Catechin-induced changes in PODXL, DNMTs, and miRNA expression in NALM6 cells: An integrated in silico and in vitro approach

Catechin-induced changes in PODXL, DNMTs, and miRNA expression in NALM6 cells: An integrated in silico and in vitro approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Catechin-induced changes in PODXL, DNMTs, and miRNA expression in NALM6 cells: An integrated in silico and in vitro approach Ali Afgar, Alireza Keyhani, Amirreza Afgar, Mohamad Javad Mirzaei-Parsa, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3873363/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jun, 2024 Read the published version in BMC Complementary Medicine and Therapies → Version 1 posted 3 You are reading this latest preprint version Abstract Background This study explored the impact of predicted miRNAs on DNA methyltransferases (DNMTs) and the PODXL gene in NALM6 cells, revealing the significance of these miRNAs in acute lymphocytic leukemia (ALL). Methods We employed a multifaceted approach comprising bioinformatic analyses (protein structure prediction, molecular docking, dynamics, ADMET study) and miRNA evaluations to explore the therapeutic effects of catechin compounds on DNMTs . Results Our evaluation revealed a nuanced relationship in which catechin treatment induced increased miRNA expression and decreased DNMT1 and DNMT3B levels in NALM6 cells. This indirect modulation impacted PODXL expression, contributing to cancer characteristics. Conclusion The overexpression of DNMT1 and DNMT3B in NALM6 cells may promote ALL development via a mechanism regulated by microRNAs, particularly miR-548 and miR-200c. Altered DNMT1 and DNMT3B expression is correlated with decreased miR-548 and miR-200c expression before and after catechin treatment, respectively, leading to the dysregulation of tumor suppressor genes, such as PODXL , and cancer cell characteristics. These findings underscore the therapeutic potential of catechin compounds targeting DNMTs and miRNAs in ALL treatment. PODXL Methyltransferase miRNAs Docking MD simulation ALL and catechin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction ALL is one of the most common cancers in children and is occasionally observed in adults. ALL is a malignant bone marrow disease in which primary lymphoid precursors proliferate and replace normal bone marrow hematopoietic cells ( 1 ). Today, molecular changes associated with the pathogenesis of leukemia are being studied and identified. These changes are used as diagnostic markers to minimize false-negative results and enable timely diagnosis of the disease before it progresses to metastatic stages. This attention is aimed at identifying appropriate treatment options. Among the molecular alterations involved in the disease process, the expression of genes and epigenetic factors plays a significant role ( 2 ). A wide range of genes that promote apoptosis, unlimited cell growth, angiogenesis, and tumor metastasis have been identified. These genes include NPMI, WTI, BAALC , and FLT3 ( 3 ). Another important gene is PODXI. gene, which is upregulated in a wide range of cancers, including malignant brain tumors; breast, prostate, testicular, liver, pancreas, and kidney cancers; and leukemia. Additionally, research has demonstrated that the expression of this protein is associated with severe malignancy, poor prognosis, and metastasis ( 4 ). The expression of PODXL in ALL is significant for several reasons. These included the expression of proteins associated with PODXL and CD34 in most leukemic blasts and the expression of PODXL in normal precursor cells. Additionally, the expression of the PODXL transcriptional regulator Wilms' tumor I is observed in many blast cells of ALL and acute myelocytic leukemia ( 5 ). These new findings highlight the role of PODXL in survival, migration, cell proliferation, drug resistance development, and metabolic reprogramming in non-Hodgkin lymphoma ( 6 ). Therefore, considering the significant role of PODXL in the development of acute leukemia, as well as in invasion and metastasis, the expression level of PODXL has been regarded as a diagnostic and prognostic factor ( 7 ). In this regard, identifying the factors involved in the effective expression of this gene will be very helpful. One of these factors is microRNAs. MicroRNAs are endogenous, single-stranded, small 20–23 noncoding RNAs that regulate the expression of approximately 60% of the genes encoding proteins at the posttranscriptional level by binding to the 3'UTR region. These molecules affect the expression of mRNAs, causing them to degrade or preventing their translation ( 8 , 9 ). Each miRNA can regulate the expression of numerous target genes and can be controlled by several other miRNAs. Therefore, miRNAs can affect the expression of the PODXL gene, and changes in miRNA expression determine the expression of this gene. Among the studies conducted in this field, one study demonstrated an inverse association between miR-125b and the PODXL gene in umbilical artery endothelial cells and aortic smooth muscle cells (HAVSMCs), suggesting that inhibiting miR-125b to reduce PODXL expression could be considered a treatment option for atherosclerosis ( 10 ). Furthermore, abnormal expression of the podocalyxin gene in AML is associated with a decrease in miR-199b ( 11 ). As a result, the use of this miRNA can serve as both a therapeutic and prognostic tool for this type of cancer. In this context, additional epigenetic mechanisms, such as methylation, play important roles in regulating these genes. Abnormal DNA methylation is a prominent feature of ALL, and numerous studies indicate that it can play a significant role in the development and progression of ALL ( 4 ). Abnormal epigenetic regulation, particularly gene promoter DNA hypermethylation, is a recurring gene silencing mechanism associated with disease prognosis and treatment response in patients with B-cell progenitors (ALL-B). Studies on ALL leukemia have shown that the expression of several microRNAs is decreased, and their levels can be restored by treatment with methyltransferase inhibitors, such as zebularine ( 12 ). DNMTI, DNMT3A, and DNMT3B enzymes collaborate to establish DNA methylation patterns. While DNMTIs are primarily responsible for preserving methylation patterns after DNA replication, DNMT3A and DNMT3B function as de novo DNMTs, initiating methylation patterns from scratch ( 13 , 14 ). Catechins, as bioactive polyphenol compounds, have attracted considerable interest because of their diverse biological activities and potential health advantages. In particular, epigallocatechin-3-gallate (EGCG) has emerged as a potent inducer of apoptosis through mechanisms that involve activating caspases, influencing Bel-2 family proteins, and disrupting survival signaling pathways. Moreover, recent findings indicate that Catechins found in the green tea variety can also influence epigenetic changes, such as DNA methylation and histone modifications ( 15 ). This feature is found in all types of cancers, such as myeloid and lymphoid leukemia, when exposed to various green tea catechins (or polyphenols), both in vitro and in vivo. It is important to note that the effectiveness of this action is influenced by the dosage and duration of treatment ( 16 , 17 ). In this study, we evaluated the expression and function of miRNAs and the PODXL gene following treatment with catechin using advanced bioinformatics software. We specifically predicted new miRNAs by analyzing the 3'UTRs of genes involved in methylation, namely, DNMT3B, DNMT3A , and DNMTI . Through a combination of bioinformatics analysis and experimental methods, we were able to evaluate the effect of catechin on miRNA expression and the function of PODXL and DNMT genes. 2. Materials and methods 2.1. Availability of sequences and BLAST queries. To identify suitable structures for our study, we retrieved the amino acid sequences of DNMT1, DNMT3A, and DNMT3B from the NCBI protein database ( https://www.ncbi.nlm.nih.gov/protein/ ) in FASTA format. Using these sequences as queries, a BLAST search against "Protein Data Bank proteins" revealed experimentally confirmed structures for each target protein. This process facilitated the selection of an appropriate template structure for investigating interactions with catechin compounds. 2.2. Prediction of protein secondary structure and topology The secondary structures of DNMT1, DNMT3A, and DNMT3B were determined using the SOPMAserver ( https://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html ). With respect to the neural network, SOPMA can predict a significant portion of the amino acids involved in the secondary structure, ultimately leading to the generation of 3D models from the 2D structures. 2.3. The physicochemical characteristics were determined from the sequences. The structural and functional characteristics of a protein can be estimated by analyzing its physical and chemical properties. To determine these characteristics, the protein structure sequence was submitted to the ProtParam web server ( https://web.expasy.org/protparam/ ). 2.4. Prediction of functional pockets and residues The online service HotSpot Wizard 3 ( https://loschmidt.chemi.muni.cz ) was used to predict the functional amino acids of the proteins DNMT1, DNMT3A, and DNMT3B. To obtain the core structural pockets and cavities, the CASTp web server at http://sts.bioe.uic.edu/castp/index.html?2cpk was used. The output of the CASTp server provides measurements in angstroms, ranging from 0.0 to 10.0. 2.5. Predicting the ADMET of chemical compounds For a drug to be considered suitable, it must possess favorable biochemical activity, pharmacokinetics, safety, high potency and selectivity, as well as ADMET. An ideal drug must be able to distribute itself effectively into various tissues and organs, undergo metabolism without an immediate decrease in activity, and be excreted from the body properly ( 18 ). Due to the incomplete medicinal properties of catechin in the DrugBank database, it was subjected to evaluation using the ADMETlab 2.0 server ( https://admetmesh.scbdd.com/service/evaluation/index ) to determine its physicochemical, medical chemistry, and ADMET parameters ( 19 ). 2.6. Evaluation of pharmaceutical criteria by R software The chemical compound catechin was analyzed using the R data mining tool to determine the number of physicochemical, medicinal chemical, and ADMET criteria met. With this software, a score was assigned to each pharmacological criterion that the catechin chemical compound successfully passed, and these scores were subsequently summed. 2.7. Energy minimization of proteins and ligands Energy minimization is crucial for accurate determination of the molecular spatial arrangement. In protein system modeling, adjusting hydrogen bond networks is essential for eliminating disruptive contacts and minimizing the overall system energy, considering components such as stretching, bending, and torsion as potential energy ( 20 ). The YASARA server ( http://www.yasara.org/minimizationserver.htm ) used the Amber force field to minimize the energy needed for DNMTs and catechin compounds. Its optimized energy functions resulted in superior structural models, leveraging the minimal energy of empirical structural models ( 21 ). 2.7. Docking of catechin and methyltransferases. The HDOCK web server ( http://hdock.phys.hust.edu.cn ) and AutoDock4 were utilized to investigate the interactions between catechin and DNMT proteins ( 22 ). HDOCK employs a hybrid approach that combines template-based modeling and ab initio-free docking to achieve protein‒protein and protein‒DNA/RNA docking. Moreover, AutoDock4 is an integrated platform for predicting protein–ligand interactions. Open Babel software was used to convert the necessary file formats for the server and program ( 23 ). 2.8. Two-dimensional interaction diagram To identify the amino acids involved in protein‒protein interactions, a 2D interaction plot was generated for the inhibitory chemical catechin with the DNMT1, DNMT3A, and DNMT3B proteins. The computations were performed using LigPlot + software, accessible at https://www.ebi.ac.uk/thornton-srv/software/LigPlus/ . Protein–ligand interactions were also analyzed using Discovery Studio software, which is built upon the SciTegic Enterprise Server, an open operating platform. This tool also provides the possibility to analyze other aspects related to protein–ligand interactions. 2.9. Molecular dynamics simulations The superior docking results of catechin with the DNMT1 and DNMT3B proteins, which exhibited significant changes before and after treatment, were subjected to MDs using the CHARMM 27 all-atomic force field. The protein–ligand complex was solvated in a Triclinic box using periodic boundary conditions and the TIP3P water model. The Na + and Cl- ions were added to neutralize the system. The SwissParam server was used to determine the ligand parameters and topology. The internal constraints of the protein‒ligand complex were relaxed by 50000 steps of steepest descent energy minimization, leading to restriction of the positions of all heavy atoms. Before the MDs, the systems were heated using a V-rescale thermostat to obtain a temperature of 300 K with 0.1 ps as the coupling constant, and equilibration was achieved in NVT. Then, the solvent density was sustained using a Parrinello-Rahman barostat with a pressure of 1 bar, a coupling constant of 0.1 ps, and a temperature of 300 K to obtain equilibration in the NPT by gradually discharging the restraint on heavy atoms step by step. Finally, an MDS was performed for the complexes for 40 ns with an integration time step of 2 fs. Finally, trajectory analyses, such as RMSD, RMSF, Rg, SASA, and H-bonds of protein–ligand complexes, were performed using the Gromacs package. 2.10. miRNA and target mRNA prediction To identify target genes, miRDB, RNAhybrid, PICTAR4, DIANAmT, miRWalk, miRanda, DIANAmT, RNAhybrid, PITA, RNA22, PICTAR5, and TargetScan software were used for predicting target microRNAs. The mentioned programs generated a substantial number of miRNA predictions, which were then filtered using four criteria: 1) longest seed region binding to the target mRNA, 2) conserved pairing of the seed region, 3) the number of target miRNA prediction software, and 4) simultaneous targeting of DNMT genes in the 3'UTR. Finally, a miRNA was selected from the eligible microRNAs based on its presence in the 3'UTR of the DNMT gene region. 2.11 Special probes, primers, and stem‒loop design for the expression of microRNAs First, to generate predicted miRNAs for all DNMT genes, the microRNA sequences of interest were obtained from the miRBase database by completing the registration process at www.mirbase.org . The specific miRNAs targeted were miR-548, miR-200c, miR-193a, and miR-148a-5p. To determine the smallest detectable number and ensure high sensitivity for these target miRNAs in the sample, we utilized the loop sequence published by Faridi et al. ( 24 ). The stem‒loop design includes a 6-nucleotide sequence at the end, which is complementary to the 3' end region of each microRNA, allowing for specific detection of each miRNA. For the forward primers, most mature miRNAs were utilized, with minor modifications to the 5' primer. To evaluate primer specificity, the BLAST primer page was used at https://٫www.ncbi.nlm.nih.gov٫tools٫primer-blast٫ on the NCBI website was utilized. The Tm values of the primers and probes were adjusted using Gene Runner v. 6.0.04 software (Hastings Software, Inc.) following the standard conditions of real-time PCR. The specificity of each miRNA was confirmed by real-time PCR amplification of the target sequence using cDNA from the miRNA stem loops. Finally, relative expression and/or fold change analyses were conducted by comparing the CT values of the target miRNAs to those of the U6 reference gene, as shown in Tables 1 and 2 . 2.12. Cell lines and drugs Catechin, with a purity greater than 95%, was generously provided as a gift by Reza Fotouhi Ardakani. The NALM6 and PBS cell lines, which served as normal cells, were obtained from the Institute Pasteur of the Iran cell bank by our investigative team. To culture the NALM6 cells, we used RPMI 1640 (Gibco BRL) supplemented with 100 mg/m2. Streptomycin, 100 mg/mL. Penicillin and 15% fetal bovine serum were obtained from Gibco BRL. All cells were maintained in a humidified atmosphere containing 5% CO2 at 37°C. The reagents used in this study were high-glucose Dulbecco's modified Eagle's medium (DMEM) supplemented with glutamic acid and fetal bovine serum (FBS). Glucose-free DMEM (0 g/mL), stereotyped bacteria, and penicillin antibiotics were purchased from Gibco. Additionally, phosphate-buffered saline (PBS), dimethyl sulfoxide (DMSO), bicarbonate powder, and other necessary materials were purchased from Merck (Darmstadt, Germany). 2.13. MTT assay Initially, 2 × 10 4 cells were seeded in a 96-well plate in a volume of 100 µl and incubated. Then, the NALM6 cells were treated with different concentrations of catechins (0, 2.5, 5, 10, 20, 40, 60, 80, or 110 µM) ( 25 , 26 ) for 24 hours. Next, MTT dye was added to the sample to a final concentration of 0.45 mg/mL, 100 µl of DMSO was added to each well, and the solution was mixed. Finally, the absorbance of the sample was measured at 570 nm to calculate the IC50 using Prism 8.0.2 software. The rate of cell proliferation inhibition was determined using the formula [1-(OD value of compound ٫OD value of the control)] 100%. 2.14. Catechin-induced morphological alterations. A total of 1x10 6 NALM6 cells/ml were seeded in 12-well plates. After treatment with different concentrations of catechin (0, 10, 15, or 20 µM), the morphology of the cells was evaluated under a microscope ( 26 ) for 24 hours. Subsequently, the samples were stained with DAPI (20 mM) to investigate the impact of different concentrations of catechins on the cytoplasmic morphology of the target cells. 2.15. Annexin V and propidium iodide flow cytometry assay To achieve this objective, a total of 5 × 10 5 NALM6 cells were subjected to treatment with the "IC50" compound, specifically 35 µM, in 6-well plates for 24 hours. After incubation, the cell pellet was isolated by centrifugation and then washed with PBS. Afterward, the cells were exposed to 500 µl of 1X binding buffer. Subsequently, 5 µl of annexin V was added to the samples, which were subsequently allowed to incubate in the dark for 10 minutes. Next, 5 µl of Pl dye was added, and the mixture was incubated for 10 minutes in the dark. Flow cytometry was then used to measure the percentage of phosphatidylserine released on the cell surface, and the results were analyzed using FlowJo v 7.6 software. 2.12. Extraction of target microRNA and RNAs For extraction of target RNA genes, especially microRNAs, the YTzol Pure RNA Kit (Yekta Tajhiz Azma Co.) was used with modifications to increase the purity and integrity of the RNA extraction. The NALM6 cells were detached and incubated on ice for 5 min. Afterward, 200 µl of chloroform solution was added, and the mixture was stirred for 2 min and then centrifuged at 12000 rpm for 30 min at 4°C. Afterward, the mixture was transferred to a clean tube, after which the previous step was instantly repeated with 100 µl of 1-bromo-3-chloropropane. The aqueous phase was transferred to a clean tube, and an equal volume of absolute alcohol was added. The tubes were kept at -20°C overnight and then centrifuged at 12,000 rpm and 4°C for 1 hour. The supernatant was discarded, and 1 ml of 70% ethanol was added. The mixture was subsequently centrifuged at 12000 and 4°C for 45 minutes. Next, the supernatant was removed, and the RNA was inverted at room temperature. Then, 50 µl of DEPC-treated water was added. In each tube, 5 units of RNase-free DNase I were added and incubated for 5 minutes at room temperature, followed by inactivation for 5 minutes at 70°C. Finally, the concentration and purity of the extracted RNA were determined using a Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). All the tubes were kept at -70°C until analysis. 2.13. cDNA synthesis and real-time PCR cDNA was synthesized from 1000 ng of total RNA using Mu-MLV reverse transcriptase according to the kit protocol (Yekta Tajhiz, cat: YT4500). The cDNA was kept at -70°C until analysis. The 12.5 µl PCR mixture was composed of 6.5 µl of SYBR Green master mix, 0.2 µM of each primer oligonucleotide, and 2 µl of cDNA. The real-time program was performed as follows. The initial denaturation step was 95°C for 40 s, followed by 45 cycles of denaturation at 95°C for 40 s and 60°C for 20 s and a final extension at 72°C for 35 s. The UBE2D2 gene was used as the internal reference gene ( 27 ). Finally, the PCR efficiency and expression of each mRNA were assessed using LinRegPCR software. The PCR efficiency was between 95 and 108%. 2.14. Synthesis of microRNA cDNA and real-time PCR Following miRNA extraction, cDNA was synthesized using Mu-MLV reverse transcriptase. Four microliters of extracted miRNA, adjusted to 1200 ng of RNA, was added to 1.5 µl of stem‒loop (diluted 1.100% of the original 100 µM solution), followed by 5 µl of double-distilled water. The 10.5 µl mixture was incubated for 5 minutes at 65°C in a thermocycler (Bio-Rad). Immediately, the tubes were transferred to a cold container. A mixture of 2 µl of dNTPs (10 mM) and 4 µl of 4X buffer was used. Then, 0.5 µl of RNase inhibitor (20 units), 2 µl of DTT (10 mM), and 1 µl of reverse transcription enzyme were added. cDNA synthesis was performed for 1 hour at 44°C and 10 min at 70°C to inactivate the enzyme. The synthesized cDNA was kept at -20°C until use. The reverse transcription products were amplified by real-time PCR. A universal reverse primer and probes with a specific primer for each miRNA were applied. Each microtube contained 6.25 µl of 2x qPCR Master Mix. The primers used were 0.74 µM reverse primer, 0.5 µM forward primer, and 0.2 µM probe for a final volume of 12.5 µl. qPCR was performed on a Rotor-Gene Q. The enzyme was initially activated at 95°C for 30 seconds, followed by 45 cycles of 95°C for 15 seconds and 60°C for 45 seconds. The U6 gene was selected as the reference gene. The relative expression of each miRNA was statistically analyzed using the Pfafil method. P values and fold changes were calculated with GraphPad Prism software version 9.2.0 (GraphPad Software, Inc., San Diego, CA). 3. Results 3.1. BLAST search of the amino acid sequence The reference amino acid sequence of DNMT can be found in the NCBI protein database, which is available at https://www.ncbi.nlm.nih.gov/ . There are three accession numbers for the DNMT protein: NP 001124295.1, NP 072046.2, and NP_008823. These accession numbers were utilized to produce a number of nearly complete structures with satisfactory resolution. These structures can be accessed in the PDB database using the following accession numbers: "DNMT1:4 WXX, DNMT3A:6PA7, and DNMT3B:6KDA" (Fig. 1 ). 3.2. Prediction of protein secondary structure and topology The protein secondary structure, which is crucial for docking and molecular dynamics simulations, was analyzed using the SOPMA server. Figure 2 shows that DNMT1 is composed of a random coil (46.50%), an alpha helix (28.90%), an extended strand (18.87%), and a beta-turn (5.73%). DNMT3A comprises random coils (46.88%), alpha helices (30.48%), extended strands (16.55%), and beta-turns (6.10%). The composition of DNMT3B included random coils (53.25%), alpha helices (26.36%), extended strands (14.94%), and beta-turns (5.45%). These findings offer insights into the factors influencing protein structure and function before and after docking and MD simulation. 3.3. The extraction of physicochemical properties from the sequence Utilizing the ProtParam server on DNMT sequences in FASTA format, DNMT1 exhibited an aliphatic index of 70.26, indicating a high proportion of aliphatic amino acids. Its GRAVY score of -0.553 implies a slightly hydrophilic nature, with an instability index of 47.52, signifying instability. The protein contains 166 negatively charged residues and 166 positively charged residues. Similarly, DNMT3A had an aliphatic index of 69.45, a GRAVY score of -0.438, and an instability index of 46.31. It has 92 negatively charged residues and 85 positively charged residues. DNMT3B displayed an aliphatic index of 63.19, a GRAVY score of -0.629, and an instability index of 58.88, indicating high instability. It includes 100 negatively charged residues and 104 positively charged residues. The Hydropathic Average (GRAVY) aids in assessing the distribution of polar and nonpolar groups within a protein's 3D structure; this parameter is essential for pre- and post-docking, MD simulation, and 2D plot analyses (Table 3 details residue distribution). Table 1 Designed primers, probes, and RT Stem-loops miRNA Accession number RT specific stem‒loop primer RT-primer miR-548 MIMAT0031890 GTATGCGGCTACCCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGGAGCCGCATAC AAAGTA RT-primer miR-200c MI0000650 GTATGCGGCTACCCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGGAGCCGCATAC TCCATC RT-primer miR-193a MI0000487 GTATGCGGCTACCCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGGAGCCGCATAC ACTGGG RT-primer miR-148a-5p MIMAT0004549 GTATGCGGCTACCCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGGAGCCGCATAC AGTCGG RT primer U6 NR_004394.1 GTATGCTGCTACCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGAGCAGCATAC CGAATT F- miR-548 MIMAT0031890 CCCGCAAAAACTGCAGTTACTTT F- miR-200c MI0000650 TAATACTGCCGGGTAATGATGGA F- miR-193a MI0000487 AATGGCCTACAAAGTCCCAGT F- miR-148a-5p MIMAT0004549 AAAGTTCTGAGACACTCCGACT F-U6 NR_004394.1 GCAAGGATGACACGCAAATT Taq man probe : FAM 5’AGTGCCATGCCTGCCATCGAGC 3’ BHQ-1 Universal reverse : GCTGCTACCTCGGACCCT; miRNA complementary specific sequences are underlined. Table 2 Primer sequences for real-time PCR Gene Accession number Primer 5’-3’ F-DNMT3A NM_001130823.3 AACAGGCCGTTGGCATCC R-DNMT3A NM_001130823.3 GTAATGGTCCTCACTTTGCTGAAC F- DNMT1 NM_001375819.1 TTATCCGAGGAGGGCTACCTG R- DNMT1 NM_001375819.1 TCCCGGTTGTAAGCATGAGC F-DNMT3B NM_175848.2 GACTTGACAGGCGATGGCG R-DNMT3B NM_175848.2 CTGTTGTTATTTCGAGTTCGGACA F-reference gene (UBE2D2) NM_181838.2 AGAATCCACAAGCTCCCTCC R-reference gene (UBE2D2) NM_181838.2 TGCCACCCAAGAGGTAAGTG F- PODXL XM_034965004.1 ACGAGAGTAACTGGGCAAAGTG R- PODXL XM_034965004.1 GTGAAGGTGGCTTTGACTGC Table 3 The frequency (percentage) of residues in DNMTs. Residue DNMT1.A DNMT3A DNMT3B Ala 76 (6.1) 45 (6.5) 47 (6.1) Arg 78 (6.2) 44 (6.4) 64 (8.3) Asn 50 (4.0) 26 (3.8) 32 (4.2) Asp 79 (6.3) 38 (5.5) 51 (6.6) Cys 39 (3.1) 26 (3.8) 23 (3.0) Gln 51 (4.1) 24 (3.5) 22 (2.9) Glu 87 (6.9) 54 (7.8) 49 (6.4) Gly 90 (7.2) 53 (7.7) 66 (8.6) His 26 (2.1) 13 (1.9) 14 (1.8) Ile 55 (4.4) 27 (3.9) 22 (2.9) Leu 99 (7.9) 44 (6.4) 58 (7.5) Lys 88 (7.0) 41 (6.0) 40 (5.2) Met 27 (2.1) 19 (2.8) 16 (2.1) Phe 56 (4.5) 31 (4.5) 32 (4.2) Pro 81 (6.4) 48 (7.0) 44 (5.7) Ser 84 (6.7) 39 (5.7) 69 (9.0) The 56 (4.5) 25 (3.6) 39 (5.1) Trp 16 (1.3) 16 (2.3) 14 (1.8) Tyr 47 (3.7) 22 (3.2) 24 (3.1) Val 71 (5.7) 54 (7.8) 44 (5.7) Pyl 0 (0) 0 (0) 0 (0) Sec 0 (0) 0 (0) 0 (0) 3.4. Functional residues and pockets in DNMTs The functional amino acids of the DNMT1, DNMT3A, and DNMT3B proteins were determined through the use of the HotSpot Wizard web server. The NCBI identified specific hotspots within the beta chain of the DNMT1 enzyme, including CYS1226, CYS353, CYS356, CYS414, HIS418, CYS653, CYS656, CYS659, CYS664, CYS667, CYS670, CYS686, CYS691, SER1146, GLU1168, MET1169, GLY1150, LEU1151, ASP1190, CYS1191, ASN1578, and VAL1580. For DNMT3A, the hotspots were found in the K chain and included CYS710, PHE640, ASP641, SER663, GLU664, VAL665, CYS666, ASP686, VAL687, GLY707, LEU730, GLU756, ARG891, SER892, and TRP893. In the case of DNMT3B, the predicted functional amino acids were located in the L chain and included LEU651, VAL582, ALA583, SER584, GLU585, VAL586, VAL605, GLY627, GLY628, and SER629. These hotspots were mutable residues with different scores based on the web server's scoring system, and they were situated in the catalytic pocket and/or access tunnels. The analysis also involved the identification and quantification of geometric and topological features. It was discovered that surface pockets and cavities play a role in hindering the functional development of protein targets. The methyltransferase enzymes were shown to possess several central pockets and cavities. Furthermore, the largest predicted pockets of the 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. Additionally, the following functional residues were common in the pockets of the enzymes DNMT1 and DNMT3A but not in those of DNMT3B: CYS1226, CYS356, CYS656, CYS664, CYS667, CYS670, CYS686, CYS691, SER1146, GLU1168, MET1169, GLY1150, LEU1151, ASP1190, CYS1191, ASN1578, and VAL1580. Therefore, according to the results of the pocket, cavity, and position of the functional residues included in Table 4 , docking and 2D diagram analyses were possible. Table 4 Structural and chemical pocket and cavities of Castp . DNMT1.B DNMT3A DNMT3B Area( Å 2 ) Volume( Å 3 ) Area( Å 2 ) Volume( Å 3 ) Area( Å 2 ) Volume( Å 3 ) Packet 5747.91 5801.38 531.86 480.92 385.82 225.80 Cavities 1 2434.66 2952.50 365.03 165.93 269.45 150.28 2 474.28 331.95 233.93 94.30 200.83 124.32 3 336.37 260.90 86.24 68.37 177.04 57.18 4 136.26 199.23 56.34 21.28 119.14 52.76 3.5. Drug-based ADMET prediction The ADMETlab2.0 server analyzed catechin, evaluating its physicochemical properties, medicinal chemistry metrics, absorption, distribution, metabolism, excretion, and toxicology. These included parameters such as molecular weight, QED score, Caco-2 permeability, enzyme interactions, and various toxicity assessments. The results are summarized in Table 5 . These assessments guide drug selection, ensuring desirable efficacy and safety profiles in the design and rescreening of catechin. Table 5. Calculation of Catechin ADMET by ADMETlab2.0 Physicochemical Property Medicinal Chemistry Distribution Absorption Environmental toxicity Property Value Property Value Property Value Property Value Property Value Molecular Weight 290.08 QED 0.51 PPB 92.35% Caco-2 Permeability -6.213 Bioconcentration Factors 0.937 Volume 279.249 SAscore 3.344 VD 0.652 MDCK Permeability 4e-06 IGC 50 4.412 Density 1.039 Fsp3 0.2 BBB Penetration 0.025 Pgp-inhibitor 0.007 LC 50 FM 4.788 nHA 6 MCE-18 60.0 Fu 8.351% Pgp-substrate 0.004 LC 50 DM 5.299 nHD 5 NPscore 2.304 Toxicity HIA 0.037 Tox21 pathway nRot 1 Lipinski Rule Accepted Property Value F20% 0.998 Property Value nRing 3 Pfizer Rule Accepted hERG Blockers 0.03 F30% 0.999 NR-AR 0.011 MaxRing 10 GSK Rule Accepted H-HT 0.099 Metabolism NR-AR-LBD 0.092 nHet 6 Golden Triangle Accepted DILI 0.101 Property Value NR-AhR 0.81 fChar 0 PAINS 1 alerts AMES Toxicity 0.616 CYP1A2 inhibitor 0.393 NR-Aromatase 0.316 nRig 17 ALARM NMR 2 alerts Rat Oral Acute Toxicity 0.43 CYP1A2 substrate 0.224 NR-ER 0.753 Flexibility 0.059 BMS 0 alerts FDAMDD 0.146 CYP2C19 inhibitor 0.031 NR-ER-LBD 0.459 Stereo Centers 2 Chelator Rule 1 alerts Skin Sensiti zation 0.947 CYP2C19 substrate 0.054 NR-PPAR-gamma 0.128 TPSA 110.38 Excretion Carcinogen city 0.159 CYP2C9 inhibitor 0.323 SR-ARE 0.146 logS -2.72 Property Value Eye Corrosion 0.003 CYP2C9 substrate 0.827 SR-ATAD5 0.019 logP 1.213 CL 16.512 Eye Irritation 0.914 CYP2D6 inhibitor 0.139 SR-HSE 0.846 logD 1.243 T 1/2 0.884 Respiratory Toxicity 0.117 CYP2D6 substrate 0.31 SR-MMP 0.779 CYP3A4 inhibitor 0.371 SR-p53 0.185 CYP3A4 substrate 0.18 3.6. Evaluation of pharmaceutical criteria by R software R software was used to analyze the catechin compounds, which are considered 86 medicinal factors. Each factor received a score, and these scores were categorized. The analysis revealed approximately 46 medicinal properties associated with the drug, with scores aggregated to reach this conclusion. The corresponding R software codes are provided in supplementary file 2. 3.7. Energy minimization of proteins and ligands The energy values of DNMTI, DNMTA, DNMTB, and the catechin chemical compound before energy minimization by YASARA tools were 6597613120.10. -139468.80, 97477.50, and − 545, respectively. The scores assigned to them prior to undergoing energy minimization were as follows: -2.06, -1.34, -2.13, and − 0.22. However, after energy minimization by YASARA, the energy values of DNMTI, DNMTA, DNMTB, and the catechin chemical compound were − 642864.90 and − 166608.10, respectively. -118058.20, and − 566, respectively. The score after energy minimization was − 0.61. -0.06, -0.62, and − 0.23, respectively. 3.8. Molecular docking In this study, the chemical compound catechin was subjected to docking analysis with DNMTI, DNMT3A, and DNMT3B using both the HDock server and AutoDock4 software. The docking process was carried out with a genetic algorithm in 50 runs employing the specific docking method illustrated in Fig. 3 . The results obtained from this analysis revealed specific docking scores and energies, which demonstrated the superior performance of the HDock server compared to that of another program, as presented in Table 6 . Table 6 Calculation of docking energy and score by AutoDock4 software and the HDOCK server Kcal/Mol DNMT1 Autodock4 HDOCK Pose Binding energy H-bond Docking Score Confidence Score Ligand rmsd (Å) 1 -7.92 1312B,1150B,1266B,1149B,1151 -80.2 0.1985 50.45 2 -7.61 463B,600B -99.01 0.2651 35.97 3 -7.61 600B,462B,463B -60.61 0.1433 48.76 4 -7.44 428B,424B,462B -99.18 0.2657 51.31 5 -7.41 428B,424B,462B -78.36 0.1927 53.44 6 -7.30 463B,595B -108.15 0.3022 33.79 7 -7.23 595B,552B,1490B,553B -93.43 0.2439 37.7 8 -7.15 462B -93.14 0.2428 35.59 9 -7.13 428B,427B,424B(two-times),463B,597B -87.64 0.2232 38.49 10 -7.00 428B,427B,424B(two times),463B -106.55 0.2955 34.7 Pose DNMT3A 1 -9.84 893K,643K(two times),890K,708K,710K,645K 24.64 0.0295 360.08 2 -9.80 710K,641K,708K,643K,707K 37.41 0.023 357.04 3 -7.73 638K,708K,641K,640K -104.62 0.2875 360.82 4 -7.72 710K,891K,640K,641K,708K -95.54 0.2518 354.55 5 -7.55 (641K,643K),710K -94.77 0.2489 355.07 6 -7.37 893K,710K,643K,645K -66.83 0.1593 363.78 7 -7.22 710K -129.71 0.3999 360.16 8 -6.39 891K,711K,640K,714K(two times) -116.5 0.3385 363.52 9 -6.36 664K,891K,663K -108.76 0.3047 354.89 10 -6.34 663K,891K -93.42 0.2439 355.45 Pose DNMT3B 1 -5.50 585 N(three times),606 N,595 N -145.99 0.48 364.17 2 -5.45 606 N,585 N(two-times),595 N -138.26 0.4416 359.94 3 -5.37 585 N(three times),606 N,595 N -137.12 0.436 363.4 4 -5.34 586 N,585 N -128.63 0.3947 357.04 5 -5.33 585 N(three times),606 N,595 N -124.47 0.3751 350.12 6 -5.21 585 N(three times),606 N -123.72 0.3715 350.17 7 -5.17 607 N,585 N,588 N -120.99 0.3589 366.41 8 -5.17 607 N,585 N -120.6 0.3571 357.17 9 -5.13 588 N,607 N -117.14 0.3414 362.12 10 -5.13 607 N,585 N,588 N -110.7 0.313 351.45 3.9. Hydrogen bonds and hydrophobic interactions in the central pocket The residues involved in the interaction between the main pockets of DNMT enzymes and the selected inhibitor compound, catechin, were identified through the application of the PDBsum page generation method. The ensuing analysis delineated specific interactions for each pose. In the DNMT1-B to catechin pose 6, four conventional hydrogen bonds were formed with GLU573, ARG69, ASP569, and GLN687. Additionally, four van der Waals interactions were observed with ASN1236, ASP571, ALA669, and SER570. Two Pi-Cation bonds were present with ARG690 and LYS668, along with one Pi-Alkyl bond involving ARG1238. For DNMT3A-K to catechin pose 7, five conventional hydrogen bonds were formed with SER714, GLU756, ARG8891, GLY707, and PHE640. Additionally, three van der Waals bonds were present with VAL758, ASN757, and ARG792. Two Pi-cation and Pi-anion bonds were formed with ARG790 and GLU756. Moreover, two Pi-alkyl bonds were observed with CYS710 and ARG891. Five Pi-Donor hydrogen bonds were formed with TRP893, GLY706, PRO709, SER708, and ARG891. In the DNMT3B-L to catechin pose 1, six conventional hydrogen bonds were formed with ARG663, LYS542, CYS696, TYR544, ASN718, and PHE672. Three van der Waals bonds were present with LEU659, ARG670, and PHE673. Additionally, four Pi-Donor hydrogen bonds and carbon‒hydrogen bonds were formed with PRO664, TRP674, and PRO671, as was a Pi-Sulfur bond with CYS696. Furthermore, a Pi‒Pi stacking interaction was observed with TYR544. Based on the analysis of two plots (Fig. 4 ), it can be concluded that catechin strongly interacts with the proteins under investigation. 3.10. Molecular dynamics simulation analyses 3.10.1. Root-mean-square deviation analysis We analyzed the RMSD of the backbone atoms using the standard g rms function in GROMACS over a total simulation time of 40 ns. As shown in the plot in Fig. 5 A, DNMT3B bound to the chemical compound catechin plateaued earlier than did DNMT1. The average deviations were 0.367 nm and 0.492 nm, respectively. These findings indicated that DNMT3B combined with the catechin chemical compound was superior to DNMTI when it was combined with the same compound. 3.10.2. Residue flexibility analysis The stability and flexibility of the residues were evaluated by calculating the root-mean-square fluctuations (RMSFs) during a 40 ns simulation using the gmx_rmsf module of GROMACS. The RMSF plot, depicted in Fig. 5 B, showed multiple peaks at residues PHE676:B, LYS385:B, LYS675:B, and GLU384:B of DNMTI. Additionally, peaks were observed for the residues PHE726:N, ILE725:N, ARG740:N, and LYS542:N. Similarly, the peak at ARG724:N was assigned to DNMT3B following its interaction with the catechin chemical compound. These findings suggested that the residues of DNMTI exhibit high dynamics and flexibility, while DNMT3B demonstrates minimal fluctuations, indicating superior stability and limited mobility. 3.10.3. Solvent accessible surface area analysis The solvent-accessible surface area (SASA) values for all the complexes were calculated using the gmx sasa function in GROMACS over a simulation time of 40 ns. Figure 5 C clearly shows that the DNMTI and DNMT3B docked catechin chemical compounds exhibited average SASA values of 581.37 nm2 and 125.93 nm2, respectively. Notably, DNMT3B demonstrated the lowest SASA among the two proteins docked with the catechin chemical compound, indicating that it has a stronger interaction with DNMT3B than with water molecules. 3.10.4. Compactness analysis The radius of gyration (Rg) was calculated using the GROMACS gmx gyrate function with a simulation time of 40 ns. Rg is defined 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 alters the Rg. Compact protein structures tend to maintain low mean Rg deviations, indicating dynamic stability. According to Fig. 5 D, the DNMTI and DNMT3B proteins attached to the catechin chemical compound exhibited mean Rg deviations of 3.71 nm and 1.74 nm, respectively. 3.10.5. Hydrogen bonding and bond distribution analysis H-bonding analysis was performed on all protein‒ligand systems during a 40 ns simulation run. The number of H-bonds was recorded using the GROMACS gmx bond tool and is shown in Fig. 5 E. During the simulation period, 0.6 hydrogen bonds were formed between DNMTI and DNMT3B via the chemical catechin compound. Furthermore, the average number of hydrogen bonds between DNMT3B and the catechin chemical compound was greater. 3.11. Inhibitory effects of catechin on the NALM6 cell line The use of catechin in NALM6 cells resulted in significant suppression of cell growth across a range of concentrations, from 0 to 110 µM. The inhibitory effect was observed at concentrations of 2.5, 5, 10, 20, 40, 60, 80, and 110 µM. After 24 hours, the IC50 value of catechin was determined to be 35 µM, with a 95% confidence interval ranging from 19.5-39.94. The data obtained from this experiment exhibited a strong correlation with an R-squared value of 0.941, as depicted in Fig. 6 . 3.12. Effects of Catechin on the Morphology and Cytoplasm of NALM6 With different concentrations of catechin (10, 15, and 20 µM), ( 26 )the cell count started to decrease within 24 hours. This decrease was more noticeable at a concentration of 20 µg/ml. Furthermore, the characteristic morphology of the cells gradually changed, resulting in a decrease in cell count and the formation of cell aggregates (Fig. 7 ). DAPI staining revealed that as the concentration of catechin increased, the distance between the cells also increased, the nuclei became almost larger, chromatin pyknosis became somewhat apparent, the cell shape became rounder, and the nuclei started to fragment. Ultimately, catechin has been demonstrated to inhibit the growth and proliferation of NALM6 cells. 3.13. Catechin was found to induce apoptosis, as indicated by the flow cytometry data for annexin PI٫V. As depicted in Fig. 8 , catechin has demonstrated a remarkable ability to enhance apoptosis in NALM6 cells. This enhancement was evident not only in the increased number of annexin V-positive cells but also in the percentage of annexin PI٫V-positive cells. The percentage of annexin V-positive cells increased from 0.11 in untreated cells to 1.05 in treated cells, with a cell treatment IC50 value of 35 µM. Furthermore, compared with those in the untreated group, the catechin concentration in the group treated with catechin exhibited a significant change of 35 µM, representing a quarter of the total catechin concentration in the intervention group. These changes were 29.11% and 24.84% in the early and late apoptosis quadrants, respectively. These findings strongly suggest that catechin exerts its cytotoxic effects on NALM6 cells by inducing early apoptosis, particularly late apoptosis. The administered catechin groups showed a significant increase in cell apoptosis (* and **, P < 0.05 ). Compared to those in the untreated group, the concentrations in the catechin group exhibited a significant change of 35 µM in the quarter following the catechin intervention. Specifically, there were changes of 29.11 and 24.84 in the early and late apoptosis quadrants, respectively. 3.14. Prediction of target miRNAs and design of primers, probes, and stem‒loop primers. The 3'UTR targets of DNMT3B, DNMT3A, and DNMT1 mRNA can be detected using various websites. More than hundreds of miRNAs confirmed by several miRNA prediction algorithms were selected based on the highest scores. The authors of these studies met several criteria: the number of algorithms, longest seed region, conserved seed region, and simultaneous 3'UTR targeting of DNMT genes; these analyses were not previously performed in the ALL cohort (Tables S1 and S2). Six complementary nucleotides were added to the 3' ends of the stem‒loop RTs, which were specific for each miRNA. Forward primers and a universal reverse primer were designed along with the TaqMan probe for qPCR. The NCBI Primer-BLAST results for each miRNA revealed that the primer sequences did not bind to any other sequences besides the target miRNA. The results showed 100% specificity for each miRNA (Tables 1 and 2 ). 3.15. DNMT, PODXL, miR-548, miR-200c, miR-193a, and miR-148a-5p gene expression In the NALM6 cell line, miR-548, miR-200c, miR-193a, and miR-148a-5p were selected from a multitude of microRNAs predicted by various software algorithms. As depicted in Fig. 9 , the expression of these miRNAs was markedly lower in the NALM6 cell line than in the peripheral blood cell group. After catechin treatment, there was a noteworthy increase in the levels of microRNAs 548 and 200 (1.65 and 2.87, respectively; p value < 0.05). The PODXL protein, known for inducing cancer via interaction with the actin-binding protein EZR, thereby promoting migration and cell invasion, was significantly upregulated in the NALM6 cell line. However, this upregulation was significantly reversed following catechin treatment (P value < 0.05). Conversely, gene expression analysis of DNMTI and DNMT3B revealed significant increases and decreases, respectively, in the NALM6 cell line before and after treatment compared to those in the PBC group, as illustrated in Fig. 2 . Although DNMT3A expression in NALM6 cells was lower in the treatment group than in the PBC group, these differences were not significant (P value < 0.05). Treatment of NALM6 cells with catechin led to decreased expression levels of DNMTI , DNMT3B , and PODXL compared to those in the PBC group. However, the decrease in DNMT3A expression was not statistically significant (fig. B; p value > 0.05). Additionally, the expression of miR-548 and miR-200c increased after treatment with catechin in NALM6 cells, but the increase in miR-193a and miR-148a-5p was not statistically significant (p value < 0.05). Discussion Various mechanisms are involved in gene expression, one of which involves the methylation of gene regulatory regions, including promoter regions containing CpG islands. Methylation is carried out by a group of DNA methyltransferase enzymes, including DNMT3A , DNMT3B , and DNMT1 ( 28 ). Increased expression of this enzyme has been observed in numerous types of cancer, such as breast cancer, pancreatic cancer, blood malignancies, and cholangiocarcinoma ( 29 ). Mutations in the DNMT3A gene have been shown to result in abnormal methylation of genomic DNA, leading to blood malignancies and various developmental defects ( 28 ). Additionally, overexpression of the DNMT3A enzyme suppressed the BASPI gene in AML-positive A٫E cells ( 29 ). However, it should be noted that overexpression of the DNMT3B enzyme is responsible for another abnormal pattern of DNA methylation that can be observed in a wide range of cancers, including ovarian hepatocellular carcinoma and colon cancer ( 30 ). The genes that are overexpressed in association with DNMT3B include RASSFIA, p53, CDHI, OСТ4, hMLH1 , and p16 ( 29 ). One of the critical cellular regulatory factors that can significantly influence the expression of DNMT methylation enzymes, or vice versa, is their interaction with miRNAs. For example, miR-133a was shown to play a specific role in regulating the expression of the DNMT-1 and 3A genes ( 30 , 31 ). On the other hand, the enzyme methyltransferase can affect the function of miRNA promoter regions through methylation ( 32 ). The results of our study revealed a decrease in the predicted expression of miR-548 and miR-200c, which significantly increased after catechin treatment. However, the levels of miR-193a and miR-148a-5p did not significantly change. Several factors can contribute to this decline. One possible mechanism involves changes in gene expression, specifically through the occurrence of single nucleotide polymorphisms (SNPs) in gene regions such as promoters and miRNA regions ( 33 ). Furthermore, the methylation of particular promoter sequences could also serve as a potential underlying factor ( 34 ). In the present study, increased expression of the DNMT3B enzyme was observed, leading to abnormal methylation of the miR-548 and miR-200c gene promoters and resulting in decreased expression. However, when combined with catechin, the opposite effect occurred, leading to increased expression of microRNAs (miR-548 and miR-200c) and decreased levels of the DNMT3B enzyme. Moreover, additional bioinformatics analyses, including docking and molecular dynamics simulations, confirmed that catechin can induce significant changes in gene expression levels and apoptosis in nalm6 cells. PODXL plays a crucial role in numerous types of cancer. Increased expression of PODXL in aggressive pancreatic cancers has been shown to increase the invasion rate. Conversely, inhibiting the negative expression of PODXL has been shown to decrease cancer cell mortality and invasion ( 7 ). Numerous studies have demonstrated a functional correlation between the PODXL gene and miRNAs in cancer. For example, miR-199a-5p has been shown to inhibit PODXL expression in testicular cancer ( 33 , 35 ). Moreover, inhibiting PODXL in the NT2 cell line results in decreased invasion ( 33 ). The potential indirect relationships between the deregulation of PODXL , miR-548, and miR-200c and methyltransferase genes ( DNMT3B, DNMT3B , and DNMT1 ) arise from the impact of these enzymes. Cheung and colleagues conducted a similar evaluation of the role of the PODXL gene and its association with miR-199a in testicular cancer. They discovered that suppressing miR-199a expression in testicular cancer led to an increase in the expression of the PODXL gene, which, in turn, enhanced the ability of cancer cells to invade and migrate. Furthermore, suppression of the PODXL gene inhibited the invasion and migration of cancer cells ( 33 ). Moreover, the direct effect of miR-5100 on the POXDL gene through binding to the 3'UTR can reduce migration, invasion, and colony formation in pancreatic cancer ( 36 ). The present study predicted that the increase in PODXL gene expression accompanies the decrease in miR-548 and miR-200c expression in the NALM6 cell line. The variation in the expression levels of these miRNAs and PODXL can be attributed to the influence of these macromolecules on the expression of the PODXL gene or methyltransferases, either directly or indirectly. A slight increase in the expression of DNMTI, DNMT3B , and PODXL was observed, potentially indicating a relationship between these DNMTs and PODXL . However, regulation of the PODXL gene by the DNMT3B enzyme is possible, as PODXL expression was significantly increased after catechin treatment. Catechin, a phytochemical found in various plants, has been found to affect cancer by influencing microRNAs ( 37 , 38 ). The expression of miRNAs can be altered by phytochemicals, such as catechins, leading to alterations in oncogenes, tumor suppressors, and proteins associated with cancer. Studies have demonstrated that the modulation of miRNAs by catechin can inhibit tumor growth, inhibit metastasis, reverse epithelial-to-mesenchymal transition (EMT), and increase the sensitivity of cancer cells to drugs ( 39 , 40 ). Catechins have been found to interact with various molecules and exhibit chaperone-like properties, indicating their potential as agents for cancer prevention ( 41 ). Recent research has also demonstrated that catechins can play a role in anticancer therapy by inhibiting cell proliferation and promoting apoptosis ( 42 ). Furthermore, the field of bioinformatics offers new solutions for analyzing cancer-related data obtained from high-throughput methods, such as DNA microarrays, docking, and molecular dynamics simulations. ( 43 ). Based on the available information, this study is the first to focus on the simultaneous effects of microRNAs, epigenetic agents (methylation), tumor suppressor genes ( PODXL ), and catechins, which are effective substances with anticarcinogenic properties. These findings suggested that these substances and plant derivatives, including nalm6, can reverse the phenotype or induce apoptosis in cancer cells to varying degrees. Overall, Catechins have shown promise in both preventing and treating cancer. Bioinformatics can contribute to a better understanding of the molecular mechanisms involved in cancer development and response to treatment. Conclusion These findings indicate that the overexpression of DNMTI and DNMT3B may contribute to the development of cancer in NALM6 cells. It is important to acknowledge the significant regulatory role of microRNAs in this context. As a result, increased expression of DNMTI and DNMT3B can lead to decreased expression of miR-548 and miR-200c before catechin treatment and vice versa. This decrease in miRNA expression eventually leads to the dysregulation of other tumor suppressor genes, such as PODXL , resulting in aberrant expression and subsequent development of cancer cell characteristics. However, further research is needed to determine the interactions between the miR-548, mik-200c, DNMT1, DNMT3B, and PODXL genes in ALL. Declarations Ethics approval and consent to participate This study was approved by the Clinical Research and Ethical Committee of the Faculty of Allied Medicine of Kerman University of Medical Sciences and complied with all the relevant laws and international ethics guidelines outlined in the Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analyzed during the current study are available in the [NCBI protein database] repository, [https://www.ncbi.nlm.nih.gov/protein/]; [SOPMA server] repository, [https://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html]; [ProtParam web server] repository, [https://web.expasy.org/protparam/]; [HotSpot Wizard 3] repository, [https://loschmidt.chemi.muni.cz]; [CASTp web server] repository, [http://sts.bioe.uic.edu/castp/index.html?2cpk]; [ADMETlab 2.0 server] repository, [https://admetmesh.scbdd.com/service/evaluation/index]; [YASARA server ] repository, [http://www.yasara.org/minimizationserver.htm]. To identify target genes, miRDB, RNAhybrid, PICTAR4, DIANAmT, miRWalk, miRanda, DIANAmT, RNAhybrid, PITA, RNA22, PICTAR5, and TargetScan software were used. Competing interests The authors have no conflicts of interest to declare that are relevant to the content of this article. 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. 98001036. Acknowledgments We would like to thank the Kerman University of Medical Sciences for supporting this research (Grant No . 98001036). We 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. Author's Contributions V.R. and M.SB. conceived the study; Al.A. designed the research and in silico study, Am.A and M.RK. performed in silico study, M.SB. performed the laboratory research; A.K and MJ.MP. analyzed the data; and M.E. and M.R. wrote and revised the manuscript, with minor contributions from the other authors. All the authors read and approved the final manuscript . References Terwilliger T, Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J. 2017;7(6):e577. Bacher U, Schnittger S, Haferlach C, Haferlach T. Molecular diagnostics in acute leukemias. Clin Chem Lab Med. 2009;47(11):1333–41. Oyekunle A, Haferlach T, Kröger N, Klyuchnikov E, Zander AR, Schnittger S, et al. Molecular diagnostics, targeted therapy, and the indication for allogeneic stem cell transplantation in acute lymphoblastic leukemia. Adv Hematol. 2011;2011:154745. Doyonnas R, Nielsen JS, Chelliah S, Drew E, Hara T, Miyajima A, et al. 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Methylation of an intronic region regulates miR-199a in testicular tumor malignancy. Oncogene. 2011;30(31):3404–15. Yuan Jhang, Yang F, Chen B, feng, Lu Z, Huo X, song, Zhou W et al. ping,. The histone deacetylase 4/SP1/microrna-200a regulatory network contributes to aberrant histone acetylation in hepatocellular carcinoma. Hepatol Baltim Md. 2011;54(6):2025–35. Lin YY, Wang CY, Phan NN, Chiao CC, Li CY, Sun Z, et al. PODXL2 maintains cellular stemness and promotes breast cancer development through the Rac1/Akt pathway. Int J Med Sci. 2020;17(11):1639–51. Chijiiwa Y, Moriyama T, Ohuchida K, Nabae T, Ohtsuka T, Miyasaka Y, et al. Overexpression of microRNA-5100 decreases the aggressive phenotype of pancreatic cancer cells by targeting PODXL. Int J Oncol. 2016;48(4):1688–700. Kashyap N, Kushwaha PP, Singh AK, Maurya S, Sahoo AK, Kumar S, Phytochemicals. Cancer and miRNAs: An in-silico Approach. In: Kumar S, Egbuna C, editors. Phytochemistry: An in-silico and in vitro Update: Advances in Phytochemical Research [Internet]. Singapore: Springer; 2019 [cited 2024 Jan 7]. p. 421–59. https://doi.org/10.1007/978-981-13-6920-9_23 . W SS. R, S P, A M, S L. Flavonoid display ability to target microRNAs in cancer pathogenesis. Biochem Pharmacol [Internet]. 2021 Jul [cited 2024 Jan 7];189. Available from: https://pubmed.ncbi.nlm.nih.gov/33428895/ . Srivastava SK, Arora S, Singh S, Singh AP, Phytochemicals. microRNAs, and Cancer: Implications for Cancer Prevention and Therapy. In: Chandra D, editor. Mitochondria as Targets for Phytochemicals in Cancer Prevention and Therapy [Internet]. New York, NY: Springer; 2013 [cited 2024 Jan 7]. p. 187–206. https://doi.org/10.1007/978-1-4614-9326-6_9 . Debnath T, Deb Nath NC, Kim EK, Lee KG. Role of phytochemicals in the modulation of miRNA expression in cancer. Food Funct. 2017;8(10):3432–42. Meutia Sari L, Catechin. Molecular mechanism of Anticancer Effect. Dentika Dent J. 2019;22:20–5. Michel O, Przystupski D, Saczko J, Szewczyk A, Niedzielska N, Rossowska J, et al. The favorable effect of catechin in electrochemotherapy in human pancreatic cancer cells. Acta Biochim Pol. 2018;65(2):173–84. Dopazo J. Bioinformatics and cancer: an essential alliance. Clin Transl Oncol Off Publ Fed Span Oncol Soc Natl Cancer Inst Mex. 2006;8(6):409–15. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1.docx Supplementaryfile2.docx Cite Share Download PDF Status: Published Journal Publication published 15 Jun, 2024 Read the published version in BMC Complementary Medicine and Therapies → Version 1 posted Editor invited by journal 25 Feb, 2024 Submission checks completed at journal 25 Feb, 2024 First submitted to journal 17 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3873363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275141198,"identity":"048212b7-e45e-48cf-8e15-6d6c7be1485b","order_by":0,"name":"Ali Afgar","email":"","orcid":"","institution":"Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran, Iran, Islamic Republic Of","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Afgar","suffix":""},{"id":275141199,"identity":"b406667e-c205-46f3-9bdf-59997abd1871","order_by":1,"name":"Alireza Keyhani","email":"","orcid":"","institution":"Kerman University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Keyhani","suffix":""},{"id":275141200,"identity":"1e511140-01ee-4d56-ab6c-49b033cc1de3","order_by":2,"name":"Amirreza Afgar","email":"","orcid":"","institution":"Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran, Iran, Islamic Republic Of","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amirreza","middleName":"","lastName":"Afgar","suffix":""},{"id":275141201,"identity":"325247f0-ca12-48c6-a662-3f6f41193932","order_by":3,"name":"Mohamad Javad Mirzaei-Parsa","email":"","orcid":"","institution":"Department of Hematology and Medical Laboratory Sciences, Faculty of Allied Medicine, Kerman University of Medical Science, Iran","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"Javad","lastName":"Mirzaei-Parsa","suffix":""},{"id":275141202,"identity":"dfc804b0-6e62-45ef-84f9-7b008ed3affd","order_by":4,"name":"Mahdiyeh Ramezani Zadeh Kermani","email":"","orcid":"","institution":"Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran, Iran, Islamic Republic Of","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahdiyeh","middleName":"Ramezani Zadeh","lastName":"Kermani","suffix":""},{"id":275141203,"identity":"16b02a1f-9fea-4da5-9868-755adccbf6d0","order_by":5,"name":"Masoud Rezaei","email":"","orcid":"","institution":"Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran, Iran, Islamic Republic Of","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Rezaei","suffix":""},{"id":275141204,"identity":"62631046-4e28-4fb7-9289-120810f9a525","order_by":6,"name":"Mohammad Ebrahimipour","email":"","orcid":"","institution":"Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran, Iran, Islamic Republic Of","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Ebrahimipour","suffix":""},{"id":275141205,"identity":"4a894f62-9b1f-4446-a8e9-aff0bea4bccf","order_by":7,"name":"Ladan Langroudi","email":"","orcid":"","institution":"Department of Immunology, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ladan","middleName":"","lastName":"Langroudi","suffix":""},{"id":275141206,"identity":"89ed9360-13eb-4db9-810b-fede09d4adb1","order_by":8,"name":"Mahla Sattarzadeh Bardsiri","email":"","orcid":"","institution":"Kerman University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahla","middleName":"Sattarzadeh","lastName":"Bardsiri","suffix":""},{"id":275141207,"identity":"cfe635fc-a74f-49f0-b74c-1d9ae162a160","order_by":9,"name":"Reza Vahidi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBCDBDaGxMYHCQY2ciDegQdEamk2+FCRZgzWkkCMFpAuyRlnDic2QLk4gXl7+8MPjG12eXzsyQ3SvG2H0+eHHX4ItMVOTrcBuxaZMweSJRjbkovZeB42GPO2peduvJ1mANSSbGx2ALsWCYmEAxIMZ5gT2yQSG5J526xzN85OAGk5kLgNlxb5h80/GM7Ug7Uc5m1jTjecnf4BvxYJZjYJhorDIC2NjTPOOCfIS+cQsIUnjc0ioeJ4YhvPw2YGYCAbbpDOKTiQYIDHL+zHH9/4YFCdOL89/fkPYFTKy89O3/zhQ4WdHC4tYJCAzDEAqzTAoxwDyDeQonoUjIJRMApGAgAA/RNjvoLskpcAAAAASUVORK5CYII=","orcid":"","institution":"Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran, Iran, Islamic Republic Of","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Vahidi","suffix":""}],"badges":[],"createdAt":"2024-01-17 16:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3873363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3873363/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12906-024-04521-2","type":"published","date":"2024-06-15T14:55:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51763686,"identity":"f427d2ae-8268-4291-9e84-1d8a02914313","added_by":"auto","created_at":"2024-02-28 17:33:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242800,"visible":true,"origin":"","legend":"\u003cp\u003eStructures of A: DNMTI, B: DNMT3A, C: DNMT3B, and D: catechin (the structures were visualized with PyMOL software).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/07ec0f4ab4af1a00a54e93aa.png"},{"id":51763538,"identity":"79e8fcbb-797d-42e3-b6cd-d9e4d9459d7c","added_by":"auto","created_at":"2024-02-28 17:25:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":574714,"visible":true,"origin":"","legend":"\u003cp\u003eSecondary structure plot of A: DNMTI, B: DNMT3A, and C: DNMT3B\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/b8284e6e5d741affa930f17f.png"},{"id":51763541,"identity":"4bf93d17-8ab0-476a-838d-757afb9c44af","added_by":"auto","created_at":"2024-02-28 17:25:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":343218,"visible":true,"origin":"","legend":"\u003cp\u003e3D docking of catechin with \u003cstrong\u003eA:\u003c/strong\u003e DNMT1\u003cstrong\u003e, B:\u003c/strong\u003e DNMT3A, and \u003cstrong\u003eC:\u003c/strong\u003e DNMT3B determined by the HDOCK server(the structures were visualized with PyMOL software).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/8d3c65f7490ae6d9a09368b9.png"},{"id":51763540,"identity":"74afed33-8f1e-4ac5-9396-dec38c2107f4","added_by":"auto","created_at":"2024-02-28 17:25:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":249819,"visible":true,"origin":"","legend":"\u003cp\u003e2D interactions of docking catechin with A: DNMT1, B: DNMT3A, and C: DNMT3B determined by Ligplot+.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/525f4f389cf773b1d92009db.png"},{"id":51763544,"identity":"0cf9d1fa-2cd1-45a4-99d3-e854af792f05","added_by":"auto","created_at":"2024-02-28 17:25:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":315945,"visible":true,"origin":"","legend":"\u003cp\u003eConvergence analysis of the catechin-DNMT complex MDS.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/eea6b89bf593f1046aebc70a.png"},{"id":51763543,"identity":"3d9ba654-4e6c-4e8e-9319-b2f4a3e4fd8f","added_by":"auto","created_at":"2024-02-28 17:25:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":45174,"visible":true,"origin":"","legend":"\u003cp\u003eCatechin-induced increase in NALM6 cell growth and IC50 concentration\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/9fcf2aa0f414c01820b3bc89.png"},{"id":51763539,"identity":"f7e92642-e88d-4c41-9197-f62fe428ced8","added_by":"auto","created_at":"2024-02-28 17:25:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":413271,"visible":true,"origin":"","legend":"\u003cp\u003eThe cytotoxicity and antiproliferative effects of different catechin concentrations (A: 10, B: 15, and C: 20 µM) on NALM6 cells detected via DAPI staining at ×100 magnification\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/ccaca5223c5042daee43cb01.png"},{"id":51763545,"identity":"e05855cb-f53a-41a8-b411-17b413568b8b","added_by":"auto","created_at":"2024-02-28 17:25:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":290897,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of catechin on the induction of cell death in the NALM6 cell line\u003c/p\u003e\n\u003cp\u003eThe administered catechin groups showed a significant increase in cell apoptosis (* and **, \u003cem\u003eP\u0026lt; 0.05\u003c/em\u003e). Compared to those in the untreated group, the concentrations in the catechin group exhibited a significant change of 35 µM in the quarter following the catechin intervention. Specifically, there were changes of 29.11 and 24.84 in the early and late apoptosis quadrants, respectively.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/dce117174c57a272dc026415.png"},{"id":51763542,"identity":"047c7dd2-f5dd-484e-8d70-8cee4efdb914","added_by":"auto","created_at":"2024-02-28 17:25:55","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":88875,"visible":true,"origin":"","legend":"\u003cp\u003eDNMTI, DNMT3A, DNMT3B, miRNA, and PODXL expression in the NALM6 cell line.\u003c/p\u003e\n\u003cp\u003eTreatment of NALM6 cells with catechin led to decreased expression levels of \u003cem\u003eDNMTI\u003c/em\u003e, \u003cem\u003eDNMT3B\u003c/em\u003e, and \u003cem\u003ePODXL\u003c/em\u003e compared to those in the PBC group. However, the decrease in \u003cem\u003eDNMT3A\u003c/em\u003eexpression was not statistically significant (fig. B; p value\u0026gt;0.05). Additionally, the expression of miR-548 and miR-200c increased after treatment with catechin in NALM6 cells, but the increase in miR-193a and miR-148a-5p was not statistically significant (p value \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/dd87b2cadc7ef07f6331269e.png"},{"id":58822461,"identity":"be8b6b91-7fcf-49bd-b5f5-7b0466bb6db0","added_by":"auto","created_at":"2024-06-21 16:44:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4470360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/5030f1c4-c08e-4325-903b-db37037360b9.pdf"},{"id":51763685,"identity":"e5fdd5b4-7168-4338-8aee-cbdbb84d53a1","added_by":"auto","created_at":"2024-02-28 17:33:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18196,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/0b6590cb6446bcc4c7892968.docx"},{"id":51763535,"identity":"41446070-e7ad-4abc-96ed-233ad98dd3d7","added_by":"auto","created_at":"2024-02-28 17:25:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14858,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3873363/v1/7bfc61f6fa4204c2c02581a7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Catechin-induced changes in PODXL, DNMTs, and miRNA expression in NALM6 cells: An integrated in silico and in vitro approach","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eALL is one of the most common cancers in children and is occasionally observed in adults. ALL is a malignant bone marrow disease in which primary lymphoid precursors proliferate and replace normal bone marrow hematopoietic cells (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Today, molecular changes associated with the pathogenesis of leukemia are being studied and identified. These changes are used as diagnostic markers to minimize false-negative results and enable timely diagnosis of the disease before it progresses to metastatic stages. This attention is aimed at identifying appropriate treatment options. Among the molecular alterations involved in the disease process, the expression of genes and epigenetic factors plays a significant role (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). A wide range of genes that promote apoptosis, unlimited cell growth, angiogenesis, and tumor metastasis have been identified. These genes include \u003cem\u003eNPMI, WTI, BAALC\u003c/em\u003e, and \u003cem\u003eFLT3\u003c/em\u003e (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Another important gene is \u003cem\u003ePODXI.\u003c/em\u003e gene, which is upregulated in a wide range of cancers, including malignant brain tumors; breast, prostate, testicular, liver, pancreas, and kidney cancers; and leukemia. Additionally, research has demonstrated that the expression of this protein is associated with severe malignancy, poor prognosis, and metastasis (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The expression of \u003cem\u003ePODXL\u003c/em\u003e in ALL is significant for several reasons. These included the expression of proteins associated with \u003cem\u003ePODXL\u003c/em\u003e and CD34 in most leukemic blasts and the expression of \u003cem\u003ePODXL\u003c/em\u003e in normal precursor cells. Additionally, the expression of the \u003cem\u003ePODXL\u003c/em\u003e transcriptional regulator Wilms' tumor I is observed in many blast cells of ALL and acute myelocytic leukemia (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These new findings highlight the role of \u003cem\u003ePODXL\u003c/em\u003e in survival, migration, cell proliferation, drug resistance development, and metabolic reprogramming in non-Hodgkin lymphoma (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, considering the significant role of \u003cem\u003ePODXL\u003c/em\u003e in the development of acute leukemia, as well as in invasion and metastasis, the expression level of \u003cem\u003ePODXL\u003c/em\u003e has been regarded as a diagnostic and prognostic factor (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In this regard, identifying the factors involved in the effective expression of this gene will be very helpful. One of these factors is microRNAs. MicroRNAs are endogenous, single-stranded, small 20\u0026ndash;23 noncoding RNAs that regulate the expression of approximately 60% of the genes encoding proteins at the posttranscriptional level by binding to the 3'UTR region. These molecules affect the expression of mRNAs, causing them to degrade or preventing their translation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Each miRNA can regulate the expression of numerous target genes and can be controlled by several other miRNAs. Therefore, miRNAs can affect the expression of the \u003cem\u003ePODXL\u003c/em\u003e gene, and changes in miRNA expression determine the expression of this gene. Among the studies conducted in this field, one study demonstrated an inverse association between miR-125b and the \u003cem\u003ePODXL\u003c/em\u003e gene in umbilical artery endothelial cells and aortic smooth muscle cells (HAVSMCs), suggesting that inhibiting miR-125b to reduce PODXL expression could be considered a treatment option for atherosclerosis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, abnormal expression of the podocalyxin gene in AML is associated with a decrease in miR-199b (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). As a result, the use of this miRNA can serve as both a therapeutic and prognostic tool for this type of cancer. In this context, additional epigenetic mechanisms, such as methylation, play important roles in regulating these genes. Abnormal DNA methylation is a prominent feature of ALL, and numerous studies indicate that it can play a significant role in the development and progression of ALL (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Abnormal epigenetic regulation, particularly gene promoter DNA hypermethylation, is a recurring gene silencing mechanism associated with disease prognosis and treatment response in patients with B-cell progenitors (ALL-B). Studies on ALL leukemia have shown that the expression of several microRNAs is decreased, and their levels can be restored by treatment with methyltransferase inhibitors, such as zebularine (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). DNMTI, DNMT3A, and DNMT3B enzymes collaborate to establish DNA methylation patterns. While DNMTIs are primarily responsible for preserving methylation patterns after DNA replication, DNMT3A and DNMT3B function as de novo DNMTs, initiating methylation patterns from scratch (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Catechins, as bioactive polyphenol compounds, have attracted considerable interest because of their diverse biological activities and potential health advantages. In particular, epigallocatechin-3-gallate (EGCG) has emerged as a potent inducer of apoptosis through mechanisms that involve activating caspases, influencing Bel-2 family proteins, and disrupting survival signaling pathways. Moreover, recent findings indicate that Catechins found in the green tea variety can also influence epigenetic changes, such as DNA methylation and histone modifications (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This feature is found in all types of cancers, such as myeloid and lymphoid leukemia, when exposed to various green tea catechins (or polyphenols), both in vitro and in vivo. It is important to note that the effectiveness of this action is influenced by the dosage and duration of treatment (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In this study, we evaluated the expression and function of miRNAs and the \u003cem\u003ePODXL\u003c/em\u003e gene following treatment with catechin using advanced bioinformatics software. We specifically predicted new miRNAs by analyzing the 3'UTRs of genes involved in methylation, namely, \u003cem\u003eDNMT3B, DNMT3A\u003c/em\u003e, and \u003cem\u003eDNMTI\u003c/em\u003e. Through a combination of bioinformatics analysis and experimental methods, we were able to evaluate the effect of catechin on miRNA expression and the function of \u003cem\u003ePODXL\u003c/em\u003e and \u003cem\u003eDNMT\u003c/em\u003e genes.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Availability of sequences and BLAST queries.\u003c/h2\u003e \u003cp\u003eTo identify suitable structures for our study, we retrieved the amino acid sequences of DNMT1, DNMT3A, and DNMT3B from the NCBI protein database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/protein/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/protein/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in FASTA format. Using these sequences as queries, a BLAST search against \"Protein Data Bank proteins\" revealed experimentally confirmed structures for each target protein. This process facilitated the selection of an appropriate template structure for investigating interactions with catechin compounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Prediction of protein secondary structure and topology\u003c/h2\u003e \u003cp\u003eThe secondary structures of DNMT1, DNMT3A, and DNMT3B were determined using the SOPMAserver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html\u003c/span\u003e\u003cspan address=\"https://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). With respect to the neural network, SOPMA can predict a significant portion of the amino acids involved in the secondary structure, ultimately leading to the generation of 3D models from the 2D structures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The physicochemical characteristics were determined from the sequences.\u003c/h2\u003e \u003cp\u003eThe structural and functional characteristics of a protein can be estimated by analyzing its physical and chemical properties. To determine these characteristics, the protein structure sequence was submitted to the ProtParam web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/protparam/\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/protparam/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.4. Prediction of functional pockets and residues\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe online service HotSpot Wizard 3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://loschmidt.chemi.muni.cz\u003c/span\u003e\u003cspan address=\"https://loschmidt.chemi.muni.cz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict the functional amino acids of the proteins DNMT1, DNMT3A, and DNMT3B. To obtain the core structural pockets and cavities, the CASTp web server at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sts.bioe.uic.edu/castp/index.html?2cpk\u003c/span\u003e\u003cspan address=\"http://sts.bioe.uic.edu/castp/index.html?2cpk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e was used. The output of the CASTp server provides measurements in angstroms, ranging from 0.0 to 10.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Predicting the ADMET of chemical compounds\u003c/h2\u003e \u003cp\u003eFor a drug to be considered suitable, it must possess favorable biochemical activity, pharmacokinetics, safety, high potency and selectivity, as well as ADMET. An ideal drug must be able to distribute itself effectively into various tissues and organs, undergo metabolism without an immediate decrease in activity, and be excreted from the body properly (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Due to the incomplete medicinal properties of catechin in the DrugBank database, it was subjected to evaluation using the ADMETlab 2.0 server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://admetmesh.scbdd.com/service/evaluation/index\u003c/span\u003e\u003cspan address=\"https://admetmesh.scbdd.com/service/evaluation/index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to determine its physicochemical, medical chemistry, and ADMET parameters (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Evaluation of pharmaceutical criteria by R software\u003c/h2\u003e \u003cp\u003eThe chemical compound catechin was analyzed using the R data mining tool to determine the number of physicochemical, medicinal chemical, and ADMET criteria met. With this software, a score was assigned to each pharmacological criterion that the catechin chemical compound successfully passed, and these scores were subsequently summed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Energy minimization of proteins and ligands\u003c/h2\u003e \u003cp\u003eEnergy minimization is crucial for accurate determination of the molecular spatial arrangement. In protein system modeling, adjusting hydrogen bond networks is essential for eliminating disruptive contacts and minimizing the overall system energy, considering components such as stretching, bending, and torsion as potential energy (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The YASARA server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.yasara.org/minimizationserver.htm\u003c/span\u003e\u003cspan address=\"http://www.yasara.org/minimizationserver.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) used the Amber force field to minimize the energy needed for DNMTs and catechin compounds. Its optimized energy functions resulted in superior structural models, leveraging the minimal energy of empirical structural models (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Docking of catechin and methyltransferases.\u003c/h2\u003e \u003cp\u003eThe HDOCK web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdock.phys.hust.edu.cn\u003c/span\u003e\u003cspan address=\"http://hdock.phys.hust.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and AutoDock4 were utilized to investigate the interactions between catechin and DNMT proteins (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). HDOCK employs a hybrid approach that combines template-based modeling and ab initio-free docking to achieve protein‒protein and protein‒DNA/RNA docking. Moreover, AutoDock4 is an integrated platform for predicting protein\u0026ndash;ligand interactions. Open Babel software was used to convert the necessary file formats for the server and program (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Two-dimensional interaction diagram\u003c/h2\u003e \u003cp\u003eTo identify the amino acids involved in protein‒protein interactions, a 2D interaction plot was generated for the inhibitory chemical catechin with the DNMT1, DNMT3A, and DNMT3B proteins. The computations were performed using LigPlot\u0026thinsp;+\u0026thinsp;software, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/thornton-srv/software/LigPlus/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/thornton-srv/software/LigPlus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Protein\u0026ndash;ligand interactions were also analyzed using Discovery Studio software, which is built upon the SciTegic Enterprise Server, an open operating platform. This tool also provides the possibility to analyze other aspects related to protein\u0026ndash;ligand interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Molecular dynamics simulations\u003c/h2\u003e \u003cp\u003eThe superior docking results of catechin with the DNMT1 and DNMT3B proteins, which exhibited significant changes before and after treatment, were subjected to MDs using the CHARMM 27 all-atomic force field. The protein\u0026ndash;ligand complex was solvated in a Triclinic box using periodic boundary conditions and the TIP3P water model. The Na\u0026thinsp;+\u0026thinsp;and Cl- ions were added to neutralize the system. The SwissParam server was used to determine the ligand parameters and topology. The internal constraints of the protein‒ligand complex were relaxed by 50000 steps of steepest descent energy minimization, leading to restriction of the positions of all heavy atoms. Before the MDs, the systems were heated using a V-rescale thermostat to obtain a temperature of 300 K with 0.1 ps as the coupling constant, and equilibration was achieved in NVT. Then, the solvent density was sustained using a Parrinello-Rahman barostat with a pressure of 1 bar, a coupling constant of 0.1 ps, and a temperature of 300 K to obtain equilibration in the NPT by gradually discharging the restraint on heavy atoms step by step. Finally, an MDS was performed for the complexes for 40 ns with an integration time step of 2 fs. Finally, trajectory analyses, such as RMSD, RMSF, Rg, SASA, and H-bonds of protein\u0026ndash;ligand complexes, were performed using the Gromacs package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.10. miRNA and target mRNA prediction\u003c/h2\u003e \u003cp\u003eTo identify target genes, miRDB, RNAhybrid, PICTAR4, DIANAmT, miRWalk, miRanda, DIANAmT, RNAhybrid, PITA, RNA22, PICTAR5, and TargetScan software were used for predicting target microRNAs. The mentioned programs generated a substantial number of miRNA predictions, which were then filtered using four criteria: 1) longest seed region binding to the target mRNA, 2) conserved pairing of the seed region, 3) the number of target miRNA prediction software, and 4) simultaneous targeting of DNMT genes in the 3'UTR. Finally, a miRNA was selected from the eligible microRNAs based on its presence in the 3'UTR of the DNMT gene region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Special probes, primers, and stem‒loop design for the expression of microRNAs\u003c/h2\u003e \u003cp\u003eFirst, to generate predicted miRNAs for all DNMT genes, the microRNA sequences of interest were obtained from the miRBase database by completing the registration process at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/protein/\" target=\"_blank\"\u003ewww.mirbase.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.mirbase.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The specific miRNAs targeted were miR-548, miR-200c, miR-193a, and miR-148a-5p. To determine the smallest detectable number and ensure high sensitivity for these target miRNAs in the sample, we utilized the loop sequence published by Faridi et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The stem‒loop design includes a 6-nucleotide sequence at the end, which is complementary to the 3' end region of each microRNA, allowing for specific detection of each miRNA. For the forward primers, most mature miRNAs were utilized, with minor modifications to the 5' primer. To evaluate primer specificity, the BLAST primer page was used at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://٫www.ncbi.nlm.nih.gov٫tools٫primer-blast٫\u003c/span\u003e\u003cspan address=\"https://٫www.ncbi.nlm.nih.gov٫tools٫primer-blast٫\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e on the NCBI website was utilized. The Tm values of the primers and probes were adjusted using Gene Runner v. 6.0.04 software (Hastings Software, Inc.) following the standard conditions of real-time PCR. The specificity of each miRNA was confirmed by real-time PCR amplification of the target sequence using cDNA from the miRNA stem loops. Finally, relative expression and/or fold change analyses were conducted by comparing the CT values of the target miRNAs to those of the U6 reference gene, as shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Cell lines and drugs\u003c/h2\u003e \u003cp\u003eCatechin, with a purity greater than 95%, was generously provided as a gift by Reza Fotouhi Ardakani. The NALM6 and PBS cell lines, which served as normal cells, were obtained from the Institute Pasteur of the Iran cell bank by our investigative team. To culture the NALM6 cells, we used RPMI 1640 (Gibco BRL) supplemented with 100 mg/m2. Streptomycin, 100 mg/mL. Penicillin and 15% fetal bovine serum were obtained from Gibco BRL. All cells were maintained in a humidified atmosphere containing 5% CO2 at 37\u0026deg;C. The reagents used in this study were high-glucose Dulbecco's modified Eagle's medium (DMEM) supplemented with glutamic acid and fetal bovine serum (FBS).\u003c/p\u003e \u003cp\u003eGlucose-free DMEM (0 g/mL), stereotyped bacteria, and penicillin antibiotics were purchased from Gibco. Additionally, phosphate-buffered saline (PBS), dimethyl sulfoxide (DMSO), bicarbonate powder, and other necessary materials were purchased from Merck (Darmstadt, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.13. MTT assay\u003c/h2\u003e \u003cp\u003eInitially, 2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells were seeded in a 96-well plate in a volume of 100 \u0026micro;l and incubated. Then, the NALM6 cells were treated with different concentrations of catechins (0, 2.5, 5, 10, 20, 40, 60, 80, or 110 \u0026micro;M) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) for 24 hours. Next, MTT dye was added to the sample to a final concentration of 0.45 mg/mL, 100 \u0026micro;l of DMSO was added to each well, and the solution was mixed. Finally, the absorbance of the sample was measured at 570 nm to calculate the IC50 using Prism 8.0.2 software. The rate of cell proliferation inhibition was determined using the formula [1-(OD value of compound ٫OD value of the control)] 100%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.14. Catechin-induced morphological alterations.\u003c/h2\u003e \u003cp\u003eA total of 1x10\u003csup\u003e6\u003c/sup\u003e NALM6 cells/ml were seeded in 12-well plates. After treatment with different concentrations of catechin (0, 10, 15, or 20 \u0026micro;M), the morphology of the cells was evaluated under a microscope (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) for 24 hours. Subsequently, the samples were stained with DAPI (20 mM) to investigate the impact of different concentrations of catechins on the cytoplasmic morphology of the target cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.15. Annexin V and propidium iodide flow cytometry assay\u003c/h2\u003e \u003cp\u003eTo achieve this objective, a total of 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e NALM6 cells were subjected to treatment with the \"IC50\" compound, specifically 35 \u0026micro;M, in 6-well plates for 24 hours. After incubation, the cell pellet was isolated by centrifugation and then washed with PBS. Afterward, the cells were exposed to 500 \u0026micro;l of 1X binding buffer. Subsequently, 5 \u0026micro;l of annexin V was added to the samples, which were subsequently allowed to incubate in the dark for 10 minutes. Next, 5 \u0026micro;l of Pl dye was added, and the mixture was incubated for 10 minutes in the dark. Flow cytometry was then used to measure the percentage of phosphatidylserine released on the cell surface, and the results were analyzed using FlowJo v 7.6 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Extraction of target microRNA and RNAs\u003c/h2\u003e \u003cp\u003eFor extraction of target RNA genes, especially microRNAs, the YTzol Pure RNA Kit (Yekta Tajhiz Azma Co.) was used with modifications to increase the purity and integrity of the RNA extraction. The NALM6 cells were detached and incubated on ice for 5 min. Afterward, 200 \u0026micro;l of chloroform solution was added, and the mixture was stirred for 2 min and then centrifuged at 12000 rpm for 30 min at 4\u0026deg;C. Afterward, the mixture was transferred to a clean tube, after which the previous step was instantly repeated with 100 \u0026micro;l of 1-bromo-3-chloropropane. The aqueous phase was transferred to a clean tube, and an equal volume of absolute alcohol was added. The tubes were kept at -20\u0026deg;C overnight and then centrifuged at 12,000 rpm and 4\u0026deg;C for 1 hour. The supernatant was discarded, and 1 ml of 70% ethanol was added. The mixture was subsequently centrifuged at 12000 and 4\u0026deg;C for 45 minutes. Next, the supernatant was removed, and the RNA was inverted at room temperature. Then, 50 \u0026micro;l of DEPC-treated water was added. In each tube, 5 units of RNase-free DNase I were added and incubated for 5 minutes at room temperature, followed by inactivation for 5 minutes at 70\u0026deg;C. Finally, the concentration and purity of the extracted RNA were determined using a Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). All the tubes were kept at -70\u0026deg;C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.13. cDNA synthesis and real-time PCR\u003c/h2\u003e \u003cp\u003ecDNA was synthesized from 1000 ng of total RNA using Mu-MLV reverse transcriptase according to the kit protocol (Yekta Tajhiz, cat: YT4500). The cDNA was kept at -70\u0026deg;C until analysis. The 12.5 \u0026micro;l PCR mixture was composed of 6.5 \u0026micro;l of SYBR Green master mix, 0.2 \u0026micro;M of each primer oligonucleotide, and 2 \u0026micro;l of cDNA. The real-time program was performed as follows. The initial denaturation step was 95\u0026deg;C for 40 s, followed by 45 cycles of denaturation at 95\u0026deg;C for 40 s and 60\u0026deg;C for 20 s and a final extension at 72\u0026deg;C for 35 s. The \u003cem\u003eUBE2D2\u003c/em\u003e gene was used as the internal reference gene (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Finally, the PCR efficiency and expression of each mRNA were assessed using LinRegPCR software. The PCR efficiency was between 95 and 108%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2.14. Synthesis of microRNA cDNA and real-time PCR\u003c/h2\u003e \u003cp\u003eFollowing miRNA extraction, cDNA was synthesized using Mu-MLV reverse transcriptase. Four microliters of extracted miRNA, adjusted to 1200 ng of RNA, was added to 1.5 \u0026micro;l of stem‒loop (diluted 1.100% of the original 100 \u0026micro;M solution), followed by 5 \u0026micro;l of double-distilled water. The 10.5 \u0026micro;l mixture was incubated for 5 minutes at 65\u0026deg;C in a thermocycler (Bio-Rad). Immediately, the tubes were transferred to a cold container. A mixture of 2 \u0026micro;l of dNTPs (10 mM) and 4 \u0026micro;l of 4X buffer was used. Then, 0.5 \u0026micro;l of RNase inhibitor (20 units), 2 \u0026micro;l of DTT (10 mM), and 1 \u0026micro;l of reverse transcription enzyme were added. cDNA synthesis was performed for 1 hour at 44\u0026deg;C and 10 min at 70\u0026deg;C to inactivate the enzyme. The synthesized cDNA was kept at -20\u0026deg;C until use. The reverse transcription products were amplified by real-time PCR. A universal reverse primer and probes with a specific primer for each miRNA were applied. Each microtube contained 6.25 \u0026micro;l of 2x qPCR Master Mix. The primers used were 0.74 \u0026micro;M reverse primer, 0.5 \u0026micro;M forward primer, and 0.2 \u0026micro;M probe for a final volume of 12.5 \u0026micro;l. qPCR was performed on a Rotor-Gene Q. The enzyme was initially activated at 95\u0026deg;C for 30 seconds, followed by 45 cycles of 95\u0026deg;C for 15 seconds and 60\u0026deg;C for 45 seconds. The \u003cem\u003eU6\u003c/em\u003e gene was selected as the reference gene. The relative expression of each miRNA was statistically analyzed using the Pfafil method. P values and fold changes were calculated with GraphPad Prism software version 9.2.0 (GraphPad Software, Inc., San Diego, CA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.1. BLAST search of the amino acid sequence\u003c/h2\u003e \u003cp\u003eThe reference amino acid sequence of DNMT can be found in the NCBI protein database, which is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. There are three accession numbers for the DNMT protein: NP 001124295.1, NP 072046.2, and NP_008823. These accession numbers were utilized to produce a number of nearly complete structures with satisfactory resolution. These structures can be accessed in the PDB database using the following accession numbers: \"DNMT1:4 WXX, DNMT3A:6PA7, and DNMT3B:6KDA\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Prediction of protein secondary structure and topology\u003c/h2\u003e \u003cp\u003eThe protein secondary structure, which is crucial for docking and molecular dynamics simulations, was analyzed using the SOPMA server. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that DNMT1 is composed of a random coil (46.50%), an alpha helix (28.90%), an extended strand (18.87%), and a beta-turn (5.73%). DNMT3A comprises random coils (46.88%), alpha helices (30.48%), extended strands (16.55%), and beta-turns (6.10%). The composition of DNMT3B included random coils (53.25%), alpha helices (26.36%), extended strands (14.94%), and beta-turns (5.45%). These findings offer insights into the factors influencing protein structure and function before and after docking and MD simulation.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The extraction of physicochemical properties from the sequence\u003c/h2\u003e \u003cp\u003eUtilizing the ProtParam server on DNMT sequences in FASTA format, DNMT1 exhibited an aliphatic index of 70.26, indicating a high proportion of aliphatic amino acids. Its GRAVY score of -0.553 implies a slightly hydrophilic nature, with an instability index of 47.52, signifying instability. The protein contains 166 negatively charged residues and 166 positively charged residues. Similarly, DNMT3A had an aliphatic index of 69.45, a GRAVY score of -0.438, and an instability index of 46.31. It has 92 negatively charged residues and 85 positively charged residues. DNMT3B displayed an aliphatic index of 63.19, a GRAVY score of -0.629, and an instability index of 58.88, indicating high instability. It includes 100 negatively charged residues and 104 positively charged residues. The Hydropathic Average (GRAVY) aids in assessing the distribution of polar and nonpolar groups within a protein's 3D structure; this parameter is essential for pre- and post-docking, MD simulation, and 2D plot analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e details residue distribution).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDesigned primers, probes, and RT Stem-loops\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccession number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT specific stem‒loop primer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT-primer miR-548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIMAT0031890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTATGCGGCTACCCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGGAGCCGCATAC\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAAAGTA\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT-primer miR-200c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI0000650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTATGCGGCTACCCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGGAGCCGCATAC \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eTCCATC\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT-primer miR-193a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI0000487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTATGCGGCTACCCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGGAGCCGCATAC \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eACTGGG\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT-primer miR-148a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIMAT0004549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTATGCGGCTACCCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGGAGCCGCATAC\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAGTCGG\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT primer U6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNR_004394.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTATGCTGCTACCTCGGACCCTGCTTAGTGCCATGCCTGCCATCGAGCAGCATAC \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eCGAATT\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF- miR-548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIMAT0031890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCCGCAAAAACTGCAGTTACTTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF- miR-200c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI0000650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAATACTGCCGGGTAATGATGGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF- miR-193a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI0000487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAATGGCCTACAAAGTCCCAGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF- miR-148a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIMAT0004549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAAGTTCTGAGACACTCCGACT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-U6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNR_004394.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCAAGGATGACACGCAAATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTaq man probe\u003c/b\u003e: FAM 5\u0026rsquo;AGTGCCATGCCTGCCATCGAGC 3\u0026rsquo; BHQ-1\u003c/p\u003e \u003cp\u003e\u003cb\u003eUniversal reverse\u003c/b\u003e: GCTGCTACCTCGGACCCT;\u003c/p\u003e \u003cp\u003emiRNA complementary specific sequences are underlined.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePrimer sequences for real-time PCR\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccession number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer 5\u0026rsquo;-3\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-DNMT3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_001130823.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAACAGGCCGTTGGCATCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DNMT3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_001130823.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTAATGGTCCTCACTTTGCTGAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF- DNMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_001375819.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTATCCGAGGAGGGCTACCTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR- DNMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_001375819.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCCGGTTGTAAGCATGAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-DNMT3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_175848.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGACTTGACAGGCGATGGCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-DNMT3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_175848.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTGTTGTTATTTCGAGTTCGGACA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-reference gene (UBE2D2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_181838.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGAATCCACAAGCTCCCTCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-reference gene (UBE2D2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_181838.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGCCACCCAAGAGGTAAGTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF- PODXL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXM_034965004.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACGAGAGTAACTGGGCAAAGTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR- PODXL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXM_034965004.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTGAAGGTGGCTTTGACTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe frequency (percentage) of residues in DNMTs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNMT1.A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNMT3A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDNMT3B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAla\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGln\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (9.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePyl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSec\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Functional residues and pockets in DNMTs\u003c/h2\u003e \u003cp\u003eThe functional amino acids of the DNMT1, DNMT3A, and DNMT3B proteins were determined through the use of the HotSpot Wizard web server. The NCBI identified specific hotspots within the beta chain of the DNMT1 enzyme, including CYS1226, CYS353, CYS356, CYS414, HIS418, CYS653, CYS656, CYS659, CYS664, CYS667, CYS670, CYS686, CYS691, SER1146, GLU1168, MET1169, GLY1150, LEU1151, ASP1190, CYS1191, ASN1578, and VAL1580. For DNMT3A, the hotspots were found in the K chain and included CYS710, PHE640, ASP641, SER663, GLU664, VAL665, CYS666, ASP686, VAL687, GLY707, LEU730, GLU756, ARG891, SER892, and TRP893. In the case of DNMT3B, the predicted functional amino acids were located in the L chain and included LEU651, VAL582, ALA583, SER584, GLU585, VAL586, VAL605, GLY627, GLY628, and SER629. These hotspots were mutable residues with different scores based on the web server's scoring system, and they were situated in the catalytic pocket and/or access tunnels. The analysis also involved the identification and quantification of geometric and topological features. It was discovered that surface pockets and cavities play a role in hindering the functional development of protein targets. The methyltransferase enzymes were shown to possess several central pockets and cavities. Furthermore, the largest predicted pockets of the 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 \u0026Aring;2/\u0026Aring;3, respectively. Additionally, the following functional residues were common in the pockets of the enzymes DNMT1 and DNMT3A but not in those of DNMT3B: CYS1226, CYS356, CYS656, CYS664, CYS667, CYS670, CYS686, CYS691, SER1146, GLU1168, MET1169, GLY1150, LEU1151, ASP1190, CYS1191, ASN1578, and VAL1580. Therefore, according to the results of the pocket, cavity, and position of the functional residues included in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, docking and 2D diagram analyses were possible.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eStructural and chemical pocket and cavities of Castp\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDNMT1.B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDNMT3A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDNMT3B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eArea(\u003c/b\u003e\u0026Aring;\u003csup\u003e2\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eVolume(\u003c/b\u003e\u0026Aring;\u003csup\u003e3\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eArea(\u003c/b\u003e\u0026Aring;\u003csup\u003e2\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eVolume(\u003c/b\u003e\u0026Aring;\u003csup\u003e3\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eArea(\u003c/b\u003e\u0026Aring;\u003csup\u003e2\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eVolume(\u003c/b\u003e\u0026Aring;\u003csup\u003e3\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePacket\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5747.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5801.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e531.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e480.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e385.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e225.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCavities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2434.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2952.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e365.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e269.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e474.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e331.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e177.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Drug-based ADMET prediction\u003c/h2\u003e \u003cp\u003eThe ADMETlab2.0 server analyzed catechin, evaluating its physicochemical properties, medicinal chemistry metrics, absorption, distribution, metabolism, excretion, and toxicology. These included parameters such as molecular weight, QED score, Caco-2 permeability, enzyme interactions, and various toxicity assessments. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. These assessments guide drug selection, ensuring desirable efficacy and safety profiles in the design and rescreening of catechin.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 5. Calculation of Catechin ADMET by ADMETlab2.0\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;'\u003e\n \u003ctable style=\"width: 100%;border: none;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width:22.16%;border:none;border-bottom:solid windowtext 1.0pt;background:#ED7D31;padding:0in 5.4pt 0in 5.4pt;height:39.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ePhysicochemical Property\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:18.38%;border:none;border-bottom:solid windowtext 1.0pt;background:#FFC000;padding:0in 5.4pt 0in 5.4pt;height:39.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eMedicinal Chemistry\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:18.24%;border:none;border-bottom:solid windowtext 1.0pt;background:#5B9BD5;padding:0in 5.4pt 0in 5.4pt;height:39.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eDistribution\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:21.1%;border:none;border-bottom:solid windowtext 1.0pt;background:#58CB5D;padding:0in 5.4pt 0in 5.4pt;height:39.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eAbsorption\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:20.12%;border:none;border-bottom:solid windowtext 1.0pt;background:#2A9ACE;padding:0in 5.4pt 0in 5.4pt;height:39.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eEnvironmental toxicity\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:14.48%;border:solid windowtext 1.0pt;border-top:none;background: #F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eProperty\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.68%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eValue\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:9.98%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eProperty\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.4%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eValue\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:10.42%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#B4C6E7;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eProperty\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.82%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#B4C6E7;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eValue\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.88%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#AFFFB3;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eProperty\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#AFFFB3;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eValue\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.46%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#96D7F6;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eProperty\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.66%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#96D7F6;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eValue\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:14.48%;border:solid windowtext 1.0pt;border-top:none;background: #F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:39.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eMolecular Weight\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.68%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:39.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e290.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:9.98%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:39.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eQED\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.4%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 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style=\"width:9.98%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:16.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eMCE-18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.4%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:16.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New 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style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e8.351%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.88%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#AFFFB3;padding:0in 5.4pt 0in 5.4pt;height:16.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003ePgp-substrate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#AFFFB3;padding:0in 5.4pt 0in 5.4pt;height:16.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e0.004\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.46%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#96D7F6;padding:0in 5.4pt 0in 5.4pt;height:16.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eLC\u003csub\u003e50\u003c/sub\u003eDM\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.66%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#96D7F6;padding:0in 5.4pt 0in 5.4pt;height:16.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e5.299\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:14.48%;border:solid windowtext 1.0pt;border-top:none;background: #F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003enHD\u003c/span\u003e\u003c/p\u003e\n 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Roman\",serif;'\u003eNPscore\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.4%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e2.304\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:18.24%;border:none;border-right:solid black 1.0pt;background:#DC3939;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eToxicity\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.88%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#AFFFB3;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eHIA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#AFFFB3;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e0.037\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:20.12%;background:#596EF3;padding:0in 5.4pt 0in 5.4pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eTox21 pathway\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:14.48%;border:solid windowtext 1.0pt;border-top:none;background: #F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003enRot\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.68%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:9.98%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eLipinski Rule\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.4%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eAccepted\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:10.42%;border:solid windowtext 1.0pt;border-left:none;background: #FF7474;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eProperty\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.82%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FF7474;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eValue\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.88%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#AFFFB3;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eF20%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#AFFFB3;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e0.998\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.46%;border:solid windowtext 1.0pt;border-left:none;background: #96A2F0;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eProperty\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.66%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#96A2F0;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eValue\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:14.48%;border:solid windowtext 1.0pt;border-top:none;background: #F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003enRing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.68%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:9.98%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003ePfizer Rule\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.4%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FFE599;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eAccepted\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:10.42%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#FF7474;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New 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style=\"width:14.48%;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:7.68%;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:9.98%;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:8.4%;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:10.42%;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:7.82%;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:12.88%;border:solid windowtext 1.0pt;border-top: none;background:#E5A6DD;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eCYP3A4 substrate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background:#E5A6DD;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Calibri\",sans-serif;color:black;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e0.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.46%;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:7.66%;padding:0in 5.4pt 0in 5.4pt;height:26.2pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Evaluation of pharmaceutical criteria by R software\u003c/h2\u003e \u003cp\u003eR software was used to analyze the catechin compounds, which are considered 86 medicinal factors. Each factor received a score, and these scores were categorized. The analysis revealed approximately 46 medicinal properties associated with the drug, with scores aggregated to reach this conclusion. The corresponding R software codes are provided in supplementary file 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Energy minimization of proteins and ligands\u003c/h2\u003e \u003cp\u003eThe energy values of DNMTI, DNMTA, DNMTB, and the catechin chemical compound before energy minimization by YASARA tools were 6597613120.10. -139468.80, 97477.50, and \u0026minus;\u0026thinsp;545, respectively. The scores assigned to them prior to undergoing energy minimization were as follows: -2.06, -1.34, -2.13, and \u0026minus;\u0026thinsp;0.22. However, after energy minimization by YASARA, the energy values of DNMTI, DNMTA, DNMTB, and the catechin chemical compound were \u0026minus;\u0026thinsp;642864.90 and \u0026minus;\u0026thinsp;166608.10, respectively. -118058.20, and \u0026minus;\u0026thinsp;566, respectively. The score after energy minimization was \u0026minus;\u0026thinsp;0.61. -0.06, -0.62, and \u0026minus;\u0026thinsp;0.23, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Molecular docking\u003c/h2\u003e \u003cp\u003eIn this study, the chemical compound catechin was subjected to docking analysis with DNMTI, DNMT3A, and DNMT3B using both the HDock server and AutoDock4 software. The docking process was carried out with a genetic algorithm in 50 runs employing the specific docking method illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results obtained from this analysis revealed specific docking scores and energies, which demonstrated the superior performance of the HDock server compared to that of another program, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalculation of docking energy and score by AutoDock4 software and the HDOCK server\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKcal/Mol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eDNMT1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAutodock4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eHDOCK\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinding energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH-bond\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDocking Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConfidence Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLigand rmsd (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1312B,1150B,1266B,1149B,1151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-80.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463B,600B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-99.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600B,462B,463B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-60.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428B,424B,462B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-99.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428B,424B,462B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-78.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463B,595B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-108.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e595B,552B,1490B,553B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-93.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-93.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428B,427B,424B(two-times),463B,597B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-87.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428B,427B,424B(two times),463B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-106.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDNMT3A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e893K,643K(two times),890K,708K,710K,645K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e710K,641K,708K,643K,707K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e357.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e638K,708K,641K,640K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-104.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e710K,891K,640K,641K,708K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-95.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e354.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(641K,643K),710K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-94.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e355.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e893K,710K,643K,645K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-66.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e363.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e710K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-129.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e891K,711K,640K,714K(two times)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-116.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e363.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e664K,891K,663K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-108.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e354.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e663K,891K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-93.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e355.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDNMT3B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585 N(three times),606 N,595 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-145.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e364.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e606 N,585 N(two-times),595 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-138.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e359.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585 N(three times),606 N,595 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-137.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e363.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e586 N,585 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-128.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e357.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585 N(three times),606 N,595 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-124.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e350.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585 N(three times),606 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-123.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e350.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e607 N,585 N,588 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-120.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e366.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e607 N,585 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-120.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e357.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e588 N,607 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-117.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e362.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e607 N,585 N,588 N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-110.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e351.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Hydrogen bonds and hydrophobic interactions in the central pocket\u003c/h2\u003e \u003cp\u003eThe residues involved in the interaction between the main pockets of DNMT enzymes and the selected inhibitor compound, catechin, were identified through the application of the PDBsum page generation method. The ensuing analysis delineated specific interactions for each pose.\u003c/p\u003e \u003cp\u003eIn the DNMT1-B to catechin pose 6, four conventional hydrogen bonds were formed with GLU573, ARG69, ASP569, and GLN687. Additionally, four van der Waals interactions were observed with ASN1236, ASP571, ALA669, and SER570. Two Pi-Cation bonds were present with ARG690 and LYS668, along with one Pi-Alkyl bond involving ARG1238.\u003c/p\u003e \u003cp\u003eFor DNMT3A-K to catechin pose 7, five conventional hydrogen bonds were formed with SER714, GLU756, ARG8891, GLY707, and PHE640. Additionally, three van der Waals bonds were present with VAL758, ASN757, and ARG792. Two Pi-cation and Pi-anion bonds were formed with ARG790 and GLU756. Moreover, two Pi-alkyl bonds were observed with CYS710 and ARG891. Five Pi-Donor hydrogen bonds were formed with TRP893, GLY706, PRO709, SER708, and ARG891.\u003c/p\u003e \u003cp\u003eIn the DNMT3B-L to catechin pose 1, six conventional hydrogen bonds were formed with ARG663, LYS542, CYS696, TYR544, ASN718, and PHE672. Three van der Waals bonds were present with LEU659, ARG670, and PHE673. Additionally, four Pi-Donor hydrogen bonds and carbon‒hydrogen bonds were formed with PRO664, TRP674, and PRO671, as was a Pi-Sulfur bond with CYS696. Furthermore, a Pi‒Pi stacking interaction was observed with TYR544.\u003c/p\u003e \u003cp\u003eBased on the analysis of two plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), it can be concluded that catechin strongly interacts with the proteins under investigation.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.10. Molecular dynamics simulation analyses\u003c/h2\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.10.1. Root-mean-square deviation analysis\u003c/h2\u003e \u003cp\u003eWe analyzed the RMSD of the backbone atoms using the standard g rms function in GROMACS over a total simulation time of 40 ns. As shown in the plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, DNMT3B bound to the chemical compound catechin plateaued earlier than did DNMT1. The average deviations were 0.367 nm and 0.492 nm, respectively. These findings indicated that DNMT3B combined with the catechin chemical compound was superior to DNMTI when it was combined with the same compound.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.10.2. Residue flexibility analysis\u003c/h2\u003e \u003cp\u003eThe stability and flexibility of the residues were evaluated by calculating the root-mean-square fluctuations (RMSFs) during a 40 ns simulation using the gmx_rmsf module of GROMACS. The RMSF plot, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, showed multiple peaks at residues PHE676:B, LYS385:B, LYS675:B, and GLU384:B of DNMTI. Additionally, peaks were observed for the residues PHE726:N, ILE725:N, ARG740:N, and LYS542:N. Similarly, the peak at ARG724:N was assigned to DNMT3B following its interaction with the catechin chemical compound. These findings suggested that the residues of DNMTI exhibit high dynamics and flexibility, while DNMT3B demonstrates minimal fluctuations, indicating superior stability and limited mobility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e3.10.3. Solvent accessible surface area analysis\u003c/h2\u003e \u003cp\u003eThe solvent-accessible surface area (SASA) values for all the complexes were calculated using the gmx sasa function in GROMACS over a simulation time of 40 ns. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC clearly shows that the DNMTI and DNMT3B docked catechin chemical compounds exhibited average SASA values of 581.37 nm2 and 125.93 nm2, respectively. Notably, DNMT3B demonstrated the lowest SASA among the two proteins docked with the catechin chemical compound, indicating that it has a stronger interaction with DNMT3B than with water molecules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e3.10.4. Compactness analysis\u003c/h2\u003e \u003cp\u003eThe radius of gyration (Rg) was calculated using the GROMACS gmx gyrate function with a simulation time of 40 ns. Rg is defined 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 alters the Rg. Compact protein structures tend to maintain low mean Rg deviations, indicating dynamic stability. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, the DNMTI and DNMT3B proteins attached to the catechin chemical compound exhibited mean Rg deviations of 3.71 nm and 1.74 nm, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e3.10.5. Hydrogen bonding and bond distribution analysis\u003c/h2\u003e \u003cp\u003eH-bonding analysis was performed on all protein‒ligand systems during a 40 ns simulation run. The number of H-bonds was recorded using the GROMACS gmx bond tool and is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE. During the simulation period, 0.6 hydrogen bonds were formed between DNMTI and DNMT3B via the chemical catechin compound. Furthermore, the average number of hydrogen bonds between DNMT3B and the catechin chemical compound was greater.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e3.11. Inhibitory effects of catechin on the NALM6 cell line\u003c/h2\u003e \u003cp\u003eThe use of catechin in NALM6 cells resulted in significant suppression of cell growth across a range of concentrations, from 0 to 110 \u0026micro;M. The inhibitory effect was observed at concentrations of 2.5, 5, 10, 20, 40, 60, 80, and 110 \u0026micro;M. After 24 hours, the IC50 value of catechin was determined to be 35 \u0026micro;M, with a 95% confidence interval ranging from 19.5-39.94. The data obtained from this experiment exhibited a strong correlation with an R-squared value of 0.941, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e3.12. Effects of Catechin on the Morphology and Cytoplasm of NALM6\u003c/h2\u003e \u003cp\u003eWith different concentrations of catechin (10, 15, and 20 \u0026micro;M), (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)the cell count started to decrease within 24 hours. This decrease was more noticeable at a concentration of 20 \u0026micro;g/ml. Furthermore, the characteristic morphology of the cells gradually changed, resulting in a decrease in cell count and the formation of cell aggregates (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). DAPI staining revealed that as the concentration of catechin increased, the distance between the cells also increased, the nuclei became almost larger, chromatin pyknosis became somewhat apparent, the cell shape became rounder, and the nuclei started to fragment. Ultimately, catechin has been demonstrated to inhibit the growth and proliferation of NALM6 cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.13. Catechin was found to induce apoptosis, as indicated by the flow cytometry data for annexin PI٫V.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, catechin has demonstrated a remarkable ability to enhance apoptosis in NALM6 cells. This enhancement was evident not only in the increased number of annexin V-positive cells but also in the percentage of annexin PI٫V-positive cells. The percentage of annexin V-positive cells increased from 0.11 in untreated cells to 1.05 in treated cells, with a cell treatment IC50 value of 35 \u0026micro;M. Furthermore, compared with those in the untreated group, the catechin concentration in the group treated with catechin exhibited a significant change of 35 \u0026micro;M, representing a quarter of the total catechin concentration in the intervention group. These changes were 29.11% and 24.84% in the early and late apoptosis quadrants, respectively. These findings strongly suggest that catechin exerts its cytotoxic effects on NALM6 cells by inducing early apoptosis, particularly late apoptosis.\u003c/p\u003e \u003cp\u003eThe administered catechin groups showed a significant increase in cell apoptosis (* and **, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Compared to those in the untreated group, the concentrations in the catechin group exhibited a significant change of 35 \u0026micro;M in the quarter following the catechin intervention. Specifically, there were changes of 29.11 and 24.84 in the early and late apoptosis quadrants, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e3.14. Prediction of target miRNAs and design of primers, probes, and stem‒loop primers.\u003c/h2\u003e \u003cp\u003eThe 3'UTR targets of DNMT3B, DNMT3A, and DNMT1 mRNA can be detected using various websites. More than hundreds of miRNAs confirmed by several miRNA prediction algorithms were selected based on the highest scores. The authors of these studies met several criteria: the number of algorithms, longest seed region, conserved seed region, and simultaneous 3'UTR targeting of DNMT genes; these analyses were not previously performed in the ALL cohort (Tables S1 and S2). Six complementary nucleotides were added to the 3' ends of the stem‒loop RTs, which were specific for each miRNA. Forward primers and a universal reverse primer were designed along with the TaqMan probe for qPCR. The NCBI Primer-BLAST results for each miRNA revealed that the primer sequences did not bind to any other sequences besides the target miRNA. The results showed 100% specificity for each miRNA (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e3.15. DNMT, PODXL, miR-548, miR-200c, miR-193a, and miR-148a-5p gene expression\u003c/h2\u003e \u003cp\u003eIn the NALM6 cell line, miR-548, miR-200c, miR-193a, and miR-148a-5p were selected from a multitude of microRNAs predicted by various software algorithms. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the expression of these miRNAs was markedly lower in the NALM6 cell line than in the peripheral blood cell group. After catechin treatment, there was a noteworthy increase in the levels of microRNAs 548 and 200 (1.65 and 2.87, respectively; p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe PODXL protein, known for inducing cancer via interaction with the actin-binding protein EZR, thereby promoting migration and cell invasion, was significantly upregulated in the NALM6 cell line. However, this upregulation was significantly reversed following catechin treatment (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, gene expression analysis of \u003cem\u003eDNMTI\u003c/em\u003e and \u003cem\u003eDNMT3B\u003c/em\u003e revealed significant increases and decreases, respectively, in the NALM6 cell line before and after treatment compared to those in the PBC group, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Although \u003cem\u003eDNMT3A\u003c/em\u003e expression in NALM6 cells was lower in the treatment group than in the PBC group, these differences were not significant (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eTreatment of NALM6 cells with catechin led to decreased expression levels of \u003cem\u003eDNMTI\u003c/em\u003e, \u003cem\u003eDNMT3B\u003c/em\u003e, and \u003cem\u003ePODXL\u003c/em\u003e compared to those in the PBC group. However, the decrease in \u003cem\u003eDNMT3A\u003c/em\u003e expression was not statistically significant (fig. B; p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, the expression of miR-548 and miR-200c increased after treatment with catechin in NALM6 cells, but the increase in miR-193a and miR-148a-5p was not statistically significant (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eVarious mechanisms are involved in gene expression, one of which involves the methylation of gene regulatory regions, including promoter regions containing CpG islands. Methylation is carried out by a group of DNA methyltransferase enzymes, including \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eDNMT3B\u003c/em\u003e, and \u003cem\u003eDNMT1\u003c/em\u003e (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Increased expression of this enzyme has been observed in numerous types of cancer, such as breast cancer, pancreatic cancer, blood malignancies, and cholangiocarcinoma (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Mutations in the DNMT3A gene have been shown to result in abnormal methylation of genomic DNA, leading to blood malignancies and various developmental defects (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Additionally, overexpression of the DNMT3A enzyme suppressed the BASPI gene in AML-positive A٫E cells (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, it should be noted that overexpression of the DNMT3B enzyme is responsible for another abnormal pattern of DNA methylation that can be observed in a wide range of cancers, including ovarian hepatocellular carcinoma and colon cancer (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The genes that are overexpressed in association with \u003cem\u003eDNMT3B include RASSFIA, p53, CDHI, OСТ4, hMLH1\u003c/em\u003e, and \u003cem\u003ep16\u003c/em\u003e (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). One of the critical cellular regulatory factors that can significantly influence the expression of \u003cem\u003eDNMT\u003c/em\u003e methylation enzymes, or vice versa, is their interaction with miRNAs. For example, miR-133a was shown to play a specific role in regulating the expression of the \u003cem\u003eDNMT-1\u003c/em\u003e and 3A genes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). On the other hand, the enzyme methyltransferase can affect the function of miRNA promoter regions through methylation (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The results of our study revealed a decrease in the predicted expression of miR-548 and miR-200c, which significantly increased after catechin treatment. However, the levels of miR-193a and miR-148a-5p did not significantly change. Several factors can contribute to this decline. One possible mechanism involves changes in gene expression, specifically through the occurrence of single nucleotide polymorphisms (SNPs) in gene regions such as promoters and miRNA regions (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Furthermore, the methylation of particular promoter sequences could also serve as a potential underlying factor (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In the present study, increased expression of the DNMT3B enzyme was observed, leading to abnormal methylation of the miR-548 and miR-200c gene promoters and resulting in decreased expression. However, when combined with catechin, the opposite effect occurred, leading to increased expression of microRNAs (miR-548 and miR-200c) and decreased levels of the DNMT3B enzyme. Moreover, additional bioinformatics analyses, including docking and molecular dynamics simulations, confirmed that catechin can induce significant changes in gene expression levels and apoptosis in nalm6 cells. \u003cem\u003ePODXL\u003c/em\u003e plays a crucial role in numerous types of cancer. Increased expression of \u003cem\u003ePODXL\u003c/em\u003e in aggressive pancreatic cancers has been shown to increase the invasion rate. Conversely, inhibiting the negative expression of PODXL has been shown to decrease cancer cell mortality and invasion (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Numerous studies have demonstrated a functional correlation between the \u003cem\u003ePODXL\u003c/em\u003e gene and miRNAs in cancer. For example, miR-199a-5p has been shown to inhibit \u003cem\u003ePODXL\u003c/em\u003e expression in testicular cancer (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Moreover, inhibiting PODXL in the NT2 cell line results in decreased invasion (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The potential indirect relationships between the deregulation of \u003cem\u003ePODXL\u003c/em\u003e, miR-548, and miR-200c and methyltransferase genes (\u003cem\u003eDNMT3B, DNMT3B\u003c/em\u003e, and \u003cem\u003eDNMT1\u003c/em\u003e) arise from the impact of these enzymes. Cheung and colleagues conducted a similar evaluation of the role of the \u003cem\u003ePODXL\u003c/em\u003e gene and its association with miR-199a in testicular cancer. They discovered that suppressing miR-199a expression in testicular cancer led to an increase in the expression of the \u003cem\u003ePODXL\u003c/em\u003e gene, which, in turn, enhanced the ability of cancer cells to invade and migrate. Furthermore, suppression of the \u003cem\u003ePODXL\u003c/em\u003e gene inhibited the invasion and migration of cancer cells (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Moreover, the direct effect of miR-5100 on the \u003cem\u003ePOXDL\u003c/em\u003e gene through binding to the 3'UTR can reduce migration, invasion, and colony formation in pancreatic cancer (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The present study predicted that the increase in PODXL gene expression accompanies the decrease in miR-548 and miR-200c expression in the NALM6 cell line. The variation in the expression levels of these miRNAs and \u003cem\u003ePODXL\u003c/em\u003e can be attributed to the influence of these macromolecules on the expression of the PODXL gene or methyltransferases, either directly or indirectly. A slight increase in the expression of \u003cem\u003eDNMTI, DNMT3B\u003c/em\u003e, and \u003cem\u003ePODXL\u003c/em\u003e was observed, potentially indicating a relationship between these \u003cem\u003eDNMTs\u003c/em\u003e and \u003cem\u003ePODXL\u003c/em\u003e. However, regulation of the \u003cem\u003ePODXL\u003c/em\u003e gene by the DNMT3B enzyme is possible, as \u003cem\u003ePODXL\u003c/em\u003e expression was significantly increased after catechin treatment. Catechin, a phytochemical found in various plants, has been found to affect cancer by influencing microRNAs (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The expression of miRNAs can be altered by phytochemicals, such as catechins, leading to alterations in oncogenes, tumor suppressors, and proteins associated with cancer. Studies have demonstrated that the modulation of miRNAs by catechin can inhibit tumor growth, inhibit metastasis, reverse epithelial-to-mesenchymal transition (EMT), and increase the sensitivity of cancer cells to drugs (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Catechins have been found to interact with various molecules and exhibit chaperone-like properties, indicating their potential as agents for cancer prevention (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Recent research has also demonstrated that catechins can play a role in anticancer therapy by inhibiting cell proliferation and promoting apoptosis (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Furthermore, the field of bioinformatics offers new solutions for analyzing cancer-related data obtained from high-throughput methods, such as DNA microarrays, docking, and molecular dynamics simulations. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Based on the available information, this study is the first to focus on the simultaneous effects of microRNAs, epigenetic agents (methylation), tumor suppressor genes (\u003cem\u003ePODXL\u003c/em\u003e), and catechins, which are effective substances with anticarcinogenic properties. These findings suggested that these substances and plant derivatives, including nalm6, can reverse the phenotype or induce apoptosis in cancer cells to varying degrees. Overall, Catechins have shown promise in both preventing and treating cancer. Bioinformatics can contribute to a better understanding of the molecular mechanisms involved in cancer development and response to treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings indicate that the overexpression of \u003cem\u003eDNMTI\u003c/em\u003e and \u003cem\u003eDNMT3B\u003c/em\u003e may contribute to the development of cancer in NALM6 cells. It is important to acknowledge the significant regulatory role of microRNAs in this context. As a result, increased expression of \u003cem\u003eDNMTI\u003c/em\u003e and \u003cem\u003eDNMT3B\u003c/em\u003e can lead to decreased expression of miR-548 and miR-200c before catechin treatment and vice versa. This decrease in miRNA expression eventually leads to the dysregulation of other tumor suppressor genes, such as \u003cem\u003ePODXL\u003c/em\u003e, resulting in aberrant expression and subsequent development of cancer cell characteristics. However, further research is needed to determine the interactions between the miR-548, mik-200c, DNMT1, DNMT3B, and PODXL genes in ALL.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Clinical Research and Ethical Committee of the Faculty of Allied Medicine of Kerman University of Medical Sciences and complied with all the relevant laws and international ethics guidelines outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the [NCBI protein database] repository, [https://www.ncbi.nlm.nih.gov/protein/]; [SOPMA server] repository, [https://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html]; [ProtParam web server] repository, [https://web.expasy.org/protparam/]; [HotSpot Wizard 3] repository, [https://loschmidt.chemi.muni.cz]; [CASTp web server] repository, [http://sts.bioe.uic.edu/castp/index.html?2cpk]; [ADMETlab 2.0 server] repository, [https://admetmesh.scbdd.com/service/evaluation/index]; [YASARA server ] repository, [http://www.yasara.org/minimizationserver.htm].\u003c/p\u003e\n\u003cp\u003eTo identify target genes, miRDB, RNAhybrid, PICTAR4, DIANAmT, miRWalk, miRanda, DIANAmT, RNAhybrid, PITA, RNA22, PICTAR5, and TargetScan software were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research leading to these results received funding from the Vice-Chancellor of Research and\u0026nbsp;\u0026nbsp;Technology, Kerman University of Medical Sciences, under Grant Agreement No. 98001036.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003eAcknowledgments\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Kerman University of Medical Sciences for supporting this research\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(Grant No\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e 98001036).\u003c/p\u003e\n\u003cp\u003eWe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eV.R. and M.SB. conceived the study; Al.A. designed the research and in silico study, Am.A and M.RK. performed in silico study, M.SB. performed the laboratory research; A.K and MJ.MP. analyzed the data; and M.E. and M.R. wrote and revised the manuscript, with minor contributions from the other authors. 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Acta Biochim Pol. 2018;65(2):173\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDopazo J. Bioinformatics and cancer: an essential alliance. Clin Transl Oncol Off Publ Fed Span Oncol Soc Natl Cancer Inst Mex. 2006;8(6):409\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-complementary-medicine-and-therapies","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcam","sideBox":"Learn more about [BMC Complementary Medicine and Therapies](https://bmccomplementmedtherapies.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Complementary Medicine and Therapies","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PODXL, Methyltransferase, miRNAs, Docking, MD simulation, ALL, and catechin","lastPublishedDoi":"10.21203/rs.3.rs-3873363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3873363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study explored the impact of predicted miRNAs on DNA methyltransferases (DNMTs) and the \u003cem\u003ePODXL\u003c/em\u003e gene in NALM6 cells, revealing the significance of these miRNAs in acute lymphocytic leukemia (ALL).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed a multifaceted approach comprising bioinformatic analyses (protein structure prediction, molecular docking, dynamics, ADMET study) and miRNA evaluations to explore the therapeutic effects of catechin compounds on \u003cem\u003eDNMTs\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur evaluation revealed a nuanced relationship in which catechin treatment induced increased miRNA expression and decreased \u003cem\u003eDNMT1\u003c/em\u003e and \u003cem\u003eDNMT3B\u003c/em\u003e levels in NALM6 cells. This indirect modulation impacted \u003cem\u003ePODXL\u003c/em\u003e expression, contributing to cancer characteristics.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe overexpression of \u003cem\u003eDNMT1\u003c/em\u003e and \u003cem\u003eDNMT3B\u003c/em\u003e in NALM6 cells may promote ALL development via a mechanism regulated by microRNAs, particularly miR-548 and miR-200c. Altered \u003cem\u003eDNMT1\u003c/em\u003e and \u003cem\u003eDNMT3B\u003c/em\u003e expression is correlated with decreased miR-548 and miR-200c expression before and after catechin treatment, respectively, leading to the dysregulation of tumor suppressor genes, such as \u003cem\u003ePODXL\u003c/em\u003e, and cancer cell characteristics. These findings underscore the therapeutic potential of catechin compounds targeting \u003cem\u003eDNMTs\u003c/em\u003e and miRNAs in ALL treatment.\u003c/p\u003e","manuscriptTitle":"Catechin-induced changes in PODXL, DNMTs, and miRNA expression in NALM6 cells: An integrated in silico and in vitro approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 17:25:50","doi":"10.21203/rs.3.rs-3873363/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"","date":"2024-02-25T20:26:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-25T20:24:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Complementary Medicine and Therapies","date":"2024-01-17T16:24:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-complementary-medicine-and-therapies","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcam","sideBox":"Learn more about [BMC Complementary Medicine and Therapies](https://bmccomplementmedtherapies.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Complementary Medicine and Therapies","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fff3d68-c60f-4017-859a-0cf72c7340ec","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T14:55:51+00:00","versionOfRecord":{"articleIdentity":"rs-3873363","link":"https://doi.org/10.1186/s12906-024-04521-2","journal":{"identity":"bmc-complementary-medicine-and-therapies","isVorOnly":false,"title":"BMC Complementary Medicine and Therapies"},"publishedOn":"2024-06-15 14:55:51","publishedOnDateReadable":"June 15th, 2024"},"versionCreatedAt":"2024-02-28 17:25:50","video":"","vorDoi":"10.1186/s12906-024-04521-2","vorDoiUrl":"https://doi.org/10.1186/s12906-024-04521-2","workflowStages":[]},"version":"v1","identity":"rs-3873363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3873363","identity":"rs-3873363","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}