mass spectrometry17. (c), Correlation between Braak score (NFT, neurofibrillary tangle) and tau levels measured by LFQ of the microtubule binding region (MTBR). Correlations were performed using biweight midcorrelation and corrected by the Benjamini-Hochberg method. CERAD, Consortium to Establish a Registry for Alzheimer’s disease Aβ plaque score (higher scores represent greater plaque burden); Braak, tau neurofibrillary tangle staging score (higher scores represent greater extent of tangle burden); Aβ, amyloid-β; α-Syn, alpha synuclein; TDP-43, TAR DNA-binding protein 43; MMSE, mini-mental status examination score (higher scores represent better cognitive function); CDR, clinical dementia rating score (higher scores representing worse functional status); CASI, Cognitive Abilities Screening Instrument (higher scores represent better cognitive function). MMSE is from Banner, CDR is from MSSB, and CASI is from ACT.

Extended Data Fig. 5 AD Protein Network Validation in a Longitudinal Cohort of Aging.

(a–c), Preservation of AD protein network modules and trait correlations in the Religious Orders Study and Memory and Aging Project (ROS/MAP) cohorts. (a), Protein levels from dorsolateral prefrontal cortex (DLPFC) in a total of 340 control, AsymAD, and AD cases (control, n=84; AsymAD, n=148; AD, n=108) from the ROS/MAP cohorts were measured using a different mass spectrometry platform and quantification approach compared to the cases used to generate the AD network as shown in Fig. 1. The resulting data were used to assess conservation of the AD brain protein network in the ROS/MAP cohorts. (b), AD brain protein network module preservation in the ROS/MAP cohorts. Module preservation was calculated using a composite zsummary score as described by Langfelder et al.74 The dashed blue line indicates a zsummary score of 1.96, or FDR q value <0.05, above which module preservation was considered statistically significant. The dashed red line indicates a zsummary score of 10, or FDR q value ~ 1e-23, above which module preservation was considered highly statistically significant. (c) Case status and trait preservation in the ROS/MAP cohorts. The top 20% of proteins by kME value in each AD brain protein network module was used to create a synthetic eigenprotein, which was then measured by case status in ROS/MAP and correlated with amyloid plaque load (CERAD score), tau neurofibrillary tangle burden (Braak stage), and cognitive function (global cognitive function composite z score). Synthetic eigenprotein analyses for modules M1, M3, M4, and M10 are shown. Analyses for all modules, with additional trait correlations, are provided in Supplementary Fig. 4. Differences in module synthetic eigenproteins by case status were assessed by Kruskal-Wallis one-way ANOVA. Module synthetic eigenprotein correlations were performed using biweight midcorrelation with Benjamini-Hochberg correction. Boxplots represent the median, 25th, and 75th percentiles, and whiskers represent measurements to the 5th and 95th percentiles. Cntl, control; AsymAD, asymptomatic Alzheimer’s disease; AD, Alzheimer’s disease.

Extended Data Fig. 6 AD Protein Network Module Changes in Other Neurodegenerative Diseases by PRM Analysis.

a–c, Protein levels for 323 proteins across 108 brains from the UPenn cohort were measured by parallel reaction monitoring targeted mass spectrometry (PRM-MS) (a), Targeted peptides and individual protein measurements by disease group are provided in Supplementary Table 4 and Supplementary Fig. 11, respectively. (b), Protein levels across all cases were highly correlated between LFQ and PRM measurements (n=307 paired protein measurements). Correlation was performed by Pearson’s rho and Student’s significance (p). (c), A synthetic eigenprotein was created from proteins that mapped to an AD network module and measured across the different disease groups (control samples n=46, AD n=49, ALS n=59, FTLD-TDP n=29, PSP n=27, CBD n=17, PD/PDD n=80, and MSA n=23 after network connectivity outlier removal). Analyses for all modules are provided in Supplementary Fig. 12. Differences in module synthetic eigenproteins were assessed by Kruskal-Wallis one-way ANOVA. Differences between AD and other case groups were assessed by two-sided Dunnett’s test, the results of which are provided in Supplementary Table 4. Boxplots represent the median, 25th, and 75th percentiles, and whiskers represent measurements to the 5th and 95th percentiles.

Extended Data Fig. 7 Protein Differential Abundance in AD Brain.

a–c, Differential protein abundance for AD versus control (a), AD versus AsymAD (b), and AsymAD versus control (c) brain, represented by fold-change versus t statistic for the given comparison (n=230 AD, n=98 AsymAD, n=91 control samples after network connectivity outlier removal). Differential abundance data are from the consensus analysis described in Fig. 1a. Proteins are colored by the module in which they reside according to the scheme shown in Fig. 1b. For instance, proteins that reside in module M4 are colored yellow. Pairwise comparisons were performed using one-way ANOVA with Tukey test. The bold horizontal dashed line represents p < 0.05. AsymAD, asymptomatic Alzheimer’s disease; AD, Alzheimer’s disease.

Extended Data Fig. 8 Differential Abundance of Reactive Astrocyte Protein Markers in AD Brain.

a–c, Proteins expressed in different astrocytic response states to acute injury26 were analyzed for changes in AD. Astrocyte mRNAs that were upregulated greater than four-fold after acute injury by LPS administration (“A1” Inflammatory) (a), middle cerebral artery occlusion (“A2” Tissue Repair) (b), or both (“A1/A2 Mixed”) (c) were analyzed for changes in abundance between AD and control. Results are shown as protein fold-change versus t statistic for the given comparison (n=230 AD, n=98 AsymAD, n=91 control samples after network connectivity outlier removal). Pairwise comparisons were performed using one-way ANOVA with Tukey test. The bold horizontal dashed line represents p < 0.05. Proteins are colored by the module in which they reside according to the scheme shown in Fig. 1b. AD, Alzheimer’s disease.

Extended Data Fig. 9 Differential Abundance of Microglial Phenotypic Protein Markers in AD Brain.

a–c, Proteins corresponding to microglial mRNAs that were found to be associated with different microglial phenotypic states27 were analyzed for changes in AD. Proteins from microglial co-expression modules corresponding to a disease-associated anti-inflammatory (a), disease-associated pro-inflammatory (b), and homeostatic (c) response phenotype were analyzed for changes in abundance between AD and control. Results are shown as protein fold-change versus t statistic for the given comparison (n=230 AD, n=98 AsymAD, n=91 control samples after network connectivity outlier removal). Pairwise comparisons were performed using one-way ANOVA with Tukey test. The bold horizontal dashed line represents p < 0.05. Proteins are colored by the module in which they reside according to the scheme shown in Fig. 1b. AD, Alzheimer’s disease.

Extended Data Fig. 10 M4 Astrocyte/Microglial Metabolism Module Members Increased at the Transcript Level in Microglia Undergoing Active Amyloid-β Plaque Phagocytosis.

mRNA transcripts increased in microglia undergoing active amyloid-β plaque phagocytosis (XO4+)33 were overlapped with cognate proteins in the M4 module. There were 23 transcripts that overlapped with M4 module members. Proteins that also overlapped with the top 30 disease-associated microglia (DAM) markers in the M4 module (Fig. 5d) are shown in blue. Proteins that did not overlap with the top 30 DAM markers are shown in cyan. Proteins in cyan are therefore M4 members that may be more specifically elevated in microglia undergoing active amyloid-β plaque phagocytosis.

Supplementary Information

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Supplementary Figs. 1–14.

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Johnson, E.C.B., Dammer, E.B., Duong, D.M. et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med 26, 769–780 (2020). https://doi.org/10.1038/s41591-020-0815-6

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