Implementation of advanced analytical methods MeD-seq and LC-MS/MS for assessing the epigenetic profile of high-risk myelodysplastic syndrome

Implementation of advanced analytical methods MeD-seq and LC-MS/MS for assessing the epigenetic profile of high-risk myelodysplastic syndrome | 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 Article Implementation of advanced analytical methods MeD-seq and LC-MS/MS for assessing the epigenetic profile of high-risk myelodysplastic syndrome Theodoros Nikolopoulos, Eleftherios Bochalis, Theodora Chatzilygeroudi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4424582/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Treatment decision and response assessment in myelodysplastic syndromes (MDS) can be enhanced by the implementation of advanced diagnostic and prognostic assays for the detection of multiple molecular features. Higher-risk (HR) MDS, ineligible for allogeneic hematopoietic stem cell transplantation (alloHSCT), require prompt therapeutic interventions such as treatment with hypomethylating agents (HMAs) to restore normal DNA methylation levels, mainly of oncosuppressor genes and consequently to delay disease progression and increase overall survival (OS). However, response assessment to HMA treatment relies on conventional methods with limited capacity to uncover a wide spectrum of molecular events. We studied bone marrow aspirates from twenty-one HR MDS patients pre- and post-HMA treatment and seven healthy controls. Genomic DNA was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) for 5’ methyl-cytosine (5mC), 5’ hydroxy-methyl cytosine (5hmC) levels detection, and global adenosine/thymidine ([dA]/[T]) ratio, to correlate differences during treatment course and at baseline state prior drug therapy. Results from methylation DNA sequencing (MeD-seq) from the same HR MDS cohort were also analyzed to identify targeted differentially methylated regions (DMRs). LC/MS-MS analysis revealed a significant hypomethylation status in responders (Rs) already established at baseline and a trend for further DNA methylation reduction post-HMA treatment. Non-responders (NRs) reached statistical significance for DNA hypomethylation only post-HMA treatment. MeD-seq confirmed results globally for both Rs and NRs and more specifically, identified DMRs associated with HMA treatment. Additionally, within statistically significant selected chromosomal bins, genes encoding for proteins and non-coding RNAs were highlighted with reversed methylation profiles between Rs and NRs. Dynamic DNA methylation changes in HR MDS patients undergoing HMA therapy demonstrated that response to treatment is associated only with few specific hypomethylated DMRs rather than presenting a global effect across genome. The 5hmC epigenetic mark was only rarely detected in Rs and NRs, in contrast to healthy controls. Global [dA]/[T] ratio was lower in both R and NR subgroups compared to controls suggesting high frequences of baseline transitions from 5mC to thymidine. Conclusively, LC-MS/MS methodology provided broad-based but rapid and cost-effective results on the molecular HR MDS background, potentially translatable into responsive phenotypes to HMA treatment. Biological sciences/Cancer Biological sciences/Chemical biology Biological sciences/Computational biology and bioinformatics Biological sciences/Molecular biology Health sciences/Medical research Health sciences/Molecular medicine Myelodysplastic Syndromes Hypomethylating agents response assessment DNA methylation LC-MS/MS analysis MeD-seq data analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key messages Genomic DNA of high-risk MDS exhibit lower methylation levels prior to HMA-treatment, than healthy controls Global and targeted differences in DNA methylation signatures pre-and post-HMA treatment discriminate responders from non-responders MDS patients Spontaneous deamination of 5’ methyl-cytosine to thymidine is increased in MDS patients versus healthy controls 5’ hydroxy-methyl cytosine is rarely detected in MDS compared to healthy controls indicating impairment in DNA demethylation pathway Introduction Myelodysplastic syndromes (MDS) are clonal hematopoietic stem cell disorders, mainly characterized by abnormal development and maturation of hematopoietic progenitor cells in the bone marrow, resulting in peripheral blood cytopenias and by an increased tendency of disease progression towards acute myelogenous leukemia (AML). Morphological examination of the bone marrow typically reveals dysplastic changes in the hematopoietic cell precursors indicating abnormal cell proliferation and maturation, leading to an heterogeneous clonal development [1]. At the molecular level MDS patients exhibit not only DNA-based genetic abnormalities in their bone marrow hematopoietic progenitors, but also aberrations related to their immunophenotype, gene expression, epigenetic profile and bone marrow microenvironment [2-4]. Several sensitive laboratory tests are implemented to guide treatment decisions and evaluate effective treatment responses. Currently, with the increasing application of interventional treatment approaches, assessment of minimal residual disease (MRD) is the indicative method for response assessment in AML patients and emerges also for the HR MDS monitoring. However, the precise evaluation of the MRD requires the application of better analytical methods with higher sensitivity than the conventional ones [5]. The study of MDS epigenetics has revealed the significant role of DNA methylation in the pathophysiology of this disease. Mutations affecting genes responsible for epigenetic modifications, such as DNMT3A, TET2 , and ASXL1 are prevalent among the elderly population and are considered as founding and driver mutations in MDS[6, 7]. These mutations at medium to high variant allele frequency (VAF) reflect an evolutionary status of clonal hematopoiesis of indeterminate potential (CHIP) towards MDS. When present at low VAF, these mutations may escape detection systems, and consequently it is uncertain if they evolve and directly contribute to MDS pathobiology[8]. Co-existence of mutations in DNMT3A, TET2 , and ASXL1 affecting epigenome with mutations in RUNX1 , EZH2, NRAS and/or TP53 genetic loci, is highly predictive for already established hematopoietic stem cell neoplasms[9] and accounts for decreased overall survival (OS) even post-allogeneic hematopoietic stem cell transplantation (alloHSCT)[10]. Apart from mutation involvement in bone marrow clonogenicity, aberrant DNA methylation patterns are also very common in MDS however, their contribution to disease development and progression has not yet been elucidated. The widespread genome hypomethylation is associated with genomic instability and activation of transposable elements and simultaneously results in increased oncogene expression, both of which can contribute to MDS development and progression and to hematopoietic clonal expansion. Additionally, MDS is characterized by gene-specific hypermethylation changes. Certain genes, involved in hematopoietic differentiation, apoptosis, and cell cycle regulation become hypermethylated, leading to their silencing and consequently resulting in undisturbed transition towards the S phase, therefore establishing a leukemic cell phenotype. For example, hypermethylation of tumor suppressor genes such as Cyclin Dependent Kinase Inhibitor 2B ( CDKN2B or P15 )[11, 12], adenosine 5'-Monophosphoramidase ( HINT1 )[13] and a set of 10 hypermethylated genes including P15 [ 14 ] are associated with adverse clinical outcomes in MDS patients. The integration of epigenetic information into existing prognostic models may improve risk stratification and guide treatment decisions for MDS patients, since certain methylation signatures have been associated with disease progression, transformation to AML and decreased OS[15, 16]. MDS are classified in distinct subtypes (various editions of the WHO classification) and prognostic groups (IPSS, IPSS-R, WPSS, IPSS-M etc classification systems) and among them, the higher-risk (HR) MDS group requires prompt treatment initiation, considering interventional therapies, such as hypomethylating agents (HMAs) or alloHSCT. HMAs and particularly the cytosine analogs azacytidine (5-azacytidine) and decitabine (5-aza-2′-deoxycytidine) are common treatment options for HR MDS patients, aiming to slow down abnormal clonal growth in the bone marrow and to improve peripheral blood cell counts. HMA treatment is continued for as long as a favorable outcome is maintained and HMA remains well-tolerated by each individual patient, since relapse with or without disease progression is unfortunately anticipated. However, only a minority of MDS patients experience prolonged (>24 months) responses to HMA treatment, depending on their overall performance, and on cytogenetic and genetic/epigenetic disease background[17]. The high diversity in response to HMA treatment has been extensively reported, but the specific factors contributing to the induction of a favorable response still remain to a substantial degree, undetermined. Nevertheless, without the administration of HMA treatment HR MDS quickly progresses, and AML develops faster in the majority of cases. Following treatment with HMAs, patients are typically assessed for response by internationally accepted and well-established criteria, such as hematologic improvement, transfusion independence, cytogenetic response, or complete remission. Close monitoring of MDS patients is essential to evaluate treatment response, manage side effects, and estimate disease progression. The recently (2022) introduced Molecular International Prognostic Scoring System (IPSS-M) provides additional prognostic value for the outcome, by merging results from clinical variables, karyotype, and myeloid detected mutations[18]. Cytogenetic evaluation including chromosome analysis, fluorescence in situ hybridization (FISH), chromosomal microarray as well as the more advanced high sensitivity multiparameter flow cytometry and mutational testing by Next Generation Sequencing (NGS) platforms, provide a comprehensive stepwise approach in clinical-morphologic correlation of MDS, selection of the appropriate therapeutic strategies, and in predicting the risk for progression to AML. However, criteria for MDS response have not yet been standardized and thus, they are not formally included in the MRD latest modification of the International Working Group (IWG) response criteria, as has been established for AML[19]. We have conducted a comprehensive evaluation of genomic DNA methylation status in twenty-one HR MDS patients prior and post-HMA-treatment and seven healthy controls. Global 5’ methyl-cytosine (5mC) and 5’ hydroxy-methyl-cytosine (5hmC) as well as targeted methylation signatures (DMRs) were assessed to enable discrimination between responders from non-responders to HMA therapy. Further to methylation analysis we have compared the randomness of spontaneous deamination of 5mC to thymidine in HR MDS patients versus controls to estimate potential increase in the mutagenesis rate attributed to HMA-treatment. Two complementary highly sensitive approaches, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and methylation DNA sequencing (MeD-seq) ( Fig. 1 ), have been implemented to address the above queries, taking into consideration their relevant advantages and limitations. Materials and Methods MDS patient and healthy cohorts In total 21 patients with HR MDS, exerting ≤20% bone marrow blasts, received azacytidine (AZA) or decitabine (DAC) by standard scheduling (75mg/m 2 x 7 days, 28-day cycle for AZA and 20mg/m 2 x 5 days, 28-day cycle, for DAC) at the University Hospital of Patras. Treatment responders (R) versus non-responders (NR) were classified according to International Working Group (IWG) response criteria for HR MDS released in 2006[20]. Bone marrow aspirate samples were collected from the whole patient’s cohort before treatment initiation as well as at the evaluation of response (after 5-7 treatment cycles). Peripheral blood from 7 elderly donors with matching age and gender was collected and represented the control samples for the LC-MS/MS analytical method. Peripheral blood samples provide an adequate picture of the bone marrow environment since, high concordance of variant detection and gene expression profile between peripheral blood and bone marrow has been repeatedly reported [21-23]. Genomic DNA was extracted using phenol: chloroform: isoamyl alcohol in 25:24:1 ratio (Sigma-Aldrich Pty Ltd, Merck KGaA, Darmstadt, Germany) and subjected to downstream analysis. Characteristics of the patient and healthy cohorts participating in this study are summarized in table 1 . Table 1: List of MDS and healthy patient samples. MDS samples were collected pre- and post-HMA treatment. A/A Healthy control (C)/ Responders (R)/Non Responders (NR) Male (M)/Female (F) Age AZA/DAC treated Tissue BM/PB 1 C1 M 70 - PB 2 C2 M 70 - PB 3 C3 M 75 - PB 4 C4 M 85 - PB 5 C5 M 70 - PB 6 C6 M 70 - PB 7 C7 M 75 - PB 8 R1 M 60 AZA BM 9 R2 M 64 AZA BM 10 R3 M 57 AZA BM 11 R4 M 69 AZA BM 12 R5 M 76 AZA BM 13 R6 M 76 AZA BM 14 R7 M 68 AZA BM 15 R8 F 78 AZA BM 16 R9 M 79 AZA BM 17 NR1 M 79 DAC BM 18 NR2 M 75 AZA BM 19 NR3 M 82 AZA BM 20 NR4 M 85 AZA BM 21 NR5 M 69 AZA BM 22 NR6 M 70 AZA BM 23 NR7 M 61 AZA BM 24 NR8 M 73 DAC BM 25 NR9 M 84 DAC BM 26 NR10 M 69 AZA BM 27 NR11 M 83 AZA BM 28 NR12 M 66 AZA BM All patients provided their written informed consent according to the Declaration of Helsinki, being informed about both clinical and translational investigations and the study was approved by the University General Hospital of Patras Ethics & Scientific Committee (approval number 33807/24.12.2020). LC-MS/MS analysis of genomic DNA Liquid chromatography-mass spectrometry technique combines Liquid Chromatography (LC) with a triple quadrupole (QqQ) mass spectrometer (LCMS-8050 system, Shimadzu, Japan). DNA samples from HR MDS were analyzed pre- and post- HMA treatment and compared with control samples for baseline methylation pre-treatment. Prior to LC-MS/MS analysis 400 ng of genomic DNA was digested and dephosphorylated by a Nucleoside Digestion Mix (NEB#M0649) to generate single nucleosides for further quantitative analysis, following manufacturer’s instructions. Reactions were purified with a special centrifugal filter 3kDa MWCO Amicon ® Ultra (Merck Millipore). LC-MS/MS system features a heated Electrospray Ionization (ESI) system and Multiple Reaction Monitoring (MRM) capabilities with high sensitivity and high speed. The column oven was set at 35 °C. A Shim-pack Scepter C18-120 column (4.6 mm x 100 mm, 5 µm, Shimadzu) with a 4.6 mm pre-column was used for the separation of nucleosides. The mobile phase was passed through the column by gradient elution with acidified H 2 O (1% CH 3 COOH) (solvent A) and acetonitrile (ACN) (solvent B). The flow rate was set at 0.2 ml/min with the solvent ratio starting at 95% A / 5% B and reaching 35% A / 65% B at 15 min. The total analysis time was 20 min and the injection volume was 10 µL. Mass spectrometry detection was performed under positive electrospray ionization (ESI) mode. Nucleosides and derivatives were monitored by multiple reaction monitoring (MRM) modes using the mass transitions (precursor ions → product ions) of dG (deoxy guanosine) (268.4 → 152.4), 5-mC (242.1 → 126.1), 5-hmC (258.1 → 142.1), T (thymidine) (243.3 → 127.2) and dA (deoxyadenosine) (252.4 → 136.2)[24]. Calibration curves of 5-mC and 5-hmC were constructed by plotting the peak area ratios of 5-mC/dG and 5-hmC/dG versus the molar ratios of 5-mC/10 2 dG and 5-hmC/10 5 dG, respectively, based on data obtained from LC-ESI-MS/MS analysis. Calibration curves were also constructed for T and dA.[25]. Linearity was within the concentration range 0.4–6 for 5-mC/10 2 dG and 60–500 for 5-hmC/10 5 dG, with a coefficient of determination (R 2 ) greater than 0.99. Results for 5mC, 5hmC, T and dA are given as mean values derived from four independent runs. Statistical analysis was performed using SPSS software version 20. To assess statistical differences of 5mC levels among control and patients’ groups before HMA treatment, a one-way ANOVA test with Bonferroni multiple comparison post hoc test was used. Paired t-test was performed to identify 5mC differences before and after treatment between each MDS patient group. Normal distribution of data was assessed by the Shapiro-Wilk test. P-values less than 0.05 were considered statistically significant. MeD-seq data analysis Raw sequencing files for 13 MDS samples pre- and post-HMA treatment (26 samples that belong to our HR MDS cohort) were retrieved from the SRA database under: PRJNA1075483 (https://www.ncbi.nlm.nih.gov/bioproject/1075483). Data processing was carried out as outlined in[26] with modifications to accommodate our study design. Specifically created Python scripts were used to trim the Universal Illumina adaptor and filter reads based on the presence of the LpnPI restriction site. Filtered reads were aligned to the hg38 human genome and were annotated for Genes (Transcription Start Sites, Gene bodies, and Transcription End Sites) and CpG islands using annotations from ENSEMBL. Differentially Methylated Region (DMR) detection was performed between two data sets (pre- and post-HMA treatment) for each MDS sample containing the regions of interest. Required filtering criteria for DMRs were the following: q-value less than 0.05, fold change greater than 2, and inclusion of at least 20 LpnPI recognition sites to ensure selection of genomic areas enriched in CGs. DMRs present in alternative and random genomic contigs were excluded from this analysis. DMRs were retrieved by using the χ 2 test on read counts. Significance was called by either Bonferroni or FDR using the Benjamini-Hochberg procedure. Using this pipeline a methylation ratio is obtained for each DMR indicating an increase or decrease in methylation post-HMA patient’s treatment. To reduce the right skewness of the data and mitigate the presence of outliers, log10 -transformation was performed on the methylation ratios for each region, since they possess only positive values that span a wide range. Post log10-transformations, positive and negative methylation values indicated increase and decrease in methylation status within the studied area, respectively. To synthesize a comprehensive methylation profile for each chromosome, DMRs were split into two groups: those with increased methylation (upwards) and those with decreased methylation (downwards). A weighted mean methylation ratio was computed separately for each response category using log10-transformed methylation ratios, thus accommodating the bimodal distribution characteristic of the methylation data. The composite weighted mean for each chromosome (WM chr ) was derived using the equation: WM chr =( m u × w u )+( m d × w d ) [1] where WM chr represents the total weighted mean methylation for a given chromosome, m u is the mean methylation level of the upwards methylated regions, w u is the proportion of upwards methylated DMRs to the total number of DMRs within the chromosome, m d is the mean methylation level of the downwards methylated regions, and w d is the proportion of downwards methylated DMRs relative to the chromosome's total DMR count. This calculation provided a weighted average methylation value that reflects the overall methylation status while accounting for the distribution and abundance of methylated regions across the chromosome. Also, for each category a weighted standard error was calculated for each chromosome (WSE chr ) using the following equation: To perform a more targeted and in-depth analysis, the hg38 human genome was segmented into bins of 100,000 base pairs, with each DMR being assigned to a bin based on coverage. The Mann-Whitney U test, a non-parametric method suitable for the bimodal distribution of our methylation data, was utilized to detect statistically significant differences in methylation levels across genomic bins between R and NR groups of HR MDS patients. Results Detection of diverse epigenetic marks (5mC/5hmC) by LC-MS/MS LC-MS/MS is a powerful analytical method which combines accuracy, high sensitivity and reproducibility although has not yet been fully established for nucleic acid analysis. However, the challenge of quantifying global levels of DNA methylation derivatives can be gauged by the low abundance of these epigenetic marks. In humans, about 1% of the total DNA bases consists of 5mC[27] whereas, 5hmC abundance is about 10 to 100-fold lower than that of 5mC[28, 29]. 5hmC has been reported as a stable epigenetic mark highly enriched within gene bodies of transcriptionally active genes, promoters and enhancers[30]. Moreover, global 5hmC content is dramatically reduced in multiple human cancers[31, 32], a sign which can potentially be associated with tumorigenesis. Chromatography-based techniques such as LC-MS/MS dominate in similar bioanalyses and is considered the gold standard method for the global analysis of DNA methylation derivatives in human cancers since enzymatic digests as well as bisulfite treatment of DNA prior to NGS reactions fail to discriminate between 5mC and 5hmC, both of which are detected as 5mC[33]. In the present study, levels of 5’ methyl-cytosine (5mC) and 5’ hydroxy-methyl cytosine (5hmC) residues, prior to and post-HMA treatment, were estimated by LC/MS-MS and further compared between MDS patients and the independent group of healthy controls ( Fig. 2 ). Total 5mC across genomic DNA was calculated by the equation: [5mC]/10 2 [dG] (further details in materials and methods section). Concentration of deoxy guanosine [dG] was selected as internal standard against [dC], based on the assumption that [dG] = {[dC] + [5mC] + [5hmC] + [other C modifications]} in genomic DNA. Therefore, [dG] is considered a unique and more accurate value rather than measurement of the independent cytosine modified nucleosides as a sum: {[dC] + [5mC] + [5hmC] + [other C modifications]}, potentially leading to experimental errors. Many of these cytosine derivatives are below detection limits of the method and additionally, guanosine modifications are much less prevalent in genomic DNA compared to methylated cytosines and its derivatives[24]. To correlate global DNA methylation profiles (5mC levels) among the different samples and in relation to HMA treatment response, MDS patients were categorized after clinical monitoring as responders (R) and non-responders (NR). Although a trend for global hypomethylated status was documented for both R and NR groups compared to healthy controls, statistical significance was demonstrated only between controls and Rs (p=0.014) ( Fig. 2A ). Comparison between the NR and R groups of HR MDS patients post-HMA treatment ( Fig. 2B ) revealed an actual significance for 5mC lower values only within the NR group (p=0.029). The R group yielded comparable 5mC values pre- and post-HMA treatment. Data are represented as boxplots with jittered points. The range between 25% and 75% of the values are within boxes, whereas median values of each dataset are represented as lines inside the boxes and the individual points outside the boxes indicate each value considered (including outliers) for the boxplot construction. A similar calibration curve, as previously described, was applied for the 5hmC calculation, but with a 10 5 as a divisor ([5hmC]/10 5 [dG]), since 5hmC is represented at a frequency approximately ~10 - 100-fold lower than 5mC. Among MDS patients tested, only 2 Rs and 3 NRs displayed detectable 5hmC pre- and post-HMA treatment (almost 1/3 of the total MDS patients), even when analyzed in the high concentration mode. On the contrary, 5hmC was consistently documented in all healthy control samples ( Fig. 2C ). This observation implies a potential dysregulation of α-ketoglutarate-dependent DNA dioxygenases (TET1-3 enzymes), implicated in the natural biochemical DNA demethylation pathway by which 5mC finally reverses to C, with 5hmC representing the first product along the oxidative reaction pathway. Deviation of adenosine: thymidine ratio (≤ 1) highlights the frequent spontaneous deamination of 5mC to thymidine The ratio of adenosine (A) to thymidine (T) in double stranded DNA is expected to be approximately 1:1 due to the complementary base pairing in double helix DNA structure. Deviations in the human genome from 1:1 [dA]/[T] ratio often result from the spontaneous deamination reaction of the modified cytosine 5mC that produces thymidine, which is unrecognizable and unable to be corrected by the repairing enzymatic complexes that monitor the human genome for non-complementary bases. This reaction, if not corrected, converts a C-G base pair to a T-A during DNA replication. The deamination of 5mC to thymidine is a significant source of mutations in DNA and leads to a deviating [dA]/[T] ratio. Certain repetitive sequences, regions with high mutation rates or highly methylated CpG islands may exhibit deviations from the expected 1:1 [dA]/[T] ratio. This is particularly relevant in epigenetics, since methylation of cytosine at CpG dinucleotides is an important epigenetic mark involved in gene regulation (mainly repression). Deamination of 5mC can lead to changes in DNA methylation patterns and gene expression regulation simultaneously with the appearance of mutated sequence. Beyond this spontaneous process, the frequency of such mutational patterns produced in the genome under the frame of specific disorders is also important, as it contributes to genetic variation and can have implications in tissue homeostasis, including development or disease progress. To this end we have comprehensively estimated the global [dA]/[T] ratio across HR MDS and healthy samples( Fig. 3 ). Calibration curves were plotted independently for A and T with values retrieved from LC-MS/MS analysis from escalating concentrations of Adenosine and Thymidine standards, respectively. [dA] and [T] concentrations were calculated separately, and then [dA]/[T] ratio was calculated for each human sample under investigation. Mean value of healthy samples was 1.02, which is considered an expectable value. Rs and NRs pre- and post-HMA treatment display [dA]/[T] ratios < 1 (0.727-0.633). These results highlight the pre-existing deviation from normal values of thymine [T] concentration in HR MDS patients, already at baseline and before starting HMA treatment. The observed [dA]/[T] ratios <1 in MDS implies increased levels of [T], attributed to deamination of 5mC to thymine, leading to the reduction of [dA]/[T] ratio. The potential of increased deamination rates of 5mC to thymidine was clearly demonstrated by LC-MS/MS analysis and may account for the generation of mutations within CpG islands, flanking genetic loci and exerting transcriptional regulatory properties. Chromosome-wide mapping of DNA methylation patterns derived from MeD-seq analysis Methylated DNA sequencing (MeD-seq) is a high-throughput methodology that constitutes of targeted capture of differentially methylated genomic regions (DMRs) by restriction enzyme digests into recognition sites of methylated over unmethylated cytosines, followed by NGS. MeD-seq facilitates the comprehensive analysis of genome-wide CpG methylation patterns. The methylation-sensitive restriction enzyme LpnPI, targets tetranucleotides containing methylated or hydroxymethylated CG dinucleotides, cleaving the DNA at 16 nucleotides downstream of the enzyme recognition site. In the present study MeD-seq data from 13 HR MDS patients (constituting a representative part of our MDS cohort) pre- and post-treatment with HMAs, were retrieved and further interpreted (details in materials and methods section). Patients were sub-categorized into two independent groups: 6 responders (Rs) and 7 non-responders (NRs) to HMA therapy. For each human chromosome a weighted mean of methylation was calculated using the equation [1] from materials and methods section. The weighted mean provides a robust way for calculating total chromosomal methylation by utilizing the log10-transformed methylation ratios as well as by taking into account their respective weights. By assigning weights to each data point based on their significance or relevance, the weighted mean ensures that these differences are appropriately considered in the calculation. The equation [1] was applied to both R and NR MDS patients, who exhibited bimodal methylation distributions on each chromosome, documenting the presence of DMRs with both increased and decreased methylation associated with HMA treatment. Results are represented as violin plots displayed for each chromosome separately ( Fig. 4A ) to effectively summarize and visualize the distribution, central tendency, and spread of hypo- and hypermethylation. Typically, NRs showed an asymmetric pattern of chromosomal methylation distribution, where the majority followed a modest reduction in methylation, as evidenced by the red density peak between 0 and -1. Specifically, chromosomes 7, 9, 11, 12, 16, 18, and 22 demonstrated a skewed distribution towards regions of reduced methylation, alongside a sparse presence of regions undergoing methylation increase. In contrast, Rs displayed a more balanced bimodal distribution of methylation changes, with a slight preference for areas with increased methylation (as shown by a green peak from 0 to +1). Chromosomes 21, X, and Y warrant special attention due to their distinctive methylation patterns in the NR subgroup, in which the majority of chromosomal regions were either non-differentially methylated or predominantly hypomethylated. Moreover, the NR subgroup exhibited a substantial proportion of regions undergoing extreme hypermethylation, with methylation elevation ranging approximately from 100 to 1000-fold (corresponding to log10 values of 2 to 3), particularly noticeable within the +1 to +3 range. The significant methylation increase of these specific regions is also highlighted in Fig. 4B , underscoring a marked rise in methylation levels that surpass the distribution of hypomethylated regions on chromosomes 21, X, and Y. In contrast, the R subgroup maintained the bimodal distribution characteristic, with a subtle inclination towards hypermethylated and not differentially methylated regions. In the bar chart provided ( Fig. 4B ), the distribution of weighted mean methylation values further corroborates these findings. The chart displays a notable differential methylation pattern between Rs and NRs, indicating potential epigenetic distinctions correlating with the HMA response categories. For the majority of chromosomes, Rs exhibited positive mean methylation values, indicating an increase in methylation post-HMA treatment, contrasting with the negative values observed in NRs, which are in line with results obtained from respective LC-MS/MS analysis, exerting a globally reduced methylation profile. This consistent inverse relationship across the chromosomal spectrum suggests that methylation status may be a significant factor for the differential response observed following HMA treatment. Moreover, the variability in methylation patterns is not uniform across all chromosomes, underscoring the intricate nature of epigenetic regulation in relation to phenotypic outcomes. The biological significance of the observed epigenetic disparities must be evaluated among a large cohort of HR MDS patients to corroborate the established hypomethylated status pre-HMA treatment and the modest hypomethylation effect post-HMA treatment in Rs group, which is in discordance with their improved clinical phenotype. Targeted methylation analysis by MeD-seq reveals significant chromosomal regions discriminating responders from non-responders to HMA-therapy Further analysis of MeD-seq data was performed to decode methylation profiles of targeted chromosomal regions. Bioinformatics analysis finalized and divided genomic MeD-seq methylation data into discrete segments or genomic regions of equal size (of max 100 kb), defined as chromosomal bins. Chromosomal bins facilitate the analysis and interpretation of genomic data by providing a systematic framework for organizing and comparing genomic features, such as methylation profiles, across different regions of the genome. The sequencing reads obtained from MeD-seq experiment were assigned to the appropriate bins based on their genomic coordinates. The assignment to chromosomal bins provided a universal way to compare both within and across patient samples. The obtained patterns reveal that HMA treatment response among HR MDS patients is associated with the methylation status of certain genomic regions rather than with widespread genomic methylation changes. In the most statistically significant bins, Rs show slight variations in methylation levels, whereas NRs exhibit substantial increases, up to 100-fold. To further deepen our analysis, chromosomal bins were searched for sequences representing genes, either protein coding or non-coding RNA species. Results are summarized in table 2 . Table 2: Genes and Non-Coding RNAs identified within statistically significant genomic bins . Predicted and alternative gene transcripts and hairpin miRNAs are not presented in this table. Also, different isoforms of the same lncRNA are aggregated under a single representation. Genomic Bin Protein coding Genes Non-coding RNAs-circRNAs Non-coding RNAs-miRNAs Non-coding RNAs-lncRNAs chr1_bin24 SKI, MORN1, RER1 hsa_circ_0007120, hsa_circ_0009371, hsa_circ_0009373, hsa_circ_0009376, hsa_circ_0009377, hsa_circ_0009378, hsa_circ_0009379, hsa_circ_0009372, hsa_circ_0009374, hsa_circ_0009375 Not found lnc-SKI, lnc-PEX10 chr4_bin492 Not found Not found Not found lnc-CWH43 chr8_bin858 REXO1L2P Not found Not found lnc-ATP6V0D2 chr19_bin363 ZNF565, ZNF146 Not found Not found lnc-CAPNS1, lnc-ZNF146, lnc-COX7A1 chr19_bin364 ZFP14, ZFP82 hsa_circ_0050766, hsa_circ_0050767 Not found lnc-ZNF146, LINC00665, lnc-ZFP14 chr21_bin83 CDC27P9, RNA28SN2, RNA18SN2, RNA5-8SN2 Not found hsa-miR-6724-1-5p, hsa-miR-6724-2-5p, hsa-miR-10401-5p, hsa-miR-10401-3p, hsa-miR-3648 lnc-KCNE1B, lnc-SMIM11B chr21_bin85 RNA45SN3, CDC27P10, RNA28SN1, RNA45SN1, RNA18SN1, RNA5-8SN1 Not found hsa-miR-6724-5p, hsa-miR-10401-5p, hsa-miR-10401-3p, hsa-miR-10396b-5p, hsa-miR-10396b-3p lnc-KCNE1B, lnc-SMIM11B chrX_bin1159 Not found Not found Not found DANT1, lnc-PLS3, DANT2, lnc-LRCH2 Most genes identified encode for ncRNA species, including circular, micro-RNAs (miRNAs) and long non-coding RNAs (lncRNAs). Among the protein-coding genes presented within the study, the Sloan-Kettering Institute proto-oncogene ( SKI ) gene located in chr1_bin24, holds a pivotal role in moderating Transforming Growth Factor-beta (TGF-β) signaling pathway. Previous studies have highlighted the important role of SKI in managing chronic TGF-β signaling, further affecting stem cell fitness by influencing aberrant splicing. Dysregulation of the SKI -TGF-β signaling axis may influence the spliceosome function and alternative splicing events, and thus providing a link to underpinning aberrant splicing patterns observed in MDS[34]. These findings reinforce the hypothesis that specific genomic bins may act as potential biomarkers for predicting treatment efficacy and merit further investigation to expand our knowledge on epigenetic regulation events guided by either protein coding genes or aberrant expression of non-coding RNAs. Discussion DNA methylation abnormalities play a crucial role in the pathophysiology of MDS and have clear implications in diagnosis, prognosis, and treatment of the disease. The accumulation of various somatic mutations in MDS by granting a proliferation advantage to hematopoietic progenitor cells, promote their expansion over time[6, 7]. In addition, the perturbed epigenetic landscape is complemented by the underlying mutational background, which either pre-exists or is induced as the neoplastic hematopoietic clones continue to expand[35]. However, in contrast to genetic mutations, epigenetic alterations are potentially reversable therefore, decoding epigenetic abnormalities spread across genome, may prove to be essential for developing targeted therapeutic strategies for MDS and improving patient outcomes by integrating accessible and accurate methodologies. So far, the dysregulated DNA methylation patterns in MDS have prompted interest in epigenetic therapies aimed to reverse aberrant DNA methylation, utilizing HMAs to restore normal DNA methylation patterns and malignant cells clearance. However, the overall response rates are ranging between 40-60% and may vary among the different MDS subtypes [17]. Response to HMA treatment may include hematological improvement, transfusion independence, depth of remission, or disease stabilization with varying duration of response, depending on specific cytogenetic abnormalities (e.g., deletion of chromosome 5q, monosomy 7 etc)[36] or with other, as yet uncertain prognostic factors influencing the probability and duration of response to HMAs. Response assessment is typically performed by standardized criteria, considering various parameters such as blood counts, bone marrow blast cell percentage, and transfusion requirements. Current molecular-based approaches that utilize NGS platforms for whole genome or partial mutational analysis can detect and monitor aberrations in multiple genetic loci however, they are unable to detect large structural abnormalities and copy number variants as well as fusion genes, which are common in MDS. Furthermore, the clinical significance for specific sets of mutations has not yet been established[37], probably due to the inability to adapt uniform algorithms for bioinformatics interpretation of the results and quality control protocols to standardize and harmonize the methodological steps between different labs. In the current study we have implemented two different advanced techniques with high analytical sensitivity to assess DNA methylation levels in HR-MDS patients and make direct comparisons with healthy controls and to identify differences in methylation status between pre- and post-HMA treatment conditions. NGS-methylation analysis employs a slightly different methodology aiming to map sequencing reads to a reference genome. This kind of analysis prerequisites bisulfite treatment or methylation-sensitive restriction enzyme digest prior to sequencing reactions, thus distinguishing methylated from unmethylated cytosines, and identifying differentially methylated regions (DMRs) between tested samples. MeD-seq analysis data which were interpreted in our study enabled the identification of global changes in DNA methylation that are associated with HMA treatment response. We have also identified distinct DNA methylation profiles across each human chromosome between responders (Rs) and non-responders (NRs), which were manifested by uniform rates of slight hypermethylation in Rs and hypomethylation among NRs apart from chromosomes 21, X and Y. NRs also exhibited extreme values of hypermethylated DMRs by approximately 100 to 1000-fold, whereas Rs displayed more balanced rates between hypo- and hypermethylation with a total tendency towards hypermethylation ( Fig 4A, B ). Methylation discrepancies within chromosomal bins revealed the existence of common chromosomal sites between Rs and NRs exerting significant methylation alterations associated with HMA treatment ( Fig. 5 ), which can further facilitate the characterization of novel targets for therapeutic interventions. This kind of analysis represents a powerful and comprehensive diagnostic approach for studying the epigenetic dysregulation in MDS patients and compare it between baseline and post-HMA treatment, with the potential to advance our understanding on disease progression and improve management strategies. In particular, the identification of chromosomal bins with the highest significance between Rs and NRs encompassed several genes encoding for circular RNAs, miRNAs and lncRNAs ( table 2 ). These ncRNAs as part of the epigenetic regulatory compartment require further examination to establish their potential significance as biomarkers for response to HMA treatment. LC-MS/MS analysis on DNA methylation levels confirmed the corresponding results obtained from MeD-seq. NR patient group exhibited significantly reduced levels of 5mC, whereas Rs displayed insignificant differences post-HMA treatment ( Fig. 2B ). Both NRs and Rs when compared at baseline displayed lower levels of 5mC throughout their genome, although statistical significance was reached only for Rs compared to healthy controls ( Fig. 2A ). Moreover, among both Rs and NRs, the epigenetic signature of 5hmC was only rarely detected (in about 1/3 of the total MDS samples) in contrast to healthy controls, whose DNA comprised 5hmC mark universally ( Fig. 2C ) and with no exception. The discovery of 5hmC, which is a product of the 5mC oxidation by the α-ketoglutarate-dependent DNA dioxygenases (TET1-3), as an epigenetic unit disrupted the simplicity of the traditional epigenetic paradigm and led to a re-evaluation of the DNA methylation landscape. Distribution patterns of 5hmC in the genome, such as its high enrichment within promoters, enhancers and transcriptionally active genes indicates a distinct biological role from 5mC [38], which is considered as a transcriptionally repressing epigenetic mark. However, methods assessing the presence of DNA methylation sites globally, such as the NGS technology combined either with methylation-sensitive restriction enzymes or bisulfite treatment, are unable to discriminate between 5mC and 5hmC[39]. Our findings in HR MDS samples highlight the absence of 5hmC in about 30-40% of DNA samples tested, a characteristic sign also observed in other malignancies [31]. This result suggests an impairment of the active DNA demethylation pathway catalyzed by the TET family of enzymes. Interpretation of our data provide clear evidence for the qualitative and quantitative methylation alterations across genomic DNA in HR MDS, which can be summarized within the following observations: a) the already significant hypomethylated DNA status among HR-MDS patients at baseline (prior treatment) ( Fig. 2A ), b) the response of NRs to HMA-treatment by further lowering their DNA methylation values ( Fig. 2B, 4A, 4B ), which is a negative complication, associated with the HMA treatment and c) more than half of the HR MDS patients have a dysfunctional DNA demethylation pathway via oxidation reactions catalyzed by the TET enzymes ( Fig. 2C ), d) apart from DNA methylation status, epigenetic deregulation post-HMA treatment is further anticipated by the altered methylated levels of several sets of ncRNAs ( table 2 ) that may promote their aberrant expression. Expanding the possibilities of LC-MS/MS analysis we have assessed and compared the [A]/[T] deviation between the R and NR patient group of HR MDS DNA samples and the group of DNA samples from the healthy donors. Theoretical background refers to Chargaff's rule, who defined the base pair equality: A% = T% and G% = C% for the double-stranded DNA molecules[40]. Within this context, potential deviations observed underlies the spontaneous deamination of methylated cytosine (5mC) to thymidine (T), leading to increased rates of mutagenicity either at baseline or post-HMA treatment associated with HMA properties. Our results estimated a [dA]/[T] ratio of 1.02 for healthy DNA samples and a range of 0.727-0.633 among HR MDS. [dA]/[T] differences pre- and post-HMA treatment were insignificant ( Fig. 3 ). Conclusively, an extensively mutated genomic background was demonstrated by both Rs and NRs prior to HMA treatment, which is unrelated to HMA mechanism of action. To overcome the substantial epigenetic heterogeneity of MDS at clinical presentation, disease progression, and treatment response, high resolution methods for discriminating MDS patients eligible for HMA treatment option and response assessment are vital. LC-MS/MS and MeD-seq methylation analysis utilized in the present study allowed for the characterization of discrete epigenetic features within the MDS patient cohort, providing novel insights with diagnostic, prognostic, or predictive value for the HMA response assessment. The small HR MDS patient sample size is considered as a potential limitation of this study, as well as the overrepresentation of male samples, since the epigenetic landscape between the two genders is likely to differ. Additionally, some strengths and limitations arising from each methodology are also considered: a) MeD-seq provides higher resolution and genome-wide coverage compared to LC-MS/MS, which estimates global methylation levels, b) LC-MS/MS can distinguish between different epigenetic marks (5mC and 5hmC), while MeD-seq provides information for both marks as 5mC, c) MeD-seq generally has higher upfront costs due to NGS performance, but it offers higher throughput and greater information content per sample. MeD-seq also requires computing power and high expertise for bioinformatics analysis. The logistical and technological complexity involved in data processing and analysis of LC-MS/MS methodology, although is high, can overcome this limitation and be applied in the clinical setting as a valuable tool for quantifying global methylation levels to gain comprehensive insights into DNA methylation dynamics during HMA therapy. Conclusion Response to hypomethylating treatment in HR MDS patients is not associated with global DNA hypomethylation, rather than with significant methylation reduction across specific chromosomal regions, which mainly include genes encoding for various ncRNA molecules. This observation highlights new epigenetic features underlying response to HMA therapy in HR MDS and merits further investigation. Also, LC-MS/MS technology acquires all those advantages for first-line HR-MDS monitoring, to provide rapid, accurate and cost-effective results (compared to NGS) on the broad molecular background translatable into responsive phenotypes to HMA treatment, and consideration for inclusion in MRD concept. Declarations Acknowledgments N/A Ethics approval and consent to participate All patients and healthy participants provided their written informed consent according to the Declaration of Helsinki, being informed about both clinical and translational investigations and the study was approved by the University General Hospital of Patras Ethics Committee (approval number 33807/24.12.2020). Author’s contributions TN, ZJ, DI and BK designed, performed and evaluated LC-MS/MS results. CT and SymA recruited MDS patients and healthy participants. They also monitored HMA treatment and collected clinical data to discriminate Rs from NRs. CV and AK performed DNA sample preparation and writing of first draft. BE analyzed MeD-seq data and drafted the manuscript. PG, SgA and SymA conceived the study, raised funding and SgA wrote the manuscript. Consent for publication All authors contributed to the article and approved the submitted version. Funding This study was funded by the Special Account for Research Funds of Hellenic Open University, Greece, ELKE_HOU_2022-2024, Grant No 80250. Availability of data and material The raw MeD-seq data for 13 MDS samples pre- and post-HMA treatment are available at the SRA database under: PRJNA1075483 (https://www.ncbi.nlm.nih.gov/bioproject/1075483). LC-MS/MS raw data presented in this article will be made available by the authors upon request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Li H, Hu F, Gale RP, Sekeres MA, Liang Y (2022) Myelodysplastic syndromes. Nature reviews Disease primers 8: 74. DOI 10.1038/s41572-022-00402-5 Maggioni G, Della Porta MG (2023) Molecular landscape of myelodysplastic neoplasms in disease classification and prognostication. Current opinion in hematology 30: 30-37. 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Experientia 8: 143-145. DOI 10.1007/BF02170221 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4424582","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":307000312,"identity":"02097d14-583f-4c91-b765-86a115aa7c71","order_by":0,"name":"Theodoros Nikolopoulos","email":"","orcid":"","institution":"Hellenic Open University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Theodoros","middleName":"","lastName":"Nikolopoulos","suffix":""},{"id":307000313,"identity":"74233a8a-3430-4c58-9dc3-d628685f27cd","order_by":1,"name":"Eleftherios Bochalis","email":"","orcid":"","institution":"Hellenic Open 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20:13:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":873032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLC-MS/MS analysis (left) vs MeD-seq methodology (right).\u003c/strong\u003e In \u003cstrong\u003eLC-MS/MS \u003c/strong\u003eanalysis DNA is hydrolyzed into nucleosides, which are then separated by liquid chromatography and quantified by mass spectrometry. By this method different forms of deoxy cytosine modifications, such as 5mC and 5hmC can be detectable and distinguished in genomic DNA in contrast to conventional NGS analysis that prerequisites DNA digestion with methylation-sensitive enzymes or bisulfite conversion. LC-MS/MS is considered a method of high sensitivity and specificity, however provides information only about universal cytosine modifications rather than site-specific DNA methylation patterns. \u003cstrong\u003eMeD-seq \u003c/strong\u003emethod is based on next-generation sequencing (NGS) technology and provides genome-wide profiling of DNA methylation patterns. DNA is first treated with methylation-dependent restriction enzyme LpnPI and then is subjected to NGS. Computational analysis is finally performed to determine methylation status at specific genomic loci. By this method genome-wide, base-resolution DNA methylation profiling is provided. Limitations are the first-step enzyme digest which can introduce bias, especially in regions with high GC content and secondly the bioinformatics expertise requirements for data analysis. Another limitation are the indispensable higher costs compared to LC-MS/MS. 5mC: 5’ methyl cytosine, 5hmC: 5’ hydroxy-methyl cytosine.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4424582/v1/a6be400aa61c7df29b644764.jpg"},{"id":57449746,"identity":"196e47d7-6e0b-44cf-bea1-bab6a72d88a3","added_by":"auto","created_at":"2024-05-30 20:13:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eAbundance of 5mC among different HR MDS groups (R and NR) and healthy controls (C) pre-HMA treatment (** p ≤ 0.01). \u003cstrong\u003eB)\u003c/strong\u003e Mean 5mC levels pre- and post-HMA treatment within R and NR group of HR MDS (* p ≤ 0.05). Data are presented as boxplots including maximum and minimum values, the median value of each data set, outliers (°) and significant p-values. \u003cstrong\u003eC) \u003c/strong\u003eGlobal levels of 5hmC among all groups of HR MDS and healthy controls, pre-HMA treatment. 5hmC appeared consistently in all healthy samples, but only in 1/3 among HR MDS samples either pre- or post-HMA treatment. C, R and NR indicate healthy controls, responders and non-responders respectively. Error bars depict the standard error present in the calculation of the global 5hmC levels for each MDS category. HR MDS: High-risk MDS, 5mC: 5’ methyl cytosine, 5hmC: 5’ hydroxy-methyl cytosine.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4424582/v1/fa40daf77c9675dad0e7396f.jpeg"},{"id":57449747,"identity":"cb2acf9b-a249-427f-99cb-4b45c047cbc5","added_by":"auto","created_at":"2024-05-30 20:13:18","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":385827,"visible":true,"origin":"","legend":"\u003cp\u003e[dA]/[T] ratio among Rs and NRs at baseline and post-HMA treatment, compared to healthy controls. Rs and NRs deviated from [dA]/[T]=1, in contrast to healthy controls. Values at baseline and post-HMA treatment ranged between 0.727-0.633 with insignificant differences between R and NR subgroups of HR MDS, indicating a high mutational rate at baseline, not associated with HMA therapy. Error bars depict the standard error present in the calculation of the mean [dA]/[T] ratio for each category. R: responders, NR: non-responders, HR MDS: High-risk MDS.\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4424582/v1/917e14aeafefb4eb51116569.jpeg"},{"id":57449750,"identity":"e3a87ae1-5bf8-4c36-9aa9-1d68d6678bb1","added_by":"auto","created_at":"2024-05-30 20:13:19","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":540193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eΑ) \u003c/strong\u003eViolin plots depict chromosomal methylation profiles by response category among HR MDS samples. Dotted lines inside the violin plots present the first quartile, median and third quartile values.\u003cstrong\u003e \u003c/strong\u003eDifferentially methylated regions (DMRs) identified within each subgroup (R and NR) of HR MDS patients were catalogued by chromosome, and their methylation profiles were visualized using violin plots, which depict the distribution of methylation levels post-log10 transformation, with chromosomal location specified on the x-axis and methylation intensity on the y-axis. The shape of the plots reflects the density of methylation at various levels, revealing prevalent methylation states within R or NR subgroup. \u003cstrong\u003eB) \u003c/strong\u003eBar plots showcasing the weighted average of methylation for each chromosome across subgrouped (R and NR) HR MDS patients according to HMA-response. Error bars depict the weighted standard error present in the calculation of the weighted average of methylation for each chromosome. Rs exhibit positive methylation values post-HMA treatment, in contrast to NRs who show mainly hypomethylation profiles in all autosomal chromosomes apart from chromosome 21 and both chromosomes X and Y. \u0026nbsp;R: responders, NR: non-responders, HR MDS: High-risk MDS.\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4424582/v1/81dfd6e5a7baf2df6dd78b62.jpeg"},{"id":57449749,"identity":"21aafbfe-aea0-4563-97ee-af30397a15af","added_by":"auto","created_at":"2024-05-30 20:13:19","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":355629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox Plots of Methylation Ratios within Genomic Bins. \u003c/strong\u003eBox plots depict log10-transformed methylation ratios across selected chromosomal bins with the highest statistical significance. Rs and NRs to HMA-treatment can be discriminated via their reversed methylation profiles within common chromosomal bins. Bins are indicated on the x-axis, and methylation ratios on the y-axis. Statistical significance was determined by the Mann-Whitney U test, which is indicated by asterisks above each bin (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001). R: responders, NR: non-responders, HR MDS: High-risk MDS.\u003c/p\u003e","description":"","filename":"Figure5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4424582/v1/675da61cf3692082fb6d53ea.jpeg"},{"id":60966763,"identity":"445a54af-96fb-4ac7-b78a-40712888a5e6","added_by":"auto","created_at":"2024-07-24 06:11:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3258447,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4424582/v1/41df76ed-84d9-467e-a4aa-9fb8bf4fc8ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eImplementation of advanced analytical methods MeD-seq and LC-MS/MS for assessing the epigenetic profile of high-risk myelodysplastic syndrome\u003c/p\u003e","fulltext":[{"header":"Key messages","content":"\u003cul\u003e\n \u003cli\u003eGenomic DNA of high-risk MDS exhibit lower methylation levels prior to HMA-treatment, than healthy controls\u003c/li\u003e\n \u003cli\u003eGlobal and targeted differences in DNA methylation signatures pre-and post-HMA treatment discriminate responders from non-responders MDS patients\u003c/li\u003e\n \u003cli\u003eSpontaneous deamination of 5\u0026rsquo; methyl-cytosine to thymidine is increased in MDS patients versus healthy controls\u003c/li\u003e\n \u003cli\u003e5\u0026rsquo; hydroxy-methyl cytosine is rarely detected in MDS compared to healthy controls indicating impairment in DNA demethylation pathway\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eMyelodysplastic syndromes (MDS) are clonal hematopoietic stem cell disorders, mainly characterized by abnormal development and maturation of hematopoietic progenitor cells in the bone marrow, resulting in peripheral blood cytopenias and by an increased tendency of disease progression towards acute myelogenous leukemia (AML). Morphological examination of the bone marrow typically reveals dysplastic changes in the hematopoietic cell precursors indicating abnormal cell proliferation and maturation, leading to an heterogeneous clonal development [1]. At the molecular level MDS patients exhibit not only DNA-based genetic abnormalities in their bone marrow hematopoietic progenitors, but also aberrations related to their immunophenotype, gene expression, epigenetic profile and bone marrow microenvironment [2-4]. Several sensitive laboratory tests are implemented to guide treatment decisions and evaluate effective treatment responses. Currently, with the increasing application of interventional treatment approaches, assessment of minimal residual disease (MRD) is the indicative method for response assessment in AML patients and emerges also for the HR MDS monitoring. However, the precise evaluation of the MRD requires the application of better analytical methods with higher sensitivity than the conventional ones [5]. \u003c/p\u003e\n\u003cp\u003eThe study of MDS epigenetics has revealed the significant role of DNA methylation in the pathophysiology of this disease. Mutations affecting genes responsible for epigenetic modifications, such as \u003cem\u003eDNMT3A, TET2\u003c/em\u003e, and \u003cem\u003eASXL1 \u003c/em\u003eare prevalent among the elderly population and are considered as founding and driver mutations in MDS[6, 7]. These mutations at medium to high variant allele frequency (VAF) reflect an evolutionary status of clonal hematopoiesis of indeterminate potential (CHIP) towards MDS. When present at low VAF, these mutations may escape detection systems, and consequently it is uncertain if they evolve and directly contribute to MDS pathobiology[8]. Co-existence of mutations in \u003cem\u003eDNMT3A, TET2\u003c/em\u003e, and \u003cem\u003eASXL1 \u003c/em\u003eaffecting epigenome with mutations in \u003cem\u003eRUNX1\u003c/em\u003e, \u003cem\u003eEZH2, NRAS \u003c/em\u003eand/or\u003cem\u003e TP53\u003c/em\u003e genetic loci, is highly predictive for already established hematopoietic stem cell neoplasms[9] and accounts for decreased overall survival (OS) even post-allogeneic hematopoietic stem cell transplantation (alloHSCT)[10]. Apart from mutation involvement in bone marrow clonogenicity, aberrant DNA methylation patterns are also very common in MDS however, their contribution to disease development and progression has not yet been elucidated. The widespread genome hypomethylation is associated with genomic instability and activation of transposable elements and simultaneously results in increased oncogene expression, both of which can contribute to MDS development and progression and to hematopoietic clonal expansion. Additionally, MDS is characterized by gene-specific hypermethylation changes. Certain genes, involved in hematopoietic differentiation, apoptosis, and cell cycle regulation become hypermethylated, leading to their silencing and consequently resulting in undisturbed transition towards the S phase, therefore establishing a leukemic cell phenotype. For example, hypermethylation of tumor suppressor genes such as Cyclin Dependent Kinase Inhibitor 2B (\u003cem\u003eCDKN2B\u003c/em\u003e or \u003cem\u003eP15\u003c/em\u003e)[11, 12], adenosine 5\u0026apos;-Monophosphoramidase (\u003cem\u003eHINT1\u003c/em\u003e)[13] and a set of 10 hypermethylated genes including \u003cem\u003eP15\u003c/em\u003e\u003cem\u003e[\u003c/em\u003e\u003cem\u003e14\u003c/em\u003e\u003cem\u003e]\u003c/em\u003e are associated with adverse clinical outcomes in MDS patients.\u003c/p\u003e\n\u003cp\u003eThe integration of epigenetic information into existing prognostic models may improve risk stratification and guide treatment decisions for MDS patients, since certain methylation signatures have been associated with disease progression, transformation to AML and decreased OS[15, 16]. \u003c/p\u003e\n\u003cp\u003eMDS are classified in distinct subtypes (various editions of the WHO classification) and prognostic groups (IPSS, IPSS-R, WPSS, IPSS-M etc classification systems) and among them, the higher-risk (HR) MDS group requires prompt treatment initiation, considering interventional therapies, such as hypomethylating agents (HMAs) or alloHSCT. HMAs and particularly the cytosine analogs azacytidine (5-azacytidine) and decitabine (5-aza-2\u0026prime;-deoxycytidine) are common treatment options for HR MDS patients, aiming to slow down abnormal clonal growth in the bone marrow and to improve peripheral blood cell counts. HMA treatment is continued for as long as a favorable outcome is maintained and HMA remains well-tolerated by each individual patient, since relapse with or without disease progression is unfortunately anticipated. However, only a minority of MDS patients experience prolonged (\u0026gt;24 months) responses to HMA treatment, depending on their overall performance, and on cytogenetic and genetic/epigenetic disease background[17]. The high diversity in response to HMA treatment has been extensively reported, but the specific factors contributing to the induction of a favorable response still remain to a substantial degree, undetermined. Nevertheless, without the administration of HMA treatment HR MDS quickly progresses, and AML develops faster in the majority of cases.\u003c/p\u003e\n\u003cp\u003eFollowing treatment with HMAs, patients are typically assessed for response by internationally accepted and well-established criteria, such as hematologic improvement, transfusion independence, cytogenetic response, or complete remission. Close monitoring of MDS patients is essential to evaluate treatment response, manage side effects, and estimate disease progression. The recently (2022) introduced Molecular International Prognostic Scoring System (IPSS-M) provides additional prognostic value for the outcome, by merging results from clinical variables, karyotype, and myeloid detected mutations[18]. Cytogenetic evaluation including chromosome analysis, fluorescence in situ hybridization (FISH), chromosomal microarray as well as the more advanced high sensitivity multiparameter flow cytometry and mutational testing by Next Generation Sequencing (NGS) platforms, provide a comprehensive stepwise approach in clinical-morphologic correlation of MDS, selection of the appropriate therapeutic strategies, and in predicting the risk for progression to AML. However, criteria for MDS response have not yet been standardized and thus, they are not formally included in the MRD latest modification of the International Working Group (IWG) response criteria, as has been established for AML[19].\u003c/p\u003e\n\u003cp\u003eWe have conducted a comprehensive evaluation of genomic DNA methylation status in twenty-one HR MDS patients prior and post-HMA-treatment and seven healthy controls. Global 5\u0026rsquo; methyl-cytosine (5mC) and 5\u0026rsquo; hydroxy-methyl-cytosine (5hmC) as well as targeted methylation signatures (DMRs) were assessed to enable discrimination between responders from non-responders to HMA therapy. Further to methylation analysis we have compared the randomness of spontaneous deamination of 5mC to thymidine in HR MDS patients versus controls to estimate potential increase in the mutagenesis rate attributed to HMA-treatment. Two complementary highly sensitive approaches, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and methylation DNA sequencing (MeD-seq) (\u003cstrong\u003eFig. 1\u003c/strong\u003e), have been implemented to address the above queries, taking into consideration their relevant advantages and limitations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eMDS patient and healthy cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total 21 patients with HR MDS, exerting ≤20% bone marrow blasts, received azacytidine (AZA) or decitabine (DAC) by standard scheduling (75mg/m\u003csup\u003e2\u003c/sup\u003e x 7 days, 28-day cycle for AZA and 20mg/m\u003csup\u003e2\u003c/sup\u003e x 5 days, 28-day cycle, for DAC) at the University Hospital of Patras. Treatment responders (R) versus non-responders (NR) were classified according to International Working Group (IWG) response criteria for HR MDS released in 2006[20]. Bone marrow aspirate samples were collected from the whole patient’s cohort before treatment initiation as well as at the evaluation of response (after 5-7 treatment cycles). Peripheral blood from 7 elderly donors with matching age and gender was collected and represented the control samples for the LC-MS/MS analytical method. Peripheral blood samples provide an adequate picture of the bone marrow environment since, high concordance of variant detection and gene expression profile between peripheral blood and bone marrow has been repeatedly reported [21-23]. Genomic DNA was extracted using phenol: chloroform: isoamyl alcohol in 25:24:1 ratio (Sigma-Aldrich Pty Ltd, Merck KGaA, Darmstadt, Germany) and subjected to downstream analysis. Characteristics of the patient and healthy cohorts participating in this study are summarized in \u003cstrong\u003etable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e List of MDS and healthy patient samples. MDS samples were collected pre- and post-HMA treatment.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA/A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy control (C)/ Responders (R)/Non Responders (NR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (M)/Female (F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAZA/DAC treated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTissue BM/PB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n 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width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eR9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eDAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR3\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eDAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eDAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR10\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.31205673758865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.75886524822695%\" valign=\"top\"\u003e\n \u003cp\u003eNR12\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.879432624113475%\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.71631205673759%\" valign=\"top\"\u003e\n \u003cp\u003eAZA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.907801418439718%\" valign=\"top\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll patients provided their written informed consent according to the Declaration of Helsinki, being informed about both clinical and translational investigations and the study was approved by the University General Hospital of Patras Ethics \u0026amp; Scientific Committee (approval number 33807/24.12.2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS/MS analysis of genomic DNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiquid chromatography-mass spectrometry technique combines Liquid Chromatography (LC) with a triple quadrupole (QqQ) mass spectrometer (LCMS-8050 system, Shimadzu, Japan). DNA samples from HR MDS were analyzed pre- and post- HMA treatment and compared with control samples for baseline methylation pre-treatment. Prior to LC-MS/MS analysis 400 ng of genomic DNA was digested and dephosphorylated by a Nucleoside Digestion Mix (NEB#M0649) to generate single nucleosides for further quantitative analysis, following manufacturer’s instructions. Reactions were purified with a special centrifugal filter 3kDa MWCO Amicon\u003csup\u003e®\u003c/sup\u003e Ultra (Merck Millipore).\u003c/p\u003e\n\u003cp\u003eLC-MS/MS system features a heated Electrospray Ionization (ESI) system and Multiple Reaction Monitoring (MRM) capabilities with high sensitivity and high speed. The column oven was set at 35 °C. A Shim-pack Scepter C18-120 column (4.6 mm x 100 mm, 5 µm, Shimadzu) with a 4.6 mm pre-column was used for the separation of nucleosides. The mobile phase was passed through the column by gradient elution with acidified H\u003csub\u003e2\u003c/sub\u003eO (1% CH\u003csub\u003e3\u003c/sub\u003eCOOH) (solvent A) and acetonitrile (ACN) (solvent B). The flow rate was set at 0.2 ml/min with the solvent ratio starting at 95% A / 5% B and reaching 35% A / 65% B at 15 min. The total analysis time was 20 min and the injection volume was 10 µL. Mass spectrometry detection was performed under positive electrospray ionization (ESI) mode. Nucleosides and derivatives were monitored by multiple reaction monitoring (MRM) modes using the mass transitions (precursor ions → product ions) of dG (deoxy guanosine) (268.4 → 152.4), 5-mC (242.1 → 126.1), 5-hmC (258.1 → 142.1), T (thymidine) (243.3 → 127.2) and dA (deoxyadenosine) (252.4 → 136.2)[24]. Calibration curves of 5-mC and 5-hmC were constructed by plotting the peak area ratios of 5-mC/dG and 5-hmC/dG versus the molar ratios of 5-mC/10\u003csup\u003e2\u003c/sup\u003edG and 5-hmC/10\u003csup\u003e5\u003c/sup\u003edG, respectively, based on data obtained from LC-ESI-MS/MS analysis. Calibration curves were also constructed for T and dA.[25]. Linearity was within the concentration range 0.4–6 for 5-mC/10\u003csup\u003e2\u003c/sup\u003edG and 60–500 for 5-hmC/10\u003csup\u003e5\u003c/sup\u003edG, with a coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) greater than 0.99. Results for 5mC, 5hmC, T and dA are given as mean values derived from four independent runs.\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS software version 20. To assess statistical differences of 5mC levels among control and patients’ groups before HMA treatment, a one-way ANOVA test with Bonferroni multiple comparison post hoc test was used. Paired t-test was performed to identify 5mC differences before and after treatment between each MDS patient group. Normal distribution of data was assessed by the Shapiro-Wilk test. P-values less than 0.05 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeD-seq data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequencing files for 13 MDS samples pre- and post-HMA treatment (26 samples that belong to our HR MDS cohort) were retrieved from the SRA database under: PRJNA1075483 (https://www.ncbi.nlm.nih.gov/bioproject/1075483). Data processing was carried out as outlined in[26] with modifications to accommodate our study design.\u003c/p\u003e\n\u003cp\u003eSpecifically created Python scripts were used to trim the Universal Illumina adaptor and filter reads based on the presence of the LpnPI restriction site. Filtered reads were aligned to the hg38 human genome and were annotated for Genes (Transcription Start Sites, Gene bodies, and Transcription End Sites) and CpG islands using annotations from ENSEMBL. Differentially Methylated Region (DMR) detection was performed between two data sets (pre- and post-HMA treatment) for each MDS sample containing the regions of interest. Required filtering criteria for DMRs were the following: q-value less than 0.05, fold change greater than 2, and inclusion of at least 20 LpnPI recognition sites to ensure selection of genomic areas enriched in CGs. DMRs present in alternative and random genomic contigs were excluded from this analysis. DMRs were retrieved by using the χ\u003csup\u003e2\u003c/sup\u003e test on read counts. Significance was called by either Bonferroni or FDR using the Benjamini-Hochberg procedure. Using this pipeline a methylation ratio is obtained for each DMR indicating an increase or decrease in methylation post-HMA patient’s treatment. To reduce the right skewness of the data and mitigate the presence of outliers, log10 -transformation was performed on the methylation ratios for each region, since they possess only positive values that span a wide range. Post log10-transformations, positive and negative methylation values indicated increase and decrease in methylation status within the studied area, respectively.\u003c/p\u003e\n\u003cp\u003eTo synthesize a comprehensive methylation profile for each chromosome, DMRs were split into two groups: those with increased methylation (upwards) and those with decreased methylation (downwards). A weighted mean methylation ratio was computed separately for each response category using log10-transformed methylation ratios, thus accommodating the bimodal distribution characteristic of the methylation data.\u003c/p\u003e\n\u003cp\u003eThe composite weighted mean for each chromosome (WM\u003csub\u003echr\u003c/sub\u003e) was derived using the equation:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWM\u003csub\u003echr\u003c/sub\u003e\u003c/em\u003e=(\u003cem\u003em\u003csub\u003eu\u003c/sub\u003e\u003c/em\u003e×\u003cem\u003ew\u003csub\u003eu\u003c/sub\u003e\u003c/em\u003e)+(\u003cem\u003em\u003csub\u003ed\u003c/sub\u003e\u003c/em\u003e×\u003cem\u003ew\u003csub\u003ed\u003c/sub\u003e\u003c/em\u003e) [1]\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eWM\u003csub\u003echr\u003c/sub\u003e\u003c/em\u003e represents the total weighted mean methylation for a given chromosome, \u003cem\u003em\u003csub\u003eu\u003c/sub\u003e\u003c/em\u003e is the mean methylation level of the upwards methylated regions, \u003cem\u003ew\u003csub\u003eu\u003c/sub\u003e\u003c/em\u003e is the proportion of upwards methylated DMRs to the total number of DMRs within the chromosome, \u003cem\u003em\u003csub\u003ed\u003c/sub\u003e\u003c/em\u003e is the mean methylation level of the downwards methylated regions, and \u003cem\u003ew\u003csub\u003ed\u003c/sub\u003e\u003c/em\u003e is the proportion of downwards methylated DMRs relative to the chromosome's total DMR count. This calculation provided a weighted average methylation value that reflects the overall methylation status while accounting for the distribution and abundance of methylated regions across the chromosome. Also, for each category a weighted standard error was calculated for each chromosome (WSE\u003csub\u003echr\u003c/sub\u003e) using the following equation:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTo perform a more targeted and in-depth analysis, the hg38 human genome was segmented into bins of 100,000 base pairs, with each DMR being assigned to a bin based on coverage. The Mann-Whitney U test, a non-parametric method suitable for the bimodal distribution of our methylation data, was utilized to detect statistically significant differences in methylation levels across genomic bins between R and NR groups of HR MDS patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDetection of diverse epigenetic marks (5mC/5hmC) by LC-MS/MS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLC-MS/MS is a powerful analytical method which combines accuracy, high sensitivity and reproducibility although has not yet been fully established for nucleic acid analysis. However, the challenge of quantifying global levels of DNA methylation derivatives can be gauged by the low abundance of these epigenetic marks. In humans, about 1% of the total DNA bases consists of 5mC[27] whereas, 5hmC abundance is\u0026thinsp;about\u0026thinsp;10 to 100-fold lower than that of 5mC[28, 29]. 5hmC has been reported as a stable epigenetic mark highly enriched within gene bodies of transcriptionally active genes, promoters and enhancers[30]. Moreover, global 5hmC content is dramatically reduced in multiple human cancers[31, 32], a sign which can potentially be associated with tumorigenesis. Chromatography-based techniques such as LC-MS/MS dominate in similar bioanalyses and is considered the gold standard method for the global analysis of DNA methylation derivatives in human cancers since enzymatic digests as well as bisulfite treatment of DNA prior to NGS reactions fail to discriminate between 5mC and 5hmC, both of which are detected as 5mC[33].\u003c/p\u003e\n\u003cp\u003eIn the present study, levels of 5\u0026rsquo; methyl-cytosine (5mC) and 5\u0026rsquo; hydroxy-methyl cytosine (5hmC) residues, prior to and post-HMA treatment, were estimated by LC/MS-MS and further compared between MDS patients and the independent group of healthy controls (\u003cstrong\u003eFig. 2\u003c/strong\u003e). Total 5mC across genomic DNA was calculated by the equation: [5mC]/10\u003csup\u003e2\u003c/sup\u003e[dG] (further details in materials and methods section). Concentration of deoxy guanosine [dG] was selected as internal standard against [dC], based on the assumption that [dG] = {[dC] + [5mC] + [5hmC] + [other C modifications]} in genomic DNA. Therefore, [dG] is considered a unique and more accurate value rather than measurement of the independent cytosine modified nucleosides as a sum: {[dC] + [5mC] + [5hmC] + [other C modifications]}, potentially leading to experimental errors. Many of these cytosine derivatives are below detection limits of the method and additionally, guanosine modifications are much less prevalent in genomic DNA compared to methylated cytosines and its derivatives[24].\u003c/p\u003e\n\u003cp\u003eTo correlate global DNA methylation profiles (5mC levels) among the different samples and in relation to HMA treatment response, MDS patients were categorized after clinical monitoring as responders (R) and non-responders (NR). Although a trend for global hypomethylated status was documented for both R and NR groups compared to healthy controls, statistical significance was demonstrated only between controls and Rs (p=0.014) (\u003cstrong\u003eFig. 2A\u003c/strong\u003e). Comparison between the NR and R groups of HR MDS patients post-HMA treatment (\u003cstrong\u003eFig. 2B\u003c/strong\u003e) revealed an actual significance for 5mC lower values only within the NR group (p=0.029). The R group yielded comparable 5mC values pre- and post-HMA treatment. Data are represented as boxplots with jittered points. The range between 25% and 75% of the values are within boxes, whereas median values of each dataset are represented as lines inside the boxes and the individual points outside the boxes indicate each value considered (including outliers) for the boxplot construction.\u003c/p\u003e\n\u003cp\u003eA similar calibration curve, as previously described, was applied for the 5hmC calculation, but with a 10\u003csup\u003e5\u003c/sup\u003e as a divisor ([5hmC]/10\u003csup\u003e5\u003c/sup\u003e[dG]), since 5hmC is represented at a frequency approximately ~10 - 100-fold lower than 5mC. Among MDS patients tested, only 2 Rs and 3 NRs displayed detectable 5hmC pre- and post-HMA treatment (almost 1/3 of the total MDS patients), even when analyzed in the high concentration mode. On the contrary, 5hmC was consistently documented in all healthy control samples (\u003cstrong\u003eFig. 2C\u003c/strong\u003e). This observation implies a potential dysregulation of \u0026alpha;-ketoglutarate-dependent DNA dioxygenases (TET1-3 enzymes), implicated in the natural biochemical DNA demethylation pathway by which 5mC finally reverses to C, with 5hmC representing the first product along the oxidative reaction pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeviation of adenosine: thymidine ratio (\u0026le; 1) highlights the frequent spontaneous deamination of 5mC to thymidine\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ratio of adenosine (A) to thymidine (T) in double stranded DNA is expected to be approximately 1:1 due to the complementary base pairing in double helix DNA structure. Deviations in the human genome from 1:1 [dA]/[T] ratio often result from the spontaneous deamination reaction of the modified cytosine 5mC that produces thymidine, which is unrecognizable and unable to be corrected by the repairing enzymatic complexes that monitor the human genome for non-complementary bases. This reaction, if not corrected, converts a C-G base pair to a T-A during DNA replication. The deamination of 5mC to thymidine is a significant source of mutations in DNA and leads to a deviating [dA]/[T] ratio. Certain repetitive sequences, regions with high mutation rates or highly methylated CpG islands may exhibit deviations from the expected 1:1 [dA]/[T] ratio. This is particularly relevant in epigenetics, since methylation of cytosine at CpG dinucleotides is an important epigenetic mark involved in gene regulation (mainly repression). Deamination of 5mC can lead to changes in DNA methylation patterns and gene expression regulation simultaneously with the appearance of mutated sequence. Beyond this spontaneous process, the frequency of such mutational patterns produced in the genome under the frame of specific disorders is also important, as it contributes to genetic variation and can have implications in tissue homeostasis, including development or disease progress.\u003c/p\u003e\n\u003cp\u003eTo this end we have comprehensively estimated the global [dA]/[T] ratio across HR MDS and healthy samples(\u003cstrong\u003eFig. 3\u003c/strong\u003e). Calibration curves were plotted independently for A and T with values retrieved from LC-MS/MS analysis from escalating concentrations of Adenosine and Thymidine standards, respectively. [dA] and [T] concentrations were calculated separately, and then [dA]/[T] ratio was calculated for each human sample under investigation. Mean value of healthy samples was 1.02, which is considered an expectable value. Rs and NRs pre- and post-HMA treatment display [dA]/[T] ratios \u0026lt; 1 (0.727-0.633).\u003c/p\u003e\n\u003cp\u003eThese results highlight the pre-existing deviation from normal values of thymine [T] concentration in HR MDS patients, already at baseline and before starting HMA treatment. The observed [dA]/[T] ratios \u0026lt;1 in MDS implies increased levels of [T], attributed to deamination of 5mC to thymine, leading to the reduction of [dA]/[T] ratio. The potential of increased deamination rates of 5mC to thymidine was clearly demonstrated by LC-MS/MS analysis and may account for the generation of mutations within CpG islands, flanking genetic loci and exerting transcriptional regulatory properties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChromosome-wide mapping of DNA methylation patterns derived from MeD-seq analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethylated DNA sequencing (MeD-seq) is a high-throughput methodology that constitutes of targeted capture of differentially methylated genomic regions (DMRs) by restriction enzyme digests into recognition sites of methylated over unmethylated cytosines, followed by NGS. MeD-seq facilitates the comprehensive analysis of genome-wide CpG methylation patterns. The methylation-sensitive restriction enzyme LpnPI, targets tetranucleotides containing methylated or hydroxymethylated CG dinucleotides, cleaving the DNA at 16 nucleotides downstream of the enzyme recognition site. In the present study MeD-seq data from 13 HR MDS patients (constituting a representative part of our MDS cohort) pre- and post-treatment with HMAs, were retrieved and further interpreted (details in materials and methods section). Patients were sub-categorized into two independent groups: 6 responders (Rs) and 7 non-responders (NRs) to HMA therapy.\u003c/p\u003e\n\u003cp\u003eFor each human chromosome a weighted mean of methylation was calculated using the equation [1] from materials and methods section. The weighted mean provides a robust way for calculating total chromosomal methylation by utilizing the log10-transformed methylation ratios as well as by taking into account their respective weights. By assigning weights to each data point based on their significance or relevance, the weighted mean ensures that these differences are appropriately considered in the calculation. The equation [1] was applied to both R and NR MDS patients, who exhibited bimodal methylation distributions on each chromosome, documenting the presence of DMRs with both increased and decreased methylation associated with HMA treatment. Results are represented as violin plots displayed for each chromosome separately (\u003cstrong\u003eFig. 4A\u003c/strong\u003e) to effectively summarize and visualize the distribution, central tendency, and spread of hypo- and hypermethylation. Typically, NRs showed an asymmetric pattern of chromosomal methylation distribution, where the majority followed a modest reduction in methylation, as evidenced by the red density peak between 0 and -1. Specifically, chromosomes 7, 9, 11, 12, 16, 18, and 22 demonstrated a skewed distribution towards regions of reduced methylation, alongside a sparse presence of regions undergoing methylation increase. In contrast, Rs displayed a more balanced bimodal distribution of methylation changes, with a slight preference for areas with increased methylation (as shown by a green peak from 0 to +1).\u003c/p\u003e\n\u003cp\u003eChromosomes 21, X, and Y warrant special attention due to their distinctive methylation patterns in the NR subgroup, in which the majority of chromosomal regions were either non-differentially methylated or predominantly hypomethylated. Moreover, the NR subgroup exhibited a substantial proportion of regions undergoing extreme hypermethylation, with methylation elevation ranging approximately from 100 to 1000-fold (corresponding to log10 values of 2 to 3), particularly noticeable within the +1 to +3 range. The significant methylation increase of these specific regions is also highlighted in \u003cstrong\u003eFig. 4B\u003c/strong\u003e, underscoring a marked rise in methylation levels that surpass the distribution of hypomethylated regions on chromosomes 21, X, and Y. In contrast, the R subgroup maintained the bimodal distribution characteristic, with a subtle inclination towards hypermethylated and not differentially methylated regions.\u003c/p\u003e\n\u003cp\u003eIn the bar chart provided (\u003cstrong\u003eFig. 4B\u003c/strong\u003e), the distribution of weighted mean methylation values further corroborates these findings. The chart displays a notable differential methylation pattern between Rs and NRs, indicating potential epigenetic distinctions correlating with the HMA response categories. For the majority of chromosomes, Rs exhibited positive mean methylation values, indicating an increase in methylation post-HMA treatment, contrasting with the negative values observed in NRs, which are in line with results obtained from respective LC-MS/MS analysis, exerting a globally reduced methylation profile. This consistent inverse relationship across the chromosomal spectrum suggests that methylation status may be a significant factor for the differential response observed following HMA treatment. Moreover, the variability in methylation patterns is not uniform across all chromosomes, underscoring the intricate nature of epigenetic regulation in relation to phenotypic outcomes. The biological significance of the observed epigenetic disparities must be evaluated among a large cohort of HR MDS patients to corroborate the established hypomethylated status pre-HMA treatment and the modest hypomethylation effect post-HMA treatment in Rs group, which is in discordance with their improved clinical phenotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeted methylation analysis by MeD-seq reveals significant chromosomal regions discriminating responders from non-responders to HMA-therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther analysis of MeD-seq data was performed to decode methylation profiles of targeted chromosomal regions. Bioinformatics analysis finalized and divided genomic MeD-seq methylation data into discrete segments or genomic regions of equal size (of max 100 kb), defined as chromosomal bins. Chromosomal bins facilitate the analysis and interpretation of genomic data by providing a systematic framework for organizing and comparing genomic features, such as methylation profiles, across different regions of the genome. The sequencing reads obtained from MeD-seq experiment were assigned to the appropriate bins based on their genomic coordinates. The assignment to chromosomal bins provided a universal way to compare both within and across patient samples.\u003c/p\u003e\n\u003cp\u003eThe obtained patterns reveal that HMA treatment response among HR MDS patients is associated with the methylation status of certain genomic regions rather than with widespread genomic methylation changes. In the most statistically significant bins, Rs show slight variations in methylation levels, whereas NRs exhibit substantial increases, up to 100-fold. To further deepen our analysis, chromosomal bins were searched for sequences representing genes, either protein coding or non-coding RNA species. Results are summarized in \u003cstrong\u003etable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Genes and Non-Coding RNAs identified within statistically significant genomic bins\u003c/strong\u003e. Predicted and alternative gene transcripts and hairpin miRNAs are not presented in this table. Also, different isoforms of the same lncRNA are aggregated under a single representation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenomic Bin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein coding Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-coding RNAs-circRNAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-coding RNAs-miRNAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-coding RNAs-lncRNAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echr1_bin24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eSKI, MORN1, RER1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003ehsa_circ_0007120, hsa_circ_0009371, hsa_circ_0009373, hsa_circ_0009376, hsa_circ_0009377, hsa_circ_0009378, hsa_circ_0009379, hsa_circ_0009372, hsa_circ_0009374, hsa_circ_0009375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003elnc-SKI, lnc-PEX10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echr4_bin492\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003elnc-CWH43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echr8_bin858\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eREXO1L2P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003elnc-ATP6V0D2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echr19_bin363\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eZNF565, ZNF146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003elnc-CAPNS1, lnc-ZNF146, lnc-COX7A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echr19_bin364\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eZFP14, ZFP82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003ehsa_circ_0050766, hsa_circ_0050767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003elnc-ZNF146,\u0026nbsp;LINC00665, lnc-ZFP14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echr21_bin83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eCDC27P9, RNA28SN2,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRNA18SN2, RNA5-8SN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003ehsa-miR-6724-1-5p,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehsa-miR-6724-2-5p, hsa-miR-10401-5p,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehsa-miR-10401-3p,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehsa-miR-3648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003elnc-KCNE1B, lnc-SMIM11B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echr21_bin85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eRNA45SN3, CDC27P10, RNA28SN1, RNA45SN1, RNA18SN1, RNA5-8SN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003ehsa-miR-6724-5p,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehsa-miR-10401-5p,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehsa-miR-10401-3p,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehsa-miR-10396b-5p,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehsa-miR-10396b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003elnc-KCNE1B, lnc-SMIM11B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.853932584269664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echrX_bin1159\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.669341894060995%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.879614767255216%\" valign=\"top\"\u003e\n \u003cp\u003eNot found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003eDANT1, lnc-PLS3, DANT2, lnc-LRCH2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMost genes identified encode for ncRNA species, including circular, micro-RNAs (miRNAs) and long non-coding RNAs (lncRNAs). Among the protein-coding genes presented within the study, the Sloan-Kettering Institute proto-oncogene (\u003cem\u003eSKI\u003c/em\u003e) gene located in chr1_bin24, holds a pivotal role in moderating Transforming Growth Factor-beta (TGF-\u0026beta;) signaling pathway. Previous studies have highlighted the important role of \u003cem\u003eSKI\u003c/em\u003e in managing chronic TGF-\u0026beta; signaling, further affecting stem cell fitness by influencing aberrant splicing. Dysregulation of the \u003cem\u003eSKI\u003c/em\u003e-TGF-\u0026beta; signaling axis may influence the spliceosome function and alternative splicing events, and thus providing a link to underpinning aberrant splicing patterns observed in MDS[34]. These findings reinforce the hypothesis that specific genomic bins may act as potential biomarkers for predicting treatment efficacy and merit further investigation to expand our knowledge on epigenetic regulation events guided by either protein coding genes or aberrant expression of non-coding RNAs.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDNA methylation abnormalities play a crucial role in the pathophysiology of MDS and have clear implications in diagnosis, prognosis, and treatment of the disease. The accumulation of various somatic mutations in MDS by granting a proliferation advantage to hematopoietic progenitor cells, promote their expansion over time[6, 7]. In addition, the perturbed epigenetic landscape is complemented by the underlying mutational background, which either pre-exists or is induced as the neoplastic hematopoietic clones continue to expand[35]. However, in contrast to genetic mutations, epigenetic alterations are potentially reversable therefore, decoding epigenetic abnormalities spread across genome, may prove to be essential for developing targeted therapeutic strategies for MDS and improving patient outcomes by integrating accessible and accurate methodologies. So far, the dysregulated DNA methylation patterns in MDS have prompted interest in epigenetic therapies aimed to reverse aberrant DNA methylation, utilizing HMAs to restore normal DNA methylation patterns and malignant cells clearance. However, the overall response rates are ranging between 40-60% and may vary among the different MDS subtypes [17]. Response to HMA treatment may include hematological improvement, transfusion independence, depth of remission, or disease stabilization with varying duration of response, depending on specific cytogenetic abnormalities (e.g., deletion of chromosome 5q, monosomy 7 etc)[36] or with other, as yet uncertain prognostic factors influencing the probability and duration of response to HMAs.\u003c/p\u003e\n\u003cp\u003eResponse assessment is typically performed by standardized criteria, considering various parameters such as blood counts, bone marrow blast cell percentage, and transfusion requirements. Current molecular-based approaches that utilize NGS platforms for whole genome or partial mutational analysis can detect and monitor aberrations in multiple genetic loci however, they are unable to detect large structural abnormalities and copy number variants as well as fusion genes, which are common in MDS. Furthermore, the clinical significance for specific sets of mutations has not yet been established[37], probably due to the inability to adapt uniform algorithms for bioinformatics interpretation of the results and quality control protocols to standardize and harmonize the methodological steps between different labs.\u003c/p\u003e\n\u003cp\u003eIn the current study we have implemented two different advanced techniques with high analytical sensitivity to assess DNA methylation levels in HR-MDS patients and make direct comparisons with healthy controls and to identify differences in methylation status between pre- and post-HMA treatment conditions. \u003c/p\u003e\n\u003cp\u003eNGS-methylation analysis employs a slightly different methodology aiming to map sequencing reads to a reference genome. This kind of analysis prerequisites bisulfite treatment or methylation-sensitive restriction enzyme digest prior to sequencing reactions, thus distinguishing methylated from unmethylated cytosines, and identifying differentially methylated regions (DMRs) between tested samples. MeD-seq analysis data which were interpreted in our study enabled the identification of global changes in DNA methylation that are associated with HMA treatment response. We have also identified distinct DNA methylation profiles across each human chromosome between responders (Rs) and non-responders (NRs), which were manifested by uniform rates of slight hypermethylation in Rs and hypomethylation among NRs apart from chromosomes 21, X and Y. NRs also exhibited extreme values of hypermethylated DMRs by approximately 100 to 1000-fold, whereas Rs displayed more balanced rates between hypo- and hypermethylation with a total tendency towards hypermethylation (\u003cstrong\u003eFig 4A, B\u003c/strong\u003e). Methylation discrepancies within chromosomal bins revealed the existence of common chromosomal sites between Rs and NRs exerting significant methylation alterations associated with HMA treatment (\u003cstrong\u003eFig. 5\u003c/strong\u003e), which can further facilitate the characterization of novel targets for therapeutic interventions. This kind of analysis represents a powerful and comprehensive diagnostic approach for studying the epigenetic dysregulation in MDS patients and compare it between baseline and post-HMA treatment, with the potential to advance our understanding on disease progression and improve management strategies. In particular, the identification of chromosomal bins with the highest significance between Rs and NRs encompassed several genes encoding for circular RNAs, miRNAs and lncRNAs (\u003cstrong\u003etable 2\u003c/strong\u003e). These ncRNAs as part of the epigenetic regulatory compartment require further examination to establish their potential significance as biomarkers for response to HMA treatment.\u003c/p\u003e\n\u003cp\u003eLC-MS/MS analysis on DNA methylation levels confirmed the corresponding results obtained from MeD-seq. NR patient group exhibited significantly reduced levels of 5mC, whereas Rs displayed insignificant differences post-HMA treatment (\u003cstrong\u003eFig. 2B\u003c/strong\u003e). Both NRs and Rs when compared at baseline displayed lower levels of 5mC throughout their genome, although statistical significance was reached only for Rs compared to healthy controls (\u003cstrong\u003eFig. 2A\u003c/strong\u003e). Moreover, among both Rs and NRs, the epigenetic signature of 5hmC was only rarely detected (in about 1/3 of the total MDS samples) in contrast to healthy controls, whose DNA comprised 5hmC mark universally (\u003cstrong\u003eFig. 2C\u003c/strong\u003e) and with no exception. The discovery of 5hmC, which is a product of the 5mC oxidation by the \u0026alpha;-ketoglutarate-dependent DNA dioxygenases (TET1-3), as an epigenetic unit disrupted the simplicity of the traditional epigenetic paradigm and led to a re-evaluation of the DNA methylation landscape. Distribution patterns of 5hmC in the genome, such as its high enrichment within promoters, enhancers and transcriptionally active genes indicates a distinct biological role from 5mC [38], which is considered as a transcriptionally repressing epigenetic mark. However, methods assessing the presence of DNA methylation sites globally, such as the NGS technology combined either with methylation-sensitive restriction enzymes or bisulfite treatment, are unable to discriminate between 5mC and 5hmC[39]. Our findings in HR MDS samples highlight the absence of 5hmC in about 30-40% of DNA samples tested, a characteristic sign also observed in other malignancies [31]. This result suggests an impairment of the active DNA demethylation pathway catalyzed by the TET family of enzymes.\u003c/p\u003e\n\u003cp\u003eInterpretation of our data provide clear evidence for the qualitative and quantitative methylation alterations across genomic DNA in HR MDS, which can be summarized within the following observations: a) the already significant hypomethylated DNA status among HR-MDS patients at baseline (prior treatment) (\u003cstrong\u003eFig. 2A\u003c/strong\u003e), b) the response of NRs to HMA-treatment by further lowering their DNA methylation values (\u003cstrong\u003eFig. 2B, 4A, 4B\u003c/strong\u003e), which is a negative complication, associated with the HMA treatment and c) more than half of the HR MDS patients have a dysfunctional DNA demethylation pathway via oxidation reactions catalyzed by the TET enzymes (\u003cstrong\u003eFig. 2C\u003c/strong\u003e), d) apart from DNA methylation status, epigenetic deregulation post-HMA treatment is further anticipated by the altered methylated levels of several sets of ncRNAs (\u003cstrong\u003etable 2\u003c/strong\u003e) that may promote their aberrant expression.\u003c/p\u003e\n\u003cp\u003eExpanding the possibilities of LC-MS/MS analysis we have assessed and compared the [A]/[T] deviation between the R and NR patient group of HR MDS DNA samples and the group of DNA samples from the healthy donors. Theoretical background refers to Chargaff\u0026apos;s rule, who defined the base pair equality: A% = T% and G% = C% for the double-stranded DNA molecules[40]. Within this context, potential deviations observed underlies the spontaneous deamination of methylated cytosine (5mC) to thymidine (T), leading to increased rates of mutagenicity either at baseline or post-HMA treatment associated with HMA properties. Our results estimated a [dA]/[T] ratio of 1.02 for healthy DNA samples and a range of 0.727-0.633 among HR MDS. [dA]/[T] differences pre- and post-HMA treatment were insignificant (\u003cstrong\u003eFig. 3\u003c/strong\u003e). Conclusively, an extensively mutated genomic background was demonstrated by both Rs and NRs prior to HMA treatment, which is unrelated to HMA mechanism of action.\u003c/p\u003e\n\u003cp\u003eTo overcome the substantial epigenetic heterogeneity of MDS at clinical presentation, disease progression, and treatment response, high resolution methods for discriminating MDS patients eligible for HMA treatment option and response assessment are vital. LC-MS/MS and MeD-seq methylation analysis utilized in the present study allowed for the characterization of discrete epigenetic features within the MDS patient cohort, providing novel insights with diagnostic, prognostic, or predictive value for the HMA response assessment. The small HR MDS patient sample size is considered as a potential limitation of this study, as well as the overrepresentation of male samples, since the epigenetic landscape between the two genders is likely to differ. Additionally, some strengths and limitations arising from each methodology are also considered: a) MeD-seq provides higher resolution and genome-wide coverage compared to LC-MS/MS, which estimates global methylation levels, b) LC-MS/MS can distinguish between different epigenetic marks (5mC and 5hmC), while MeD-seq provides information for both marks as 5mC, c) MeD-seq generally has higher upfront costs due to NGS performance, but it offers higher throughput and greater information content per sample. MeD-seq also requires computing power and high expertise for bioinformatics analysis. The logistical and technological complexity involved in data processing and analysis of LC-MS/MS methodology, although is high, can overcome this limitation and be applied in the clinical setting as a valuable tool for quantifying global methylation levels to gain comprehensive insights into DNA methylation dynamics during HMA therapy. \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResponse to hypomethylating treatment in HR MDS patients is not associated with global DNA hypomethylation, rather than with significant methylation reduction across specific chromosomal regions, which mainly include genes encoding for various ncRNA molecules. This observation highlights new epigenetic features underlying response to HMA therapy in HR MDS and merits further investigation. Also, LC-MS/MS technology acquires all those advantages for first-line HR-MDS monitoring, to provide rapid, accurate and cost-effective results (compared to NGS) on the broad molecular background translatable into responsive phenotypes to HMA treatment, and consideration for inclusion in MRD concept.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients and healthy participants provided their written informed consent according to the Declaration of Helsinki, being informed about both clinical and translational investigations and the study was approved by the University General Hospital of Patras Ethics Committee (approval number 33807/24.12.2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTN, ZJ, DI and BK designed, performed and evaluated LC-MS/MS results. CT and SymA recruited MDS patients and healthy participants. They also monitored HMA treatment and collected clinical data to discriminate Rs from NRs. CV and AK performed DNA sample preparation and writing of first draft. BE analyzed MeD-seq data and drafted the manuscript. PG, SgA and SymA conceived the study, raised funding and SgA wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Special Account for Research Funds of Hellenic Open University, Greece, ELKE_HOU_2022-2024, Grant No 80250.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw MeD-seq data for 13 MDS samples pre- and post-HMA treatment are available at the SRA database under: PRJNA1075483 (https://www.ncbi.nlm.nih.gov/bioproject/1075483). LC-MS/MS raw data presented in this article will be made available by the authors upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi H, Hu F, Gale RP, Sekeres MA, Liang Y (2022) Myelodysplastic syndromes. Nature reviews Disease primers 8: 74. DOI 10.1038/s41572-022-00402-5\u003c/li\u003e\n\u003cli\u003eMaggioni G, Della Porta MG (2023) Molecular landscape of myelodysplastic neoplasms in disease classification and prognostication. Current opinion in hematology 30: 30-37. DOI 10.1097/MOH.0000000000000752\u003c/li\u003e\n\u003cli\u003eKhoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, Bejar R, Berti E, Busque L, Chan JKC, et al. 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DOI 10.1007/BF02170221\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Myelodysplastic Syndromes, Hypomethylating agents, response assessment, DNA methylation, LC-MS/MS analysis, MeD-seq data analysis","lastPublishedDoi":"10.21203/rs.3.rs-4424582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4424582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Treatment decision and response assessment in myelodysplastic syndromes (MDS) can be enhanced by the implementation of advanced diagnostic and prognostic assays for the detection of multiple molecular features. Higher-risk (HR) MDS, ineligible for allogeneic hematopoietic stem cell transplantation (alloHSCT), require prompt therapeutic interventions such as treatment with hypomethylating agents (HMAs) to restore normal DNA methylation levels, mainly of oncosuppressor genes and consequently to delay disease progression and increase overall survival (OS). However, response assessment to HMA treatment relies on conventional methods with limited capacity to uncover a wide spectrum of molecular events. We studied bone marrow aspirates from twenty-one HR MDS patients pre- and post-HMA treatment and seven healthy controls. Genomic DNA was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) for 5’ methyl-cytosine (5mC), 5’ hydroxy-methyl cytosine (5hmC) levels detection, and global adenosine/thymidine ([dA]/[T]) ratio, to correlate differences during treatment course and at baseline state prior drug therapy. Results from methylation DNA sequencing (MeD-seq) from the same HR MDS cohort were also analyzed to identify targeted differentially methylated regions (DMRs). LC/MS-MS analysis revealed a significant hypomethylation status in responders (Rs) already established at baseline and a trend for further DNA methylation reduction post-HMA treatment. Non-responders (NRs) reached statistical significance for DNA hypomethylation only post-HMA treatment. MeD-seq confirmed results globally for both Rs and NRs and more specifically, identified DMRs associated with HMA treatment. Additionally, within statistically significant selected chromosomal bins, genes encoding for proteins and non-coding RNAs were highlighted with reversed methylation profiles between Rs and NRs. Dynamic DNA methylation changes in HR MDS patients undergoing HMA therapy demonstrated that response to treatment is associated only with few specific hypomethylated DMRs rather than presenting a global effect across genome. The 5hmC epigenetic mark was only rarely detected in Rs and NRs, in contrast to healthy controls. Global [dA]/[T] ratio was lower in both R and NR subgroups compared to controls suggesting high frequences of baseline transitions from 5mC to thymidine. Conclusively, LC-MS/MS methodology provided broad-based but rapid and cost-effective results on the molecular HR MDS background, potentially translatable into responsive phenotypes to HMA treatment.","manuscriptTitle":"Implementation of advanced analytical methods MeD-seq and LC-MS/MS for assessing the epigenetic profile of high-risk myelodysplastic syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 20:13:11","doi":"10.21203/rs.3.rs-4424582/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e188111-d6e5-4c1f-a576-92a18f82ddc0","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32437854,"name":"Biological sciences/Cancer"},{"id":32437855,"name":"Biological sciences/Chemical biology"},{"id":32437856,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":32437857,"name":"Biological sciences/Molecular biology"},{"id":32437858,"name":"Health sciences/Medical research"},{"id":32437859,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2024-07-24T06:03:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-30 20:13:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4424582","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4424582","identity":"rs-4424582","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}