Identification of hub genes associated with cognition in the hippocampus of Alzheimer's Disease

SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer's disease

The slow-developing neurological disorder Alzheimer's disease (AD) has no recognized etiology. A bioinformatics investigation verified copper metabolism indicators for AD development. GEO contributed AD-related datasets GSE1297 and GSE5281. Differential expression analysis and WGCNA confirmed biomarker candidate genes. Each immune cell type in AD and control samples was scored using single sample gene set enrichment analysis. Receiver Operating Characteristic (ROC) analysis, short Time-series Expression Miner (STEM) grouping, and expression analysis between control and AD samples discovered copper metabolism indicators that impacted AD progression. We test clinical samples and cellular function to ensure study correctness. Biomarker-targeting miRNAs and lncRNAs were predicted by starBase. Trust website anticipated biomarker-targeting transcription factors. In the end, Cytoscape constructed the TF/miRNA-mRNA and lncRNA-miRNA networks. The DGIdb database predicted biomarker-targeted drugs. We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38. A TF/miRNA-mRNA regulation network was then established with two miRNAs (hsa-miR-34a-5p and 34c-5p), six TFs (NFKB1, RELA, MYC, HIF1A, JUN, and SP1), and four biomarkers. The DGIdb database contained 171 drugs targeting ten copper metabolism-relevant biomarkers (BRCA1, MITF, NFKBIA, CD38, CCK2, HPRT1, SPHK1, LDHA, SCG5, and SYT1). Copper metabolism biomarkers CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38 alter AD progression, laying the groundwork for disease pathophysiology and novel AD diagnostic and treatment.

Cholecystokinin (CCK): a neuromodulator with therapeutic potential in Alzheimer's and Parkinson's disease

Cholecystokinin (CCK) is a neuropeptide modulating digestion, glucose levels, neurotransmitters and memory. Recent studies suggest that CCK exhibits neuroprotective effects in Alzheimer's disease (AD) and Parkinson's disease (PD). Thus, we review the physiological function and therapeutic potential of CCK. The neuropeptide facilitates hippocampal glutamate release and gates GABAergic basket cell activity, which improves declarative memory acquisition, but inhibits consolidation. Cortical CCK alters recognition memory and enhances audio-visual processing. By stimulating CCK-1 receptors (CCK-1Rs), sulphated CCK-8 elicits dopamine release in the substantia nigra and striatum. In the mesolimbic pathway, CCK release is triggered by dopamine and terminates reward responses via CCK-2Rs. Importantly, activation of hippocampal and nigral CCK-2Rs is neuroprotective by evoking AMPK activation, expression of mitochondrial fusion modulators and autophagy. Other benefits include vagus nerve/CCK-1R-mediated expression of brain-derived neurotrophic factor, intestinal protection and suppression of inflammation. We also discuss caveats and the therapeutic combination of CCK with other peptide hormones.

Brain circuits for retching-like behavior

Nausea and vomiting are important defensive responses to cope with pathogens and toxins that invade the body. The nucleus of the solitary tract (NTS) is important for initiating these responses. However, the molecular heterogeneities and cellular diversities of the NTS occlude a better understanding of these defensive responses. Here, we constructed the single-nucleus transcriptomic atlas of NTS cells and found multiple populations of NTS neurons that may be involved in these defensive responses. Among these, we identified Calbindin1-positive (Calb1+) NTS neurons that are molecularly distinct from Tac1+ neurons. These Calb1+ neurons are critical for nausea and retching induced by cereulide; an emetic toxin secreted by Bacillus Cereus. Strikingly, we found that cereulide can directly modulate vagal sensory neurons that innervate Calb1+ NTS neurons, a novel mechanism distinct from that for nausea and retching induced by Staphylococcal enterotoxin A. Together, our transcriptomic atlas of NTS neurons and the functional analyses revealed the neural mechanism for cereulide-induced retching-like behavior. These results demonstrate the molecular and cellular complexities in the brain that underlie defensive responses to the diversities of pathogens and toxins.

Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer's disease

Background

Alzheimer's disease (AD) is an age-associated neurodegenerative disease, and the currently available diagnostic modalities and therapeutic agents are unsatisfactory due to its high clinical heterogeneity. Necroptosis is a common type of programmed cell death that has been shown to be activated in AD.

Methods

In this study, we first investigated the expression profiles of necroptosis-related genes (NRGs) and the immune landscape of AD based on GSE33000 dataset. Next, the AD samples in the GSE33000 dataset were extracted and subjected to consensus clustering based upon the differentially expressed NRGs. Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. Finally, we developed a diagnostic model for AD by comparing four different machine learning approaches. The discrimination performance and clinical relevance of the diagnostic model were assessed using various evaluation metrics, including the nomogram, calibration plot, decision curve analysis (DCA), and independent validation datasets.

Results

Aberrant expression patterns of NRGs and specific immune landscape were identified in the AD samples. Consensus clustering revealed that patients in the GSE33000 dataset could be classified into two necroptosis clusters, each with distinct immune landscapes and enriched pathways. The Extreme Gradient Boosting (XGB) was found to be the most optimal diagnostic model for the AD based on the predictive ability and reliability of the models constructed by four machine learning approaches. The five most important variables, including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1, were used to construct a five-gene diagnostic model. The constructed nomogram, calibration plot, DCA, and external independent validation datasets exhibited outstanding diagnostic performance for AD and were closely related with the pathologic hallmarks of AD.

Conclusion

This work presents a novel diagnostic model that may serve as a framework to study disease heterogeneity and provide a plausible mechanism underlying neuronal loss in AD.

Cholecystokinin Signaling can Rescue Cognition and Synaptic Plasticity in the APP/PS1 Mouse Model of Alzheimer's Disease

Synaptic impairment and loss are an important pathological feature of Alzheimer's disease (AD). Memory is stored in neural networks through changes in synaptic activity, and synaptic dysfunction can cause cognitive dysfunction and memory loss. Cholecystokinin (CCK) is one of the major neuropeptides in the brain, and plays a role as a neurotransmitter and growth factor. The level of CCK in the cerebrospinal fluid is decreased in AD patients. In this study, a novel CCK analogue was synthesized on the basis of preserving the minimum bioactive fragment of endogenous CCK to investigate whether the novel CCK analogue could improve synaptic plasticity in the hippocampus of the APP/PS1 transgenic mouse model of AD and its possible molecular biological mechanism. Our study found that the CCK analogue could effectively improve spatial learning and memory, enhance synaptic plasticity in the hippocampus, normalize synapse numbers and morphology and the levels of key synaptic proteins, up-regulate the PI3K/Akt signaling pathway and normalize PKA, CREB, BDNF and TrkB receptor levels in APP/PS1 mice. The amyloid plaque load in the brain was reduced by CCK, too. The use of a CCKB receptor antagonist and targeted knockdown of the CCKB receptor (CCKBR) attenuated the neuroprotective effect of the CCK analogue. These results demonstrate that the neuroprotective effect of CCK analogue is achieved by activating the PI3K/Akt as well as the PKA/CREB-BDNF/TrkB signaling pathway that leads to protection of synapses and cognition.

β-Amyloid targeting nanodrug for neuron-specific delivery of nucleic acids in Alzheimer's disease mouse models

Delivery of therapeutic substances into the brain poses a significant challenge in the treatment of neurological disorders. This is primarily due to the blood-brain barrier (BBB), which restricts access, alongside the limited stability and distribution of these agents within the brain tissue. Here we demonstrate an efficient delivery of microRNA (miRNA) and antisense RNA preferentially to neurons compared to astroglia in the brain of healthy and Alzheimer's disease mice, via disulfide-linked conjugation with poly(ß-L-malic acid-trileucine)-copolymer a biodegradable, amphiphilic, and multivalent platform. By conjugating a D-configured (D3)-peptide (vector) for specific targeting, highly efficient delivery across the BBB is achieved through the Low-Density Lipoprotein Receptor-Related Protein-1 (LRP-1) transcytosis pathway, amyloid beta (Aβ) peptides. Nanodrug distribution was determined by fluorescent labeling and analyzed by microscopy in neurons, astroglia, and in extracellular amyloid plaques typical for Alzheimer's disease. Whereas D-configured BBB-vectors can efficiently target neurons, L-configured (e.g., AP2-peptide) guided vector can only cross BBB but not seem to bind neurons. An analysis of post-injection fluorescence distribution, and RNA-seq followed by real-time PCR validation, confirmed a successful in vivo delivery of morpholino-miRNA-186 nanoconjugates into mouse brain. The size and fluorescence intensity of the intracellular nanodrug particulates were analyzed and verified by a competition with non-fluorescent conjugates. Differentially expressed genes (DEGs) from RNA-seq were identified in the nanodrug injected mice, and the changes of selected DEGs related to Alzheimer's disease were further validated by western blot and real-time PCR. Collectively, these results demonstrated that D3-peptide-conjugated nanopolymer drug is able to achieve neuron-selective delivery of miRNA and can serve as an efficient brain delivery vehicle in Alzheimer's disease (AD) mouse models.

Exploring the Mechanism of Arctium Lappa L. Leaves in the Treatment of Alzheimer's Disease Based on Chemical Profile, Network Pharmacology and Molecular Docking

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease, which urgently needs more effective treatment strategies. Arctium lappa L. leaf (burdock leaf) performs wide pharmacological activities, increasing evidence hinted that burdock leaves can ameliorate AD. This research aims to explore the bioactive ingredients and mechanisms of burdock leaves against AD by performing chemical profiles, network pharmacology, and molecular docking. 61 components are identified by liquid chromatography equipped with mass spectrometry. 792 targets of ingredients and 1661 AD-related genes are retrieved from public databases. Ten critical ingredients are identified from the topology analysis of the compound-target network. CytoNCA, AlzData database, and Aging Atlas database contribute to the foundation of 36 potential targets and four clinically significant targets (STAT3, RELA, MAPK8, and AR). The gene ontology (GO) analysis manifests that the included processes are close to the pathogenesis of AD. PI3K-Akt signaling pathway and AGE-RAGE signaling pathway may be important therapeutic mechanisms. Molecular docking results imply that network pharmacology results are reliable. Furthermore, the clinical meanings of core targets are also evaluated with the Gene Expression Omnibus (GEO) database. This research will provide research direction for the application of burdock leaves in the treatment of AD.

Integrated analysis of the lncRNA-associated ceRNA network in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease that worsens with age. Long non-coding RNAs (lncRNAs) dysregulation and its associated competing endogenous RNA (ceRNA) network have a potential connection with the occurrence and development of AD. A total of 358 differentially expressed genes (DEGs) were screened via RNA sequencing, including 302 differentially expressed mRNAs (DEmRNAs) and 56 differential expressed lncRNAs (DElncRNAs). Anti-sense lncRNA is the main type of DElncRNA, which plays a major role in the cis and trans regulation. The constructed ceRNA network consisted of 4 lncRNAs (NEAT1, LINC00365, FBXL19-AS1, RAI1-AS1719) and 4 microRNAs (miRNAs) (HSA-Mir-27a-3p, HSA-Mir-20b-5p, HSA-Mir-17-5p, HSA-Mir-125b-5p), and 2 mRNAs (MKNK2, F3). Functional enrichment analysis revealed that DEmRNAs are involved in related biological functions of AD. The co-expressed DEmRNAs (DNAH11, HGFAC, TJP3, TAC1, SPTSSB, SOWAHB, RGS4, ADCYAP1) of humans and mice were screened and verified by real-time quantitative polymerase chain reaction (qRT-PCR). In this study, we analyzed the expression profile of human AD-related lncRNA genes, constructed a ceRNA network, and performed functional enrichment analysis of DEmRNAs between human and mice. The obtained gene regulatory networks and target genes can be used to further analyze AD-related pathological mechanisms to optimize AD diagnosis and treatment.

Identification of TAC1 Associated with Alzheimer's Disease Using a Robust Rank Aggregation Approach

BACKGROUND

Alzheimer's disease (AD) brings heavy burden to society and family. There is an urgent need to find effective methods for disease diagnosis and treatment. The robust rank aggregation (RRA) approach that could aggregate the resulting gene lists has been widely utilized in genomic data analysis.

OBJECTIVE

To identify hub genes using RRA approach in AD.

METHODS

Seven microarray datasets in frontal cortex from GEO database were used to identify differential expressed genes (DEGs) in AD patients using RRA approach. STRING was performed to explore the protein-to-protein interaction (PPI). Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were utilized for enrichment analysis. Human Gene Connectome and Gene Set Enrichment Analysis were used for functional annotation. Finally, the expression levels of hub genes were validated in the cortex of 5xFAD mice by quantitative real-time polymerase chain reaction.

RESULTS

After RRA analysis, 473 DEGs (216 upregulated and 257 downregulated) were identified in AD samples. PPI showed that DEGs had a total of 416 nodes and 2750 edges. These genes were divided into 17 clusters, each of which contains at least three genes. After functional annotation and enrichment analysis, TAC1 is identified as the hub gene and may be related to synaptic function and inflammation. In addition, Tac1 was found downregulated in cortices of 5xFAD mice.

CONCLUSION

In the current study, TAC1 is identified as a key gene in the frontal cortex of AD, providing insight into the possible pathogenesis and potential therapeutic targets for this disease.

The potential role of the cholecystokinin system in declarative memory

As one of the most abundant neuropeptides in the central nervous system, cholecystokinin (CCK) has been suggested to be associated with higher brain functions, including learning and memory. In this review, we examined the potential role of the CCK system in declarative memory. First, we summarized behavioral studies that provide evidence for an important role of CCK in two forms of declarative memory-fear memory and spatial memory. Subsequently, we examined the electrophysiological studies that support the diverse roles of CCK-2 receptor activation in neocortical and hippocampal synaptic plasticity, and discussed the potential mechanisms that may be involved. Last but not least, we discussed whether the reported CCK-mediated synaptic plasticity can explain the strong influence of the CCK signaling system in neocortex and hippocampus dependent declarative memory. The available research supports the role of CCK-mediated synaptic plasticity in neocortex dependent declarative memory acquisition, but further study on the association between CCK-mediated synaptic plasticity and neocortex dependent declarative memory consolidation and retrieval is necessary. Although a direct link between CCK-mediated synaptic plasticity and hippocampus dependent declarative memory is missing, noticeable evidence from morphological, behavioral, and electrophysiological studies encourages further investigation regarding the potential role of CCK-dependent synaptic plasticity in hippocampus dependent declarative memory.

Bioinformatics and systems biology approaches to identify the effects of COVID-19 on neurodegenerative diseases: A review

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing coronavirus disease (COVID-19), has been devastated by COVID-19 in an increasing number of countries and health care systems around the world since its announcement of a global pandemic on 11 March 2020. During the pandemic, emerging novel viral mutant variants have caused multiple outbreaks of COVID-19 around the world and are prone to genetic evolution, causing serious damage to human health. As confirmed cases of COVID-19 spread rapidly, there is evidence that SARS-CoV-2 infection involves the central nervous system (CNS) and peripheral nervous system (PNS), directly or indirectly damaging neurons and further leading to neurodegenerative diseases (ND), but the molecular mechanisms of ND and CVOID-19 are unknown. We employed transcriptomic profiling to detect several major diseases of ND: Alzheimer 's disease (AD), Parkinson' s disease (PD), and multiple sclerosis (MS) common pathways and molecular biomarkers in association with COVID-19, helping to understand the link between ND and COVID-19. There were 14, 30 and 19 differentially expressed genes (DEGs) between COVID-19 and Alzheimer 's disease (AD), Parkinson' s disease (PD) and multiple sclerosis (MS), respectively; enrichment analysis showed that MAPK, IL-17, PI3K-Akt and other signaling pathways were significantly expressed; the hub genes (HGs) of DEGs between ND and COVID-19 were CRH, SST, TAC1, SLC32A1, GAD2, GAD1, VIP and SYP. Analysis of transcriptome data suggests multiple co-morbid mechanisms between COVID-19 and AD, PD, and MS, providing new ideas and therapeutic strategies for clinical prevention and treatment of COVID-19 and ND.