Abstract
This investigation delves into the influence of predicted microRNAs on DNA methyltransferases (DNMTs) and the PODXL gene within the NB4 cell line, aiming to elucidate their roles in the pathogenesis of acute myeloid leukemia (AML). A comprehensive methodological framework was adopted to explore the therapeutic implications of 6-gingerol on DNMTs. This encompassed a suite of bioinformatics tools for protein structure prediction, docking, molecular dynamics, and ADMET profiling, alongside empirical assessments of miRNA and PODXL expression levels. Such a multifaceted strategy facilitated an in-depth understanding of 6-gingerol’s potential efficacy in DNMT modulation. The findings indicate a nuanced interplay where 6-gingerol administration modulated miRNA expression levels, decreasing in DNMT1 and DNMT3A expression in NB4 cells. This alteration indirectly influenced PODXL expression, contributing to the manifestation of oncogenic phenotypes. The overexpression of DNMT1 and DNMT3A in NB4 cells may contribute to AML, which appears modulable via microRNAs such as miR-193a and miR-200c. Post-treatment with 6-gingerol, DNMT1 and DNMT3A expression alterations were observed, culminating in the upregulation of miR-193a and miR-200c. This cascade effect led to the dysregulation of tumor suppressor genes in cancer cells, including downregulation of PODXL, and the emergence of cancerous traits. These insights underscore the therapeutic promise of 6-gingerol in targeting DNMTs and microRNAs within the AML context.
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Introduction
Acute myeloid leukemia (AML) is a malignant disease, known for its diversity of phenotype and genetics, with the highest mortality rate among hematopoietic neoplasms. It is characterized by the clonal transformation of myeloid precursors with lower differentiation abilities. It has been demonstrated that 4 out 5 of AML patients are adults1. Acute promyelocytic leukemia (APL) accounted for one in ten cases of AML. It usually affects younger individuals compared to other subtypes of AML2. APL is mainly distinguished by a chromosome translocation t (15; 17) (q24; q21), which is involved in the PML/RARA gene fusion. Retinoids are implicated in the activation of retinoic acid receptors (RARs α, β, and γ), and retinoid X receptors (RXRs). RARα is capable of interacting with RXR, and the RARα-RXR heterodimer can manage the corepressor or coactivator involved in the regulation of its gene’s transcription3. Apart from PML/RARA gene fusion formation, mutations in other genes such as WT1, FLT3-ITD, FLT3-TKD, and KRAS as well as repeated deleterious mutations in STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2, and PTPN11 have high chances of involvement in APL pathogenesis and were found in APL patients4,5,6. Previous studies have proven that besides mutations in oncogenes and tumor-suppressive genes, epigenetic changes are also key determiners of the onset of many malignancies. In this regard, DNA methylation of cytosine in the cytosine–guanine (CpG) dinucleotide is considered the most common epigenetic mechanism7. DNMTs are capable of catalyzing the transfer of a methyl group onto the carbon 5 position of the cytosine ring. Different DNMT isoforms, e.g., DNMT1 play a crucial role in the regulation of differentiation, hematopoietic stem cell growth, and survival. DNMT3A and DNMT3B regulate DNA methylation de novo, whereas DNMT1 preserves DNA methylation status during replication8. MiRNAs are also key regulators of DNMTs expression. For instance, miR-29b reduces the mRNA and protein expression levels of DNMT1, DNMT3A, and DNMT3B, which leads to decreased global DNA methylation. MiR-29b is directly involved in targeting DNMT3A and DNMT3B in their 3′ UTRs, while it indirectly downregulates DNMT1 by targeting SP1, a trans-activator of DNMT1 expression8. Moreover, DNA methylation and telomere recombination can be regulated by miR-290 via retinoblastoma-like 2 (Rbl2)-dependent expression of DNMTs9. The PODXL gene is a member of the sialomucin protein family, initially identified as an essential component of glomerular podocytes. It plays a role in the development and progression of cancer through cell invasion and migration10 and increasing or decreasing the expression of the PODXL gene by methylating its promoter regions by methyltransferase enzymes can play a role in the development of cancer11. Extensive research has been conducted on 6-gingerol, a compound found in 6-ginger, to explore its potential in the field of cancer treatment. Numerous studies have demonstrated its ability to regulate several cellular signaling pathways associated with cancer, such as nuclear factors, growth factor receptors, and proinflammatory mediators12. The apoptotic cell death of drug-resistant acute myeloid leukemia AML cells has been observed as a result of the cytotoxic effects exhibited by 6-ginger extract13. The anti-leukemic effects of ginger extract on acute lymphoblastic leukemia (ALL) cells have been observed both in vitro and in vivo14. In this study, sophisticated bioinformatics tools were employed to elucidate the regulatory functions of microRNAs (miRNAs) and the PODXL gene after administering 6-gingerol. The research primarily concentrated on predicting hitherto unidentified miRNAs through the analysis of methylation-associated 3ʹ untranslated regions (3’ UTRs) of genes, notably DNMT3B, DNMT3A, and DNMT1. Utilizing an integrative approach that combined bioinformatics methodologies with empirical procedures, the investigation effectively delineated the influence of 6-gingerol on the expression profiles of miRNAs, in addition to examining the functional dynamics of the PODXL gene and DNMTs.
Materials and methods
Provision of sequence data and execution of BLAST searches
We obtained the amino acid sequences of DNMT1, DNMT3A, and DNMT3B from the NCBI protein database (https://www.ncbi.nlm.nih.gov/protein/) in FASTA format to find suitable structures for our study. Using these sequences as queries, we conducted a BLAST search against “Protein Data Bank” (https://www.rcsb.org/) and successfully identified experimentally confirmed structures for each target protein. This approach greatly assisted us in selecting the most suitable template structure for studying the interactions with 6-gingerol compounds. The values derived from the BLAST analysis for the proteins DNMT1/4WXX, DNMT3A/6PA7_7, and DNMT3B/6PA7_8 are as follows: The maximum score, query coverage, E-value, and percent identity were 2640, 76%, 0.0, and 100.00%; 970, 50%, 0.0, and 99.57%; and 1361, 75%, 0.0, and 100.00%, respectively.
Estimating the topology and secondary structure of proteins
The SOPMA server (https://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html) was utilized to determine the secondary structures of DNMT1, DNMT3A, and DNMT3B. By employing a neural network, SOPMA can predict a substantial number of amino acids that contribute to the secondary structure. This prediction ultimately facilitates the creation of 3D models based on the initial 2D structure. The 3D structures of DNMT1, DNMT3A, DNMT3B, and 6-gingerol were built with PyMOL software Version 2.5.8.
Extraction of physicochemical attributes from the sequence
Analyzing the physical and chemical properties of a protein allows for the estimation of its structural and functional characteristics. To determine these characteristics, the protein structure sequence was submitted to the ProtParam web server (https://web.expasy.org/protparam/).
Identifying and characterizing functional pockets and residues in proteins
The prediction of functional amino acids for the DNMT1, DNMT3A, and DNMT3B was carried out using the online service HotSpot Wizard 3 available at https://loschmidt.chemi.muni.cz. To identify the central structural pockets and cavities, the CASTp web server at http://sts.bioe.uic.edu/castp/index.html?2cpk was utilized. The default value used by the CASTp server is 1.4 Angstroms. Only values between 0.0 and 10.0 will be accepted.
ADMET properties of chemical compounds
A drug’s suitability is determined by its favorable biochemical activity, pharmacokinetics, safety, high potency, selectivity, and ADMET. An ideal drug should have the ability to effectively distribute itself throughout various tissues and organs, undergo metabolism without an immediate decrease in activity, and be properly excreted from the body. To evaluate the medicinal properties of 6-gingerol, which were incomplete in the DrugBank database (https://go.drugbank.com), the ADMETlab 2.0 server (https://admetmesh.scbdd.com/) was utilized. This server is an integrated online platform for accurate and comprehensive predictions of ADMET properties. It helped determine the physicochemical, medical chemistry, and ADMET parameters of 6-gingerol. These parameters encompass 17 physicochemical properties, 13 medicinal chemistry measures, 23 ADME endpoints, and 27 toxicity endpoints, ensuring a thorough investigation of 6-gingerol’s suitability as a drug candidate. ADME is an acronym for Absorption, Distribution, Metabolism, and Excretion. Following drug exposure to an organism, several stages occur. The Absorption phase is the process by which the drug enters the bloodstream and plasma from the administered route. Distribution involves the penetration and spread of pharmaceutical substances into tissues and interstitial fluid. Metabolism refers to the breakdown of pharmaceutical substances into active components. Excretion is the process of clearing and removing the remnants of pharmaceutical substances from the body.
Pharmaceutical criteria evaluation with R software
The R data mining tool was employed to analyze the chemical compound 6-gingerol to assess its compliance with various physicochemical, medicinal chemical, and ADMET criteria. By utilizing this software, a numerical score was assigned to each pharmacological criterion that the 6-gingerol compound fulfilled, and these individual scores were then aggregated.
Protein–ligand energy minimization
The precise determination of the molecular spatial arrangement heavily relies on energy minimization. In the modeling of protein systems, the adjustment of hydrogen bond networks plays a vital role in eliminating disruptive contacts and reducing the overall energy of the system. This process takes into account various components such as stretching, bending, and torsion, which contribute to the potential energy15. To achieve this, the YASARA server (http://www.yasara.org/) utilized the Amber force field and its minimization capabilities to decrease the energy required for DNMTs and 6-gingerol compounds. The YASARA server successfully produced high-quality structural models by utilizing optimized energy functions, taking advantage of the low energy levels found in empirical structural models16.
Docking of 6-gingerol and DNMTs
Investigation of the interactions between 6-gingerol and DNMT proteins17 was conducted using the HDOCK web server (http://hdock.phys.hust.edu.cn) and AutoDock4. HDOCK utilizes a hybrid approach that combines template-based modeling and ab initio-free docking to achieve protein–protein and protein-DNA/RNA docking. Additionally, AutoDock4 is an integrated platform that predicts protein–ligand interactions. To facilitate the process, the necessary file formats for the server and program were converted using Open Babel software 2.4.1 version18.
2D interaction plots
A 2D interaction plot was created to determine the amino acids involved in protein–protein interactions by utilizing the inhibitory chemical 6-gingerol with the DNMT1, DNMT3A, and DNMT3B proteins. The LigPlot + software version 2.2, available at https://www.ebi.ac.uk/thornton-srv/software/LigPlus/, was employed for the computations. Additionally, the Discovery Studio software, built on the SciTegic Enterprise Server, an open operating platform, was used to analyze protein–ligand interactions. This tool also offers the capability to examine other aspects of protein–ligand interactions.
Molecular dynamics simulations of protein-ligands
The MDs were conducted on the protein–ligand complex of 6-gingerol with the DNMT1 and DNMT3A proteins, which displayed significant changes pre-and post-treatment. The CHARMM 27 all-atomic force field was employed for the MD simulations. To create a suitable environment, the protein–ligand complex was solvated in a triclinic box with periodic boundary conditions, using the TIP3P water model. To neutralize the system, Na+ and Cl− ions were added. The ligand parameters and topology were determined using the SwissParam server. The internal constraints of the protein–ligand complex were relaxed through the steepest descent energy minimization, which involved 50,000 steps and restricted the positions of all heavy atoms. Before the MD simulations, the systems were heated using a V-rescale thermostat to achieve a temperature of 300 K, with a coupling constant of 0.1 ps, and equilibration was achieved in NVT. The solvent density was maintained using a Parrinello-Rahman barostat with a pressure of 1 bar, a coupling constant of 0.1 ps, and a temperature of 300 K, gradually releasing the restraint on heavy atoms to achieve equilibration in the NPT. Finally, the complexes underwent a 40 ns MDS with an integration time step of 2 fs. Trajectory analyses, including RMSD, RMSF, Rg, SASA, and H-bonds, were performed on the protein–ligand complexes using the GROMACS package.
MiRNA and target mRNA prediction
DNAMTs’ corresponding miRNA was predicted using DIANAmT, miRanda, miRDB, miRWalk, RNAhybrid, PICTAR4, PICTAR5, PITA, RNA22, and Targetscan. The software predicts miRNA based on 3 criteria: 1. the longest seed region, 2. conserved pairing of the seed region, and 3. the number of algorithms with the same result. Finally, four microRNAs were selected based on the minimum listed cases and without duplication in previous studies on DNMT gene binding (see Tables S1, S2 in Supplementary 1).
Primers, special probes, and stem-loop design for microRNAs
The sequence of miRNAs with potential interference with DNMTs was retrieved from the miRbase database (http://www.mirbase.org/). The selected miRNAs were miR-548, miR-200c miR-193a, and miR-148a-5p. To increase the sensitivity of the miRNA sequence, we used the stem-loop sequence of Faridi et al.19 (Table 1).
The miRNAs that were designed could detect the final 6 nucleotides at the end of the stem-loop, which is involved in complementing the 3ʹ region of the miRNA sequence. These miRNAs were used as forward primers. The Tm temperature of the probes and primers was adjusted using Gene Runner software. The probe and stem-loop sequence selected were based on the sequence reported by Faridi et al. The secondary structures were checked using Gene Runner software and the Mfold web server (http://mfold.rna.albany.edu/?q=mfold/). The primer-blast analysis was conducted to verify the specificity of each primer, using the website https://www.ncbi.nlm.nih.gov/tools/primer-blast/. To ensure specificity, real-time PCR was performed using the cDNA of the stem-loops of each miRNA. The relative expression (fold change) was determined by comparing the CT value of the microRNAs to the U6.
Cell culture and active pharmaceutical ingredient
The compound (5S)-5-hydroxy-1-(4-hydroxy-3-methoxyphenyl) decan-3-one, with a purity of more than 98%, was kindly provided by Reza Fotouhi Ardakani from Abcam Company (Abcam, ab145635). The compound, also known as 6-gingerol, is a natural product isolated from Zingiber officinale (ginger) with various biological activities. Its molecular weight is 294.4 g/mol and its PubChem identifier is 442793. The NB4 cell line was purchased from the cell bank of the Institute Pasteur of Iran and peripheral blood cells (PBCs) were extracted in our laboratory. All cells were seeded in RPMI 1640 medium, containing 15% fetal bovine serum, 2 mM glutathione, 100 units of penicillin, and 100 μg of streptomycin per mL. When the cells reached 70–80% confluency, they were transferred to the 6-well cell culture plate.
MTT-based cytotoxicity assay
The overall cytotoxicity of 6-gingerol on NB4 cells was assessed by the MTT colorimetric assay. This test measures the reduction of thiazolyl blue tetrazolium bromide into a purple structure (formazan) by viable cell enzymes. The cells (2 × 104/well) were seeded into a 96-well plate in 100 µL of medium supplemented with different concentrations (25, 50, 100, 150, 200, 250, 300, 350, and 400 µM) of 6-gingerol. After 24 h, the medium was replaced with 20 μL of MTT solution (5 mg/mL), and the plate was incubated for 4 h at 37 °C. Subsequently, 100 μL dimethylsulfoxide (DMSO) was used for dissolvation of formazan crystals and finally, the optical densities (OD) of supernatants were determined using an ELISA reader device at 570 nm. By following the formula, the cytotoxicity corresponding to each concentration was calculated:
Annexin V and propidium iodide as flow cytometry markers of apoptosis
The quantitative assessment of apoptosis was carried out using ApoFlowEx® FITC kit (ExBio, Vestec, Czech Republic). NB4 cells (1 × 106) were cultured in two flasks and the old culture media were replaced by new media containing DMSO (control group) and IC50 concentration of 6-gingerol (treated group) when the confluency of 90% was obtained. After 24 h of incubation, the cells were separated and rinsed in ice-cold PBS and then suspended in 500 µL binding buffer, 5 µL propidium iodide (PI), and 5 µL Annexin V. After gentle vortex of the suspension and 15 min incubation (at room temperature and dark environment), the added materials were removed and new binding buffer was added to each microtube. The examination was done using the flow cytometry device (CyFlow® Space, Sysmex Partec). Viable cells were negative for Annexin V and PI (lower left quadrant), early apoptotic cells were negative for PI and positive for Annexin V (lower right quadrant), late apoptotic cells were positive for Annexin V and PI (upper right quadrant), and necrotic cells were negative and positive for Annexin V and PI, respectively (upper left quadrant).
MiRNA and RNA extraction
Target microRNAs and mRNA were extracted using the RNA extraction protocol of Sinaclon RNX-plus with slight modification. RNX-plus (1 mL) was added to 5 to 6 × 106 cells, followed by incubation on ice for 5 min. Furthermore, 200 μL of the 1-Bromo-3-chloropropane solution was added to each tube and was entirely shaken for 2 min, followed by centrifugation at 4 °C for 25 min (12,000 rpm). The supernatant was then transferred to a clean microtube and continued by repeating the previous step using 100 μL of 1-Bromo-3-chloropropane. Finally, after transferring the aqueous phase to a clean microtube, an equal volume of isopropanol alcohol was added to the tube.
The tubes were stored overnight at − 20 °C, followed by centrifugation (12,000 rpm) at 4 °C for 1 h. After discarding the supernatant, 1 mL of 70% ethanol was added and centrifugation was performed (12,000 rpm) at 4 °C for 30 min, followed by discarding the supernatant. Finally, 50 μL of DEPC-treated water was added to the tube. To eliminate genomic DNA contamination, RNase free-DNase I enzyme (5 units) was added to each tube, followed by incubation at room temperature for 10 min and inactivation for 5 min at 80 °C. Finally, the concentration and purity of our RNA extracts were measured by Nanodrop (Microdigital Nabi). All microtubes were stored at − 70 °C until use.
cDNA synthesis and real-time PCR
1000 ng of the sample was reversed transcribed by Mu-MLV reverse transcriptase based on the manufacturer’s instruction (Thermo Fisher Scientific, 00452644). The cDNA was maintained at − 70°C until further evaluation. The qPCR reaction of the samples was at a total volume of 12.5 μL of SYBR™ Green master mix, primers (0.2 μM), oligonucleotide, and cDNA (2 μL) (Table 2).
Initial denaturation at 95 °C was performed for the 40 s in real-time program, followed by 40 denaturation cycles at 90 °C for 40 s, 60 °C for 20 s, and final expansion at 72 °C for 35 s. The UBE2D2 gene was applied as the internal reference gene20. Furthermore, LinReg PCR software was used to evaluate PCR efficiency (90 and 108%) and expression of each mRNA.
MicroRNA cDNA synthesis and real-time PCR
Following miRNA extraction, cDNA synthesis was performed using Mu-MLV reverse transcriptase. 4 μL of extracted miRNA (Adjusted less than 1500 ng RNA), was next added to 1.5 μL of stem-loop (1% solution of the original 100 μM), followed by the addition of 5 μL of double distilled water, and incubation of the mixture (10.5 μL) for 5 min at 65 °C in a thermocycler (ABI Veriti Gradient Thermal Cycler). Instantly, the tubes were transferred to a cold container. A mixture consisting of 2 μL of dNTP (10 mM), 4 μL of 4× buffer, 0.5 μL of RNase inhibitor (20 units), 2 μL of DTT (10 mM), and 1 μL of reverse transcription enzyme (50 μL) was then prepared. The cDNA was then synthesized for 1 h at 44 °C and the enzyme inactivated during thermal processing for 5 min at 85 °C. The synthesized cDNA was stored at − 20 °C. Real-time PCR was performed to amplify reverse transcription products. Universal reverse primers and specific probes for each target miRNA were applied. Each microtube contained 2 × qPCR Master Mix (6.25 μL) of reverse primer (0.7 μM), forward primer (0.5 μM), and probe (0.2 μM) in a final volume of 12.5 μL. The qPCR reaction was performed on Rotor-Gene Q. The protocol used a 30 s enzyme activation step at 95 °C, followed by 45 cycles (95 °C, 15 s; 60 °C, 45 s). The U6 (RNU6-1) snRNA was applied as the internal control gene.
For statistical analyses, GraphPad Prism (Pfaffl method) was used to determine the relative expression ratios of each miRNA. We used the Graph Pad Prism 8.0 software to calculate fold change and P-value.
Ethics statement
This study was approved by the Clinical Research and Ethical Committee of the Faculty of Allied Medicine of Kerman University of Medical Sciences and complies with all laws and international ethics guidelines, outlined in the Declaration of Helsinki.
Results
Provision of sequence data and execution of BLAST searches
The NCBI protein database (https://www.ncbi.nlm.nih.gov/protein/) contains reference amino acid sequences of DNMTs with accession numbers NP_001124295.1, NP_072046.2, NP_008823. These sequences have been used to generate several nearly complete structures with the appropriate resolution, which can be accessed under the accession numbers “DNMT1/4WXX, DNMT3A/6PA7_7, and DNMT3B/6PA7_8” in the PDB database.
Estimating the topology, secondary, and 3D structures of proteins and compounds
The secondary structure of the protein predicted by the SOPMA server concluded (Fig. 1) that DNMT1 is composed of a random coil (46.50% or 584/1256 residues), alpha helix (28.90% or 363/1256 residues), extended strand (18.87% or 237/1256 residues), beta-turn (5.73% or 72/1256 residues), respectively, and the secondary structure of the protein predicted by the SOPMA server concluded that DNMT3A is composed of a random coil (46.88% or 323/689 residues), alpha helix (30.48% or 210/689 residues), extended strand (16.55% or 114/689 residues), beta-turn (6.10% or 42/689 residues), respectively. The secondary structure of the protein predicted by the SOPMA server concluded that DNMT3B is composed of a random coil (53.25% or 410/770 residues), alpha helix (26.36% or 203/770 residues), extended strand (14.94% or 115/770 residues), beta-turn (5.45% or 42/770 residues), respectively. The 3D structures of DNMT1 (A), DNMT3A (B), DNMT3B (C), and 6-gingerol (D) are depicted. Visualization was performed using PyMOL software, Version 2.5.8. The four illustrated conformations represent the 3D structure of the molecules before Docking and MD simulations (Fig. 2).
Secondary structure plot of DNMT1 (A), DNMT3A (B), and DNMT3B (C). The SOPMA server predicted the secondary structure of three proteins: DNMT1, DNMT3A, and DNMT3B. The results showed that DNMT1 had 46.50% random coil, 28.90% alpha helix, 18.87% extended strand, and 5.73% beta-turn. DNMT3A had 46.88% random coil, 30.48% alpha helix, 16.55% extended strand, and 6.10% beta-turn. DNMT3B had 53.25% random coil, 26.36% alpha helix, 14.94% extended strand, and 5.45% beta-turn.
3D structures of DNMT1 (A), DNMT3A (B), DNMT3B (C), and 6-gingerol (D). The structures were visualized with Pymol software Version 2.5.8. The four proposed shapes represent the three-dimensional structures of the obtained molecules before docking and molecular dynamics simulations.
Extraction of physicochemical attributes from the sequence
DNMTs sequences were given to the Protparam server in FASTA format and it was observed that the aliphatic index of DNMT1 protein was 70.26, its total hydropathic average (GRAVY) was − 0.553, its instability index (II) was 47.52 and this protein is unstable. Accordingly, the total number of negatively charged residues (Asp + Glu) in the protein was 166 and the positively charged ones (Arg + Lys) were 166. Moreover, the aliphatic index of DNMT3A structure was 69.45, its total hydropathic average (GRAVY) was − 0.438, its instability index (II) was 46.31 and this protein was unstable. By this, we mean that the total number of negatively charged residues (Asp + Glu) in the protein was 92, and the total number of positively charged residues (Arg + Lys) was 85. Also, the aliphatic index of DNMT3B structure is 63.19, its total hydropathic average (GRAVY) was − 0.629, its instability index (II) was 58.88 and this protein was unstable; the total number of negatively charged residues (Asp + Glu) in the protein is 100 and the total number of positively charged residues (Arg + Lys) was 104. The number and percentage of each residue in the structure of the receptor can be seen in Table 3. Therefore, the degree of stability or instability of target proteins (DNMTs) can be effective and important in the results of Docking and Molecular Dynamics Simulation.
Identifying and characterizing functional pockets and residues in proteins
The functional amino acids of DNMT1, DNMT3A, and DNMT3B proteins were predicted using the NCBI web server. The NCBI identified the hotspots “CYS1226, CYS353, CYS356, CYS414, HIS418, CYS653, CYS656, CYS659, CYS664, CYS667, CYS670, CYS686, CYS691, SER1146, GLU1168, MET1169, GLY1150, LEU1151, ASP1190, CYS1191, ASN1578, VAL1580” in the beta chain as functional amino acids for the DNMT1 enzyme. For DNMT3A, the hotspots included CYS710, PHE640, ASP641, SER663, GLU664, VAL665, CYS666, ASP686, VAL687, GLY707, LEU730, GLU756, ARG891, SER892, and TRP893 in the K chain. As for DNMT3B, the predicted functional amino acids were LEU651, VAL582, ALA583, SER584, GLU585, VAL586, VAL605, GLY627, GLY628, and SER629 in the L chain. These hotspots were mutable residues with varying scores based on the web server’s scoring system, and they were located in the catalytic pocket and/or access tunnels. The analysis also involved the identification and quantification of geometric and topological features. It was found that surface pockets and cavities play a role in reducing the functional development process against protein targets. The methyltransferase enzymes were observed to have several central pockets and cavities. Additionally, the largest predicted pocket of DNMT1, DNMT3A, and DNMT3B enzymes had solvent-accessible surface areas/volumes of 5747.91/5801.38, 531.86/480.92, and 385.82/225.80 Å2/Å3, respectively (Table 4, Fig. 2).
Notably, the enzymes DNMT1 and DNMT3A shared functional residues in their pockets, which were absent in the pockets of DNMT3B, CYS1226, CYS356, CYS656, CYS664, CYS667, CYS670, CYS686, CYS691, SER1146, GLU1168, MET1169, GLY1150, LEU1151, ASP1190, CYS1191, ASN1578, VAL1580. The pocket, cavity, and position of the functional residues, as outlined in Table 4, allowed for the successful execution of Docking and 2D diagram analyses.
ADMET properties of chemical compounds
After checking the properties of the chemical composition of 6-gingerol by ADMETlab2.0 server, the results of Table 5 were presented.
Pharmaceutical criteria evaluation with R software
The codes written by the R software have evaluated the 6-gingerol chemical compound in terms of 86 medicinal factors, in such a way that for each factor passed by the 6-gingerol chemical compound, one score was given to drug and the scores were added together and finally, it was concluded that this drug has approximately 50 medicinal factors. Hence, it can demonstrate the desired characteristics of possessing both anticancer and medicinal properties (see R Code in Supplementary 2).
Protein–ligand energy minimization
DNMT1, DNMTA, DNMTB, and 6-gingerol chemical compound energy before energy minimization by Yasara were 6,597,613,120.10, − 139,468.80, − 97,477.50, − 245 and their score before energy minimization were − 2.06, − 1.34, − 2.13, − 0.34, respectively. DNMT1, DNMTA, DNMTB, and 6-gingerol chemical compound energy after energy minimization by Yasara were − 642,864.90, − 166,608.10, − 118,058.20, − 269.70, and their score after energy minimization were − 0.61, − 0.06, − 0.62, − 0.39, respectively. This showed that our structures reached their lowest energy levels and the results of our Docking and MD simulations are reliable.
Docking of 6-gingerol and DNMTs
The 6-gingerol chemical compound was docked to DNMT1, DNMTA, and DNMTB by HDOCK Server and AutoDock4 software with a genetic algorithm in 50 runs using a specific docking method (Fig. 3). The obtained specific docking scores and energies show that the HDOCK Server has performed better than another program (Table 6).
3D Docking gingerol with DNMT1 (A), DNMT3A (B), and DNMT3B (C) by HDOCK Server (The structures were visualized with Pymol software Version 2.5.8). As shown in the figure above, gingerol is correctly positioned in the binding pockets or cavities of the target proteins.
2D interaction plots
The results presented in the text below indicate the 2D binding interactions of the drug compound 6-gingerol with three different enzymes, as analyzed by two distinct software tools. However, only the graphical representation from the Ligplot + software is included in the Fig. 4.
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DNMT1-B to 6-gingerol pose 1: 7 van der Waals bonds with SER520, PRO579, GLN1227, GLN573, SER1230, GLN698, VAL1268, 1 Pi–anion interaction with ASP702, 4 Alkyl and Pi–alkyl interactions with CYS1226, PRO1225, LEU577, ALA699, 1 unfavorable acceptor–acceptor interaction with ASP521.
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DNM3A-K to 6-gingerol pose 4: 3 conventional hydrogen bonds with ARG891, PHE640, GLY707, 10 van der Waals bonds with ASN717, SER714, CYS710, ARG790, VAL758, PRO709, SER708, GLY706, ARG792, ASN711, 1 Pi–Anion interaction with GLU756, 2 Pi–donor hydrogen bond and carbon–hydrogen bond with ASP641, ARG891, 1 Pi–Pi stacked interaction with TRP893, 1 Pi–sigma interaction with TRP893, 1 Pi–anion interaction with GLU756
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DNMT3B-L to 6-gingerol pose 1: 2 conventional hydrogen bonds with PHE672, ARG670, 6 van der Waals bonds with LEU694, ASN697, ASN718, GLY717, LYS542, PRO671, 2 Pi-donor hydrogen bond and carbon-hydrogen bond with TYR544, ASP668, 1 Pi–Pi stacked and Pi–Pi T-shaped interaction with PHE672, 2 Alkyl and Pi–alkyl interactions with PRO664, VAL699, 2 unfavorable bumps with CYS696, TRP674.
2D interactions of docking 6-gingerol with DNMT1 (A), DNMT3A (B), and DNMT3B (C) by Ligplot+. The results indicate that 6-gingerol, the active ingredient, can form multiple chemical bonds with the enzymes, including hydrogen and hydrophobic bonds. This demonstrates the potential effect of 6-gingerol on the enzymatic activity.
These findings collectively suggest that 6-gingerol may have a significant influence on the activity of these enzymes. The common results from the two software tools are highlighted, but Fig. 4 only displays the visualization from Ligplot + software.
Molecular dynamics simulations of protein–ligands
The top three ranked 6-gingerol chemical compounds with DNMT1 and DNMT3A proteins were subjected to MDS using Charmm 27 all-atom force field. The protein–ligand complex was solvated in a triclinic box using periodic boundary conditions and a TIP3P water model. The Na+ and Cl− ions were added to neutralize the system. The Swiss param server was used to generate the ligand parameters and topology. After that, internal constraints of the protein–ligand complex were relaxed by 50,000 steps of steepest descent energy minimization, leading to restraining positions of all heavy atoms. Before MDS, the systems were heated using a V-rescale thermostat to attain the temperature of 300 K with 0.1 ps as the constant of coupling and achieved equilibration in NVT. Then solvent density was sustained using a Parrinello-Rahman barostat with a pressure of 1 bar, coupling constant of 0.1 ps, and temperature of 300 K to obtain equilibration in NPT by gradually discharging the restraint on heavy atoms step by step. In the following, MDS was performed for the complexes for 40 ns with an integration time step of 2 fs. Finally, the trajectory analysis such as RMSD, RMSF, Rg, SASA, and H-bonds of protein–ligand complexes was performed using GROMACS utilities. Based on the experimental data, only the DNMT1 and DNMT3A enzymes showed alterations and responses to 6-gingerol treatment. Thus, the DNMT3B enzyme was excluded from the molecular dynamics simulation for this study.
Root-mean-square deviation analysis
We have analyzed the RMSD of backbone atoms using the standard g_rms function of GROMACS for an overall time of 40 ns simulation run. In the plot depicted in Fig. 5A, it is concluded that the DNMT3A attached to the 6-gingerol chemical compound reached the plateau earlier than the DNMT1. The average deviation is in them 0.352 nm and 0.403 nm, respectively. This shows that the DNMT3A docked with a 6-gingerol chemical compound is better than DNMT1 docked with a 6-gingerol chemical compound.
Convergence assessment of Molecular Dynamics Simulations of 6-gingerol-DNMTs. The RMSD of backbone atoms was calculated using the g_rms function of GROMACS for 40 ns. The plot in (A) showed that DNMT3A reached a stable conformation with less deviation faster than DNMT1, indicating better docking with 6-gingerol. The Rg of the protein–ligand complexes was calculated using the gmx_gyrate function of GROMACS for 40 ns. The plot in (B) showed that DNMT1 and DNMT3A had mean Rg deviations of 3.695 nm and 1.766 nm, respectively, suggesting that DNMT3A had a more compact structure than DNMT1. The RMSFs of the residues were calculated using the gmx_rmsf module of GROMACS for 40 ns. The plot in (C) showed that some residues of DNMT1 had high peaks, indicating high dynamics and flexibility, while some residues of DNMT3A had low peaks, indicating low fluctuations and stability. The number of H-bonds between the protein and the ligand was recorded using the gmx_hbond tool of GROMACS for 40 ns. The plot in (D) showed that 6-gingerol formed 0_4 and 0_5 H-bonds with DNMT1 and DNMT3A, respectively, and that DNMT3A had a higher average number of H-bonds than DNMT1, implying a better interaction with 6-gingerol. The SASA values of the complexes were computed using the gmx_sasa function of GROMACS for 40 ns. The plot in (E) showed that DNMT1 and DNMT3B had average SASA values of 571.168 nm2 and 128.233 nm2, respectively, while DNMT3A had the lowest SASA value, indicating that 6-gingerol had a stronger bond with DNMT3A than with water molecules.
Compactness analysis
The radius of gyration (Rg) is calculated using the GROMACS gmx_gyrate function with a simulation time of 40 ns. Rg is described as the distance measured during the simulation between the termini of the protein and its center of mass. When a ligand binds to a protein, a conformational change occurs that changes the Rg. Compact protein structures tend to maintain low mean Rg deviations, indicating dynamic stability. According to Fig. 5B, the DNMT1 and DNMT3A proteins attached to the 6-gingerol chemical compound exhibit mean Rg deviations of 3.695 nm and 1.766 nm, respectively.
Residue flexibility analysis
To check the stability and flexibility of the residues, we have calculated the RMSFs for an entire simulation run of 40 ns using the gmx_rmsf module of GROMACS. In the RMSF plot observed in Fig. 5C, several peaks can be seen in ARG681:B, PHE676:B, GLY680:B, GLY677:B, LYS683:B, SER682:B, SER679:B residues of DNMT1, and several peaks can be seen in LEU543:N, LYS542:N, TYR544:N, ARG740:N, PRO545:N residues of DNMT3A after binding to the 6-gingerol chemical compound, which indicates that the residues of DNMT1 have high dynamics and flexibility, and DNMT3A have shown the small fluctuations, best stability, and low mobility.
Hydrogen bonding and bond distribution analysis
H-bonding analysis was performed for all protein–ligand systems over a total of 40 ns simulation run. The number of H-bonds was recorded using the GROMACS gmx_hbond tool and is shown in Fig. 5D. During the simulation period, the chemical compound 6-gingerol formed 0_4 and 0_5 H bonds with DNMT1 and DNMT3A, respectively. Moreover, DNMT3A had a higher average number of hydrogen bonds with 6-gingerol than DNMT1.
Solvent accessible surface area analysis
The SASA values of all the complexes were computed using the gmx_sasa function of GROMACS for a simulation time of 40 ns. In the illustrated plot in Fig. 5E, it is deduced that DNMT1 and DNMT3A docked the 6-gingerol chemical compounds show an average SASA value of 571.168 nm2 and 128.233 nm2, respectively. Eventually, among these proteins docked with the 6-gingerol chemical compound, DNMT3A had the lowest SASA, which shows that the 6-gingerol chemical compound has established a stronger bond with DNMT3A instead of bonding with water molecules.
The molecular dynamics simulation results suggest that 6-gingerol forms stable complexes with the enzyme targets “DNMT1 and DNMT3A” and maintains their stability throughout the binding duration.
MiRNA and target mRNA prediction
The 3ʹ UTR targets of DNMT3B, DNMT3A, and DNMT1 mRNA can be identified using various online platforms. Numerous miRNAs, which were confirmed by multiple miRNA prediction algorithms, were selected based on their high scores. These miRNAs were chosen based on several criteria, including the number of algorithms that predicted them, the length of the seed region, the conservation of the seed region, their simultaneous targeting of the 3ʹ UTR of DNMT genes, and the fact that they had not been previously evaluated in the AML study (see Tables S1, S2 in Supplementary 1).
To ensure specificity, six complementary nucleotides were added to the 3ʹ ends of the stem-loop RTs, which were unique for each miRNA. Forward primers, universal reverse primer, and TaqMan probe were designed for qPCR analysis. The NCBI Primer-BLAST results for each miRNA confirmed that the primer sequences were only bound to the target miRNA and not to any other sequences. These results demonstrated 100% specificity for each miRNA (see Tables S1, S2 in Supplementary 1).
Cell culture and active pharmaceutical ingredient
The NB4 cell line was subjected to the inhibitory effects of 6-gingerol, resulting in significant suppression of cell growth at various concentrations ranging from 25 to 400 µM21. Following a 24-h, the IC50 value of 6-gingerol was determined to be 183 µM, with a 95% confidence interval ranging from 89.93 to 278.8. The obtained data from this experiment demonstrated a strong correlation, as evidenced by an R-squared value of 0.7878, as depicted in Fig. 6A.
Cell viability and flow cytometry analysis of 6-gingerol effect on NB4 cells. As presented in (A), the 6-gingerol treatment induced a dose-dependent inhibition of cell viability, and the IC50 value was determined as 183 µM. Consistent with the cytotoxic effect, the apoptotic effect was observed after 24-h exposure to IC50 concentration of the 6-gingerol (B,D). In other words, unlike the control group (C), treatment of cells with 183 µM of 6-gingerol decreased the number of viable cells and increased (P value < 0.05) the number of apoptotic cells (Annexin V + cells).
Impact of 6-gingerol on NB4 cells morphology and DAPI staining
With varying concentrations of 6-gingerol (100, 150, and 200 µM)22, the cell number decreased within 24 h. This decrease was particularly noticeable at a concentration of 200 µM. Additionally, the distinct morphology of the cells underwent gradual changes, leading to the reduction of cell count and the formation of cell aggregates, Fig. 7. DAPI staining revealed that as the concentration of 6-gingerol increased, the gap between the cells widened, chromatin pyknosis became somewhat evident, the cell shape became more rounded, and the nuclei started to fragment. Ultimately, it has been demonstrated that 6-gingerol inhibits the growth and proliferation of NB4 cells.
Morphologic characterization of NB4 cells before (A) and after treatment with 100 (B), 150 (C), and 200 µM (D) of 6-gingerol (DAPI staining, × 100 magnification). As shown, 6-gingerol reduced the number of viable cells in a dose-dependent manner, with the most significant reduction observed at 200 µM. Furthermore, DAPI staining revealed that 6-gingerol also caused nuclear changes, such as chromatin condensation/fragmentation (red arrow) and nuclear blebbing (white arrow).
Annexin V and propidium iodide as flow cytometry markers of apoptosis
As illustrated in Fig. 6B–D, 6-gingerol has a remarkable capacity to increase apoptosis in NB4 cells. This augmentation was not only evident in the increased number of annexin V-positive cells, but also in the percentage of Annexin V and PI-positive cells. These findings strongly indicate that 6-gingerol exerts cytotoxic effects on NB4 cells by inducing early and late apoptosis.
Gene expression profiles of DNMTs, PODXL, and a subset of miRNAs
Various microRNAs were predicted by different software algorithms in the NB4 cell line, and from these, miR-548, miR-200c, miR-193a, and miR-148a-5p were selected. As shown in Fig. 8A the expression levels of these miRNAs (except miR-200c) were decreased in the NB4 cell line relative to the peripheral blood cell (control) group before treatment. Compared to untreated NB4 cells, 6-gingerol-treated cells expressed a notable level of miR-193a and miR-200c (1.6 and 2.8, respectively; P value < 0.05). The NB4 cells expressed higher levels of the PODXL gene compared to PBCs. This gene induces cancer through its interaction with the actin-binding protein EZR, thereby promoting migration and cell invasion. Interestingly, this expression was significantly reversed after 6-gingerol treatment (P value < 0.05). In addition, gene expression analysis of DNMT1 and DNMT3A showed a significant (P value < 0.05 and P value < 0.01) decrease after treatment (Fig. 8B). It should be noted that exposure to 6-gingerol does not significantly (P value > 0.05) increase the expression of DNMT3B in NB4 cells.
(A) Upon comparative analysis, NB4 cells subjected to 6-gingerol exhibited a significant upregulation of miR-193a and miR-200c, as evidenced by P values less than 0.05. Conversely, in comparison to the control group of untreated NB4 cells, the expression levels of miR-548 and miR-148a-5p remained statistically unchanged. Furthermore (B), the study observed a discernible decrease in the expression of DNMT1, DNMT3A, and PODXL in the 6-gingerol-treated NB4 cells relative to their untreated counterparts, with P value < 0.05 for DNMT1 and PODXL, and (P value < 0.01) for DNMT3A. The expression of DNMT3B, however, did not demonstrate a significant difference.
Discussion
Epigenetic modification of deoxynucleotide acids at the C-5 position of cytosine in the cytosine–guanine (CpG) dinucleotide is one of the most common forms of gene expression regulation, which is carried out by the family of DNA methyltransferases enzymes8. These CpG islands are usually located in promoters. DNMT1 plays an important role in epigenetically regulating tumor suppressor genes such as PTEN, p16, CDH1, etc.23. DNMT1 is highly expressed in several malignancies like cholangiocarcinoma, hematological malignancies, pancreatic cancer, and breast cancer. It is required for maintaining the cancer stem cell status24. Mutations in DNMT3A leave abnormal methylation fingerprints on genomic DNA and are observed in different developmental defects and hematological malignancies8. Overexpression of DNMT3A silences BASP1 in A/E positive AML23. Overexpression of DNMT3B is correlated with aberrant DNA methylation pattern which is a common phenomenon in lung, breast, and ovarian cancer as well as hepatocellular carcinoma. The overexpression of DNMT3B affects the expression of RASSF1A, OCT4, hMLH1, p16 and p53, CDH1, etc.23. MiRNAs have a bidirectional relationship with DNMTs. On the one hand, miRNAs can modulate the transcription of DNMTs. For example, miR-133a influences the expression of DNMT1, 3A, and 3B25. Conversely, DNMTs modulate the transcription of miRNAs by methylation of their promoter region26. The expression of microRNAs miR-548, miR-193a, and miR-148a-5p were decreased before treatment with 6-gingerol. This phenomenon could be attributed to several factors. For example, miRNAs could have undergone acetylation in the promoter regions9. Single nucleotide polymorphisms (SNPs) are frequent occurrences in miRNA genes10. These SNPs could have influenced the expression level of these miRNAs. Additionally, there was an upregulation in the expression of the DNMTs before treatment with 6-gingerol. This upregulation could have resulted in the downregulation of miR-548, miR-193a, and miR-148a-5p by methylation of their gene promoter. Furthermore, a decrease in the expression of miR-548, miR-193a, and miR-148a-5p could have stimulated the upregulation of DNMTs. This study demonstrated that before treatment with 6-gingerol, the expression level of DNMT3A and DNMT1 enzymes was increased, which could be associated with aberrant methylation of miR200c and miR-193a gene promoters, and consequently lead to a reduction in their expression. The application of 6-gingerol to the aforementioned enzymes yielded a remarkable finding, as it elicited an inverse effect, inducing an upregulation of microRNA (miR-193a and miR-200c) and a downregulation of DNMT3A and DNMT1 enzymes. Moreover, further investigation using bioinformatics methods such as docking and molecular dynamics simulations have provided additional evidence supporting the notion that 6-gingerol can induce substantial changes in gene expression levels and induce apoptosis in NB4 cells. The PODXL has a crucial role in various cancer types, and its enhanced expression has been correlated with higher invasion rates in aggressive pancreatic cancers. Conversely, inhibiting the negative expression of PODXL has shown promising results in reducing cancer cell mortality and invasion27. Several studies have highlighted the functional relationship between the PODXL gene and miRNAs in cancer. For example, the manifestation of miR-199a-5p has been shown to inhibit PODXL expression in testicular neoplasia28.
The present study hypothesized that the increased importance of the PODXL gene is associated with decreased expression of miR-193a and miR-200c in the NB4 cell line. Dysregulation in the expression level of these miRNAs and PODXL can be directly or indirectly attributed to the effect of these macromolecules on PODXL gene expression or methyltransferases. By contrasting the expression levels of DNMT1, DNMT3A, and PODXL, a marginal increase in the expression of this methyltransferase was detected, possibly implying a correlation between these DNMTs and PODXL. Nevertheless, the putative regulation of the PODXL gene by the DNMT3A and DNMT1 enzymes can be discerned through the considerable increase in PODXL expression after 6-gingerol treatment. 6-gingerols, which are the predominant pungent compounds present in ginger rhizomes, have been revealed to wield an influence on microRNAs, thereby affecting cancer progression29,30. Phenolic phytochemical compounds, including 6-gingerol, can modify the expression of miRNAs, which are small RNA molecules associated with cancer. Recent research has shown that 6-gingerol can effectively modulate miRNAs, leading to the inhibition of tumor growth, suppression of metastasis, reversal of epithelial-to-mesenchymal transition (EMT), and increased sensitivity of cancer cells to drugs. These findings highlight the potential of 6-gingerol as a promising therapeutic agent in cancer treatment31,32. In another study, researchers found another miRNA, miR-5100, that directly regulates the POXDL gene post-transcriptionally by binding to the 3ʹ untranslated region of POXDL. Upregulation of miR-5100 decreased the rate of cell invasion, migration, and colony formation in pancreatic cancer33. An expression and clinical significance study in China showed a significantly lower expression of downregulated miR-148/152 in AML patients compared with the control group. In addition, a relationship between the expression level of miR-148/152 and clinicopathological features of AML patients was observed34. Another study showed decreased microRNA-148b in circulating PBMCs in chronic myeloid leukemia patients. Based on the data presented in the mentioned studies, the miR148/152 family may prove to be a biomarker for AML and CML35. Findings of a study indicate that microRNA-193a represses c-kit expression has a critical role in myeloid leukemogenesis and functions as a methylation-silenced tumor suppressor in AML36.
Zhou et al. investigated the expression and clinical implication of miR-200s clusters in healthy donors, newly diagnosed AML patients, and 35 AML patients who achieved complete remission (CR). In this study, expression of miR-200a/200b/429 cluster decreased in newly diagnosed AML patients compared to healthy donors and AML patients achieved CR. Since the original findings highlighted above, the research on the miR-548, miR-200c, miR-193a, and miR-148a-5p family, is promising for the research on the target genes in tumor-related diseases37. Finally, the analysis of cancer-related data obtained from high-throughput methods, such as DNA microarrays, docking, and molecular dynamics simulations, has been greatly facilitated by the field of bioinformatics38. In this investigation, we delved into the synergistic effects of microRNA dynamics, epigenetic modifications through methylation, the role of tumor suppressor genes such as PODXL, and the bioactive compound 6-gingerol, renowned for its anticancer properties. The findings of this study suggest that phytochemicals like 6-gingerol and their derivatives possess the capability to modulate the cellular phenotype or instigate apoptotic pathways in oncogenic cells, including NB4, by orchestrating the activity of methylating enzymes and microRNA expression. Collectively, these insights underscore the promising potential of 6-gingerol as a multifaceted agent in cancer prophylaxis and treatment. Furthermore, the integration of bioinformatics tools enhances our understanding of the intricate molecular mechanisms underpinning tumorigenesis and therapeutic responsiveness.
Conclusion
The findings of this study suggested that the excessive production of DNMT1 and DNMT3A proteins may play a role in the formation of cancerous cells in NB4 cells. It is crucial to recognize the significant regulatory function of microRNAs in this particular context. Consequently, the intensified expression of DNMT1 and DNMT3A can result in decreased expression of miR-193a and miR-200c before 6-gingerol treatment, and vice versa. This reduction in miRNA expression ultimately leads to the disruption of other genes that suppress tumor growth, such as PODXL, which consequently leads to abnormal expression and subsequent development of cancerous cell characteristics. However, further investigation is necessary to determine the interactions between miR-193a, miR-200c, DNMT1, DNMT3A, and PODXL genes in AML.
Data availability
Data is provided within the manuscript or Supplementary Information files.
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Acknowledgements
The authors would like to thank the Kerman University of Medical Sciences for supporting this research (Grant No. 98001041). The authors would also like to take this opportunity to express our gratitude to the artificial intelligence tools, including the AI Paragraph Rewriter at https://ahrefs.com/writing-tools/paragraph-rewriter and the Free Proofreading Tool at https://wordvice.ai/proofreading/30eea6c6-37fd-4634-afcb-ac0231dc4d52, which helped the authors edit and improve this manuscript.
Funding
The research leading to these results received funding from the Vice-Chancellor of Research and Technology, Kerman University of Medical Sciences, under Grant Agreement No. 98001041.
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RV and AP conceived the study; AA principal designer of the study and in silico study; AMR, MRZK, BK, and HA in silico study: MSB performed the laboratory research; BK and HR analyzed the data; AA, RV, SZ, MJS, HA, and MRZK wrote and revised the manuscript with minor contributions from the other authors. All the authors read and approved the final manuscript.
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Afgar, A., Ramezani Zadeh Kermani, M., Pabarja, A. et al. 6-Gingerol modulates miRNAs and PODXL gene expression via methyltransferase enzymes in NB4 cells: an in silico and in vitro study. Sci Rep 14, 18356 (2024). https://doi.org/10.1038/s41598-024-68069-4
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DOI: https://doi.org/10.1038/s41598-024-68069-4
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