Linking Diabetes Mellitus with Alzheimer's Disease: Bioinformatics Analysis for the Potential Pathways and Characteristic Genes

From Genes to Metabolites: HSP90B1's Role in Alzheimer's Disease and Potential for Therapeutic Intervention

Alzheimer's disease (AD) is a prototypical neurodegenerative disorder, predominantly affecting individuals in the presenile and elderly populations, with an etiology that remains elusive. This investigation aimed to elucidate the alterations in anoikis-related genes (ARGs) in the AD brain, thereby expanding the repertoire of biomarkers for the disease. Using publically available gene expression data for the hippocampus from both healthy and AD subjects, differentially expressed genes (DEGs) were identified. Subsequent intersection with a comprehensive list of 575 ARGs yielded a subset for enrichment analysis. Machine learning algorithms were employed to identify potential biomarker, which was validated in an AD animal model. Additionally, gene set enrichment analysis was conducted on the biomarker and its interacting genes and microRNAs were predicted through online databases. To assess its biological functions, the expression of the marker was suppressed in an in vitro model to examine cell viability and inflammation-related indicators. Furthermore, following treatment with the inhibitor, the dysregulated metabolites in the hippocampus of the model mice were evaluated. Forty-seven ARGs were ultimately identified, with HSP90B1 emerging as a central marker. HSP90B1 was found to be significantly up-regulated in AD hippocampal samples and its inhibition conferred increased cell viability and reduced levels of inflammatory factors in amyloid β-protein (Aβ)-treated cells. A total of 24 differentially expressed metabolites were confidently identified between model mice and those with low HSP90B1 expression, with bioinformatics analysis shedding light on the molecular underpinnings of HSP90B1's involvement in AD. Collectively, these findings may inform novel insights into the pathogenesis, mechanisms, or therapeutic strategies for AD.

Comparative Analysis of Human Brain RNA-seq Reveals the Combined Effects of Ferroptosis and Autophagy on Alzheimer's Disease in Multiple Brain Regions

Ferroptosis and autophagy are closely associated with Alzheimer's disease (AD). Elevated ferric ion levels can induce oxidative stress and chronic inflammatory responses, resulting in brain tissue damage and further neurological cell damage. Autophagy in Alzheimer's has a dual role. On one hand, it protects neurons by removing β-amyloid and cellular damage products caused by oxidative stress and inflammation. On the other hand, abnormal autophagy is linked to neuronal apoptosis and neurodegeneration. However, the intricate interplay between ferroptosis and autophagy in AD remains insufficiently explored. This study focuses on the roles of ferroptosis and autophagy in AD and their interconnection through bioinformatics analysis, shedding light on the disease. Ferroptosis and autophagy significantly correlate with the development and course of AD. Using PPI network analysis and unsupervised consistency clustering analysis, we uncovered a complex network of interactions between ferroptosis and autophagy during disease progression, demonstrating a significant congruence in their modification patterns. Functional analyses further demonstrated that ferroptosis and autophagy together affect the immunological status and synaptic regulation in hippocampal regions in patients with AD, which significantly impacts the start and progression of the disease.

Cross-disease modeling of peripheral blood identifies biomarkers of type 2 diabetes predictive of Alzheimer's disease

Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD). Despite multiple studies reporting this connection, the mechanism by which T2D exacerbates AD is poorly understood. It is challenging to design studies that address co-occurring and comorbid diseases, limiting the number of existing evidence bases. To address this challenge, we expanded the applications of a computational framework called Translatable Components Regression (TransComp-R), initially designed for cross-species translation modeling, to perform cross-disease modeling to identify biological programs of T2D that may exacerbate AD pathology. Using TransComp-R, we combined peripheral blood-derived T2D and AD human transcriptomic data to identify T2D principal components predictive of AD status. Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD. The same T2D PC predictive of AD outcomes unveiled sex-based differences across the AD datasets. We performed a gene expression correlational analysis to identify therapeutic hypotheses tailored to the T2D-AD axis. We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state. Finally, we assessed our blood-based T2DxAD biomarker signature in post-mortem human AD and control brain gene expression data from the hippocampus, entorhinal cortex, superior frontal gyrus, and postcentral gyrus. Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples. Our methodological advance in cross-disease modeling identified biological programs in T2D that may predict the future onset of AD in this population. This, paired with our therapeutic gene expression correlational analysis, also revealed alogliptin, a T2D medication that may help prevent the onset of AD in T2D patients.

Exacerbated ischemic brain damage in type 2 diabetes via methylglyoxal-mediated miR-148a-3p decline

BACKGROUND

Although microvascular dysfunction is a widespread phenomenon in type 2 diabetes (T2D) and is recognized as a main cause of T2D-aggravated ischemic stroke injury, the underlying mechanisms by which T2D-mediated exacerbation of cerebral damage after ischemic stroke is still largely uncharacterized. Here, we found that methylglyoxal-mediated miR-148a-3p decline can trigger blood-brain barrier dysfunction, thereby exacerbating cerebrovascular injury in diabetic stroke.

METHODS

Using T2D models generated with streptozotocin plus a high-fat diet or db/db mice, and then inducing focal ischemic stroke through middle cerebral artery occlusion and reperfusion (MCAO/R), we established a diabetic stroke mouse model. RNA-sequencing was applied to identify the differentially expressed miRNAs in peri-cerebral infarction of diabetic stroke mice. RT-qPCR confirmed the potential miRNA in the plasma of ischemic stroke patients with or without T2D. Fluorescence in situ hybridization was used to image the localization of the miRNA. Brain pathology was analyzed using magnetic resonance imaging, laser-Doppler flowmetry, and transmission electron microscope in diabetic stroke mice. Immunofluorescence and immunoblotting were performed to elucidate the molecular mechanisms.

RESULTS

miR-148a-3p level was downregulated in the peri-infarct cortex of stroke mice and this downregulation was even more enhanced in diabetic stroke mice. A similar decrease in miR-148a-3p expression was also confirmed in the plasma of ischemic stroke patients with T2D compared to patients with ischemic stroke only. This miR-148a-3p downregulation intensified the severity of BBB damage, infarct size, and neurological function impairment caused by stroke. Notably, the reduction in miR-148a-3p levels was primarily triggered by methylglyoxal, a toxic byproduct of glucose metabolism commonly associated with T2D. Furthermore, methylglyoxal somewhat replicated the influence of T2D in exacerbating BBB damage and increasing infarct size caused by ischemia. Mechanistically, we found that downregulation of miR-148a-3p de-repressed SMAD2 and activated matrix metalloproteinase 9 signaling pathway, promoting blood-brain barrier impairment, and exacerbating the cerebral ischemic injury.

CONCLUSIONS

Blood-brain barrier damage caused by methylglyoxal-mediated miR-148a-3p downregulation may provide a novel target for the therapeutic intervention for the treatment of stroke patients with diabetes.

Upregulation of C1QC as a Mediator of Blood-Brain Barrier Damage in Type 2 Diabetes Mellitus

The blood-brain barrier (BBB) is a neurovascular structure that safeguards the brain by inhibiting the passage of harmful substances. In individuals with type 2 diabetes mellitus (T2DM), the heightened blood glucose may cause damage to endothelial cells and neurons, increase collagen protein content, and elevate BBB permeability. Although the impact of blood glucose regulation on the structure and function of BBB has been documented, the exact mechanism remains incompletely elucidated. The primary aim of this investigation was to uncover the pivotal dysregulation of specific genes observed within the cerebral microvascular endothelial cells of diabetic patients, with a particular focus on understanding its biological implications in the disruption of the BBB. By integrating bioinformatics analysis, we identified C1QC as a potential upregulated marker. The expression level of C1QC was subsequently verified in both in vivo and in vitro models. Our experiments have discovered that, under diabetic conditions, suppressing C1QC leads to the mitigation of BBB damage. The presence of a high level of C1QC, through its binding to discoidin domain receptor 2 (DDR2), may trigger the activation of its downstream MMP9, a calcium-dependent enzyme that is capable of degrading protein components in the extracellular matrix, consequently leading to the structural and functional disruption of BBB. In summary, the findings of this study indicate that the aberrantly upregulated expression of C1QC may exert deleterious effects on the BBB under diabetes. To alleviate neurological impairments in individuals with T2DM, C1QC may emerge as a promising therapeutic target worthy of further investigation.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND

The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease.

AIM

To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment.

METHODS

To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro. To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD).

RESULTS

A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24, Rac3, and Tfrc). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro, consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD.

CONCLUSION

Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro, consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC. These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Association of FHL5 and LPA genetic polymorphisms with diabetes mellitus risk: a case-control study

BACKGROUND

China is one of the countries with the fastest growing prevalence of diabetes mellitus (DM) in the world. This study intended to investigate the association of single nucleotide polymorphisms (SNPs) of FHL5 and LPA with DM risk in the Chinese population.

METHODS

This case-control study involved 1,420 Chinese individuals (710 DM patients and 710 controls). Four candidate loci (rs2252816/rs9373985 in FHL5 and rs3124784/rs7765781 in LPA) were successfully screened. The association of SNPs with DM risk was assessed by logistic regression analysis. Differences in clinical characteristics among subjects with different genotypes were analyzed by one-way analysis of variance.

RESULTS

Overall analysis indicated that rs3124784 was associated with an increased risk of DM. Stratification analysis showed that rs3124784 significantly increased DM risk in different subgroups (male, non-smoking, non-drinking, and BMI > 24), while rs7765781 increased DM risk only in participants with BMI ≤ 24. Rs2252816 was associated with the course of DM. We also found that rs2252816 GG genotype and rs9373985 GG genotype were linked to the increased cystatin c in DM patients.

CONCLUSION

The genetic polymorphisms of LPA may be associated with DM risk in the Chinese population, which will provide useful information for the prevention and diagnosis of DM.

Metabolic Profile of Alzheimer's Disease: Is 10-Hydroxy-2-decenoic Acid a Pertinent Metabolic Adjuster?

Alzheimer's disease (AD) represents a significant public health concern in modern society. Metabolic syndrome (MetS), which includes diabetes mellitus (DM) and obesity, represents a modifiable risk factor for AD. MetS and AD are interconnected through various mechanisms, such as mitochondrial dysfunction, oxidative stress, insulin resistance (IR), vascular impairment, inflammation, and endoplasmic reticulum (ER) stress. Therefore, it is necessary to seek a multi-targeted and safer approach to intervention. Thus, 10-hydroxy-2-decenoic acid (10-HDA), a unique hydroxy fatty acid in royal jelly, has shown promising anti-neuroinflammatory, blood-brain barrier (BBB)-preserving, and neurogenesis-promoting properties. In this paper, we provide a summary of the relationship between MetS and AD, together with an introduction to 10-HDA as a potential intervention nutrient. In addition, molecular docking is performed to explore the metabolic tuning properties of 10-HDA with associated macromolecules such as GLP-1R, PPARs, GSK-3, and TREM2. In conclusion, there is a close relationship between AD and MetS, and 10-HDA shows potential as a beneficial nutritional intervention for both AD and MetS.

Exploring novel derivatives of isatin-based Schiff bases as multi-target agents: design, synthesis, in vitro biological evaluation, and in silico ADMET analysis with molecular modeling simulations

Recently, scientists developed a powerful strategy called "one drug-multiple targets" to discover vital and unique therapies to fight the most challenging diseases. Novel derivatives of isatin-based Schiff bases 2-7 have been synthesized by the reaction of 3-hydrazino-isatin (1) with aryl aldehydes, hetero-aryl aldehydes, and dialdehydes. The structure of the synthesized derivatives was proved by physical and spectral analysis. Additionally, in vitro biological studies were performed, including antioxidant, anti-diabetic, anti-Alzheimer, and anti-arthritic activities. The four derivatives 3b, 5a, 5b, and 5c possess the highest activities. Among the four potent derivatives, compound 5a exhibited the highest antioxidant (TAC = 68.02 ± 0.15 mg gallic acid per g; IRP = 50.39 ± 0.11) and scavenging activities (ABTS = 53.98 ± 0.12% and DPPH = 8.65 ± 0.02 μg mL-1). Furthermore, compound 5a exhibited an α-amylase inhibitory percentage of 57.64 ± 0.13% near the acarbose (ACA = 69.11 ± 0.15%) and displayed inhibitor activity of the acetylcholinesterase (AChE) enzyme = 36.38 ± 0.08%. Moreover, our work extended to determining the anti-arthritic effect, and compound 5a revealed good inhibitor activities with very close values for proteinase denaturation (PDI) = 39.59 ± 0.09% and proteinase inhibition (PI) = 36.39 ± 0.08%, compared to diclofenac sodium PDI = 49.33 ± 0.11% and PI = 41.88 ± 0.09%. Additionally, the quantum chemical calculations, including HOMO, LUMO, and energy band gap were determined, and in silico ADMET properties were predicted, and their probability was recorded. Finally, molecular docking simulations were performed inside α-amylase and acetylcholinesterase enzymes.

Differential expression of pyroptosis-related genes in the hippocampus of patients with Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder with insidious onset. Some scholars believe that there is a close relationship between pyroptosis and AD. However, studies with evidence supporting this relationship are lacking.

MATERIALS AND METHODS

The microarray data of AD were retrieved from the Gene Expression Omnibus (GEO) database with the datasets merged using the R package inSilicoMerging. R software package Limma was used to perform the differential expression analysis to identify the differentially expressed genes (DEGs). We further performed the enrichment analyses of the DEGs based on Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to identify the metabolic pathways with a significant difference. The Gene Set Enrichment Analysis (GSEA) was applied to identify the significant pathways. The protein-protein interaction (PPI) network was constructed based on the STRING database with the hub genes identified. Quantitative real-time PCR (qRT-PCR) analyses based on HT22 cells were performed to validate the findings based on the microarray analysis. Gene expression correlation heatmaps were generated to evaluate the relationships among the genes.

RESULTS

A new dataset was derived by merging 4 microarray datasets in the hippocampus of AD patients in the GEO database. Differential gene expression analysis yielded a volcano plot of a total of 20 DEGs (14 up-regulated and 6 down-regulated). GO analysis revealed a group of GO terms with a significant difference, e.g., cytoplasmic vesicle membrane, vesicle membrane, and monocyte chemotaxis. KEGG analysis detected the metabolic pathways with a significant difference, e.g., Rheumatoid arthritis and Fluid shear stress and atherosclerosis. The results of the Gene Set Enrichment Analysis of the microarray data showed that gene set ALZHEIMER_DISEASE and the gene set PYROPTOSIS were both up-regulated. PPI network showed that pyroptosis-related genes were divided into two groups. In the Aβ-induced HT22 cell model, three genes (i.e., BAX, IL18, and CYCS) were revealed with significant differences. Gene expression correlation heatmaps revealed strong correlations between pyroptotic genes and AD-related genes.

CONCLUSION

The pyroptosis-related genes BAX, IL18, and CYCS were significantly different between AD patients and normal controls.

Identification of ferroptosis related markers by integrated bioinformatics analysis and In vitro model experiments in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is an autoimmune disease characterized by destructive and symmetrical joint diseases and synovitis. This research attempted to explore the mechanisms involving ferroptosis in RA, and find the biological markers by integrated analysis.

METHODS

Gene expression data (GSE55235 and GSE55457) of synovial tissues from healthy and RA individuals were downloaded. By filtering the differentially expressed genes (DEGs) and intersecting them with the 484 ferroptosis-related genes (FRGs), the overlapping genes were identified. After the enrichment analysis, the machine learning-based approaches were introduced to screen the potential biomarkers, which were further validated in other two datasets (GSE77298 and GSE93272) and cell samples. Besides, we also analyze the infiltrating immune cells in RA and their correlation with the biomarkers.

RESULTS

With the criteria, 635 DEGs in RA were included, and 29 of them overlapped in the reported 484 FRGs. The enrichments of the 29 differentially expressed ferroptosis-related genes indicated that they may involve in the FoxO signaling pathway and inherited metabolic disorder. RRM2, validating by the external datasets and western blot, were identified as the biomarker with the high diagnostic value, whose associated immune cells, such as Neutrophils and Macrophages M1, were also further evaluated.

CONCLUSION

We preliminary explored the mechanisms between ferroptosis and RA. These results may help us better comprehend the pathophysiological changes of RA in basic research, and provide new evidences for the clinical transformation.

Diabetes and dementia: Clinical perspective, innovation, knowledge gaps

The world faces a pandemic-level prevalence of type 2 diabetes. In parallel with this massive burden of metabolic disease is the growing prevalence of dementia as the population ages. The two health issues are intertwined. The Lancet Commission on dementia prevention, intervention, and care was convened to tackle the growing global concern of dementia by identifying risk factors. It concluded, along with other studies, that diabetes as well as obesity and the metabolic syndrome more broadly, which are frequently comorbid, raise the risk of developing dementia. Type 2 diabetes is a modifiable risk factor; however, it is uncertain whether anti-diabetic drugs mitigate risk of developing dementia. Reasons are manifold but constitute a critical knowledge gap in the field. This review outlines studies of type 2 diabetes on risk of dementia, illustrating key concepts. Moreover, it identifies knowledge gaps, reviews strategies to help fill these gaps, and concludes with a series of recommendations to mitigate risk and advance understanding of type 2 diabetes and dementia.

Identification of Biomarkers in Intracranial Aneurysm and Their Immune Infiltration Characteristics

BACKGROUND

Intracranial aneurysm (IA), known as the intracranial "unscheduled bomb," is one of the most dangerous cerebrovascular diseases, with unclear pathogenesis. This study aimed to show the mechanisms and identify the new biological targets by applying bioinformatics analysis.

METHODS

Expression profiling for control superficial temporal artery and IA walls in GSE26969 and GSE75436 datasets were downloaded. By executing the LIMMA package in R software, the differentially expressed genes (DEGs) were filtered, and the functional enrichments were consequently performed. Further cross-linking with the 2483 immune-related genes (IRGs) from the ImmPort database, the differentially expressed IRGs were identified. Based on them, the least absolute shrinkage and selection operator logistic regression and support vector machine-recursive feature elimination algorithms were used to screen the biomarkers, which were validated in the GSE54083 datasets. The CIBERSORT algorithm was applied to evaluate the infiltration of immune cells in tissues.

RESULTS

A total of 668 DEGs were obtained, and the functional enrichment suggested that they were closely related to the immune process. After intersecting them with the IRGs, 90 differentially expressed IRGs emerged, and ADIPOQ and ESM1 were identified as the biomarkers. Besides, we found that the infiltrated immune cells, such as the mast cells resting, might be associated with them.

CONCLUSIONS

We explored the contributing factors involving IA, which may generate a better understanding of the complex interactions among them and inspire a promising strategy for clinical works.

Identification and Experimental Validation of Marker Genes between Diabetes and Alzheimer’s Disease

Currently, Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are widely prevalent in the elderly population, and accumulating evidence implies a strong link between them. For example, patients with T2DM have a higher risk of developing neurocognitive disorders, including AD, but the exact mechanisms are still unclear. This time, by combining bioinformatics analysis and in vivo experimental validation, we attempted to find a common biological link between AD and T2DM. We firstly downloaded the gene expression profiling (AD: GSE122063; T2DM: GSE161355) derived from the temporal cortex. To find the associations, differentially expressed genes (DEGs) of the two datasets were filtered and intersected. Based on them, enrichment analysis was carried out, and the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify the specific genes. After verifying in the external dataset and in the samples from the AD and type 2 diabetes animals, the shared targets of the two diseases were finally determined. Based on them, the ceRNA networks were constructed. Besides, the logistic regression and single-sample gene set enrichment analysis (ssGSEA) were performed. As a result, 62 DEGs were totally identified between AD and T2DM, and the enrichment analysis indicated that they were much related to the function of synaptic vesicle and MAPK signaling pathway. Based on the evidence from external dataset and RT-qPCR, CARTPT, EPHA5, and SERPINA3 were identified as the marker genes in both diseases, and their clinical significance and biological functions were further analyzed. In conclusion, discovering and exploring the marker genes that are dysregulated in both 2 diseases could help us better comprehend the intrinsic relationship between T2DM and AD, which may inspire us to develop new strategies for facing the dilemmas of clinical or basic research in cognitive dysfunction.