Edited by: Daniel Victor Guebel, Universidad de La Laguna, Spain
Reviewed by: Gokhan Ertaylan, Flemish Institute for Technological Research, Belgium; Lina Lim, National University of Singapore, Singapore
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Alzheimer’s disease (AD) is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20–50 years), aging (70–99 years), and AD (70–99 years). The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein–protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein–protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14-3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies.
Aging is associated with decline in cognitive abilities, including memory and executive function supported by prefrontal cortex and hippocampus (
Genome-wide expression profiling of hippocampus have been widely used to investigate the aging and pathogenesis of AD in human post-mortem brain tissues (
On the other hand, few studies have compared the gene expression profiles in young, aging, and AD (
The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20–50 years), aging (70–99 years), and AD (70–99 years). We combined the weighted gene co-expression network and weighted PPI network-level approaches to study the transition from young to aging to AD. The co-expression network analysis clusters genes into functional modules based on the gene expression profiles and helps to identify core biological processes and pathways associated with the sample group. The weighted PPI network uses the expression data to calculate edge weights in the network and helps to identify edges and subnetworks that are significantly affected between groups. We found modules associated with neuron, glial and endothelial cells in the co-expression network of young, aging, and AD. These modules significantly correlate with both aging and AD. We also show the preservation of these modules in five different hippocampus datasets of AD. Mapping the gene expression to PPI network helped to identify the upregulated and downregulated subnetworks of aging and AD.
Gene expression data of different age groups and AD samples with accession no: GSE48350 was downloaded from Gene Expression Omnibus (GEO)
Workflow used to study young, aging, and AD.
The WGCNA package in R was used to construct a signed co-expression network from the expression data (
The correlation matrix was converted to adjacency matrix using the function,
The resultant adjacency matrix was transformed into topological overlap matrix (TOM) and a dendrogram was constructed using 1-TOM as a distance measure (
In addition, cell-type specific gene lists obtained from
The reliability of the identified modules was checked by performing module preservation analysis using hippocampal test datasets of whole tissue: GSE1297, GSE36980, GSE84422, GSE29378 (both CA1 and CA3) and neuron enriched samples: GSE28146, GSE5281. These datasets were independently proces-sed depending on the platform (
A comprehensive human PPI network constructed by
The edge betweenness centrality measure was computed using igraph R package (
We performed WGCNA using 18,754 genes to identify and characterize modules that are related to aging and AD. A co-expression network was constructed independent of clinical information, age and gender using all the samples. We found 15 modules of co-expressed genes (
Correlation between module eigengene (ME) expression value and age, stage (0-young, 1-aging, 2-AD), AD (young and aging-0, AD-1) for each module. Pearson correlation is reported with the
Module eigengene (ME) expression values (
The overlap of upregulated and downregulated DEGs between young vs. aging, young vs. AD and aging vs. AD.
We also grouped samples into male and female, and explored the correlation of modules to aging and AD in a gender-specific manner. The ME expression values of M4, M5, and M7 show difference between young and aging depending on the gender (
We analyzed the overlap of cell-type specific genes with modules (
The overlap between cell-type specific genes and modules.
Module | Astrocytes | Endothelial | Microglia | Neurons | Oligodendrocytes |
---|---|---|---|---|---|
M1 | 1.0 | 0.998 | 1.0 | 1.0 | |
M2 | 0.99 | 0.667 | 1.0 | 1.0 | 0.95 |
M3 | 0.244 | 1.0 | 1.0 | 1.0 | |
M4 | 0.998 | 0.489 | 1.0 | 1.0 | |
M5 | 0.489 | 1.0 | 0.989 | ||
M6 | 0.723 | 0.292 | 0.496 | 0.99 | 1.0 |
M7 | 0.949 | 1.0 | 1.0 | 0.897 | 1.0 |
M8 | 0.947 | 0.929 | 0.983 | 0.98 | |
M9 | 1.0 | 1.0 | 1.0 | 1.0 | |
M10 | 1.0 | 1.0 | 1.0 | 0.998 | 1.0 |
M11 | 0.18 | 1.0 | 1.0 | 0.077 | |
M12 | 0.995 | 1.0 | 1.0 | 1.0 | |
M13 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
M14 | 1.0 | 0.96 | 1.0 | 1.0 | 1.0 |
Module preservation analysis with hippocampal datasets.
We characterized the biological processes and KEGG pathways associated with modules using DAVID for the functional enrichment analysis. We found that microglia module M4 is associated with the biological process inflammatory response and KEGG pathways phagosome, Toll-like receptor signaling and cytokine-cytokine receptor interactions. Further, it is associated with the cellular component MHC class II protein complex (
Enrichment of Gene Ontology (GO) terms and KEGG pathways associated with aging and AD-specific modules.
Module (genes) | KEGG pathway | Biological process | Cellular components | Hub genes |
---|---|---|---|---|
M2 (2072) | Ribosome (1.7E-10), Spliceosome (1.2E-2), RNA transport (2.4E-4∗) | rRNA processing (2.4E-13), mRNA splicing (3.6E-5), mRNA processing (1.1E-4) | Nucleolus (1.6E-10), Ribosome (4.1E-6) | TFEB, PAN2, ARHGAP17 |
M3 (675) | Fatty acid degradation (8.2E-3), Hippo signaling (2.7E-2), PPAR signaling (9.7E-4∗) | Cell adhesion (4.1E-2), Oxidation–reduction process (3.0E-4∗), Fatty acid beta-oxidation (8.5E-4∗) | Extracellular exosome (1.5E-2), Focal adhesion (3.9E-4∗), Extracellular space (8.9E-4∗) | CDC42EP4, EZR, ARHGEF26, TCF7L1, SOX9, ARHGEF6 |
M4 (701) | Phagosome (4.3E-8), Toll-like receptor signaling (3.1E-6), Cytokine–cytokine receptor interaction (2.7E-4) | Inflammatory response (1.4E-18), Signal transduction (4.0E-12), Toll-like receptor signaling (7.6E-8) | MHC class II protein complex (1.8E-6), Integral component of membrane (2.6E-3), Phagocytic vesicle membrane (4.3E-3) | TYROBP, TREM2, ITGB2, MYO1F, C1Qs, TGFB1 |
M5 (798) | TNF signaling (1.9E-6), Complement and coagulation cascades (5.6E-5), HIF-1 signaling (4.1E-4) | Inflammatory response (4.8E-16), Response to LPS (1.5E-8), Cellular response to TNF (2.2E-8) | Extracellular matrix (3.5E-7), Extracellular exosome (5.8E-7), MHC class 1 complex (1.4E-4) | TNFRSF1A, MSN, CLIC1, IFITM2 |
M7 (333) | ECM-receptor interaction (6.4E-3∗), Focal adhesion (3.6E-2∗) | Outer dynein arm assembly (6.4E-12), Inner dynein arm assembly (3.8E-9), Cilium morphogenesis (9.0E-9) | Axoneme (1.3E-18), Motile cilium (5.5E-17) | ZMYND10, ARMC3, CFAP43 |
M8 (695) | Axon guidance (5.9E-3), Oxytocin signaling (1.6E-3∗), Rap1 signaling (4.8E-3∗) | Calcium ion transport (8.1E-3∗), Potassium ion transport (9.9E-3∗), Dendritic spine morphogenesis (1.1E-2∗) | Cell junction (9.7E-9), Postsynaptic density (6.4E-8), Dendritic spine (9.1E-5) | ICAM5, PRKCG, JPH3, SPTBN2 |
M9 (2377) | Synaptic vesicle (8.3E-10), Glutamatergic synapse (3.8E-5), Long term potentiation (4.2E-3) | Chemical synaptic transmission (2.3E-16), Neurotransmitter secretion (1.5E-6), Nervous system development (5.8E-5) | Neuron projection (2.5E-10), Dendrite (1.9E-10), Axon (4.9E-9) | UCHL1, STMN2, SYN1, SYT5, SNAP91, PAK3 |
M10 (2660) | Oxidative phosphorylation (1.8E-19), Proteasome (6.6E-11), Spliceosome (7.5E-7), Protein processing in ER (3.8E-5) | Mitochondria electron transport (1.4E-12), Protein folding (9.3E-9) | Mitochondrion (2.4E-62), Mitochondrial matrix (2.4E-19), Proteasome complex (3.4E-9), Ribosome (2.5E-8) | NDUFAB1, VDAC3, ATP5G3, COPS4, RTCA, POP4 |
M12 (584) | Retrograde endocannabinoid signaling (4.8E-4), Circadian entrainment (7.1E-3), Glutamatergic synapse (7.2E-3), GABAergic synapse (2.9E-2) | Peptidyl-serine phosphorylation (2.9E-4∗), Neuron cell–cell adhesion (2.1E-3∗) | Postsynaptic density (3.5E-2), Postsynaptic membrane (2.9E-2) | YWHAZ, GADP1, SYNJ1, MAPK9, G3BP2, ATP6AP2 |
The astrocyte module M3 is associated with the biological process cell adhesion and KEGG pathways fatty acid degradation and HIPPO signaling pathway (
The neuron module M9 is associated with biological processes chemical synaptic transmission, neurotransmitter secretion and nervous system development, and KEGG pathways synaptic vesicle, chemical synapses (glutamatergic, cholinergic, GABAergic, serotonergic, and dopaminergic) and long-term potentiation (
Further, the module M10 is another downregulated module related to AD and it is associated with mitochondria, ribosome, and protein folding (
In the oligodendrocyte module M1, we note that most genes are downregulated with aging but in AD the extent of downregulation decrease and in some patients this module is upregulated (
We integrated the gene expression of young, aging, and AD with the PPI network to obtain weighted PPI networks. The integrated PPI networks were used to identify active/inactive interactions between young vs. aging and young vs. AD using the edge betweenness network measure. Only those interactions with an edge betweenness value difference of 2000 and adj
We found nodes CD44, VEGFA, HIF1A, VIM, FOS, CEBPB, CDKN1A, SHC1, TGFβ1, and SYK as hub genes of the upregulated aging subnetwork. CD44 is expressed in both glial and neuronal cells and it is associated with astrocytes migration and differentiation, astrogliosis, oligodendrocytes differentiation, inflammatory response, dendritic arborization, actin polymerization, and synaptic transmission (
Expression profiles of genes associated with aging and AD. The fold changes of
VEGFA interaction with HIF1A is upregulated in aging. We also found key interactions between HIF1A, PFKFB3, and LDHA, which regulate the metabolic switch toward aerobic glycolysis. This is also further supported by the upregulation of hexokinase 2 (HK2) and pyruvate dehydrogenase kinase 1 (PDK1) genes with aging compared to the young. DNA damage responsive gene CDKN1A is upregulated in aging along with its interacting partner GADD45B. Further, the upregulated aging subnetwork also includes hub genes related to immune and inflammatory response (CEBPB, FOS, STAT3, TGFβ1, and SYK). In young vs. AD, we found that the upregulated subnetwork expands with newer interactions and hub genes also include RXRA, ACTB, RELA, NFKBIA, FYN, MYC, and YBX1. NFKβ subunit, RELA interactions increase compared to the aging subnetwork and is related to the immune response. We found interactions involving YBX1, MYC, MAX, MXI1, SGK1, and FOXO3 in AD that are linked to cell proliferation and cell death decision-making. The repressor element 1-silencing transcription factor (REST) interactions are also part of the upregulated subnetwork. REST is linked to the stress resistance in aging and AD (
The hub genes of downregulated aging subnetwork include MAPK1, RAD21, YWHAZ, ATXN1, SRC, CTCF, CALM1, and PRKCZ. In AD, the node degree of MAPK1, CALM1, YWHAZ, PRKCZ, SRC, and CTCF increases while that of RAD21 decreases. Further, we also found hub genes PIN1, YWHAH, YWHAG, YWHAQ, EGR1, CDC42, DNM1, SST, and SNAP25 that are specific for AD. YWHAZ, YWHAH, YWHAG, and YWHAQ encode proteins of the 14-3-3 family that are highly expressed in the brain tissue and are involved in the brain development, memory and learning (
CDC42 interaction with GRB2 is downregulated, which has a preventive role in the cytoskeleton disassembly. Further, SNAP25 encoding a SNARE complex protein and DNM1 encoding a dynamin subfamily of GTP-binding protein, are downregulated affecting the synaptic vesicle exocytosis and endocytosis, respectively. The neuropeptide somatostatin (SST) gene is also downregulated, which suggest that the SST group of GABAergic interneurons are affected (
In this study, we extend the work of
Module hub genes in aging and AD.
Gene | Role/function | Reference |
---|---|---|
TFEB | Involved in Aβ-induced pathogenesis of AD by regulating the autophagy-lysosome pathway | |
EZR | Role in immune synapse along with MSN | |
TCF7L1 | Mediates Wnt signaling pathway; Altered in AD patients in the hippocampus | |
SOX9 | Glial fate specification | |
TYROBP | Activates immune response; genetic variants are risk factor for AD | |
TREM2 | Activates immune response; genetic variants are risk factor for AD | |
C1Qs | Associated with early synaptic loss in AD mice models | |
TGFB1 | Major role in the activation of microglia | |
ITGB2 | Identified as one of key inflammatory gene in AD mice models | |
MSN | Role in immune synapse along with EZR; identified as highly expressed in the AD brain using proteomic analysis | |
CLIC1 | Identified as highly expressed in the AD brain using proteomic analysis; involved in Aβ induced generation of ROS | |
IFITM2 | Identified as part of microglial sensome in aging with neuroprotective role | |
TNFRSF1A | Identified as AD associated gene using genome wide haplotype association study | |
UCHL1 | Regulates the production of Aβ by interacting with APP | |
SNAP91 | Role in vesicle mediated transport; downregulated in AD patients and AD mice models | |
PAK3 | Reduced activity in AD patients and AD mice models | |
NDUFAB1 | Role in energy metabolism; downregulated in AD | |
YWHAZ | Identified as AD biomarker using proteomic analysis; reported as hub gene in aging and AD | |
SYNJ1 | Accelerate Aβ clearance and attenuates cognitive deterioration | |
ATP6AP2 | Downregulation induces neurodegeneration |
The upregulation of glial cells-associated modules in aging is consistent with the study by
An increase in the expression of the endothelial cell module genes and VEGFA are also observed in aging. On the other hand, the expression of VEGFA decreased in AD (
Further, two neuron modules M8 and M9 are significantly downregulated with AD than aging and another neurons module M12 is downregulated as continuum of aging (
We found genes that encode protein kinases as hubs of upregulated (FYN and SYK) and downregulated (SRC, MAPK1, PRKCZ) PPI subnetworks. FYN is a tyrosine kinase that is implicated in AD and its known targets are PTK2B and Tau (
Further, we found more proteins (14-3-3 proteins, PIN1, ATXN1, and BDNF) with neuroprotective function in aging to be part of the downregulated AD subnetwork (
In summary, our study provides network-level insights into the complex relationship between aging and AD. The co-expression network of young, aging, and AD helped to identify modules, pathways and genes that are stage-specific, cell-type specific and continuum in the hippocampus, which were unclear in the previous studies that focused on either aging or AD. We identified the genes and their interactions that protect aging brain from AD and that make it susceptible to AD. We also demonstrated the validity of our study by identifying pathways and genes that are previously implicated in aging and AD. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing treatment strategies. This study can be further extended to characterize the global and local alterations in the other areas of the brain in young, aging, and AD.
PKV and VL designed the study. VL and SM carried out the analysis. VL, SM, DR, and PKV analyzed the data, wrote the manuscript, and gave the final approval for publication.
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.
PKV acknowledges financial support from the Early Career Research Award Scheme (ECR/2016/000488), Science and Engineering Research Board, Department of Science and Technology, India.
The Supplementary Material for this article can be found online at:
Up-regulated and down-regulated interactions of young vs. aging, aging vs. AD and young vs. AD.
Scale free topology fit showing the relation between
Modular organization of the co-expression network. The modules are shown in different colors below the dendrogram. The gray module consists of genes not assigned to any module.
Module eigengene (ME) expression values (
Oligodendrocyte module eigengene (ME) expression values (
The upregulated
The downregulated
The overlap of upregulated and downregulated nodes and interactions between aging and AD subnetworks.
Datasets used for the module preservation analysis.
The distribution of DEGs in the different modules of co-expression network.
Alzheimer’s disease
Database for Annotation, Visualization and Integrated Discovery
differentially expressed gene
Gene Ontology
Linear Models for Microarray
mild cognitive impairment
module eigengene
protein–protein interaction
weighted gene co-expression network analysis