Abstract
There is limited available information regarding the biological activity of Abyssinone-I, apart from its recognized antioxidant and cytotoxic properties. So, we aimed to evaluate the molecular processes underlying the promising effect of Abyssinone-I on Alzheimer’s disease (AD). The Swiss Target Predictor, GeneCard, GeneMania, Metascape, SwissADME, Cytoscape, the Panther classification system, MIENTURNET, WebGestalt, PASS online, Autodock Vina, and molecular dynamic simulation were the main methods for this analysis. Abyssinone-I exhibits antioxidative, anti-inflammatory, and MAO inhibitory activities and maintains membrane integrity. These properties may target 79 proteins, four miRNAs (hsa-miR-128-3p, hsa-miR-124-3p, hsa-miR-16-5p, and hsa-miR-335-5p), three transcription factors (PPARG, MEF2B, and MYBL2), and two chromosomes (chr9q22.2, chr12q24.12). Key pathways affected include the amyloid-beta response, protein autophosphorylation, and dopamine metabolism. Among these, five hub targets (PPARG, mTOR, EGFR, ESR1, and MAPK1) were highlighted for their significant roles in AD pathogenesis. Despite its promising properties, abyssinone-I has low bioavailability and may interact with other drugs. Future in vivo and in vitro studies are necessary to validate these findings and optimize therapeutic usage. This study provides a foundation for Abyssinone-I as a potential AD treatment, pending further experimental confirmation.
Graphical Abstract
![](http://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs44337-024-00009-7/MediaObjects/44337_2024_9_Figa_HTML.png)
Highlights
-
PPARG, mTOR, EGFR, ESR1 & MAPK1 were linked with Abyssinone I & Alzheimer’s Disease
-
Key pathways: dopamine metabolic process, regulation of neurotransmitter levels
-
Core miRNAs: hsa-miR-128-3p, 124-3p, 16-5p, and 335-5p
-
Transcription factors and chromosomes: PPARG, MEF2B, MYBL2, chr9q22.2, chr12q24.12
-
Membrane integrity agonists, and monoamine oxidase inhibitors were highlighted
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
The incidence of Alzheimer’s disease (AD) tends to increase due to population growth, an aging population, and exposure to chemicals during urbanization and industrialization [1,2,3]. AD affects not only patients (e.g., their physiology, psychology, society, and finances), but also their families, caregivers, and society as a whole [4]. Therefore, drugs and improving the quality of life for people with AD were put at the top of the list of strategies to deal with the public health problem of AD.
Although there have been extensive studies on the pathogenetic and pathophysiology of AD (Aβ amyloid accumulation, tau hyperphosphorylation, mitochondrial dysfunction, central cholinergic damage, infection, gut microbes’ abnormal growth, and environmental and genetic factors) [2, 3, 5,6,7], the etiopathology of AD remains poorly understood. Several medicines, like donepezil, rivastigmine, tacrine, galantamine, memantine, and N-methyl-D-aspartate (NMDA) receptor antagonists, have been approved to treat AD, but they can only slow the disease's progression or lessen its symptoms. Furthermore, patients who used these medications reported various side effects (e.g., diarrhea, sickness, insomnia, and hepatotoxicity) [8]. On the other hand, poor drug pharmacokinetics and pharmacodynamics and poor blood–brain barrier (BBB) permeability are also known to be common causes of therapeutic failure [9]. Until now, no effective cures have been available. Due to the limitations of current medicine, identifying or developing new therapies for AD prevention and treatment is necessary and urgent. Natural products exert anti-inflammatory, anti-oxidant, anti-infective, and anti-apoptotic properties, which are promising alternative therapies for AD [10,11,12,13].
Abyssinones are flavonoids that have been prenylated and isolated from the plant Erythrina Abyssinica. These compounds have attracted a substantial amount of attention because Abyssinone-II has antioxidant, cytotoxic, and anticancer properties [14, 15]. In hormone-dependent cancer, Hatti et al. reported the role of Abyssinones as steroidogenesis modulators [16]. A recent study discovered that Abyssinone V-4’ methyl ether, which was extracted from Erythrina droogmansiana, has demonstrated anti-mammary tumor effects in mice and anti-breast cancer activity in MDA-MB-231 cells [17]. In addition, studies have demonstrated that Abyssinone I and II can trigger apoptosis in human cervical cancer cells via the mitochondrial pathway [14]. Though Abyssinone-I is extracted from natural sources, there has been little information on its biological activity except for its antioxidant and cytotoxic properties [14]. Therefore, further research is necessary to determine the effectiveness of these compounds in treating neurodegenerative diseases. Due to the rapid development of existing bioinformatics, publicly available databases offer big data to facilitate drug research. Therefore, integrating the data from these databases with the appropriate bioinformatical techniques can help clarify the biological properties of natural compounds. This study aimed to elucidate the molecular mechanisms of Abyssinone-I against AD.
2 Materials and methods
2.1 Drug likeness assessment
We first assessed the drug likeness of the studied compound. Abyssinone-I (C20H18O4, Fig. 1A) was obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/), and it was then submitted to SwissADME (www.swissadme.ch) to analyze the physicochemical properties, drug likeness, pharmacokinetics, and medicinal chemistry. In this study, drug likeness was evaluated following Lipinski’s rule [18].
2.2 Target identification
Next, we identify the important target of Abyssinone-I by using Swiss Target Predictor (www.swisstargetprediction.ch) [19]. After that, we analyzed targets involved in the pathogenesis of AD using GeneCard (https://www.genecards.org) [20]. The JVenn tool (http://jvenn.toulouse.inra.fr/app/example.html) was used to classify the overlapping targets. Finally, these shared targets were assessed to categorize the protein class using the Panther classification system (http://www.pantherdb.org/) [21]. In this study, Homo sapiens was selected as a target organism when required.
2.3 Enrichment analyses
We first proceeded by examining the gene–gene interactions. The gene–gene interactions were analyzed using GeneMANIA, a free Cytoscape plug-in (http://geneMANIA.org/plug-in/) [22]. Second, cytoscape software 3.9.1 (http://www.cytoscape.org/) and the CytoscapeClueGO plug-in version 2.5.8 were utilized to identify and visualize the biological processes of the shared targets. In this analysis, the GoBiologicalProcess databases were selected to attain the list of Cytoscape biological process plugins [11, 23,24,25]. After that, the CytoHubba plug-in was applied to obtain the Hub targets based on three key parameters (degree, closeness, and betweenness) [26]. To examine protein–protein interaction enrichment (PPIE), STRING (String version 11.5, available at https://string-db.org) and Metascape (available at https://metascape.org) were utilized. Only physical interactions were looked at in STRING (physical score > 0.132) [22]. In networks with between 3 and 500 proteins, the Molecular Complex Detection (MCODE) approach was performed to narrow down highly related network components [27, 28]. Additionally, ChIP-X Enrichment Analysis Version 3 (CHEA3) was carried out to identify the transcription factors responsible for the shared targets (https://maayanlab.cloud/CHEA3). Using Cytoscape, the integrated regulatory network was visualized using the top ten transcription factors as determined by the mean rank score [26, 29,30,31]. We further analyzed the cellular components and chromosomes related to AD etiology targeted by Abyssinone-I by using WebGestalt (www.webgestalt.org). In these analyses, over-representative analysis, gene ontology, chromosome location, and illumine humanht 12 v3 were selected. A volcano plot and a bar plot were used to visualize the outcomes [32]. Finally, we analyzed the interactions between miRNA and studied targets by using the MicroRNA ENrichment TURned NETwork (MIENTURNET, http://userver.bio.uniroma1.it/apps/mienturnet/) [33].
2.4 Biological activities and metabolisms
In this analysis, the biological activities and metabolisms of Abyssinone-I were investigated using PASS online (Prediction of Activity Spectra for Substances) [34]. We used "probable activity (Pa, probability to be active)" to assess the likelihood of common biological activities of the studied compound. A number of cytochrome (CYP) P450 isoforms' metabolic processes, including those of their substrates, inducers, and inhibitors, were also studied. Pa values are reported as percentages of probability (%) and range from 0.000 to 1.000.
2.5 Molecular docking
The key targets (PPARG, mTOR, EGFR, ESR1, and MAPK1) thought to be responsible for Abyssinone-I's effects against AD were utilized in this investigation's in-silico docking study using Autodock Vina [35]. In the present study, proteins exhibiting significant levels of x-ray crystallization were chosen. Prior to completing the analysis, the proteins were corrected using MOE software (version 2014). The PubChem database (https://www.ncbi.nlm.nih.gov/) was used to retrieve the Abyssinone-I structure, which was then converted to a PDB file format. Using the Avogadro software, the MMFF-94 force field, and the steepest descent algorithm, the structure of Abyssinone-I was then optimized over 5000 iterations using the MMFF-94 force field [36]. To obtain the target structures in PDB format, we accessed the RCSB website at http://www.rcsb.org/pdb. For this analysis, the PPARG structure that showed higher levels of x-ray crystallization was obtained. The key targets were prepared using MOE software (version 2014) prior to performing the analysis [37]. The protein–ligand complexes were cleared of water molecules and crystal ligands prior to docking. Hydrogen atoms and charges were inserted, while default values for other properties were used. The exhaustiveness setting was assigned a value of 8, and the energy range setting was assigned a value of 4. The AUTOGRID method is used to make a three-dimensional grid so that the total binding energy between abyssinone-I and the key target can be calculated. The grid is generated in the shape of a rectangle. The grid is structured with dimensions of 40 units on the x-axis, 40 units on the y-axis, and 40 units on the z-axis. According to the information provided in reference, the distance between each point on the grid is 0.375 Å [26]. Abyssinone-I's binding affinities to amino acids are estimated using the docking score. The visualization of the results was done using the BIOVIA Discovery Studio Visualizer 2016 program.
2.6 Molecular dynamic simulation, principal component analysis, and binding energy calculation
In this study, a 100-ns molecular dynamic simulation (MDS) analysis of PPARG was carried out using the simulation tool Gromacs 5.1.4, which revealed associations and binding affinities with Abyssinone-I. The topology parameter files for the proteins were constructed using the GROMOS96 force field, while the ligand topology file was generated using the PRODRG2 online service [38]. The docked complexes were included in a cubic box with 1.0 periodic boundary conditions using the straightforward point-charge water model. The system was then neutralized by the addition of Couturier ions. The processed systems were subjected to energy reduction utilizing the steepest descent method and conjugate gradient reduction. For steady temperature (up to 300 K) and pressure (1 bar) stability at 5000 ps, respectively, the systems were modified using the NPT and NVT groups. Each component's temperature was controlled using the Parrinello-Rahman barostat. The stability, flexibility, and rigidity and compactness of Abyssinone-I were assessed via four key parameters: the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration, and hydrogen bond interaction patterns. Principal component analysis (PCA) was performed using the condacolab package in Python version 3.0, Gromacs, and Pymol. PCA was executed on Colab, a cloud-based Jupyter Notebook platform that offers complimentary access to computational resources such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). The g_mmpbsa tool was utilized to measure the binding free energy of protein–ligand complexes. The binding free energy of the complex structure for the last 5 ns was determined using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method by g_mmpbsa. The potential energy of solvation was calculated, including the electrostatic and van der Waals interactions, the free energy of solvation, and the polar and non-polar solvation energies.
3 Results
3.1 Drug-likeness assessment
We first evaluate Abyssinone-I's druggability. As shown in Fig. 1B, the physicochemical properties of Abyssinone-I reached Lipinski’s rule of five. Abyssinone-I possessed the ideal range for solubility, lipophilicity, size, saturation, polarity, and flexibility (Fig 1C). Furthermore, the studied compound exerted high human gastrointestinal absorption and blood-brain barrier (BBB) permeation (Fig 1D). Therefore, Abyssinone-I can be a promising compound to optimize as an oral treatment for AD.
3.2 Overlapping target evaluation
Figure 2A shows 79 overlapping targets involved in the pathogenesis of AD and related to Abyssione-I. As shown in Fig. 2B, most of these targets (29/79, 37%) were implicated in the processes of protein-modifying enzymes, followed by metabolite interconversion enzymes (22/79, 28%), transmembrane signal receptors (15/79, 19%), and transporters (6/79, 8%). Most of the interactions between these targets were found to be physical interactions and co-expression (Fig. 2C).
3.3 Molecular mechanism analysis
We looked at the enrichment analysis for biological processes, cellular components, and PPIs to figure out how Abyssinone-I is involved in the cause of AD through shared targets. The most enriched biological processes involved in Abyssinone-I and AD were “response to amyloid-beta”, “protein autophosphorylation”, “protein tyrosine kinase activity”, “multicellular organismal response to stress”, “dopamine metabolic process”, and “peptidyl-serine modification” (Fig. 3A). The most enriched cellular components were the plasma membrane region, post-synapse, and dendrite (Fig. 3B). PPIE analysis highlighted “dopamine metabolic process”, “regulation of neurotransmitter levels”, and protein phosphorylation-related pathways, implicating that Abyssinone-I may combat AD by regulating these pathways (Fig. 3C and Table S1).
We next identify factors related to shared targets, including Hub targets, transcription factors, chromosomes, and miRNAs. As shown in Fig. 4A–C and Figure S1A–C, five Hub targets (PPARG, mTOR, EGFR, ESR1, and MAPK1), three transcription factors (PPARG (regulated 25/79 targets), MEF2B (regulated 17/79 targets), and MYBL2 (regulated 17/79 targets), and two chromosomes (chr9q22.2 and chr12q24.12) were listed as the top 10 most enriched terms.
In terms of miRNAs, hsa-miR-128-3p was found to interact with stemness-related targets four times (Fig. 5A). A network representation of key miRNAs (hsa-miR-128-3p, hsa-miR-124-3p, hsa-miR-16-5p, and hsa-miR-335-5p) and their hub targets was predicted in Fig. 5B. “Cell cycle”, “prolactin signaling pathway”, “PI3K-AKT-mTOR signaling pathway and therapeutic opportunities” and “Alzheimer’s disease”, and “tauopathy” were the important oncology processes targeted by Abyssinone-I.
The beneficial effects of Abyssinone-I against Alzheimer's disease are mediated via the structural interactions of hub targets, including PPARG (A), mTOR (B), EGRF (C), ESR1 (D), and MAPK1 (E). The functional residues that comprise binding pocket residues are denoted by three-letter amino acid codes, and the different kinds of interactions are denoted by different color schemes. For the donor and acceptor, respectively, in the H-bond, pink and green are displayed
3.4 Molecular docking and stability of the protein–ligand complex
We next assessed the binding mode between five hub targets and Abyssinone-I. The binding affinity of these complexes ranged from −8.9 to −8.5 kcal/mol (Fig. 6A–E and Table 1). PPARG was found to have the lowest affinity energy (−8.9 kcal/mol), implying that PPARG plays critical roles in underlying processes combating AD. The binding energy between PPAGR and Abyssinone-I was identified via a Pi-anion interaction with Asp441 and a Pi-alkyl interaction with Arg443.
The beneficial effects of Abyssinone-I against Alzheimer's disease are mediated via the structural interactions of hub targets, including PPARG (A), mTOR (B), EGRF (C), ESR1 (D), and MAPK1 (E). The functional residues that comprise binding pocket residues are denoted by three-letter amino acid codes, and the different kinds of interactions are denoted by different color schemes. For the donor and acceptor, respectively, in the H-bond, pink and green are displayed
The RMSD graph demonstrates the stability of Abyssinone-I in PPARG. With an estimated backbone RMSD of 0.375 nm, the system and proteins are stable due to the low variation (Fig. 7A). The loop's various amino acid residues can be seen in the RMSF figure. However, these modifications did not significantly affect the average RMSF of 0.3 nm, and Abyssinone-I and PPARG were still able to achieve their conformations (Fig. 7B). In the simulation, 100 ns-long stable 2H-bonds were formed between Abyssinone-I and PPARG (Fig. 7C). Figure 7D demonstrates that PPARG is rigid and compact, and the small but consistent variation in its radius of gyration demonstrates the protein's stable folding. Based on the results obtained, it was predicted that the average supersurface area (SASA) value of the Abyssinone-I and PPARG complex combination would be 240 nm2 (Fig. 7E). It can be inferred from this prediction that the systems being examined did not exhibit any significant alterations in their conformational structure over the course of the simulation.
We conducted PCA to predict significant coordinated movements that occur during ligand binding. The initial eigenvectors are recognized as the primary factors that depict the general movements of the protein. We used the initial 100 eigenvectors to determine the highly correlated movements during the final 100 ns of the runs. We computed the eigenvalues by diagonalizing the covariance matrix of atomic fluctuations. Figure 8A displays the structures of the protein–ligand complex, including surface and non-surface. Figure 8B displays the graph of eigenvalues representing the variability in the protein–ligand combination. The analysis of 100 eigenvectors revealed that slight motion variations may not influence the behavior of Abyssinone-I and the PPARG complex. This suggests that the protein–ligand complexes are stable. Figure 8B illustrates that the average variance of this dimension was around 0.53 ∂^2. The atoms located at the protein's N-terminus and C-terminus that are involved in the principal component movement were measured. The atoms located in the middle and at the end exhibit minimal movement, providing supportive evidence for the stability of this intricate structure (Fig. 8C). Figure 8D displays the PCA of the three components. The binding free energy corresponds to the sum of all the non-covalent interactions. We employed the MM-PBSA method to determine the values for Abyssinone-I and the PPARG complex. The last 5 ns of the molecular dynamics’ trajectory were used to figure out the interaction energies. These included Van der Waal's energy, polar solvation energy, electrostatic energy, SASA energy, and binding energy (Table 2). Figure 9A–D shows binding energy, molecular mechanics potential energy, free energies of polarization, and nonpolar energy. The Abyssinone-I-PPARG complex exhibits a binding free energy of -212.451 kJmol-1. The data clearly demonstrate that Abyssinone-I exhibits a substantial binding affinity with PPARG.
Principle component analysis (PCA) of Abyssinone-I and the PPARG complex. The structures of the protein–ligand complex, including surface and non-surface (A). The eigenvalues display the variability in the protein–ligand combination (B). C Measurement of the atoms involved in the principal component movement. D PCA of three components related to Abyssinone-I and the PPARG complex
3.5 Biological activities and metabolisms
We further analyzed the biological activities and metabolisms of Abyssinone-I. As shown in Fig. 10A, the main potential mechanisms against AD were antioxidative stress, anti-inflammatory, and anti-oxidant activities, membrane integrity agonists, and MAO inhibitors. Furthermore, Abyssinone-I acted as substrates, inhibitors, and inducers for various CYP450 enzymes (Fig. 10B).
4 Discussion
This study gave mechanistic insight into the potential effects of Abyssinone-I on AD. Overall, Abyssinone-I exerts various biological properties against AD (e.g., antioxidative stress, anti-inflammatory, anti-oxidant activities, membrane integrity agonists, and MAO inhibitors). Furthermore, Abyssinone-I possesses its therapeutic effects on AD via 79 targets, four miRNAs (hsa-miR-128-3p, hsa-miR-124-3p, hsa-miR-16-5p, and hsa-miR-335-5p), three transcription factors (PPARG, MEF2B, and MYBL2), and two chromosomes (chr9q22.2 and chr12q24.12). These targets were found to be linked with “response to amyloid-beta”, “protein autophosphorylation”, “protein tyrosine kinase activity”, “multicellular organismal response to stress”, “dopamine metabolic process”, and “peptidyl-serine modification”, while miRNAs were related to “cell cycle”, “prolactin signaling pathway”, “PI3K-AKT-mTOR signaling pathway and therapeutic opportunities”, “Alzheimer’s disease”, and “tauopathy”.
4.1 Shared targets
It has been known that one of the main causes of AD is serotonergic synapse dysregulation [39]. This study observed that Abyssinone-I combats AD by regulating 79 shared targets. Most of these targets were protein-modifying enzymes, metabolite interconversion enzymes, transmembrane signal receptors, and transporters. [7] found that these targets were involved in interactions between serotonergic, cholinergic, and neuroactive ligands and receptors. Coinciding with a cohort study of 136 AD patients and 183 healthy controls, these proteins were observed to be both upregulated and downregulated in AD patients compared with their counterparts [40]. These findings suggest that the effects of Abyssinone-I combat AD via modulating serotonergic synapse-related pathways.
On the other hand, 5 hub targets (PPARG, mTOR, EGFR, ESR1, and MAPK1) were identified among 79 overlapped targets. These hub targets were also found to be linked with AD pathogenesis. For example, there are higher levels of the peroxisome proliferator-activated receptor gamma (PPARG) in the brains of people with AD, which regulates lipid, glucose, and energy metabolism [41]. Through molecular docking, it has been shown how Abyssinone-I and PPARG work together on a molecular level. The characterization of the binding of Abyssinone-I to PPARG involved the identification of two distinct types of interactions. These interactions collectively contribute to the compound's potential therapeutic efficacy in the treatment of Alzheimer's disease through its targeted action on PPARG. The first type of interaction, called a Pi-anion interaction, was found between Abyssinone-I and an essential amino acid residue, Asp441, in the binding site of PPARG. For this interaction to happen, the dispersed electron cloud of the aromatic ring system of Abyssinone-I must line up with the negatively charged area of Asp441. The Pi-anion interaction plays a crucial role in enhancing the stability of the ligand-receptor complex, thereby strengthening the binding affinity between Abyssinone-I and PPARG. The particular interaction described plays a crucial role in facilitating the initial identification and secure placement of Abyssinone-I within the binding pocket. The second interaction, which can be classified as a Pi-alkyl interaction, was observed between Abyssinone-I and Arg443, an additional crucial residue located within the binding site of PPARG. The Pi-alkyl interaction is a result of the close proximity between the aromatic portion of Abyssinone-I and the hydrophobic alkyl side chain of Arg443. The present interaction serves to enhance the stability of the ligand–protein complex through its contribution to the overall molecular cohesion and interaction between Abyssinone-I and PPARG. Furthermore, PPARG is a key regulator of glucose and lipid metabolism [42]. It has been linked to neuroinflammation and neurodegeneration, which means it could be a target for treating Alzheimer's disease [41]. By figuring out how Abyssinone-I binds to PPARG, scientists can get a better idea of how it could be used as a drug to change how PPARG works. This has the potential to contribute to the improvement of Alzheimer's disease pathology. In short, Abyssinone-I and PPARG interact molecularly through the Pi-anion's interaction with Asp441 and the Pi-alkyl's interaction with Arg443. These interactions give us an idea of how Abyssinone-I might work to fight Alzheimer's disease by specifically targeting and interacting with PPARG.
During the early AD stages, soluble amyloid-β induced the hyperactivity of the mammalian target of rapamycin (mTOR) and thus reduced autophagy induction. However, autophagosomes fail to integrate with lysosomes during the last stages of AD [43]. In in-vivo investigations, the epidermal growth factor receptor (EGFR) has been found to be a favorable target for treating Aβ-induced memory loss [44]. A meta-analysis of 8288 AD patients and healthy controls observed an association between AD and estrogen receptor 1 (ESR1) [45]. Also, the levels of mitogen-activated protein kinase phosphatase 1 (MAKP-1) were lower in the brain tissue of AD patients and an animal model of AD [46].
4.2 Biological processes
It has been known that tau hyperphosphorylation, amyloid-β accumulation, oxidative stress, and an altered dopaminergic system are risk factors for AD [47, 48]. In the enrichment analysis, “response to amyloid-beta”, “protein autophosphorylation”, “protein tyrosine kinase activity”, “multicellular organismal response to stress”, “dopamine metabolic process”, and “peptidyl-serine modification” were listed as the top six important biological processes. PPI enrichment analysis also indicated that “dopamine metabolic process”, “regulation of neurotransmitter levels”, and protein phosphorylation-related pathways were the core signaling pathways potentially regulated by Abyssinone-I. These findings highlight that dopamine metabolic processes and protein phosphorylation-related pathways are the most important in the processes by which Abyssinone-I combats AD.
4.3 Transcription factors, miRNAs, and chromosomes
Transcription factors and miRNAs play important roles in regulating gene expression. This study found a link between AD, Abyssinone-I, and four miRNAs (hsa-miR-128-3p, hsa-miR-124-3p, hsa-miR-16-5p, and hsa-miR-335-5p). In accordance with this, various studies have reported the association between altered miRNAs and AD [3, 11, 30, 49, 50]. Three transcription factors (PPARG, MEF2B, and MYBL2) regulated the most targets related to AD and Abyssinone-I that were found. Coinciding with previous studies, these transcription factors were also reported to be implicated in the pathophysiology of AD [51,52,53]. This study also found an association between Abyssinone-I, AD, and two key chromosomes (chr9q22.2 and chr12q24.12). It has been known that chromosomes 9 and 12 have been reported to be linked with AD [54, 55]. These findings may partly bring a new understanding of AD and move treatment for AD closer.
4.4 Biological activities, physicochemical properties, and pharmacokinetics
Natural products exert multi-functional properties; therefore, they could become promising compounds as adjuvants or treatments for AD [10, 11, 13]. This analysis found that Abyssinone-I has several important properties that are needed to treat AD, such as antioxidative stress, anti-inflammatory, anti-oxidant activities, membrane integrity agonists, and MAO inhibitors. These biological activities are widely recognized as important factors that can ameliorate AD [10, 11, 13, 49, 56, 57]. These results were supported by the physical and chemical properties of Abyssinone-I, such as the fact that it is well absorbed by the GI tract and can pass through the BBB.
Although Abyssinone-I could be a promising compound for AD treatment, its bioavailability is still poor (a bioavailability score of 0.55). Furthermore, Abyssinone-I is a P-glycoprotein substrate, indicating it may be eliminated from the central nervous system by the P-glycoprotein. Based on its synthesis accessibility score of 3.93, Abyssinone-I is a chemical that should be suggested for synthesis (Figure S2). Rao et al. were able to synthesize Abyssinone I, which worked as an antioxidant and killed cells in living organisms [15].
Cytochrome P450 enzymes, mostly in the liver, play a role in drug metabolism, especially phase I reactions (hydrolysis, oxidation, and reduction) [58]. Abyssinone-I is an inducer or substrate for various cytochrome P450 enzymes (e.g., CYB, CYP2C, CYP3A, CAY1A1, CYP3A4, (Fig. 10B)). However, it is also an inhibitor for CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, which are important enzymes implicated in the phase I reaction (Figure S2). These enzymes (e.g., CYP1A2, CYP2D6, and CYP3A4) are involved in the metabolism of various approved AD medications, including donepezil, tacrine, and galantamine. Given that AD frequently requires polytherapy (e.g., antiarrhythmics, antidepressants, analgesics, antiemetics, and neuroleptics) for both more effective treatment and the management of comorbidities, Abyssinone-I may cause drug-drug interactions. Given that AD frequently requires polytherapy (antidepressants, neuroleptics, antiarrhythmics, analgesics, and antiemetics) for both more effective treatment and the management of comorbidities, Abyssinone-I may cause drug-drug interactions [10, 13]. Additionally, CYP-related enzymes, including CYP2D6, CYP3A4, and CYP1A2, are involved in the metabolism of various standard AD medications, including tacrine, donepezil, and galantamine [59]. This suggests that additional investigation is needed to determine the most effective pharmaceutical dosage of this compound for the treatment of AD. To be used in the possible treatment of AD, therapeutic optimization is necessary.
4.5 Limitations
Our findings provide insight into the molecular processes behind Abyssinone's beneficial effects on AD. However, the present study has certain limitations. The reliability of the analysis depends on online databases (Swiss Target Predictor and GeneCard) [12, 23, 26, 60]. The sensitivities of the tested subjects, the route and period of exposure, as well as the dose–response relationship of Abyssinone-I exposure, were not analyzed [13, 23, 24, 31, 60]. The metabolism, multi-drug effects, interactions, and binding-specific structures of Abyssinone-I and other compounds were not explored due to a lack of high-quality databases [10, 11, 26, 30, 49]. No experiments were performed to validate our findings. Therefore, the results can only point to important molecular processes against AD that need to be studied further in vivo and in vitro. In conclusion, this study provides a mechanistic understanding of Abyssinone-I's ability to combat AD. To confirm our findings, more experiments will need to be done, with a focus on hub targets and other important molecular processes.
Data availability
The data supporting this study was obtained from Swiss Target Predictor (www.swisstargetprediction.ch) and GeneCard (https://www.genecards.org).
References
Nichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, Abdoli A, Abualhasan A, Abu-Gharbieh E, Akram TT, Al Hamad H, Alahdab F, Alanezi FM, Alipour V, Almustanyir S, Amu H, Ansari I, Arabloo J, Ashraf T, Astell-Burt T, Ayano G, Ayuso-Mateos JL, Baig AA, Barnett A, Barrow A, Baune BT, Béjot Y, Bezabhe WMM, Bezabih YM, Bhagavathula AS, Bhaskar S, Bhattacharyya K, Bijani A, Biswas A, Bolla SR, Boloor A, Brayne C, Brenner H, Burkart K, Burns RA, Cámera LA, Cao C, Carvalho F, Castro-de-Araujo LFS, Catalá-López F, Cerin E, Chavan PP, Cherbuin N, Chu D-T, Costa VM, Couto RAS, Dadras O, Dai X, Dandona L, Dandona R, De la Cruz-Góngora V, Dhamnetiya D, Dias da Silva D, Diaz D, Douiri A, Edvardsson D, Ekholuenetale M, El Sayed I, El-Jaafary SI, Eskandari K, Eskandarieh S, Esmaeilnejad S, Fares J, Faro A, Farooque U, Feigin VL, Feng X, Fereshtehnejad S-M, Fernandes E, Ferrara P, Filip I, Fillit H, Fischer F, Gaidhane S, Galluzzo L, Ghashghaee A, Ghith N, Gialluisi A, Gilani SA, Glavan I-R, Gnedovskaya EV, Golechha M, Gupta R, Gupta VB, Gupta VK, Haider MR, Hall BJ, Hamidi S, Hanif A, Hankey GJ, Haque S, Hartono RK, Hasaballah AI, Hasan MT, Hassan A, Hay SI, Hayat K, Hegazy MI, Heidari G, Heidari-Soureshjani R, Herteliu C, Househ M, Hussain R, Hwang B-F, Iacoviello L, Iavicoli I, Ilesanmi OS, Ilic IM, Ilic MD, Irvani SSN, Iso H, Iwagami M, Jabbarinejad R, Jacob L, Jain V, Jayapal SK, Jayawardena R, Jha RP, Jonas JB, Joseph N, Kalani R, Kandel A, Kandel H, Karch A, Kasa AS, Kassie GM, Keshavarz P, Khan MAB, Khatib MN, Khoja TAM, Khubchandani J, Kim MS, Kim YJ, Kisa A, Kisa S, Kivimäki M, Koroshetz WJ, Koyanagi A, Kumar GA, Kumar M, Lak HM, Leonardi M, Li B, Lim SS, Liu X, Liu Y, Logroscino G, Lorkowski S, Lucchetti G, Lutzky Saute R, Magnani FG, Malik AA, Massano J, Mehndiratta MM, Menezes RG, Meretoja A, Mohajer B, Mohamed Ibrahim N, Mohammad Y, Mohammed A, Mokdad AH, Mondello S, Moni MAA, Moniruzzaman M, Mossie TB, Nagel G, Naveed M, Nayak VC, Neupane Kandel S, Nguyen TH, Oancea B, Otstavnov N, Otstavnov SS, Owolabi MO, Panda-Jonas S, Pashazadeh Kan F, Pasovic M, Patel UK, Pathak M, Peres MFP, Perianayagam A, Peterson CB, Phillips MR, Pinheiro M, Piradov MA, Pond CD, Potashman MH, Pottoo FH, Prada SI, Radfar A, Raggi A, Rahim F, Rahman M, Ram P, Ranasinghe P, Rawaf DL, Rawaf S, Rezaei N, Rezapour A, Robinson SR, Romoli M, Roshandel G, Sahathevan R, Sahebkar A, Sahraian MA, Sathian B, Sattin D, Sawhney M, Saylan M, Schiavolin S, Seylani A, Sha F, Shaikh MA, Shaji KS, Shannawaz M, Shetty JK, Shigematsu M, Shin JI, Shiri R, Silva DAS, Silva JP, Silva R, Singh JA, Skryabin VY, Skryabina AA, Smith AE, Soshnikov S, Spurlock EE, Stein DJ, Sun J, Tabarés-Seisdedos R, Thakur B, Timalsina B, Tovani-Palone MR, Tran BX, Tsegaye GW, Valadan Tahbaz S, Valdez PR, Venketasubramanian N, Vlassov V, Vu GT, Vu LG, Wang Y-P, Wimo A, Winkler AS, Yadav L, Yahyazadeh Jabbari SH, Yamagishi K, Yang L, Yano Y, Yonemoto N, Yu C, Yunusa I, Zadey S, Zastrozhin MS, Zastrozhina A, Zhang Z-J, Murray CJL, Vos T. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019. Lancet Public Health. 2022;7(2):e105–25. https://doi.org/10.1016/S2468-2667(21)00249-8.
Nguyen HD, Jo WH, Hoang NHM, Yu BP, Chung HY, Kim M-S. 1,2-Diacetylbenzene impaired hippocampal memory by activating proinflammatory cytokines and upregulating the prolactin pathway: an in vivo and in vitro study. Int Immunopharmacol. 2022;108:108901. https://doi.org/10.1016/j.intimp.2022.108901.
Nguyen HD, Kim M-S. Exposure to a mixture of heavy metals induces cognitive impairment: genes and microRNAs involved. Toxicology. 2022;15(471):153164. https://doi.org/10.1016/j.tox.2022.153164.
W.H. Organization, Dementia, 2021. https://www.who.int/news-room/fact-sheets/detail/dementia. Accessed 09 Nov 2021.
Sochocka M, Zwolińska K, Leszek J. The infectious etiology of Alzheimer’s disease. Curr Neuropharmacol. 2017;15(7):996–1009. https://doi.org/10.2174/1570159x15666170313122937.
Hu X, Wang T, Jin F. Alzheimer’s disease and gut microbiota. Sci China Life sci. 2016;59(10):1006–23. https://doi.org/10.1007/s11427-016-5083-9.
Hampel H, Mesulam MM, Cuello AC, Farlow MR, Giacobini E, Grossberg GT, Khachaturian AS, Vergallo A, Cavedo E, Snyder PJ, Khachaturian ZS. The cholinergic system in the pathophysiology and treatment of Alzheimer’s disease. Brain. 2018;141(7):1917–33. https://doi.org/10.1093/brain/awy132.
Watkins PB, Zimmerman HJ, Knapp MJ, Gracon SI, Lewis KW. Hepatotoxic effects of tacrine administration in patients with Alzheimer’s disease. JAMA. 1994;271(13):992–8. https://doi.org/10.1001/jama.1994.03510370044030.
Tiwari S, Atluri V, Kaushik A, Yndart A, Nair M. Alzheimer’s disease: pathogenesis, diagnostics, and therapeutics. Int J Nanomed. 2019. https://doi.org/10.2147/IJN.S200490.
Nguyen HD. Resveratrol, endocrine disrupting chemicals, neurodegenerative diseases and depression: genes, transcription factors, microRNAs, and sponges involved. Neurochem Res. 2022. https://doi.org/10.1007/s11064-022-03787-7.
Nguyen HD, Jo WH, Hoang NHM, Kim M-S. Curcumin-attenuated TREM-1/DAP12/NLRP3/caspase-1/IL1B, TLR4/NF-κB pathways, and Tau hyperphosphorylation induced by 1,2-diacetyl benzene: an in vitro and in silico study. Neurotox Res. 2022. https://doi.org/10.1007/s12640-022-00535-1.
Nguyen HD, Kim M-S. The role of mixed B vitamin intakes on cognitive performance: modeling, genes and miRNAs involved. J Psychiatr Res. 2022;152:38–56. https://doi.org/10.1016/j.jpsychires.2022.06.006.
Nguyen HD, Kim M-S. Roles of curcumin on cognitive impairment induced by a mixture of heavy metals. Neurotox Res. 2022. https://doi.org/10.1007/s12640-022-00583-7.
Samaga KKL, Rao GV, Chandrashekara Reddy G, Kush AK, Diwakar L. Synthetic racemates of Abyssinone I and II induces apoptosis through mitochondrial pathway in human cervix carcinoma cells. Bioorg Chem. 2014;56:54–61. https://doi.org/10.1016/j.bioorg.2014.06.004.
Maiti A, Cuendet M, Croy VL, Endringer DC, Pezzuto JM, Cushman M. Synthesis and biological evaluation of (+/-)-Abyssinone II and its analogues as aromatase inhibitors for chemoprevention of breast cancer. J Med Chem. 2007;50(12):2799–806. https://doi.org/10.1021/jm070109i.
Hatti KS, Diwakar L, Rao GV, Kush A, Reddy GC. Abyssinones and related flavonoids as potential steroidogenesis modulators. Bioinformation. 2009;3(9):399–402. https://doi.org/10.6026/97320630003399.
Zingue S, Gbaweng Yaya AJ, Cisilotto J, Kenmogne LV, Talla E, Bishayee A, Njamen D, Creczynski-Pasa TB, Ndinteh DT. Abyssinone V-4’ Methyl Ether, a flavanone isolated from Erythrina Droogmansiana, exhibits cytotoxic effects on human breast cancer cells by induction of apoptosis and suppression of invasion. Evid Based Complement Alternat Med. 2020;2020:6454853. https://doi.org/10.1155/2020/6454853.
Benet LZ, Hosey CM, Ursu O, Oprea TI. BDDCS, the rule of 5 and drugability. Adv Drug Del Rev. 2016;101:89–98. https://doi.org/10.1016/j.addr.2016.05.007.
Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46(W1):W296-w303. https://doi.org/10.1093/nar/gky427.
Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics. 1998;14(8):656–64. https://doi.org/10.1093/bioinformatics/14.8.656.
Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003;13(9):2129–41. https://doi.org/10.1101/gr.772403.
Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010. https://doi.org/10.1093/nar/gkq537.
Nguyen HD, Kim M-S. Effects of chemical mixtures on liver function biomarkers in the Korean adult population: thresholds and molecular mechanisms for non-alcoholic fatty liver disease involved. Environ Sci Pollut Res. 2022. https://doi.org/10.1007/s11356-022-21090-4.
Nguyen HD, Kim M-S. Cadmium, lead, and mercury mixtures interact with non-alcoholic fatty liver diseases. Environ Pollut. 2022;309:119780. https://doi.org/10.1016/j.envpol.2022.119780.
Nguyen HD, Oh H, Kim M-S. Mixtures modeling identifies vitamin B1 and B3 intakes associated with depression. J Affect Disord. 2022;301:68–80. https://doi.org/10.1016/j.jad.2021.12.133.
Nguyen HD, Kim M-S. The protective effects of curcumin on metabolic syndrome and its components: In-silico analysis for genes, transcription factors, and microRNAs involved. Arch Biochem Biophys. 2022;727:109326. https://doi.org/10.1016/j.abb.2022.109326.
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat commun. 2019;10(1):1523. https://doi.org/10.1038/s41467-019-09234-6.
Van Parys T, Melckenbeeck I, Houbraken M, Audenaert P, Colle D, Pickavet M, Demeester P, Van de Peer YJB. A Cytoscape app for motif enumeration with ISMAGS. Bioinformaticsn. 2017;33(3):461–3.
Keenan AB, Torre D, Lachmann A, Leong AK, Wojciechowicz ML, Utti V, Jagodnik KM, Kropiwnicki E, Wang Z, Ma’ayan A. ChEA3: transcription factor enrichment analysis by orthogonal omics integration. Nucleic Acids Res. 2019;47(W1):W212–24.
Nguyen HD, Jo WH, Hoang NHM, Kim M-S. In silico identification of the potential molecular mechanisms involved in protective effects of prolactin on motor and memory deficits induced by 1,2-Diacetylbenzene in young and old rats. Neurotoxicology. 2022;93:45–59. https://doi.org/10.1016/j.neuro.2022.09.002.
Nguyen HD, Kim M-S. The effects of a mixture of cadmium, lead, and mercury on metabolic syndrome and its components, as well as cognitive impairment: genes, MicroRNAs, transcription factors, and sponge relationships. Biol Trace Elem Res. 2022. https://doi.org/10.1007/s12011-022-03343-y.
Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47(W1):W199-w205. https://doi.org/10.1093/nar/gkz401.
Licursi V, Conte F, Fiscon G, Paci P. MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis. BMC Bioinformat. 2019;20(1):545. https://doi.org/10.1186/s12859-019-3105-x.
Lagunin A, Stepanchikova A, Filimonov D, Poroikov VJB. PASS: prediction of activity spectra for biologically active substances. Bioinformatics. 2000;16(8):747–8.
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–61.
Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform. 2012;4(1):17. https://doi.org/10.1186/1758-2946-4-17.
Nguyen HD, Kim M-S. In silico exploration of promising heterocyclic molecules against both acetylcholinesterase and butyrylcholinesterase enzymes. J Biomol Struct Dyn. 2023. https://doi.org/10.1080/07391102.2023.2238068.
Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001.
Vakalopoulos C. Alzheimer’s disease: the alternative serotonergic hypothesis of cognitive decline. J Alzheimers Dis. 2017;60:859–66. https://doi.org/10.3233/JAD-170364.
Kerdsaeng N, Roytrakul S, Chanprasertyothin S, Charernwat P, Chansirikarnjana S, Sritara P, Sirivarasai J. Serum glycoproteomics and identification of potential mechanisms underlying Alzheimer’s disease. Behav Neurol. 2021;2021:1434076. https://doi.org/10.1155/2021/1434076.
Jiang Q, Heneka M, Landreth GE. The role of peroxisome proliferator-activated receptor-gamma (PPARgamma) in Alzheimer’s disease: therapeutic implications. CNS Drugs. 2008;22(1):1–14. https://doi.org/10.2165/00023210-200822010-00001.
Ahmadian M, Suh JM, Hah N, Liddle C, Atkins AR, Downes M, Evans RM. PPARγ signaling and metabolism: the good, the bad and the future. Nat Med. 2013;19(5):557–66. https://doi.org/10.1038/nm.3159.
Oddo S. The role of mTOR signaling in Alzheimer disease. Front Biosci. 2012;4(3):941–52. https://doi.org/10.2741/s310.
Wang L, Chiang H-C, Wu W, Liang B, Xie Z, Yao X, Ma W, Du S, Zhong Y. Epidermal growth factor receptor is a preferred target for treating Amyloid-β–induced memory loss. Proc Natl Acad Sci. 2012;109(41):16743–8. https://doi.org/10.1073/pnas.1208011109.
Luckhaus C, Sand PG. Estrogen receptor 1 gene (ESR1) variants in Alzheimer’s disease. results of a meta-analysis. Aging Clin Exp Res. 2007;19(2):165–8. https://doi.org/10.1007/bf03324684.
Du Y, Du Y, Zhang Y, Huang Z, Fu M, Li J, Pang Y, Lei P, Wang YT, Song W, He G, Dong Z. MKP-1 reduces Aβ generation and alleviates cognitive impairments in Alzheimer’s disease models. Sig Transduct Targeted Ther. 2019;4(1):58. https://doi.org/10.1038/s41392-019-0091-4.
Martorana A, Koch G. Is dopamine involved in Alzheimer’s disease? Front Aging Neurosci. 2014. https://doi.org/10.3389/fnagi.2014.00252.
Rajmohan R, Reddy PH. Amyloid-beta and phosphorylated Tau accumulations cause abnormalities at synapses of Alzheimer’s disease neurons. J Alzheimers Dis. 2017;57(4):975–99. https://doi.org/10.3233/jad-160612.
Duc Nguyen H, Hee Jo W, Hong Minh Hoang N, Kim M-S. Anti-inflammatory effects of B vitamins protect against tau hyperphosphorylation and cognitive impairment induced by 1,2 diacetyl benzene: An in vitro and in silico study. Int Immunopharmacol. 2022;108:108736. https://doi.org/10.1016/j.intimp.2022.108736.
Kang Q, Xiang Y, Li D, Liang J, Zhang X, Zhou F, Qiao M, Nie Y, He Y, Cheng J, Dai Y, Li Y. MiR-124–3p attenuates hyperphosphorylation of Tau protein-induced apoptosis via caveolin-1-PI3K/Akt/GSK3β pathway in N2a/APP695swe cells. Oncotarget. 2017;8(15):24314–26. https://doi.org/10.18632/oncotarget.15149.
Heneka MT, Reyes-Irisarri E, Hüll M, Kummer MP. Impact and therapeutic potential of PPARs in Alzheimer’s disease. Curr Neuropharmacol. 2011;9(4):643–50. https://doi.org/10.2174/157015911798376325.
Li H, Wang F, Guo X, Jiang Y. Decreased MEF2A expression regulated by its enhancer methylation inhibits autophagy and may play an important role in the progression of Alzheimer’s disease. Front Neurosci. 2021. https://doi.org/10.3389/fnins.2021.682247.
Liu DX, Biswas SC, Greene LA. B-myb and C-myb play required roles in neuronal apoptosis evoked by nerve growth factor deprivation and DNA damage. J Neurosci. 2004;24(40):8720–5. https://doi.org/10.1523/jneurosci.1821-04.2004.
Miller BL. The C9ORF72 mutation brings more answers and more questions. Alzheimers Res Ther. 2013;5(1):7. https://doi.org/10.1186/alzrt161.
Poduslo SE, Yin X. Chromosome 12 and late-onset Alzheimer’s disease. Neurosci Lett. 2001;310(2–3):188–90. https://doi.org/10.1016/s0304-3940(01)02130-9.
Cai Z. Monoamine oxidase inhibitors: promising therapeutic agents for Alzheimer’s disease (Review). Mol Med Rep. 2014;9(5):1533–41. https://doi.org/10.3892/mmr.2014.2040.
Fabiani C, Antollini SS. Alzheimer’s disease as a membrane disorder: spatial cross-talk among beta-amyloid peptides, nicotinic Acetylcholine receptors and lipid rafts. Front Cell Neurosci. 2019;13:309. https://doi.org/10.3389/fncel.2019.00309.
Nebert DW, Wikvall K, Miller WL. Human cytochromes P450 in health and disease. Phil Trans R Soc London Series B, Biol Sci. 2013;368(1612):20120431–20120431. https://doi.org/10.1098/rstb.2012.0431.
Jann MW, Shirley KL, Small GW. Clinical pharmacokinetics and pharmacodynamics of cholinesterase inhibitors. Clin Pharmacokinet. 2002;41(10):719–39. https://doi.org/10.2165/00003088-200241100-00003.
Nguyen HD, Kim M-S. Effects of heavy metals on cardiovascular diseases in pre and post-menopausal women: from big data to molecular mechanism involved. Environ Sci Pollut Res. 2022. https://doi.org/10.1007/s11356-022-21208-8.
Acknowledgements
Not applicable
Funding
None.
Author information
Authors and Affiliations
Contributions
HDN: Conceptualization. HDN: Data collection. HDN: Data analysis and interpretations. HDN: Methodology. HDN: Resources. HDN: Writing–original draft. HDN:Critical revision of the manuscript.
Corresponding author
Ethics declarations
Ethics approval and content to participate
The manuscript does not involve any animal or human study.
Competing interests
The authors have no conflict of interest to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Nguyen, H.D. The molecular mechanisms of Abyssinone-I protect against Alzheimer’s disease: an in-silico study. Discov Med 1, 8 (2024). https://doi.org/10.1007/s44337-024-00009-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s44337-024-00009-7