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Functional genomics identify causal variant underlying the protective CTSH locus for Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) is the most prevalent age-related neurodegenerative disease, which has a high heritability of up to 79%. Exploring the genetic basis is essential for understanding the pathogenic mechanisms underlying AD development. Recent genome-wide association studies (GWASs) reported an AD-associated signal in the Cathepsin H (CTSH) gene in European populations. However, the exact functional/causal variant(s), and the genetic regulating mechanism of CTSH in AD remain to be determined. In this study, we carried out a comprehensive study to characterize the role of CTSH variants in the pathogenesis of AD. We identified rs2289702 in CTSH as the most significant functional variant that is associated with a protective effect against AD. The genetic association between rs2289702 and AD was validated in independent cohorts of the Han Chinese population. The CTSH mRNA expression level was significantly increased in AD patients and AD animal models, and the protective allele T of rs2289702 was associated with a decreased expression level of CTSH through the disruption of the binding affinity of transcription factors. Human microglia cells with CTSH knockout showed a significantly increased phagocytosis of Aβ peptides. Our study identified CTSH as being involved in AD genetic susceptibility and uncovered the genetic regulating mechanism of CTSH in pathogenesis of AD.

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Fig. 1: Functional genomics analysis identifies rs2289702 as a functional variant in the CTSH gene.
Fig. 2: SNP rs2289702 affects the binding affinity of transcription factors.
Fig. 3: Alterations of the CTSH mRNA level in the brain tissues of AD patients and AD mouse models.
Fig. 4: Knockout of the CTSH gene in human microglial cells alters gene expression pattern and increases phagocytosis of Aβ42.

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References

  1. De Strooper B, Karran E. The cellular phase of Alzheimer’s disease. Cell. 2016;164:603–15.

    Article  PubMed  Google Scholar 

  2. Querfurth HW, LaFerla FM. Alzheimer’s disease. N Engl J Med. 2010;362:329–44.

    Article  CAS  PubMed  Google Scholar 

  3. Guo T, Zhang D, Zeng Y, Huang TY, Xu H, Zhao Y. Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer’s disease. Mol Neurodegener. 2020;15:40.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Karch CM, Goate AM. Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry. 2015;77:43–51.

    Article  CAS  PubMed  Google Scholar 

  5. Bertram L, Lill CM, Tanzi RE. The genetics of Alzheimer disease: back to the future. Neuron. 2010;68:270–81.

    Article  CAS  PubMed  Google Scholar 

  6. Zhang DF, Xu M, Bi R, Yao YG. Genetic analyses of Alzheimer’s disease in China: achievements and perspectives. ACS Chem Neurosci. 2019;10:890–901.

    Article  PubMed  Google Scholar 

  7. Gatz M, Reynolds CA, Fratiglioni L, Johansson B, Mortimer JA, Berg S, et al. Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry. 2006;63:168–74.

    Article  PubMed  Google Scholar 

  8. Bellenguez C, Kucukali F, Jansen IE, Kleineidam L, Moreno-Grau S, Amin N, et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat Genet. 2022;54:412–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wingo AP, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, et al. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nat Genet. 2021;53:143–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jia L, Li F, Wei C, Zhu M, Qu Q, Qin W, et al. Prediction of Alzheimer’s disease using multi-variants from a Chinese genome-wide association study. Brain. 2021;144:924–37.

    Article  PubMed  Google Scholar 

  11. Jiao B, Xiao X, Yuan Z, Guo L, Liao X, Zhou Y, et al. Associations of risk genes with onset age and plasma biomarkers of Alzheimer’s disease: a large case-control study in mainland China. Neuropsychopharmacology. 2022;47:1121–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang HZ, Bi R, Hu QX, Xiang Q, Zhang C, Zhang DF, et al. Validating GWAS identified risk loci for Alzheimer’s disease in Han Chinese populations. Mol Neurobiol. 2016;53:379–90.

    Article  CAS  PubMed  Google Scholar 

  13. Tan L, Yu JT, Zhang W, Wu ZC, Zhang Q, Liu QY, et al. Association of GWAS-linked loci with late-onset Alzheimer’s disease in a northern Han Chinese population. Alzheimers Dement. 2013;9:546–53.

    Article  PubMed  Google Scholar 

  14. Bi R, Zhang W, Zhang DF, Xu M, Fan Y, Hu QX, et al. Genetic association of the cytochrome c oxidase-related genes with Alzheimer’s disease in Han Chinese. Neuropsychopharmacology. 2018;43:2264–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhang DF, Fan Y, Xu M, Wang G, Wang D, Li J, et al. Complement C7 is a novel risk gene for Alzheimer’s disease in Han Chinese. Natl Sci Rev. 2019;6:257–74.

    Article  CAS  PubMed  Google Scholar 

  16. Luo R, Fan Y, Yang J, Ye M, Zhang DF, Guo K, et al. A novel missense variant in ACAA1 contributes to early-onset Alzheimer’s disease, impairs lysosomal function, and facilitates amyloid-β pathology and cognitive decline. Signal Transduct Target Ther. 2021;6:325.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Liao X, Cai F, Sun Z, Zhang Y, Wang J, Jiao B, et al. Identification of Alzheimer’s disease-associated rare coding variants in the ECE2 gene. JCI Insight. 2020;5:135119.

    Article  PubMed  Google Scholar 

  18. Khaire AS, Wimberly CE, Semmes EC, Hurst JH, Walsh KM. An integrated genome and phenome-wide association study approach to understanding Alzheimer’s disease predisposition. Neurobiol Aging. 2022;118:117–23.

    Article  CAS  PubMed  Google Scholar 

  19. Campion D, Pottier C, Nicolas G, Le Guennec K, Rovelet-Lecrux A. Alzheimer disease: modeling an Aβ-centered biological network. Mol Psychiatry. 2016;21:861–71.

    Article  CAS  PubMed  Google Scholar 

  20. Kikuchi M, Hara N, Hasegawa M, Miyashita A, Kuwano R, Ikeuchi T, et al. Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping. BMC Med Genomics. 2019;12:128.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Nott A, Holtman IR, Coufal NG, Schlachetzki JCM, Yu M, Hu R, et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science. 2019;366:1134–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Johnson ECB, Dammer EB, Duong DM, Ping L, Zhou M, Yin L, et al. 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. 2020;26:769–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Corces MR, Shcherbina A, Kundu S, Gloudemans MJ, Fresard L, Granja JM, et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat Genet. 2020;52:1158–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wingo AP, Fan W, Duong DM, Gerasimov ES, Dammer EB, Liu Y, et al. Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain. Nat Neurosci. 2020;23:696–700.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Nativio R, Lan Y, Donahue G, Sidoli S, Berson A, Srinivasan AR, et al. An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease. Nat Genet. 2020;52:1024–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ioannidis JP, Thomas G, Daly MJ. Validating, augmenting and refining genome-wide association signals. Nat Rev Genet. 2009;10:318–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gallagher MD, Chen-Plotkin AS. The post-GWAS era: from association to function. Am J Hum Genet. 2018;102:717–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cannon ME, Mohlke KL. Deciphering the emerging complexities of molecular mechanisms at GWAS Loci. Am J Hum Genet. 2018;103:637–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kumasaka N, Knights AJ, Gaffney DJ. High-resolution genetic mapping of putative causal interactions between regions of open chromatin. Nat Genet. 2019;51:128–37.

    Article  CAS  PubMed  Google Scholar 

  30. Iotchkova V, Ritchie GRS, Geihs M, Morganella S, Min JL, Walter K, et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat Genet. 2019;51:343–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rojnik M, Jevnikar ZR, Doljak B, Turk S, Zidar N, Kos J. The influence of differential processing of procathepsin H on its aminopeptidase activity, secretion and subcellular localization in human cell lines. Eur J Cell Biol. 2012;91:757–64.

    Article  CAS  PubMed  Google Scholar 

  32. Ruiz-Blazquez P, Pistorio V, Fernandez-Fernandez M, Moles A. The multifaceted role of cathepsins in liver disease. J Hepatol. 2021;75:1192–202.

    Article  CAS  PubMed  Google Scholar 

  33. Ou YN, Yang YX, Deng YT, Zhang C, Hu H, Wu BS, et al. Identification of novel drug targets for Alzheimer’s disease by integrating genetics and proteomes from brain and blood. Mol Psychiatry. 2021;26:6065–73.

    Article  CAS  PubMed  Google Scholar 

  34. Kibinge NK, Relton CL, Gaunt TR, Richardson TG. Characterizing the causal pathway for genetic variants associated with neurological phenotypes using human brain-derived proteome data. Am J Hum Genet. 2020;106:885–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet. 2019;51:404–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015;526:68–74.

    Article  Google Scholar 

  38. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5.

    Article  CAS  PubMed  Google Scholar 

  39. Wallace C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet. 2020;16:e1008720.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Consortium GT. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  Google Scholar 

  41. Wang G, Zhang DF, Jiang HY, Fan Y, Ma L, Shen Z, et al. Mutation and association analyses of dementia-causal genes in Han Chinese patients with early-onset and familial Alzheimer’s disease. J Psychiatr Res. 2019;113:141–7.

    Article  PubMed  Google Scholar 

  42. Wang D, Fan Y, Malhi M, Bi R, Wu Y, Xu M, et al. Missense variants in HIF1A and LACC1 contribute to leprosy risk in Han Chinese. Am J Hum Genet. 2018;102:794–805.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Xu M, Liu Q, Bi R, Li Y, Zeng C, Yan Z, et al. A multiple-causal-gene-cluster model underlying GWAS signals of Alzheimer’s disease. bioRxiv. 2021;https://doi.org/10.1101/2021.05.14.444131.

  46. Encode Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.

    Article  Google Scholar 

  47. Davis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM, Gabdank I, et al. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 2018;46:D794–D801.

    Article  CAS  PubMed  Google Scholar 

  48. Song M, Yang X, Ren X, Maliskova L, Li B, Jones IR, et al. Mapping cis-regulatory chromatin contacts in neural cells links neuropsychiatric disorder risk variants to target genes. Nat Genet. 2019;51:1252–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Fullard JF, Hauberg ME, Bendl J, Egervari G, Cirnaru MD, Reach SM, et al. An atlas of chromatin accessibility in the adult human brain. Genome Res. 2018;28:1243–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Novakovic B, Habibi E, Wang SY, Arts RJW, Davar R, Megchelenbrink W, et al. β-Glucan reverses the epigenetic state of LPS-induced immunological tolerance. Cell. 2016;167:1354–68.e14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol. 1994;2:28–36.

    CAS  PubMed  Google Scholar 

  52. Zuo C, Shin S, Keles S. atSNP: transcription factor binding affinity testing for regulatory SNP detection. Bioinformatics. 2015;31:3353–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Consortium GT, Laboratory DA, Coordinating Center -Analysis Working G, Statistical Methods groups-Analysis Working G, Enhancing Gg, Fund NIHC. et al. Genetic effects on gene expression across human tissues. Nature. 2017;550:204–13.

    Article  Google Scholar 

  54. Consortium GT. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.

    Article  Google Scholar 

  55. Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019;570:332–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Xu M, Zhang DF, Luo R, Wu Y, Zhou H, Kong LL, et al. A systematic integrated analysis of brain expression profiles reveals YAP1 and other prioritized hub genes as important upstream regulators in Alzheimer’s disease. Alzheimers Dement. 2018;14:215–29.

    Article  PubMed  Google Scholar 

  57. Matarin M, Salih DA, Yasvoina M, Cummings DM, Guelfi S, Liu W, et al. A genome-wide gene-expression analysis and database in transgenic mice during development of amyloid or tau pathology. Cell Rep. 2015;10:633–44.

    Article  CAS  PubMed  Google Scholar 

  58. Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome engineering using the CRISPR-Cas9 system. Nat Protoc. 2013;8:2281–308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Concordet JP, Haeussler M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 2018;46:W242–W5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gao Z, Harwig A, Berkhout B, Herrera-Carrillo E. Mutation of nucleotides around the +1 position of type 3 polymerase III promoters: the effect on transcriptional activity and start site usage. Transcription. 2017;8:275–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Wu Y, Liang D, Wang Y, Bai M, Tang W, Bao S, et al. Correction of a genetic disease in mouse via use of CRISPR-Cas9. Cell Stem Cell. 2013;13:659–62.

    Article  CAS  PubMed  Google Scholar 

  62. Jiao L, Su LY, Liu Q, Luo R, Qiao X, Xie T, et al. GSNOR deficiency attenuates MPTP-induced neurotoxicity and autophagy by facilitating CDK5 S-nitrosation in a mouse model of Parkinson’s disease. Free Radic Biol Med. 2022;189:111–21.

    Article  CAS  PubMed  Google Scholar 

  63. Li H, Su LY, Yang L, Li M, Liu Q, Li Z, et al. A cynomolgus monkey with naturally occurring Parkinson’s disease. Natl Sci Rev. 2021;8:nwaa292.

    Article  CAS  PubMed  Google Scholar 

  64. Yao YG, Kajigaya S, Young NS. Mitochondrial DNA mutations in single human blood cells. Mutat Res. 2015;779:68–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D13.

    Article  CAS  PubMed  Google Scholar 

  67. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102:15545–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Dolgalev I msigdbr: MSigDB gene sets for multiple organisms in a tidy data format. R package. 2022, https://CRAN.R-project.org/package=msigdbr.

  69. Lau SF, Cao H, Fu AKY, Ip NY. Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease. Proc Natl Acad Sci USA 2020;117:25800–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Grubman A, Chew G, Ouyang JF, Sun G, Choo XY, McLean C, et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat Neurosci. 2019;22:2087–97.

    Article  CAS  PubMed  Google Scholar 

  71. Korin B, Ben-Shaanan TL, Schiller M, Dubovik T, Azulay-Debby H, Boshnak NT, et al. High-dimensional, single-cell characterization of the brain’s immune compartment. Nat Neurosci. 2017;20:1300–9.

    Article  CAS  PubMed  Google Scholar 

  72. Heckmann BL, Teubner BJW, Tummers B, Boada-Romero E, Harris L, Yang M, et al. LC3-associated endocytosis facilitates β-amyloid clearance and mitigates neurodegeneration in murine Alzheimer’s disease. Cell. 2019;178:536–51.e14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. McAlpine CS, Park J, Griciuc A, Kim E, Choi SH, Iwamoto Y, et al. Astrocytic interleukin-3 programs microglia and limits Alzheimer’s disease. Nature. 2021;595:701–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Kaksonen M, Roux A. Mechanisms of clathrin-mediated endocytosis. Nat Rev Mol Cell Biol. 2018;19:313–26.

    Article  CAS  PubMed  Google Scholar 

  75. Wu LG, Hamid E, Shin W, Chiang HC. Exocytosis and endocytosis: modes, functions, and coupling mechanisms. Annu Rev Physiol. 2014;76:301–31.

    Article  CAS  PubMed  Google Scholar 

  76. Shin W, Wei L, Arpino G, Ge L, Guo X, Chan CY, et al. Preformed omega-profile closure and kiss-and-run mediate endocytosis and diverse endocytic modes in neuroendocrine chromaffin cells. Neuron. 2021;109:3119–34.

    Article  CAS  PubMed  Google Scholar 

  77. Zhao B, Wang Q, Du J, Luo S, Xia J, Chen YG. PICK1 promotes caveolin-dependent degradation of TGF-beta type I receptor. Cell Res. 2012;22:1467–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Bourguignon V, Flamion B. Respective roles of hyaluronidases 1 and 2 in endogenous hyaluronan turnover. FASEB J. 2016;30:2108–14.

    Article  CAS  PubMed  Google Scholar 

  79. Lee HW, Kim Y, Han K, Kim H, Kim E. The phosphoinositide 3-phosphatase MTMR2 interacts with PSD-95 and maintains excitatory synapses by modulating endosomal traffic. J Neurosci. 2010;30:5508–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Malecz N, McCabe PC, Spaargaren C, Qiu R, Chuang Y, Symons M. Synaptojanin 2, a novel Rac1 effector that regulates clathrin-mediated endocytosis. Curr Biol. 2000;10:1383–6.

    Article  CAS  PubMed  Google Scholar 

  81. Cheng ZJ, Singh RD, Holicky EL, Wheatley CL, Marks DL, Pagano RE. Co-regulation of caveolar and Cdc42-dependent fluid phase endocytosis by phosphocaveolin-1. J Biol Chem. 2010;285:15119–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Saftig P, Klumperman J. Lysosome biogenesis and lysosomal membrane proteins: trafficking meets function. Nat Rev Mol Cell Biol. 2009;10:623–35.

    Article  CAS  PubMed  Google Scholar 

  83. Braulke T, Bonifacino JS. Sorting of lysosomal proteins. Biochim Biophys Acta. 2009;1793:605–14.

    Article  CAS  PubMed  Google Scholar 

  84. Nakanishi H. Cathepsin regulation on microglial function. Biochim Biophys Acta Proteins Proteom. 2020;1868:140465.

    Article  CAS  PubMed  Google Scholar 

  85. Lopez-Otin C, Bond JS. Proteases: multifunctional enzymes in life and disease. J Biol Chem. 2008;283:30433–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Oberstein TJ, Utz J, Spitzer P, Klafki HW, Wiltfang J, Lewczuk P, et al. The role of cathepsin B in the degradation of Aβ and in the production of Aβ peptides starting with Ala2 in cultured astrocytes. Front Mol Neurosci. 2020;13:615740.

    Article  CAS  PubMed  Google Scholar 

  87. Chai YL, Chong JR, Weng J, Howlett D, Halsey A, Lee JH, et al. Lysosomal cathepsin D is upregulated in Alzheimer’s disease neocortex and may be a marker for neurofibrillary degeneration. Brain Pathol. 2019;29:63–74.

    Article  CAS  PubMed  Google Scholar 

  88. Suire CN, Abdul-Hay SO, Sahara T, Kang D, Brizuela MK, Saftig P, et al. Cathepsin D regulates cerebral Aβ42/40 ratios via differential degradation of Aβ42 and Aβ40. Alzheimers Res Ther. 2020;12:80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Hook V, Funkelstein L, Wegrzyn J, Bark S, Kindy M, Hook G. Cysteine Cathepsins in the secretory vesicle produce active peptides: Cathepsin L generates peptide neurotransmitters and cathepsin B produces beta-amyloid of Alzheimer’s disease. Biochim Biophys Acta. 2012;1824:89–104.

    Article  CAS  PubMed  Google Scholar 

  90. Sundelof J, Sundstrom J, Hansson O, Eriksdotter-Jonhagen M, Giedraitis V, Larsson A, et al. Higher cathepsin B levels in plasma in Alzheimer’s disease compared to healthy controls. J Alzheimers Dis. 2010;22:1223–30.

    Article  PubMed  Google Scholar 

  91. Kim KR, Cho EJ, Eom JW, Oh SS, Nakamura T, Oh CK, et al. S-Nitrosylation of cathepsin B affects autophagic flux and accumulation of protein aggregates in neurodegenerative disorders. Cell Death Differ. 2022;29:2137–50.

    Article  CAS  PubMed  Google Scholar 

  92. Zamolodchikov D, Duffield M, Macdonald LE, Alessandri-Haber N. Accumulation of high molecular weight kininogen in the brains of Alzheimer’s disease patients may affect microglial function by altering phagocytosis and lysosomal cathepsin activity. Alzheimers Dement. 2022;18:1919–29.

    Article  CAS  PubMed  Google Scholar 

  93. Xie Z, Meng J, Kong W, Wu Z, Lan F, Narengaowa, et al. Microglial cathepsin E plays a role in neuroinflammation and amyloid β production in Alzheimer’s disease. Aging Cell. 2022;21:e13565.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Li Y, Ma C, Li W, Yang Y, Li X, Liu J, et al. A missense variant in NDUFA6 confers schizophrenia risk by affecting YY1 binding and NAGA expression. Mol Psychiatry. 2021;26:6896–911.

    Article  CAS  PubMed  Google Scholar 

  95. Chen ZY, Zhang Y. Animal models of Alzheimer’s disease: applications, evaluation, and perspectives. Zool Res. 2022;43:1026–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Dr. Ian Logan for his helpful comments and language editing. We would like to thank the Institutional Center for Shared Technologies and Facilities of Kunming Institute of Zoology, Chinese Academy of Sciences for providing us with confocal microscopy image acquisition and flow cytometric analysis. We are grateful to Cong Li for his technical support.

Funding

This study was supported by the National Natural Science Foundation of China (32230021, 31970560, and 31730037), The STI2030-Major Projects (2021ZD0200900), Yunnan Province (202003AD150009, 2019FA027, and 202101AT070285), and Strategic Priority Research Program (B) of the Chinese Academy of Sciences (CAS) (XDB32020200), the International Partnership Program of Chinese Academy of Sciences (152453KYSB20170031), the Bureau of Frontier Sciences and Education, CAS (QYZDJ-SSW-SMC005) and the Youth Innovation Promotion Association of CAS (2020000017).

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YL, YGY, and RB conceived and designed the study. YL, YGY, and RB compiled the figures and wrote the manuscript with help and input from all authors. YL, RB, BLX, MX and XL performed experiments. MX, DFZ, and RB performed the data processing and analysis. HZ revised the draft. All authors revised the manuscript and approved the publication.

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Correspondence to Rui Bi or Yong-Gang Yao.

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Li, Y., Xu, M., Xiang, BL. et al. Functional genomics identify causal variant underlying the protective CTSH locus for Alzheimer’s disease. Neuropsychopharmacol. 48, 1555–1566 (2023). https://doi.org/10.1038/s41386-023-01542-2

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