Inhibition of CXCR4/CXCL12 signaling: a translational perspective for Alzheimer's disease treatment

Inhibition of CXCR4: A perspective on miracle fruit seed for Alzheimer's disease treatment

Alzheimer's disease (AD) is the most prevalent type of dementia, and its causes are currently diverse and not fully understood. In a previous study, we discovered that short-term treatment with miracle fruit seed (MFS) had a therapeutic effect on AD model mice, however, the precise mechanism behind the effect remains unclear. In this research, we aimed to establish the efficacy and safety of long-term use of MFS in AD model mice. A variety of cytokines and chemokines have been implicated in the development of AD. Previous studies have validated a correlation between the expression levels of C-X-C chemokine receptor type 4 (CXCR4) and disease severity in AD. In this research, we observed an upregulation of CXCR4 expression in hippocampal tissues in the AD model group, which was then reversed after MFS treatment. Moreover, CXCR4 knockout led to improving cognitive function in AD model mice, and MFS showed the ability to regulate CXCR4 expression. Finally, our findings indicate that CXCR4 knockout and long-term MFS treatment produce comparable effects in treating AD model mice. In conclusion, this research demonstrates that therapeutic efficacy and safety of long-term use of MFS in AD model mice. MFS treatment and the subsequent reduction of CXCR4 expression exhibit a neuroprotective role in the brain, highlighting their potential as therapeutic targets for AD.

Evaluating serum CXCL12, sCD22, Lp-PLA2 levels and ratios as biomarkers for diagnosis of Alzheimer's disease

BACKGROUND

Grasping the underlying mechanisms of Alzheimer's disease (AD) is still a work in progress, and existing diagnostic techniques encounter various obstacles. Therefore, the discovery of dependable biomarkers is essential for early detection, tracking the disease's advancement, and steering treatment strategies.

AIM

To explore the diagnostic potential of serum CXCL12, sCD22, Lp-PLA2, and their ratios in AD, aiming to enhance early detection and inform targeted treatment strategies.

METHODS

The study was conducted in Dongying people's Hospital from January 2021 to December 2022. Participants included 60 AD patients (AD group) and 60 healthy people (control group). Using a prospective case-control design, the levels of CXCL12, sCD22 and Lp-PLA2 and their ratios were detected by enzyme-linked immunosorbent assay kit in the diagnosis of AD. The differences between the two groups were analyzed by statistical methods, and the corresponding ratio was constructed to improve the specificity and sensitivity of diagnosis.

RESULTS

Serum CXCL12 levels were higher in the AD group (47.2 ± 8.5 ng/mL) than the control group (32.8 ± 5.7 ng/mL, P < 0.001), while sCD22 levels were lower (14.3 ± 2.1 ng/mL vs 18.9 ± 3.4 ng/mL, P < 0.01). Lp-PLA2 levels were also higher in the AD group (112.5 ± 20.6 ng/mL vs 89.7 ± 15.2 ng/mL, P < 0.05). Significant differences were noted in CXCL12/sCD22 (3.3 vs 1.7, P < 0.001) and Lp-PLA2/sCD22 ratios (8.0 vs 5.2, P < 0.05) between the groups. Receiver operating characteristic analysis confirmed high sensitivity and specificity of these markers and their ratios in distinguishing AD, with area under the curves ranging from 0.568 to 0.787.

CONCLUSION

Serum CXCL12 and Lp-PLA2 levels were significantly increased, while sCD22 were significantly decreased, as well as increases in the ratios of CXCL12/sCD22 and Lp-PLA2/sCD22, are closely related to the onset of AD. These biomarkers and their ratios can be used as potential diagnostic indicators for AD, providing an important clinical reference for early intervention and treatment.

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

BACKGROUND AND OBJECTIVES

Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes.

RESULTS

The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases.

CONCLUSION

MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.

The Relationships between Cerebrospinal Fluid Glial (CXCL12, CX3CL, YKL-40) and Synaptic Biomarkers (Ng, NPTXR) in Early Alzheimer's Disease

In addition to amyloid and tau pathology in the central nervous system (CNS), inflammatory processes and synaptic dysfunction are highly important mechanisms involved in the development and progression of dementia diseases. In the present study, we conducted a comparative analysis of selected pro-inflammatory proteins in the CNS with proteins reflecting synaptic damage and core biomarkers in mild cognitive impairment (MCI) and early Alzheimer's disease (AD). To our knowledge, no studies have yet compared CXCL12 and CX3CL1 with markers of synaptic disturbance in cerebrospinal fluid (CSF) in the early stages of dementia. The quantitative assessment of selected proteins in the CSF of patients with MCI, AD, and non-demented controls (CTRL) was performed using immunoassays (single- and multiplex techniques). In this study, increased CSF concentration of CX3CL1 in MCI and AD patients correlated positively with neurogranin (r = 0.74; p < 0.001, and r = 0.40; p = 0.020, respectively), ptau181 (r = 0.49; p = 0.040), and YKL-40 (r = 0.47; p = 0.050) in MCI subjects. In addition, elevated CSF levels of CXCL12 in the AD group were significantly associated with mini-mental state examination score (r = -0.32; p = 0.040). We found significant evidence to support an association between CX3CL1 and neurogranin, already in the early stages of cognitive decline. Furthermore, our findings indicate that CXCL12 might be a useful marker for tract severity of cognitive impairment.

Common molecular signatures between coronavirus infection and Alzheimer's disease reveal targets for drug development

Cognitive decline has been reported as a common consequence of COVID-19, and studies have suggested a link between COVID-19 infection and Alzheimer's disease (AD). However, the molecular mechanisms underlying this association remain unclear. To shed light on this link, we conducted an integrated genomic analysis using a novel Robust Rank Aggregation method to identify common transcriptional signatures of the frontal cortex, a critical area for cognitive function, between individuals with AD and COVID-19. We then performed various analyses, including the KEGG pathway, GO ontology, protein-protein interaction, hub gene, gene-miRNA, and gene-transcription factor interaction analyses to identify molecular components of biological pathways that are associated with AD in the brain also show similar changes in severe COVID-19. Our findings revealed the molecular mechanisms underpinning the association between COVID-19 infection and AD development and identified several genes, miRNAs, and TFs that may be targeted for therapeutic purposes. However, further research is needed to investigate the diagnostic and therapeutic applications of these findings.

Preliminary study to identify CXCR4 inhibitors as potential therapeutic agents for Alzheimer's and Parkinson's diseases

Neurodegenerative disorders (NDDs) are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information. The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). A total of 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction networks of the DEGs were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the four common hub genes between two datasets, C-X-C chemokine receptor type 4 (CXCR4) was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor was performed to find two novel molecules, ZINC49067615 and ZINC103242147. This preliminary study might help predict molecular targets and diagnostic markers for treating Alzheimer's and Parkinson's diseases. Insight Box Our research substantiates the therapeutic relevance of CXCR4 inhibitors for the treatment of Alzheimer's and Parkinson's diseases. We would like to disclose the following insights about this study. We found common signatures between Alzheimer's and Parkinson's diseases at transcriptional levels by analyzing mRNA sequencing data. These signatures were used to identify putative therapeutic agents for these diseases through computational analysis. Thus, we proposed two novel compounds, ZINC49067615 and ZINC103242147, that were stable, showed a strong affinity with CXCR4, and exhibited good pharmacokinetic properties. The interaction of these compounds with major residues of CXCR4 has also been described.

Common Molecular Signatures Between Coronavirus Infection and Alzheimer's Disease Reveal Targets for Drug Development

BACKGROUND

Cognitive decline is a common consequence of COVID-19, and studies suggest a link between COVID-19 and Alzheimer's disease (AD). However, the molecular mechanisms underlying this association remain unclear.

OBJECTIVE

To understand the potential molecular mechanisms underlying the association between COVID-19 and AD development, and identify the potential genetic targets for pharmaceutical approaches to reduce the risk or delay the development of COVID-19-related neurological pathologies.

METHODS

We analyzed transcriptome datasets of 638 brain samples using a novel Robust Rank Aggregation method, followed by functional enrichment, protein-protein, hub genes, gene-miRNA, and gene-transcription factor (TF) interaction analyses to identify molecular markers altered in AD and COVID-19 infected brains.

RESULTS

Our analyses of frontal cortex from COVID-19 and AD patients identified commonly altered genes, miRNAs and TFs. Functional enrichment and hub gene analysis of these molecular changes revealed commonly altered pathways, including downregulation of the cyclic adenosine monophosphate (cAMP) signaling and taurine and hypotaurine metabolism, alongside upregulation of neuroinflammatory pathways. Furthermore, gene-miRNA and gene-TF network analyses provided potential up- and downstream regulators of identified pathways.

CONCLUSION

We found that downregulation of cAMP signaling pathway, taurine metabolisms, and upregulation of neuroinflammatory related pathways are commonly altered in AD and COVID-19 pathogenesis, and may make COVID-19 patients more susceptible to cognitive decline and AD. We also identified genetic targets, regulating these pathways that can be targeted pharmaceutically to reduce the risk or delay the development of COVID-19-related neurological pathologies and AD.

Identification of immune microenvironment subtypes and signature genes for Alzheimer's disease diagnosis and risk prediction based on explainable machine learning

Background

Using interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.

Methods

ssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.

Results

It was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.

Conclusion

The hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.

Bone Marrow Mesenchymal Stem Cell (BMSC) Restrains the Angiogenesis in Melanoma Through Stromal-Derived-Factor-1/C-X-C Chemokine Receptor Type 4 (SDF-1/CXCR4)

This study analyzes the effect of BMSC on restraining the angiogenesis in melanoma through inducing SDF-1/CXCR4 channel. 50 female naked rates were equally assigned into NC group, model group, BMSC group, agonist group and positive NC group randomly followed by analysis of pathological changes, and the level of HIF-1, VEG, MVD, SDF-1 and CXCR4. Agonist group showed the highest level of HIF-1α and VEGF and MVD followed by, model group BMSC group, positive NC group and NC group with no different between BMSC group and positive NC group. SDF-1 and CXCR4 expression was highest in agonist group, followed by that in model group, positive NC group, BMSC group and NC group without difference between model group and positive NC group. In conclusion, SDF-1/CXCR4 activity could be restrained by BMSC partly along with reduced level of HIF-1α and VEGF. This is mainly related with restraining the SDF-1/CXCR4 channel, indicating that it could be adopted as a brand-new therapeutic target for treating melanoma.