Molecular properties of curcumin
The Lipinski rule of five (RO5) establish pharmacokinetic properties of drugs such as distribution, metabolism, absorption, and excretion on specific molecular properties. In accordance with RO5, the potential compound’s molecular weight should be at most 500 Dalton, the rotatable bond below10, the hydrogen bond acceptors below10, and the hydrogen bond donors below 5, calculated (XLogP3-AA) octanol /water partition coefficient (XLogP3-AA) of no more than 5 and Polar surface area (PSA) no more than 140 A2. Our results indicated that our results were in accordance with RO5, 0.55 Bioavailability score and High gastrointestinal absorption showing that curcumin has good drug-like properties without any violations (Table 1). The drugs with bioavailability score 0.55 or more have good pharmacokinetic properties and are considered potential candidates for oral drugs (Bojarska et al. 2020).
Table 1
Curcumin’s Molecular Properties
Property | Value |
Molecular Weight | 368.38 g/mol |
XLogP3-AA | 3.20 |
PSA | 93.06 Ų |
Rotable Bonds | 8 |
H-bonds donor | 2 |
H-bonds acceptor | 6 |
Molar refractivity | 102.80 |
Bioavailability Score | 0.55 |
GI Absorption | High |
Blood Brain Barrier Permeability | No |
Analysis Between Targets Of Curcumin And Ad
In this study, the targets of the curcumin were predicted using the STITCH, the Binding DB, the PharmMapper and SwissTargetPrediction databases. After merging target proteins obtained, 383 targets for curcumin were obtained. Additionally, AD-linked genes were retrieved from the OMIM, CTD, TTD, and CooLGeN databases. 512 targets were obtained after redundant data were deleted. Finally, 74 genes were selected from the intersection of the curcumin targets and AD targets. A drug exhibits characteristics called as polypharmacology or drug promiscuity which means it can bind to multiple targets. Thereby, it is important to understand the drug-target interactions. To study the relationship between curcumin and the targets, a compound-target network (C-T network) was built. This network had 75 nodes and 74 edges and is presented in Fig. 2.
Gene Enrichment Analysis
We conducted KEGG pathway and GO enrichment analyses to expound the potential biological functions of 74 target genes. GO annotation and KEGG pathway analyses for the 74 targeted genes were performed. The significant enrichment of these targeted genes are shown in Fig. 3.The targeted gene were enriched in molecular function (MF) including transmembrane receptor protein kinase activity, transition of metal ion binding, MAP kinase activity etc (Fig. 3A). As for cellular component the targeted genes are involved in dendrite, neuron projection, axon, nucleus, intracellular organelle lumen etc (Fig. 3B). According to the GO enrichment results these targets not only regulate Protein phosphorylation, transferase activity, inflammatory response and peptidyl-tyrosine modification (Fig. 3A) but also regulate the Protein kinase binding, Protein homodimerization activity and DNA binding (Fig. 3C). As shown in these Fig. 3C top 10 terms in biological process and Molecular process were related to “protein phosphorylation” (GO: 0006468), regulation of “inflammatory response” (GO: 0050727) and regulation of “kinase activity” (GO: 0043549). All these are mainly related to development and spread of the AD.
Additionally, the pharmacological mechanism of the curcumin against the AD was studied using the KEGG analysis using the Enrichr tool and DisGeNET analysis using the Metascape online database. After the enrichment analysis of the pathway 74 genes were mapped to 171 pathways. The results showed that many targets played role in multiple biological pathways simultaneously. For example, both KDR and MAPK1 were both involved in the Ras signalling pathway (Fig. 3D and Fig. 3E). It has been studies that the activation of the Ras-ERK signalling is responsible for the hyperphosphorylation of the tau proteins another major hallmark for the AD (Kirouac et al. 2017). The results indicated that the curcumin could have therapeutic effect against AD through targeting various pathways and proteins.
Protein-protein Interaction Network Analysis And Key Gene Identification
Using the GeneMANIA database, PPI networks were built, visualized and analyzed by Cytoscape software. The PPI data and network is shown in Fig. 4A. Network Analyzer (cytoscope plugin) were utilized to analyse the network topological properties. (Supplementary file 1). Here, the highest degree is 99 and lowest degree is 1. In order to quantify and visualize the relation between the genes and understand the functions of proteins at the systematic level the PPI network was obtained. The data was then analysed on the basis of the topological parameters like betweenness, degree and closeness and then it was found that APP was present in the centre of the network having the largest degree (degree = 53), closeness (closeness = 0.699) and betweenness (betweenness = 0.06) followed by MAPK1, PPARγ, STAT3, CTSD, AGTR1, RARA, MET, STAT1, and KDR. A gene with the higher degree (k) and centralities (betweenness and closeness) value can aid to recognize a biological system having main role in the network. Therefore, we have computed degree (k), centralities betweenness (CB) and closeness (CC) by using NetworkAnalyzer. The details of top ten degree and centrality measurement (CB and CC) of PPI network are given in Fig. 4B, Fig. 4C and Fig. 4D respectively. First, we have selected the first10 genes based on ranking of degree, betweenness and closeness. After that find, the common genes in degree, betweenness and closeness of network (Fig. 4E). We identified key genes in regulatory network of AD. Therefore, we have identified five key gene RARA, APP, PRARG, STAT3 and MAPK1 in PPI network (Fig. 4A). These entire key genes play a great role in the progression of AD. The PPI network of 74 genes is shown in Fig. 4A with the key gene highlighted in yellow. The regulatory network of these key gene is given in Fig. 4F
Mapping Of Targeted Ad Genes With All Brain Expressed Genes Analysis
SynGO (Koopmans et al. 2019) showed clear enrichment in ontological categories correlated with synaptic signalling and synapse organisation for inhibitory neuron genes found with AD targeted gene. The evaluation of the results indicated that 15 genes like CTSD, APP, and MAPK1 are synaptic and are localised in the synaptic membrane. 10 genes are the presynaptic and are localised in pre-synaptic membrane, endosome, cytosol and exoskeleton. Out of the inputted genes 16 genes were also showing the role in process of synapse. Studies have also been established that CTSD and APP protein promotes synapse formation and neuronal migration. The impairment of CTSD and APP protein can lead to dysregulation of synaptic transmission therefore the protein have a part in the patho-physiology of the cerebral degenerative disease (Niemeyer et al. 2020) (Hefter et al. 2020). The Fig. 4G below shows the biological function of genes in the synapses.
Varelect Analysis Of The Key Genes
For the genotypic-phenotypic analysis of 74 intersection genes were loaded in the VarElect online server. The result is shown in Table 2. Amyloid precursor protein (APP) is a single pass trans-membrane protein belonging to the family of the proteins called amyloid precursor-like proteins (APLP) in Drosophila, APLP1 and APLP2 of mammals. This APP protein is responsible for the lodgement of the neurotoxic β-amyloid peptide (Aβ) in the brain which is key factor for the development of AD (O'Brien and Wong 2011). In humans, APP gene is present on the chromosome number 21 which translates into three isoforms i.e., APP695, APP770 and APP751. These forms vary on the basis of presence of Kunitz Protease Inhibitor (KPI) domain within their extracellular regions (Kang and Müller-Hill 1990). The APP695 lacks this domain and it has been reported that in AD patients this KPI lacking isoform gets converted to KPI containing isoforms i.e. APP770 and APP751 which is found associated with increased production of Aβ (Bordji et al. 2010). Another Protein called Cathepsin D (CTSD) is a gene which is involved in the processing of APP protein. The impaired activity of the CTSD protein leads to the accumulation of the β-amyloid peptide (Aβ) thereby causing the progression of AD (Di Domenico, Tramutola, and Perluigi 2016) (Schuur et al. 2011). The development of the amyloid peptides causes the inflammatory response in the brain. The peroxisome proliferator-activated receptor gamma (PPAR-gamma) is transcription factor which suppress that inflammatory gene expression by regulating the glucose and lipid metabolism (Tyagi et al. 2011) (Govindarajulu et al. 2018).
Table 2
VarElect Analysis of target proteins of Alzheimer with the targets of Curcumin. The score tells the relation between the genes and the target phenotype.
Symbol | Description | Category | Score | Average Disease-Causing Likelihood |
APP | Amyloid Beta Precursor Protein | Alzheimer | 41.00 | 77.65% |
PLAU | Plasminogen Activator, Urokinase | Alzheimer | 28.26 | 32.24% |
NOS3 | Nitric Oxide Synthase 3 | Alzheimer | 28.26 | 58.50% |
GBA | Glucosylceramidase Beta | Alzheimer | 13.62 | 61.31% |
BACE1 | Beta-Secretase 1 | Alzheimer | 6.81 | 78.70% |
GSK3B | Glycogen Synthase Kinase 3 Beta | Alzheimer | 6.11 | 83.18% |
MAOB | Monoamine Oxidase B | Alzheimer | 5.67 | 84.51% |
PPARA | Peroxisome Proliferator Activated Receptor Alpha | Alzheimer | 5.56 | 36.99% |
CTSD | Cathepsin D | Alzheimer | 5.52 | 68.84% |
PPARγ | Peroxisome Proliferator Activated Receptor Gamma | Alzheimer | 5.50 | 58.38% |
TTR | Transthyretin | Alzheimer | 5.32 | 43.90% |
MME | Membrane Metalloendo peptidase | Alzheimer | 5.26 | 69.79% |
AKT1 | AKT Serine/Threonine Kinase 1 | Alzheimer | 5.15 | 90.77% |
IL2 | Interleukin 2 | Alzheimer | 5.12 | 75.55% |
HTR7 | 5-Hydroxytryptamine Receptor 7 | Alzheimer | 5.12 | 82.77% |
ESR1 | Estrogen Receptor 1 | Alzheimer | 5.12 | 68.53% |
CASP3 | Caspase 3 | Alzheimer | 5.12 | 64.18% |
MAPK8 | Mitogen-Activated Protein Kinase 8 | Alzheimer | 5.09 | 87.00% |
MAPK10 | Mitogen-Activated Protein Kinase 10 | Alzheimer | 5.09 | 78.02% |
HMOX1 | Heme Oxygenase 1 | Alzheimer | 5.09 | 31.92% |
CDK1 | Cyclin Dependent Kinase 1 | Alzheimer | 5.06 | 79.71% |
MAPK1 | Mitogen-Activated Protein Kinase 1 | Alzheimer | 5.06 | 73.83% |
CDK5R1 | Cyclin Dependent Kinase 5 Regulatory Subunit 1 | Alzheimer | 5.06 | 88.63% |
ALOX5 | Arachidonate 5-Lipoxygenase | Alzheimer | 5.06 | 77.22% |
CYP19A1 | Cytochrome P450 Family 19 Subfamily A Member 1 | Alzheimer | 5.06 | 49.30% |
PTGS2 | Prostaglandin-Endoperoxide Synthase 2 | Alzheimer | 5.06 | 66.54% |
REG1A | Regenerating Family Member 1 Alpha | Alzheimer | 5.06 | 82.07% |
HMGCR | 3-Hydroxy-3-Methylglutaryl-CoA Reductase | Alzheimer | 5.06 | 83.06% |
REN | Renin | Alzheimer | 5.06 | 75.65% |
INSR | Insulin Receptor | Alzheimer | 5.06 | 75.63% |
NOS2 | Nitric Oxide Synthase 2 | Alzheimer | 5.06 | 46.59% |
EPHA1 | EPH Receptor A1 | Alzheimer | 2.93 | 34.44% |
AGTR1 | Angiotensin II Receptor Type 1 | Alzheimer | 2.60 | 63.82% |
ADH1C | Alcohol Dehydrogenase 1C (Class I), Gamma Polypeptide | Alzheimer | 2.05 | 0.00% |
S100A9 | S100 Calcium Binding Protein A9 | Alzheimer | 1.69 | 67.27% |
HTR1A | 5-Hydroxytryptamine Receptor 1A | Alzheimer | 0.72 | 56.88% |
MAOA | Monoamine Oxidase A | Alzheimer | 0.62 | 84.12% |
DRD4 | Dopamine Receptor D4 | Alzheimer | 0.62 | 0.00% |
HSP90AA1 | Heat Shock Protein 90 Alpha Family Class A Member 1 | Alzheimer | 0.51 | 79.59% |
ESR2 | Estrogen Receptor 2 | Alzheimer | 0.51 | 54.14% |
MMP9 | Matrix Metallopeptidase 9 | Alzheimer | 0.36 | 20.28% |
PARP1 | Poly (ADP-Ribose) Polymerase 1 | Alzheimer | 0.36 | 45.76% |
PDE5A | Phosphodiesterase 5A | Alzheimer | 0.36 | 29.70% |
ALB | Albumin | Alzheimer | 0.36 | 82.33% |
SOD2 | Superoxide Dismutase 2 | Alzheimer | 0.36 | 35.48% |
NFE2L2 | Nuclear Factor, Erythroid 2 Like 2 | Alzheimer | 0.36 | 54.66% |
Spatiotemporal Expression Patterns Of Five Key Genes In Various Regions Of Human Brain
Additionally, to test if the key genes PPARγ, MAPK1, STAT3, RARA and APP causes the risk of AD, we searched the expression profiling of PPARγ, MAPK1, STAT3, RARA and APP in different regions human brain tissues utilizing the BRAINEAC data (Fig. 5). We identified that PPARγ, MAPK1, STAT3, RARA and APP are expressed differently in various brain regions, with the highest transcript level was observed for MAPK1 in the putamen, followed by hippocampus, temporal cortex, frontal cortex, occipital cortex and thalamus. The least expression was observed in cerebral cortex for PPARγ, RARA and APP. Both RARA and PPARγ showed highest expression in the thalamus.
In order to evaluate comparative temporospatial expression of PPARγ, MAPK1, STAT3, RARA and APP in human central nervous system (CNS), we searched the Human Brain Transcriptome (HBT) dataset (https://hbatlas.org/), a database based on Affymetrix GeneChip arrays (Keil, Qalieh, and Kwan 2018). As it is seen in Fig. 6, PPARγ, MAPK1, STAT3, RARA and APP mRNA expression is consistent from conception to adulthood in all brain regions shown here which includes: mediodorsal nucleus of the thalamus (MD), cerebellar cortex (CBC), amygdala region (AMY), striatum (STR), hippocampus (HIP) and neocortex (NCX)The highest expression was seen for APP followed by MAPK1 and STAT3. The temporal expression analysis showed that in the case of RARA the expression level was high during the fetal development, but the expression level gradually decreased as the human brain developed. The trajectory plot of PPARγ further tells that the expression of PPARγ mRNA expression is quite uniform from conception to adulthood in all depicted brain regions, including NCX, AMY, STR, MD, and CBC. All regions indicate comparatively lower level of expression in early embryonal life which slowly increases during fetal development till birth and increase again postnatal life and early childhood up to adolescence (Fig. 6), which remains quite stable thereafter. It must be noted however, that PPARγ expression in hippocampal region (blue line) behaves differently indicating a more significant rise in early postnatal life and persisting at higher levels (in comparison to other tissues) up to adulthood.
Molecular Docking
Molecular docking studies helped us in better understanding the binding interactions of the key proteins with the ligand molecule. The molecular docking of key proteins RARA, APP, PPARγ, STAT3 and MAPK1 with the curcumin was carried out using Glide. The glide score and glide binding energy for all the key genes are shown in the Table 3. The results showed that the stronger interaction is present between PPARγ molecule and curcumin having Glide energy 49.03 kcal/mol and glide score as -7.269. The 2-D structure of the docking complex reveals that Curcumin interacts with the amino acids GLU 295 in the active site (Fig. 7). The interacting amino acids of the various docking complex is given in (Table 3).
Table 3
Molecular Docking analysis results of Curcumin and shortlisted targets of protein showing Glide Score, energy of the docking and interacting amino acids
Target Proteins | Glide energy (Kcal/mol) | Glide Score | Interacting Amino acids |
PPARγ | − 49.03 | -7.269 | PHE 282, CYS 285, GLN 286, ARG 288, SER289, GLU 291, ALA 292, GLU 295, VAL 339, LEU 340, ILE 341, SER342, HIE 449, PHE 226, HIE 323, ILE 325, ILE 326, TYR 327, MET 329, LEU 330, LEU333 |
MAPK1 | − 43.637 | -5.635 | VAL 37, ALA 33, ASP109, GLY32, GLU 31, ILE 29, MET 106, LEU105, ASP104, GLN 103, ALA 50, LYS 52, LEU154, ASN 152, LYS 149, ASP 147, ARG 65, THR188, LEU168, ASP 165, CYS 164, GLU 69 |
STAT3 | − 43.489 | -5.329 | ILE 258, ASN257, PRO 256, GLY 253, CYS 251, ALA 250, GLN 247, SER 514, TRP 474, PRO 336, ASP 334, PRO 333, ARG 325, GLN 326, CYS 328 |
RARA | − 34.122 | -5.312 | TRP 225, PHE 228, SER 229, SER 232, PRO 407, PRO 408, LEU 409, ILE 410, GLN 411, VAL 395, ALA 392, ALA 389, SER 388, ARG 385, LYS 262, ALA 263, CYS 265, LEU 266, ASP 267, LEU 269, ARG 339 |
APP | -36.255 | -3.980 | LYS 314, LEU 311, PHE 310, HIS 307, VAL 373, HIS 376, ALA 377, VAL 380, ILE 381, VAL 434, HIS 433, HIS 430, ARG 429, HIS 426 |