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Identification of hub proteins in cerebrospinal fluid as potential biomarkers of Alzheimer’s disease by integrated bioinformatics

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Abstract

Background

Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disease with complex pathophysiology. Therefore, the identification of novel effective fluid biomarkers is essential for Alzheimer’s disease diagnosis and drug development. This study aimed to identify potential candidate hub proteins in cerebrospinal fluid for precise Alzheimer’s disease diagnosis using bioinformatics methods.

Methods

A total of 29 co-significant differentially expressed proteins were identified by differential protein expression analysis in four different cohorts. Functional enrichment analysis revealed that most of these proteins were enriched in pathways related to glycometabolism. Using the Least Absolute Shrinkage and Selection Operator (LASSO) and random forest feature selection methods, six hub proteins [14-3-3 protein zeta/delta (YWHAZ), SPARC-related modular calcium-binding protein 1 (SMOC1), aldolase A (ALDOA), pyruvate kinase isoenzyme type M2 (PKM), chitinase-3-like protein 1 (CHI3L1), and secreted phosphoprotein 1 (SPP1)] were identified.

Results

These six hub proteins were upregulated in the cerebrospinal fluid of patients with Alzheimer’s disease compared with cognitively unimpaired control individuals. Meanwhile, SMOC1, ALDOA, and PKM were specifically upregulated in the cerebrospinal fluid of patients with Alzheimer’s disease but not in other neurodegenerative diseases. Build AD diagnostic models showed that a single hub protein or six hub proteins combination had an excellent ability to discriminate Alzheimer’s disease.

Conclusions

In conclusion, our study suggests that these identified hub proteins, which are related to glycometabolism, may be potential biomarkers for further basic and clinical research in Alzheimer’s disease.

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Data availability statement

Publicly available datasets were analyzed in this study. These data can be found here: the Synapse dataset (https://www.synapse.org/) and PRIDE proteomic dataset (https://www.ebi.ac.uk/pride/archive/).

References

  1. Long JM, Holtzman DM (2019) Alzheimer disease: an update on pathobiology and treatment strategies. Cell 179(2):312–339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lashley T et al (2018) Molecular biomarkers of Alzheimer’s disease: progress and prospects. Dis Model Mech. https://doi.org/10.1242/dmm.031781

    Article  PubMed  PubMed Central  Google Scholar 

  3. Duyckaerts C, Delatour B, Potier MC (2009) Classification and basic pathology of Alzheimer disease. Acta Neuropathol 118(1):5–36

    Article  CAS  PubMed  Google Scholar 

  4. Hampel H et al (2018) Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol 14(11):639–652

    Article  PubMed  PubMed Central  Google Scholar 

  5. Blennow K, Zetterberg H (2018) Biomarkers for Alzheimer’s disease: current status and prospects for the future. J Intern Med 284(6):643–663

    Article  CAS  PubMed  Google Scholar 

  6. Guzman-Martinez L et al (2019) Biomarkers for Alzheimer’s disease. Curr Alzheimer Res 16(6):518–528

    Article  CAS  PubMed  Google Scholar 

  7. Niemantsverdriet E et al (2017) Alzheimer’s disease CSF biomarkers: clinical indications and rational use. Acta Neurol Belg 117(3):591–602

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bateman RJ et al (2012) Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 367(9):795–804

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Blennow K, Hampel H (2003) CSF markers for incipient Alzheimer’s disease. Lancet Neurol 2(10):605–613

    Article  CAS  PubMed  Google Scholar 

  10. Higginbotham L et al (2020) Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer’s disease. Sci Adv. https://doi.org/10.1126/sciadv.aaz9360

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bader JM et al (2020) Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer’s disease. Mol Syst Biol 16(6):e9356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Auso E, Gomez-Vicente V, Esquiva G (2020) Biomarkers for Alzheimer’s disease early diagnosis. J Pers Med 10(3):114

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hampel H et al (2021) Omics sciences for systems biology in Alzheimer’s disease: state-of-the-art of the evidence. Ageing Res Rev 69:101346

    Article  CAS  PubMed  Google Scholar 

  14. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gao F et al (2022) A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer’s dementia: China Aging and Neurodegenerative Initiative (CANDI) study. Alzheimers Dement. https://doi.org/10.1002/alz.12700

    Article  PubMed  PubMed Central  Google Scholar 

  16. Jack CR Jr et al (2018) NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14(4):535–562

    Article  PubMed  PubMed Central  Google Scholar 

  17. McKhann GM et al (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7(3):263–269

    Article  PubMed  PubMed Central  Google Scholar 

  18. Winblad B et al (2004) Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med 256(3):240–246

    Article  CAS  PubMed  Google Scholar 

  19. Dayon L et al (2018) Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimers Res Ther 10(1):66

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gene Ontology, C. (2015) Gene ontology consortium: going forward. Nucleic Acids Res 43(Database issue):D1049–D1056

    Article  Google Scholar 

  21. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kawabata T (2018) Gaussian-input Gaussian mixture model for representing density maps and atomic models. J Struct Biol 203(1):1–16

    Article  CAS  PubMed  Google Scholar 

  23. Balasa AF, Chircov C, Grumezescu AM (2020) Body fluid biomarkers for Alzheimer’s disease-an up-to-date overview. Biomedicines 8(10):421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Van Hulle C et al (2021) An examination of a novel multipanel of CSF biomarkers in the Alzheimer’s disease clinical and pathological continuum. Alzheimers Dement 17(3):431–445

    Article  PubMed  Google Scholar 

  25. Muszynski P et al (2017) YKL-40 as a potential biomarker and a possible target in therapeutic strategies of Alzheimer’s disease. Curr Neuropharmacol 15(6):906–917

    Article  PubMed  PubMed Central  Google Scholar 

  26. Suarez-Calvet M et al (2019) Early increase of CSF sTREM2 in Alzheimer’s disease is associated with tau related-neurodegeneration but not with amyloid-beta pathology. Mol Neurodegener 14(1):1

    Article  PubMed  PubMed Central  Google Scholar 

  27. Tang BL (2020) Glucose, glycolysis, and neurodegenerative diseases. J Cell Physiol 235(11):7653–7662

    Article  CAS  PubMed  Google Scholar 

  28. Johnson ECB et al (2020) 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(5):769–780

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Le Douce J et al (2020) Impairment of glycolysis-derived l-serine production in astrocytes contributes to cognitive deficits in Alzheimer’s disease. Cell Metab 31(3):503–517 (e8)

    Article  PubMed  Google Scholar 

  30. Vlassenko AG et al (2018) Aerobic glycolysis and tau deposition in preclinical Alzheimer’s disease. Neurobiol Aging 67:95–98

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lananna BV et al (2020) Chi3l1/YKL-40 is controlled by the astrocyte circadian clock and regulates neuroinflammation and Alzheimer's disease pathogenesis. Sci Transl Med 12(574):eaax3519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Craig-Schapiro R et al (2010) YKL-40: a novel prognostic fluid biomarker for preclinical Alzheimer’s disease. Biol Psychiatry 68(10):903–912

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wilczynska K, Waszkiewicz N (2020) Diagnostic utility of selected serum dementia biomarkers: amyloid beta-40, amyloid beta-42, tau protein, and ykl-40: a review. J Clin Med 9(11):3452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kester MI et al (2015) Cerebrospinal fluid VILIP-1 and YKL-40, candidate biomarkers to diagnose, predict and monitor Alzheimer’s disease in a memory clinic cohort. Alzheimers Res Ther 7(1):59

    Article  PubMed  PubMed Central  Google Scholar 

  35. Baldacci F et al (2017) Diagnostic function of the neuroinflammatory biomarker YKL-40 in Alzheimer’s disease and other neurodegenerative diseases. Expert Rev Proteomics 14(4):285–299

    Article  CAS  PubMed  Google Scholar 

  36. Zhang F et al (2017) Elevated transcriptional levels of aldolase A (ALDOA) associates with cell cycle-related genes in patients with NSCLC and several solid tumors. BioData Min 10:6

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ashizawa K et al (1991) An in vitro novel mechanism of regulating the activity of pyruvate kinase M2 by thyroid hormone and fructose 1, 6-bisphosphate. Biochemistry 30(29):7105–7111

    Article  CAS  PubMed  Google Scholar 

  38. Dombrauckas JD, Santarsiero BD, Mesecar AD (2005) Structural basis for tumor pyruvate kinase M2 allosteric regulation and catalysis. Biochemistry 44(27):9417–9429

    Article  CAS  PubMed  Google Scholar 

  39. Noguchi T, Inoue H, Tanaka T (1986) The M1- and M2-type isozymes of rat pyruvate kinase are produced from the same gene by alternative RNA splicing. J Biol Chem 261(29):13807–13812

    Article  CAS  PubMed  Google Scholar 

  40. Yamada K, Noguchi T (1999) Nutrient and hormonal regulation of pyruvate kinase gene expression. Biochem J 337(Pt 1):1–11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bluemlein K et al (2011) No evidence for a shift in pyruvate kinase PKM1 to PKM2 expression during tumorigenesis. Oncotarget 2(5):393–400

    Article  PubMed  PubMed Central  Google Scholar 

  42. Han J et al (2021) Aberrant role of pyruvate kinase M2 in the regulation of gamma-secretase and memory deficits in Alzheimer’s disease. Cell Rep 37(10):110102

    Article  CAS  PubMed  Google Scholar 

  43. Okada I et al (2011) SMOC1 is essential for ocular and limb development in humans and mice. Am J Hum Genet 88(1):30–41

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Montgomery MK et al (2020) SMOC1 is a glucose-responsive hepatokine and therapeutic target for glycemic control. Sci Transl Med. https://doi.org/10.1126/scitranslmed.aaz8048

    Article  PubMed  PubMed Central  Google Scholar 

  45. Vannahme C et al (2002) Characterization of SMOC-1, a novel modular calcium-binding protein in basement membranes. J Biol Chem 277(41):37977–37986

    Article  CAS  PubMed  Google Scholar 

  46. Lehallier B et al (2019) Undulating changes in human plasma proteome profiles across the lifespan. Nat Med 25(12):1843–1850

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Arnold SE et al (2018) Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol 14(3):168–181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hashiguchi M, Sobue K, Paudel HK (2000) 14-3-3zeta is an effector of tau protein phosphorylation. J Biol Chem 275(33):25247–25254

    Article  CAS  PubMed  Google Scholar 

  49. Qureshi HY et al (2013) Overexpression of 14-3-3z promotes tau phosphorylation at Ser262 and accelerates proteosomal degradation of synaptophysin in rat primary hippocampal neurons. PLoS One 8(12):e84615

    Article  PubMed  PubMed Central  Google Scholar 

  50. Seyfried NT et al (2017) A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease. Cell Syst 4(1):60–72 (e4)

    Article  CAS  PubMed  Google Scholar 

  51. Hong S et al (2016) Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352(6286):712–716

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wingo AP et al (2020) Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain. Nat Neurosci 23(6):696–700

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang H et al (2020) Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease. Mol Neurodegener 15(1):43

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Chinese Academy of Sciences (QYZDY-SSW-SMC012 and XDB39000000), the National Natural Sciences Foundation of China (31530089 and 82030034), and the Guangzhou Key Research Program on Brain Science (202007030008).

Author information

Author notes

  1. Yang Li and Zuolong Chen have contributed equally to this work.

Authors and Affiliations

  1. Institute on Aging and Brain Disorders, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China

    Yang Li, Qiong Wang, Xinyi Lv, Zhaozhao Cheng, Yan Wu, Fang Tang, Yong Shen & Feng Gao

  2. Division of Life Sciences and Medicine, Neurodegenerative Disorder Research Center, University of Science and Technology of China, Hefei, China

    Yang Li, Zuolong Chen, Qiong Wang & Yong Shen

  3. Anhui Province Key Laboratory of Biomedical Aging Research, University of Science and Technology of China, Hefei, China

    Yong Shen & Feng Gao

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  1. Yang Li
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Contributions

YL, YS, and FG conceived and designed the study. YL and ZC contributed to the research design, data analysis, and article writing. YL, YS, and FG revised the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Yong Shen or Feng Gao.

Ethics declarations

Conflicts of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical standard statement

This study was approved by the Ethics Committee of the First Affiliated Hospital of the University of Science and Technology of China (2019KY-26)

Informed consent

All patients provided written informed consent to participate in the study.

Supplementary Information

Below is the link to the electronic supplementary material.

415_2022_11476_MOESM1_ESM.pdf

Figure S1. LASSO feature selection in cohort 2. (A) Cohort 2 binomial deviance plots present the suitable classification loss for different λ choices. λ is the L1 regularization coefficient. (B) Cohort 2 coefficients of the p value selected protein change by the changes of λ. Figure S2. Correlation between hub proteins, age, sex, and other indexes of Alzheimer’s disease. Blue indicates a positive correlation, and red refers to a negative correlation. The intensity of the color and the size of the circle are related to the correlation coefficients. Crossed cells refer to nonsignificant correlations. MoCA: Montreal Cognitive Assessment. Figure S3. Determination of CSF Aβ42 and P-tau cut-offs. The graph shows the two Gaussian mixture model (GMM) curves that were used to derive the cut-off for Aβ and P-tau in cohort 1 (A-B), cohort 2 (C-D) and cohort 3 (E-F). The Gaussian distribution of control CSF values is depicted in green, and AD pathology of CSF values is depicted in orange. The Gaussian distribution of the mixture is depicted in blue. The data-driven cut-off is the point where the lines of two fitted normal distributions crossed each other. Cut-off values are as follows: cohort 1, Aβ42 = 448.818 pg/mL (A); P-tau = 49.253 pg/mL (B); cohort 2, Aβ42 = 850.088 pg/mL (C); P-tau = 75.642 pg/mL (D); cohort 3, Aβ42 = 639.901 pg/mL, P-tau = 55.520 pg/mL. Figure S4. Receiver operating characteristics (ROC) curve analysis of hub proteins in cohort 2, cohort 3 and cohort 4. ROC curves represent which protein can best differentiate AD from controls. ROC curves of single hub proteins in cohort 2 (A), cohort 3 (C) and cohort 4 (E) of the classified logistic regression. ROC curves of hub protein combinations in cohort 2 (B), cohort 3 (D) and cohort 4 (F) through logistic regression, SVM, decision tree, and naïve Bayes models. Cross-validation was used in all datasets. Figure S5. Receiver operating characteristics (ROC) curve analysis of hub proteins combined with Aβ42 or P-tau in cohort 1. ROC curves of hub proteins combined with Aβ42 in the training set (A) and testing set (B) by cross-validation. ROC curves of hub proteins combined with P-tau in the training set (C) and testing set (D) by cross-validation. (PDF 2151 KB)

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Li, Y., Chen, Z., Wang, Q. et al. Identification of hub proteins in cerebrospinal fluid as potential biomarkers of Alzheimer’s disease by integrated bioinformatics. J Neurol 270, 1487–1500 (2023). https://doi.org/10.1007/s00415-022-11476-2

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