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Changes in proteome solubility indicate widespread proteostatic disruption in mouse models of neurodegenerative disease

Abstract

The deposition of pathologic misfolded proteins in neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia and amyotrophic lateral sclerosis is hypothesized to burden protein homeostatic (proteostatic) machinery, potentially leading to insufficient capacity to maintain the proteome. This hypothesis has been supported by previous work in our laboratory, as evidenced by the perturbation of cytosolic protein solubility in response to amyloid plaques in a mouse model of Alzheimer’s amyloidosis. In the current study, we demonstrate changes in proteome solubility are a common pathology to mouse models of neurodegenerative disease. Pathological accumulations of misfolded tau, α-synuclein and mutant superoxide dismutase 1 in CNS tissues of transgenic mice were associated with changes in the solubility of hundreds of CNS proteins in each model. We observed that changes in proteome solubility were progressive and, using the rTg4510 model of inducible tau pathology, demonstrated that these changes were dependent upon sustained expression of the primary pathologic protein. In all of the models examined, changes in proteome solubility were robust, easily detected, and provided a sensitive indicator of proteostatic disruption. Interestingly, a subset of the proteins that display a shift towards insolubility were common between these different models, suggesting that a specific subset of the proteome is vulnerable to proteostatic disruption. Overall, our data suggest that neurodegenerative proteinopathies modeled in mice impose a burden on the proteostatic network that diminishes the ability of neural cells to prevent aberrant conformational changes that alter the solubility of hundreds of abundant cellular proteins.

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Acknowledgements

We thank the Interdisciplinary Center for Biotechnology Research (ICBR), specifically the Proteomics and Mass Spectrometry Core for assistance in processing LC–MS/MS samples. We also thank personnel within the University of Florida Animal Care Services for assistance with animal care for the mice used in this study. We also acknowledge the generous contribution of wild-type tau K18 fibril preparations from Kevin Strang within the laboratory of Dr. Benoit Giasson.

Funding

This work was supported by a grants from the National Institute of Neurological Disorders and Stroke (R21NS083006 to D.R.B. and J.L.; R01NS089622 to B.I.G.; T32NS082128 supported M.P.), the National Institute on Aging (P50AG047266; R01AG049456 to D.R.B. and J.L.), and the BrightFocus Foundation (A20141085) to G.X.; and by funding from the Santa Fe HealthCare Alzheimer’s Disease Research Center.

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Authors

Contributions

Conceptualization, DRB, JL, and GX; methodology, DRB, JL, GX, and BIG; formal analysis, MCP and GX; investigation, MCP, GX, KWC, SF, and JH; resources, DRB, JL, and BIG; writing—original draft, MCP; writing—reviewing and editing, MCP, DRB, JL, GX, and BIG.; supervision, DRB, JL, and GX; funding acquisition, DRB, JL, and GX.

Corresponding authors

Correspondence to Jada Lewis or David R. Borchelt.

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Ethical approval

All applicable international, national and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the University of Florida Institutional Animal Care and Use Committee (IACUC). This article does not contain any studies with human participants performed by any of the authors.

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

401_2018_1895_MOESM1_ESM.docx

Table S1. Statistical information for different proteinopathy animal groups analyzed via LC–MS/MS. Supplemental Materials and Methods. Supplementary material 1 (DOCX 46 kb)

401_2018_1895_MOESM2_ESM.xlsx

Table S13 Overlapping protein identifications between SDS-insoluble fractions from rTg4510 mice and previous proteomic studies of disease-associated pathological features. Table S2. Spectral counts exported from Scaffold (version Scaffold_4.7.3, Proteome Software Inc., Portland, OR) used for analysis of rTg4510 mice (with and without doxycycline treatment) and rTg21221 mice. Table S3. Comprehensive lists of proteins that were aberrantly detected in SDS-insoluble fractions in 7.0 and 9.5 month-old based upon fold-change and G-test criteria. Analysis was conducted on rTg4510 7.0-month-old (n = 10) and 9.5-month-old (n = 3) mice. For 7.0-month-old mice, any given protein must have reached our criterion in 7 out of 10 analyzed mice. Table S4. Comprehensive list of proteins that met SAINT score thresholds of 0.9 as over-represented in SDS-insoluble fractions in all of the proteinopathy models. Analysis was conducted on JNPL3 (n = 7), rTg4510 7-month (n = 10), rTg4510 9.5 month (n = 3), M83 (n = 6), M83 seeded (n = 4), APPswe/PS1dE9 20-month (n = 3) and G93A SOD1 (n = 4) mice. Models of spinal proteinopathy were harvested at end-stage phenotype (paresis in one or more hind limbs). Proteins were accepted if they reached a SAINT score of ≥ 0.9 between control (NTg) mice and each corresponding transgenic model of neurodegenerative proteinopathy. Table S5. Comparison of proteins in SDS-insoluble fractions across cortical models (rTg4510 & L85) and spinal models (JNPL3, M83, M83 seeded, and G93A SOD1). Table S6. Spectral count data from proteins analyzed by immunoblotting Fig. 2a. Table S7. List of proteins that are most highly over-represented in SDS-insoluble fractions from spinal cords of end-stage G93A SOD1 mice. Individual animal spectral counts are separated by a comma in the SDS-insoluble columns. All spectral count comparisons exhibited a G-test value of < 0.01 (n = 4). nTg = nontransgenic, Tg = transgenic, PBS-S = PBS soluble. Table S8. List of the 91 common proteins that lose solubility in the G93A SOD1, APPswe/PS1dE9, and rTg4510 models of neurodegenerative proteinopathy. The protein list was compiled based upon the lists of proteins generated in Online Resource 2 Table S4. Table S9 List of proteins common to SDS-insoluble fractions across all spinal proteinopathy models (JNPL3, M83, M83 seeded, and G93A SOD1). The protein list was compiled based upon the lists of proteins generated in Online Resource, Table S4. Table S10 Compiled lists of proteins that lose solubility in rTg4510 mice that either overlap (Column A) or do not overlap (Column B) with previous studies identifying proteins that may be interacting with tau (see Online Resource 1, Table S13). Table S11 Compiled lists of proteins that lose solubility in rTg4510 mice that either overlap (Column A) or do not overlap (Column B) with previous studies identifying proteins that may interact with pathologic features of AD (see Online Resource 1, Table S13). Table S12 Complete lists of overlapping proteins between rTg4510 SDS-insoluble forebrains and the two first studies listed in Online Resource 1, Table S13. Supplementary material 2 (XLSX 1107 kb)

401_2018_1895_MOESM3_ESM.tif

Two-way clustering of spectral count data from rTg4510 mice. Clustering is based upon detergent-insoluble peptide spectra for 206 total proteins identified as affected in rTg4510 mice of any age (with and without DOX treatment). Red is indicative of the highest number of peptide spectra for a given protein relative to nontransgenic control mice, while blue is indicative of an absence of the peptide in SDS-insoluble fractions, or absence of a difference between transgenic and nontransgenic samples. Figure generated using JMP Pro Statistical Discovery from SAS (version 13.0, Cary, NC, USA). Supplementary material 3 (TIFF 1165 kb)

401_2018_1895_MOESM4_ESM.tif

Venn diagram of common proteins identified in APPswe/PS1dE9 (L85) across different analysis timepoints. Newly analyzed L85 mice were compared to previously analyzed animals. Supplementary material 4 (TIFF 3643 kb)

401_2018_1895_MOESM5_ESM.xlsx

Spectral counts exported from Scaffold (version Scaffold_4.7.3, Proteome Software Inc., Portland, OR) used for analysis of the APPswe/PS1E9 model of amyloidosis, the JNPL3, M83, and G93A models of spinal proteinopathy, and the seeded N2a cells. Supplementary material 5 (XLSX 428 kb)

401_2018_1895_MOESM6_ESM.tif

Immunoreactivity for Hspa4 protein is not highly co-localized with neurofibrillary tangle pathology of rTg4510 mice. Nontransgenic mice (a & b) exhibit minimal positive staining of Hspa4 puncta (green). rTg4510 (c & d) exhibit dramatic increases in Hspa4 puncta (green) that do not directly co-localize with neurofibrillary tangles (stained using the MC1 antibody to misfolded human tau, red). rTg4510 mice that received doxycycline to suppress mutant tau expression from 4.5 – 7.0 months of age (e & f) exhibited reduced Hspa4 immunostaining compared to rTg4510 mice that did not receive doxycycline. The graph shows the number of spectral counts for Hspa4 in SDS-insoluble fractions from the forebrains of NTg, 7-month-old rTg4510 mice, and 7-month-old rTg4510 that began DOX treatment at 4.5 months of age (g). Supplementary material 6 (TIFF 24679 kb)

401_2018_1895_MOESM7_ESM.tif

Two-way clustering of SDS-insoluble spectra for rTg4510, M83, M83 seeded, JNPL3, and G93A SOD1 models. Clustering is based upon detergent-insoluble peptide spectra for 310 total proteins identified as affected in any spinal proteinopathy model. Red is indicative of the highest number of peptide spectra for a given protein relative to nontransgenic control mice, while blue is indicative of an absence of the peptide in SDS-insoluble fractions, or absence of a difference between transgenic and nontransgenic samples. Figure generated using JMP Pro Statistical Discovery from SAS (version 13.0, Cary, NC, USA). Supplementary material 7 (TIFF 6224 kb)

401_2018_1895_MOESM8_ESM.tif

Bioinformatic analysis of protein classes that are statistically over-represented in SDS-insoluble fractions rTg4510 mice. Pie chart of protein classes uniquely affected in rTg4510 mice. The protein list was compiled based upon the lists of proteins generated in Online Resource 2, Table S4. Supplementary material 8 (TIFF 4701 kb)

401_2018_1895_MOESM9_ESM.tif

Combined Venn diagram representative of both diagrams from Fig. 8, encompassing the numbers of overlapping proteins from all types of methodologies used in previous literature (IP, Detergent-Insoluble, LCM, and Other). Supplementary material 9 (TIFF 25481 kb)

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Pace, M.C., Xu, G., Fromholt, S. et al. Changes in proteome solubility indicate widespread proteostatic disruption in mouse models of neurodegenerative disease. Acta Neuropathol 136, 919–938 (2018). https://doi.org/10.1007/s00401-018-1895-y

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Keywords

  • Proteostasis
  • Protein misfolding
  • Neurodegeneration
  • Proteinopathy
  • Proteomics