Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease

Background: Alzheimer's disease (AD) is the most common cause of dementia. As a heterogenous disease, there are several clinically and pathobiological defined subtypes with different molecular signatures. Neuroinflammation contributed to AD pathogenesis, however, the roles it played in the heterogeneity of AD was unclear. Objective: We aimed to illustrate the roles neuroinflammation played in the heterogeneity of AD. Method: An integrative network analysis based on transcriptomics, miRNOmics, and proteomics was performed to illustrate the heterogeneous characters of AD. Combined-functional-networks and hypothesis-network were constructed and analyzed to explore the roles neuroinflammation played in AD heterogeneity. Results: Astrocytes, microglia, ‘M2 macrophage-Neuron’, and ‘Microglia- Neuron’ were shown to be enriched in neuroinflammation related functional terms in a cell- and spatial-specific way. The microglia and neurons could interact with each other in three different ways including indirect interactions via intermediate cells, indirect interactions via soluble factors, and direct interactions established localized and functionally distinct signaling, all of which were used to control different biological processes. The combined network analyses exhibited the key roles neuroinflammation plays in the 'AD hypothesis network’. Conclusion : The AD heterogeneity may be caused by the heterogeneous cells involved in neuroinflammation and the crosstalks between spatial-specific molecular signatures.

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P4-031: Integrative Network Analysis of Multiple Alzheimer's Disease Rnaseq Studies From the Accelerating Medicine Partnership-Alzheimer's Disease Consortium

Alzheimer s & Dementia ◽

10.1016/j.jalz.2016.06.2120 ◽

2016 ◽

Vol 12 ◽

pp. P1026-P1027 ◽

Cited By ~ 1

Author(s):

Benjamin A. Logsdon ◽

Bin Zhang ◽

Vitalina Komashko ◽

Sara Mostafavi ◽

Mingming Chen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Analysis ◽

Integrative Network

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Predicting Brain Regions Related to Alzheimer’s Disease Based on Global Feature

10.1101/2020.11.27.401950 ◽

2020 ◽

Author(s):

Qi Wang ◽

Siwei Chen ◽

He Wang ◽

Luzeng Chen ◽

Yongan Sun ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Analysis ◽

Diffusion Tensor ◽

The Elderly ◽

Brain Regions ◽

Imaging Method ◽

Global Feature ◽

Objective Monitoring ◽

The Brain

AbstractAlzheimer’s disease (AD) is a common neurodegenerative disease in the elderly, early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most of the brain regions related to AD were identified based on the imaging method, which can only identify some atrophic brain regions. In this work, we used mathematical models to find out the potential brain regions related to AD. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, we set a new local feature index 2hop-connectivity to measure the correlation among different areas. And for this, we proposed a novel algorithm named 2hopRWR to measure 2hop-connectivity. At last, we proposed a new index GFS (Global Feature Score) based on global feature by combing 5 local features: degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are likely related to Alzheimer’s Disease. As a result, all the top ten brain regions in GFS scoring difference between the AD group and the non-AD group were related to AD by literature verification. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the mini-mental state examination (MMSE) scale and montreal cognitive assessment (MoCA) scale. So, we believe the GFS can also be used as a new index to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method in other areas of network analysis.

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Molecular differences in Alzheimer's disease between male and female patients determined by integrative network analysis

Journal of Cellular and Molecular Medicine ◽

10.1111/jcmm.13852 ◽

2018 ◽

Vol 23 (1) ◽

pp. 47-58 ◽

Cited By ~ 7

Author(s):

Lin-Lin Sun ◽

Song-Lin Yang ◽

Hui Sun ◽

Wei-Da Li ◽

Shu-Rong Duan

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Analysis ◽

Male And Female ◽

Female Patients ◽

Integrative Network

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Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets

Science Advances ◽

10.1126/sciadv.abb5398 ◽

2021 ◽

Vol 7 (2) ◽

pp. eabb5398

Author(s):

Ryan A. Neff ◽

Minghui Wang ◽

Sezen Vatansever ◽

Lei Guo ◽

Chen Ming ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Mouse Models ◽

Molecular Subtypes ◽

Amyloid Β ◽

Brain Regions ◽

Molecular Heterogeneity ◽

Sequencing Data ◽

Integrative Network ◽

Pathophysiologic Mechanisms

Alzheimer’s disease (AD), the most common form of dementia, is recognized as a heterogeneous disease with diverse pathophysiologic mechanisms. In this study, we interrogate the molecular heterogeneity of AD by analyzing 1543 transcriptomes across five brain regions in two AD cohorts using an integrative network approach. We identify three major molecular subtypes of AD corresponding to different combinations of multiple dysregulated pathways, such as susceptibility to tau-mediated neurodegeneration, amyloid-β neuroinflammation, synaptic signaling, immune activity, mitochondria organization, and myelination. Multiscale network analysis reveals subtype-specific drivers such as GABRB2, LRP10, MSN, PLP1, and ATP6V1A. We further demonstrate that variations between existing AD mouse models recapitulate a certain degree of subtype heterogeneity, which may partially explain why a vast majority of drugs that succeeded in specific mouse models do not align with generalized human trials across all AD subtypes. Therefore, subtyping patients with AD is a critical step toward precision medicine for this devastating disease.

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Preservation Analysis on Spatiotemporal Specific Co-expression Networks Suggests the Immunopathogenesis of Alzheimer’s Disease

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.727928 ◽

2021 ◽

Vol 13 ◽

Author(s):

Liyuan Guo ◽

Yushan Liu ◽

Jing Wang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Analysis ◽

Expression Profiling ◽

Clinical Symptoms ◽

Brain Regions ◽

Postmortem Brain ◽

Postmortem Brain Tissue ◽

Key Genes ◽

Disease Stages

The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.

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Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.659838 ◽

2021 ◽

Vol 15 ◽

Author(s):

Qi Wang ◽

Siwei Chen ◽

He Wang ◽

Luzeng Chen ◽

Yongan Sun ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Analysis ◽

Diffusion Tensor ◽

The Elderly ◽

Local Features ◽

Brain Regions ◽

Global Feature ◽

Objective Monitoring ◽

Peking University

Alzheimer's disease (AD) is a neurodegenerative disease that commonly affects the elderly; early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most brain regions related to AD were identified based on imaging methods, and only some atrophic brain regions could be identified. In this work, the authors used mathematical models to identify the potential brain regions related to AD. In this study, 20 patients with AD and 13 healthy controls (non-AD) were recruited by the neurology outpatient department or the neurology ward of Peking University First Hospital from September 2017 to March 2019. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, the authors set a new local feature index 2hop-connectivity to measure the correlation between different regions. Compared with the traditional graph theory index, 2hop-connectivity exploits the higher-order information of the graph structure. And for this purpose, the authors proposed a novel algorithm called 2hopRWR to measure 2hop-connectivity. Then, a new index global feature score (GFS) based on a global feature was proposed by combing five local features, namely degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are related to AD. As a result, the top ten brain regions identified using the GFS scoring difference between the AD and the non-AD groups were associated to AD by literature verification. The results of the literature validation comparing GFS with the local features showed that GFS was superior to individual local features. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the Mini-Mental State Examination (MMSE) scale and the Montreal Cognitive Assessment (MoCA) scale. Therefore, the authors believe the GFS can also be used as a new biomarker to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method for network analysis in other domains.

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A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

Current Alzheimer Research ◽

10.2174/1567205014666171106150309 ◽

2018 ◽

Vol 15 (5) ◽

pp. 429-442 ◽

Cited By ~ 3

Author(s):

Nishant Verma ◽

S. Natasha Beretvas ◽

Belen Pascual ◽

Joseph C. Masdeu ◽

Mia K. Markey ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Cognitive Impairment ◽

Latent Variable ◽

Brain Regions ◽

Disease Assessment ◽

Latent Variable Analysis ◽

The Relationship ◽

Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

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Genome-wide analysis identifies a novel LINC-PINT splice variant associated with vascular amyloid pathology in Alzheimer’s disease

Acta Neuropathologica Communications ◽

10.1186/s40478-021-01199-2 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Joseph S. Reddy ◽

Mariet Allen ◽

Charlotte C. G. Ho ◽

Stephanie R. Oatman ◽

Özkan İş ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genome Wide Association Study ◽

Transcriptome Profiling ◽

Brain Regions ◽

Common Finding ◽

Amyloid Angiopathy ◽

Genome Wide ◽

A Genome ◽

Male Sex

AbstractCerebral amyloid angiopathy (CAA) contributes to accelerated cognitive decline in Alzheimer’s disease (AD) dementia and is a common finding at autopsy. The APOEε4 allele and male sex have previously been reported to associate with increased CAA in AD. To inform biomarker and therapeutic target discovery, we aimed to identify additional genetic risk factors and biological pathways involved in this vascular component of AD etiology. We present a genome-wide association study of CAA pathology in AD cases and report sex- and APOE-stratified assessment of this phenotype. Genome-wide genotypes were collected from 853 neuropathology-confirmed AD cases scored for CAA across five brain regions, and imputed to the Haplotype Reference Consortium panel. Key variables and genome-wide genotypes were tested for association with CAA in all individuals and in sex and APOEε4 stratified subsets. Pathway enrichment was run for each of the genetic analyses. Implicated loci were further investigated for functional consequences using brain transcriptome data from 1,186 samples representing seven brain regions profiled as part of the AMP-AD consortium. We confirmed association of male sex, AD neuropathology and APOEε4 with increased CAA, and identified a novel locus, LINC-PINT, associated with lower CAA amongst APOEε4-negative individuals (rs10234094-C, beta = −3.70 [95% CI −0.49—−0.24]; p = 1.63E-08). Transcriptome profiling revealed higher LINC-PINT expression levels in AD cases, and association of rs10234094-C with altered LINC-PINT splicing. Pathway analysis indicates variation in genes involved in neuronal health and function are linked to CAA in AD patients. Further studies in additional and diverse cohorts are needed to assess broader translation of our findings.

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Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications ◽

10.1007/s00521-021-06105-4 ◽

2021 ◽

Author(s):

Antonio Giovannetti ◽

Gianluca Susi ◽

Paola Casti ◽

Arianna Mencattini ◽

Sandra Pusil ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Brain Regions ◽

Ensemble Classification ◽

The Novel ◽

Ensemble Classifiers ◽

Deep Convolutional Neural Networks ◽

Early Signs ◽

Data Representations ◽

The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

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