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Quantitative phosphoproteomics uncovers dysregulated kinase networks in Alzheimer’s disease

Nature Aging volume 1, pages 550–565 (2021)Cite this article

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Abstract

Alzheimer’s disease (AD) is a form of dementia characterized by amyloid-β plaques and tau neurofibrillary tangles that progressively disrupt neural circuits in the brain. The signaling networks underlying AD pathological changes are poorly characterized at the phosphoproteome level. Using mass spectrometry, we analyzed the proteome and tyrosine, serine and threonine phosphoproteomes of temporal cortex tissue from patients with AD and aged-matched controls. We identified cocorrelated peptide clusters that were linked to varying levels of phospho-tau, oligodendrocyte, astrocyte, microglia and neuron pathologies. We found that neuronal synaptic protein abundances were strongly anti-correlated with markers of microglial reactivity. We also observed that phosphorylation sites on kinases targeting tau and other new signaling factors were correlated with these peptide modules. Finally, we used data-driven statistical modeling to identify individual peptides and peptide clusters that were predictive of AD histopathologies. Together, these results build a map of pathology-associated phosphorylation signaling events occurring in AD.

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Fig. 1: Combined phosphoproteomics and proteomics analysis captures the molecular signaling profile of AD.
Fig. 2: Clustering analysis identifies pathology-associated peptide modules.
Fig. 3: Correlation analysis finds pathology-associated change in the full peptidome.
Fig. 4: Kinome map of the pathology-associated phosphoproteome.
Fig. 5: PLSR model ties clusters and peptides to AD histopathologies.

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Data availability

Data supporting findings from this study are available from the lead contact, F.M.W., upon request. The MS raw and searched proteomics data files have been deposited to the ProteomeXchange Consortium via the PRIDE114 partner repository with the dataset accession no. PXD020087 and 10.6019/PXD020087. Patient clinical histopathology data and MS-run information are available in Supplementary Data 1. Processed peptide data are available in Supplementary Data 2. All calculated peptide feature correlations and group fold-changes are available in Supplementary Data 4 and 5. Cell type-specific sequencing data were downloaded from BrainRNASeq (https://web.stanford.edu/group/barres_lab/brainseq2/TableS4-HumanMouseMasterFPKMList.xlsx). Kinase–substrate and homology mapping data were downloaded from PhosphoSite Plus (https://www.phosphosite.org/downloads/Kinase_Substrate_Dataset.gz, https://phosphosite.org/downloads/Phosphorylation_site_dataset.gz). BRSK1 and BRSK2 substrates were derived from MaxQuant processed ‘_phosphoSites_filtered_imputed.txt’ file, shared directly by Tamir et al.112. For GO analysis, ‘gene2go’ (ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz) and ‘go-basic.obo’ (http://geneontology.org/ontology/go-basic.obo) were downloaded using goatools. Mouse phosphoproteome data for analysis was sourced from Morshed et al.22: dataset EV1. Mouse source MS data are available on PRIDE via accession no. PXD018757.

Code availability

The proteomics data integration software is available on GitHub (https://github.com/white-lab/pyproteome). This repository also includes detailed software for concatenating peptide fractions on a Gibson FC 204 Fraction Collection (https://github.com/white-lab/fc-cycle) and an updated tool for validating PSMs (https://github.com/white-lab/CAMV). Sources for other code used in the present study are indicated in Supplementary Information.

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Acknowledgements

We thank members of the laboratories of F.M.W., L.-H. Tsai and D.A.L. for numerous discussions and feedback. N.M. was partially supported by the NIH Biotechnology Training grant (no. T32GM008334). M.J.L. was partially supported through the US Army Research Office Cooperative Agreement (no. W911NF-19-2-0026) for the Institute for Collaborative Biotechnologies and the National Science Foundation Graduate Research Fellowship Program (award no. 1745302). We thank the BSHRI Brain and Body Donation Program of Sun City, AZ for the provision of human brain tissue. The Brain and Body Donation Program has been supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the National Institute on Aging (P30 AG19610 Arizona Alzheimer’s Disease Core Center), the Arizona Department of Health Services (contract no. 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contract nos. 4001, 0011, 05-901 and 1001 to the Arizona Parkinson’s Disease Consortium) and the Michael J. Fox Foundation for Parkinson’s Research.

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Authors and Affiliations

  1. Department of Bioengineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Nader Morshed, Meelim J. Lee, Douglas A. Lauffenburger & Forest M. White

  2. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Nader Morshed & Forest M. White

  3. Department of Chemical and Materials Engineering, New Mexico State University, Las Cruces, NM, USA

    Felicia H. Rodriguez

  4. Arizona State University–Banner Neurodegenerative Disease Research Center, Tempe, AZ, USA

    Diego Mastroeni

  5. Center for Precision Cancer Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA

    Forest M. White

Authors
  1. Nader Morshed
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  2. Meelim J. Lee
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  3. Felicia H. Rodriguez
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  4. Douglas A. Lauffenburger
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  5. Diego Mastroeni
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Contributions

N.M. and F.M.W. designed the study. D.M. collected human brain tissue samples. N.M. and F.H.R. collected proteomics datasets. N.M. wrote proteomics data integration and enrichment software. N.M. and M.J.L. performed statistical analyses with methodology and analysis oversight from D.A.L. N.M., M.J.L. and F.M.W. wrote the manuscript.

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Correspondence to Forest M. White.

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Extended data

Extended Data Fig. 1 Proteome and phosphoproteome dataset statistics and quantification reproducibility.

a–c, Venn diagram showing peptide overlap between (a) pTyr sites, (b) pSer/pThr sites, and (c) proteins identified in each 10-plex analysis. d, Cumulative number of peptides identified compared to the number of patients with peptide quantification. e-f, Reproducibility analysis of peptide quantification after data integration and normalization. Shown are the log2 quantification ratios between two separate brain samples from patients (e) 06-49 and (f) 06-03. Peptides are separated on the x-axis by log10 total TMT intensity from all PSMs in both 10-plex analyses. The standard deviation of abundance ratios and Pearson’s correlation coefficient between all peptide replicates are listed in the panels. g,h, Reproducibility of peptide quantification between technical replicates of peptide aliquots for each TMT10-plex analysis. Shown are (g) the standard deviation of abundance ratios and (h) the Pearson’s correlation coefficient calculated over all peptides. Box plot indicates quartiles, and whiskers indicate the last datum within 1.5 interquartile range. Individual data points are shown for each technical replicate.

Extended Data Fig. 2 Accessory clustering and dataset comparison analyses.

a-b, Properties of clusters generated by clustering with increasing number of clusters. Shown are the (a) mean Pearson correlation coefficient for cluster peptides with their respective cluster centroid, averaged over all clusters, and (b) minimum absolute Pearson correlation coefficient for cluster peptides with their respective cluster centroid, summed and normalized over all clusters. Dotted line indicates the final cluster count used for downstream analysis. c, Co-correlation heatmap between all cluster centroids, group by cluster. Selected named clusters are shown on the y-axis. d, Phosphopeptide composition of all peptides associated with cluster centroids. Peptides are labeled as ‘pY’ if they contain at least one pTyr site, ‘pST’ if they contain at least one pSer or pThr site, or ‘no phospho’ if they are not phosphorylated. Statistics calculations are same as Fig. 2g. e, Binomial enrichment for proteome module marker proteins identified in Johnson et al. (2020). Heatmap shows log-odds enrichment (LOE) values for the overlap of marker transcripts with pTyr, pSer/pThr, and proteome datasets that are correlated with cluster centroids. f, Binomial enrichment for single-nuclei populations markers identified in Mathys et al. (2019). Legend is same as (e). g, Venn diagram overlap between proteins correlated with Oligo in our dataset, M2 Oligo proteome module from (e) and Oli0 marker transcripts from (f). Numbers indicate the total number of proteins in each overlapping set and selected protein names are shown. h, Transcript log2 Fold Change values Oli0 population from Mathys et al. (2019) for markers of the Oligo cluster.

Extended Data Fig. 3 Peptide cluster principle component and stratification analyses.

a, Principal component analysis (PCA) of peptides quantified in all samples. Patients are colored by (a) AD or ND status, (b) Tau, (c) Oligo, (d) Astro, (e) Micro, (f) pNeuro, and (g) Neuro cluster centroid values. h–j, PCA of samples colored by patient stratifications using (h) Tau and Oligo, (i) Astro and Micro, and (j) pNeuro and Neuro cluster centroids. k,l, Patients numbers for (k) Astro;Micro or (l) pNeuro;Neuro patient stratifications. m,n, Overlap between Tau;Oligo and (m) Astro;Micro or (n) pNeuro;Neuro patient stratifications.

Extended Data Fig. 4 Peptide correlation analyses.

a,b, Distribution of Spearman’s correlation coefficient for peptides identified in ≥15 patients with (a) scrambled cluster centroids or (b) true cluster centroids. c,d, Distribution of Spearman’s correlation p-values for peptides identified in ≥15 patients with (c) scrambled cluster centroids or (d) true cluster centroids. (e–g) Venn diagram showing the number of correlated peptides with (e) Tau and Oligo, (f) Astro and Micro, and (g) pNeuro and Neuro cluster centroids. Plots include the total number of associated peptides for each cluster, as well as the number of overlapping peptides between similar clusters. h, Heatmap showing phosphopeptide fold changes for peptides that are significantly correlated with Tau cluster centroid. Samples are ordered by centroid values. Left color bars indicate proteins that were predicted to be cell type-specific. Green = Astrocyte, Purple = Oligodendrocyte, Yellow = Neuron, Blue = Microglia. Missing values are indicated as white boxes. i, Heatmap showing fold changes cell type-specific proteins that are significantly correlated with Oligo cluster centroid. Samples are ordered by centroid value. Left color bar legend is same as (h).

Extended Data Fig. 5 Analyses of peptide partial cluster correlation.

a-d, Fold changes for (a) MAPT pS579 (pS262), (b) MAPT pT534 (pS217), (c) MBP protein, and (d) PLP1 protein. Protein levels were estimated from the median abundance of all unmodified peptides that map uniquely to that protein. Values are normalized to the median of Tau− Oligo− patients. Box plot indicates quartiles, and whiskers indicate the last datum within 1.5 interquartile range. Individual data points are shown for each sample. Stars indicate two-sided t-test values for each group compared with Tau− Oligo− and Tau− Oligo+ patients (n = 8-14 patients in each group). ns, not significant (p > 0.05); * p < 5e-2, **p < 1e-2; ***p < 1e-3; ****p < 1e-4. e, All peptides mapping to the peripheral nervous system (PNS) isoform of MAPT. Colored bars indicate directional correlation for non-phosphorylated peptides. Red = correlated, blue = anti-correlated, grey = uncorrelated, light-grey = only phosphopeptides were seen in that region. Colored circles indicate phosphorylation sites that were quantified. Red circle = correlated, blue circle = anti-correlated, black circle = uncorrelated. Green dividers indicate the borders between the N-terminal projection, proline-rich domain, microtubule-binding repeats, and C-terminal flanking region. f, All peptides mapping to the full-length isoform of MBP. Legend is same as (e). g–r, Fold changes across Astro+ and Micro+ groups for (g) GFAP protein, (h) VIM protein, (i) GNA13 protein, (j) AIF1 protein, (k) ITGB2 protein, (l) SPP1 pS270, (m) C1QA protein, (n) C1QB protein, (o) C1QC protein, (p) C3 protein, (q) C4A protein, and (r) C4B protein. Legend is same as (a-d). Stars indicate two-sided t-test values for each group compared with Astro− Micro− and Astro+ Micro− patients (n = 12-14 patients in each group). ns, not significant (p > 0.05); * p < 5e-2, **p < 1e-2; ***p < 1e-3; ****p < 1e-4.

Extended Data Fig. 6 Tau kinase and CDK phosphosite abundances.

a-d, Fold changes for (a) TNIK pS680, (b) BRSK1 pS443, (c) BRSK2 pS423, and (d) TTBK1 pS483 across Tau and Oligo groups. Values are normalized to the median of Tau− Oligo− patients. Box plot indicates quartiles, and whiskers indicate the last datum within 1.5 interquartile range. Individual data points are shown for each sample. Stars indicate two-sided t-test values for each group compared with Tau− Oligo− and Tau− Oligo+ patients (n = 8-14 patients in each group). ns, not significant (p > 0.05); * p < 5e-2, **p < 1e-2; ***p < 1e-3; ****p < 1e-4. e–h, Fold changes for all non-phospho peptides and significantly changing phosphosites on (e) TNIK, (f) BRSK1, (g) BRSK2, (h) TTBK1. Each dot indicates one unique peptide. Phosphosite ranges are estimated from peptides with missed cleavages. Box plot indicates quartiles, and whiskers indicate the last datum within 1.5 interquartile range. Phosphosites labeled with ‘*’ are on peptides that map ambiguously to more than one protein. i–m, Fold changes for (i) CDK5 pY15, (j) CDKL5 pY171, (k) CDK16 pS153, (l) CDK17 pS180, and (m) CDK18 pS132 across Tau and Oligo groups. Legend is same as (a-d). n–r, Fold changes for all non-phospho peptides and significantly changing phosphosites on (n) CDK5, (o) CDKL5, (p) CDK16, (q) CDK17, and (r) CDK18. Legend is same as (e-h).

Extended Data Fig. 7 Accessory kinome analyses.

a, Maximum phosphosite fold changes and median protein abundances for all kinases shown in Fig. 4d. Changes are calculated for each Tau;Oligo group compared to Tau−;Oligo−. Stars indicate two-sided t-test values for each group compared with Tau− Oligo− patients (n = 8-14 patients in each group). *p < 5e-2; **p < 1e-3; ***p < 1e-4. b–m, Fold changes for all non-phospho peptides and significantly changing phosphosites on (b) MAPK8 (c) MAPK10, (d) MAPK11, (e) MAPK14, (f) PRKACA, (g) PRKCA, (h) all CaMKII subunits, (i) SRC, (j) LMTK2, (k) PTK2, (l) DDR1, and (m) EGFR. Legend is same as Extended Data Figure 6e.

Extended Data Fig. 8 Kinase-substrate and AD mouse model analyses.

a–d, Volcano plots showing Spearman’s correlation coefficient and p-values between PKA Cα substrates and (a) Tau, (b) Astro, (d) Oligo, and (d) pNeuro cluster centroids. Labels are colored using the same scheme as Extended Data Figure 4h. e, Number of phosphosites from AD mouse models identified in three mouse models of neurodegeneration that could be translated to human and were significantly correlated with each cluster centroid and shared directional changes. f, Phosphoproteins that were identified to have at least one shared phosphosite with three mouse models of neurodegeneration. Proteins are shown as a Venn diagram between downregulated phosphosites and pNeuro or Neuro cluster centroids.

Extended Data Fig. 9 PLSR accessory analyses

a–c, Latent variable plots for multivariate PLSR analysis for X scores. Points are colored by patient (a) NIA-R score, (b) CERAD-NP score, and (c) TangleT score. d, K-fold validation of the multivariate PLSR model for all included histopathology variables. R2 and Q2 values are shown for training and test data predictions respectively. e, Scatter plot showing PLSR patient scores generated from cluster centroids compared with subsets of the matrix of peptides quantified in all patients. f, Fold changes for all non-phospho peptides and significantly changing phosphosites on CKB. Legend is same as Extended Data Fig. 6e. g–j, Fold changes for (g) SYN1, (h) SYT1, (i) CAMKK1 pS52, and (j) CASKIN pS935 across pNeuro and Neuro groups. Box plot indicates quartiles, and whiskers indicate the last datum within 1.5 interquartile range. Individual data points are shown for each sample. Stars indicate two-sided t-test values for each group compared with pNeuro− Neuro− patients (n = 5-16 patients in each group). ns, not significant (p > 0.05); * p < 5e-2, **p < 1e-2; ***p < 1e-3; ****p < 1e-4. k–m, Fold changes for (k) EMILIN3, (l) C4A, and (m) VGF across Tau and Oligo groups. Legend is same as (g-j). Stars indicate two-sided t-test values for each group compared with Tau− Oligo− and Tau− Oligo+ patients (n = 8-14 patients in each group). ns, not significant (p > 0.05); * p < 5e-2, **p < 1e-2; ***p < 1e-3; ****p < 1e-4.

Extended Data Fig. 10 Additional ROC and PMI analyses.

a,b, ROC curves predicting AD status from (a) protein and (b) phosphosites highlighted in Fig. 5f–h.

Supplementary information

Supplementary Information

Supplementary Note 1, Table 1 and Fig. 1.

Reporting Summary

Supplementary Data 1

Demographic information and clinical histopathology measurements for individual patients.

Supplementary Data 2

Full peptidome dataset with individual quantification values.

Supplementary Data 3

Peptide cluster composition: cluster identities and centroid values for peptides quantified in all samples.

Supplementary Data 4

Proteome and phosphoproteome cluster correlations: the table includes the Spearman’s correlation and associated P values between each peptide and each cluster centroid.

Supplementary Data 5

Proteome and phosphoproteome cluster fold-changes: the table includes fold-changes and P values for each peptide and cluster group against their respective negative cases.

Supplementary Data 6

AD mouse phosphoproteome overlap: AD mouse model phosphosites from Morshed et al.22 that had homologous phosphosites detected in the human AD phosphoproteome.

Supplementary Data 7

Peptide scores and VIP values for peptide-centric PLSR models: the table includes tabs for PLSR models built from pTyr, pSer/pThr, protein and all data.

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Morshed, N., Lee, M.J., Rodriguez, F.H. et al. Quantitative phosphoproteomics uncovers dysregulated kinase networks in Alzheimer’s disease. Nat Aging 1, 550–565 (2021). https://doi.org/10.1038/s43587-021-00071-1

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