Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing

Elucidation of the effects of 2,5-hexandione as a metabolite of n-hexane on cognitive impairment in leptin-knockout mice (C57BL/6-Lepem1Shwl/Korl)

Exposure to n-hexane and its metabolite 2,5-hexandione (HD) is a well-known cause of neurotoxicity, particularly in the peripheral nervous system. To date, few studies have focused on the neurotoxic effects of HD on cognitive impairment. Exposure to HD and diabetes mellitus can exacerbate neurotoxicity. There are links among HD, diabetes mellitus, and cognitive impairment; however, the specific mechanisms underlying them remain unclear. Therefore, we aimed to elucidate the neurotoxic effects of HD on cognitive impairment in ob/ob (C57BL/6-Lepem1Shwl/Korl) mice. We found that HD induced cognitive impairment by altering the expression of genes (FN1, AGT, ACTA2, MYH11, MKI67, MET, CTGF, and CD44), miRNAs (mmu-miR15a-5p, mmu-miR-17-5p, and mmu-miR-29a-3p), transcription factors (transcription factor AP-2 alpha [TFAP2A], serum response factor [Srf], and paired box gene 4 [PAX4]), and signaling pathways (ERK/CERB, PI3K/AKT, GSK-3β/p-tau/amyloid-β), as well as by causing neuroinflammation (TREM1/DAP12/NF-κB), oxidative stress, and apoptosis. The prevalent use of n-hexane in various industrial applications (for instance, shoe manufacturing, printing inks, paints, and varnishes) suggests that individuals with elevated body weight and glucose levels and those employed in high-risk workplaces have greater probability of cognitive impairment. Therefore, implementing screening strategies for HD-induced cognitive dysfunction is crucial.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s43188-024-00228-1.

Humoral immune disorders affect clinical outcomes of oral lichen planus

OBJECTIVES

The molecular characteristics of oral lichen planus (OLP) are still unclear, and it is not possible to distinguish the clinical outcome of OLP patients in a short period of time for follow-up. Here, we investigate the molecular characteristics of lesions in patients with stable lichen planus (SOLP) and recalcitrant erosive oral lichen planus (REOLP).

METHODS

Our clinical follow-up cohort was split into SOLP and REOLP groups based on the follow-up clinical data. The core modules associated with the clinical information were identified by weighted gene co-expression network analysis (WGCNA). The OLP cohort samples were divided into two groups by molecular typing, and a prediction model for OLP was created by training neural networks with the neuralnet package.

RESULTS

We screened 546 genes in five modules. After doing a molecular type of OLP, it was determined that B cells might have a significant impact on the clinical outcome of OLP. In addition, by means of machine learning, a prediction model was developed to predict the clinical regression of OLP with greater accuracy than the existing clinical diagnostic.

CONCLUSIONS

Our study revealed humoral immune disorders may make an important contribution to the clinical outcome of OLP.

Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing

Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (n S1 = 53, n S2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions (p Bonf = 5.9 × 10-3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain.

Astragaloside IV: A promising natural neuroprotective agent for neurological disorders

Neurological disorders are characterized by high morbidity, disability, and mortality rates, which seriously threaten human health. However, clinically satisfactory agents for treatment are still currently lacking. Therefore, finding neuroprotective agents with minimum side effects and better efficacy is a challenge. Chinese herbal medicine, particularly natural preparations extracted from herbs or plants, has become an unparalleled resource for discovering new agent candidates. Astragali Radix is an important Qi tonic drug in traditional Chinese medicine and has a long medicinal history. As a natural medicine, it has a good prevention and treatment effect on neurological disorders. Here, the role and mechanism of astragaloside IV in the treatment of neurological disorders were evaluated and discussed through previous research results. Related information from major scientific databases, such as PubMed, MEDLINE, Web of Science, ScienceDirect, Embase, BIOSIS Previews, and the Cochrane Central Register of Controlled Trials and Cochrane Library, covering between 2001 and 2021 was compiled, using "Astragaloside IV" and "Neurological disorders," "Astragaloside IV," and "Neurodegenerative diseases" as reference terms. By summarizing previous research results, we found that astragaloside IV may play a neuroprotective role through various mechanisms: anti-inflammatory, anti-oxidative, anti-apoptotic protection of nerve cells and regulation of nerve growth factor, as well as by inhibiting neurodegeneration and promoting nerve regeneration. Astragaloside IV is a promising natural neuroprotective agent. By determining its pharmacological mechanism, astragaloside IV may be a new candidate drug for the treatment of neurological disorders.

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models

Background

Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.

Methods

By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.

Results

Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.

Conclusion

The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

Potential biomarkers of Alzheimer’s disease and cerebral small vessel disease

Background

Cerebral small vessel disease (CSVD) is associated with the pathogenesis of Alzheimer’s disease (AD). Effective treatments to alleviate AD are still not currently available. Hence, we explored markers and underlying molecular mechanisms associated with AD by utilizing gene expression profiles of AD and CSVD patients from public databases, providing more options for early diagnosis and its treatment.

Methods

Gene expression profiles were collected from GSE63060 (for AD) and GSE162790 (for CSVD). Differential analysis was performed between AD and mild cognitive impairment (MCI) or CSVD progression and CSVD no-progression. In both datasets, differentially expressed genes (DEGs) with the same expression direction were identified as common DEGs. Then protein-protein interaction (PPI) network was constructed for common DEGs. Differential immune cells and checkpoints were calculated between AD and MCI.

Results

A total of 146 common DEGs were identified. Common DEGs were mainly enriched in endocytosis and oxytocin signaling pathways. Interestingly, endocytosis and metabolic pathways were shown both from MCI to AD and from CSVD no-progression to CSVD progression. Moreover, SIRT1 was identified as a key gene by ranking degree of connectivity in the PPI network. SIRT1 was associated with obesity-related genes and metabolic disorders. Additionally, SIRT1 showed correlations with CD8 T cells, NK CD56 bright cells, and checkpoints in AD.

Conclusion

The study revealed that the progression of AD is associated with abnormalities in gene expression and metabolism and that the SIRT1 gene may serve as a promising therapeutic target for the treatment of AD.

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Neurodegenerative diseases such as Alzheimer’s disease (AD) are an increasing public health challenge. There is an urgent need to shift the focus to accurate detection of clinical AD at the physical examination stage. The purpose of this study was to identify biomarkers for AD diagnosis. Differential expression analysis was performed on a dataset including prefrontal cortical samples and peripheral blood samples of AD to identify shared differentially expressed genes (DEGs) shared between the two datasets. In addition, a minimum absolute contraction and selection operator (LASSO) model based on shared-DEGs identified nine signature genes (MT1X, IGF1, DLEU7, TRIM36, PTPRC, WNK2, SPG20, C8orf59, and BRWD1) that accurately predict AD occurrence. Enrichment analysis showed that the signature gene was significantly associated with the AD-related p53 signaling pathway, T-cell receptor signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Thus, our results identify not only biomarkers for diagnosing AD but also potentially specific pathways. The AD biomarkers proposed in this study could serve as indicators for prevention and diagnosis during physical examination.

REPS1 as a Potential Biomarker in Alzheimer’s Disease and Vascular Dementia

Vascular dementia (VD) and Alzheimer’s disease (AD) are common types of dementia for which no curative therapies are known. In this study, we identified hub genes associated with AD and VD in order to explore new potential therapeutic targets. Genes differentially expressed in VD and AD in all three datasets (GSE122063, GSE132903, and GSE5281) were identified and used to construct a protein–protein interaction network. We identified 10 modules containing 427 module genes in AD and VD. Module genes showing an area under the diagnostic curve > 0.60 for AD or VD were used to construct a least absolute shrinkage and selection operator model and were entered into a support vector machine-recursive feature elimination algorithm, which identified REPS1 as a hub gene in AD and VD. Furthermore, REPS1 was associated with activation of pyruvate metabolism and inhibition of Ras signaling pathway. Module genes, together with differentially expressed microRNAs from the dataset GSE46579, were used to construct a regulatory network. REPS1 was predicted to bind to the microRNA hsa_miR_5701. Single-sample gene set enrichment analysis was used to explore immune cell infiltration, which suggested a negative correlation between REPS1 expression and infiltration by plasmacytoid dendritic cells in AD and VD. In conclusion, our results suggest core pathways involved in both AD and VD, and they identify REPS1 as a potential biomarker of both diseases. This protein may aid in early diagnosis, monitoring of treatment response, and even efforts to prevent these debilitating disorders.

Identification of molecular signatures associated with sleep disorder and Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.

METHODS

Differentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.

RESULTS

DEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.

CONCLUSION

AD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.