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.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12877-024-05289-3.
Keywords: Delirium, Alzheimer’s disease, Common signaling pathways,
Essential drug targets, Drug repurposing, Molecular docking simulation
Background
Delirium among older adults results in a higher economic burden for the
family and caregivers and increases cognitive and physical dysfunctions
[[32]1, [33]2]. Due to mysterious and multifactorial properties, more
than half of this neurologically complicated condition remains
undiagnosed in intensive care units (ICU) [[34]3–[35]5]. This is mainly
caused by inconsistency in delirium’s definition and subsequent
identification, and its complex molecular pathophysiology [[36]6].
Delirium is triggered by multiple potential factors and causes,
including the predisposing of older persons and potential frailty
[[37]7], pre-diagnosed cognitive impairment [[38]8, [39]9], any
psychological illness [[40]10, [41]11], use of alcohol, and associated
malnutrition [[42]12, [43]13] other precipitating factors including
chronic and acute medical conditions, severe diseases, trauma, major
surgery and stress, and medications [[44]4, [45]14]. The molecular
pathophysiological mechanism of delirium involves different important
signaling pathways and biological mechanisms. The oxidative
stress-associated medical condition, hypoxia, is also considered a
driver of delirium [[46]15, [47]16]. Severe systemic cytokines-derived
inflammation and peripheral neuroinflammation are widely reported and
described as influencers and triggers of delirium [[48]17, [49]18]. The
functional disruption of brain neurotransmitter systems, including the
dopamine, acetylcholine (ACh), and GABA associate pathways, and
cholinergic synapses neurodegeneration, are closely connected with
delirium [[50]19, [51]20].
AD is an elusive neurodegenerative disease characterized by chronic and
persistent cognitive impairment and is considered one of the major
causes of dementia [[52]21]. The molecular pathophysiology of AD
reveals a wide range of neurobiological functions, including amyloid
plaques, neurofibrillary tangles, neuroinflammation, oxidative stress,
and damage to cholinergic neurons [[53]22, [54]23]. The chemical
synapse-associated pathways, notably axonal dystrophy, loss of
pre-synaptic terminal loss, and dendritic spines loss, lead to the
primary stage of AD, introducing memory dysfunction [[55]24–[56]26].
Both neurodegenerative diseases, delirium, and AD, have a complex
interrelationship among their pathophysiological mechanisms in which
they can act interactively, independently, and simultaneously [[57]27].
Delirium has been treated as a vulnerability marker for AD which can
alter the potential neuronal injury that leads to AD. The risk of
incident dementia is considerably increased by delirium, frequently
misdiagnosed or confused with AD. Studies suggest that 22%-89% of
patients with dementia experience delirium during critical medical
events [[58]28]. The outcomes of studies examining delirium-related
biomarkers in people with AD have been mixed. However, there are
associations between delirium and AD-associated biomarkers, suggesting
that the underlying AD pathology might impact the development of
delirium [[59]29, [60]30]. Moreover, AD patients with delirium are at
greater risk of suffering negative consequences, including death or
being admitted to a nursing home and experiencing hastened cognitive
loss [[61]31]. Glucose utilization and insulin signaling are
significantly decreased in AD patients [[62]32, [63]33], which are also
linked with delirium [[64]34, [65]35].
This introduction indicates a complex and mysterious relationship
between the etiology of delirium and AD. Even though a few weakly
powered genetic investigations have been carried out, no persistent
potential genes linked to delirium risk have been found
[[66]35–[67]37]. To better understand the underlying pathophysiology of
both diseases, we consider that conducting an in-depth investigation is
necessary to decipher the common biomarkers and signaling pathways
shared by both diseases. In addition, the conjugial nature of these two
neurological diseases increases the challenge of developing successful
treatments for both. Although very few drugs are being prescribed or
treating the symptoms of AD [[68]21], no specific drugs are being
considered for delirium treatment, despite ongoing research
[[69]38–[70]41].
We have carried out an integrated bioinformatics and system biology
analysis to explore the typical potential molecular relationship
between delirium and AD, exploring the common molecular signatures and
pathways and the repurposable drug investigation for delirium and AD.
The study was designed to capture the delirium pathophysiology
associated with AD and dementia. The study also sought to elucidate the
typical pathophysiological association between delirium and
AD/dementia. The outcomes of this study were hypothesized to generate
evidence for more profound knowledge and understanding of delirium and
AD as well as better therapeutic development, especially for delirium.
Materials and methods
We used delirium-associated proteins and AD-related proteins from
different independent sources, to identify the common proteomic
biomarker candidates. Then, a network-based analysis approach was used
to decipher the pathophysiological processes and their regulators. The
entire study diagram has been presented in Fig. [71]1.
Fig. 1.
[72]Fig. 1
[73]Open in a new tab
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
Data sources and descriptions
Due to a lack of delirium-gene expression data, the current study
searched for delirium-associated gene expression data. For this study,
the delirium-associated protein dataset was collected through a
systematic literature review (SLR) (please see the Supplementary File 1
for details about the SLR) and the Comparative Toxicogenomics Database
(CTD, [74]http://ctdbase.org/) [[75]42], a widely used database for
investigating chemical genes or proteins relationships. In our study,
we searched the proteins from CTD against delirium. Combining these two
protein datasets, we have compiled a delirium-associated total protein
seed dataset containing 524 unique gene encoded proteins (Table [76]1).
Table 1.
Description of delirium and AD-associated genomic datasets
Diseases Data Sources Databases Period # of collected gene encoded
proteins # of selected encoded proteins
Delirium Through a SLR following PRISMA guidelines [[77]43] PubMed,
Scopus, and EBSCOhost (CINAHL, Medline) databases 1^st January 2000, to
31^st December 2023 A = 189 unique genes were collected from 78
included studies The final combined dataset, P[D] = (AUB)
Database search Comparative Toxicogenomics Database (CTD) [[78]42]
Accessed on 13 June 2023 B = 350 unique genes were collected having
inference score > 40 [[79]44]
AD Gene expression data ([80]GSE36980) [[81]45] NCBI-GEO Published in
2014 The dataset contained the expression data of 27,925 probes Only
the significant DEGs were included
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The AD-associated gene expression transcriptomic dataset was downloaded
from the National Center for Biotechnology Information Gene Expression
Omnibus (NCBI-GEO) data repository. The AD-associated microarray
dataset ([83]GSE36980 [[84]45]) contained 80 samples of human
postmortem brains, of which 33 samples were from AD-affected brains,
and others were from non-AD brains collected from the area of the
frontal cortex, temporal cortex, and hippocampus of brains.
Identification of differentially expressed genes (DEGs) between AD and
control samples
We used the well-established linear model method for microarray data
(LIMMA) [[85]46] to identify AD-associated DEGs. The conventional LIMMA
procedure employs an empirical Bayes estimation procedure to ‘moderate’
the ordinary t-test statistic by adjusting the sample variance using
the distribution of all standard deviations. The p-values were adjusted
using the Benjamini and Hochberg approach to control the multiple
testing false discovery rate (FDR) [[86]47]. Statistical significance
was considered by an adjusted P-value < 0.05 and the |log[2](FC)|> 0.5
(where FC means average fold change value) to identify the significant
DEGs. The statistical tests were performed and implemented by the
NCBI-GEO2R web tool ([87]https://www.ncbi.nlm.nih.gov/geo/geo2r/,
accessed on 22 June 2023).
In this statistical test, the k^th gene (where k = 1,2, …, 27,925) was
considered as DEG altering between the AD and non-AD groups if the
adjusted P[k]-value < 0.05 with the |log[2]([a]FC[k])|> 0.5 after
controlling the FDR at 5% level or else, it was an equally expressed
gene (EEG). If the k^th gene's adjusted P[k]-value were less than 0.05
and log2([a]FC[k]) > 0.5 and log2([a]FC[k]) < − 0.5, the gene was
classified as either up- or down-regulated DEG, respectively. Here, the
[a]FC[k] represents the average normalized fold change value of the
k^th gene’s expression arrays concerning AD and non-AD samples which
can be defined as [a]FC[k] =
[MATH:
x¯k/y¯k :MATH]
(where
[MATH:
x¯kand :MATH]
[MATH: y¯k
:MATH]
is the average value of the normalized expression array count of the
k^th gene in AD and non-AD samples). For instance, if the
[MATH: x¯k
:MATH]
=8 in the AD sample and
[MATH:
y¯k=2 :MATH]
, then the [a]FC[k] = 4, indicating that the k^th gene is fourfold
upregulated in AD compared to non-AD conditions and vice versa for
downregulated genes.
Identification of shared DEGs (sDEGs) between AD and delirium
First, we collected DEGs between delirium and control samples from the
literature. Suppose we denoted the delirium-associated DEGs encoded
protein dataset by P[D] and the AD-associated DEGs encoded protein
dataset by Q[AD]. Then we selected the shared DEGs (sDEGs) encoded
protein dataset between delirium and AD by Z =
[MATH: (PD∩QAD
:MATH]
) which has been used as the final combined analytical dataset for this
study.
sDEGs-set enrichment (GSE) and annotation analysis
Gene ontology enrichment and functional signaling pathway analysis were
conducted to identify the significant biological and molecular
functions. The gene set enrichment and ontology analysis were performed
using g:GOSt embedded in g:Profiler web server. The significant
signaling pathways were retrieved from four databases including
BioCarta, WikiPathways, KEGG, and Reactome. The significant signaling
pathways and ontology terms were considered based on the adjusted
P-value < 0.05 and the Benjamini and Hochberg [[88]47] procedure for
controlling FDR.
Identification of common key genes associated with delirium and AD
It is a common practice to investigate key proteins using
protein–protein interaction (PPI) network analysis [[89]48, [90]49]. In
the current study, the STRING, a protein interactome database [[91]50]
was used to build the PPI network of the sDEGs encoded proteins between
delirium and AD. The highly representative key common proteins, also
known as hub-proteins, were retrieved using a topological investigation
based on dual-metric measurement degree and betweenness on the PPI
network using the Cytoscape [[92]51]. ClueGO, a plug-in Cytoscape was
used to create a network of functions for GO enrichment analysis
utilizing the key hub genes under the statistical significance of
P-value < 0.05.
Pre- and post-transcriptional gene regulatory network analysis
In this study, we have identified the pre- and post-transcriptional
gene regulatory factors-microRNA (miRNA) and the transcriptional factor
(TF) analyzing the interaction networks among the shared hub-genes
encoded proteins and miRNAs and TFs, respectively. The interaction
network of TFs and shared hub-DEGs was constructed using the JASPAR
[[93]52] TF database as well as the TarBase V8.0 and miRTarBase
[[94]53, [95]54] miRNAs databases were utilized to construct the
miRNA-hub-DEGs interaction network. The Network Analyst [[96]55] online
server revealed all the regulatory networks. Dual-metric measurement
degree and betweenness were implemented in network analysis and
visualization.
Disease-gene interaction analysis
The disease multimorbidity association of the sDEGs was investigated
using the disease-gene interaction network. To discover this, the
DisGeNET database [[97]56, [98]57] was utilized and then Cytoscape was
used to analyze the network under the dual-metric topological
measurement condition degree and betweenness to capture the important
and significant disease interactions with the hub-DEGs. The significant
diseases were also highlighted which are highly comorbid with delirium
and AD.
Drug repurposing and molecular docking
Since we observed the internal pathophysiological relationship, we
attempted to identify the potential repurposable drugs against delirium
and AD using computational molecular docking analysis. The FDA-approved
drugs that are used for neurological treatment (like AD) have been
retrieved from the online drug-repositioning tool and database
Connectivity Map (CMap) [[99]58] (Supplementary File 2). The drug
repurposing database was used to search the drug molecules against
hub-proteins. Only the top-ranked associated repurposable drug
molecules were collected for the docking analysis with our
hub-proteins. Then we performed molecular docking analysis between the
top-ranked repurposable drug molecules and target proteins as in-silico
validation. The binding affinity of the repurposable drugs with the top
key common hub-proteins and TFs was investigated by a molecular docking
simulation study. The 3D structures of the drug target key
hub-proteins, TF proteins, and repurposable drugs were downloaded from
the Protein Data Bank (PDB) [[100]59] and SWISS-MODEL [[101]60], a
homology modeling-based database as well as from PubChem database
[[102]61]. The 3D drug target proteins were visualized and preprocessed
by removing the co-crystal ligands and water molecules using Discovery
Studio Visualizer 2019 [[103]55], Swiss PDB Viewer Software, and
MGLTools Software [[104]62]. The energy minimization of the drug
compounds was performed by applying the MMFF94 force field [[105]63].
Molecular docking analysis was performed using AutoDockTools 4.2
[[106]64] and AutoDock Vina [[107]65]. For the drug-protein
interaction, the highest docking score with the best-fit posture was
considered to select the best repurposable drug for delirium as well as
AD.
Molecular Dynamic (MD) simulations
The MD simulation was employed to explore the dynamic nature of the
top-ranked protein-drug complex by using the YASARA Dynamics software
[[108]66], and the AMBER14 force field [[109]67]. The simulation
included the top four protein-drug complexes, ALB-Donepezil,
ALB-Tacrine, GAD2-Donepezil, and GAD2-Tacrine from molecular docking
analysis. The hydrogen bonding network of the target protein-drug
complex was optimized and solvated using a TIP3P [[110]68] water model
in a simulation cell before simulation. Given a solvent density of
0.9971 gL-1, periodic boundary conditions were maintained. The
ALB-Donepezil, ALB-Tacrine, GAD2-Donepezil, and GAD2-Tacrine complex
contained 115,942, 114,258, 158,429, and 159,893 atoms respectively at
the initial energy minimization process using the simulated annealing
method with the steepest gradient approach (5000 cycles). Under
physiological circumstances (298 K, pH 7.4, 0.9% NaCl) [[111]69], every
simulation was run with a repeated time-step method [[112]70] utilizing
a time-step interval of 2.50 fs. The linear constraint solver (LINCS)
[[113]71] algorithm was used to limit all bond lengths and SETTLE
[[114]72] was used for water molecules. The root-mean-squared deviation
(RMSD) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA)
binding free energy was calculated for up to 100 ns MD simulation under
the Berendsen thermostat [[115]73] and constant pressure. The analysis
was performed using the default script of YASARA macro and SciDAVis
software available at [116]http://scidavis.sourceforge.net/. The
MM-PBSA binding free energy was calculated by the following equation
[[117]74], using YASARA built-in macros using AMBER 14 as a force
field, with larger positive energies indicating better binding
[[118]75],
[MATH: Binding free
Energy=EpotReceptor+EsolvReceptor+EpotLigand+
EsolvLigand-EpotComplex-EsolvComplex :MATH]
Results
Identification of DEGs between AD and control samples
The statistical analysis revealed a total of 2257 DEGs (i.e., 534
up-regulated and 1723 down-regulated genes) with their official gene
symbol identified from the gene expression data analysis. The
significant upregulated and downregulated genes and their mean
expression difference (AD vs. non-AD) plot are shown in Fig. [119]2A
and B respectively.
Fig. 2.
[120]Fig. 2
[121]Open in a new tab
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
Identification of DEGs between delirium and control samples
A total of 189 unique delirium-associated genes and their encoded
proteins were found from 78 included studies (Table [122]1) in the
comprehensive SLR. Under the cutoff inference score (> 40), the CTD
database revealed a total of 350 delirium-associated gene encoded
proteins. Then, the two protein datasets from separate sources (SLR and
CDT database) were combined (mathematical union) to create an
integrated delirium-associated protein dataset with 524 unique proteins
(Table [123]1).
Identification of sDEGs between AD and delirium
A total of 99 common shared DEGs (sDEGs) between delirium and AD were
identified as displayed in Fig. [124]2C. The distribution of up and
downregulated genes with the delirium-associated genes shows that a
total of 79 downregulated genes and 20 upregulated genes were common
between delirium and AD (Table [125]2). The shared genes and their
encoded proteins were utilized to identify the common regulatory
biomolecules and common pathophysiological relationships between
delirium and AD in the downstream analysis.
Table 2.
The common shared proteins between delirium and AD
Delirium and AD-associated common genes (N=94) AD-associated up
regulated genes (n=20) AD-associated down regulated genes (n=79)
ABCC2, ABCC3, AGT, ALB, CALCA, CASP8, CCL5, CP, EDN1, FSTL1, GFAP,
GJA1, GNAI2, IL12A, MICA, MUC1, NR0B2, NUPR1, SLC47A2, VCAM1 ACHE,
ALDOA, AP1S1, APP, ASNS, ATP1A1, BACE1, BDNF, BNIP3, CACNA1C, CADM3,
CAMK2D, CAMKK2, CDK5, CFL1, CHRM1, CHRM2, CHRM4, CHRNB2, CREB1, CREM,
CX3CL1, CYCS, DLG4, DRD1, ENO1, FNDC4, GABRA1, GABRA4, GABRB2, GABRB3,
GABRG2, GAD1, GAD2, GAP43, GOT2, GRIN2A, GRIN2B, GSR, HAPLN1, HOMER1,
HRH1, HSP90AA1, HSPA8, HTR2A, H2AFZ, INSIG2, MAN1A1, MAP2, MAPK1,
MAPK8, MTOR, NPY, NREP, NSF, ODC1, PDYN, PER2, PID1, PLCB1, PPIA,
PRKCB, PTEN, RGS4, RTN4R, SLC17A6, SLC17A7, SLC1A1, SLC2A3, SMPD1,
SNCA, STX1A, SYN1, SYP, TAC1, THY1, VEGFA, VGF, VLDLR
[126]Open in a new tab
Note: The official gene symbols are presented here
sDEGs set enrichment analysis with GO-terms and pathways
Based on the statistical significance criteria (AdjP-value < 0.05)
under the controlled FDR, the top significant functional pathways and
enriched GO shared by the common genes of delirium and AD are shown in
Fig. [127]3. The bubble plots in Fig. [128]3A and B have been
constructed from g:GOSt server, representing the top significant GO
terms (Fig. [129]3A) and the functional pathways (Fig. [130]3B). The
analysis revealed the significant GO terms including the biological
process (BP) molecular functions (MF) and cellular components (CC). The
most significant GO terms are represented in Fig. [131]3A. According to
the GO enrichment analysis, the chemical synaptic transmission, cell
communication, memory, anterograde trans-synaptic signaling, response
to stimulus, protein-binding activity, neurotransmitter receptor
activities, and other synoptic signaling activities are highly enriched
and are significant GO terms commonly shared by delirium and
AD-associated genes (Fig. [132]3A).
Fig. 3.
[133]Fig. 3
[134]Open in a new tab
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
The entire neurotransmission system along with the different signaling
pathways were highly enriched pathways among the common genes of
delirium and AD. The enriched functional pathways commonly linked with
delirium and AD that have been identified, are mostly associated with
the nervous system and their signaling synapse mechanism as well as the
chemical reaction of receptors with ligand chemical molecules
(Fig. [135]3). For example, among the most significant enriched
pathways shared by delirium and AD, the transmission across chemical
synapses, pathways of neurodegeneration-multiple diseases, signal
transduction, neuroinflammation, and glutamatergic signaling pathway,
brain-derived neurotrophic factor (BDNF) signaling pathway, fragile-X
syndrome, oxidative stress, and hypoxia associated pathways are the
most important. The detailed analysis output of GSE and functional
pathway analysis has been provided in Supplementary File 3.
PPI network analysis of sDEGs
The PPI network of the sDEGs-encoded proteins revealed the most highly
connected shared key proteins which are also known as drug-targeted
hub-proteins. Among the signature proteins, three key proteins (ALB,
AGT and GFAP) were AD-associated upregulated, and the others were found
downregulated. The top 10 hub-proteins including ALB, APP, BDNF, CREB1,
DLG4, GAD1, GAD2, GFAP, GRIN2B and GRIN2A were found in the PPI network
(Fig. [136]4) and utilized for further downstream analysis.
Fig. 4.
[137]Fig. 4
[138]Open in a new tab
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
Figure [139]4 demonstrated that the two upregulated and five
downregulated gene-encoded proteins were not interconnected with any
other proteins in the network (Fig. [140]4). With the PPI network, we
observed the engagement and association of the signature proteins with
delirium and AD. The ClueGO-derived GO network also revealed the
association of mental dysfunction-related pathways and biological
function which were consistent with the overall GSE analysis
(Supplementary Fig. 1).
The sDEGs regulatory network analysis
The common sDEGs-TFs and DEGs-miRNAs interaction network is presented
in Fig. [141]5. The gene-TFs regulatory network showed the key
regulatory TF including the FOXC1, GATA2, and FOXL1. The highly
connected TFs are represented in Fig. [142]5A with the green diamond
nodes. The common DEGs-miRNA network analysis revealed the potential
key miRNAs namely, miR-16-5p, miR-1-3p, and miR-34a-5p (Fig. [143]5B).
Fig. 5.
[144]Fig. 5
[145]Open in a new tab
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
Disease-gene network fetched the key neurological disorders associated with
the shared DEGs
The interaction network analysis revealed the associated diseases with
the common hub-genes associated with delirium and AD. The interaction
network is displayed in Fig. [146]6. Most importantly Alzheimer’s
disease 2, dementia, cognitive disorders, Parkinson's disease and
disorders, and brain diseases were the most significant diseases
associated with common hub-genes (Fig. [147]6). The disease interaction
and the association indicate that neurological complications and
disorders are highly comorbid with delirium and AD development.
Fig. 6.
[148]Fig. 6
[149]Open in a new tab
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
The hub-proteins guided drug discovery
In this part the top 10 common hub-DEGs encoded hub-proteins and 3 key
TFs (total = 13) proteins for molecular docking simulation were
considered. The 3D-structure of the 10 hub-proteins including ALB,
BDNF, GRIN2B, CREB1, APP, DLG4, GFAP, GAD1, GRIN2A and GAD2 were
collected from the PDB database using the codes 7VR0, 1BND, 7EU8, 5ZKO,
1AAP, 6SPV, 6A9P, 3VP6, 5H8Q, and 2OKK respectively. The 3D structure
of GATA2 TF-protein was also downloaded from the PDB database using the
code 5O9B whereas the other two 3D structures of TFs FOXC1 and FOXL1
were collected from the SWISS-MODEL using UniProt with IDs [150]Q12948
and [151]Q12952. The 3D structure of eight FDA-approved neurological
drugs was collected from the PubChem database and used for molecular
docking against the drug receptor proteins associated with delirium and
AD. Based on the binding affinity scores (kcal/mol) between the
receptor proteins and the drug agents, the top repurposable drug
molecules and the most effective drug targets were confirmed. The
affinity scores were ordered and plotted in a heatmap against the
receptor proteins in Fig. [152]7. In our investigation, the ALB and
GAD2 were found as leading and prominent drug target receptor proteins
associated with delirium and AD where donepezil (with ALB:
-8.8 kcal/mol and with GAD2: -9.0 kcal/mol) and tacrine (with ALB:
-8.0 kcal/mol) showed the maximum binding affinity scores with the two
target proteins compared to others lead components (Fig. [153]7). The
docking analysis revealed that most of the drug agents performed well
with the target proteins which resulted in the GRIN2B protein also
docking well with citicoline (-7.5 kcal/mol) and tacrine
(-7.6 kcal/mol) drug molecules. The details docking score matrix is
provided in Supplementary File 4.
Fig. 7.
Fig. 7
[154]Open in a new tab
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
Table [155]3 represents the gist of the molecular docking interaction
summary of our top drug target proteins (ALB and GAD2) with the
prominent drug candidates (Tacrine and Donepezil) scoring maximum
binding affinity. The best docking pose (3D) of the drug molecule, the
interaction complex (2D), and the adjacent interacting residues along
with the bond and distance (Å) are reported in Table [156]3. The
interaction pose of the target receptor and drug molecule indicates
that the drug molecule fits on the target protein’s pocket with
significant binding affinities.
Table 3.
The interacting characteristics and outcomes of the top-ranked drug
molecules and drug target key proteins. The Hydrogen Bond (HB),
Electrostatic Bond (EB), and Hydrophobic Bond (HpB) are presented in
the 4^th column along with their bond distance in the last column
Potential Target Proteins 2D Drug Structure and Binding Affinity
(kcal/mol) 3D Pose View of Interaction Complex graphic file with name
12877_2024_5289_Figa_HTML.gif Interacting Residues Bond Types Distance
(Å)
graphic file with name 12877_2024_5289_Figb_HTML.gif
GAD2
graphic file with name 12877_2024_5289_Figc_HTML.gif
Donepezil (-9.0 kcal/mol)
graphic file with name 12877_2024_5289_Figd_HTML.gif graphic file with
name 12877_2024_5289_Fige_HTML.gif
GLN429
ASP236
TYR218
LEU435
HIS422
PHE427
HB
HB
HpB
HpB
HpB
HpB
2.30
3.57
4.80
4.01
4.78
5.15
graphic file with name 12877_2024_5289_Figf_HTML.gif
ALB
graphic file with name 12877_2024_5289_Figg_HTML.gif
Tacrine (-8.0 kcal/mol)
graphic file with name 12877_2024_5289_Figh_HTML.gif graphic file with
name 12877_2024_5289_Figi_HTML.gif
LEU238
ALA291
LEU238
ALA291
HIS242
ARG257
LEU260
ALA261
ILE290
ALA291
HpB
HpB
HpB
HpB
HpB
HpB
HpB
HpB
HpB
3.96
3.74
4.99
3.86
4.89
5.21
4.76
4.99
4.59
5.13
graphic file with name 12877_2024_5289_Figj_HTML.gif
ALB
graphic file with name 12877_2024_5289_Figk_HTML.gif
Donepezil (-8.8 kcal/mol)
graphic file with name 12877_2024_5289_Figl_HTML.gif graphic file with
name 12877_2024_5289_Figm_HTML.gif
ALA210
LYS351
ALA210
ALA213
ALA213
VAL216
LEU347
VAL482
ALA210
ALA213
LYS351
HB
EL
HpB
HpB
HpB
HpB
HpB
HpB
HpB
HpB
HpB
3.46
4.14
3.72
3.99
4.34
5.40
4.25
4.33
5.40
3.89
4.48
[157]Open in a new tab
MD simulation
The complex stability analysis through MD simulation between the
top-ranked drug target and drug molecules showed significant stability
between the initial drug target and complex moving variation over the
100 ns MD-PBSA simulation. Fig. [158]8A shows the calculated RMSD for
all four protein-drug complexes ALB-Donepezil, ALB-Tacrine,
GAD2-Donepezil, and GAD2-Tacrine.
Fig. 8.
[159]Fig. 8
[160]Open in a new tab
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
The system provided an average RMSD of 2.185 Å (Range: 0.428 Å to
3.148 Å), 2.255 Å (Range: 0.449 Å to 3.177 Å), 7.540 Å (Range: 0.483 Å
to 8.434 Å) and 7.590 Å (Range: 0.504 Å to 8.727 Å) for the
ALB-Donepezil, ALB-Tacrine, GAD2-Donepezil and GAD2-Tacrine
respectively. The GAD2 complex structures fluctuated for the drug
molecules up to 15 ns and became stable during the remaining
simulation. The RMSD plot indicates that the ALB complexes were more
stable with the reported drugs during the entire simulation than GAD2.
The MM-PBSA binding energy for four complexes shows the average binding
energy 307.061 kJ/mol, 192.694 kJ/mol, 105.350 kJ/mol, and
111.743 kJ/mol for ALB-Donepezil, ALB-Tacrine, GAD2-Donepezil, and
GAD2-Tacrine complex respectively (Fig. [161]8B).
Discussion
This study has focused on deciphering the interactions and
pathophysiological pathways shared by the shared key proteomic
biomarkers between delirium and AD. The significant AD-associated DEGs
were compared with the delirium-associated genes to identify the common
genomic signatures and found 99 common genes between the two
conditions. Among the common genes shared by delirium and AD, it was
observed that the common genes are differentially expressed in AD where
20 genes were upregulated and 79 were downregulated.
The shared functional pathways between the two diseases show the
epidemiological and internal pathophysiological relationship between
them. For instance, transmission across chemical synapses is one of the
most important and significant pathways shared by the common genes for
transferring chemical neurotransmitters across the neurons [[162]76].
One neuron can quickly and efficiently stimulate or inhibit the
neuronal activity of another neuron via chemical synapses. The
neurodegeneration pathways caused by multiple diseases play a
significant role in enhancing the progression of memory loss and
ultimately developing distinct brain-dysfunctional diseases like AD,
dementia, and Parkinson’s disease [[163]77]. As the common genes of
delirium and AD were significantly enriched in this pathway, it
indicates that delirium is also associated with permanent cognitive
and/or motor dysfunction, supported by different studies [[164]27,
[165]78]. Another significant shared pathway was the neuroinflammation
and glutamatergic signaling pathway. Neuroinflammation is considered
the primary process that triggers delirium under any critical medical
condition. The neuronal and synaptic dysfunction is changed due to the
neuroinflammatory, which is after the abnormal neurobehavioral and
mental disorder symptoms [[166]79, [167]80]. The common genes also
enriched the BDNF signaling pathway, which plays a vital role in the
pathogenesis of neurodegenerative diseases by accelerating
TrkB-mediated neuronal events [[168]81]. Oxidative stress is a
significant mechanism linked to chronic inflammation and age-related
disorders that may also be connected to delirium pathogenesis
[[169]82]. Among the other important pathways, the glutamatergic
signaling in the central nervous system, and cholinergic functional
pathways are crucial pathways associated with delirium and AD
development [[170]83]. Studies show that anesthesia drugs during
surgery can directly act on the central cholinergic system which leads
to postoperative delirium and mental dysfunction [[171]84]. Drugs used
for anesthesia could be one of the major factors for postoperative
delirium [[172]85, [173]86] and it demands rigorous research to
decipher the molecular interaction of the drug molecules with the
delirium-associated target proteins. The pathway indicates the
interrelationship of neuronal activity, cognitive impairment, and
conditions like AD and dementia. Investigating these pathways enhances
the influence of delirium on chronic brain diseases.
The PPI network analysis of sDEGs-encoded proteins revealed the top key
hub-proteins where most of them came from the down-regulated genes.
Among the top ten hub-proteins, the ALB and GFAP were upregulated from
an AD perspective and others were downregulated. The expression
profiles of the common genes between delirium and AD indicate that
delirium superimposed on AD might also be triggered by the
downregulated genes associated with delirium. Among the key hub-genes,
albumin (ALB) is considered a potential biomarker to diagnose delirium
among surgical patients [[174]87]. The studies support that lower
albumin levels are highly associated with delirium development
postoperatively [[175]88–[176]91]. The astrocyte-expressed glial
fibrillary acidic protein (GFAP) [[177]92], is used to identify
astrocytosis in cases of neurodegeneration and is associated with
traumatic brain injury that might involve mental illness like AD. Among
the other key downregulated hub-genes, BDNF, GRIN2B, and CREB1 are
highly connected with the other genes. The increased protein level of
BDNF is associated with delirium diagnosis and quick recovery from
postoperative delirium [[178]93–[179]95] whereas it has been found in
lower levels among AD patients [[180]96]. Studies suggested that
genetic variation of the GRIN2B gene might be associated with the
molecular mechanism of AD [[181]97, [182]98] and its molecular
variations may offer a crucial tip for understanding the molecular
causes of AD [[183]99]. The GSE analysis of the common genes revealed
the cMAP-signaling pathway, mainly enhanced by CREB1 genes associated
with mental depression [[184]100]. The pathway enrichment analysis of
the hub-genes using ClueGo revealed the important pathways, namely
BDNF-TrkB signaling pathway, NMDA glutamate receptor activity, synaptic
signaling pathways, fragile X syndrome, cocaine addiction, and
amphetamine addiction (Supplementary Fig. 1). The pathways are aligned
with the overall GSE analysis results. The top significant hub-genes
play a substantial role in delirium development and AD. The genes might
influence higher delirium occurrence with the concurrence of critical
medical conditions of AD-affected patients. Therefore, the hub-genes
can serve as a potential biomarker to diagnose delirium and could be
treated as a potential therapeutic target for drug development.
The gene regulatory network (GRN) analysis detected some key
transcription factors (TFs), namely FOXC1, GATA2, and FOXL1, as the
transcriptional regulators of shared key genes as well as key miRNAs,
notably miR-16-5p, miR-1-3p, and miR-34a-5p as the post-transcriptional
regulators. Neuroinflammation and neuronal death are linked to the
FOXC1 TF whereas, neurodegenerative consequences including Alzheimer's
disease, dementia, and Parkinson's disease are strongly correlated with
neuroinflammation [[185]101–[186]103]. The key TF GATA2 is associated
with the Neuroglobin (NGB) gene expression when the neural disease
(like AD) is connected with the expression level of the NGB gene
[[187]104]. The results suggest that FOXL1, a transcriptional repressor
regulates the development of the central nervous system in Zebra fish
[[188]105] and is also associated with AD [[189]106] as reported in
previous studies. The miR-16-5p miRNA plays a significant role in
neuronal cell apoptosis in AD [[190]107, [191]108]. The miR-1-3p is
directly involved in Fas Apoptotic Inhibitory Molecule (FAIM)
expression which is closely associated with the physiological and
pathological processes of Alzheimer’s and Parkinson’s diseases
[[192]109]. Different clinical and molecular studies suggested that the
plasma level of miR-34a-5p miRNA is being considered as an early
biomarker [[193]110, [194]111], which also decreases oxidative stress
and apoptosis condition by inhibiting β-amyloid (Aβ)-induced
neurotoxicity in AD [[195]112]. The EGR1 TF regulates the AChE
expression contributing to cholinergic function alteration in AD
development [[196]113] which may contribute to delirium as well. The
SP1 is known as a pro-inflammatory TF that regulates the AD causal
genes including amyloid precursor protein (APP) and β-secretase (BACE1)
gene expression [[197]114, [198]115]. Besides the TF, the miR-103 and
miR-107 are directly associated with neurodegenerative diseases like AD
and neurodegeneration-associated pathways which play an important role
in delirium as well [[199]116, [200]117]. Since the regulatory
molecules are associated with the common hub-genes of delirium and AD
their functionalities are directly involved with neurological
complications and pathophysiology and they might have close
connectivity with delirium occurrence and development.
The disease-gene interaction network revealed the comorbidities
associated with delirium and AD. The common genes associated with
diseases contain mental dysfunctions and disorder-related complications
that might influence delirium and AD development, or they could boost
medical complications in patients with delirium and AD. The comorbidity
analysis revealed alcoholic intoxication which could lead to delirium.
The results are consistent with the GSE and ClueGO GO group analysis
results including cocaine and nicotine addiction pathways. Studies
suggested that the alcohol withdrawal/alteration could result in
delirium [[201]118–[202]120] where the key hub-genes may have been
involved.
Repurposable drugs are considered a great source of treatment in any
emergency. In this aspect, the FDA-approved neurological drugs
especially used for AD treatment were retrieved from the CMap database.
The computational molecular docking simulation study was implemented to
investigate the drug target properties of our proposed drug target
proteins which might be investigated further for more effective
therapeutic development against delirium and AD. The docking analysis
revealed that the drug molecules significantly interact with the target
protein pockets. Among the drug target proteins, ALB and GAD2 were
found to be highly interacting drug targets compared to others. Tacrine
and donepezil showed the highest binding affinity scores which indicate
the primary properties of drug candidate molecules. Tacrine was
approved by the FDA as one of the first drugs to treat AD [[203]121]
although it has been prescribed with limitations for easing AD symptoms
[[204]122, [205]123]. Studies suggest that tacrine-related drugs could
be a potential source for AD treatment [[206]124]. Tacrine also showed
effective improvement in treatment of cholinergic delirium [[207]125].
On the other hand, donepezil, an acetylcholinesterase (AChE) inhibitor
has already been investigated as a drug for neurological or psychiatric
complications including delirium and AD [[208]126, [209]127]. Studies
supported that the donepezil showed strong significant binding affinity
with AChE which triggers the cholinergic pathways on AD therapeutics
[[210]128–[211]130]. Clinical improvement was investigated by using the
donepezil for the Alzheimer dementia patients [[212]131]. Research
indicates that donepezil has being investigated as a potential
medication for treating delirium [[213]132, [214]133]. Donepezil
medication also improved the critical condition of dementia patients
and reduced the delirium development [[215]134]. The computational
analysis in this study revealed consistent findings about donepezil as
a prominent therapeutic candidate which will influence the therapeutic
development for delirium as well. The 100 ns MD-based simulation
revealed the stability of the reported protein-drug complexes
suggesting significant structural consistency according to the physical
law [[216]135]. Based on the prominent properties of our proposed drug
molecules, further clinical and pharmacological research is needed for
effective therapeutic development against delirium and AD targeting the
key drug target biomolecules reported in this study.
Strength and limitations
The current study utilized AD-associated gene expression data collected
from human brain samples, which could explain a greater genomic
signature than blood and other tissue samples. This study collected a
comprehensive delirium-associated gene encoded protein dataset which
included an SLR and an independent CTD database that can be explored
for further delirium research. The study outlined key drug-target
biomolecules including hub-proteins, TFs, and miRNAs which are jointly
functional in delirium and AD. These important biomarker genes will
open a new dimension of research in diagnosis, prognosis, and
therapeutic development. The proposed repurposable neurological drugs
showed significant binding affinity against the therapeutic targets
which augers well for therapeutic development for delirium and AD.
While the study identified common molecular signatures, it may not
fully elucidate the intricate biological pathways linking delirium and
AD. Further research is needed to gain a comprehensive understanding of
the shared pathophysiological processes. There might be some
inconsistencies about the common molecular functionality between
delirium and AD, since AD is highly accountable for dementia whereas
delirium is generally a short-term cognitive impairment. Therefore, the
common pathophysiological functions between delirium and AD would be
considered when they act conjugately. In this aspect, the identified
key proteins and their functionalities may differ for independent
delirium episodes and their subtypes. Predominantly, both neurological
conditions are highly prevalent among older patients and have a great
chance to be comorbid to each other when occur together. Although the
present study reported several important key proteins, further research
needs to be conducted to identify a single biomarker of delirium and
AD. The study's suggestions for drug repurposing for delirium might be
influenced by bias or limited data availability. Rigorous clinical
trials are needed to validate the effectiveness and safety of
repurposed drugs. Moreover, the delirium associated gene expression
data should be generated to elucidate the genetic engagement on disease
pathophysiology.
Implementation
This study aimed to understand the pathophysiological relationship
between delirium and AD along with identifying potential drug targets
and repurposable drug candidates. The outcomes will significantly
contribute to a better understanding of the common key genomic
biomarkers and shared signaling pathways associated with delirium and
AD. This will enrich the pathophysiological knowledge about delirium
and AD, their cooccurrence, and also delirium superimposed on dementia.
Healthcare policies should prioritize biomarker-based early detection
of individuals at risk for both delirium and AD. The comorbidity
analysis associated with delirium and AD reported significant symptoms
which will contribute to healthcare practitioner knowledge for good
practice of diagnosis, monitoring, and management of delirium and AD.
Healthcare systems might adopt integrated care models that bring
together specialists in geriatric medicine, neurology, psychiatry, and
genetics to comprehensively address the overlapping risk factors and
underlying genetic connections between delirium and AD. Finally, if the
reported repurposable drugs are considered for in-depth clinical
investigation for further validation, they will be a potential source
for enhancing precision medicine and the process of therapeutic
development against delirium and AD. This information could guide
personalized risk assessment and early intervention strategies.
Conclusion
The literature supports that delirium individually is a common
phenomenon among older patients and significantly increases the
economic burden, mortality, and morbidity. When AD-affected patients
develop delirium in a medical setting, the consequences are more
severe. This study identified several significant biomarker proteins
such as ALB, BDNF, GRIN2A, and GAD2 as potential candidates for
diagnosis, prognosis, and therapeutic development against delirium and
AD. The transmission across chemical synapses, neurodegeneration,
signal transduction, neuroinflammation and glutamatergic signaling
pathway, BDNF signaling pathway, fragile-X syndrome, oxidative stress,
hypoxia, and cholinergic functional pathways were most significantly
associated with delirium and AD-associated pathophysiology. Moreover,
the MD analysis and simulation study (100 ns) among the common
hub-proteins and neurological repurposable drugs provided the
top-ranked drug candidates (tacrine and donepezil) and the prominent
drug target proteins (ALB and GAD2), significant for therapeutic
development against delirium and AD. The findings were consistent with
and supported by the outcomes of previous studies as we discussed
earlier. The findings of this study will strengthen the molecular
research foundation for delirium and AD pathophysiological mechanisms.
Furthermore, the reported drug targets and the drug molecules will
enhance efficient therapeutic development for delirium and AD by
further validation under in-depth pharmacological and clinical
research.
Supplementary Information
[217]Supplementary Material 1.^ (1MB, pdf)
[218]Supplementary Material 2.^ (2.3MB, docx)
[219]Supplementary Material 3.^ (9.8KB, xlsx)
[220]Supplementary Material 4.^ (28.5KB, xlsx)
[221]Supplementary Material 5.^ (12.1KB, xlsx)
Acknowledgements