Common molecular and pathophysiological underpinnings of delirium and Alzheimer's disease: molecular signatures and therapeutic indications

Abstract

Background: Delirium and Alzheimer's disease (AD) are common causes of cognitive dysfunction among older adults. These neurodegenerative diseases share a common and complex relationship, and can occur individually or concurrently, increasing the chance of permanent mental dysfunction. However, the common molecular pathophysiology, key proteomic biomarkers, and functional pathways are largely unknown, whereby delirium is superimposed on AD and dementia.

Methods: We employed an integrated bioinformatics and system biology analysis approach to decipher such common key proteomic signatures, pathophysiological links between delirium and AD by analyzing the gene expression data of AD-affected human brain samples and comparing them with delirium-associated proteins. The present study identified the common drug target hub-proteins examining the protein-protein interaction (PPI) and gene regulatory network analysis. The functional enrichment and pathway analysis was conducted to reveal the common pathophysiological relationship. Finally, the molecular docking and dynamic simulation was used to computationally identify and validate the potential drug target and repurposable drugs for delirium and AD.

Results: We detected 99 shared differentially expressed genes (sDEGs) associated with AD and delirium. The sDEGs-set enrichment analysis detected the transmission across chemical synapses, neurodegeneration pathways, neuroinflammation and glutamatergic signaling pathway, oxidative stress, and BDNF signaling pathway as the most significant signaling pathways shared by delirium and AD. The disease-sDEGs interaction analysis highlighted the other disease risk factors with delirium and AD development and progression. Among the sDEGs of delirium and AD, the top 10 hub-proteins including ALB, APP, BDNF, CREB1, DLG4, GAD1, GAD2, GFAP, GRIN2B and GRIN2A were found by the PPI network analysis. Based on the maximum molecular docking binding affinities and molecular dynamic simulation (100 ns) results, the ALB and GAD2 were found as prominent drug target proteins when tacrine and donepezil were identified as potential drug candidates for delirium and AD.

Conclusion: The study outlined the common key biomolecules and biological pathways shared by delirium and AD. The computationally reported potential drug molecules need a deeper investigation including clinical trials to validate their effectiveness. The outcomes from this study will help to understand the typical pathophysiological relationship between delirium and AD and flag future therapeutic development research for delirium.

Keywords: Alzheimer’s disease; Common signaling pathways; Delirium; Drug repurposing; Essential drug targets; Molecular docking simulation.

Fig. 1

This study’s pipeline and flow diagram. The diagram illustrates the data collection process, integrated bioinformatics analysis, and the computational cross-validation of protein targets and repurposable drugs conjugates. This involves using molecular docking and dynamic simulation to identify the best lead pairs

Fig. 2

Selection of shared DEGs (sDEGs) between AD and delirium. A The volcano plot denotes the DEGs associated with the AD. The green dots represent significantly downregulated genes, and the orange dots are for upregulated genes. B The mean-difference plot of their expression in AD and Non-AD samples. The red color dots are significantly upregulated and the blue color dots show the downregulated genes. C The Ven diagram shows the datasets that have been collected from different diseases and then combined. The common genes, N = 99, have been utilized in this study for further downstream analysis

Fig. 3

The sDEGs-set enrichment analysis results with GO-terms and pathways, A represents the top significant GO terms and B shows the significant functional pathways, respectively that were retrieved from the g:GOSt server. The GO and pathway terms and IDs have been added to the y-axis and the x-axis represents the -log10(AdjP-value). The figure legend size indicates the number of enriched genes in a particular GO term and pathway

Fig. 4

The PPI network of sDEGs-encoded proteins shared by delirium and AD. The hub-proteins were shown with large node names. The AD-associated up and downregulated genes are indicated separately in the figure

Fig. 5

The gene regulatory network analysis of (A) shared DEGs-TFs, (B) shared DEGs -miRNA. The red color square-shaped and green color diamond-shaped nodes represent the miRNAs and TFs respectively and other nodes represent the common DEGs

Fig. 6

The disease-gene interaction network represents the significant comorbidities associated with delirium and AD development. The hub-proteins are pink diamond-shaped nodes. The highly significant comorbidities are in V-shaped nodes. The most critical diseases are marked by red colored V-shaped

Fig. 7

AutoDock Vina findings for molecular docking simulation analysis between the key drug target hub-proteins encoded from hub-DEGs and the TFs. The redder color in the heatmap indicates the stronger binding affinity between the drug target proteins and the drug molecules. The repurposable drugs used for neurological treatments are on the Y-axis and the drug target proteins are represented on the X-axis. The top-scored repurposable medicines and the drug targets are presented in red color

Fig. 8

A The RMSD (in Å) plot of backbone atoms (C, C and N) for every single docked complex over the MD simulation. B The MM-PBSA analysis computed binding free energy for every complex during the simulation which indicates the alteration of binding stability. The positive values indicate better binding. In both figures, black, red, green, and blue lines are for ALB-Donepezil, ALB-Tacrine, GAD2-Donepezil and GAD2-Tacrine complex respectively