A Bioinformatics Approach Toward Unravelling the Synaptic Molecular Crosstalk Between Alzheimer’s Disease and Diabetes

ZiBu PiYin Recipe Regulates Central and Peripheral Aβ Metabolism and Improves Diabetes-associated Cognitive Decline in ZDF Rats

ETHNOPHARMACOLOGICAL RELEVANCE

Cognitive impairment caused by central neuropathy in type 2 diabetes mellitus (T2DM), namely diabetes-associated cognitive decline (DACD), is one of the common complications in patients with T2DM. Studies have shown that brain β-amyloid (Aβ) deposition is a typical pathological change in patients with DACD, and that there is a close relationship between intestinal microorganisms and cognitive impairment. However, the specific mechanism(s) of alteration in Aβ metabolism in DACD, and of the correlation between Aβ metabolism and intestinal microorganisms remain unknown.

AIM OF THE STUDY

Revealing the mechanism of ZBPYR regulating Aβ metabolism and providing theoretical basis for clinical evaluation and diagnosis of DACD.

MATERIALS AND METHODS

We characterized Aβ metabolism in the central and peripheral tissues of Zucker diabetic fatty (ZDF) rats with DACD, and then explored the preventive and therapeutic effects of ZiBu PiYin Recipe (ZBPYR). Specifically, we assessed these animals for the formation, transport, and clearance of Aβ; the morphological structure of the blood-brain barrier (BBB); and the potential correlation between Aβ metabolism and intestinal microorganisms.

RESULTS

ZBPYR provided improvements in the structure of the BBB, attenuation of Aβ deposition in the central and peripheral tissues, and a delay in the development of DACD by improving the expression of Aβ production, transport, and clearance related protein in ZDF rats. In addition, ZBPYR improved the diversity and composition of intestinal microorganisms, decreased the abundance of Coprococcus, a bacterium closely related to Aβ production, and up regulate the abundance of Streptococcus, a bacterium closely related to Aβ clearance.

CONCLUSION

The mechanism of ZBPYR ability to ameliorate DACD may be closely related to changes in the intestinal microbiome.

Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson's disease, and periodontitis using integrated bioinformatics analyses

Objective

To identify the genetic linkage mechanisms underlying Parkinson's disease (PD) and periodontitis, and explore the role of immunology in the crosstalk between both these diseases.

Methods

The gene expression omnibus (GEO) datasets associated with whole blood tissue of PD patients and gingival tissue of periodontitis patients were obtained. Then, differential expression analysis was performed to identify the differentially expressed genes (DEGs) deregulated in both diseases, which were defined as crosstalk genes. Inflammatory response-related genes (IRRGs) were downloaded from the MSigDB database and used for dividing case samples of both diseases into different clusters using k-means cluster analysis. Feature selection was performed using the LASSO model. Thus, the hub crosstalk genes were identified. Next, the crosstalk IRRGs were selected and Pearson correlation coefficient analysis was applied to investigate the correlation between hub crosstalk genes and hub IRRGs. Additionally, immune infiltration analysis was performed to examine the enrichment of immune cells in both diseases. The correlation between hub crosstalk genes and highly enriched immune cells was also investigated.

Results

Overall, 37 crosstalk genes were found to be overlapping between the PD-associated DEGs and periodontitis-associated DEGs. Using clustering analysis, the most optimal clustering effects were obtained for periodontitis and PD when k = 2 and k = 3, respectively. Using the LASSO feature selection, five hub crosstalk genes, namely, FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1, were identified. In periodontitis, MANSC1 was negatively correlated and the other four hub crosstalk genes (FMNL1, PLAUR, RNASE6, and TCIRG1) were positively correlated with five hub IRRGs, namely, AQP9, C5AR1, CD14, CSF3R, and PLAUR. In PD, all five hub crosstalk genes were positively correlated with all five hub IRRGs. Additionally, RNASE6 was highly correlated with myeloid-derived suppressor cells (MDSCs) in periodontitis, and MANSC1 was highly correlated with plasmacytoid dendritic cells in PD.

Conclusion

Five genes (i.e., FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1) were identified as crosstalk biomarkers linking PD and periodontitis. The significant correlation between these crosstalk genes and immune cells strongly suggests the involvement of immunology in linking both diseases.

Unveiling the m6A Methylation Regulator Links between Prostate Cancer and Periodontitis by Transcriptomic Analysis

Objective

To identify the N6-methyladenosine (m6A) methylation regulator genes linking prostate adenocarcinoma (PRAD) and periodontitis (PD).

Materials and Methods

PD and TCGA-PRAD GEO datasets were downloaded and analyzed through differential expression analysis to determine the differentially expressed genes (DEGs) deregulated in both conditions. Twenty-three m6A RNA methylation-related genes were downloaded in total. The m6A-related genes that overlapped between PRAD and PD were identified as crosstalk genes. Survival analysis was performed on these genes to determine their prognostic values in the overall survival outcomes of prostate cancer. The KEGG pathways were the most significantly enriched by m6A-related crosstalk genes. We also performed lasso regression analysis and univariate survival analysis to identify the most important m6A-related crosstalk genes, and a protein-protein interaction (PPI) network was built from these genes.

Results

Twenty-three m6A methylation-related regulator genes were differentially expressed and deregulated in PRAD and PD. Among these, seven (i.e., ALKBH5, FMR1, IGFBP3, RBM15B, YTHDF1, YTHDF2, and ZC3H13) were identified as m6A-related cross-talk genes. Survival analysis showed that only the FMR1 gene was a prognostic indicator for PRAD. All other genes had no significant influence on the overall survival of patients with PRAD. Lasso regression analysis and univariate survival analysis identified four m6A-related cross-talk genes (i.e., ALKBH5, IGFBP3, RBM15B, and FMR1) that influenced risk levels. A PPI network was constructed from these genes, and 183 genes from this network were significantly enriched in pathogenic Escherichia coli infection, p53 signaling pathway, nucleocytoplasmic transport, and ubiquitin-mediated proteolysis.

Conclusion

Seven m6A methylation-related genes (ALKBH5, FMR1, IGFBP3, RBM15B, YTHDF1, YTHDF2, and ZC3H13) were identified as cross-talk genes between prostate cancer and PD.

Biomarker Candidates for Alzheimer’s Disease Unraveled through In Silico Differential Gene Expression Analysis

Alzheimer’s disease (AD) is neurodegeneration that accounts for 60–70% of dementia cases. Symptoms begin with mild memory difficulties and evolve towards cognitive impairment. The underlying risk factors remain primarily unclear for this heterogeneous disorder. Bioinformatics is a relevant research tool that allows for identifying several pathways related to AD. Open-access databases of RNA microarrays from the peripheral blood and brain of AD patients were analyzed after background correction and data normalization; the Limma package was used for differential expression analysis (DEA) through statistical R programming language. Data were corrected with the Benjamini and Hochberg approach, and genes with p-values equal to or less than 0.05 were considered to be significant. The direction of the change in gene expression was determined by its variation in the log2-fold change between healthy controls and patients. We performed the functional enrichment analysis of GO using goana and topGO-Limma. The functional enrichment analysis of DEGs showed upregulated (UR) pathways: behavior, nervous systems process, postsynapses, enzyme binding; downregulated (DR) were cellular component organization, RNA metabolic process, and signal transduction. Lastly, the intersection of DEGs in the three databases showed eight shared genes between brain and blood, with potential use as AD biomarkers for blood tests.