Long RNA Profiles of Human Brain Extracellular Vesicles Provide New Insights into the Pathogenesis of Alzheimer's Disease

Small RNA Profiles of Brain Tissue-Derived Extracellular Vesicles in Alzheimer's Disease

Background

Extracellular vesicles (EVs) and non-coding RNAs (ncRNAs) are emerging contributors to Alzheimer's disease (AD) pathophysiology. Differential abundance of ncRNAs carried by EVs may provide valuable insights into underlying disease mechanisms. Brain tissue-derived EVs (bdEVs) are particularly relevant, as they may offer valuable insights about the tissue of origin. However, there is limited research on diverse ncRNA species in bdEVs in AD.

Objective

This study explored whether the non-coding RNA composition of EVs isolated from post-mortem brain tissue is related to AD pathogenesis.

Methods

bdEVs from age-matched late-stage AD patients (n = 23) and controls (n = 10) that had been separated and characterized in our previous study were used for RNA extraction, small RNA sequencing, and qPCR verification.

Results

Significant differences of non-coding RNAs between AD and controls were found, especially for miRNAs and tRNAs. AD pathology-related miRNA and tRNA differences of bdEVs partially matched expression differences in source brain tissues. AD pathology had a more prominent association than biological sex with bdEV miRNA and tRNA components in late-stage AD brains.

Conclusions

Our study provides further evidence that EV non-coding RNAs from human brain tissue, including but not limited to miRNAs, may be altered and contribute to AD pathogenesis.

Assess Alzheimer's Disease via Plasma Extracellular Vesicle-derived mRNA

Alzheimer's disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern, particularly due to the increasing aging population. Recently, it has been revealed that extracellular vesicles (EVs) originating from neurons play a critical role in AD pathogenesis and progression. These neuronal EVs can cross the blood-brain barrier and enter peripheral circulation, offering a less invasive means for assessing blood-based AD biomarkers. In this study, we analyzed plasma EV-derived messenger RNA (mRNA) from 82 subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls, using next-generation sequencing (NGS) to profile their gene expression for functional enrichment and pathway analysis. Based on the differentially expressed genes identified in both MCI and AD groups, we established a diagnostic model by implementing a machine learning classifier. The refined model demonstrated an average diagnostic accuracy over 98% and showed a strong correlation with different AD stages, suggesting the potential of plasma EV-derived mRNA as a promising non-invasive biomarker for early detection and ongoing monitoring of AD.

Multi-layered transcriptomic analysis reveals a pivotal role of FMR1 and other developmental genes in Alzheimer's disease-associated brain ceRNA network

Alzheimer's disease (AD) is an increasingly neurodegenerative disorder that causes progressive cognitive decline and memory impairment. Despite extensive research, the underlying causes of late-onset AD (LOAD) are still in progress. This study aimed to establish a network of competing regulatory interactions involving circular RNAs (circRNAs), microRNAs (miRNAs), RNA-binding proteins (RBPs), and messenger RNAs (mRNAs) connected to LOAD. A systematic analysis of publicly available expression data was conducted to identify integrated differentially expressed genes (DEGs) from the hippocampus of LOAD patients. Subsequently, gene co-expression analysis identified modules comprising highly expressed DEGs that act cooperatively. The competition between co-expressed DEGs and miRNAs/RBPs and the simultaneous interactions between circRNA and miRNA/RBP revealed a complex ceRNA network responsible for post-transcriptional regulation in LOAD. Hippocampal expression data for miRNAs, circRNAs, and RBPs were used to filter relevant relationships for AD. An integrated topological score was used to identify the highly connected hub gene, from which a brain core ceRNA subnetwork was generated. The Fragile X Messenger Ribonucleoprotein 1 (FMR1) coding for the RBP FMRP emerged as the prominent driver gene in this subnetwork. FMRP has been previously related to AD but not in a ceRNA network context. Also, the substantial number of neurodevelopmental genes in the ceRNA subnetwork and their related biological pathways strengthen that AD shares common pathological mechanisms with developmental conditions. Our results enhance the current knowledge about the convergent ceRNA regulatory pathways underlying AD and provide potential targets for identifying early biomarkers and developing novel therapeutic interventions.

Construction of an immune-related lncRNA-miRNA-mRNA regulatory network in radiation-induced esophageal injury in rats

Radiation-induced esophageal injury (RIEI) is an adverse reaction of radiation therapy in patients with esophageal cancer, lung cancer and other malignant tumors. Competitive endogenous RNA (ceRNA) network is known to play a significant role in the onset and progression of many diseases, but the exact mechanism of ceRNA in RIEI has not been fully elucidated. In this study, rat esophaguses were obtained after conducting irradiation under different doses (0 Gy, 25 Gy, 35 Gy). Total RNA was extracted and mRNA, lncRNA, circRNA, and miRNA sequencing was performed. Multiple dose-dependent differentially expressed RNAs (dd-DERs), including 870 lncRNAs, 82 miRNAs, 2478 mRNAs, were obtained through the integration of differential expression analysis and dose-dependent screening (35 Gy ≥ 25 Gy > 0 Gy, or 35 Gy ≤ 25 Gy < 0 Gy). Co-expression analysis and prediction of the binding site in dd-DER were conducted and 27 lncRNAs, 20 miRNAs, and 168 mRNAs were selected to construct a ceRNA network. As the immune microenvironment is crucial for RIEI progression, we constructed an immune-related ceRNA network consisting of 11 lncRNAs, 9 miRNAs, and 9 mRNAs. The expression levels of these immune-related RNAs were verified by RT-qPCR. Immune infiltration analysis showed that the RNAs in the immune-related ceRNA network were mainly associated with the proportion of monocytes, M2 macrophages, activated NK cells, and activated CD4+ memory T cells. Drug sensitivity analysis was conducted based on the expression levels of mRNAs in the immune-related ceRNA network, and small molecule drugs with preventive and therapeutic effects on RIEI were identified. In summary, an immune-related ceRNA network associated with RIEI progression was constructed in this study. The findings provide useful information on new potential targets for the prevention and treatment of RIEI.