Epigenetic dysregulation of transposable elements in cognitive impairment and Alzheimer’s disease
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE270454 | GEO | 2024/08/01
REPOSITORIES: GEO
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| GSE270454_AD-vs-MCI_peaks.xlsx | Xlsx | |||
| GSE270454_AD-vs-middleaged_peaks.xlsx | Xlsx | |||
| GSE270454_MCI-vs-middleaged_peaks.xlsx | Xlsx | |||
| GSE270454_Older-vs-middleaged_peaks.xlsx | Xlsx |
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Project description:Mild cognitive impairment (MCI) is a clinical precursor of Alzheimer’s disease (AD). Many MCI subjects convert to AD, although others remain stable as MCI or sometimes return to cognitive normal. Currently, there are no curative treatments for patients who are already in AD, and therefore biomarkers for early detection of high risk MCI-to-AD conversion subjects are rapidly required. Here, we investigated potential biomarkers from blood-based miRNA (miR) expression profiles and genomic data of 197 Japanese MCI patients. Using the candidate biomarkers, we constructed a prognosis prediction model based on a Cox proportional hazard model. The final prediction model, composed of 24 miR-eQTLs (i.e. SNP-miRNA pairs) and three clinical factors (age, sex and ApoE ε4 carriers), successfully classified MCI patients into two groups, low and high, in terms of risk of MCI-to-AD conversion (log-rank trend test P=3.44×10^(-4)), and achieved a concordance index of 0.702 on an independent test set. Network-based meta-analysis using target genes of the miR-eQTLs further revealed four important hub genes (SHC1, FOXO1, GSK3B, and PTEN) associated with the pathogenesis of AD. Statistically significant differences were observed in PTEN expression between MCI and AD and SHC1 expression between cognitively normal elder subjects (CN) and AD when examining RNA-seq data from 610 blood samples (PTEN, P=0.023; SHC1, P=0.049), although FOXO1 and GSK3B showed low levels of expression in blood. Accurate prediction of MCI-to-AD conversion enables earlier appropriate intervention for those MCI patients and can lead to a reduction of MCI patients that convert to AD with high risk. We believe that further investigation with larger sample sizes will contribute to practical clinical use of our approach in MCI-to-AD conversion in the near future.
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Project description:<h4><strong>INTRODUCTION </strong>Alzheimer's disease is a prevalent disease with a heavy global burden and is suggested to be a metabolic disease in the brain in recent years. The metabolome is considered to be the most promising phenotype which reflects changes in genetic, transcript, and protein profiles as well as environmental effects. Aiming to obtain a comprehensive understanding and convenient diagnosis of MCI and AD from another perspective, researchers are working on AD metabolomics. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.</h4><p><strong>METHODS </strong>We first enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples and conducted an LC-MS/MS analysis. In parallel, clinical data were collected and clinical examinations were performed. After statistical and bioinformatics analyzes, significant risk factors and differential urinary metabolites were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.</p><p><strong>RESULTS </strong>57 AD patients, 43 MCI patients and 62 CN subjects were enrolled. A total of 2,140 metabolites were identified among which 125 significantly differed between the AD and CN groups, including 46 upregulated ones and 79 downregulated ones. In parallel, there were 93 significant differential metabolites between the MCI and CN groups, including 23 upregulated ones and 70 downregulated ones. AD diagnostic panel (30 metabolites+ age + APOE) achieved an AUC of 0.9575 in the test set while MCI diagnostic panel (45 metabolites+ age + APOE) achieved an AUC of 0.7333 in the test set. Atropine, S-Methyl-L-cysteine-S-oxide, D-Mannose 6-phosphate (M6P), Spiculisporic Acid, N-Acetyl-L-methionine, 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Pyridoxal 5'-Phosphate (PLP) and 17(S)-HpDHA were considered valuable for both AD and MCI diagnosis and defined as hub metabolites. Besides, diagnostic metabolites were weakly correlated with cognitive functions.</p><p><strong>DISCUSSION </strong>In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Atropine, M6P and PLP were evidence-based hub metabolites in AD.</p>
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Project description:Extensive literature has explored the beneficial effects of music in age-related cognitive disorders (ACD), but limited knowledge exists regarding its impact on gene expression. We analyzed transcriptomes of ACD patients and healthy controls, pre-post a music session (n=60), and main genes/pathways were compared to those dysregulated in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) as revealed by a multi-cohort study (n=1269 MCI/AD and controls). Music was associated with 2.3 times more whole-genome gene expression, particularly on neurodegeneration-related genes, in ACD than controls. Co-expressed gene-modules and pathways analysis demonstrated that music impacted autophagy, vesicle and endosome organization, biological processes commonly dysregulated in MCI/AD. Notably, the data indicated a strong negative correlation between musically-modified genes/pathways in ACD and those dysregulated in MCI/AD. These findings highlight the compensatory effect of music on genes/biological processes affected in MCI/AD, providing insights into the molecular mechanisms underlying the benefits of music on these disorders.
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Project description:Background: Mild cognitive impairment (MCI) is an intermediate state between normal aging, and Alzheimer’s disease, and other dementias. Early detection of dementia, and MCI, is a crucial issue in terms of secondary prevention. Blood biomarker detection is a possible way for early detection of MCI. Although disease biomarkers are detected by, in general, using single molecular analysis such as t-test, another possible approach is based on interaction between molecules. Results: Differential correlation analysis, which detects difference on correlation of two variables in case/control study, was carried out to the dataset with 745 microRNAs (miRNAs) from plasma samples of 30 age-matched controls and 23 MCI patients in Japan. The 20 pairs of miRNAs, which consist of 20 miRNAs, were selected as MCI markers. Two pairs of miRNAs (hsa-miR-191 and hsa-miR-101, and hsa-miR-103 and hsa-miR-222) out of 20 attained the highest area under the curve (AUC) value of 0.962 for MCI detection. Other two miRNA pairs that include hsa-miR-191 and hsa-miR-125b also attained high AUC value of ≥ 0.95. Pathway analysis was performed to the MCI markers for further understanding of biological implications. As a result, collapsed correlation on hsa-miR-191 and emerged correlation on hsa-miR-125b may have key role in MCI, and dementia progression. Conclusion: Differential correlation analysis, a bioinformatics tool to elucidate complicated and interdependent biological systems behind diseases, detects effective MCI markers that cannot be found by single molecule analysis such as t-test.
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