Abstract
Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF.
Similar content being viewed by others
Data Availability
The data used in this paper are all from ADNI database and GEO database.
References
Aggleton JP, Pralus A, Nelson AJ, Hornberger M (2016) Thalamic pathology and memory loss in early Alzheimer’s disease: moving the focus from the medial temporal lobe to Papez circuit. Brain 139:1877–1890
Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine JC, Geurts P, Aerts J et al (2017) SCENIC: single-cell regulatory network inference and clustering. Nat Methods 14:1083–1086
Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR et al (2019) Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 20:163–172
Ban Y, Lao H, Li B, Su W, Zhang X (2023) Diagnosis of Alzheimer’s disease using hypergraph p-Laplacian regularized multi-task feature learning. J Biomed Inform 140:104326
Bhalla M, Lee CJ (2024) Long-term inhibition of ODC1 in APP/PS1 mice rescues amyloid pathology and switches astrocytes from a reactive to active state. Mol Brain 17:3
Breijyeh Z, Karaman R (2020) Comprehensive review on Alzheimer’s disease: causes and treatment. Molecules 25
da Rocha TJ, Silva Alves M, Guisso CC, de Andrade FM, Camozzato A, de Oliveira AA, Fiegenbaum M (2018) Association of GPX1 and GPX4 polymorphisms with episodic memory and Alzheimer’s disease. Neurosci Lett 666:32–37
Deng J, Zeng W, Luo S, Kong W, Shi Y, Li Y, Zhang H (2021) Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization. Inf Sci
Dickson DW (2004) Apoptotic mechanisms in Alzheimer neurofibrillary degeneration: cause or effect? J Clin Invest 114:23–27
Ephrame SJ, Cork GK, Marshall V, Johnston MA, Shawa J, Alghusen I, Qiang A, Denson AR, Carman MS, Fedosyuk H et al (2023) O-GlcNAcylation regulates extracellular signal-regulated kinase (ERK) activation in Alzheimer’s disease. Front Aging Neurosci 15:1155630
Fang Z, Li J, Cao F, Li F (2022) Integration of scRNA-Seq and bulk RNA-Seq reveals molecular characterization of the immune microenvironment in acute pancreatitis. Biomolecules 13
Fu X, Song C, Zhang R, Shi H, Jiao Z (2023) Multimodal classification framework based on hypergraph latent relation for end-stage renal disease associated with mild cognitive impairment. Bioengineering 10:958
GBD 2016 Epilepsy Collaborators (2019) Global, regional, and national burden of epilepsy, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 18(4):357–375. https://doi.org/10.1016/S1474-4422(18)30454-X
Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein DL, Jacobs AH, Wyss-Coray T, Vitorica J, Ransohoff RM et al (2015) Neuroinflammation in Alzheimer’s disease. Lancet Neurol 14:388–405
Heurtaux T, Bouvier DS, Benani A, Helgueta Romero S, Frauenknecht KBM, Mittelbronn M, Sinkkonen L (2022) Normal and pathological NRF2 signalling in the central nervous system. Antioxidants (Basel) 11
Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, Myung P, Plikus MV, Nie Q (2021) Inference and analysis of cell-cell communication using Cell Chat. Nat Commun 12:1088
Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the \(2{{^{-\triangle\triangle C_{\text T}}}}\) Method. Methods (San Diego, Calif.) 25(4):402–408. https://doi.org/10.1006/meth.2001.1262
Long HZ, Cheng Y, Zhou ZW, Luo HY, Wen DD, Gao LC (2021) PI3K/AKT signal pathway: a target of natural products in the prevention and treatment of Alzheimer’s disease and Parkinson’s disease. Front Pharmacol 12:648636
Looi JC, Rajagopalan P, Walterfang M, Madsen SK, Thompson PM, Macfarlane MD, Ching C, Chua P, Velakoulis D (2012) Differential putaminal morphology in Huntington’s disease, frontotemporal dementia and Alzheimer’s disease. Aust N Z J Psychiatry 46:1145–1158
Lu Y, He X, Zhong S (2007) Cross-species microarray analysis with the OSCAR system suggests an INSR->Pax6->NQO1 neuro-protective pathway in aging and Alzheimer’s disease. Nucleic Acids Res 35:W105-114
Marcello E, Di Luca M, Gardoni F (2018) Synapse-to-nucleus communication: from developmental disorders to Alzheimer’s disease. Curr Opin Neurobiol 48:160–166
Martinez B, Peplow PV (2023) MicroRNAs as potential biomarkers in temporal lobe epilepsy and mesial temporal lobe epilepsy. Neural Regen Res 18:716–726
McManus RM, Heneka MT (2017) Role of neuroinflammation in neurodegeneration: new insights. Alzheimers Res Ther 9:14
Meneses A, Koga S, O’Leary J, Dickson DW, Bu G, Zhao N (2021) TDP-43 pathology in Alzheimer’s disease. Mol Neurodegener 16:84
Moon S, Lee H (2021) JDSNMF: joint deep semi-non-negative matrix factorization for learning integrative representation of molecular signals in Alzheimer’s disease. J Pers Med 11
Niu Y, Ding T, Liu J, Zhang G, Tong L, Cheng X, Yang Y, Chen Z, Tang B (2021) Fluorescence switch of gold nanoclusters stabilized with bovine serum albumin for efficient and sensitive detection of cysteine and copper ion in mice with Alzheimer’s disease. Talanta 223:121745
Olsen I, Taubman MA, Singhrao SK (2016) Porphyromonas gingivalis suppresses adaptive immunity in periodontitis, atherosclerosis, and Alzheimer’s disease. J Oral Microbiol 8:33029
Pei Y, Chen S, Zhou F, Xie T, Cao H (2023) Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy. Front Aging Neurosci 15:1146660
Prokopenko D, Hecker J, Kirchner R, Chapman BA, Hoffman O, Mullin K, Hide W, Bertram L, Laird N, DeMeo DL et al (2020) Identification of novel Alzheimer’s disease loci using sex-specific family-based association analysis of whole-genome sequence data. Sci Rep 10:5029
Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, Trapnell C (2017) Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14:979–982
Rahayel S, Frasnelli J, Joubert S (2012) The effect of Alzheimer’s disease and Parkinson’s disease on olfaction: a meta-analysis. Behav Brain Res 231:60–74
Sánchez JD, Alcántara AR, González JF, Sánchez-Montero JM (2023) Advances in the discovery of heterocyclic-based drugs against Alzheimer’s disease. Expert Opin Drug Discov 18:1413–1428
Santiago JA, Quinn JP, Potashkin JA (2023) Co-expression network analysis identifies molecular determinants of loneliness associated with neuropsychiatric and neurodegenerative diseases. Int J Mol Sci 24
Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, Cummings J, van der Flier WM (2021) Alzheimer’s disease. Lancet 397:1577–1590
Sharma V, Kaur A, Singh TG (2020) Counteracting role of nuclear factor erythroid 2-related factor 2 pathway in Alzheimer’s disease. Biomed Pharmacother 129:110373
Shiga M, Seno S, Onizuka M, Matsuda H (2021) SC-JNMF: single-cell clustering integrating multiple quantification methods based on joint non-negative matrix factorization. PeerJ 9:e12087
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R (2019) Comprehensive integration of single-cell data. Cell 177:1888-1902.e1821
Su H, Na N, Zhang X, Zhao Y (2017) The biological function and significance of CD74 in immune diseases. Inflamm Res 66:209–216
Tan Z, Chen X, Zuo J, Fu S, Wang H, Wang J (2023) Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model. J Transl Med 21:223
Thornton C, Bright NJ, Sastre M, Muckett PJ, Carling D (2011) AMP-activated protein kinase (AMPK) is a tau kinase, activated in response to amyloid β-peptide exposure. Biochem J 434:503–512
Wang J, Eslinger PJ, Smith MB, Yang QX (2005a) Functional magnetic resonance imaging study of human olfaction and normal aging. J Gerontol A Biol Sci Med Sci 60:510–514
Wang HQ, Nakaya Y, Du Z, Yamane T, Shirane M, Kudo T, Takeda M, Takebayashi K, Noda Y, Nakayama KI et al (2005b) Interaction of presenilins with FKBP38 promotes apoptosis by reducing mitochondrial Bcl-2. Hum Mol Genet 14:1889–1902
Wang Y, Chen G, Shao W (2022) Identification of ferroptosis-related genes in Alzheimer’s disease based on bioinformatic analysis. Front Neurosci 16:823741
Wei K, Kong W, Wang S (2022) Integration of imaging genomics data for the study of Alzheimer’s disease using joint-connectivity-based sparse nonnegative matrix factorization. J Mol Neurosci 72:255–272
Wei L, Yang X, Wang J, Wang Z, Wang Q, Ding Y, Yu A (2023) H3K18 lactylation of senescent microglia potentiates brain aging and Alzheimer’s disease through the NFκB signaling pathway. J Neuroinflammation 20:208
Wu T, Hu E, Xu S et al (2021) clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb) 2:100141
Xi Z, Song C, Zheng J, Shi H, Jiao Z (2023) Brain functional networks with dynamic hypergraph manifold regularization for classification of end-stage renal disease associated with mild cognitive impairment. Comput Model Eng Sci 135:2243–2266
Yankner BA (1996) Mechanisms of neuronal degeneration in Alzheimer’s disease. Neuron 16:921–932
Yu L, Shen N, Shi Y, Shi X, Fu X, Li S, Zhu B, Yu W, Zhang Y (2022) Characterization of cancer-related fibroblasts (CAF) in hepatocellular carcinoma and construction of CAF-based risk signature based on single-cell RNA-seq and bulk RNA-seq data. Front Immunol 13:1009789
Yuan J, Yankner BA (2000) Apoptosis in the nervous system. Nature 407:802–809
Zhang S, Liu CC, Li W, Shen H, Laird PW, Zhou XJ (2012) Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res 40:9379–9391
Zhou D, Huang J, Schölkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10:1523
Author information
Authors and Affiliations
Contributions
Kun Tu: data curation, software, writing—original draft; Wenhui Zhou: conceptualization, software, visualization; Shubing Kong: data curation, methodology, supervision. All authors read and approved the manuscript.
Corresponding author
Ethics declarations
Ethics Approval and Consent to Participate
Not applicable.
Conflict of Interest
The authors declare no conflict of interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tu, K., Zhou, W. & Kong, S. Integrating Multi-omics Data for Alzheimer’s Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm. J Mol Neurosci 74, 43 (2024). https://doi.org/10.1007/s12031-024-02211-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12031-024-02211-9