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

2019 ◽  
Vol 137 (4) ◽  
pp. 557-569 ◽  
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 ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Brain Regions ◽  
Novel Genes

scholarly journals The Triggering Receptor Expressed on Myeloid Cells 2: “TREM-ming” the Inflammatory Component Associated with Alzheimer's Disease

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Troy T. Rohn
Keyword(s):  
Oxidative Stress ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurofibrillary Tangles ◽  
Neurodegenerative Disorder ◽  
Late Onset ◽  
Senile Plaques ◽  
Myeloid Cells ◽  
Disease Process ◽  
Age Related

Alzheimer's disease (AD) is an age-related neurodegenerative disorder characterized by a progressive loss of memory and cognitive skills. Although much attention has been devoted concerning the contribution of the microscopic lesions, senile plaques, and neurofibrillary tangles to the disease process, inflammation has long been suspected to play a major role in the etiology of AD. Recently, a novel variant in the gene encoding the triggering receptor expressed on myeloid cells 2 (TREM2) has been identified that has refocused the spotlight back onto inflammation as a major contributing factor in AD. Variants in TREM2 triple one's risk of developing late-onset AD. TREM2 is expressed on microglial cells, the resident macrophages in the CNS, and functions to stimulate phagocytosis on one hand and to suppress cytokine production and inflammation on the other hand. The purpose of this paper is to discuss these recent developments including the potential role that TREM2 normally plays and how loss of function may contribute to AD pathogenesis by enhancing oxidative stress and inflammation within the CNS. In this context, an overview of the pathways linking beta-amyloid, neurofibrillary tangles (NFTs), oxidative stress, and inflammation will be discussed.

scholarly journals Effects of Swimming Training and Royal Jelly on BDNF and NGF Gene Expression in Hippocampus Tissue of Rats with Alzheimer’s Disease

2020 ◽  
Vol 22 (2) ◽  
Author(s):  
Amin Dehbozorgi ◽  
Laleh Behbudi Tabrizi ◽  
Seyed Ali Hosseini ◽  
Masod Haj Rasoli
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Royal Jelly ◽  
Neurodegenerative Disorder ◽  
Control Group ◽  
Swimming Training ◽  
Age Related ◽  
And Training ◽  
Ngf Gene

Background: Alzheimer’s disease (AD) is an age-related neurodegenerative disorder. Evidence from neuropathological studies indicates that the levels of neurotrophins brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) are compromised in AD. Objectives: The present study aimed to review the effects of swimming training and royal jelly (RJ) on BDNF and NGF gene expression in the hippocampus tissue of rats with AD. Methods: In the present experimental study, 25 rats with AD were divided into five groups, including (1) control, (2) sham, (3) RJ, (4) training, and (5) training with RJ. Five healthy rats were selected as the healthy control group to examine the effect of AD induction by 8 mg/kg trimethyltin chloride (TMT) intra-peritoneally on BDNF and NGF. During eight weeks, groups 3 and 5 received 100 mg/kg RJ daily intra-peritoneally, and groups 4 and 5 swam in a rat swimming tank three sessions per week. One-way ANOVA with Tukey’s post hoc test was used for data analysis in SPSS 20 software (P < 0.05). Results: The induction of AD by TMT had a significant effect on the reduction of BDNF (P = 0.001) and NGF (P = 0.001). However, RJ had a significant effect on the increase of NGF (P = 0.03). Nevertheless, RJ (P = 0.99), training (P = 0.99), and training with RJ (P = 0.94) had no significant effect on BDNF and training (P = 0.99) and training with RJ (P = 0.97) had no significant effect on NGF. Conclusions: It appears that RJ has a significant effect on the increase of NGF gene expression in the hippocampus tissue of rats with AD. Nevertheless, RJ consumption simultaneously with swimming training has no significant effect on BDNF and NGF.

A Qualitative Analysis Based on Relative Expression Orderings Identifies Transcriptional Subgroups for Alzheimer’s Disease

2020 ◽  
Vol 16 (13) ◽  
pp. 1175-1182 ◽  
Author(s):  
Guini Hong ◽  
Pengming Zeng ◽  
Na Li ◽  
Hao Cai ◽  
You Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Region ◽  
Expression Patterns ◽  
Brain Regions ◽  
Gene Expression Patterns ◽  
Relative Expression ◽  
Age Related ◽  
Relative Expression Orderings

Background: Alzheimer's disease (AD) is a heterogeneous neurodegenerative disease. However, few studies have investigated the heterogeneous gene expression patterns in AD. Objective and Methods: We examined the gene expression patterns in four brain regions of AD based on the within-sample relative expression orderings (REOs). Gene pairs with significantly reversed REOs in AD samples compared to non-AD controls were identified for each brain region using Fisher’s exact test, and filtered according to their transcriptional differences between AD samples. Subgroups of AD were classified by cluster analysis. Results: REO-based gene expression profiling analyses revealed that transcriptional differences, as well as distinct disease subsets, existed within AD patients. For each brain region, two main subgroups were classified: one subgroup reported differentially expressed genes overlapped with the age-related genes, and the other might relate to neuroinflammation. Conclusion: AD transcriptional subgroups might help understand the underlying pathogenesis of AD, and lend support to a personalized approach to AD management.

scholarly journals Largest GWAS (N=1,126,563) of Alzheimer’s Disease Implicates Microglia and Immune Cells

2020 ◽  
Author(s):  
Douglas P Wightman ◽  
Iris E Jansen ◽  
Jeanne E. Savage ◽  
Alexey A Shadrin ◽  
Shahram Bahrami ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Variants ◽  
Late Onset ◽  
Gene Expression Pattern ◽  
Human Microglia ◽  
Age Related ◽  
Causal Genes ◽  
Meta Analyses

SummaryLate-onset Alzheimer’s disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases1. Late-onset Alzheimer’s disease is caused by a combination of many genetic variants with small effect sizes and environmental influences. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified2,3. Here we show that increased sample sizes allowed for identification of seven novel genetic loci contributing to Alzheimer’s disease. We highlighted eight potentially causal genes where gene expression changes are likely to explain the association. Human microglia were found as the only cell type where the gene expression pattern was significantly associated with the Alzheimer’s disease association signal. Gene set analysis identified four independent pathways for associated variants to influence disease pathology. Our results support the importance of microglia, amyloid and tau aggregation, and immune response in Alzheimer’s disease. We anticipate that through collaboration the results from this study can be included in larger meta-analyses of Alzheimer’s disease to identify further genetic variants which contribute to Alzheimer’s pathology. Furthermore, the increased understanding of the mechanisms that mediate the effect of genetic variants on disease progression will help identify potential pathways and gene-sets as targets for drug development.

Emerging Promise of Immunotherapy for Alzheimer’s Disease: A New Hope for the Development of Alzheimer’s Vaccine

2020 ◽  
Vol 20 (13) ◽  
pp. 1214-1234 ◽  
Author(s):  
Md. Tanvir Kabir ◽  
Md. Sahab Uddin ◽  
Bijo Mathew ◽  
Pankoj Kumar Das ◽  
Asma Perveen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Immune Response ◽  
Neutralizing Antibodies ◽  
Neurodegenerative Disorder ◽  
Symptomatic Relief ◽  
Aβ Oligomers ◽  
Th2 Immune Response ◽  
Age Related ◽  
Anti Inflammatory

Background: Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the characteristics of this devastating disorder include the progressive and disabling deficits in the cognitive functions including reasoning, attention, judgment, comprehension, memory, and language. Objective: In this article, we have focused on the recent progress that has been achieved in the development of an effective AD vaccine. Summary: Currently, available treatment options of AD are limited to deliver short-term symptomatic relief only. A number of strategies targeting amyloid-beta (Aβ) have been developed in order to treat or prevent AD. In order to exert an effective immune response, an AD vaccine should contain adjuvants that can induce an effective anti-inflammatory T helper 2 (Th2) immune response. AD vaccines should also possess the immunogens which have the capacity to stimulate a protective immune response against various cytotoxic Aβ conformers. The induction of an effective vaccine’s immune response would necessitate the parallel delivery of immunogen to dendritic cells (DCs) and their priming to stimulate a Th2-polarized response. The aforesaid immune response is likely to mediate the generation of neutralizing antibodies against the neurotoxic Aβ oligomers (AβOs) and also anti-inflammatory cytokines, thus preventing the AD-related inflammation. Conclusion: Since there is an age-related decline in the immune functions, therefore vaccines are more likely to prevent AD instead of providing treatment. AD vaccines might be an effective and convenient approach to avoid the treatment-related huge expense.

scholarly journals Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Angela M. Crist ◽  
Kelly M. Hinkle ◽  
Xue Wang ◽  
Christina M. Moloney ◽  
Billie J. Matchett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Digital Pathology ◽  
Selective Vulnerability ◽  
Brain Regions ◽  
Biochemical Analyses ◽  
The Brain ◽  
Relevant Gene ◽  
Tau Expression

AbstractSelective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer’s disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

scholarly journals Association and Gene–-Gene Interactions Study of Late-Onset Alzheimer’s Disease in the Russian Population

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1647
Author(s):  
Anna Bocharova ◽  
Kseniya Vagaitseva ◽  
Andrey Marusin ◽  
Natalia Zhukova ◽  
Irina Zhukova ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Factor Model ◽  
Late Onset ◽  
Interaction Network ◽  
Russian Population ◽  
Protein Protein Interaction ◽  
Protective Haplotype ◽  
Single Snps

Alzheimer’s disease (AD) is a neurodegenerative disorder, and represents the most common cause of dementia. In this study, we performed several different analyses to detect loci involved in development of the late onset AD in the Russian population. DNA samples from 472 unrelated subjects were genotyped for 63 SNPs using iPLEX Assay and real-time PCR. We identified five genetic loci that were significantly associated with LOAD risk for the Russian population (TOMM40 rs2075650, APOE rs429358 and rs769449, NECTIN rs6857, APOE ε4). The results of the analysis based on comparison of the haplotype frequencies showed two risk haplotypes and one protective haplotype. The GMDR analysis demonstrated three significant models as a result: a one-factor, a two-factor and a three-factor model. A protein–protein interaction network with three subnetworks was formed for the 24 proteins. Eight proteins with a large number of interactions are identified: APOE, SORL1, APOC1, CD33, CLU, TOMM40, CNTNAP2 and CACNA1C. The present study confirms the importance of the APOE-TOMM40 locus as the main risk locus of development and progress of LOAD in the Russian population. Association analysis and bioinformatics approaches detected interactions both at the association level of single SNPs and at the level of genes and proteins.

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.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

Sign in / Sign up

Export Citation Format

Share Document