IGF1R/INSR signaling is crucial for understanding Alzheimer's disease (AD) and may aid in the development of potent therapeutic strategies. This study investigated the expression and activity of these receptors and their potential to form functional hybrids in response to amyloid beta (Aβ). IGF1R, INSR, and ARRB1 were found to be upregulated in AD. The propensity for functional hybrid formation was also greater in the presence of Aβ. The association of IGF1R with ARRB1 reached a maximum at 60 min of Aβ treatment, which coincided with increased pERK activity at approximately the same time, indicating the importance of this association in pERK regulation. Knocking down IGF1R, INSR, and ARRB1 independently reduced cAMP, whereas overexpressing IGF1R significantly increased cAMP. Knocking down ARRB1 in IGF1R-overexpressing cells led to a reduction in cAMP, indicating that the interaction of ARRB1 and IGF1R possibly contributes to cAMP dysregulation. Since cAMP plays a crucial role in cognition and memory, alterations in cAMP after receptor hybridization could be significant in AD. Additionally, we noted hyperactivation of MAPK, which is associated with aberrant cellular activity, transcriptional control, and stress pathways. This finding highlights the importance of IGF1R and INSR dysregulation, which plays a major role in addition to conventional RTK signaling through multiple pathways. Here, we focused on the ARRB1 and IGF1R interaction and showed that picropodophyllin (PPP), an IGF1R-specific inhibitor, blocks this interaction and alters the ERK and cAMP status under disease conditions. Cell viability studies further revealed that the PPP substantially improved cell viability in the presence of Aβ. This highlights the role of the PPP in regulating these cascades and opens the arena for further therapeutic development for AD.

Long non-coding RNAs (LncRNAs) have emerged as pivotal regulators in the pathogenesis of Alzheimer's disease (AD), a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. With the capacity to modulate gene expression at various levels, LncRNAs are implicated in multiple pathological mechanisms of AD, including amyloid-beta (Aβ) accumulation, tau protein phosphorylation, neuroinflammation, and neuronal apoptosis. Recent studies have highlighted the potential of LncRNAs as diagnostic biomarkers and therapeutic targets due to their differential expression patterns in AD patients. This review synthesizes current knowledge on the role of LncRNAs in AD, focusing on their involvement in key molecular pathways and their promise as indicators for early diagnosis and prognosis. We discuss the regulatory networks of LncRNAs in the context of AD, their interaction with miRNAs, and the implications for developing novel therapeutic strategies. Despite the complexity and variability in LncRNA function, the prospect of harnessing these molecules for precision medicine in AD is gaining momentum. The translational potential of LncRNA-based interventions offers a new frontier in the quest for effective treatments and a deeper understanding of the molecular underpinnings of AD.

Neurodegeneration occurs naturally as humans age, but the presence of additional pathogenic mechanisms yields harmful and consequential effects on the brain. Alzheimer's disease (AD), the most common form of dementia, is a composite of such factors. Despite extensive research to identify the exact causes of AD, therapeutic approaches for treating the disease continue to be ineffective, indicating important gaps in our understanding of disease mechanisms. Long non-coding RNAs (lncRNAs) are an endogenous class of regulatory RNA transcripts longer than 200 nucleotides, involved in various regulatory networks, whose dysregulation is evident in several neural and extraneural diseases. LncRNAs are ubiquitously expressed across all tissues with a wide range of functions, including controlling cell differentiation and development, responding to environmental stimuli, and other physiological processes. Several lncRNAs have been identified as potential contributors in worsening neurodegeneration due to altered regulation during abnormal pathological conditions. Within neurological disease, lncRNAs are prime candidates for use as biomarkers and pharmacological targets. Gender-associated lncRNA expression is altered in a gender-dependent manner for AD, suggesting more research needs to be focused on this relationship. Overall, research on lncRNAs and their connection to neurodegenerative disease is growing exponentially, as commercial enterprises are already designing and employing RNA therapeutics. In this review we offer a comprehensive overview of the current state of knowledge on the role of lncRNAs in AD and discuss the potential implications of lncRNA as potential therapeutic targets and diagnostic biomarkers in patients with Alzheimer's disease.

Recent studies on the regulatory networks implicated in Alzheimer's disease (AD) evince long non-coding RNAs (lncRNAs) as crucial regulatory players, albeit a poor understanding of the mechanism. Analyzing differential gene expression in the RNA-seq data from the post-mortem AD brain hippocampus, we categorized a list of AD-dysregulated lncRNA transcripts into functionally similar communities based on their k-mer profiles. Using machine-learning-based algorithms, their subcellular localizations were mapped. We further explored the functional relevance of each community through AD-dysregulated miRNA, RNA-binding protein (RBP) interactors, and pathway enrichment analyses. Further investigation of the miRNA-lncRNA and RBP-lncRNA networks from each community revealed the top RBPs, miRNAs, and lncRNAs for each cluster. The experimental validation community yielded ELAVL4 and miR-16-5p as the predominant RBP and miRNA, respectively. Five lncRNAs emerged as the top-ranking candidates from the RBP/miRNA-lncRNA networks. Further analyses of these networks revealed the presence of multiple regulatory triads where the RBP-lncRNA interactions could be augmented by the enhanced miRNA-lncRNA interactions. Our results advance the understanding of the mechanism of lncRNA-mediated AD regulation through their interacting partners and demonstrate how these functionally segregated but overlapping regulatory networks can modulate the disease holistically.

Perioperative neurocognitive disorders (PND) refer to neurocognitive abnormalities during perioperative period, which are a great challenge for elderly patients and associated with increased morbidity and mortality. Our studies showed that long non-coding RNAs (lncRNAs) regulate mitochondrial function and aging-related pathologies in the aged hippocampus after anesthesia, and lncRNAs are associated with multiple neurodegenerations. However, the regulatory role of lncRNAs in PND-related pathological processes remains unclear.

A total of 18-month mice were assigned to control and surgery (PND) groups, mice in PND group received sevoflurane anesthesia and laparotomy. Cognitive function was assessed with fear conditioning test. Hippocampal RNAs were isolated for sequencing, lncRNA and microRNA libraries were constructed, mRNAs were identified, Gene Ontology (GO) analysis were performed, and lncRNA-microRNA-mRNA networks were established. qPCR was performed for gene expression verification.

A total of 312 differentially expressed (DE) lncRNAs, 340 DE-Transcripts of Uncertain Coding Potential (TUCPs), and 2,003 DEmRNAs were identified in the hippocampus between groups. The lncRNA-microRNA-mRNA competing endogenous RNA (ceRNA) network was constructed with 29 DElncRNAs, 90 microRNAs, 493 DEmRNAs, 148 lncRNA-microRNA interaction pairs, 794 microRNA-mRNA interaction pairs, and 110 lncRNA-mRNA co-expression pairs. 795 GO terms were obtained. Based on the frequencies of involved pathological processes, BP terms were divided into eight categories: neurological system alternation, neuronal development, metabolism alternation, immunity and neuroinflammation, apoptosis and autophagy, cellular communication, molecular modification, and behavior changes. LncRNA-microRNA-mRNA ceRNA networks in these pathological categories were constructed, and involved pathways and targeted genes were revealed. The top relevant lncRNAs in these ceRNA networks included RP23-65G6.4, RP24-396L14.1, RP23-251I16.2, XLOC_113622, RP24-496E14.1, etc., and the top relevant mRNAs in these ceRNA networks included Dlg4 (synaptic function), Avp (lipophagy), Islr2 (synaptic function), Hcrt (regulation of awake behavior), Tnc (neurotransmitter uptake).

In summary, we have constructed the lncRNA-associated ceRNA network during PND development in mice, explored the role of lncRNAs in multiple pathological processes in the mouse hippocampus, and provided insights into the potential mechanisms and therapeutic gene targets for PND.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.