Analyzing the genes related to Alzheimer’s disease via a network and pathway-based approach

Abstract

BackgroundOur understanding of the molecular mechanisms underlying Alzheimer’s disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease.MethodIn this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm.ResultsWe compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules—neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module—indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified.ConclusionBy means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13195-017-0252-z) contains supplementary material, which is available to authorized users.

A core transcriptional signature of human microglia: Derivation and utility in describing region‐dependent alterations associated with Alzheimer's disease

“…Moreover, several members of TYROBP signaling pathway were differentially expressed ( SAMSN1, SIRPB2, CD37, IL10RA, PIK3CG, and BIN2 ), a pathway dysregulated in microglia during Alzheimer's (Keren‐Shaul et al, ; Ma, Jiang, Tan, & Yu, ; Zhang et al, ). Of the differentially expressed genes, 11 were MAGs including LYZ , RPS6KA1, and SLA , with known associations to Alzheimer's (Ellison, Bradley‐Whitman, & Lovell, ; Hu, Xin, Hu, Zhang, & Wang, ; Tuppo & Arias, ). Interestingly, certain classical microglial marker genes were differentially upregulated in Alzheimer's, for example, ITGAM and PTPRC , while others showed a downward trend, including CX3CR1 and P2RY12 ; the latter consistent with a loss of homeostatic microglial identity observed in Alzheimer's mouse models (Keren‐Shaul et al, ; Krasemann et al, ).…”

“…Interestingly, certain classical microglial marker genes were differentially upregulated in Alzheimer's, for example, ITGAM and PTPRC , while others showed a downward trend, including CX3CR1 and P2RY12 ; the latter consistent with a loss of homeostatic microglial identity observed in Alzheimer's mouse models (Keren‐Shaul et al, ; Krasemann et al, ). Alternatively, whilst tissue gene expression can be influenced by cell activation and cell numbers, certain genes found differentially upregulated in both Alzheimer's and aging, such as TSPO, MS4A6A, and MHC class 2 genes, are known features of microglial activation (Bergen, Kaing, Jacoline, Gorgels, & Janssen, ; Hamelin et al, ; Hu et al, ). Overall, these observations demonstrate the value of the refined microglial signature we have derived in deducing changes in microglial profile (numbers and functional status) in Alzheimer's and are consistent with the region‐specific vulnerability and progression of Alzheimer's pathology.…”

A Network of Genetic Effects on Non-Demented Cognitive Aging: Alzheimer’s Genetic Risk (CLU + CR1 + PICALM) Intensifies Cognitive Aging Genetic Risk (COMT + BDNF) Selectively for APOE ɛ4 Carriers

“…A recent study applied a systems biology approach to examine common pathways and molecular networks among AD related genes. Three inter-connected pathway modules (neuronal and metabolic; cell growth/survival and neuroendocrine; immunological cluster) were identified [50]. Identifying genes involved in the same pathways will contribute to our understanding of how select clusters of genes operate to influence neurocognitive phenotypes associated with cognitive impairment and AD.…”

“…AD genetic network based approaches focus on using large molecular networks [50] to identify and understand specific AD-related biological functions. For example, recent network approaches include (1) co-expression networks (gene-gene correlations), (2) genetic integration (protein-protein interactions), (3) tissue specificity (network for tissues associated with AD), (4) robustness (strength in patterns of gene co-expressions in specific regions) [51,52], (5) network-based stratification (protein-protein interaction to stratify patients and identify disease molecules within the network), (6) analysis of directed networks (predicts specific signals), and (7) disease state-specific networks (networks that are only significant in specific disease states) [12,51,52].…”

Analyzing the genes and pathways related to major depressive disorder via a systems biology approach

“…Comparison of the molecules involved in the two diseases shows that they share a number of genes, regulatory elements like miRNAs, and quite several biological processes and pathways (Hu, Xin, Xin, Hu, Zhang, F I G U R E 2 Cross talk between pathways related to major depressive disorder (MDD). The nodes labeled with numbers represent the following pathways: 1, "vascular smooth muscle contraction"; 2, "dilated cardiomyopathy"; 3, "estrogen signaling pathway"; 4, "gap junction"; 5, "inflammatory mediator regulation of TRP channels"; 6, "long-term potentiation"; 7, "longevity regulated pathway-multiple species"; 8, "Rap1 signaling pathway"; 9, "neuroactive ligand-receptor interaction"; 10, "amyotrophic lateral sclerosis"; 11, "taste transduction"; 12, "insulin resistance"; 13, "apoptosis"; 14, "AGE-RAGE signaling pathway in diabetic complications"; and 15, "prolactin signaling pathway" & Wang, 2017;Mendes-Silva et al, 2016), which is consistent with the prior knowledge that depression may be a risk factor for Alzheimer's disease or part of the symptoms of dementia. The nodes labeled with numbers represent the following pathways: 1, "vascular smooth muscle contraction"; 2, "dilated cardiomyopathy"; 3, "estrogen signaling pathway"; 4, "gap junction"; 5, "inflammatory mediator regulation of TRP channels"; 6, "long-term potentiation"; 7, "longevity regulated pathway-multiple species"; 8, "Rap1 signaling pathway"; 9, "neuroactive ligand-receptor interaction"; 10, "amyotrophic lateral sclerosis"; 11, "taste transduction"; 12, "insulin resistance"; 13, "apoptosis"; 14, "AGE-RAGE signaling pathway in diabetic complications"; and 15, "prolactin signaling pathway" & Wang, 2017;Mendes-Silva et al, 2016), which is consistent with the prior knowledge that depression may be a risk factor for Alzheimer's disease or part of the symptoms of dementia.…”