Long non-coding RNA MALAT1 protects against Aβ1-42 induced toxicity by regulating the expression of receptor tyrosine kinase EPHA2 via quenching miR-200a/26a/26b in Alzheimer's disease

The receptor tyrosine kinase IGF1R and its associated GPCRs are co-regulated by the noncoding RNA NEAT1 in Alzheimer's disease

The study is based on the complexity of Insulin like growth factor receptor (IGF1R) signaling and its regulation by noncoding RNAs (ncRNAs). IGF1R signaling is an important cascade in Alzheimer's disease (AD); however, its regulation and roles are poorly understood. Due to the presence of β-arrestin and GPCR Receptor Kinase binding sites, this protein has been termed a 'functional hybrid', as it can take part in both kinase and GPCR signaling pathways, further adding to its complexity. The objective of this study is to understand the underlying ncRNA regulation controlling IGF1R and GPCRs in AD to find commonalities in the network. We found through data mining that 45 GPCRs were reportedly deregulated in AD and built clusters based on GO/KEGG pathways to show shared functionality with IGF1R. Eight miRs were further discovered that could coregulate IGF1R and GPCRs. We validated their expression in an AD cell model and probed for common lncRNAs downstream that could regulate these miRs. Seven such candidates were identified and further validated. A combined network comprising IGF1R with nine GPCRs, eight miRs, and seven lncRNAs was created to visualize the interconnectivity within pathways. Betweenness centrality analysis showed a cluster of NEAT1, hsa-miR-15a-5p, hsa-miR-16-5p, and IGF1R to be crucial form a competitive endogenous RNA-based (ceRNA) tetrad that could relay information within the network, which was further validated by cell-based studies. NEAT1 emerged as a master regulator that could alter the levels of IGF1R and associated GPCRs. This combined bioinformatics and experimental study for the first time explored the regulation of IGF1R through ncRNAs from the perspective of neurodegeneration.

Non-Coding RNAs in Neurological and Neuropsychiatric Disorders: Unraveling the Hidden Players in Disease Pathogenesis

Neurological and neuropsychiatric disorders pose substantial challenges to public health, necessitating a comprehensive understanding of the molecular mechanisms underlying their pathogenesis. In recent years, the focus has shifted toward the intricate world of non-coding RNAs (ncRNAs), a class of RNA molecules that do not encode proteins but play pivotal roles in gene regulation and cellular processes. This review explores the emerging significance of ncRNAs in the context of neurological and neuropsychiatric disorders, shedding light on their diverse functions and regulatory mechanisms. The dysregulation of various ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), has been implicated in the pathophysiology of conditions such as Alzheimer's disease, Parkinson's disease, schizophrenia, and mood disorders. This review delves into the specific roles these ncRNAs play in modulating key cellular processes, including synaptic plasticity, neuroinflammation, and apoptosis, providing a nuanced understanding of their impact on disease progression. Furthermore, it discusses the potential diagnostic and therapeutic implications of targeting ncRNAs in neurological and neuropsychiatric disorders. The identification of specific ncRNA signatures holds promise for the development of novel biomarkers for early disease detection, while the manipulation of ncRNA expression offers innovative therapeutic avenues. Challenges and future directions in the field are also considered, highlighting the need for continued research to unravel the complexities of ncRNA-mediated regulatory networks in the context of neurological and neuropsychiatric disorders. This review aims to provide a comprehensive overview of the current state of knowledge and stimulate further exploration into the fascinating realm of ncRNAs in the brain's intricate landscape.

Tanshinone IIA improves Alzheimer's disease via RNA nuclear-enriched abundant transcript 1/microRNA-291a-3p/member RAS oncogene family Rab22a axis

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition characterized by oxidative stress and neuroinflammation. Tanshinone IIA (Tan-IIA), a bioactive compound isolated from Salvia miltiorrhiza plants, has shown potential neuroprotective effects; however, the mechanisms underlying such a function remain unclear.

AIM

To investigate potential Tan-IIA neuroprotective effects in AD and to elucidate their underlying mechanisms.

METHODS

Hematoxylin and eosin staining was utilized to analyze structural brain tissue morphology. To assess changes in oxidative stress and neuroinflammation, we performed enzyme-linked immunosorbent assay and western blotting. Additionally, the effect of Tan-IIA on AD cell models was evaluated in vitro using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Genetic changes related to the long non-coding RNA (lncRNA) nuclear-enriched abundant transcript 1 (NEAT1)/microRNA (miRNA, miR)-291a-3p/member RAS oncogene family Rab22a axis were assessed through reverse transcription quantitative polymerase chain reaction.

RESULTS

In vivo, Tan-IIA treatment improved neuronal morphology and attenuated oxidative stress and neuroinflammation in the brain tissue of AD mice. In vitro experiments showed that Tan-IIA dose-dependently ameliorated the amyloid-beta 1-42-induced reduction of neural stem cell viability, apoptosis, oxidative stress, and neuroinflammation. In this process, the lncRNA NEAT1 - a potential therapeutic target - is highly expressed in AD mice and downregulated via Tan-IIA treatment. Mechanistically, NEAT1 promotes the transcription and translation of Rab22a via miR-291a-3p, which activates nuclear factor kappa-B (NF-κB) signaling, leading to activation of the pro-apoptotic B-cell lymphoma 2-associated X protein and inhibition of the anti-apoptotic B-cell lymphoma 2 protein, which exacerbates AD. Tan-IIA intervention effectively blocked this process by inhibiting the NEAT1/miR-291a-3p/Rab22a axis and NF-κB signaling.

CONCLUSION

This study demonstrates that Tan-IIA exerts neuroprotective effects in AD by modulating the NEAT1/miR-291a-3p/Rab22a/NF-κB signaling pathway, serving as a foundation for the development of innovative approaches for AD therapy.

Long Non-Coding RNAs: New Insights in Neurodegenerative Diseases

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), are gradually becoming a burden to society. The adverse effects and mortality/morbidity rates associated with these NDDs are a cause of many healthcare concerns. The pathologic alterations of NDDs are related to mitochondrial dysfunction, oxidative stress, and inflammation, which further stimulate the progression of NDDs. Recently, long non-coding RNAs (lncRNAs) have attracted ample attention as critical mediators in the pathology of NDDs. However, there is a significant gap in understanding the biological function, molecular mechanisms, and potential importance of lncRNAs in NDDs. This review documents the current research on lncRNAs and their implications in NDDs. We further summarize the potential implication of lncRNAs to serve as novel therapeutic targets and biomarkers for patients with NDDs.

Urine biomarkers for Alzheimer's disease: A new opportunity for wastewater-based epidemiology?

While Alzheimer's disease (AD) diagnosis, management, and care have become priorities for healthcare providers and researcher's worldwide due to rapid population aging, epidemiologic surveillance efforts are currently limited by costly, invasive diagnostic procedures, particularly in low to middle income countries (LMIC). In recent years, wastewater-based epidemiology (WBE) has emerged as a promising tool for public health assessment through detection and quantification of specific biomarkers in wastewater, but applications for non-infectious diseases such as AD remain limited. This early review seeks to summarize AD-related biomarkers and urine and other peripheral biofluids and discuss their potential integration to WBE platforms to guide the first prospective efforts in the field. Promising results have been reported in clinical settings, indicating the potential of amyloid β, tau, neural thread protein, long non-coding RNAs, oxidative stress markers and other dysregulated metabolites for AD diagnosis, but questions regarding their concentration and stability in wastewater and the correlation between clinical levels and sewage circulation must be addressed in future studies before comprehensive WBE systems can be developed.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease

BACKGROUND

Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

METHODS

We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.

RESULTS

Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.

CONCLUSIONS

Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease

Background

Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

Methods

We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.

Results

Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules, and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and apoE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.

Conclusions

Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

The Emerging Role of Autophagy-Associated lncRNAs in the Pathogenesis of Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) have become a significant global public health problem and a major societal burden. The World Health Organization predicts that NDDs will overtake cancer as the second most common cause of human mortality within 20 years. Thus, it is urgently important to identify pathogenic and diagnostic molecular markers related to neurodegenerative processes. Autophagy is a powerful process for removing aggregate-prone proteins in neurons; defects in autophagy are often associated with the pathogenesis of NDDs. Long non-coding RNAs (lncRNAs) have been suggested as key regulators in neurodevelopment; aberrant regulation of lncRNAs contributes to neurological disorders. In this review, we summarize the recent progress in the study of lncRNAs and autophagy in the context of neurodegenerative disorders, especially Alzheimer's disease (AD) and Parkinson's disease (PD). The information presented here should provide guidance for future in-depth investigations of neurodegenerative processes and related diagnostic molecular markers and treatment targets.

New Insights into lncRNAs in Aβ Cascade Hypothesis of Alzheimer's Disease

Alzheimer's disease (AD) is the most common type of dementia, but its pathogenesis is not fully understood, and effective drugs to treat or reverse the progression of the disease are lacking. Long noncoding RNAs (lncRNAs) are abnormally expressed and deregulated in AD and are closely related to the occurrence and development of AD. In addition, the high tissue specificity and spatiotemporal specificity make lncRNAs particularly attractive as diagnostic biomarkers and specific therapeutic targets. Therefore, an in-depth understanding of the regulatory mechanisms of lncRNAs in AD is essential for developing new treatment strategies. In this review, we discuss the unique regulatory functions of lncRNAs in AD, ranging from Aβ production to clearance, with a focus on their interaction with critical molecules. Additionally, we highlight the advantages and challenges of using lncRNAs as biomarkers for diagnosis or therapeutic targets in AD and present future perspectives in clinical practice.