scholarly journals Differential Network Analyses of Alzheimer’s Disease Identify Early Events in Alzheimer’s Disease Pathology

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Jing Xia ◽  
David M. Rocke ◽  
George Perry ◽  
Monika Ray
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Renal Diseases ◽  
Middle Temporal Gyrus ◽  
Specific Gene ◽  
Network Analyses ◽  
Coexpression Networks

In late-onset Alzheimer’s disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with low topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early events occurring in AD pathology. Cellular defense mechanisms try to intervene but fail, finally resulting in AD pathology as the disease progresses. Furthermore, disease annotation of the low TO genes in two independent protein interaction networks has resulted in association between cancer, diabetes, renal diseases, and cardiovascular diseases.

scholarly journals Failure To Detect Chlamydia pneumoniaein the Late-Onset Alzheimer's Brain

2000 ◽  
Vol 38 (7) ◽  
pp. 2591-2594 ◽  
Author(s):  
Robert H. Ring ◽  
Joseph M. Lyons
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Chlamydia Pneumoniae ◽  
Late Onset ◽  
Brain Regions ◽  
Postmortem Brain ◽  
Respiratory Pathogen ◽  
Specific Gene ◽  
Tissue Samples ◽  
Postmortem Brain Tissue

Epidemiological studies have yet to identify a single cause for the most common late-onset form of Alzheimer's disease. The common respiratory pathogen Chlamydia pneumoniae recently has been implicated as a risk factor for this form of Alzheimer's disease. Were this true, there would be a dramatic shift in current paradigms of Alzheimer's disease research and treatment. In the absence of published confirmation, we obtained postmortem brain tissue from late-onset Alzheimer's disease patients (n = 15) and representative controls (n = 5) and extracted DNA from up to six separate brain regions in each instance, including those areas particularly relevant to Alzheimer's disease neuropathology. Each sample of DNA (n = 101) was assayed five times or more for the presence of C. pneumoniae DNA using a nested-PCR protocol targeting a species-specific gene sequence coding for the major outer membrane protein of this organism. We were unable unequivocally to detect C. pneumoniae in any of the 101 samples tested by PCR and failed to culture the organism from tissue samples. We conclude that C. pneumoniae is neither strongly nor uniquely associated with the neuropathology seen in late-onset Alzheimer's disease.

scholarly journals Molecular Networks and Key Regulators of the Dysregulated Neuronal System in Alzheimer’s Disease

2019 ◽  
Author(s):  
Minghui Wang ◽  
Aiqun Li ◽  
Michiko Sekiya ◽  
Noam D. Beckmann ◽  
Xiuming Quan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Brain Regions ◽  
Molecular Networks ◽  
Parahippocampal Gyrus ◽  
Specific Gene ◽  
Neuronal System ◽  
Full Spectrum

SUMMARYTo study the molecular mechanisms driving the pathogenesis and identify novel therapeutic targets of late onset Alzheimer’s Disease (LOAD), we performed an integrative network analysis of whole-genome DNA and RNA sequencing profiling of four cortical areas, including the parahippocampal gyrus, across 364 donors spanning the full spectrum of LOAD-related cognitive and neuropathological disease severities. Our analyses revealed thousands of molecular changes and uncovered for the first-time multiple neuron specific gene subnetworks most dysregulated in LOAD. ATP6V1A, a critical subunit of vacuolar-type H+-ATPase (v-ATPase), was predicted to be a key regulator of one neuronal subnetwork and its role in disease-related processes was evaluated through CRISPR-based manipulation of human induced pluripotent stem cell derived neurons and RNAi-based knockdown in transgenic Drosophila models. This study advances our understanding of LOAD pathogenesis by providing the global landscape and detailed circuits of complex molecular interactions and regulations in several key brain regions affected by LOAD and the resulting network models provide a blueprint for developing next generation therapeutics against LOAD.

scholarly journals A Machine Learning Approach to Unmask Novel Gene Signatures and Prediction of Alzheimer’s Disease Within Different Brain Regions

2021 ◽  
Author(s):  
Abhibhav Sharma ◽  
Pinki Dey
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Non Coding Rna ◽  
Machine Learning Approach ◽  
Microarray Datasets ◽  
Rna Genes

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unrevealed the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.

scholarly journals Network propagation of rare variants in Alzheimer’s disease reveals tissue-specific hub genes and communities

2021 ◽  
Vol 17 (1) ◽  
pp. e1008517
Author(s):  
Marzia Antonella Scelsi ◽  
Valerio Napolioni ◽  
Michael D. Greicius ◽  
Andre Altmann ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rare Variants ◽  
Late Onset ◽  
Integrative Approach ◽  
Specific Gene ◽  
Tissue Specific ◽  
Association Testing ◽  
Wide Range ◽  
Network Propagation

State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer’s disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

scholarly journals Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer’s disease

2018 ◽  
Author(s):  
Stephen A. Semick ◽  
Rahul A. Bharadwaj ◽  
Leonardo Collado-Torres ◽  
Ran Tao ◽  
Joo Heon Shin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Neurodegenerative Disorder ◽  
Late Onset ◽  
Brain Regions ◽  
Epigenetic Mechanism ◽  
Novel Genes ◽  
Age Related

AbstractBackgroundLate-onset Alzheimer’s disease (AD) is a complex age-related neurodegenerative disorder that likely involves epigenetic factors. To better understand the epigenetic state associated with AD represented as variation in DNA methylation (DNAm), we surveyed 420,852 DNAm sites from neurotypical controls (N=49) and late-onset AD patients (N=24) across four brain regions (hippocampus, entorhinal cortex, dorsolateral prefrontal cortex and cerebellum).ResultsWe identified 858 sites with robust differential methylation, collectively annotated to 772 possible genes (FDR<5%, within 10kb). These sites were overrepresented in AD genetic risk loci (p=0.00655), and nearby genes were enriched for processes related to cell-adhesion, immunity, and calcium homeostasis (FDR<5%). We analyzed corresponding RNA-seq data to prioritize 130 genes within 10kb of the differentially methylated sites, which were differentially expressed and had expression levels associated with nearby DNAm levels (p<0.05). This validated gene set includes previously reported (e.g. ANK1, DUSP22) and novel genes involved in Alzheimer’s disease, such as ANKRD30B.ConclusionsThese results highlight DNAm changes in Alzheimer’s disease that have gene expression correlates, implicating DNAm as an epigenetic mechanism underlying pathological molecular changes associated with AD. Furthermore, our framework illustrates the value of integrating epigenetic and transcriptomic data for understanding complex disease.

scholarly journals Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia

2021 ◽  
Vol 12 ◽  
Author(s):  
Carmen Lage ◽  
Sara López-García ◽  
Alexandre Bejanin ◽  
Martha Kazimierczak ◽  
Ignacio Aracil-Bolaños ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eye Tracking ◽  
Frontotemporal Dementia ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Machine Learning Classification

Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

scholarly journals Characterization of mitochondrial DNA quantity and quality in the human aged and Alzheimer's disease brain

2021 ◽  
Author(s):  
Hans-Ulrich Klein ◽  
Caroline Trumpff ◽  
Hyun-Sik Yang ◽  
Annie J Lee ◽  
Martin Picard ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mitochondrial Dna ◽  
Human Brain ◽  
Neurodegenerative Diseases ◽  
Late Onset ◽  
Multivariable Analysis ◽  
Amyloid Β ◽  
Brain Regions ◽  
Mtdna Copy Number

Mitochondrial dysfunction is a feature of neurodegenerative diseases, including Alzheimer's disease (AD). Using whole-genome sequencing, we assessed mitochondrial DNA (mtDNA) heteroplasmy levels and mtDNA copy number (mtDNAcn) in 1,361 human brain samples of five brain regions from three studies. Multivariable analysis of ten common brain pathologies identified tau pathology in the dorsolateral prefrontal cortex and TDP-43 pathology in the posterior cingulate cortex as primary drivers of reduced mtDNAcn in the aged human brain. Amyloid-β pathology, age, and sex were not associated with mtDNAcn. Further, there is evidence for a direct effect of mitochondrial health on cognition. In contrast, while mtDNA heteroplasmy levels increase by about 1.5% per year of life in the cortical regions, we found little evidence for an association with brain pathologies or cognitive functioning. Thus, our data indicates that mtDNA heteroplasmy burden is unlikely to be involved in the pathogenesis of late-onset neurodegenerative diseases.

scholarly journals Exome-wide age-of-onset analysis reveals exonic variants in ERN1, TACR3 and SPPL2C associated with Alzheimer’s disease

2020 ◽  
Author(s):  
Liang He ◽  
Yury Loika ◽  
Yongjin Park ◽  
David A. Bennett ◽  
Manolis Kellis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rare Variants ◽  
Age Of Onset ◽  
Missense Variant ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Minor Allele ◽  
Eqtl Analysis ◽  
Apoe Ε4

AbstractDespite recent discovery in GWAS of genomic variants associated with Alzheimer’s disease (AD), its underlying biological mechanisms are still elusive. Discovery of novel AD-associated genetic variants, particularly in coding regions and from APOE ε4 non-carriers, is critical for understanding the pathology of AD. In this study, we carried out an exome-wide association analysis of age-of-onset of AD with ~20,000 subjects and placed more emphasis on APOE ε4 non-carriers. Using Cox mixed-effects models, we find that age-of-onset shows a stronger genetic signal than AD case-control status, capturing many known variants with stronger significance, and also revealing new variants. We identified two novel rare variants, rs56201815, a synonymous variant in ERN1, from the analysis of APOE ε4 non-carriers, and a missense variant rs144292455 in TACR3. In addition, we detected rs12373123, a common missense variant in SPPL2C in the MAPT region in APOE ε4 non-carriers. In an attempt to unravel their regulatory and biological functions, we found that the minor allele of rs56201815 was associated with lower average FDG uptake across five brain regions in ADNI. Our eQTL analyses based on 6198 gene expression samples from ROSMAP and GTEx revealed that the minor allele of rs56201815 was associated with elevated expression of ERN1, a key gene triggering unfolded protein response (UPR), in multiple brain regions, including posterior cingulate cortex and nucleus accumbens. Our cell-type-specific eQTL analysis of based on ~80,000 single nuclei in the prefrontal cortex revealed that the protective minor allele of rs12373123 significantly increased expression of GRN in microglia, and was associated with MAPT expression in astrocytes. These findings provide novel evidence supporting the hypothesis of the potential involvement of the UPR to ER stress in the pathological pathway of AD, and also give more insights into underlying regulatory mechanisms behind the pleiotropic effects of rs12373123 in multiple degenerative diseases including AD and Parkinson’s disease.

scholarly journals Relationship between the Presence of the ApoE ε4 Allele and EEG Complexity along the Alzheimer’s Disease Continuum

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3849
Author(s):  
Víctor Gutiérrez-de Pablo ◽  
Carlos Gómez ◽  
Jesús Poza ◽  
Aarón Maturana-Candelas ◽  
Sandra Martins ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Brain Activity ◽  
Developed Countries ◽  
Brain Regions ◽  
Major Health Problem ◽  
Temporal Lobes ◽  
Clinical Onset ◽  
Healthy Control

Alzheimer’s disease (AD) is the most prevalent cause of dementia, being considered a major health problem, especially in developed countries. Late-onset AD is the most common form of the disease, with symptoms appearing after 65 years old. Genetic determinants of AD risk are vastly unknown, though, ε 4 allele of the ApoE gene has been reported as the strongest genetic risk factor for AD. The objective of this study was to analyze the relationship between brain complexity and the presence of ApoE ε 4 alleles along the AD continuum. For this purpose, resting-state electroencephalography (EEG) activity was analyzed by computing Lempel-Ziv complexity (LZC) from 46 healthy control subjects, 49 mild cognitive impairment subjects, 45 mild AD patients, 44 moderate AD patients and 33 severe AD patients, subdivided by ApoE status. Subjects with one or more ApoE ε 4 alleles were included in the carriers subgroups, whereas the ApoE ε 4 non-carriers subgroups were formed by subjects without any ε 4 allele. Our results showed that AD continuum is characterized by a progressive complexity loss. No differences were observed between AD ApoE ε 4 carriers and non-carriers. However, brain activity from healthy subjects with ApoE ε 4 allele (carriers subgroup) is more complex than from non-carriers, mainly in left temporal, frontal and posterior regions (p-values < 0.05, FDR-corrected Mann–Whitney U-test). These results suggest that the presence of ApoE ε 4 allele could modify the EEG complexity patterns in different brain regions, as the temporal lobes. These alterations might be related to anatomical changes associated to neurodegeneration, increasing the risk of suffering dementia due to AD before its clinical onset. This interesting finding might help to advance in the development of new tools for early AD diagnosis.

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