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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1.

    Prince, M. et al. World Alzheimer Report 2015: The Global Impact of Dementia. (Alzheimer’s Disease International, 2015).

  2. 2.

    Jack, C. R. Jr. et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Miller, J. A., Oldham, M. C. & Geschwind, D. H. A systems level analysis of transcriptional changes in Alzheimer’s disease and normal aging. J. Neurosci. 28, 1410–1420 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Clarke, C. et al. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis 34, 2300–2308 (2013).

    CAS  PubMed  Google Scholar 

  6. 6.

    Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Huan, T. et al. Integrative network analysis reveals molecular mechanisms of blood pressure regulation. Mol. Syst. Biol. 11, 799 (2015).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Tran, L. M. et al. Inferring causal genomic alterations in breast cancer using gene expression data. BMC Syst. Biol. 5, 121 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Huan, T. et al. A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler. Thromb. Vasc. Biol. 33, 1427–1434 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Cerami, E., Demir, E., Schultz, N., Taylor, B. S. & Sander, C. Automated network analysis identifies core pathways in glioblastoma. PLoS ONE 5, e8918 (2010).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Yue, Z. et al. Repositioning drugs by targeting network modules: a Parkinson’s disease case study. BMC Bioinformatics 18, 532 (2017).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Toledo, J. B. et al. Metabolic network failures in Alzheimer’s disease: a biochemical road map. Alzheimers Dement. 13, 965–984 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Sun, M., Sun, T., He, Z. & Xiong, B. Identification of two novel biomarkers of rectal carcinoma progression and prognosis via co-expression network analysis. Oncotarget 8, 69594–69609 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    He, Z. et al. Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis. Oncotarget 8, 27904–27914 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Mirra, S. S. et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology 41, 479–486 (1991).

    CAS  PubMed  Google Scholar 

  16. 16.

    Miller, J. A., Woltjer, R. L., Goodenbour, J. M., Horvath, S. & Geschwind, D. H. Genes and pathways underlying regional and cell type changes in Alzheimer’s disease. Genome Med. 5, 48 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Seyfried, N. T. et al. A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease. Cell Syst. 4, 60–72 e64 (2017).

    CAS  PubMed  Google Scholar 

  18. 18.

    Bennett, D. A., Schneider, J. A., Arvanitakis, Z. & Wilson, R. S. Overview and findings from the religious orders study. Curr. Alzheimer Res. 9, 628–645 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Bennett, D. A. et al. Overview and findings from the rush memory and aging project. Curr. Alzheimer Res. 9, 646–663 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Bennett, D. A. et al. The Rush Memory and Aging Project: study design and baseline characteristics of the study cohort. Neuroepidemiology 25, 163–175 (2005).

    PubMed  Google Scholar 

  21. 21.

    Ping, L. et al. Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease. Sci. Data 5, 180036 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Rauniyar, N. & Yates, J. R. 3rd Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S. & Coon, J. J. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol. Cell Proteomics 11, 1475–1488 (2012).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Zamanian, J. L. et al. Genomic analysis of reactive astrogliosis. J. Neurosci. 32, 6391–6410 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Rangaraju, S. et al. Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease. Mol. Neurodegener. 13, 24 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Ransohoff, R. M. & Perry, V. H. Microglial physiology: unique stimuli, specialized responses. Annu. Rev. Immunol. 27, 119–145 (2009).

    CAS  PubMed  Google Scholar 

  29. 29.

    Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    CAS  PubMed  Google Scholar 

  30. 30.

    Holtman, I. R. et al. Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis. Acta Neuropathol. Commun. 3, 31 (2015).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271 (2019).

    CAS  Google Scholar 

  32. 32.

    Orre, M. et al. Isolation of glia from Alzheimer’s mice reveals inflammation and dysfunction. Neurobiol. Aging 35, 2746–2760 (2014).

    CAS  PubMed  Google Scholar 

  33. 33.

    Grubman, A., et al. Mouse and human microglial phenotypes in Alzheimer’s disease are controlled by amyloid plaque phagocytosis through Hif1α. Preprint at bioRxiv https://doi.org/10.1101/639054

  34. 34.

    Jack, C. R. Jr. et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539–547 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Bai, B. et al. Deep multilayer brain proteomics identifies molecular networks in Alzheimer’s disease progression. Neuron 105, 975–991.e7 (2020).

    CAS  Google Scholar 

  36. 36.

    Higginbotham, L., et al. Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer’s disease. Preprint at bioRxiv https://doi.org/10.1101/806752

  37. 37.

    Johnson, E. C. B. et al. Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease. Mol. Neurodegener. 13, 52 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Wingo, A. P., et al. Cerebral atherosclerosis contributes to Alzheimer’s dementia independently of its hallmark amyloid and tau pathologies. Preprint at bioRxiv https://doi.org/10.1101/793349

  39. 39.

    Jonsson, T. et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N. Engl. J. Med. 368, 107–116 (2013).

    CAS  PubMed  Google Scholar 

  40. 40.

    Wang, Y. et al. TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell 160, 1061–1071 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Baker, M. et al. Mutations in progranulin cause tau-negative frontotemporal dementia linked to chromosome 17. Nature 442, 916–919 (2006).

    CAS  PubMed  Google Scholar 

  42. 42.

    Cruts, M. et al. Null mutations in progranulin cause ubiquitin-positive frontotemporal dementia linked to chromosome 17q21. Nature 442, 920–924 (2006).

    CAS  PubMed  Google Scholar 

  43. 43.

    Hickman, S., Izzy, S., Sen, P., Morsett, L. & El Khoury, J. Microglia in neurodegeneration. Nat. Neurosci. 21, 1359–1369 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Swarup, V., et al. Identification of conserved proteomic networks in neurodegenerative dementia. Preprint at bioRxiv https://doi.org/10.1101/825802

  45. 45.

    Drummond, E. et al. Proteomic differences in amyloid plaques in rapidly progressive and sporadic Alzheimer’s disease. Acta Neuropathol. 133, 933–954 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Hamelin, L. et al. Early and protective microglial activation in Alzheimer’s disease: a prospective study using 18F-DPA-714 PET imaging. Brain 139, 1252–1264 (2016).

    PubMed  Google Scholar 

  47. 47.

    Ramanan, V. K. et al. GWAS of longitudinal amyloid accumulation on 18F-florbetapir PET in Alzheimer’s disease implicates microglial activation gene IL1RAP. Brain 138, 3076–3088 (2015).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Mathur, R. et al. A reduced astrocyte response to β-amyloid plaques in the ageing brain associates with cognitive impairment. PLoS ONE 10, e0118463 (2015).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Luo, Y. et al. Asymmetric dimethylarginine exacerbates aβ-induced toxicity and oxidative stress in human cell and Caenorhabditis elegans models of Alzheimer disease. Free Radic. Biol. Med. 79, 117–126 (2015).

    CAS  PubMed  Google Scholar 

  50. 50.

    Power, J. H. et al. Peroxiredoxin 6 in human brain: molecular forms, cellular distribution and association with Alzheimer’s disease pathology. Acta Neuropathol. 115, 611–622 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Clements, C. M., McNally, R. S., Conti, B. J., Mak, T. W. & Ting, J. P. DJ-1, a cancer- and Parkinson’s disease-associated protein, stabilizes the antioxidant transcriptional master regulator Nrf2. Proc. Natl Acad. Sci. USA 103, 15091–15096 (2006).

    CAS  PubMed  Google Scholar 

  52. 52.

    Dayon, L. et al. Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimers Res. Ther. 10, 66 (2018).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Sathe, G. et al. Quantitative proteomic profiling of cerebrospinal fluid to identify candidate biomarkers for Alzheimer’s disease. Proteomics Clin. Appl. 13, e1800105 (2019).

    PubMed  Google Scholar 

  54. 54.

    Dayon, L. et al. Proteomes of paired human cerebrospinal fluid and plasma: relation to blood–brain barrier permeability in older adults. J. Proteome Res. 18, 1162–1174 (2019).

    CAS  PubMed  Google Scholar 

  55. 55.

    Langfelder, P., Luo, R., Oldham, M. C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    O’Brien, R. J. et al. Neuropathologic studies of the baltimore longitudinal study of aging (BLSA). J. Alzheimers Dis. 18, 665–675 (2009).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Beach, T. G. et al. Arizona study of aging and neurodegenerative disorders and brain and body donation program. Neuropathology 35, 354–389 (2015).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Bennett, D. A. et al. Religious orders study and rush memory and aging project. J. Alzheimers Dis. 64, S161–S189 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).

    CAS  Google Scholar 

  61. 61.

    Ince, P. G., Ince, P. G., Lowe, J. & Shaw, P. J. Review. Neuropathol. Appl. Neurobiol. 24, 104–117 (1998).

    CAS  PubMed  Google Scholar 

  62. 62.

    Balsis, S., Benge, J. F., Lowe, D. A., Geraci, L. & Doody, R. S. How do scores on the ADAS-cog, MMSE, and CDR-SOB correspond? Clin. Neuropsychol. 29, 1002–1009 (2015).

    PubMed  Google Scholar 

  63. 63.

    Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell Proteomics 13, 2513–2526 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Luber, C. A. et al. Quantitative proteomics reveals subset-specific viral recognition in dendritic cells. Immunity 32, 279–289 (2010).

    CAS  PubMed  Google Scholar 

  65. 65.

    Mertins, P. et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat. Protoc. 13, 1632–1661 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Wingo, T. S. et al. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J. Proteome Res. 16, 3336–3347 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Petyuk, V. A., Qian, W. J., Smith, R. D. & Smith, D. J. Mapping protein abundance patterns in the brain using voxelation combined with liquid chromatography and mass spectrometry. Methods 50, 77–84 (2010).

    CAS  PubMed  Google Scholar 

  68. 68.

    Hulstaert, F. et al. Improved discrimination of AD patients using β-amyloid(1-42) and tau levels in CSF. Neurology 52, 1555–1562 (1999).

    CAS  PubMed  Google Scholar 

  69. 69.

    Shaw, L. M. et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann. Neurol. 65, 403–413 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Yi, L. et al. Boosting to amplify signal with isobaric labeling (BASIL) strategy for comprehensive quantitative phosphoproteomic characterization of small populations of cells. Anal. Chem. 91, 5794–5801 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Tukey, J. W. Exploratory Data Analysis (Addison-Wesley, 1977).

  72. 72.

    McKenzie, A. T. et al. Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer’s disease. Mol. Neurodegener. 12, 82 (2017).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Zhao, M., Chen, L. & Qu, H. CSGene: a literature-based database for cell senescence genes and its application to identify critical cell aging pathways and associated diseases. Cell Death Dis. 7, e2053 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Umoh, M. E. et al. A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain. EMBO Mol. Med. 10, 48–62 (2018).

    CAS  PubMed  Google Scholar 

Download references

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

Affiliations

Authors

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 or Allan I. Levey or Nicholas T. Seyfried.

Ethics declarations

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.

ad, 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.

ac, 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.

(ac), 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.

ac, 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.

ac, 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.

ac, 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.

ac, 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

Supplementary Information

Supplementary Figs. 1–14.

Reporting Summary

Supplementary Table

Supplementary Tables 1–5.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing