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Proteomics analysis of prefrontal cortex of Alzheimer’s disease patients revealed dysregulated proteins in the disease and novel proteins associated with amyloid-β pathology

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Abstract

Background

Alzheimer’s disease (AD) is a progressive, chronic, and neurodegenerative disease, and the most common cause of dementia worldwide. Currently, the mechanisms underlying the disease are far from being elucidated. Thus, the study of proteins involved in its pathogenesis would allow getting further insights into the disease and identifying new markers for AD diagnosis.

Methods

We aimed here to analyze protein dysregulation in AD brain by quantitative proteomics to identify novel proteins associated with the disease. 10-plex TMT (tandem mass tags)-based quantitative proteomics experiments were performed using frozen tissue samples from the left prefrontal cortex of AD patients and healthy individuals and vascular dementia (VD) and frontotemporal dementia (FTD) patients as controls (CT). LC–MS/MS analyses were performed using a Q Exactive mass spectrometer.

Results

In total, 3281 proteins were identified and quantified using MaxQuant. Among them, after statistical analysis with Perseus (p value < 0.05), 16 and 155 proteins were defined as upregulated and downregulated, respectively, in AD compared to CT (Healthy, FTD and VD) with an expression ratio ≥ 1.5 (upregulated) or ≤ 0.67 (downregulated). After bioinformatics analysis, ten dysregulated proteins were selected as more prone to be associated with AD, and their dysregulation in the disease was verified by qPCR, WB, immunohistochemistry (IHC), immunofluorescence (IF), pull-down, and/or ELISA, using tissue and plasma samples of AD patients, patients with other dementias, and healthy individuals.

Conclusions

We identified and validated novel AD-associated proteins in brain tissue that should be of further interest for the study of the disease. Remarkably, PMP2 and SCRN3 were found to bind to amyloid-β (Aβ) fibers in vitro, and PMP2 to associate with Aβ plaques by IF, whereas HECTD1 and SLC12A5 were identified as new potential blood-based biomarkers of the disease.

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Availability of data and materials

The mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD030751.

Abbreviations

AD:

Alzheimer’s disease

ANLN:

Anillin

Aβ:

Amyloid-β

BIN1:

Myc box-dependent-interacting protein 1

CSF:

Cerebrospinal fluid

DLPFC:

Dorsolateral prefrontal cortex

EXOC2:

Exocyst complex component 2

FTD:

Frontotemporal dementia

GFP:

Green fluorescence protein

HECTD1:

E3 ubiquitin-protein ligase

hNSCs:

Multipotent neural stem cells

HPC:

High pathology controls

HSC:

Human stem cell

IF:

Immunofluorescence

IHC:

Immunohistochemistry

LC–MS/MS:

Liquid chromatography coupled with tandem mass spectrometry

LPC:

Low-pathology controls

MBs:

Streptavidin magnetic beads

MCI:

Mild cognitive impairment

NFT:

Neurofibrillary tangles

NRGN:

Neurogranin

PMP2:

Peripheral myelin protein 2

PPP1R14A:

Protein phosphatase 1 regulatory subunit 14A

qPCR:

Real-time quantitative PCR

ROC:

Receiver operating characteristic curve

SCRN3:

Secernin-3

SLC12A5:

Solute carrier family 12 member 5

TMT:

Tandem mass tags

TSC2:

Tuberous sclerosis 2 protein

VD:

Vascular dementia

WB:

Western blot

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Acknowledgements

The protein identification by nLC–MS/MS was carried out in the Proteomics and Genomics Facility (CIB-CSIC), a member of ProteoRed-ISCIII network.

Funding

This work was supported by the financial support of the PI17CIII/00045 and PI20CIII/00019 grants from the AES-ISCIII program to R.B. The FPU predoctoral contract to A.M-C. is supported by the Spanish Ministerio de Educación, Cultura y Deporte. G.S-F. is recipient of a predoctoral contract (grant number 1193818N) supported by The Flanders Research Foundation (FWO). M. G-A. is recipient of a Margarita Salas postdoctoral grant for the requalification of the Spanish university system.

Author information

Authors and Affiliations

  1. Functional Proteomics Unit, Chronic Disease Programme (UFIEC), Instituto de Salud Carlos III, Majadahonda, E-28220, Madrid, Spain

    Ana Montero-Calle, María Garranzo-Asensio, Guillermo Solís-Fernández & Rodrigo Barderas

  2. Unidad de Regeneración Neural, Unidad Funcional de Investigación de Enfermedades Crónicas, Instituto de Salud Carlos III (ISCIII), Majadahonda, Madrid, Spain

    Raquel Coronel & Isabel Liste

  3. Molecular Imaging and Photonics Division, Chemistry Department, Faculty of Sciences, KU Leuven, Celestijnenlaan 200F, Heverlee, 3001, Louvain, Belgium

    Guillermo Solís-Fernández

  4. Alzheimer Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, E-28031, Madrid, Spain

    Alberto Rábano

  5. Centro de Investigaciones Biológicas, CSIC, E-28040, Madrid, Spain

    Vivian de los Ríos

  6. Surgical Pathology Department, Hospital Universitario Clínico San Carlos, E-28040, Madrid, Spain

    María Jesús Fernández-Aceñero

  7. Department of Infection and Immunity, Luxembourg Institute of Health, L-1445, Strassen, Luxembourg

    Marta L. Mendes

  8. Translational Oncology Division, OncoHealth Institute, Health Research Institute-University Hospital Fundación Jiménez Díaz-Universidad Autónoma de Madrid, E-28040, Madrid, Spain

    Javier Martínez-Useros

  9. Advanced Optical Microscopy Unit, UCCTs, Instituto de Salud Carlos III (ISCIII), E-28220, Majadahonda, Madrid, Spain

    Diego Megías

  10. Investén-isciii, Instituto de Salud Carlos III, E-28029, Madrid, Spain

    María Teresa Moreno-Casbas

  11. CIBERFES, E-28029, Madrid, Spain

    María Teresa Moreno-Casbas

  12. Molecular Pathology and Therapeutic Targets Group, La Paz University Hospital (IdiPAZ), E-28046, Madrid, Spain

    Alberto Peláez-García

  13. Area of Physiology, Department of Basic Health Sciences, Faculty of Health Sciences, Rey Juan Carlos University, E-28922, Madrid, Spain

    Javier Martínez-Useros

Authors
  1. Ana Montero-Calle
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  2. Raquel Coronel
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  3. María Garranzo-Asensio
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  4. Guillermo Solís-Fernández
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  6. Vivian de los Ríos
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  7. María Jesús Fernández-Aceñero
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  8. Marta L. Mendes
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  9. Javier Martínez-Useros
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  13. Isabel Liste
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  14. Rodrigo Barderas
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Contributions

Conceptualization: AM-C and RB. Methodology: AM-C, G.S-F, RC, AR, VR, AP-G, IL, and RB. Investigation: AM-C, RC, MG-A, GS-F, VR, MJF-A, MM, JM-U, DM, MTM-C, and AP-G. Writing—original draft: AM-C and RB. Writing—review and editing: AM-C, GS-F, RC, MG-A, AR, VR, MJF-A, MM, JM-U, DM, MTM-C, AP-G; IL, and RB. Resources: AR, VR, MM, IL, and RB. Supervision: AM-C, MJF-A, AP-G, IL, and RB. Funding acquisition: IL, and RB. All authors read and approved the manuscript.

Corresponding author

Correspondence to Rodrigo Barderas.

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Conflict of interest

The authors declare no competing interests.

Ethical approval and consent to participate

The Institutional Ethical Review Board of the Spanish Research Center for Neurological Diseases Foundation (CIEN) and the Instituto de Salud Carlos III approved this study on proteomics analysis and biomarker discovery of Alzheimer’s disease (CEI PI 49).

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Supplementary Information

Below is the link to the electronic supplementary material.

18_2023_4791_MOESM1_ESM.pptx

Supplementary Supplementary Fig. 1. MaxQuant data normalization. Left, box plots of the mean of log2 protein intensities before (top) and after (bottom) data normalization of each TMT reporter. Right, histogram of the log2 protein intensities for each TMT reporter before (top) and after (bottom) data normalization. After SL normalization, box plots and density distributions of each TMT reporter were aligned. file1 (PPTX 465 KB)

18_2023_4791_MOESM2_ESM.pptx

Supplementary Supplementary Fig. 2. Bioinformatics analysis of APP, MAPT, and dysregulated proteins in AD. A Graph bar representing the TMT relative protein abundance of APP, MAPT or amyloidβ peptide in the individual analyzed samples including duplicates of healthy individuals, VD, FTD patients, Braak IV, Braak V and Braak VI, as obtained from mass spectrometry analysis, confirmed higher protein levels of APP and MAPT in AD samples. B Venn Diagram analysis of the significantly downregulated (left) or upregulated (right) proteins as obtained by ANOVA and Post Hoc correction regarding AD vs healthy and DV/FTD, Braak V vs healthy and Braak VI vs healthy comparisons. C Genome-wide overview of reactome pathways. Significant pathways in which the 171 significantly dysregulated proteins are involved were represented in yellow (p value < 0.05). Light grey represents pathways with no significant representation. file2 (PPTX 1251 KB)

18_2023_4791_MOESM3_ESM.pptx

Supplementary Supplementary Fig. 3. Validation of mRNA and protein dysregulation of the 10 candidate proteins. A Differences at mRNA expression levels in healthy individuals, VD and FTD patients, Braak IV, Braak V and Braak VI AD groups were separately found for the 10 selected proteins, with HECTD1 significantly downregulated in AD patients and PPP1R14A, SCRN3, ANLN, BIN1 and NRGN statistically significant upregulated in patients. B Coomassie-blue staining (upper panel) and Ponceau red staining (lower panel) for quality control of individual protein extracts of the left prefrontal cortex brain tissue samples of AD patients and controls and for normalization of the total protein content per sample used for WB validation, respectively. C Statistically significant differences at protein level as observed by WB were separately found among healthy individuals and VD and FTD patients, and AD patients at Braak IV, Braak V and Braak VI for HECTD1, EXOC2, SLC12A5, TSC2, ANLN, NRGN and SCRN3. D Immunohistochemistry analysis revealed dysregulation of ANLN, BIN1, SCRN3, EXOC2 and HECTD1 protein levels at each Braak stage in comparison to healthy individuals. *: p value < 0.05; **: p value < 0.01. ***: p value < 0.001; ****: p value < 0.0001; n.s.: not significant; CT: controls; AD: AD patients file3 (PPTX 3532 KB)

18_2023_4791_MOESM4_ESM.pptx

Supplementary Supplementary Fig. 4. Analysis of the proteins specifically dysregulated at Braak V or Braak VI in comparison with Healthy and VD and FTD patients. A Venn diagram analysis of the significantly dysregulated proteins at Braak V (left) or Braak VI (right) stages obtained by ANOVA and Post Hoc correction and t-test comparison between AD and controls or healthy individuals. A total of 34 and 15 proteins were identified as dysregulated at Braak V or Braak VI, respectively. B Genome-wide overview of reactome pathways significantly altered at Braak V or Braak VI AD stages. Significant pathways were represented in yellow (p value < 0.05). Light grey represents pathways with no significant representation file4 (PPTX 1034 KB)

18_2023_4791_MOESM5_ESM.pdf

Supplementary Supplementary Fig. 5. Immunofluorescence staining of human Aβ plaques. A Negative control of immunofluorescence experiment. AD Braak V tissue was incubated with 4G8 and its corresponding secondary antibody (Alexa Fluor 555) and with Alexa Fluor 647 to confirm the specific staining of PMP2 and SCRN3 in AD tissue samples. B Immunofluorescence images of SCRN3 staining showing the nuclear staining with Hoechst for the confirmation of the presence of SCRN3 within the cells. C Lifetime images of SCRN3 and PMP2 staining showed the differential specific staining of both proteins in the tissue samples of AD patients and healthy individuals from the autofluorescence of tissue and lipofuscin-like structures. Shorter lifetime: positive Alexa Fluor 647 staining; Longer lifetime: lipofuscin-like structures; Scale bar: 20 µm. D λ spectra of Alexa-Fluor 647 from lipofuscin-like regions (AF) and specific 647 signals (PMP2 or SCRN3) regions as indicated in Fig. 6f,g. AF: autofluorescence. file5 (PDF 260 KB)

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Montero-Calle, A., Coronel, R., Garranzo-Asensio, M. et al. Proteomics analysis of prefrontal cortex of Alzheimer’s disease patients revealed dysregulated proteins in the disease and novel proteins associated with amyloid-β pathology. Cell. Mol. Life Sci. 80, 141 (2023). https://doi.org/10.1007/s00018-023-04791-y

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