scholarly journals Molecular subtyping of Alzheimer’s disease with consensus non-negative matrix factorization

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250278
Author(s):  
Chunlei Zheng ◽  
Rong Xu
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Matrix Factorization ◽  
Molecular Subtypes ◽  
Clinical Trial Design ◽  
Gene Expression Omnibus ◽  
Clinical Presentations ◽  
Apoe Genotypes ◽  
Non Negative Matrix Factorization

Alzheimer’s disease (AD) is a heterogeneous disease and exhibits diverse clinical presentations and disease progression. Some pathological and anatomical subtypes have been proposed. However, these subtypes provide a limited mechanistic understanding for AD. Leveraging gene expression data of 222 AD patients from The Religious Orders Study and Memory and Aging Project (ROSMAP) Study, we identified two AD molecular subtypes (synaptic type and inflammatory type) using consensus non-negative matrix factorization (NMF). Synaptic type is characterized by disrupted synaptic vesicle priming and recycling and synaptic plasticity. Inflammatory type is characterized by disrupted IL2, interferon alpha and gamma pathways. The two AD molecular subtypes were validated using independent data from Gene Expression Omnibus. We further demonstrated that the two molecular subtypes are associated with APOE genotypes, with synaptic type more prevalent in AD patients with E3E4 genotype and inflammatory type more prevalent in AD patients with E3E3 genotype (p = 0.031). In addition, two molecular subtypes are differentially represented in male and female AD, with synaptic type more prevalent in male and inflammatory type in female patients (p = 0.051). Identification of AD molecular subtypes has potential in facilitating disease mechanism understanding, clinical trial design, drug discovery, and precision medicine for AD.

Comprehensive Analysis of Differential Expression Profiles of Long Non-coding RNAs with Associated Co-expression and Competing Endogenous RNA Networks in the Hippocampus of Patients with Alzheimer's Disease

2021 ◽  
Vol 18 ◽  
Author(s):  
Jian-Jun Zhang ◽  
Ze-Xuan-Zhu ◽  
Guang-Min-Xu ◽  
Peng Su ◽  
Qian Lei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Competing Endogenous Rna ◽  
Pathogenic Genes ◽  
Non Coding Rna ◽  
Using Data ◽  
Trans Regulation

Background: Alzheimer's disease (AD) is still one of the major threats to human health. Although a satisfactory treatment for AD has not yet been discovered, it is necessary to continue to search for novel approaches to deal with this insidious and debilitating disease. Although numerous studies have shown that long non-coding RNA (lncRNA) occupy a significant role in a variety of diseases, their roles in AD remain unclear. Objectives: Using data analysis to explore the role of lncRNA in the course of AD, to further our understanding of AD, and to look forward to finding a new breakthrough for the treatment of AD. Methods: We downloaded and screened expression data of the hippocampal regions of patients with AD from the Gene Expression Omnibus database. We generated lncRNA-miRNA-mRNA networks based on the competing endogenous RNA (ceRNA) hypothesis, and according to gene expression level, we constructed a coding-noncoding co-expression (CNC) network and then executed cis- and trans-regulation analyses. Results: Through comprehensive and systematic analyses, we found that lncRNAs MALAT1, OIP5-AS1, LINC00657, and lnc-NUMB-1 regulated the expression of the key AD pathogenic genes APP, PSEN1, BACE1; and that these lncRNAs may promote the distribution of β-amyloid (Aβ protein) in the brain through exosomes. In addition, lncRNAs were found to adjust viral transcriptional expression, thereby further supporting viral pathogenesis for AD. Conclusions: The lncRNAs MALAT1, OIP5-AS1, LINC00657, and lnc-NUMB-1 that are present in the hippocampus of AD patients exert an important influence on the development of this disease.

scholarly journals Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yeonwoo Chung ◽  
Hyunju Lee ◽  
Michael W. Weiner ◽  
Paul Aisen ◽  
Ronald Petersen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Type 2 Diabetes ◽  
Alzheimer's Disease ◽  
Candidate Genes ◽  
Matrix Factorization ◽  
Differentially Expressed Gene ◽  
Gene Clustering ◽  
Type 3 ◽  
Non Negative Matrix Factorization

AbstractAlzheimer’s disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal–Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments.

O5-01-03: ATROPHY SUBTYPES IN ALZHEIMER'S DISEASE IDENTIFIED THROUGH NON-NEGATIVE MATRIX FACTORIZATION

2006 ◽  
Vol 14 (7S_Part_31) ◽  
pp. P1638-P1639
Author(s):  
Mara ten Kate ◽  
Ellen Dicks ◽  
Wiesje M. Van der Flier ◽  
Charlotte E. Teunissen ◽  
Philip Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Matrix Factorization ◽  
Non Negative Matrix Factorization

scholarly journals JDSNMF: Joint Deep Semi-Non-Negative Matrix Factorization for Learning Integrative Representation of Molecular Signals in Alzheimer’s Disease

2021 ◽  
Vol 11 (8) ◽  
pp. 686
Author(s):  
Sehwan Moon ◽  
Hyunju Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Matrix Factorization ◽  
Omics Data ◽  
Biological Interactions ◽  
Multiple Data ◽  
Age Related ◽  
Latent Features ◽  
Non Negative Matrix Factorization

High dimensional multi-omics data integration can enhance our understanding of the complex biological interactions in human diseases. However, most studies involving unsupervised integration of multi-omics data focus on linear integration methods. In this study, we propose a joint deep semi-non-negative matrix factorization (JDSNMF) model, which uses a hierarchical non-linear feature extraction approach that can capture shared latent features from the complex multi-omics data. The extracted latent features obtained from JDSNMF enabled a variety of downstream tasks, including prediction of disease and module analysis. The proposed model is applicable not only to sample-matched multiple data (e.g., multi-omics data from one cohort) but also to feature-matched multiple data (e.g., omics data from multiple cohorts), and therefore it can be flexibly applied to various cases. We demonstrate the capabilities of JDSNMF using sample-matched simulated data and feature-matched multi-omics data from Alzheimer’s disease cohorts, evaluating the feature extraction performance in the context of classification. In a test application, we identify AD- and age-related modules from the latent matrices using an explainable artificial intelligence and regression model. These results show that the JDSNMF model is effective in identifying latent features having a complex interplay of potential biological signatures.

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