scholarly journals Differential Expression of mRNAs in the Brain Tissues of Patients with Alzheimer’s Disease Based on GEO Expression Profile and Its Clinical Significance

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Guowei Ma ◽  
Mingyan Liu ◽  
Ke Du ◽  
Xin Zhong ◽  
Shiqiang Gong ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Expression ◽  
Brain Tissue ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Venn Diagram ◽  
Ad Prevention ◽  
The Brain ◽  
Brain Tissues

Background. Early diagnosis of Alzheimer’s disease (AD) is an urgent point for AD prevention and treatment. The biomarkers of AD still remain indefinite. Based on the bioinformatics analysis of mRNA differential expressions in the brain tissues and the peripheral blood samples of Alzheimer’s disease (AD) patients, we investigated the target mRNAs that could be used as an AD biomarker and developed a new effective, practical clinical examination program. Methods. We compared the AD peripheral blood mononuclear cells (PBMCs) expression dataset (GEO accession GSE4226 and GSE18309) with AD brain tissue expression datasets (GEO accessions GSE1297 and GSE5281) from GEO in the present study. The GEO gene database was used to download the appropriate gene expression profiles to analyze the differential mRNA expressions between brain tissue and blood of AD patients and normal elderly. The Venn diagram was used to screen out the differential expression of mRNAs between the brain tissue and blood. The protein-protein interaction network map (PPI) was used to view the correlation between the possible genes. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the major affected genes and the function or pathway. Results. Bioinformatics analysis revealed that there were differentially expressed genes in peripheral blood and hippocampus of AD patients. There were 4958 differential mRNAs in GSE18309, 577 differential mRNAs in GSE4226 in AD PBMCs sample, 7464 differential mRNAs in GSE5281, and 317 differential mRNAs in GSE129 in AD brain tissues, when comparing between AD patients and healthy elderly. Two mRNAs of RAB7A and ITGB1 coexpressed in hippocampus and peripheral blood were screened. Furthermore, functions of differential genes were enriched by the PPI network map, GO, and KEGG analysis, and finally the chemotaxis, adhesion, and inflammatory reactions were found out, respectively. Conclusions. ITGB1 and RAB7A mRNA expressions were both changed in hippocampus and PBMCs, highly suggested being used as an AD biomarker with AD. Also, according to the results of this analysis, it is indicated that we can test the blood routine of the elderly for 2-3 years at a frequency of 6 months or one year. When a patient continuously detects the inflammatory manifestations, it is indicated as a potentially high-risk AD patient for AD prevention.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

A fingerprint of amyloid plaques in a bitransgenic animal model of Alzheimer's disease obtained by statistical unmixing analysis of hyperspectral Raman data

The Analyst ◽  
2019 ◽  
Vol 144 (23) ◽  
pp. 7049-7056 ◽  
Author(s):  
Emerson A. Fonseca ◽  
Lucas Lafetá ◽  
Renan Cunha ◽  
Hudson Miranda ◽  
João Campos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Model ◽  
Amyloid Plaques ◽  
Model Of Alzheimer’S Disease ◽  
The Brain ◽  
Brain Tissues

We have found different Raman signatures of AB fibrils and in brain tissues from unmixed analysis, providing a detailed image of amyloid plaques in the brain, with the potential to be used as biomarkers.

MR Brain Screening using Optimization Techniques – A survey

2021 ◽  
Vol 17 ◽  
Author(s):  
Chitradevi D ◽  
Prabha S.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Symptoms ◽  
Memory Loss ◽  
Amyloid Β ◽  
Cell Loss ◽  
Optimization Techniques ◽  
Amyloid Β Protein ◽  
The Brain ◽  
Brain Tissues

Background: Alzheimer’s disease (AD) is associated with Dementia, and it is also a memory syndrome in the brain. It affects the brain tissues and causes major changes in day-to-day activities. Aging is a major cause of Alzheimer's disease. AD is characterized by two pathological hallmarks as, Amyloid β protein and neurofibrillary tangles of hyperphosphorylated tau protein. The imaging hallmarks for Alzheimer’s disease are namely, swelling, shrinkage of brain tissues due to cell loss, and atrophy in the brain due to protein dissemination. Based on the survey, 60% to 80% of dementia patients belong to Alzheimer’s disease. Introduction: AD is now becoming an increasing and important brain disease. The goal of AD pathology is to cause changes/damage in brain tissues. Alzheimer's disease is thought to begin 20 years or more before symptoms appear, with tiny changes in the brain that are undetectable to the person affected. The changes in a person's brain after a few years are noticeable through symptoms such as language difficulties and memory loss. Neurons in different parts of the brain have detected symptoms such as cognitive impairments and learning disabilities. In this case, neuroimaging tools are necessary to identify the development of pathology which relates to the clinical symptoms. Methods: Several approaches have been tried during the last two decades for brain screening to analyse AD with the process of pre-processing, segmentation and classification. Different individual such as Grey Wolf optimization, Lion Optimization, Ant Lion Optimization and so on. Similarly, hybrid optimization techniques are also attempted to segment the brain sub-regions which helps in identifying the bio-markers to analyse AD. Conclusion: This study discusses a review of neuroimaging technologies for diagnosing Alzheimer's disease, as well as the discovery of hallmarks for the disease and the methodologies for finding hallmarks from brain images to evaluate AD. According to the literature review, most of the techniques predicted higher accuracy (more than 90%), which is beneficial for assessing and screening neurodegenerative illness, particularly Alzheimer's disease.

scholarly journals Longitudinal data in peripheral blood confirm that PM20D1 is a quantitative trait locus (QTL) for Alzheimer’s disease and implicate its dynamic role in disease progression

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Qi Wang ◽  
Yinghua Chen ◽  
Benjamin Readhead ◽  
Kewei Chen ◽  
Yi Su ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Longitudinal Data ◽  
Disease Progression ◽  
Peripheral Blood ◽  
Early Stage ◽  
Underlying Mechanism ◽  
Methylation Data ◽  
Brain Tissues

Abstract Background While Alzheimer’s disease (AD) remains one of the most challenging diseases to tackle, genome-wide genetic/epigenetic studies reveal many disease-associated risk loci, which sheds new light onto disease heritability, provides novel insights to understand its underlying mechanism and potentially offers easily measurable biomarkers for early diagnosis and intervention. Methods We analyzed whole-genome DNA methylation data collected from peripheral blood in a cohort (n = 649) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compared the DNA methylation level at baseline among participants diagnosed with AD (n = 87), mild cognitive impairment (MCI, n = 175) and normal controls (n = 162), to identify differentially methylated regions (DMRs). We also leveraged up to 4 years of longitudinal DNA methylation data, sampled at approximately 1 year intervals to model alterations in methylation levels at DMRs to delineate methylation changes associated with aging and disease progression, by linear mixed-effects (LME) modeling for the unchanged diagnosis groups (AD, MCI and control, respectively) and U-shape testing for those with changed diagnosis (converters). Results When compared with controls, patients with MCI consistently displayed promoter hypomethylation at methylation QTL (mQTL) gene locus PM20D1. This promoter hypomethylation was even more prominent in patients with mild to moderate AD. This is in stark contrast with previously reported hypermethylation in hippocampal and frontal cortex brain tissues in patients with advanced-stage AD at this locus. From longitudinal data, we show that initial promoter hypomethylation of PM20D1 during MCI and early stage AD is reversed to eventual promoter hypermethylation in late stage AD, which helps to complete a fuller picture of methylation dynamics. We also confirm this observation in an independent cohort from the Religious Orders Study and Memory and Aging Project (ROSMAP) Study using DNA methylation and gene expression data from brain tissues as neuropathological staging (Braak score) advances. Conclusions Our results confirm that PM20D1 is an mQTL in AD and demonstrate that it plays a dynamic role at different stages of the disease. Further in-depth study is thus warranted to fully decipher its role in the evolution of AD and potentially explore its utility as a blood-based biomarker for AD.

Exploring the brain tissue proteome of TgCRND8 Alzheimer's Disease model mice under B vitamin deficient diet induced hyperhomocysteinemia by LC-MS top-down platform

2019 ◽  
Vol 1124 ◽  
pp. 165-172
Author(s):  
Daniela Delfino ◽  
Diana Valeria Rossetti ◽  
Claudia Martelli ◽  
Ilaria Inserra ◽  
Federica Vincenzoni ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Tissue ◽  
Disease Model ◽  
Top Down ◽  
Deficient Diet ◽  
B Vitamin ◽  
The Brain ◽  
Tissue Proteome

Cerebrospinal Fluid pH and Monoamine and Glucolytic Metabolites in Alzheimer's Disease

1974 ◽  
Vol 124 (580) ◽  
pp. 280-287 ◽  
Author(s):  
C. G. Gottfries ◽  
Åke Kjällquist ◽  
Urban Pontén ◽  
B. E. Roos ◽  
G. Sundbärg
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Brain Tissue ◽  
Homovanillic Acid ◽  
Monoamine Metabolites ◽  
Hydroxyindoleacetic Acid ◽  
The Brain ◽  
Valid Information

Determinations of acid monoamine metabolites, such as homovanillic acid (HVA) and 5-hydroxyindoleacetic acid (5-HIAA), in cerebrospinal fluid (CSF) give valid information on the metabolism of the corresponding amines in the brain tissue (Moir et al., 1970; Roos, 1970). The monoamine metabolites in the CSF are related to age. The concentrations of HVA and 5-HIAA increase with age (Gottfries et al., 1971). Probenecid blocks the elimination of HVA and 5-HIAA from brain tissue to blood (Neff et al., 1964, 1967; Werdinius, 1966) and from CSF to blood (Guldberg et al., 1966; Olsson and Roos, 1968). Probenecid thus normally induces an increase in the concentrations of the acid monoamine metabolites in the CSF, which is related to the turnover of monoamines in the brain tissue.

Neuronal Development-Related miRNAs as Biomarkers for Alzheimer's Disease, Depression, Schizophrenia and Ionizing Radiation Exposure

2020 ◽  
Vol 28 (1) ◽  
pp. 19-52 ◽  
Author(s):  
Renu Chandra Segaran ◽  
Li Yun Chan ◽  
Hong Wang ◽  
Gautam Sethi ◽  
Feng Ru Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Radiation Exposure ◽  
Brain Tissue ◽  
Neuronal Development ◽  
Radiation Induced ◽  
The Common ◽  
Ionizing Radiation Exposure ◽  
Brain Radiation ◽  
The Brain

Radiation exposure may induce Alzheimer's disease (AD), depression or schizophrenia. A number of experimental and clinical studies suggest the involvement of miRNA in the development of these diseases, and also in the neuropathological changes after brain radiation exposure. The current literature review indicated the involvement of 65 miRNAs in neuronal development in the brain. In the brain tissue, blood, or cerebral spinal fluid (CSF), 11, 55, or 28 miRNAs are involved in the development of AD respectively, 89, 50, 19 miRNAs in depression, and 102, 35, 8 miRNAs in schizophrenia. We compared miRNAs regulating neuronal development to those involved in the genesis of AD, depression and schizophrenia and also those driving radiation-induced brain neuropathological changes by reviewing the available data. We found that 3, 11, or 8 neuronal developmentrelated miRNAs from the brain tissue, 13, 16 or 14 miRNAs from the blood of patient with AD, depression and schizophrenia respectively were also involved in radiation-induced brain pathological changes, suggesting a possibly specific involvement of these miRNAs in radiation-induced development of AD, depression and schizophrenia respectively. On the other hand, we noted that radiationinduced changes of two miRNAs, i.e., miR-132, miR-29 in the brain tissue, three miRNAs, i.e., miR- 29c-5p, miR-106b-5p, miR-34a-5p in the blood were also involved in the development of AD, depression and schizophrenia, thereby suggesting that these miRNAs may be involved in the common brain neuropathological changes, such as impairment of neurogenesis and reduced learning memory ability observed in these three diseases and also after radiation exposure.

scholarly journals The role of microRNAs regulatory network in Alzheimer’s disease: a bioinformatics analysis

2021 ◽  
Author(s):  
Chenjing Sun ◽  
Xiaokun Qi ◽  
Jianguo Liu ◽  
Feng Duan ◽  
Lin Cong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bioinformatics Analysis ◽  
Time Window ◽  
Bioinformatic Analysis ◽  
Preclinical Phase ◽  
Wide Range ◽  
Important Time ◽  
Brain Tissues

IntroductionAlzheimer’s disease (AD) is a neurodegenerative disease which presents with an earlier onset age and increased symptom severity. The objective of this study was to evaluate the relationship between regulation of miRNAs and AD.Material and methodsWe completed the bioinformatic analysis of miRNAs-AD studies through multiple databases such as TargetScan, Database for Annotation, Visualization and Integrated Discovery (DAVID), FunRich and String and assessed which miRNAs are commonly elevated or decreased in brain tissues, cerebrospinal fluid (CSF) and blood of AD. All identified articles were assessed using specific inclusion and exclusion criteria.ResultsMiRNAs related to AD of twenty-eight studies were assessed in this study. A wide range of miRNAs were up-regulated or down-regulated in tissues of AD patient’s brain, blood and CSF. 27 differentially dysregulated miRNAs have identified involved in amyloidogenesis, inflammation, tau-phosphorylation, apoptosis, synaptogenesis, neurotrophism, neurons degradation, activates cell cycle entry. Additionally, our bioinformatics analysis identified the top ten functions of common miRNAs in candidate studies. The function of common up-regulated miRNAs primarily target nucleus and common down-regulated miRNAs primarily target transcription, DNA-templated.ConclusionsComprehensive analysis of all miRNAs studies reveals cooperation in miRNA signatures whether in brain tissues or in CSF and peripheral blood. More and more studies suggest that miRNAs may play crucial roles as diagnostic biomarkers and/or as new therapeutic targets in AD. According to biomarkers, we can identify the preclinical phase early that provides an important time-window for therapeutic intervention.

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