Metabolomics: a novel approach to identify potential diagnostic biomarkers and pathogenesis in Alzheimer's disease.
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ABSTRACT:
SUBMITTER: Xu XH
PROVIDER: S-EPMC5561924 | biostudies-other | 2012 Oct
REPOSITORIES: biostudies-other
Similar Datasets
Project description:BackgroundAlzheimer's disease (AD) is an extremely complicated neurodegenerative disorder, which accounts for almost 80 % of all dementia diagnoses. Due to the limited treatment efficacy, it is imperative for AD patients to take reliable prevention and diagnosis measures. This study aimed to explore potential biomarkers for AD.MethodsGSE63060 and GSE140829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEG) between AD and control groups in GSE63060 were analyzed using the limma software package. The mRNA expression data in GSE140829 was analyzed using weighted gene co-expression network analysis (WGCNA) function package. Protein functional connections and interactions were analyzed using STRING and key genes were screened based on the degree and Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the key genes.ResultsThere were 65 DEGs in GSE63060 dataset between AD patients and healthy controls. In GSE140829 dataset, the turquoise module was related to the pathogenesis of AD, among which, 42 genes were also differentially expressed in GSE63060 dataset. Then 8 genes, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were finally screened. Additionally, these 42 genes were significantly enriched in 12 KEGG pathways and 119 GO terms.ConclusionsIn conclusion, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were potential biomarkers for pathogenesis of AD, which should be further explored in AD in the future.
| S-EPMC8259215 | biostudies-literature
Project description:Background:Alzheimer's Disease (AD) is the most common form of dementia in the elderly. Due to the facts that biological causes of AD are complex in addition to increasing rates of AD worldwide, a deeper understanding of AD etiology is required for AD treatment and diagnosis. Methods:To identify molecular pathological alterations in AD brains, GSE36980 series containing microarray data samples from temporal cortex, frontal cortex and hippocampus were downloaded from Gene Expression Omnibus (GEO) database and valid gene symbols were subjected to building a gene co-expression network by a bioinformatics tool known as differential regulation from differential co-expression (DCGL) software package. Then, a network-driven integrative analysis was performed to find significant genes and underlying biological terms. Results:A total of 17088 unique genes were parsed into three independent differential co-expression networks. As a result, a small number of differentially co-regulated genes mostly in frontal and hippocampus lobs were detected as potential biomarkers related to AD brains. Ultimately differentially co-regulated genes were enriched in biological terms including response to lipid and fatty acid and pathways mainly signaling pathway such as G-protein signaling pathway and glutamate receptor groups II and III. By conducting co-expression analysis, our study identified multiple genes that may play an important role in the pathogenesis of AD. Conclusion:The study aimed to provide a systematic understanding of the potential relationships among these genes and it is hoped that it could aid in AD biomarker discovery.
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Project description:Alzheimer's disease (AD) is the most common form of dementia. Although the incidence of AD is high, the rates of diagnosis and treatment are relatively low. Moreover, effective means for the diagnosis and treatment of AD are still lacking. MicroRNAs (miRNAs, miRs) are non-coding RNAs that play regulatory roles by targeting mRNAs. The expression of miRNAs is conserved, temporal, and tissue-specific. Impairment of microRNA function is closely related to AD pathogenesis, including the beta-amyloid and tau hallmarks of AD, and there is evidence that the expression of some microRNAs differs significantly between healthy people and AD patients. These properties of miRNAs endow them with potential diagnostic and therapeutic value in the treatment of this debilitating disease. This review provides comprehensive information about the regulatory function of miRNAs in AD, as well as potential applications as diagnostic biomarkers.
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Project description:Alzheimer's disease is a progressive and neurodegenerative disorder which involves multiple molecular mechanisms. Intense research during the last years has accumulated a large body of data and the search for sensitive and specific biomarkers has undergone a rapid evolution. However, the diagnosis remains problematic and the current tests do not accurately detect the process leading to neurodegeneration. Biomarkers discovery and validation are considered the key aspects to support clinical diagnosis and provide discriminatory power between different stages of the disorder. A considerable challenge is to integrate different types of data from new potent approach to reach a common interpretation and replicate the findings across studies and populations. Furthermore, long-term clinical follow-up and combined analysis of several biomarkers are among the most promising perspectives to diagnose and manage the disease. The present review will focus on the recent published data providing an updated overview of the main achievements in the genetic and biochemical research of the Alzheimer's disease. We also discuss the latest and most significant results that will help to define a specific disease signature whose validity might be clinically relevant for future AD diagnosis.
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Project description:Exosomes are bilipid layer-enclosed vesicles derived from endosomes and are released from neural cells. They contain a diversity of proteins, mRNAs, and microRNAs (miRNAs) that are delivered to neighboring cells and/or are transported to distant sites. miRNAs released from exosomes appear to be associated with multiple neurodegenerative conditions linking to Alzheimer's disease (AD) which is marked by hyperphosphorylated tau proteins and accumulation of Aβ plaques. Exciting findings reveal that miRNAs released from exosomes modulate the expression and function of amyloid precursor proteins (APP) and tau proteins. These open up the possibility that dysfunctional exosomal miRNAs may influence AD progression. In addition, it has been confirmed that the interaction between miRNAs released by exosomes and Toll-like receptors (TLR) initiates inflammation. In exosome support-deprived neurons, exosomal miRNAs may regulate neuroplasticity to relieve neurological damage. In this review, we summarize the literature on the function of exosomal miRNAs in AD pathology, the potential of these miRNAs as diagnostic biomarkers in AD, and the use of exosomes in the delivery of miRNAs which may lead to major advances in the field of macromolecular drug delivery.
| S-EPMC5523215 | biostudies-other
Project description:Potential biomarkers for Alzheimer's disease (AD) include amyloid β1-42 (Aβ1-42), t-Tau, p-Tau181, neurofilament light chain (NFL), and neuroimaging biomarkers. Their combined use is useful for diagnosing and monitoring the progress of AD. Therefore, further development of a combination of these biomarkers is essential. We investigated whether plasma NFL/Aβ1-42 can serve as a plasma-based primary screening biomarker reflecting brain neurodegeneration and amyloid pathology in AD for monitoring disease progression and early diagnosis. We measured the NFL and Aβ1-42 concentrations in the CSF and plasma samples and performed correlation analysis to evaluate the utility of these biomarkers in the early diagnosis and monitoring of AD spectrum disease progression. Pearson's correlation analysis was used to analyse the associations between the fluid biomarkers and neuroimaging data. The study included 136 participants, classified into five groups: 28 cognitively normal individuals, 23 patients with preclinical AD, 22 amyloid-negative patients with amnestic mild cognitive impairment, 32 patients with prodromal AD, and 31 patients with AD dementia. With disease progression, the NFL concentrations increased and Aβ1-42 concentrations decreased. The plasma and CSF NFL/Aβ1-42 were strongly correlated (r = 0.558). Plasma NFL/Aβ1-42 was strongly correlated with hippocampal volume/intracranial volume (r = 0.409). In early AD, plasma NFL/Aβ1-42 was associated with higher diagnostic accuracy than the individual biomarkers. Moreover, in preclinical AD, plasma NFL/Aβ1-42 changed more rapidly than the CSF t-Tau or p-Tau181 concentrations. Our findings highlight the utility of plasma NFL/Aβ1-42 as a non-invasive plasma-based biomarker for early diagnosis and monitoring of AD spectrum disease progression.
| S-EPMC8773964 | biostudies-literature
Project description:Alzheimer's disease (AD) is an aging-related neurodegenerative disease. We aimed to investigate the metabolic mechanisms of aging and AD and to identify potential biomarkers for the early screening of AD in a natural aging population. To analyze the plasma metabolites related to aging, we conducted an untargeted metabolomics analysis using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry in a two-stage cross-sectional study. Spearman's correlation analysis and random forest were applied to model the relationship between age and each metabolite. Moreover, a systematic review of metabolomics studies of AD in the PubMed, Cochrane and Embase databases were searched to extract the differential metabolites and altered pathways from original studies. Pathway enrichment analysis was conducted using Mummichog. In total, 669 metabolites were significantly altered with aging, and 12 pathways were enriched and correlated with aging. Three pathways (purine metabolism, arginine and proline metabolism, and the TCA cycle) were shared between aging and AD. Arginine and proline metabolism play a key role in the progression from healthy to mild cognitive impairment and to AD in the natural aging population. Three metabolites, 16-a-hydroxypregnenolone, stearic acid and PC[16:0/22:5(4Z,7Z,10Z,13Z,16Z)] were finally proposed as potential markers of AD in the natural aging population. The underlying mechanism shared between aging and AD and the potential biomarkers for AD diagnosis were proposed based on multistep comparative analysis.
| S-EPMC7947003 | biostudies-literature
Project description:IntroductionGenome-wide association studies (GWAS) for late onset Alzheimer's disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity.MethodsWe use a transfer learning technique to train three-dimensional convolutional neural network (CNN) models based on structural magnetic resonance imaging (MRI) from the screening stage in the Alzheimer's Disease Neuroimaging Initiative consortium to derive image features that reflect AD progression.ResultsCNN-derived image phenotypes are significantly associated with fasting metabolites related to early lipid metabolic changes as well as insulin resistance and with genetic variants mapped to candidate genes enriched for amyloid beta degradation, tau phosphorylation, calcium ion binding-dependent synaptic loss, APP-regulated inflammation response, and insulin resistance.DiscussionThis is the first attempt to show that non-invasive MRI biomarkers are linked to AD progression characteristics, reinforcing their use in early AD diagnosis and monitoring.
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Project description:BACKGROUND Alzheimer disease (AD) is a common and fatal subtype of dementia that remains a challenge to diagnose and treat. This study aimed to identify potential biomarkers that influence the prognosis of AD. MATERIAL AND METHODS A total of 6 gene expression profiles from the Gene Expression Omnibus (GEO) database were assessed for their potential as AD biomarkers. We identified differentially expressed genes (DEGs) using the prediction analysis for microarray (PAM) algorithm and obtained hub genes through the analysis of the protein-protein interaction (PPI) network and module analysis. RESULTS We identified 6 gene expression profiles from the GEO database and assessed their potential as AD biomarkers. Shared gene sets were extracted and integrated into large expression profile matrices. We identified 2514 DEGs including 68 upregulated- and 2446 downregulated genes through analysis of the limma package. We screened 379 significant DEGs including 68 upregulated and 307 downregulated genes for their ability to distinguish AD from control samples using PAM algorithm. Functional enrichment of the 379 target genes was produced from Database for Annotation, Visualization and Integrated Discovery.(DAVID) and included histone function, beta receptor signaling, cell growth, and angiogenesis. The downregulated genes were significantly enriched in MAPK signaling, synaptic signaling, neuronal apoptosis and AD associated pathways. Upon analysis of the PPI network, 32 hub genes including ENO2, CCT2, CALM2, ACACB, ATP5B, MDH1, and PP2CA were screened. Of these hub genes, NFKBIA and ACACB were upregulated and 29 genes were downregulated in AD patients. CONCLUSIONS We screened 379 significant DEGs as potential biomarkers of AD using PAM and obtained 32 hub genes through PPI network and module analysis. These findings reveal new potential AD biomarkers with prognostic and therapeutic value.
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Project description:Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease and affects 1 in 1,000 individuals. Ultrasound is most often used to diagnose ADPKD; such a modality is only useful late in the disease after macroscopic cysts are present. There is accumulating evidence suggesting that there are common cellular and molecular mechanisms responsible for cystogenesis in human and murine PKD regardless of the genes mutated, and, in the case of complex metabolomic analysis, the use of a mouse model has distinct advantages for proof of principle over a human study. Therefore, in this study we utilized a urinary metabolomics-based investigation using gas chromatography-time of flight mass spectrometry to demonstrate that the cystic mouse can be discriminated from its wild-type counterpart by urine analysis alone. At day 26 of life, before there is serological evidence of kidney dysfunction, affected mice are distinguishable by urine metabolomic analysis; this finding persists through 45 days until 64 days, at which time body weight differences confound the results. Using functional score analysis and the KEGG pathway database, we identify several biologically relevant metabolic pathways which are altered very early in this disease, the most highly represented being the purine and galactose metabolism pathways. In addition, we identify several specific candidate biomarkers, including allantoic acid and adenosine, which are augmented in the urine of young cystic mice. These markers and pathway components, once extended to human disease, may prove useful as a noninvasive means of diagnosing cystic kidney diseases and to suggest novel therapeutic approaches. Thus, urine metabolomics has great diagnostic potential for cystic renal disorders and deserves further study.
| S-EPMC2853321 | biostudies-literature