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Global, in situ analysis of the structural proteome in individuals with Parkinson’s disease to identify a new class of biomarker

Abstract

Parkinson’s disease (PD) is a prevalent neurodegenerative disease for which robust biomarkers are needed. Because protein structure reflects function, we tested whether global, in situ analysis of protein structural changes provides insight into PD pathophysiology and could inform a new concept of structural disease biomarkers. Using limited proteolysis–mass spectrometry (LiP–MS), we identified 76 structurally altered proteins in cerebrospinal fluid (CSF) of individuals with PD relative to healthy donors. These proteins were enriched in processes misregulated in PD, and some proteins also showed structural changes in PD brain samples. CSF protein structural information outperformed abundance information in discriminating between healthy participants and those with PD and improved the discriminatory performance of CSF measures of the hallmark PD protein α-synuclein. We also present the first analysis of inter-individual variability of a structural proteome in healthy individuals, identifying biophysical features of variable protein regions. Although independent validation is needed, our data suggest that global analyses of the human structural proteome will guide the development of novel structural biomarkers of disease and enable hypothesis generation about underlying disease processes.

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Fig. 1: Schematic overview of the study.
Fig. 2: Structural variability of the proteome in healthy human CSF.
Fig. 3: Identification of proteome structural variations between the healthy and PD cohort groups.
Fig. 4: Structural changes in selected proteins that are altered between healthy individuals and those with PD.
Fig. 5: Classification of Parkinson’s disease on the basis of CSF proteome information.

Data availability

The mass spectrometry proteomics dataset generated in this study is available in the PRIDE database111 (accession number PXD034120). Source data are provided with this paper.

Code availability

Code for the main analyses (Figs. 2b, 3, and 5) has been deposited on GitHub at https://github.com/beyergroup/Global-analyses-of-the-human-structural-proteome-to-identify-a-new-type-of-disease-biomarker. Further code for plots and other analyses is available upon request. Supplementary Table 12 contains all necessary data to use with the provided scripts.

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Acknowledgements

We gratefully acknowledge all individuals who donated samples used in this project. We thank: K. van Dijk and L. Oosterveld for help collecting CSF samples and clinical datasets; N. Majbour and O. El-Agnaf for collection of the alpha-synuclein datasets; and the Netherlands Brain Bank for postmortem brain tissue samples. M.-T. M. was supported by a long-term EMBO postdoctoral fellowship (ALTF 522-2019). L. N. was funded by DFG (grant CRC 1310 and grant agreement no. 398882498) and the German Academic Exchange Service (Forschungsstipendium fuer Doktorandinnen und Doktoranden). J. G. was funded by DFG (grants CRC 680 and CRC 1310). A. B. acknowledges funding by DFG (grant CRC 1310 and grant agreement no. 398882498). P. P. was funded by a Personalized Health and Related Technologies (PHRT) grant (PHRT-506), a Sinergia grant from the Swiss National Science Foundation (SNSF grant CRSII5_177195), the Peter Bockhoff Stiftung and the ETH Zurich Foundation, Parkinson Schweiz, the European Research Council (866004), and the EPIC-XS Consortium (823839), the last two under the EU Horizon 2020 program. W. D. J. v. d. B. was financially supported by grants from Amsterdam Neuroscience, Dutch Research council (ZonMW 70-73305-98-106; 70-73305-98-102; 40-46000-98-101), Michael J. Fox foundation (17253), and Dutch Parkinson Association (2020-G01). Some figures were created with BioRender.com.

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P. P. conceived the project. M.-T. M. conceived the experimental pipeline with input from P. P. and A. B. M.-T. M., A. B. and P. P. designed the experiments. M.-T. M. performed the experiments. M.-T. M., L. N. and F. S. analyzed the data. J. M., R. B., L. R. and W. D. J. v. d. B. collected the data. P. S. and M.-T. M. designed and analyzed in vitro experiments. W. D. J. v. d. B. provided the clinical samples. L. N. and J. G. performed the statistical analysis with input from A.B. N. d. S. supervised writing of the manuscript. M.-T. M., L. N., F. S., N. d. S., A. B. and P. P. wrote the manuscript. A. B. and P. P. supervised the project. All authors discussed and revised the final manuscript prior to submission.

Corresponding authors

Correspondence to Andreas Beyer or Paola Picotti.

Ethics declarations

Competing interests

The authors RB, JM and LR are full-time employees of Biognosys AG (Zurich, Switzerland). Spectronaut is a trademark of Biognosys AG. PP is an inventor of a patent licensed by Biognosys AG that covers the LiP–MS method used in this manuscript. WvdB performed contract research and consultancy for Hoffmann-La Roche, Roche Tissue Diagnostics, Crossbeta Sciences, Discoveric Bio and received research consumables from Hoffmann-La Roche and Prothena. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Structural and Molecular Biology thanks Tiago Outeiro, Marcus Bantscheff, and Laura Parkkinen for their contribution to the peer review of this work. Primary Handling editors: Anke Sparmann and Florian Ullrich, in collaboration with the Nature Structural & Molecular Biology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Global characteristics of study population.

a, Characteristics of the study cohort. P values were estimated via Wilcoxon rank sum test. b, Age distribution within the healthy (HG) and PD (PDG) cohort groups, separated by sex. Boxplots: median, center; first and third quantile, lower and upper hinges; largest/smallest value no further than 1.5 * inter-quantile range of the hinge, whiskers; data points beyond are defined as outliers and plotted individually. P values are indicated (Wilcoxon rank sum test, n = 51 subjects in HG, n = 52 subjects PDG). c, GO enrichment of all identified proteins in the CSF proteome (trypsin-only control data) using the human proteome (UniProt FASTA, July 2019) as the background. Only the 10 terms with the highest enrichment per GO domain are shown. Numerical data for graphs in b and c are available as source data.

Source data

Extended Data Fig. 2 Comparison of structural features of variable and non-variable peptides, and the proteins containing these peptides, in CSF.

a, Distribution of secondary structures as loops, alpha-helix and beta-strands for variable/non-variable peptides, as predicted using PSIPRED. Boxplots for all panels: median, center; first and third quantile, lower and upper hinges; largest/smallest value no further than 1.5 * inter-quantile range of the hinge, whiskers; data points beyond are defined as outliers and plotted individually. P values are indicated (Wilcoxon rank sum test; ns, not significant; 9385 non-variable, 386 medium-variable, 117 high-variable peptides, from 51 subjects) b, c, Predicted propensity of peptides to bind DNA or RNA. d, Predicted solvent accessible surface area of variable/non-variable peptides. e, Number of high confidence interaction in STRING for proteins with at least one highly variable peptide (red), as compared to all other proteins (gray). f, g, Sequence length and number of domains as annotated in the PFAM database for proteins with at least one highly variable peptide (red), as compared to all other proteins (gray). h, Side-by-side view of affected peptides from in situ (left, reproduced from Fig. 2g for comparison) and in vitro experiments (right). Structure of human brain fructose bisphosphate aldolase (PDB entry 1XFB). The enzyme is a homotetramer, one subunit is shown as light blue cartoon and the other 3 subunits are shown as gray surface. The substrate is represented as yellow spheres, based on an alignment of PDB entry 1XFB with the ligand bound structure of the muscle isoform (PDB entry 4ALD). For the in situ data (left), the highly variable peptides are highlighted in dark red (bimodal) and salmon (unimodal). For the in vitro data (right), the significant peptides in the presence and absence of fructose 1,6-bisphosphate are highlighted in red (log2 fold change < −1 or > 1). The bimodal and non-bimodal peptide identified in situ are encircled. Numerical data for graphs in a-g are available as source data.

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Extended Data Fig. 3 Effects of the sex variable on the linear model and overlap between the brain and CSF data sets.

a, The histogram visualizes the P values (calculated via t-statistics) of the cohort variable estimated from the linear model describing effects of structural variation, with the indicated combinations of the sex variable and interactions with sex taken into account. For all models, the first bar (extreme left) indicates significant (<0.05) P values. b, Number of proteins and peptides of the CSF and brain samples used for estimating the linear models, visualizing the overlapping peptides and proteins between the two tissues. c, Number of candidate peptide from the CSF where the coefficients change in the same or different direction in the brain samples. Numerical data for graphs in a and c are available as source data.

Source data

Extended Data Fig. 4 Structural changes in selected CSF and brain proteins.

a, b, Structure of PSAT1 (PDB 3E77) colored according to peptides in CSF (a) and brain (b) data. PSAT catalyzes an important step in serine biosynthesis. Black indicates all analyzed peptides; red indicates the candidate peptide (a) or peptides with a significant P value (b). One candidate CSF peptide is only 6.0 Å away from the active site. c, Coverage plot for all analyzed PSAT1 peptides in CSF (top) and brain (bottom). Black represents fully tryptic; gray represents half tryptic peptides and red bars the candidate/significant peptide in the CSF/ brain. d, e, Structure of PRDX6 (PDB PRX1) colored according to peptides in CSF (d) and brain (e) data. Colors as in a, b. f, Coverage plot for all analyzed PRDX6 peptides in CSF (top) and brain (bottom). Colors as in c. g, Structure of AFM (PDB 5OKL). h, Coverage plot for AFM. AFM level in serum exosomes was linked to PD progression4. i, Scaled residual plots for AFM. Boxplots are as in Extended Data Fig. 2 (n = 51 subjects in each HG and PDG). j, Structure of SAMP (PDB entry 1GYK) as pentamer with bound calcium (yellow spheres), an abundance-based plasma biomarker candidate for PD112. k, Coverage plot of SAMP. l, Scaled residual plots for SAMP (ERVGEYSLYIGR: n = 47 subjects in HG and n = 50 subjects in PDG; IVLGQEQDSYGGK: n = 51 subjects in each HG and PDG). m, Structure of BCHE (PDB entry 1P0I) in complex with butanoic acid (yellow spheres). Activity of this enzyme is decreased in PD with dementia (PDD)113, note that BCH inhibition was not used in our cohort. n, Coverage plot for BCHE. o, Scaled residual plot for BCHE (NIAAFGGNPK: n = 51 subjects in each HG and PDG; IFFPQVSEFGK: n = 43 subjects in HG and n = 48 subjects in PDG). Peptide colors in panels G, J, M are as in D; colors in panels H, K, N are as in C. Numerical data for graphs in c, f, h, i, k, l, n and o are available as source data.

Source data

Extended Data Fig. 5 Classification of Parkinson’s Disease in CSF data.

a, ROC curves for classification of PD based on LiP peptide variation. In this case, LiP peptide intensities were neither corrected for trypsin-only peptide intensities nor for protein abundance. b, ROC curves for classification of PD based on LiP peptide variation. In this case, LiP peptide intensities were not corrected for protein abundance. c, ROC curves for classification of PD based on LiP peptide variation. In this case, LiP peptide intensities were not corrected for trypsin-only peptide intensities. d, ROC curves for classification of PD based on ELISA measurement of different a-synuclein species from. e, Total α-synuclein levels compared to the ratio of the oligomeric/total α-synuclein level across the cohort. f, Oligomeric α-synuclein levels compared to the ratio of the oligomeric/total α-synuclein level across the cohort. For (e) and (f), each dot represents a single individual. g, Comparison of classification of the PDG using the ratio of oligomeric to total a-synuclein (log odds plotted on x axis) and using a combination of five LiP peptide levels (log odds plotted on y axis). Each point represents an individual and the HY-stage is indicated by color. Numerical data for graphs in a-g are available as source data.

Source data

Supplementary information

Reporting Summary

Peer Review File

Supplementary Table 1

Raw peptide intensities of healthy and PD CSF samples measured for trypsin-only (PK-independent) conditions.

Supplementary Table 2

Raw peptide intensities of healthy and PD CSF samples measured for LiP conditions.

Supplementary Table 3

Summary of the number of peptides and proteins used in different analyses of CSF and brain data.

Supplementary Table 4

Protein and peptide usage across analyses of the CSF samples.

Supplementary Table 5

Structural peptide variability analysis. Peptides included in the analysis of the variability of the healthy CSF human proteome.

Supplementary Table 6

LiP–MS analysis of in vitro FBP aldolase and vitamin-D-binding protein with and without substrate.

Supplementary Table 7

Structural variation in PD model results.

Supplementary Table 8

PK-independent variation in PD model results.

Supplementary Table 9

Protein abundance variation in PD model results.

Supplementary Table 10

Overview of all structurally changed candidate peptides in CSF.

Supplementary Table 11

Overlap of candidate CSF biomarkers with brain data. List of CSF candidate biomarkers for structural rearrangements in PD which also show a structural change in the brain data.

Supplementary Table 12

Preprocessed data for code deposited on GitHub.

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Mackmull, MT., Nagel, L., Sesterhenn, F. et al. Global, in situ analysis of the structural proteome in individuals with Parkinson’s disease to identify a new class of biomarker. Nat Struct Mol Biol 29, 978–989 (2022). https://doi.org/10.1038/s41594-022-00837-0

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